[med-svn] [python-biopython] 04/06: Imported Upstream version 1.67+dfsg

Andreas Tille tille at debian.org
Wed Jun 8 20:01:29 UTC 2016


This is an automated email from the git hooks/post-receive script.

tille pushed a commit to branch master
in repository python-biopython.

commit f356f2ab2fe0599015334a1afc5faef3723dd7da
Author: Andreas Tille <tille at debian.org>
Date:   Wed Jun 8 21:25:42 2016 +0200

    Imported Upstream version 1.67+dfsg
---
 .gitattributes                                     |    20 +
 .gitignore                                         |    89 +
 .travis.yml                                        |    76 +
 Bio/Affy/CelFile.py                                |     2 -
 Bio/Affy/__init__.py                               |     2 -
 Bio/Align/AlignInfo.py                             |    67 +-
 Bio/Align/Applications/_ClustalOmega.py            |    12 +-
 Bio/Align/Applications/_Clustalw.py                |    12 +-
 Bio/Align/Applications/_Dialign.py                 |    19 +-
 Bio/Align/Applications/_MSAProbs.py                |    12 +-
 Bio/Align/Applications/_Mafft.py                   |     1 -
 Bio/Align/Applications/_Muscle.py                  |    14 +-
 Bio/Align/Applications/_Prank.py                   |    15 +-
 Bio/Align/Applications/_Probcons.py                |    11 +-
 Bio/Align/Applications/_TCoffee.py                 |    12 +-
 Bio/Align/Applications/__init__.py                 |     2 -
 Bio/Align/Generic.py                               |    26 +-
 Bio/Align/__init__.py                              |    11 +-
 Bio/AlignIO/ClustalIO.py                           |     4 +-
 Bio/AlignIO/EmbossIO.py                            |     2 -
 Bio/AlignIO/FastaIO.py                             |     8 +-
 Bio/AlignIO/Interfaces.py                          |     2 -
 Bio/AlignIO/NexusIO.py                             |     1 -
 Bio/AlignIO/PhylipIO.py                            |     1 -
 Bio/AlignIO/StockholmIO.py                         |    14 +-
 Bio/AlignIO/__init__.py                            |    13 +-
 Bio/Alphabet/IUPAC.py                              |     2 -
 Bio/Alphabet/Reduced.py                            |     1 -
 Bio/Alphabet/__init__.py                           |     2 -
 Bio/Application/__init__.py                        |     3 +-
 Bio/Blast/Applications.py                          |    64 +-
 Bio/Blast/NCBIStandalone.py                        |    33 +-
 Bio/Blast/NCBIWWW.py                               |    23 +-
 Bio/Blast/NCBIXML.py                               |    12 +-
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 Bio/CAPS/__init__.py                               |     2 -
 Bio/Cluster/__init__.py                            |     8 +-
 Bio/Compass/__init__.py                            |     2 -
 Bio/Crystal/__init__.py                            |     2 -
 Bio/Data/CodonTable.py                             |    25 +-
 Bio/Data/IUPACData.py                              |     2 -
 Bio/Data/SCOPData.py                               |     2 -
 Bio/Data/__init__.py                               |     2 -
 Bio/DocSQL.py                                      |     1 -
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 Bio/Emboss/Primer3.py                              |     3 +-
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 Bio/Entrez/XSDs/IPGReportSet.xsd                   |    94 +
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 Bio/File.py                                        |    25 +-
 Bio/GA/Crossover/GeneralPoint.py                   |     6 +-
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 Bio/GenBank/__init__.py                            |    15 +-
 Bio/GenBank/utils.py                               |     2 -
 Bio/Geo/Record.py                                  |     2 +-
 Bio/Graphics/BasicChromosome.py                    |    19 +-
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 Bio/Graphics/Comparative.py                        |    10 +-
 Bio/Graphics/DisplayRepresentation.py              |     3 +-
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 Bio/PDB/MMCIFParser.py                             |   241 +-
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 Bio/ParserSupport.py                               |   161 +-
 Bio/Pathway/Rep/Graph.py                           |     6 +-
 Bio/Pathway/Rep/MultiGraph.py                      |     6 +-
 Bio/Pathway/__init__.py                            |    25 +-
 Bio/Phylo/Applications/_Fasttree.py                |     1 -
 Bio/Phylo/Applications/_Phyml.py                   |     9 +-
 Bio/Phylo/Applications/_Raxml.py                   |     7 +-
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 Bio/Phylo/NeXMLIO.py                               |     5 +-
 Bio/Phylo/Newick.py                                |     2 -
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 Bio/Phylo/PAML/_paml.py                            |    17 +-
 Bio/Phylo/PAML/_parse_baseml.py                    |     7 +-
 Bio/Phylo/PAML/_parse_codeml.py                    |    44 +-
 Bio/Phylo/PAML/_parse_yn00.py                      |     2 +-
 Bio/Phylo/PAML/baseml.py                           |    53 +-
 Bio/Phylo/PAML/chi2.py                             |    10 +-
 Bio/Phylo/PAML/codeml.py                           |    68 +-
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 Bio/Phylo/PhyloXML.py                              |     8 +-
 Bio/Phylo/PhyloXMLIO.py                            |     5 +-
 Bio/Phylo/TreeConstruction.py                      |    39 +-
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 Bio/Phylo/_cdao_owl.py                             |     1 +
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 Bio/PopGen/Async/Local.py                          |    42 +-
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 Bio/Restriction/PrintFormat.py                     |    31 +-
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 Bio/Restriction/Restriction_Dictionary.py          |  3781 +-
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 Bio/SCOP/Raf.py                                    |     8 +-
 Bio/SCOP/Residues.py                               |     2 +-
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 Bio/SVDSuperimposer/__init__.py                    |    26 +-
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 Bio/SearchIO/ExonerateIO/exonerate_vulgar.py       |     2 +-
 Bio/SearchIO/FastaIO.py                            |     9 +-
 Bio/SearchIO/HmmerIO/__init__.py                   |     5 +-
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 CONTRIB                                            |    26 +-
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 Tests/test_Phylo_NeXML.py                          |     6 +-
 Tests/test_Phylo_depend.py                         |    30 +
 Tests/test_PopGen_GenePop_nodepend.py              |    30 +-
 Tests/test_PopGen_SimCoal_nodepend.py              |    11 +-
 Tests/test_Prank_tool.py                           |    10 +-
 Tests/test_QCPSuperimposer.py                      |     8 +-
 Tests/test_RCSBFormats.py                          |    69 +
 Tests/test_SCOP_Raf.py                             |     9 +
 Tests/test_SCOP_Scop.py                            |     2 +-
 Tests/test_SVDSuperimposer.py                      |    15 +-
 Tests/test_SearchIO_model.py                       |    12 +-
 Tests/test_SeqFeature.py                           |    28 +
 Tests/test_SeqIO.py                                |   260 +-
 Tests/test_SeqIO_FastaIO.py                        |    32 +-
 Tests/test_SeqIO_PdbIO.py                          |    12 +-
 Tests/test_SeqIO_QualityIO.py                      |     8 +-
 Tests/test_SeqIO_convert.py                        |     9 +-
 Tests/test_SeqIO_features.py                       |    40 +-
 Tests/test_SeqIO_index.py                          |   235 +-
 Tests/test_SeqIO_online.py                         |     4 +-
 Tests/test_SeqIO_write.py                          |     8 +-
 Tests/test_SeqRecord.py                            |    30 +
 Tests/test_Seq_objs.py                             |     1 +
 Tests/test_SffIO.py                                |   225 +-
 Tests/test_SwissProt.py                            |   295 +-
 Tests/test_TogoWS.py                               |     4 +-
 Tests/test_Tutorial.py                             |    92 +-
 Tests/test_XXmotif_tool.py                         |     2 +-
 Tests/test_codonalign.py                           |     8 +-
 Tests/test_motifs.py                               |    19 +-
 Tests/test_motifs_online.py                        |    42 +-
 Tests/test_phenotype.py                            |   304 +
 Tests/test_phenotype_fit.py                        |    63 +
 Tests/test_phyml_tool.py                           |    19 +-
 Tests/test_prodoc.py                               |   192 +-
 Tests/test_prosite1.py                             |    24 +-
 Tests/test_prosite2.py                             |    21 +-
 Tests/test_raxml_tool.py                           |     9 +-
 Tests/test_samtools_tool.py                        |    15 +-
 Tests/test_seq.py                                  |   112 +-
 setup.py                                           |    11 +-
 469 files changed, 35382 insertions(+), 69360 deletions(-)

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..12f3515
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,20 @@
+# On Windows, git can be setup to convert text files to use
+# DOS/Windows newlines (\r\n) rather than Unix style (\n).
+# This should be harmless for most of our unit tests...
+#
+# However, where testing indexing and get_raw (including the
+# BGZF tests comparing the uncompressed file to the compressed
+# file) it can be important to preserve the line endings in git.
+
+Tests/Fasta/f002 binary
+Tests/GenBank/cor6_6.gb binary
+Tests/GenBank/NC_000932.gb binary
+Tests/Quality/example.fastq binary
+Tests/Quality/example_dos.fastq binary
+Tests/Blast/wnts.xml binary
+
+# This pickle file has to be using Unix new lines otherwise at
+# least Python 3.4's C pickle parser fails with exception:
+# _pickle.UnpicklingError: the STRING opcode argument must be quoted
+
+Tests/SubsMat/acc_rep_mat.pik binary
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1dd13b8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,89 @@
+#Ignore the build directory (and its sub-directories):
+build
+
+#Ignore the distribution directory
+dist
+ 
+#Ignore backup files from some Unix editors,
+*~
+*.swp
+*.bak
+
+#Ignore patches and any original files created by patch command
+*.diff
+*.patch
+*.orig
+*.rej
+
+#Ignore these hidden files from Mac OS X
+.DS_Store
+
+#Ignore hidden files from Dolphin window manager
+.directory
+ 
+#Ignore all compiled python files (e.g. from running the unit tests):
+*.pyc
+*.pyo
+*.py{}
+*.py-e
+
+#Ignore all Jython class files (present if using Jython)
+*.class
+
+#Ignore the MANIFEST file created automatically from MANIFEST.in
+MANIFEST
+
+#Ignore potential directory created during install:
+biopython.egg-info
+ 
+#The graphics unit tests produce output files for human inspection
+#(at the time of writing, only PDF and PNG files are created)
+Tests/Graphics/*.pdf
+Tests/Graphics/*.eps
+Tests/Graphics/*.svg
+Tests/Graphics/*.png
+
+#Ignore the local BioSQL test settings:
+Tests/biosql.ini
+
+#TODO - The unit tests shouldn't leave temp files after running:
+Tests/BioSQL/temp_sqlite.db
+Tests/BioSQL/temp_sqlite.db-journal
+
+#Ignore LaTeX temp files, and compiled output
+Doc/*.aux
+Doc/*.log
+Doc/*.out
+Doc/*.toc
+Doc/*.haux
+Doc/*.htoc
+Doc/*/*.aux
+Doc/*/*.log
+Doc/*/*.out
+Doc/*/*.toc
+Doc/*/*.haux
+Doc/*/*.htoc
+Doc/*/*/*.aux
+Doc/*/*/*.log
+Doc/*/*/*.out
+Doc/*/*/*.toc
+Doc/*/*/*.haux
+Doc/*/*/*.htoc
+Doc/Tutorial.txt
+Doc/Tutorial.pdf
+Doc/Tutorial.html
+Doc/biopdb_faq.pdf
+Doc/install/Installation.txt
+Doc/install/Installation.pdf
+Doc/install/Installation.html
+Doc/cookbook/motif/motif.pdf
+Doc/cookbook/motif/motif.html
+
+#Ignore the hevea.sty file if present (used for HTML docs)
+Doc/hevea.sty
+Doc/*/hevea.sty
+Doc/*/*/hevea.sty
+
+#Ignore IntelliJ IDEA directory and project files
+.idea
+*.iml
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..cfee4f5
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,76 @@
+# Special configuration file to run tests on Travis-CI via GitHub notifications
+# See https://travis-ci.org/biopython/biopython/builds for results
+#
+# The tests are run via the coverage script, and if the tests pass the coverage
+# information is pushed to https://codecov.io/github/biopython/biopython
+#
+# Note when testing Python 3, the 'python' command will invoke Python 3
+# and similarly for PyPy too.
+
+# Environment variables setup via the matrix
+# - DEP = list of python packages to install via default pip install
+# - EXT = list of python packages to install via pip install --allow-all-external
+# - BIN = list of python packages to install via pip install --only-binary
+# - COV = yes/no; should tests be run with coverage metric collection
+language: python
+matrix:
+  include:
+    - python: "2.6"
+      env: DEP="reportlab psycopg2 mysql-python unittest2" EXT=mysql-connector-python-rf BIN=scipy COV=yes
+    - python: "2.7"
+      env: DEP="reportlab psycopg2 mysql-python" EXT=mysql-connector-python-rf COV=yes
+    - python: "3.3"
+      env: DEP="reportlab psycopg2" EXT=mysql-connector-python-rf COV=yes
+    - python: "3.4"
+      env: DEP="reportlab psycopg2" EXT=mysql-connector-python-rf COV=yes
+    - python: "3.5"
+      env: DEP=reportlab psycopg2" EXT=mysql-connector-python-rf BIN=scipy COV=yes
+    - python: "pypy"
+      env: EXT="mysql-connector-python-rf" COV=yes
+    - python: "pypy3"
+      env: COV=no
+
+sudo: false
+addons:
+  apt:
+    packages:
+    - bwa
+    - clustalo
+    - clustalw
+    - emboss
+    - mafft
+    - muscle
+    - probcons
+    - samtools
+    - wise
+
+before_install:
+  - "pip install --upgrade pip"
+  - "pip install rdflib"
+  - "pip install $DEP"
+  - "pip install --only-binary=:all: $BIN"
+  - "pip install --allow-all-external $EXT"
+  - "if [[ $COV == 'yes' ]]; then pip install coverage; fi"
+
+
+install:
+#The yes is in case we get our prompt about missing NumPy
+  - "/usr/bin/yes | python setup.py install"
+
+before_script:
+  - cd Tests
+  - cp biosql.ini.sample biosql.ini
+
+script:
+#Using just coverage should match up to the current Python version:
+  - "if [[ $COV == 'yes' ]]; then coverage run --source=Bio,BioSQL run_tests.py --offline; fi"
+  - "if [[ $COV != 'yes' ]]; then python run_tests.py --offline; fi"
+
+after_success:
+#See https://codecov.io/ and https://github.com/codecov/example-python
+  - "if [[ $COV == 'yes' ]]; then pip install codecov; fi"
+  - "if [[ $COV == 'yes' ]]; then codecov; fi"
+
+#The email defaults are too talkative
+notifications:
+  email: false
diff --git a/Bio/Affy/CelFile.py b/Bio/Affy/CelFile.py
index f683bf0..db82f4e 100644
--- a/Bio/Affy/CelFile.py
+++ b/Bio/Affy/CelFile.py
@@ -23,8 +23,6 @@ except ImportError:
     raise MissingPythonDependencyError(
         "Install NumPy if you want to use Bio.Affy.CelFile")
 
-__docformat__ = "restructuredtext en"
-
 
 class Record(object):
     """Stores the information in a cel file
diff --git a/Bio/Affy/__init__.py b/Bio/Affy/__init__.py
index e0ea1c4..925dd85 100644
--- a/Bio/Affy/__init__.py
+++ b/Bio/Affy/__init__.py
@@ -5,5 +5,3 @@
 
 """Deal with Affymetrix related data such as cel files.
 """
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/Align/AlignInfo.py b/Bio/Align/AlignInfo.py
index 2d8a386..b2fc980 100644
--- a/Bio/Align/AlignInfo.py
+++ b/Bio/Align/AlignInfo.py
@@ -20,7 +20,6 @@ from Bio.Alphabet import IUPAC
 from Bio.Seq import Seq
 from Bio.SubsMat import FreqTable
 
-__docformat__ = "restructuredtext en"
 
 # Expected random distributions for 20-letter protein, and
 # for 4-letter nucleotide alphabets
@@ -104,8 +103,8 @@ class SummaryInfo(object):
 
             if require_multiple and num_atoms == 1:
                 consensus += ambiguous
-            elif (len(max_atoms) == 1) and ((float(max_size) / float(num_atoms))
-                                            >= threshold):
+            elif (len(max_atoms) == 1) and ((float(max_size) /
+                                             float(num_atoms)) >= threshold):
                 consensus += max_atoms[0]
             else:
                 consensus += ambiguous
@@ -161,8 +160,8 @@ class SummaryInfo(object):
 
             if require_multiple and num_atoms == 1:
                 consensus += ambiguous
-            elif (len(max_atoms) == 1) and ((float(max_size) / float(num_atoms))
-                                            >= threshold):
+            elif (len(max_atoms) == 1) and ((float(max_size) /
+                                             float(num_atoms)) >= threshold):
                 consensus += max_atoms[0]
             else:
                 consensus += ambiguous
@@ -315,9 +314,9 @@ class SummaryInfo(object):
     def _get_all_letters(self):
         """Returns a string containing the expected letters in the alignment."""
         all_letters = self.alignment._alphabet.letters
-        if all_letters is None \
-        or (isinstance(self.alignment._alphabet, Alphabet.Gapped)
-        and all_letters == self.alignment._alphabet.gap_char):
+        if all_letters is None or \
+                (isinstance(self.alignment._alphabet, Alphabet.Gapped) and
+                 all_letters == self.alignment._alphabet.gap_char):
             # We are dealing with a generic alphabet class where the
             # letters are not defined!  We must build a list of the
             # letters used...
@@ -697,55 +696,3 @@ def print_info_content(summary_info, fout=None, rep_record=0):
     for pos in sorted(summary_info.ic_vector):
         fout.write("%d %s %.3f\n" % (pos, rep_sequence[pos],
                    summary_info.ic_vector[pos]))
-
-if __name__ == "__main__":
-    print("Quick test")
-    from Bio import AlignIO
-    from Bio.Align.Generic import Alignment
-
-    filename = "../../Tests/GFF/multi.fna"
-    format = "fasta"
-    expected = FreqTable.FreqTable({"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25},
-                                   FreqTable.FREQ,
-                                   IUPAC.unambiguous_dna)
-
-    alignment = AlignIO.read(open(filename), format)
-    for record in alignment:
-        print(record.seq)
-    print("=" * alignment.get_alignment_length())
-
-    summary = SummaryInfo(alignment)
-    consensus = summary.dumb_consensus(ambiguous="N")
-    print(consensus)
-    consensus = summary.gap_consensus(ambiguous="N")
-    print(consensus)
-    print("")
-    print(summary.pos_specific_score_matrix(chars_to_ignore=['-'],
-                                            axis_seq=consensus))
-    print("")
-    # Have a generic alphabet, without a declared gap char, so must tell
-    # provide the frequencies and chars to ignore explicitly.
-    print(summary.information_content(e_freq_table=expected,
-                                      chars_to_ignore=['-']))
-    print("")
-    print("Trying a protein sequence with gaps and stops")
-
-    alpha = Alphabet.HasStopCodon(Alphabet.Gapped(Alphabet.generic_protein, "-"), "*")
-    a = Alignment(alpha)
-    a.add_sequence("ID001", "MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-")
-    a.add_sequence("ID002", "MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*")
-    a.add_sequence("ID003", "MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*")
-    print(a)
-    print("=" * a.get_alignment_length())
-
-    s = SummaryInfo(a)
-    c = s.dumb_consensus(ambiguous="X")
-    print(c)
-    c = s.gap_consensus(ambiguous="X")
-    print(c)
-    print("")
-    print(s.pos_specific_score_matrix(chars_to_ignore=['-', '*'], axis_seq=c))
-
-    print(s.information_content(chars_to_ignore=['-', '*']))
-
-    print("Done")
diff --git a/Bio/Align/Applications/_ClustalOmega.py b/Bio/Align/Applications/_ClustalOmega.py
index 2e97e11..fbce5b8 100644
--- a/Bio/Align/Applications/_ClustalOmega.py
+++ b/Bio/Align/Applications/_ClustalOmega.py
@@ -13,8 +13,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
 
@@ -201,12 +199,6 @@ class ClustalOmegaCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running ClustalOmega doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_Clustalw.py b/Bio/Align/Applications/_Clustalw.py
index bb6e2f5..c328189 100644
--- a/Bio/Align/Applications/_Clustalw.py
+++ b/Bio/Align/Applications/_Clustalw.py
@@ -7,8 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 import os
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
@@ -330,12 +328,6 @@ class ClustalwCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running ClustalW doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_Dialign.py b/Bio/Align/Applications/_Dialign.py
index 409b67e..2277c37 100644
--- a/Bio/Align/Applications/_Dialign.py
+++ b/Bio/Align/Applications/_Dialign.py
@@ -7,7 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
 
 from Bio.Application import _Option, _Argument, _Switch, AbstractCommandline
 
@@ -103,7 +102,7 @@ class DialignCommandline(AbstractCommandline):
             _Switch(["-lo", "lo"],
                     "(Long Output) Additional file *.log with information "
                     "about fragments selected for pairwise alignment and "
-                    "about consistency in multi-alignment proceedure."),
+                    "about consistency in multi-alignment procedure."),
             _Switch(["-ma", "ma"],
                     "`mixed alignments' consisting of P-fragments and "
                     "N-fragments if nucleic acid sequences are aligned."),
@@ -137,7 +136,7 @@ class DialignCommandline(AbstractCommandline):
             _Switch(["-nta", "nta"],
                     "`no textual alignment' - textual alignment suppressed. "
                     "This option makes sense if other output files are of "
-                    "intrest -- e.g. the fragment files created with -ff, "
+                    "interest -- e.g. the fragment files created with -ff, "
                     "-fop, -fsm or -lo."),
             _Switch(["-o", "o"],
                     "Fast version, resulting alignments may be slightly "
@@ -161,7 +160,7 @@ class DialignCommandline(AbstractCommandline):
                     "Maximum number of `*' characters indicating degree "
                     "of local similarity among sequences. By default, no "
                     "stars are used but numbers between 0 and 9, instead.",
-                    checker_function = lambda x: x in range(0, 10),
+                    checker_function=lambda x: x in range(0, 10),
                     equate=False),
             _Switch(["-stdo", "stdo"],
                     "Results written to standard output."),
@@ -171,7 +170,7 @@ class DialignCommandline(AbstractCommandline):
                     "options, e.g. -lgs)"),
             _Option(["-thr", "thr"],
                     "Threshold T = x.",
-                    checker_function = lambda x: isinstance(x, int),
+                    checker_function=lambda x: isinstance(x, int),
                     equate=False),
             _Switch(["-xfr", "xfr"],
                     "'exclude fragments' - list of fragments can be "
@@ -184,12 +183,6 @@ class DialignCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running modules doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_MSAProbs.py b/Bio/Align/Applications/_MSAProbs.py
index 5838339..8cda414 100644
--- a/Bio/Align/Applications/_MSAProbs.py
+++ b/Bio/Align/Applications/_MSAProbs.py
@@ -8,7 +8,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
 
 from Bio.Application import _Argument, _Option, _Switch, AbstractCommandline
 
@@ -76,13 +75,6 @@ class MSAProbsCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running MSAProbs doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_Mafft.py b/Bio/Align/Applications/_Mafft.py
index 45bdaf2..a47f631 100644
--- a/Bio/Align/Applications/_Mafft.py
+++ b/Bio/Align/Applications/_Mafft.py
@@ -7,7 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"
 
 import os
 from Bio.Application import _Option, _Switch, _Argument, AbstractCommandline
diff --git a/Bio/Align/Applications/_Muscle.py b/Bio/Align/Applications/_Muscle.py
index a4e71da..8a7ab88 100644
--- a/Bio/Align/Applications/_Muscle.py
+++ b/Bio/Align/Applications/_Muscle.py
@@ -7,8 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
 
@@ -406,7 +404,7 @@ class MuscleCommandline(AbstractCommandline):
             # One of either -le, -sp, or -sv
             #
             # According to the doc, spn is default and the only option for
-            # nucleotides: this doesnt appear to be true. -le, -sp, and -sv can
+            # nucleotides: this doesn't appear to be true. -le, -sp, and -sv can
             # be used and produce numerically different logs (what is going on?)
             #
             # spn fails on proteins
@@ -468,12 +466,6 @@ class MuscleCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running MUSCLE doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_Prank.py b/Bio/Align/Applications/_Prank.py
index a60cb21..c0d362d 100644
--- a/Bio/Align/Applications/_Prank.py
+++ b/Bio/Align/Applications/_Prank.py
@@ -7,7 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
 
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
@@ -159,13 +158,13 @@ class PrankCommandline(AbstractCommandline):
             _Switch(["-printnodes", "printnodes"],
                     "Output each node; mostly for debugging"),
             # -matresize=# [matrix resizing multiplier]
-            # Doesnt specify type but Float and Int work
+            # Doesn't specify type but Float and Int work
             _Option(["-matresize", "matresize"],
                     "Matrix resizing multiplier",
                     checker_function=lambda x: isinstance(x, float) or
                                                isinstance(x, int)),
             # -matinitsize=# [matrix initial size multiplier]
-            # Doesnt specify type but Float and Int work
+            # Doesn't specify type but Float and Int work
             _Option(["-matinitsize", "matinitsize"],
                     "Matrix initial size multiplier",
                     checker_function=lambda x: isinstance(x, float) or
@@ -207,12 +206,6 @@ class PrankCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running modules doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_Probcons.py b/Bio/Align/Applications/_Probcons.py
index 7231648..ab3f452 100644
--- a/Bio/Align/Applications/_Probcons.py
+++ b/Bio/Align/Applications/_Probcons.py
@@ -7,7 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
 
 from Bio.Application import _Option, _Switch, _Argument, AbstractCommandline
 
@@ -112,12 +111,6 @@ class ProbconsCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running modules doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/_TCoffee.py b/Bio/Align/Applications/_TCoffee.py
index 537a62b..18f3bc2 100644
--- a/Bio/Align/Applications/_TCoffee.py
+++ b/Bio/Align/Applications/_TCoffee.py
@@ -7,8 +7,6 @@
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
 
@@ -104,12 +102,6 @@ class TCoffeeCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running modules doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/Applications/__init__.py b/Bio/Align/Applications/__init__.py
index 635f810..795c507 100644
--- a/Bio/Align/Applications/__init__.py
+++ b/Bio/Align/Applications/__init__.py
@@ -4,8 +4,6 @@
 # as part of this package.
 """Alignment command line tool wrappers."""
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from ._Muscle import MuscleCommandline
 from ._Clustalw import ClustalwCommandline
 from ._ClustalOmega import ClustalOmegaCommandline
diff --git a/Bio/Align/Generic.py b/Bio/Align/Generic.py
index c732855..0f1411e 100644
--- a/Bio/Align/Generic.py
+++ b/Bio/Align/Generic.py
@@ -1,6 +1,6 @@
 # Copyright 2000-2004 Brad Chapman.
 # Copyright 2001 Iddo Friedberg.
-# Copyright 2007-2010 by Peter Cock.
+# Copyright 2007-2016 by Peter Cock.
 # All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -12,9 +12,6 @@ specific to a particular program or format.
 """
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
-# biopython
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio import Alphabet
@@ -40,6 +37,7 @@ class Alignment(object):
         e.g.
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",  "ACT-CTAGCTAG")
@@ -53,8 +51,8 @@ class Alignment(object):
         import warnings
         import Bio
         warnings.warn("With the introduction of the MultipleSeqAlignment class in Bio.Align, this base class is deprecated and is likely to be removed in a future release of Biopython.", Bio.BiopythonDeprecationWarning)
-        if not (isinstance(alphabet, Alphabet.Alphabet)
-        or isinstance(alphabet, Alphabet.AlphabetEncoder)):
+        if not (isinstance(alphabet, Alphabet.Alphabet) or
+                isinstance(alphabet, Alphabet.AlphabetEncoder)):
             raise ValueError("Invalid alphabet argument")
         self._alphabet = alphabet
         # hold everything at a list of SeqRecord objects
@@ -87,6 +85,7 @@ class Alignment(object):
         single screen. e.g.
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",  "ACT-CTAGCTAG")
@@ -144,6 +143,7 @@ class Alignment(object):
         e.g.
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",  "ACT-CTAGCTAG")
@@ -210,6 +210,7 @@ class Alignment(object):
         e.g.
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",  "ACT-CTAGCTAG")
@@ -264,6 +265,7 @@ class Alignment(object):
         by finding the maximum length of sequences in the alignment.
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",  "ACT-CTAGCTAG")
@@ -343,6 +345,7 @@ class Alignment(object):
         e.g.
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha", "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",  "ACT-CTAGCTAG")
@@ -365,6 +368,7 @@ class Alignment(object):
         We'll use the following example alignment here for illustration:
 
         >>> from Bio.Alphabet import IUPAC, Gapped
+        >>> from Bio.Align.Generic import Alignment
         >>> align = Alignment(Gapped(IUPAC.unambiguous_dna, "-"))
         >>> align.add_sequence("Alpha",  "ACTGCTAGCTAG")
         >>> align.add_sequence("Beta",   "ACT-CTAGCTAG")
@@ -435,12 +439,6 @@ class Alignment(object):
             raise TypeError("Invalid index type.")
 
 
-def _test():
-    """Run the Bio.Align.Generic module's doctests."""
-    print("Running doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Align/__init__.py b/Bio/Align/__init__.py
index b74775d..2af9ab1 100644
--- a/Bio/Align/__init__.py
+++ b/Bio/Align/__init__.py
@@ -11,8 +11,6 @@ class, used in the Bio.AlignIO module.
 """
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio import Alphabet
@@ -128,6 +126,7 @@ class MultipleSeqAlignment(_Alignment):
         >>> from Bio.Alphabet import generic_dna
         >>> from Bio.Seq import Seq
         >>> from Bio.SeqRecord import SeqRecord
+        >>> from Bio.Align import MultipleSeqAlignment
         >>> a = SeqRecord(Seq("AAAACGT", generic_dna), id="Alpha")
         >>> b = SeqRecord(Seq("AAA-CGT", generic_dna), id="Beta")
         >>> c = SeqRecord(Seq("AAAAGGT", generic_dna), id="Gamma")
@@ -145,8 +144,7 @@ class MultipleSeqAlignment(_Alignment):
         compatible "hack" so as not to disrupt existing scripts and users, but
         is deprecated and will be removed in a future release.
         """
-        if isinstance(records, Alphabet.Alphabet) \
-        or isinstance(records, Alphabet.AlphabetEncoder):
+        if isinstance(records, (Alphabet.Alphabet, Alphabet.AlphabetEncoder)):
             if alphabet is None:
                 # TODO - Remove this backwards compatible mode!
                 alphabet = records
@@ -163,8 +161,7 @@ class MultipleSeqAlignment(_Alignment):
             else:
                 raise ValueError("Invalid records argument")
         if alphabet is not None:
-            if not (isinstance(alphabet, Alphabet.Alphabet)
-            or isinstance(alphabet, Alphabet.AlphabetEncoder)):
+            if not isinstance(alphabet, (Alphabet.Alphabet, Alphabet.AlphabetEncoder)):
                 raise ValueError("Invalid alphabet argument")
             self._alphabet = alphabet
         else:
@@ -315,7 +312,7 @@ class MultipleSeqAlignment(_Alignment):
         self._records.append(record)
 
     def __add__(self, other):
-        """Combines to alignments with the same number of rows by adding them.
+        """Combines two alignments with the same number of rows by adding them.
 
         If you have two multiple sequence alignments (MSAs), there are two ways to think
         about adding them - by row or by column. Using the extend method adds by row.
diff --git a/Bio/AlignIO/ClustalIO.py b/Bio/AlignIO/ClustalIO.py
index 525d9c3..aed9489 100644
--- a/Bio/AlignIO/ClustalIO.py
+++ b/Bio/AlignIO/ClustalIO.py
@@ -16,8 +16,6 @@ from Bio.SeqRecord import SeqRecord
 from Bio.Align import MultipleSeqAlignment
 from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
 
-__docformat__ = "restructuredtext en"
-
 
 class ClustalWriter(SequentialAlignmentWriter):
     """Clustalw alignment writer."""
@@ -104,7 +102,7 @@ class ClustalIterator(AlignmentIterator):
             raise StopIteration
 
         # Whitelisted headers we know about
-        known_headers = ['CLUSTAL', 'PROBCONS', 'MUSCLE', 'MSAPROBS','Kalign']
+        known_headers = ['CLUSTAL', 'PROBCONS', 'MUSCLE', 'MSAPROBS', 'Kalign']
         if line.strip().split()[0] not in known_headers:
             raise ValueError("%s is not a known CLUSTAL header: %s" %
                              (line.strip().split()[0],
diff --git a/Bio/AlignIO/EmbossIO.py b/Bio/AlignIO/EmbossIO.py
index ba7c473..d395fe3 100644
--- a/Bio/AlignIO/EmbossIO.py
+++ b/Bio/AlignIO/EmbossIO.py
@@ -19,8 +19,6 @@ from Bio.SeqRecord import SeqRecord
 from Bio.Align import MultipleSeqAlignment
 from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
 
-__docformat__ = "restructuredtext en"
-
 
 class EmbossWriter(SequentialAlignmentWriter):
     """Emboss alignment writer (WORK IN PROGRESS).
diff --git a/Bio/AlignIO/FastaIO.py b/Bio/AlignIO/FastaIO.py
index 7ff9326..eb153c8 100644
--- a/Bio/AlignIO/FastaIO.py
+++ b/Bio/AlignIO/FastaIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2011 by Peter Cock.  All rights reserved.
+# Copyright 2008-2016 by Peter Cock.  All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -10,7 +10,7 @@ Bio.SeqIO functions if you want to work directly with the gapped sequences).
 
 This module contains a parser for the pairwise alignments produced by Bill
 Pearson's FASTA tools, for use from the Bio.AlignIO interface where it is
-refered to as the "fasta-m10" file format (as we only support the machine
+referred to as the "fasta-m10" file format (as we only support the machine
 readable output format selected with the -m 10 command line option).
 
 This module does NOT cover the generic "fasta" file format originally
@@ -27,8 +27,6 @@ from Bio.Align import MultipleSeqAlignment
 from Bio.Alphabet import single_letter_alphabet, generic_dna, generic_protein
 from Bio.Alphabet import Gapped
 
-__docformat__ = "restructuredtext en"
-
 
 def _extract_alignment_region(alignment_seq_with_flanking, annotation):
     """Helper function for the main parsing code (PRIVATE).
@@ -111,7 +109,7 @@ def FastaM10Iterator(handle, alphabet=single_letter_alphabet):
                              % (query_id, match_id))
         assert query_tags, query_tags
         assert match_tags, match_tags
-        evalue = align_tags.get("fa_expect", None)
+        evalue = align_tags.get("fa_expect")
         q = "?"  # Just for printing len(q) in debug below
         m = "?"  # Just for printing len(m) in debug below
         tool = global_tags.get("tool", "").upper()
diff --git a/Bio/AlignIO/Interfaces.py b/Bio/AlignIO/Interfaces.py
index 768d4c1..2493155 100644
--- a/Bio/AlignIO/Interfaces.py
+++ b/Bio/AlignIO/Interfaces.py
@@ -14,8 +14,6 @@ import sys  # for checking if Python 2
 
 from Bio.Alphabet import single_letter_alphabet
 
-__docformat__ = "restructuredtext en"
-
 
 class AlignmentIterator(object):
     """Base class for building MultipleSeqAlignment iterators.
diff --git a/Bio/AlignIO/NexusIO.py b/Bio/AlignIO/NexusIO.py
index ab8dddb..544734b 100644
--- a/Bio/AlignIO/NexusIO.py
+++ b/Bio/AlignIO/NexusIO.py
@@ -21,7 +21,6 @@ from Bio.Align import MultipleSeqAlignment
 from .Interfaces import AlignmentWriter
 from Bio import Alphabet
 
-__docformat__ = "restructuredtext en"
 
 # You can get a couple of example files here:
 # http://www.molecularevolution.org/resources/fileformats/
diff --git a/Bio/AlignIO/PhylipIO.py b/Bio/AlignIO/PhylipIO.py
index ebd686c..95c9703 100644
--- a/Bio/AlignIO/PhylipIO.py
+++ b/Bio/AlignIO/PhylipIO.py
@@ -43,7 +43,6 @@ from Bio.SeqRecord import SeqRecord
 from Bio.Align import MultipleSeqAlignment
 from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
 
-__docformat__ = "restructuredtext en"
 
 _PHYLIP_ID_WIDTH = 10
 
diff --git a/Bio/AlignIO/StockholmIO.py b/Bio/AlignIO/StockholmIO.py
index 2904427..9358f93 100644
--- a/Bio/AlignIO/StockholmIO.py
+++ b/Bio/AlignIO/StockholmIO.py
@@ -1,8 +1,10 @@
-# Copyright 2006-2015 by Peter Cock.  All rights reserved.
+# Copyright 2006-2016 by Peter Cock.  All rights reserved.
+# Revisions copyright 2015 by Ben Woodcroft.  All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
+
 """Bio.AlignIO support for "stockholm" format (used in the PFAM database).
 
 You are expected to use this module via the Bio.AlignIO functions (or the
@@ -131,11 +133,11 @@ secondary structure string here, are also sliced:
 """
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # not just plaintext
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio.Align import MultipleSeqAlignment
 from .Interfaces import AlignmentIterator, SequentialAlignmentWriter
+from Bio._py3k import OrderedDict
 
 
 class StockholmWriter(SequentialAlignmentWriter):
@@ -337,7 +339,7 @@ class StockholmIterator(AlignmentIterator):
         # if present it agrees with our parsing.
 
         seqs = {}
-        ids = []
+        ids = OrderedDict()  # Really only need an OrderedSet, but python lacks this
         gs = {}
         gr = {}
         gf = {}
@@ -365,10 +367,10 @@ class StockholmIterator(AlignmentIterator):
                 if len(parts) != 2:
                     # This might be someone attempting to store a zero length sequence?
                     raise ValueError("Could not split line into identifier "
-                                      + "and sequence:\n" + line)
+                                     "and sequence:\n" + line)
                 id, seq = parts
                 if id not in ids:
-                    ids.append(id)
+                    ids[id] = True
                 seqs.setdefault(id, '')
                 seqs[id] += seq.replace(".", "-")
             elif len(line) >= 5:
@@ -419,7 +421,7 @@ class StockholmIterator(AlignmentIterator):
         # assert len(gs)   <= len(ids)
         # assert len(gr)   <= len(ids)
 
-        self.ids = ids
+        self.ids = ids.keys()
         self.sequences = seqs
         self.seq_annotation = gs
         self.seq_col_annotation = gr
diff --git a/Bio/AlignIO/__init__.py b/Bio/AlignIO/__init__.py
index 62026cb..57e9e26 100644
--- a/Bio/AlignIO/__init__.py
+++ b/Bio/AlignIO/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock.  All rights reserved.
+# Copyright 2008-2016 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -84,7 +84,7 @@ overwrite the existing file each time.
 Conversion
 ----------
 The Bio.AlignIO.convert(...) function allows an easy interface for simple
-alignnment file format conversions. Additionally, it may use file format
+alignment file format conversions. Additionally, it may use file format
 specific optimisations so this should be the fastest way too.
 
 In general however, you can combine the Bio.AlignIO.parse(...) function with
@@ -127,8 +127,6 @@ same length.
 from __future__ import print_function
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"  # not just plaintext
-
 # TODO
 # - define policy on reading aligned sequences with gaps in
 #   (e.g. - and . characters) including how the alphabet interacts
@@ -220,8 +218,8 @@ def write(alignments, handle, format):
             count = 0
             for alignment in alignments:
                 if not isinstance(alignment, Alignment):
-                    raise TypeError(
-                        "Expect a list or iterator of Alignment objects.")
+                    raise TypeError("Expect a list or iterator of Alignment "
+                                    "objects, got: %r" % alignment)
                 SeqIO.write(alignment, fp, format)
                 count += 1
         elif format in _FormatToIterator or format in SeqIO._FormatToIterator:
@@ -266,7 +264,7 @@ def _SeqIO_to_alignment_iterator(handle, format, alphabet=None, seq_count=None):
             if len(records) == seq_count:
                 yield MultipleSeqAlignment(records, alphabet)
                 records = []
-        if len(records) > 0:
+        if records:
             raise ValueError("Check seq_count argument, not enough sequences?")
     else:
         # Must assume that there is a single alignment using all
@@ -274,7 +272,6 @@ def _SeqIO_to_alignment_iterator(handle, format, alphabet=None, seq_count=None):
         records = list(SeqIO.parse(handle, format, alphabet))
         if records:
             yield MultipleSeqAlignment(records, alphabet)
-    raise StopIteration
 
 
 def _force_alphabet(alignment_iterator, alphabet):
diff --git a/Bio/Alphabet/IUPAC.py b/Bio/Alphabet/IUPAC.py
index 19cb4f4..a1433b5 100644
--- a/Bio/Alphabet/IUPAC.py
+++ b/Bio/Alphabet/IUPAC.py
@@ -10,8 +10,6 @@
 from Bio import Alphabet
 from Bio.Data import IUPACData
 
-__docformat__ = "restructuredtext en"
-
 
 # #################### Protein
 
diff --git a/Bio/Alphabet/Reduced.py b/Bio/Alphabet/Reduced.py
index 7ca4c08..a375ae5 100644
--- a/Bio/Alphabet/Reduced.py
+++ b/Bio/Alphabet/Reduced.py
@@ -27,7 +27,6 @@ the tables here, or a user-defined table.
 
 from Bio import Alphabet
 
-__docformat__ = "restructuredtext en"
 
 murphy_15_tab = {"L": "L",
                  "V": "L",
diff --git a/Bio/Alphabet/__init__.py b/Bio/Alphabet/__init__.py
index bf62cb9..022a2e3 100644
--- a/Bio/Alphabet/__init__.py
+++ b/Bio/Alphabet/__init__.py
@@ -10,8 +10,6 @@
 This is used by sequences which contain a finite number of similar words.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 class Alphabet(object):
     """Generic alphabet base class.
diff --git a/Bio/Application/__init__.py b/Bio/Application/__init__.py
index 661cd48..1cb7e36 100644
--- a/Bio/Application/__init__.py
+++ b/Bio/Application/__init__.py
@@ -32,7 +32,6 @@ from subprocess import CalledProcessError as _ProcessCalledError
 
 from Bio import File
 
-__docformat__ = "restructuredtext en"
 
 # Use this regular expression to test the property names are going to
 # be valid as Python properties or arguments
@@ -81,7 +80,7 @@ class ApplicationError(_ProcessCalledError):
         # get first line of any stderr message
         try:
             msg = self.stderr.lstrip().split("\n", 1)[0].rstrip()
-        except:
+        except Exception:  # TODO, ValueError? AttributeError?
             msg = ""
         if msg:
             return "Non-zero return code %d from %r, message %r" \
diff --git a/Bio/Blast/Applications.py b/Bio/Blast/Applications.py
index 982f02a..2d2f388 100644
--- a/Bio/Blast/Applications.py
+++ b/Bio/Blast/Applications.py
@@ -30,8 +30,6 @@ from __future__ import print_function
 
 from Bio.Application import _Option, AbstractCommandline, _Switch
 
-__docformat__ = "restructuredtext en"
-
 
 class _NcbibaseblastCommandline(AbstractCommandline):
     """Base Commandline object for (new) NCBI BLAST+ wrappers (PRIVATE).
@@ -89,7 +87,7 @@ class _NcbibaseblastCommandline(AbstractCommandline):
             # Miscellaneous options
             _Switch(["-parse_deflines", "parse_deflines"],
                     "Should the query and subject defline(s) be parsed?"),
-            ]
+        ]
         try:
             # Insert extra parameters - at the start just in case there
             # are any arguments which must come last:
@@ -238,7 +236,7 @@ class _NcbiblastCommandline(_NcbibaseblastCommandline):
                     """Execute search remotely?
 
                     Incompatible with: gilist, negative_gilist, subject_loc, num_threads, ..."""),
-            ]
+        ]
         try:
             # Insert extra parameters - at the start just in case there
             # are any arguments which must come last:
@@ -316,7 +314,7 @@ class _Ncbiblast2SeqCommandline(_NcbiblastCommandline):
 
                     Incompatible with: culling_limit.""",
                     equate=False),
-            ]
+        ]
         try:
             # Insert extra parameters - at the start just in case there
             # are any arguments which must come last:
@@ -359,7 +357,7 @@ class _NcbiblastMain2SeqCommandline(_Ncbiblast2SeqCommandline):
 
                     Incompatible with: db_soft_mask, subject, subject_loc""",
                     equate=False),
-            ]
+        ]
         try:
             # Insert extra parameters - at the start just in case there
             # are any arguments which must come last:
@@ -431,7 +429,7 @@ class NcbiblastpCommandline(_NcbiblastMain2SeqCommandline):
             # Miscellaneous options:
             _Switch(["-use_sw_tback", "use_sw_tback"],
                     "Compute locally optimal Smith-Waterman alignments?"),
-            ]
+        ]
         _NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
 
 
@@ -465,8 +463,8 @@ class NcbiblastnCommandline(_NcbiblastMain2SeqCommandline):
 
                     Values allowed are "both" (default), "minus", "plus".""",
                     checker_function=lambda value: value in ["both",
-                                                              "minus",
-                                                              "plus"],
+                                                             "minus",
+                                                             "plus"],
                     equate=False),
             # General search options:
             _Option(["-task", "task"],
@@ -475,10 +473,10 @@ class NcbiblastnCommandline(_NcbiblastMain2SeqCommandline):
                     Allowed values 'blastn', 'blastn-short', 'dc-megablast', 'megablast'
                     (the default), or 'vecscreen'.""",
                     checker_function=lambda value: value in ['blastn',
-                                                              'blastn-short',
-                                                              'dc-megablast',
-                                                              'megablast',
-                                                              'vecscreen'],
+                                                             'blastn-short',
+                                                             'dc-megablast',
+                                                             'megablast',
+                                                             'vecscreen'],
                     equate=False),
             _Option(["-penalty", "penalty"],
                     "Penalty for a nucleotide mismatch (integer, at most zero).",
@@ -546,12 +544,12 @@ class NcbiblastnCommandline(_NcbiblastMain2SeqCommandline):
                     Added in BLAST 2.2.23+
                     """,
                     equate=False),
-            ]
+        ]
         _NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
 
     def _validate(self):
         if (self.template_type and not self.template_length) \
-        or (self.template_length and not self.template_type):
+                or (self.template_length and not self.template_type):
             raise ValueError("Options template_type and template_type require each other.")
         _NcbiblastMain2SeqCommandline._validate(self)
 
@@ -635,7 +633,7 @@ class NcbiblastxCommandline(_NcbiblastMain2SeqCommandline):
                     "Perform ungapped alignment only?"),
             _Switch(["-use_sw_tback", "use_sw_tback"],
                     "Compute locally optimal Smith-Waterman alignments?"),
-            ]
+        ]
         _NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
 
 
@@ -718,7 +716,7 @@ class NcbitblastnCommandline(_NcbiblastMain2SeqCommandline):
                     Incompatible with: remote, query""",
                     filename=True,
                     equate=False),
-            ]
+        ]
         _NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
 
 
@@ -776,7 +774,7 @@ class NcbitblastxCommandline(_NcbiblastMain2SeqCommandline):
                     Format: "yes", "window locut hicut", or "no" to disable.
                     Default is "12 2.2 2.5""",
                     equate=False),
-            ]
+        ]
         _NcbiblastMain2SeqCommandline.__init__(self, cmd, **kwargs)
 
 
@@ -846,6 +844,13 @@ class NcbipsiblastCommandline(_Ncbiblast2SeqCommandline):
                     "File name to store ASCII version of PSSM.",
                     filename=True,
                     equate=False),
+            _Switch(["-save_pssm_after_last_round", "save_pssm_after_last_round"],
+                    "Save PSSM after the last database search."),
+            _Switch(["-save_each_pssm", "save_each_pssm"],
+                    """Save PSSM after each iteration
+
+                    File name is given in -save_pssm or -save_ascii_pssm options.
+                    """),
             _Option(["-in_msa", "in_msa"],
                     """File name of multiple sequence alignment to restart PSI-BLAST.
 
@@ -887,7 +892,7 @@ class NcbipsiblastCommandline(_Ncbiblast2SeqCommandline):
                     Incompatible with: in_pssm""",
                     filename=True,
                     equate=False),
-            ]
+        ]
         _Ncbiblast2SeqCommandline.__init__(self, cmd, **kwargs)
 
     def _validate(self):
@@ -895,7 +900,7 @@ class NcbipsiblastCommandline(_Ncbiblast2SeqCommandline):
                          "in_msa": ["in_pssm", "query"],
                          "in_pssm": ["in_msa", "query", "phi_pattern"],
                          "ignore_msa_master": ["msa_master_idx", "in_pssm",
-                                 "query", "query_loc", "phi_pattern"],
+                                               "query", "query_loc", "phi_pattern"],
                          }
         self._validate_incompatibilities(incompatibles)
         _Ncbiblast2SeqCommandline._validate(self)
@@ -971,7 +976,7 @@ class NcbirpsblastCommandline(_NcbiblastCommandline):
             # Misc options:
             _Switch(["-use_sw_tback", "use_sw_tback"],
                     "Compute locally optimal Smith-Waterman alignments?"),
-            ]
+        ]
         _NcbiblastCommandline.__init__(self, cmd, **kwargs)
 
     def _validate(self):
@@ -1009,8 +1014,8 @@ class NcbirpstblastnCommandline(_NcbiblastCommandline):
 
                     Values allowed are "both" (default), "minus", "plus".""",
                     checker_function=lambda value: value in ["both",
-                                                              "minus",
-                                                              "plus"],
+                                                             "minus",
+                                                             "plus"],
                     equate=False),
             # Input query options:
             _Option(["-query_gencode", "query_gencode"],
@@ -1026,7 +1031,7 @@ class NcbirpstblastnCommandline(_NcbiblastCommandline):
             # Extension options:
             _Switch(["-ungapped", "ungapped"],
                     "Perform ungapped alignment only?"),
-            ]
+        ]
         _NcbiblastCommandline.__init__(self, cmd, **kwargs)
 
 
@@ -1072,7 +1077,7 @@ class NcbiblastformatterCommandline(_NcbibaseblastCommandline):
                     "Maximum number of aligned sequences to keep",
                     checker_function=lambda value: value >= 1,
                     equate=False),
-            ]
+        ]
         _NcbibaseblastCommandline.__init__(self, cmd, **kwargs)
 
     def _validate(self):
@@ -1144,6 +1149,13 @@ class NcbideltablastCommandline(_Ncbiblast2SeqCommandline):
                     "File name to store ASCII version of PSSM.",
                     filename=True,
                     equate=False),
+            _Switch(["-save_pssm_after_last_round", "save_pssm_after_last_round"],
+                    "Save PSSM after the last database search."),
+            _Switch(["-save_each_pssm", "save_each_pssm"],
+                    """Save PSSM after each iteration
+
+                    File name is given in -save_pssm or -save_ascii_pssm options.
+                    """),
             # PSSM engine options
             _Option(["-pseudocount", "pseudocount"],
                     "Pseudo-count value used when constructing PSSM (integer, default 0).",
@@ -1164,7 +1176,7 @@ class NcbideltablastCommandline(_Ncbiblast2SeqCommandline):
                     """Show domain hits?
 
                     Incompatible with:  remote, subject""")
-            ]
+        ]
         _Ncbiblast2SeqCommandline.__init__(self, cmd, **kwargs)
 
 
diff --git a/Bio/Blast/NCBIStandalone.py b/Bio/Blast/NCBIStandalone.py
index 8fe0c12..d2f1f70 100644
--- a/Bio/Blast/NCBIStandalone.py
+++ b/Bio/Blast/NCBIStandalone.py
@@ -24,21 +24,22 @@ wrappers for these under Bio.Blast.Applications (see the tutorial).
 
 from __future__ import print_function
 
-from Bio import BiopythonDeprecationWarning
-import warnings
-warnings.warn("This module has been deprecated. Consider Bio.SearchIO for "
-              "parsing BLAST output instead.", BiopythonDeprecationWarning)
-
-import os
+import sys
 import re
-from Bio._py3k import StringIO
 
+from Bio._py3k import StringIO
+from Bio.ParserSupport import AbstractParser, AbstractConsumer
+from Bio.ParserSupport import read_and_call, read_and_call_until
+from Bio.ParserSupport import read_and_call_while, attempt_read_and_call
+from Bio.ParserSupport import is_blank_line, safe_peekline, safe_readline
 from Bio import File
-from Bio.ParserSupport import *
 from Bio.Blast import Record
-from Bio.Application import _escape_filename
 
-__docformat__ = "restructuredtext en"
+from Bio import BiopythonDeprecationWarning
+import warnings
+warnings.warn("This module has been deprecated. Consider Bio.SearchIO for "
+              "parsing BLAST output instead.", BiopythonDeprecationWarning)
+
 
 _score_e_re = re.compile(r'Score +E')
 
@@ -605,7 +606,7 @@ class _Scanner(object):
         try:
             read_and_call(uhandle, consumer.noevent, start='Lambda')
             read_and_call(uhandle, consumer.ka_params)
-        except:
+        except Exception:  # TODO: ValueError, AttributeError?
             pass
 
         # This blank line is optional:
@@ -1427,7 +1428,7 @@ class _ParametersConsumer(object):
 
     def threshold(self, line):
         if line[:2] == "T:":
-            # Assume its an old stlye line like "T: 123"
+            # Assume its an old style line like "T: 123"
             self._params.threshold, = _get_cols(
                 line, (1,), ncols=2, expected={0: "T:"})
         elif line[:28] == "Neighboring words threshold:":
@@ -1636,9 +1637,9 @@ class Iterator(object):
             if not line:
                 break
             # If I've reached the next one, then put the line back and stop.
-            if lines and (line.startswith('BLAST')
-                          or line.startswith('BLAST', 1)
-                          or line.startswith('<?xml ')):
+            if lines and (line.startswith('BLAST') or
+                          line.startswith('BLAST', 1) or
+                          line.startswith('<?xml ')):
                 self._uhandle.saveline(line)
                 break
             # New style files omit the BLAST line to mark a new query:
@@ -1769,7 +1770,7 @@ class BlastErrorParser(AbstractParser):
 
     Current BLAST problems this detects are:
     o LowQualityBlastError - When BLASTing really low quality sequences
-    (ie. some GenBank entries which are just short streches of a single
+    (ie. some GenBank entries which are just short stretches of a single
     nucleotide), BLAST will report an error with the sequence and be
     unable to search with this. This will lead to a badly formatted
     BLAST report that the parsers choke on. The parser will convert the
diff --git a/Bio/Blast/NCBIWWW.py b/Bio/Blast/NCBIWWW.py
index 4081afe..2145027 100644
--- a/Bio/Blast/NCBIWWW.py
+++ b/Bio/Blast/NCBIWWW.py
@@ -21,10 +21,11 @@ from Bio._py3k import urlopen as _urlopen
 from Bio._py3k import urlencode as _urlencode
 from Bio._py3k import Request as _Request
 
-__docformat__ = "restructuredtext en"
 
+NCBI_BLAST_URL = "http://blast.ncbi.nlm.nih.gov/Blast.cgi"
 
-def qblast(program, database, sequence,
+
+def qblast(program, database, sequence, url_base=NCBI_BLAST_URL,
            auto_format=None, composition_based_statistics=None,
            db_genetic_code=None, endpoints=None, entrez_query='(none)',
            expect=10.0, filter=None, gapcosts=None, genetic_code=None,
@@ -39,9 +40,18 @@ def qblast(program, database, sequence,
            format_entrez_query=None, format_object=None, format_type='XML',
            ncbi_gi=None, results_file=None, show_overview=None, megablast=None,
            ):
-    """Do a BLAST search using the QBLAST server at NCBI.
+    """Do a BLAST search using the QBLAST server at NCBI or a cloud service
+    provider.
 
     Supports all parameters of the qblast API for Put and Get.
+
+    Please note that BLAST on the cloud supports the NCBI-BLAST Common
+    URL API (http://ncbi.github.io/blast-cloud/dev/api.html). To
+    use this feature, please set url_base to
+    'http://host.my.cloud.service.provider.com/cgi-bin/blast.cgi' and
+    format_object='Alignment'. For more details, please see
+    https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=CloudBlast
+
     Some useful parameters:
 
      - program        blastn, blastp, blastx, tblastn, or tblastx (lower case)
@@ -62,6 +72,7 @@ def qblast(program, database, sequence,
     This function does no checking of the validity of the parameters
     and passes the values to the server as is.  More help is available at:
     http://www.ncbi.nlm.nih.gov/BLAST/Doc/urlapi.html
+
     """
     import time
 
@@ -116,7 +127,7 @@ def qblast(program, database, sequence,
     # Note the NCBI do not currently impose a rate limit here, other
     # than the request not to make say 50 queries at once using multiple
     # threads.
-    request = _Request("http://blast.ncbi.nlm.nih.gov/Blast.cgi",
+    request = _Request(url_base,
                        message,
                        {"User-Agent": "BiopythonClient"})
     handle = _urlopen(request)
@@ -160,7 +171,7 @@ def qblast(program, database, sequence,
         else:
             delay = 120
 
-        request = _Request("http://blast.ncbi.nlm.nih.gov/Blast.cgi",
+        request = _Request(url_base,
                            message,
                            {"User-Agent": "BiopythonClient"})
         handle = _urlopen(request)
@@ -185,7 +196,7 @@ def qblast(program, database, sequence,
 def _parse_qblast_ref_page(handle):
     """Extract a tuple of RID, RTOE from the 'please wait' page (PRIVATE).
 
-    The NCBI FAQ pages use TOE for 'Time of Execution', so RTOE is proably
+    The NCBI FAQ pages use TOE for 'Time of Execution', so RTOE is probably
     'Request Time of Execution' and RID would be 'Request Identifier'.
     """
     s = _as_string(handle.read())
diff --git a/Bio/Blast/NCBIXML.py b/Bio/Blast/NCBIXML.py
index e0d889d..8b5d32f 100644
--- a/Bio/Blast/NCBIXML.py
+++ b/Bio/Blast/NCBIXML.py
@@ -16,8 +16,6 @@ import xml.sax
 from xml.sax.handler import ContentHandler
 from functools import reduce
 
-__docformat__ = "restructuredtext en"
-
 
 class _XMLparser(ContentHandler):
     """Generic SAX Parser (PRIVATE).
@@ -61,7 +59,7 @@ class _XMLparser(ContentHandler):
         # Note could use try / except AttributeError
         # BUT I found often triggered by nested errors...
         if hasattr(self, method):
-            eval("self.%s()" % method)
+            getattr(self, method)()
             if self._debug > 4:
                 print("NCBIXML: Parsed:  " + method)
         elif self._debug > 3:
@@ -96,7 +94,7 @@ class _XMLparser(ContentHandler):
         # Note could use try / except AttributeError
         # BUT I found often triggered by nested errors...
         if hasattr(self, method):
-            eval("self.%s()" % method)
+            getattr(self, method)()
             if self._debug > 2:
                 print("NCBIXML: Parsed:  %s %s" % (method, self._value))
         elif self._debug > 1:
@@ -164,7 +162,7 @@ class BlastParser(_XMLparser):
         # These are required for "old" pre 2.2.14 files
         # where only <BlastOutput_query-ID>, <BlastOutput_query-def>
         # and <BlastOutput_query-len> were used.  Now they
-        # are suplemented/replaced by <Iteration_query-ID>,
+        # are supplemented/replaced by <Iteration_query-ID>,
         # <Iteration_query-def> and <Iteration_query-len>
         if not hasattr(self._blast, "query") \
         or not self._blast.query:
@@ -327,11 +325,11 @@ class BlastParser(_XMLparser):
     #     pass # XXX TODO PSI
 
     def _end_Parameters_sc_match(self):
-        """match score for nucleotide-nucleotide comparaison (-r) (PRIVATE)."""
+        """match score for nucleotide-nucleotide comparison (-r) (PRIVATE)."""
         self._parameters.sc_match = int(self._value)
 
     def _end_Parameters_sc_mismatch(self):
-        """mismatch penalty for nucleotide-nucleotide comparaison (-r) (PRIVATE)."""
+        """mismatch penalty for nucleotide-nucleotide comparison (-r) (PRIVATE)."""
         self._parameters.sc_mismatch = int(self._value)
 
     def _end_Parameters_gap_open(self):
diff --git a/Bio/Blast/Record.py b/Bio/Blast/Record.py
index be11d77..e48e28c 100644
--- a/Bio/Blast/Record.py
+++ b/Bio/Blast/Record.py
@@ -24,8 +24,6 @@ from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio.Align import MultipleSeqAlignment
 
-__docformat__ = "restructuredtext en"
-
 
 class Header(object):
     """Saves information from a blast header.
@@ -112,13 +110,13 @@ class HSP(object):
         - expect          Expect value.  (float)
         - num_alignments  Number of alignments for same subject.  (int)
         - identities      Number of identities (int) if using the XML parser.
-          Tuple of numer of identities/total aligned (int, int)
+          Tuple of number of identities/total aligned (int, int)
           if using the (obsolete) plain text parser.
         - positives       Number of positives (int) if using the XML parser.
-          Tuple of numer of positives/total aligned (int, int)
+          Tuple of number of positives/total aligned (int, int)
           if using the (obsolete) plain text parser.
         - gaps            Number of gaps (int) if using the XML parser.
-          Tuple of numer of gaps/total aligned (int, int) if
+          Tuple of number of gaps/total aligned (int, int) if
           using the (obsolete) plain text parser.
         - align_length    Length of the alignment. (int)
         - strand          Tuple of (query, target) strand.
@@ -133,7 +131,7 @@ class HSP(object):
         - sbjct_end       The end residue for the sbjct sequence.  (1-based)
 
     Not all flavors of BLAST return values for every attribute::
-    
+
                   score     expect     identities   positives    strand  frame
         BLASTP     X          X            X            X
         BLASTN     X          X            X            X          X
diff --git a/Bio/Blast/__init__.py b/Bio/Blast/__init__.py
index bd8e089..ea0f6db 100644
--- a/Bio/Blast/__init__.py
+++ b/Bio/Blast/__init__.py
@@ -3,5 +3,3 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 """Code for dealing with BLAST programs and output."""
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/CAPS/__init__.py b/Bio/CAPS/__init__.py
index 48d77d3..1820490 100644
--- a/Bio/CAPS/__init__.py
+++ b/Bio/CAPS/__init__.py
@@ -12,8 +12,6 @@ be found in the paper `Konieczny and Ausubel (1993)`_ (PMID 8106085).
 .. _`Konieczny and Ausubel (1993)`: http://dx.doi.org/10.1046/j.1365-313X.1993.04020403.x
 """
 
-__docformat__ = "restructuredtext en"
-
 
 class DifferentialCutsite(object):
     """Differential enzyme cutsite in an alignment.
diff --git a/Bio/Cluster/__init__.py b/Bio/Cluster/__init__.py
index a46b9b7..e3dea5c 100644
--- a/Bio/Cluster/__init__.py
+++ b/Bio/Cluster/__init__.py
@@ -7,8 +7,6 @@ import numpy
 
 from Bio.Cluster.cluster import *
 
-__docformat__ = "restructuredtext en"
-
 
 def _treesort(order, nodeorder, nodecounts, tree):
     # Find the order of the nodes consistent with the hierarchical clustering
@@ -397,7 +395,7 @@ Return values:
         return clustercentroids(self.data, self.mask, clusterid, method,
                                 transpose)
 
-    def clusterdistance(self, index1=[0], index2=[0], method='a', dist='e',
+    def clusterdistance(self, index1=0, index2=0, method='a', dist='e',
                         transpose=0):
         """Calculate the distance between two clusters.
 
@@ -526,8 +524,8 @@ Arguments:
         if geneclusters is not None and expclusters is not None and \
            type(geneclusters) != type(expclusters):
             raise ValueError("found one k-means and one hierarchical "
-                           + "clustering solution in geneclusters and "
-                           + "expclusters")
+                             "clustering solution in geneclusters and "
+                             "expclusters")
         gid = 0
         aid = 0
         filename = jobname
diff --git a/Bio/Compass/__init__.py b/Bio/Compass/__init__.py
index 577c0b1..75afe99 100644
--- a/Bio/Compass/__init__.py
+++ b/Bio/Compass/__init__.py
@@ -16,8 +16,6 @@ Tested with COMPASS 1.24.
 
 import re
 
-__docformat__ = "restructuredtext en"
-
 
 def read(handle):
     """Reads a COMPASS file containing one COMPASS record."""
diff --git a/Bio/Crystal/__init__.py b/Bio/Crystal/__init__.py
index e50736f..c381df4 100644
--- a/Bio/Crystal/__init__.py
+++ b/Bio/Crystal/__init__.py
@@ -17,8 +17,6 @@ from functools import reduce
 from Bio._py3k import map
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"
-
 
 class CrystalError(Exception):
     pass
diff --git a/Bio/Data/CodonTable.py b/Bio/Data/CodonTable.py
index edebc26..4d96cb2 100644
--- a/Bio/Data/CodonTable.py
+++ b/Bio/Data/CodonTable.py
@@ -15,7 +15,6 @@ from Bio import Alphabet
 from Bio.Alphabet import IUPAC
 from Bio.Data import IUPACData
 
-__docformat__ = "restructuredtext en"
 
 unambiguous_dna_by_name = {}
 unambiguous_dna_by_id = {}
@@ -206,8 +205,8 @@ def list_possible_proteins(codon, forward_table, ambiguous_nucleotide_values):
                         stops.append(y1 + y2 + y3)
         if stops:
             if possible:
-                raise TranslationError("ambiguous codon '%s' codes " % codon
-                                       + "for both proteins and stop codons")
+                raise TranslationError("ambiguous codon %r codes for both"
+                                       " proteins and stop codons" % codon)
             # This is a true stop codon - tell the caller about it
             raise KeyError(codon)
         return list(possible)
@@ -895,6 +894,26 @@ register_ncbi_table(name='Pterobranchia Mitochondrial',
                     start_codons=['TTG', 'CTG', 'ATG', 'GTG', ],
                     )
 
+register_ncbi_table(name='Candidate Division SR1 and Gracilibacteria',
+                    alt_name=None, id=25,
+                    table={
+     'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L', 'TCT': 'S',
+     'TCC': 'S', 'TCA': 'S', 'TCG': 'S', 'TAT': 'Y', 'TAC': 'Y',
+     'TGT': 'C', 'TGC': 'C', 'TGG': 'W', 'CTT': 'L', 'CTC': 'L',
+     'CTA': 'L', 'CTG': 'L', 'CCT': 'P', 'CCC': 'P', 'CCA': 'P',
+     'CCG': 'P', 'CAT': 'H', 'CAC': 'H', 'CAA': 'Q', 'CAG': 'Q',
+     'CGT': 'R', 'CGC': 'R', 'CGA': 'R', 'CGG': 'R', 'ATT': 'I',
+     'ATC': 'I', 'ATA': 'I', 'ATG': 'M', 'ACT': 'T', 'ACC': 'T',
+     'ACA': 'T', 'ACG': 'T', 'AAT': 'N', 'AAC': 'N', 'AAA': 'K',
+     'AAG': 'K', 'AGT': 'S', 'AGC': 'S', 'AGA': 'R', 'AGG': 'R',
+     'GTT': 'V', 'GTC': 'V', 'GTA': 'V', 'GTG': 'V', 'GCT': 'A',
+     'GCC': 'A', 'GCA': 'A', 'GCG': 'A', 'GAT': 'D', 'GAC': 'D',
+     'GAA': 'E', 'GAG': 'E', 'GGT': 'G', 'GGC': 'G', 'GGA': 'G',
+     'GGG': 'G', 'TGA': 'G', },
+                    stop_codons=['TAA', 'TAG', ],
+                    start_codons=['TTG', 'CTG', 'ATG', ]
+                    )
+
 # Basic sanity test,
 for key, val in generic_by_name.items():
     assert key in ambiguous_generic_by_name[key].names
diff --git a/Bio/Data/IUPACData.py b/Bio/Data/IUPACData.py
index 5998e02..0b39a4a 100644
--- a/Bio/Data/IUPACData.py
+++ b/Bio/Data/IUPACData.py
@@ -4,8 +4,6 @@
 
 """Information about the IUPAC alphabets."""
 
-__docformat__ = "restructuredtext en"
-
 
 protein_letters = "ACDEFGHIKLMNPQRSTVWY"
 extended_protein_letters = "ACDEFGHIKLMNPQRSTVWYBXZJUO"
diff --git a/Bio/Data/SCOPData.py b/Bio/Data/SCOPData.py
index de8ac90..25834fa 100644
--- a/Bio/Data/SCOPData.py
+++ b/Bio/Data/SCOPData.py
@@ -7,8 +7,6 @@
 See Bio.SCOP for more information about SCOP and Biopython"s SCOP module.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 protein_letters_3to1 = {
     "00C": "C", "01W": "X", "02K": "A", "03Y": "C", "07O": "C",
diff --git a/Bio/Data/__init__.py b/Bio/Data/__init__.py
index 93f5557..df21e03 100644
--- a/Bio/Data/__init__.py
+++ b/Bio/Data/__init__.py
@@ -3,5 +3,3 @@
 # as part of this package.
 
 """Collections of various bits of useful biological data."""
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/DocSQL.py b/Bio/DocSQL.py
index 16bf73a..9bace71 100755
--- a/Bio/DocSQL.py
+++ b/Bio/DocSQL.py
@@ -35,7 +35,6 @@ except:
     raise MissingPythonDependencyError("Install MySQLdb if you want to use "
                                        "Bio.DocSQL.")
 
-__docformat__ = "restructuredtext en"
 
 connection = None
 
diff --git a/Bio/Emboss/Applications.py b/Bio/Emboss/Applications.py
index 22f1772..cc1ed47 100644
--- a/Bio/Emboss/Applications.py
+++ b/Bio/Emboss/Applications.py
@@ -16,8 +16,6 @@ from __future__ import print_function
 
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
-__docformat__ = "restructuredtext en"
-
 
 class _EmbossMinimalCommandLine(AbstractCommandline):
     """Base Commandline object for EMBOSS wrappers (PRIVATE).
@@ -646,7 +644,7 @@ class FDNAParsCommandline(_EmbossCommandLine):
         _Option(["-thorough", "thorough"],
                  "more thorough search (Y/n)"),
         _Option(["-rearrange", "rearrange"],
-                 "Rearrange on jsut 1 best tree (Y/n)"),
+                 "Rearrange on just 1 best tree (Y/n)"),
         _Option(["-transversion", "transversion"],
                  "Use tranversion parsimony (y/N)"),
         _Option(["-njumble", "njumble"],
@@ -1256,11 +1254,6 @@ class SeqmatchallCommandline(_EmbossCommandLine):
         _EmbossCommandLine.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the Bio.Emboss.Applications module doctests."""
-    import doctest
-    doctest.testmod()
-
 if __name__ == "__main__":
-    # Run the doctests
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Emboss/Primer3.py b/Bio/Emboss/Primer3.py
index 86f7eb1..45e0591 100644
--- a/Bio/Emboss/Primer3.py
+++ b/Bio/Emboss/Primer3.py
@@ -17,7 +17,6 @@ function. If you ran eprimer3 with multiple targets, use the parse
 function to iterate over the retsults.
 """
 
-__docformat__ = "restructuredtext en"
 
 # --- primer3
 
@@ -62,7 +61,7 @@ class Primers(object):
         - internal_length
         - internal_tm
         - internal_gc
-    
+
     """
     def __init__(self):
         self.size = 0
diff --git a/Bio/Emboss/PrimerSearch.py b/Bio/Emboss/PrimerSearch.py
index 3fbb92c..530ce17 100644
--- a/Bio/Emboss/PrimerSearch.py
+++ b/Bio/Emboss/PrimerSearch.py
@@ -6,8 +6,6 @@
 """Code to interact with the primersearch program from EMBOSS.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 class InputRecord(object):
     """Represent the input file into the primersearch program.
diff --git a/Bio/Emboss/__init__.py b/Bio/Emboss/__init__.py
index faf1253..36032f6 100644
--- a/Bio/Emboss/__init__.py
+++ b/Bio/Emboss/__init__.py
@@ -3,5 +3,4 @@
 # as part of this package.
 #
 
-"""Code to interact with the ever-so-useful EMBOSS programs.
-"""
+"""Code to interact with the ever-so-useful EMBOSS programs."""
diff --git a/Bio/Entrez/Parser.py b/Bio/Entrez/Parser.py
index 016d974..73e8666 100644
--- a/Bio/Entrez/Parser.py
+++ b/Bio/Entrez/Parser.py
@@ -35,6 +35,7 @@ written solution, since the number of DTDs is rather large and their
 contents may change over time. About half the code in this parser deals
 wih parsing the DTD, and the other half with the XML itself.
 """
+import sys
 import re
 import os
 import warnings
@@ -47,7 +48,6 @@ from Bio._py3k import urlopen as _urlopen
 from Bio._py3k import urlparse as _urlparse
 from Bio._py3k import unicode
 
-__docformat__ = "restructuredtext en"
 
 # The following four classes are used to add a member .attributes to integers,
 # strings, lists, and dictionaries, respectively.
@@ -207,10 +207,20 @@ class DataHandler(object):
         # expects binary data
         if handle.__class__.__name__ == 'EvilHandleHack':
             handle = handle._handle
+        if handle.__class__.__name__ == 'TextIOWrapper':
+            handle = handle.buffer
         if hasattr(handle, "closed") and handle.closed:
             # Should avoid a possible Segmentation Fault, see:
             # http://bugs.python.org/issue4877
             raise IOError("Can't parse a closed handle")
+        if sys.version_info[0] >= 3:
+            # Another nasty hack to cope with a unicode StringIO handle
+            # since the Entrez XML parser expects binary data (bytes)
+            from io import StringIO
+            if isinstance(handle, StringIO):
+                from io import BytesIO
+                from Bio._py3k import _as_bytes
+                handle = BytesIO(_as_bytes(handle.read()))
         try:
             self.parser.ParseFile(handle)
         except expat.ExpatError as e:
@@ -451,10 +461,10 @@ class DataHandler(object):
             return
         # First, remove ignorable parentheses around declarations
         while (model[0] in (expat.model.XML_CTYPE_SEQ,
-                            expat.model.XML_CTYPE_CHOICE)
-          and model[1] in (expat.model.XML_CQUANT_NONE,
-                           expat.model.XML_CQUANT_OPT)
-          and len(model[3]) == 1):
+                            expat.model.XML_CTYPE_CHOICE) and
+               model[1] in (expat.model.XML_CQUANT_NONE,
+                           expat.model.XML_CQUANT_OPT) and
+               len(model[3]) == 1):
             model = model[3][0]
         # PCDATA declarations correspond to strings
         if model[0] in (expat.model.XML_CTYPE_MIXED,
diff --git a/Bio/Entrez/XSDs/IPGReportSet.xsd b/Bio/Entrez/XSDs/IPGReportSet.xsd
new file mode 100644
index 0000000..da50ae2
--- /dev/null
+++ b/Bio/Entrez/XSDs/IPGReportSet.xsd
@@ -0,0 +1,94 @@
+<?xml version="1.0" ?>
+<xs:schema
+  xmlns:xs="http://www.w3.org/2001/XMLSchema"
+  xmlns:ncbi="http://www.ncbi.nlm.nih.gov"
+  elementFormDefault="unqualified"
+  attributeFormDefault="unqualified"
+>
+    <xs:element name="CDS">
+        <xs:complexType>
+            <xs:attribute name="accver" type="xs:string" use="required"/>
+            <xs:attribute name="gi" type="xs:integer" use="required"/>
+            <xs:attribute name="kingdom" type="xs:string" use="required"/>
+            <xs:attribute name="org" type="xs:string" use="required"/>
+            <xs:attribute name="start" type="xs:integer" use="required"/>
+            <xs:attribute name="stop" type="xs:integer" use="required"/>
+            <xs:attribute name="strand" type="xs:string" use="required"/>
+            <xs:attribute name="taxid" type="xs:integer" use="required"/>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="CDSList">
+        <xs:complexType>
+            <xs:sequence maxOccurs="unbounded">
+                <xs:element ref="CDS"/>
+            </xs:sequence>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="IPGReport">
+        <xs:complexType>
+            <xs:sequence>
+                <xs:element ref="Product"/>
+                <xs:element ref="RedundantGiList"/>
+                <xs:element ref="Statistics"/>
+            </xs:sequence>
+            <xs:attribute name="accver" type="xs:string" use="required"/>
+            <xs:attribute name="gi" type="xs:integer" use="required"/>
+            <xs:attribute name="kingdom" type="xs:string" use="required"/>
+            <xs:attribute name="org" type="xs:string" use="required"/>
+            <xs:attribute name="taxid" type="xs:integer" use="required"/>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="IPGReportSet">
+        <xs:complexType>
+            <xs:sequence>
+                <xs:element ref="IPGReport"/>
+            </xs:sequence>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="Product">
+        <xs:complexType>
+            <xs:simpleContent>
+                <xs:extension base="xs:string">
+                    <xs:attribute name="accver" type="xs:string" use="required"/>
+                    <xs:attribute name="gi" type="xs:integer" use="required"/>
+                    <xs:attribute name="slen" type="xs:integer" use="required"/>
+                </xs:extension>
+            </xs:simpleContent>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="RedundantGi">
+        <xs:complexType>
+            <xs:sequence minOccurs="0">
+                <xs:element ref="CDSList"/>
+            </xs:sequence>
+            <xs:attribute name="accver" type="xs:string" use="required"/>
+            <xs:attribute name="gi" type="xs:integer" use="required"/>
+            <xs:attribute name="kingdom" type="xs:string" use="required"/>
+            <xs:attribute name="org" type="xs:string" use="required"/>
+            <xs:attribute name="priority" type="xs:string" use="required"/>
+            <xs:attribute name="source" type="xs:string" use="required"/>
+            <xs:attribute name="taxid" type="xs:integer" use="required"/>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="RedundantGiList">
+        <xs:complexType>
+            <xs:sequence maxOccurs="unbounded">
+                <xs:element ref="RedundantGi"/>
+            </xs:sequence>
+        </xs:complexType>
+    </xs:element>
+
+    <xs:element name="Statistics">
+        <xs:complexType>
+            <xs:attribute name="nuc_count" type="xs:integer" use="required"/>
+            <xs:attribute name="prot_count" type="xs:integer" use="required"/>
+        </xs:complexType>
+    </xs:element>
+
+</xs:schema>
\ No newline at end of file
diff --git a/Bio/Entrez/__init__.py b/Bio/Entrez/__init__.py
index be65ed6..d2d1f19 100644
--- a/Bio/Entrez/__init__.py
+++ b/Bio/Entrez/__init__.py
@@ -1,5 +1,7 @@
 # Copyright 1999-2000 by Jeffrey Chang.  All rights reserved.
-# Copyright 2008 by Michiel de Hoon.  All rights reserved.
+# Copyright 2008-2013 by Michiel de Hoon.  All rights reserved.
+# Revisions copyright 2011-2015 by Peter Cock. All rights reserved.
+# Revisions copyright 2015 by Eric Rasche. All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -9,13 +11,30 @@
 The main Entrez web page is available at:
 http://www.ncbi.nlm.nih.gov/Entrez/
 
-A list of the Entrez utilities is available at:
-http://www.ncbi.nlm.nih.gov/entrez/utils/utils_index.html
+Entrez Programming Utilities web page is available at:
+http://www.ncbi.nlm.nih.gov/books/NBK25501/
+
+This module provides a number of functions like ``efetch`` (short for
+Entrez Fetch) which will return the data as a handle object. This is
+a standard interface used in Python for reading data from a file, or
+in this case a remote network connection, and provides methods like
+``.read()`` or offers iteration over the contents line by line. See
+also "What the heck is a handle?" in the Biopython Tutorial and
+Cookbook: http://biopython.org/DIST/docs/tutorial/Tutorial.html
+http://biopython.org/DIST/docs/tutorial/Tutorial.pdf
+
+Unlike a handle to a file on disk from the ``open(filename)`` function,
+which has a ``.name`` attribute giving the filename, the handles from
+``Bio.Entrez`` all have a ``.url`` attribute instead giving the URL
+used to connect to the NCBI Entrez API.
+
+The Entrez module also provides an XML parser which takes a handle
+as input.
 
 Variables:
 
     - email        Set the Entrez email parameter (default is not set).
-    - tool         Set the Entrez tool parameter (default is  biopython).
+    - tool         Set the Entrez tool parameter (default is ``biopython``).
 
 Functions:
 
@@ -85,7 +104,6 @@ from Bio._py3k import HTTPError as _HTTPError
 
 from Bio._py3k import _binary_to_string_handle, _as_bytes
 
-__docformat__ = "restructuredtext en"
 
 email = None
 tool = "biopython"
@@ -99,7 +117,7 @@ def epost(db, **keywds):
     environment to use with subsequent search strategies.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/epost_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EPost
 
     Return a handle to the results.
 
@@ -118,7 +136,7 @@ def efetch(db, **keywords):
     more UIs or from user's environment.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch
 
     Return a handle to the results.
 
@@ -133,6 +151,9 @@ def efetch(db, **keywords):
     LOCUS       AY851612                 892 bp    DNA     linear   PLN 10-APR-2007
     >>> handle.close()
 
+    This will automatically use an HTTP POST rather than HTTP GET if there
+    are over 200 identifiers as recommended by the NCBI.
+
     **Warning:** The NCBI changed the default retmode in Feb 2012, so many
     databases which previously returned text output now give XML.
     """
@@ -148,11 +169,15 @@ def efetch(db, **keywords):
         if isinstance(ids, list):
             ids = ",".join(ids)
             variables["id"] = ids
+        elif isinstance(ids, int):
+            ids = str(ids)
+            variables["id"] = ids
+
         if ids.count(",") >= 200:
             # NCBI prefers an HTTP POST instead of an HTTP GET if there are
             # more than about 200 IDs
             post = True
-    return _open(cgi, variables, post)
+    return _open(cgi, variables, post=post)
 
 
 def esearch(db, term, **keywds):
@@ -163,7 +188,7 @@ def esearch(db, term, **keywds):
     for future use in the user's environment.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch
 
     Return a handle to the results which are always in XML format.
 
@@ -201,7 +226,7 @@ def elink(**keywds):
     database, or lists LinkOut URLs and attributes for multiple IDs.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/elink_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ELink
 
     Return a handle to the results, by default in XML format.
 
@@ -237,7 +262,7 @@ def einfo(**keywds):
     available links for each Entrez database.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/einfo_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EInfo
 
     Return a handle to the results, by default in XML format.
 
@@ -265,7 +290,7 @@ def esummary(**keywds):
     from the user's environment.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/esummary_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESummary
 
     Return a handle to the results, by default in XML format.
 
@@ -297,7 +322,7 @@ def egquery(**keywds):
     using Global Query.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/egquery_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EGQuery
 
     Return a handle to the results in XML format.
 
@@ -330,7 +355,7 @@ def espell(**keywds):
     ESpell retrieves spelling suggestions, if available.
 
     See the online documentation for an explanation of the parameters:
-    http://www.ncbi.nlm.nih.gov/entrez/query/static/espell_help.html
+    http://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESpell
 
     Return a handle to the results, by default in XML format.
 
@@ -454,13 +479,21 @@ def parse(handle, validate=True):
     return records
 
 
-def _open(cgi, params=None, post=False, ecitmatch=False):
+def _open(cgi, params=None, post=None, ecitmatch=False):
     """Helper function to build the URL and open a handle to it (PRIVATE).
 
     Open a handle to Entrez.  cgi is the URL for the cgi script to access.
     params is a dictionary with the options to pass to it.  Does some
     simple error checking, and will raise an IOError if it encounters one.
 
+    The arugment post should be a boolean to explicitly control if an HTTP
+    POST should be used rather an HTTP GET based on the query length.
+    By default (post=None), POST is used if the query URL would be over
+    1000 characters long.
+
+    The arugment post should be a boolean to explicitly control if an HTTP
+    POST should be used rather an HTTP GET based on the query length.
+
     This function also enforces the "up to three queries per second rule"
     to avoid abusing the NCBI servers.
     """
@@ -500,12 +533,17 @@ is A.N.Other at example.com, you can specify it as follows:
 In case of excessive usage of the E-utilities, NCBI will attempt to contact
 a user at the email address provided before blocking access to the
 E-utilities.""", UserWarning)
+
     # Open a handle to Entrez.
     options = _urlencode(params, doseq=True)
     # _urlencode encodes pipes, which NCBI expects in ECitMatch
     if ecitmatch:
         options = options.replace('%7C', '|')
     # print cgi + "?" + options
+
+    # By default, post is None. Set to a boolean to over-ride length choice:
+    if post is None and len(options) > 1000:
+        post = True
     try:
         if post:
             # HTTP POST
diff --git a/Bio/ExPASy/Enzyme.py b/Bio/ExPASy/Enzyme.py
index 2c81885..a10ce64 100644
--- a/Bio/ExPASy/Enzyme.py
+++ b/Bio/ExPASy/Enzyme.py
@@ -22,8 +22,6 @@ Classes:
 
 """
 
-__docformat__ = "restructuredtext en"
-
 
 def parse(handle):
     """Parse ENZYME records.
diff --git a/Bio/ExPASy/Prodoc.py b/Bio/ExPASy/Prodoc.py
index 41650fc..65817df 100644
--- a/Bio/ExPASy/Prodoc.py
+++ b/Bio/ExPASy/Prodoc.py
@@ -23,8 +23,6 @@ Classes:
  - Reference          Holds data from a Prodoc reference.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 def read(handle):
     """Read in a record from a file with exactly one Prodoc record."""
diff --git a/Bio/ExPASy/Prosite.py b/Bio/ExPASy/Prosite.py
index f0322bb..c195f50 100644
--- a/Bio/ExPASy/Prosite.py
+++ b/Bio/ExPASy/Prosite.py
@@ -24,8 +24,6 @@ Classes:
     - Record                Holds Prosite data.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 def parse(handle):
     """Parse Prosite records.
diff --git a/Bio/ExPASy/ScanProsite.py b/Bio/ExPASy/ScanProsite.py
index a6caf9e..881d040 100644
--- a/Bio/ExPASy/ScanProsite.py
+++ b/Bio/ExPASy/ScanProsite.py
@@ -10,8 +10,6 @@ from Bio._py3k import urlencode as _urlencode
 from xml.sax import handler
 from xml.sax.expatreader import ExpatParser
 
-__docformat__ = "restructuredtext en"
-
 
 class Record(list):
     """Represents search results returned by ScanProsite.
diff --git a/Bio/ExPASy/__init__.py b/Bio/ExPASy/__init__.py
index a8a59dd..72cc6d9 100644
--- a/Bio/ExPASy/__init__.py
+++ b/Bio/ExPASy/__init__.py
@@ -21,8 +21,6 @@ Functions:
 from Bio._py3k import urlopen as _urlopen
 from Bio._py3k import urlencode as _urlencode
 
-__docformat__ = "restructuredtext en"
-
 
 def get_prodoc_entry(id, cgi='http://www.expasy.ch/cgi-bin/get-prodoc-entry'):
     """get_prodoc_entry(id,
diff --git a/Bio/FSSP/FSSPTools.py b/Bio/FSSP/FSSPTools.py
index 7cb31cf..09f6e67 100644
--- a/Bio/FSSP/FSSPTools.py
+++ b/Bio/FSSP/FSSPTools.py
@@ -1,7 +1,16 @@
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
-#
+"""Several routines used to extract information from FSSP sections.
+
+filter: filters a passed summary section and alignment section according to a numeric
+        attribute in the summary section. Returns new summary and alignment sections
+
+For example, to filter in only  those records which have a zscore greater than
+4.0 and lesser than 7.5:
+
+new_sum, new_align = filter(sum, align, 'zscore', 4, 7.5)
+"""
 
 from Bio import FSSP
 import copy
@@ -42,14 +51,6 @@ def mult_align(sum_dict, align_dict):
                                     sum_dict[i].pdb2 + sum_dict[i].chain2))
     return fssp_align
 
-
-# Several routines used to extract information from FSSP sections
-# filter:
-# filters a passed summary section and alignment section according to a numeric
-# attribute in the summary section. Returns new summary and alignment sections
-# For example, to filter in only  those records which have a zscore greater than
-# 4.0 and lesser than 7.5:
-# new_sum, new_align = filter(sum, align, 'zscore', 4, 7.5)
 #
 # Warning: this function really slows down when filtering large FSSP files.
 # The reason is the use of copy.deepcopy() to copy align_dict into
@@ -57,6 +58,7 @@ def mult_align(sum_dict, align_dict):
 # Took me ~160 seconds for the largest FSSP file (1reqA.fssp)
 #
 
+
 def filter(sum_dict, align_dict, filter_attribute, low_bound, high_bound):
     """Filters a passed summary section and alignment section according to a numeric
     attribute in the summary section. Returns new summary and alignment sections"""
diff --git a/Bio/FSSP/__init__.py b/Bio/FSSP/__init__.py
index 6c61d27..189bb03 100644
--- a/Bio/FSSP/__init__.py
+++ b/Bio/FSSP/__init__.py
@@ -21,6 +21,8 @@ import re
 from . import fssp_rec
 from Bio.Align import Generic
 from Bio import Alphabet
+
+
 fff_rec = fssp_rec.fff_rec
 header_records = {
     'database': re.compile('^DATABASE'),
diff --git a/Bio/FSSP/fssp_rec.py b/Bio/FSSP/fssp_rec.py
index 044d9c8..6b79d88 100644
--- a/Bio/FSSP/fssp_rec.py
+++ b/Bio/FSSP/fssp_rec.py
@@ -1,4 +1,6 @@
-# A superclass for reading [f]ixed-column type [f]lat-[f]ile records. (e.g.
+"""A superclass for reading [f]ixed-column type [f]lat-[f]ile records."""
+
+
 class fff_rec(object):
     def __init__(self, inrec=''):
         self.data = inrec
@@ -21,8 +23,8 @@ class fff_rec(object):
             return self.data[index]
 
 
-# Definition of the align section in a FSSP file
 class align(object):
+    """Definition of the align section in a FSSP file."""
     abs_res_num = (0, 4)
     pdb_res_num = (4, 9)
     chain_id = 10
diff --git a/Bio/File.py b/Bio/File.py
index 2f54edd..e5c2014 100644
--- a/Bio/File.py
+++ b/Bio/File.py
@@ -43,8 +43,6 @@ except ImportError:
     _sqlite = None
     pass
 
-__docformat__ = "restructuredtext en"
-
 
 @contextlib.contextmanager
 def as_handle(handleish, mode='r', **kwargs):
@@ -229,7 +227,12 @@ class _IndexedSeqFileProxy(object):
         raise NotImplementedError("Subclass should implement this")
 
     def get_raw(self, offset):
-        """Returns bytes string (if implemented for this file format)."""
+        """Return the raw record from the file as a bytes string (if implemented).
+
+        If the key is not found, a KeyError exception is raised.
+
+        This may not have been implemented for all file formats.
+        """
         # Should be done by each sub-class (if possible)
         raise NotImplementedError("Not available for this file format.")
 
@@ -371,14 +374,9 @@ class _IndexedSeqFileDict(_dict_base):
             return d
 
     def get_raw(self, key):
-        """Similar to the get method, but returns the record as a raw string.
+        """Return the raw record from the file as a bytes string.
 
         If the key is not found, a KeyError exception is raised.
-
-        Note that on Python 3 a bytes string is returned, not a typical
-        unicode string.
-
-        NOTE - This functionality is not supported for every file format.
         """
         # Pass the offset to the proxy
         return self._proxy.get_raw(self._offsets[key])
@@ -602,7 +600,7 @@ class _SQLiteManySeqFilesDict(_IndexedSeqFileDict):
                 # Since user gave BOTH filename & index as relative paths,
                 # we will store this relative to the index file even though
                 # if it may now start ../ (meaning up a level)
-                # Note for cross platfrom use (e.g. shared data drive over SAMBA),
+                # Note for cross platform use (e.g. shared data drive over SAMBA),
                 # convert any Windows slash into Unix style / for relative paths.
                 f = os.path.relpath(filename, relative_path).replace(os.path.sep, "/")
             elif (os.path.dirname(os.path.abspath(filename)) + os.path.sep).startswith(relative_path + os.path.sep):
@@ -714,14 +712,9 @@ class _SQLiteManySeqFilesDict(_IndexedSeqFileDict):
             return d
 
     def get_raw(self, key):
-        """Similar to the get method, but returns the record as a raw string.
+        """Return the raw record from the file as a bytes string.
 
         If the key is not found, a KeyError exception is raised.
-
-        Note that on Python 3 a bytes string is returned, not a typical
-        unicode string.
-
-        **NOTE** - This functionality is not supported for every file format.
         """
         # Pass the offset to the proxy
         row = self._con.execute(
diff --git a/Bio/GA/Crossover/GeneralPoint.py b/Bio/GA/Crossover/GeneralPoint.py
index f299d8c..c98340d 100644
--- a/Bio/GA/Crossover/GeneralPoint.py
+++ b/Bio/GA/Crossover/GeneralPoint.py
@@ -40,8 +40,6 @@ import random
 
 from Bio._py3k import range
 
-__docformat__ = "restructuredtext en"
-
 
 class GeneralPointCrossover(object):
     """Perform n-point crossover between genomes at some defined rates.
@@ -132,8 +130,8 @@ class GeneralPointCrossover(object):
 
                 - new organisms
 
-               - locs (int list, int list)
-               
+                - locs (int list, int list)
+
                 - lists of locations,
                   [0, +n points+, bound]
                   for each genome (sync'd with x)
diff --git a/Bio/GA/Crossover/Uniform.py b/Bio/GA/Crossover/Uniform.py
index 8eed37e..623a688 100644
--- a/Bio/GA/Crossover/Uniform.py
+++ b/Bio/GA/Crossover/Uniform.py
@@ -21,8 +21,6 @@ rapid mutation-behavior.
 # standard modules
 import random
 
-__docformat__ = "restructuredtext en"
-
 
 class UniformCrossover(object):
     """Perform single point crossover between genomes at some defined rates.
diff --git a/Bio/GenBank/Record.py b/Bio/GenBank/Record.py
index 951d0a4..b148be9 100644
--- a/Bio/GenBank/Record.py
+++ b/Bio/GenBank/Record.py
@@ -17,8 +17,6 @@ classes:
 # local stuff
 import Bio.GenBank
 
-__docformat__ = "restructuredtext en"
-
 
 def _wrapped_genbank(information, indent, wrap_space=1, split_char=" "):
     """Write a line of GenBank info that can wrap over multiple lines.
diff --git a/Bio/GenBank/Scanner.py b/Bio/GenBank/Scanner.py
index e53da35..391e43d 100644
--- a/Bio/GenBank/Scanner.py
+++ b/Bio/GenBank/Scanner.py
@@ -30,13 +30,12 @@ from __future__ import print_function
 
 import warnings
 import re
+from collections import defaultdict
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio.Alphabet import generic_protein
 from Bio import BiopythonParserWarning
 
-__docformat__ = "restructuredtext en"
-
 
 class InsdcScanner(object):
     """Basic functions for breaking up a GenBank/EMBL file into sub sections.
@@ -809,10 +808,24 @@ class EmblScanner(InsdcScanner):
                 # Remove trailing ; at end of authors list
                 consumer.authors(data.rstrip(";"))
             elif line_type == 'PR':
-                # Remove trailing ; at end of the project reference
-                # In GenBank files this corresponds to the old PROJECT
-                # line which is being replaced with the DBLINK line.
-                consumer.project(data.rstrip(";"))
+                # In the EMBL patent files, this is a PR (PRiority) line which
+                # provides the earliest active priority within the family.
+                # The priority  number comes first, followed by the priority date.
+                #
+                # e.g.
+                # PR   JP19990377484 16-DEC-1999
+                #
+                # However, in most EMBL files this is a PR (PRoject) line which
+                # gives the BioProject reference number.
+                #
+                # e.g.
+                # PR   Project:PRJNA60715;
+                #
+                # In GenBank files this corresponds to the old PROJECT line
+                # which was later replaced with the DBLINK line.
+                if data.startswith("Project:"):
+                    # Remove trailing ; at end of the project reference
+                    consumer.project(data.rstrip(";"))
             elif line_type == 'KW':
                 consumer.keywords(data.rstrip(";"))
             elif line_type in consumer_dict:
@@ -1246,6 +1259,9 @@ class GenBankScanner(InsdcScanner):
         # handled individually.
         GENBANK_INDENT = self.HEADER_WIDTH
         GENBANK_SPACER = " " * GENBANK_INDENT
+        STRUCTURED_COMMENT_START = "-START##"
+        STRUCTURED_COMMENT_END = "-END##"
+        STRUCTURED_COMMENT_DELIM = " :: "
         consumer_dict = {
             'DEFINITION': 'definition',
             'ACCESSION': 'accession',
@@ -1258,7 +1274,6 @@ class GenBankScanner(InsdcScanner):
             'AUTHORS': 'authors',
             'CONSRTM': 'consrtm',
             'PROJECT': 'project',
-            'DBLINK': 'dblink',
             'TITLE': 'title',
             'JOURNAL': 'journal',
             'MEDLINE': 'medline_id',
@@ -1268,6 +1283,7 @@ class GenBankScanner(InsdcScanner):
         # ORIGIN (locus, size, residue_type, data_file_division and date)
         # COMMENT (comment)
         # VERSION (version and gi)
+        # DBLINK (database links like projects, newlines important)
         # REFERENCE (eference_num and reference_bases)
         # ORGANISM (organism and taxonomy)
         lines = [_f for _f in lines if _f]
@@ -1296,6 +1312,20 @@ class GenBankScanner(InsdcScanner):
                         consumer.gi(data.split(' GI:')[1])
                     # Read in the next line!
                     line = next(line_iter)
+                elif line_type == 'DBLINK':
+                    # Need to call consumer.dblink() for each line, e.g.
+                    # DBLINK      Project: 57779
+                    #             BioProject: PRJNA57779
+                    consumer.dblink(data.strip())
+                    # Read in the next line, and see if its more of the DBLINK section:
+                    while True:
+                        line = next(line_iter)
+                        if line[:GENBANK_INDENT] == GENBANK_SPACER:
+                            # Add this continuation to the data string
+                            consumer.dblink(line[GENBANK_INDENT:].strip())
+                        else:
+                            # End of the DBLINK, leave this text in the variable "line"
+                            break
                 elif line_type == 'REFERENCE':
                     if self.debug > 1:
                         print("Found reference [" + data + "]")
@@ -1366,26 +1396,54 @@ class GenBankScanner(InsdcScanner):
                     consumer.taxonomy(lineage_data.strip())
                     del organism_data, lineage_data
                 elif line_type == 'COMMENT':
+                    # A COMMENT can either be plain text or tabular (Structured Comment),
+                    # or contain both. Multiline comments are common. The code calls
+                    # consumer.comment() once with a list where each entry
+                    # is a line. If there's a structured comment consumer.structured_comment()
+                    # is called with a dict of dicts where the secondary key/value pairs are
+                    # the same as those in the structured comment table. The primary key is
+                    # the title or header of the table (e.g. Assembly-Data, FluData). See
+                    # http://www.ncbi.nlm.nih.gov/genbank/structuredcomment
+                    # for more information on Structured Comments.
+                    data = line[GENBANK_INDENT:]
                     if self.debug > 1:
                         print("Found comment")
-                    # This can be multiline, and should call consumer.comment() once
-                    # with a list where each entry is a line.
                     comment_list = []
-                    comment_list.append(data)
+                    structured_comment_dict = defaultdict(dict)
+                    structured_comment_key = ''
+
+                    if STRUCTURED_COMMENT_START in data:
+                        structured_comment_key = re.search(r"([^#]+){0}$".format(STRUCTURED_COMMENT_START), data).group(1)
+                        if self.debug > 1:
+                            print("Found Structured Comment")
+                    else:
+                        comment_list.append(data)
+
                     while True:
                         line = next(line_iter)
+                        data = line[GENBANK_INDENT:]
                         if line[0:GENBANK_INDENT] == GENBANK_SPACER:
-                            data = line[GENBANK_INDENT:]
-                            comment_list.append(data)
-                            if self.debug > 2:
-                                print("Comment continuation [" + data + "]")
+                            if STRUCTURED_COMMENT_START in data:
+                                structured_comment_key = re.search(r"([^#]+){0}$".format(STRUCTURED_COMMENT_START), data).group(1)
+                            elif structured_comment_key is not None and STRUCTURED_COMMENT_DELIM in data:
+                                match = re.search(r"(.+?)\s*{0}\s*(.+)".format(STRUCTURED_COMMENT_DELIM), data)
+                                structured_comment_dict[structured_comment_key][match.group(1)] = match.group(2)
+                                if self.debug > 2:
+                                    print("Structured Comment continuation [" + data + "]")
+                            elif STRUCTURED_COMMENT_END not in data:
+                                comment_list.append(data)
+                                if self.debug > 2:
+                                    print("Comment continuation [" + data + "]")
                         else:
                             # End of the comment
                             break
-                    consumer.comment(comment_list)
-                    del comment_list
+                    if comment_list:
+                        consumer.comment(comment_list)
+                    if structured_comment_dict:
+                        consumer.structured_comment(structured_comment_dict)
+                    del comment_list, structured_comment_key, structured_comment_dict
                 elif line_type in consumer_dict:
-                    # Its a semi-automatic entry!
+                    # It's a semi-automatic entry!
                     # Now, this may be a multi line entry...
                     while True:
                         line = next(line_iter)
diff --git a/Bio/GenBank/__init__.py b/Bio/GenBank/__init__.py
index 0b7b117..776d79a 100644
--- a/Bio/GenBank/__init__.py
+++ b/Bio/GenBank/__init__.py
@@ -1,5 +1,5 @@
 # Copyright 2000 by Jeffrey Chang, Brad Chapman.  All rights reserved.
-# Copyright 2006-2013 by Peter Cock.  All rights reserved.
+# Copyright 2006-2016 by Peter Cock.  All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -13,7 +13,7 @@ SeqRecord and SeqFeature objects (see the Biopython tutorial for details).
 
 Using Bio.GenBank directly to parse GenBank files is only useful if you want
 to obtain GenBank-specific Record objects, which is a much closer
-representation to the raw file contents that the SeqRecord alternative from
+representation to the raw file contents than the SeqRecord alternative from
 the FeatureParser (used in Bio.SeqIO).
 
 To use the Bio.GenBank parser, there are two helper functions:
@@ -53,7 +53,6 @@ from Bio import SeqFeature
 from .utils import FeatureValueCleaner
 from .Scanner import GenBankScanner
 
-__docformat__ = "restructuredtext en"
 
 # Constants used to parse GenBank header lines
 GENBANK_INDENT = 12
@@ -97,7 +96,7 @@ _simple_location = r"\d+\.\.\d+"
 _re_simple_location = re.compile(r"^%s$" % _simple_location)
 _re_simple_compound = re.compile(r"^(join|order|bond)\(%s(,%s)*\)$"
                                  % (_simple_location, _simple_location))
-_complex_location = r"([a-zA-z][a-zA-Z0-9_]*(\.[a-zA-Z0-9]+)?\:)?(%s|%s|%s|%s|%s)" \
+_complex_location = r"([a-zA-Z][a-zA-Z0-9_\.]*[a-zA-Z0-9]?\:)?(%s|%s|%s|%s|%s)" \
                     % (_pair_location, _solo_location, _between_location,
                        _within_location, _oneof_location)
 _re_complex_location = re.compile(r"^%s$" % _complex_location)
@@ -132,10 +131,12 @@ assert _re_complex_location.match("41^42")  # between
 assert _re_complex_location.match("AL121804:41^42")
 assert _re_complex_location.match("AL121804:41..610")
 assert _re_complex_location.match("AL121804.2:41..610")
+assert _re_complex_location.match("AL358792.24.1.166931:3274..3461")  # lots of dots in external reference
 assert _re_complex_location.match("one-of(3,6)..101")
 assert _re_complex_compound.match("join(153490..154269,AL121804.2:41..610,AL121804.2:672..1487)")
 assert not _re_simple_compound.match("join(153490..154269,AL121804.2:41..610,AL121804.2:672..1487)")
 assert _re_complex_compound.match("join(complement(69611..69724),139856..140650)")
+assert _re_complex_compound.match("join(complement(AL354868.10.1.164018:80837..81016),complement(AL354868.10.1.164018:80539..80835))")
 
 # Trans-spliced example from NC_016406, note underscore in reference name:
 assert _re_complex_location.match("NC_016402.1:6618..6676")
@@ -948,6 +949,9 @@ class _FeatureConsumer(_BaseGenBankConsumer):
         except KeyError:
             self.data.annotations['comment'] = "\n".join(content)
 
+    def structured_comment(self, content):
+        self.data.annotations['structured_comment'] = content
+
     def features_line(self, content):
         """Get ready for the feature table when we reach the FEATURE line.
         """
@@ -1383,6 +1387,9 @@ class _RecordConsumer(_BaseGenBankConsumer):
     def comment(self, content):
         self.data.comment += "\n".join(content)
 
+    def structured_comment(self, content):
+        self.data.structured_comment = content
+
     def primary_ref_line(self, content):
         """Data for the PRIMARY line"""
         self.data.primary.append(content)
diff --git a/Bio/GenBank/utils.py b/Bio/GenBank/utils.py
index 4220b80..a4c4304 100644
--- a/Bio/GenBank/utils.py
+++ b/Bio/GenBank/utils.py
@@ -6,8 +6,6 @@
 """Useful utilities for helping in parsing GenBank files.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 class FeatureValueCleaner(object):
     r"""Provide specialized capabilities for cleaning up values in features.
diff --git a/Bio/Geo/Record.py b/Bio/Geo/Record.py
index f7176e9..026f3fe 100644
--- a/Bio/Geo/Record.py
+++ b/Bio/Geo/Record.py
@@ -48,7 +48,7 @@ class Record(object):
                     try:
                         output += '%s: %s\n' % (key, item[:40])
                         output += out_block(item[40:])
-                    except:
+                    except Exception:  # TODO: IndexError?
                         pass
             elif isinstance(contents, str):
                 output += '%s: %s\n' % (key, contents[:40])
diff --git a/Bio/Graphics/BasicChromosome.py b/Bio/Graphics/BasicChromosome.py
index 8bdde68..72ebd92 100644
--- a/Bio/Graphics/BasicChromosome.py
+++ b/Bio/Graphics/BasicChromosome.py
@@ -30,23 +30,20 @@ http://www.reportlab.com
 This provides nice output in PDF, SVG and postscript.  If you have
 reportlab's renderPM module installed you can also use PNG etc.
 """
-# standard library
-import os
 
 # reportlab
-from reportlab.pdfgen import canvas
 from reportlab.lib.pagesizes import letter
 from reportlab.lib.units import inch
 from reportlab.lib import colors
 from reportlab.pdfbase.pdfmetrics import stringWidth
 
 from reportlab.graphics.shapes import Drawing, String, Line, Rect, Wedge, ArcPath
-from reportlab.graphics import renderPDF, renderPS
 from reportlab.graphics.widgetbase import Widget
 
 from Bio.Graphics import _write
 from Bio.Graphics.GenomeDiagram._Colors import ColorTranslator as _ColorTranslator
 
+
 _color_trans = _ColorTranslator()
 
 
@@ -255,11 +252,11 @@ class Chromosome(_ChromosomeComponent):
         # will actually be the picture of the chromosome
         cur_y_pos = self.start_y_position
         if self.scale_num:
-            y_pos_change = ((self.start_y_position * .95 - self.end_y_position)
-                            / self.scale_num)
+            y_pos_change = ((self.start_y_position * .95 - self.end_y_position) /
+                            self.scale_num)
         elif len(self._sub_components) > 0:
-            y_pos_change = ((self.start_y_position * .95 - self.end_y_position)
-                            / self.subcomponent_size())
+            y_pos_change = ((self.start_y_position * .95 - self.end_y_position) /
+                            self.subcomponent_size())
         # no sub_components to draw
         else:
             pass
@@ -598,7 +595,7 @@ def _place_labels(desired_etc, minimum, maximum, gap=0):
 class AnnotatedChromosomeSegment(ChromosomeSegment):
     def __init__(self, bp_length, features,
                  default_feature_color=colors.blue,
-                 name_qualifiers=['gene', 'label', 'name', 'locus_tag', 'product']):
+                 name_qualifiers=('gene', 'label', 'name', 'locus_tag', 'product')):
         """Like the ChromosomeSegment, but accepts a list of features.
 
         The features can either be SeqFeature objects, or tuples of values:
@@ -619,7 +616,7 @@ class AnnotatedChromosomeSegment(ChromosomeSegment):
         is used, unless the feature's qualifiers include an Artemis colour
         string (functionality also in GenomeDiagram). The caption also follows
         the GenomeDiagram approach and takes the first qualifier from the list
-        specified in name_qualifiers.
+        or tuple specified in name_qualifiers.
 
         Note additional attribute label_sep_percent controls the percentage of
         area that the chromosome segment takes up, by default half of the
@@ -661,7 +658,7 @@ class AnnotatedChromosomeSegment(ChromosomeSegment):
                 try:
                     # Handles Artemis colour integers, HTML colors, etc
                     color = _color_trans.translate(f.qualifiers['color'][0])
-                except:
+                except Exception:  # TODO: ValueError?
                     color = self.default_feature_color
                 fill_color = color
                 name = ""
diff --git a/Bio/Graphics/ColorSpiral.py b/Bio/Graphics/ColorSpiral.py
index 4163a3c..696b74b 100644
--- a/Bio/Graphics/ColorSpiral.py
+++ b/Bio/Graphics/ColorSpiral.py
@@ -23,8 +23,6 @@ import colorsys    # colour format conversions
 from math import log, exp, floor, pi
 import random      # for jitter values
 
-__docformat__ = "restructuredtext en"
-
 
 class ColorSpiral(object):
     """Implement a spiral path through HSV colour space.
diff --git a/Bio/Graphics/Comparative.py b/Bio/Graphics/Comparative.py
index c1c46fc..e44d786 100644
--- a/Bio/Graphics/Comparative.py
+++ b/Bio/Graphics/Comparative.py
@@ -10,20 +10,18 @@ compare different types of information. The most basic example is comparing
 two variables in a traditional scatter plot.
 """
 # reportlab
-from reportlab.pdfgen import canvas
 from reportlab.lib import colors
 from reportlab.graphics.charts.lineplots import LinePlot
 from reportlab.lib.pagesizes import letter
 from reportlab.lib.units import inch
 
-from reportlab.graphics.shapes import Drawing, String, Group
-from reportlab.graphics import renderPDF, renderPS
-from reportlab.graphics.charts.markers import *
+from reportlab.graphics.shapes import Drawing, String
+from reportlab.graphics.charts.markers import makeEmptySquare, makeFilledSquare
+from reportlab.graphics.charts.markers import makeFilledDiamond, makeEmptyCircle
+from reportlab.graphics.charts.markers import makeFilledCircle, makeSmiley
 
 from Bio.Graphics import _write
 
-__docformat__ = "restructuredtext en"
-
 
 class ComparativeScatterPlot(object):
     """Display a scatter-type plot comparing two different kinds of info.
diff --git a/Bio/Graphics/DisplayRepresentation.py b/Bio/Graphics/DisplayRepresentation.py
index 502b25a..a72f55c 100644
--- a/Bio/Graphics/DisplayRepresentation.py
+++ b/Bio/Graphics/DisplayRepresentation.py
@@ -15,6 +15,7 @@ from reportlab.lib import colors
 from Bio.Graphics.BasicChromosome import ChromosomeSegment
 from Bio.Graphics.BasicChromosome import TelomereSegment
 
+
 # --- constants
 # This is a default color scheme based on the light spectrum.
 # Based on my vague recollections from biology, this is our friend ROY G. BIV
@@ -91,7 +92,7 @@ class ChromosomeCounts(object):
             raise KeyError("Segment name %s not found." % segment_name)
 
     def add_label(self, segment_name, label):
-        """Add a label to a specfic segment.
+        """Add a label to a specific segment.
 
         Raises a KeyError is the specified segment name is not found.
         """
diff --git a/Bio/Graphics/Distribution.py b/Bio/Graphics/Distribution.py
index 022b78e..4422903 100644
--- a/Bio/Graphics/Distribution.py
+++ b/Bio/Graphics/Distribution.py
@@ -65,8 +65,8 @@ class DistributionPage(object):
         end_y_pos = .5 * inch
         x_pos_change = ((end_x_pos - cur_x_pos) /
                         float(self.number_of_columns))
-        num_y_rows = math.ceil(float(len(self.distributions))
-                               / float(self.number_of_columns))
+        num_y_rows = math.ceil(float(len(self.distributions)) /
+                               float(self.number_of_columns))
         y_pos_change = (cur_y_pos - end_y_pos) / num_y_rows
 
         self._draw_distributions(cur_drawing, cur_x_pos, x_pos_change,
diff --git a/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py b/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py
index 51d9380..090c2cd 100644
--- a/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py
+++ b/Bio/Graphics/GenomeDiagram/_AbstractDrawer.py
@@ -46,7 +46,6 @@ from reportlab.graphics.shapes import Polygon
 
 from math import pi, sin, cos
 
-__docformat__ = "restructuredtext en"
 
 ################################################################################
 # METHODS
@@ -273,7 +272,7 @@ def draw_arrow(point1, point2, color=colors.lightgreen, border=None,
 
 
 def angle2trig(theta):
-    """Convert anngle to a reportlab ready tuple.
+    """Convert angle to a reportlab ready tuple.
 
     Arguments:
 
@@ -346,7 +345,7 @@ class AbstractDrawer(object):
 
         Attributes:
 
-            - tracklines    Boolean for whether to draw lines dilineating tracks
+            - tracklines    Boolean for whether to draw lines delineating tracks
 
             - pagesize      Tuple describing the size of the page in pixels
 
@@ -512,7 +511,7 @@ class AbstractDrawer(object):
         self.length = self.end - self.start + 1
 
     def is_in_bounds(self, value):
-        """Check if given value is withing the region selected for drawing,
+        """Check if given value is within the region selected for drawing,
 
         Arguments:
 
diff --git a/Bio/Graphics/GenomeDiagram/_CircularDrawer.py b/Bio/Graphics/GenomeDiagram/_CircularDrawer.py
index 5b441a2..29519e6 100644
--- a/Bio/Graphics/GenomeDiagram/_CircularDrawer.py
+++ b/Bio/Graphics/GenomeDiagram/_CircularDrawer.py
@@ -16,7 +16,6 @@ from __future__ import print_function
 
 from reportlab.graphics.shapes import Drawing, String, Group, Line, Circle, Polygon
 from reportlab.lib import colors
-from reportlab.pdfbase import _fontdata
 from reportlab.graphics.shapes import ArcPath
 
 from Bio._py3k import range
@@ -27,7 +26,7 @@ from ._AbstractDrawer import _stroke_and_fill_colors
 from ._FeatureSet import FeatureSet
 from ._GraphSet import GraphSet
 
-from math import ceil, pi, cos, sin, asin
+from math import pi, cos, sin
 
 
 class CircularDrawer(AbstractDrawer):
@@ -231,7 +230,7 @@ class CircularDrawer(AbstractDrawer):
         for track in range(bot_track, top_track + 1):  # track numbers to 'draw'
             try:
                 trackheight = self._parent[track].height  # Get track height
-            except:
+            except Exception:  # TODO: ValueError? IndexError?
                 trackheight = 1
             trackunit_sum += trackheight  # increment total track unit height
             trackunits[track] = (heightholder, heightholder + trackheight)
diff --git a/Bio/Graphics/GenomeDiagram/_CrossLink.py b/Bio/Graphics/GenomeDiagram/_CrossLink.py
index 380163f..c42008f 100644
--- a/Bio/Graphics/GenomeDiagram/_CrossLink.py
+++ b/Bio/Graphics/GenomeDiagram/_CrossLink.py
@@ -58,7 +58,7 @@ class CrossLink(object):
             track, start, end = self.featureA
             assert track in tracks
             return track
-        except:
+        except Exception:  # TODO: ValueError?
             for track in tracks:
                 for feature_set in track.get_sets():
                     if hasattr(feature_set, "features"):
diff --git a/Bio/Graphics/GenomeDiagram/_Diagram.py b/Bio/Graphics/GenomeDiagram/_Diagram.py
index 02dbdd2..3a39f6e 100644
--- a/Bio/Graphics/GenomeDiagram/_Diagram.py
+++ b/Bio/Graphics/GenomeDiagram/_Diagram.py
@@ -6,204 +6,79 @@
 # Contact:       Leighton Pritchard, Scottish Crop Research Institute,
 #                Invergowrie, Dundee, Scotland, DD2 5DA, UK
 #                L.Pritchard at scri.ac.uk
-################################################################################
+"""Provides a container for information concerning the tracks to be drawn in a diagram.
 
-""" Diagram module
+It also provides the interface for defining the diagram (possibly split these
+functions in later version?).
 
-    Provides:
+For drawing capabilities, this module uses reportlab to draw and write the
+diagram:
 
-    o Diagram -   Container for information concerning the tracks to be
-                    drawn in a diagram, and the interface for defining the
-                    diagram (possibly split these functions in later version?)
+http://www.reportlab.com
 
-    For drawing capabilities, this module uses reportlab to draw and write
-    the diagram:
-
-    http://www.reportlab.com
-
-    For dealing with biological information, the package expects BioPython
-    objects - namely SeqRecord ojbects containing SeqFeature objects.
+For dealing with biological information, the package expects BioPython
+objects - namely SeqRecord objects containing SeqFeature objects.
 """
 
-# -----------------------------------------------------------------------------
-# IMPORTS
-
-# ReportLab
 try:
     from reportlab.graphics import renderPM
 except ImportError:
     # This is an optional part of ReportLab, so may not be installed.
     renderPM = None
 
-# GenomeDiagram
 from ._LinearDrawer import LinearDrawer
 from ._CircularDrawer import CircularDrawer
 from ._Track import Track
 
 from Bio.Graphics import _write
 
-# -----------------------------------------------------------------------------
-# CLASSES
-
-# -----------------------------------------------------------
-# Diagram
-
 
 class Diagram(object):
-    """ Diagram
-
-        Provides:
-
-        Attributes:
-
-        o name         String, identifier for the diagram
-
-        o tracks       List of Track objects comprising the diagram
-
-        o format       String, format of the diagram (circular/linear)
-
-        o pagesize     String, the pagesize of output
-
-        o orientation  String, the page orientation (landscape/portrait)
-
-        o x            Float, the proportion of the page to take up with even
-                              X margins
-
-        o y            Float, the proportion of the page to take up with even
-                              Y margins
-
-        o xl           Float, the proportion of the page to take up with the
-                              left X margin
-
-        o xr           Float, the proportion of the page to take up with the
-                              right X margin
-
-        o yt           Float, the proportion of the page to take up with the
-                              top Y margin
-
-        o yb           Float, the proportion of the page to take up with the
-                              bottom Y margin
-
-        o circle_core  Float, the proportion of the available radius to leave
-                       empty at the center of a circular diagram (0 to 1).
-
-        o start        Int, the base/aa position to start the diagram at
-
-        o end          Int, the base/aa position to end the diagram at
-
-        o tracklines   Boolean, True if track guidelines are to be drawn
-
-        o fragments    Int, for a linear diagram, the number of equal divisions
-                                into which the sequence is divided
-
-        o fragment_size Float, the proportion of the space available to each
-                                   fragment that should be used in drawing
-
-        o track_size   Float, the proportion of the space available to each
-                                  track that should be used in drawing
-
-        o circular     Boolean, True if the genome/sequence to be drawn is, in
-                                reality, circular.
-
-        Methods:
-
-        o __init__(self, name=None) Called on instantiation
-
-        o draw(self, format='circular', ...) Instructs the package to draw
-            the diagram
-
-        o write(self, filename='test1.ps', output='PS') Writes the drawn
-            diagram to a specified file, in a specified format.
-
-        o add_track(self, track, track_level) Adds a Track object to the
-            diagram, with instructions to place it at a particular level on
-            the diagram
-
-        o del_track(self, track_level) Removes the track that is to be drawn
-            at a particular level on the diagram
-
-        o get_tracks(self) Returns the list of Track objects to be drawn
-            contained in the diagram
-
-        o renumber_tracks(self, low=1) Renumbers all tracks consecutively,
-            optionally from a passed lowest number
-
-        o get_levels(self) Returns a list of levels currently occupied by
-            Track objects
-
-        o get_drawn_levels(self) Returns a list of levels currently occupied
-            by Track objects that will be shown in the drawn diagram (i.e.
-            are not hidden)
-
-        o range(self) Returns the lowest- and highest-numbered positions
-            contained within features in all tracks on the diagram as a tuple.
-
-        o __getitem__(self, key) Returns the track contained at the level of
-            the passed key
-
-        o __str__(self) Returns a formatted string describing the diagram
-
+    """Diagram container.
+
+    Arguments:
+        - name           - a string, identifier for the diagram.
+        - tracks         - a list of Track objects comprising the diagram.
+        - format         - a string, format of the diagram 'circular' or
+          'linear', depending on the sort of diagram required.
+        - pagesize       - a string, the pagesize of output describing the ISO
+          size of the image, or a tuple of pixels.
+        - orientation    - a string describing the required orientation of the
+          final drawing ('landscape' or 'portrait').
+        - x              - a float (0->1), the proportion of the page to take
+          up with even X margins t the page.
+        - y              - a float (0->1), the proportion of the page to take
+          up with even Y margins to the page.
+        - xl             - a float (0->1), the proportion of the page to take
+          up with the left X margin to the page (overrides x).
+        - xr             - a float (0->1), the proportion of the page to take
+          up with the right X margin to the page (overrides x).
+        - yt             - a float (0->1), the proportion of the page to take
+          up with the top Y margin to the page (overrides y).
+        - yb             - a float (0->1), the proportion of the page to take
+          up with the bottom Y margin to the page (overrides y).
+        - circle_core    - a float, the proportion of the available radius to
+          leave empty at the center of a circular diagram (0 to 1).
+        - start          - an integer, the base/aa position to start the diagram at.
+        - end            - an integer, the base/aa position to end the diagram at.
+        - tracklines     - a boolean, True if track guidelines are to be drawn.
+        - fragments      - and integer, for a linear diagram, the number of equal
+          divisions into which the sequence is divided.
+        - fragment_size  - a float (0->1), the proportion of the space
+          available to each fragment that should be used in drawing.
+        - track_size     - a float (0->1), the proportion of the space
+          available to each track that should be used in drawing with sigils.
+        - circular       - a boolean, True if the genome/sequence to be drawn
+          is, in reality, circular.
     """
     def __init__(self, name=None, format='circular', pagesize='A3',
-         orientation='landscape', x=0.05, y=0.05, xl=None,
-         xr=None, yt=None, yb=None, start=None, end=None,
-         tracklines=False, fragments=10, fragment_size=0.9,
-         track_size=0.75, circular=True, circle_core=0.0):
-        """ __init__(self, name=None)
-
-            o name  String describing the diagram
-
-            o format    String: 'circular' or 'linear', depending on the sort of
-                        diagram required
-
-            o pagesize  String describing the ISO size of the image, or a tuple
-                        of pixels
-
-            o orientation   String describing the required orientation of the
-                            final drawing ('landscape' or 'portrait')
-
-            o x         Float (0->1) describing the relative size of the X
-                        margins to the page
-
-            o y         Float (0->1) describing the relative size of the Y
-                        margins to the page
-
-            o xl        Float (0->1) describing the relative size of the left X
-                        margin to the page (overrides x)
-
-            o xl        Float (0->1) describing the relative size of the left X
-                        margin to the page (overrides x)
-
-            o xr        Float (0->1) describing the relative size of the right X
-                        margin to the page (overrides x)
-
-            o yt        Float (0->1) describing the relative size of the top Y
-                        margin to the page (overrides y)
-
-            o yb        Float (0->1) describing the relative size of the lower Y
-                        margin to the page (overrides y)
-
-            o start     Int, the position to begin drawing the diagram at
-
-
-            o end       Int, the position to stop drawing the diagram at
-
-            o tracklines    Boolean flag to show (or not) lines delineating
-                        tracks on the diagram
-
-            o fragments Int, for linear diagrams, the number of sections into
-                        which to break the sequence being drawn
-
-            o fragment_size     Float (0->1), for linear diagrams, describing
-                                the proportion of space in a fragment to take
-                                up with tracks
-
-            o track_size        Float (0->1) describing the proportion of space
-                                in a track to take up with sigils
-
-            o circular  Boolean flag to indicate whether the sequence being
-                        drawn is circular
+                 orientation='landscape', x=0.05, y=0.05, xl=None,
+                 xr=None, yt=None, yb=None, start=None, end=None,
+                 tracklines=False, fragments=10, fragment_size=0.9,
+                 track_size=0.75, circular=True, circle_core=0.0):
+        """Called on instantiation.
 
+        gdd = Diagram(name=None)
         """
         self.tracks = {}   # Holds all Track objects, keyed by level
         self.name = name    # Description of the diagram
@@ -226,16 +101,16 @@ class Diagram(object):
         self.circular = circular
         self.circle_core = circle_core
         self.cross_track_links = []
+        self.drawing = None
 
     def set_all_tracks(self, attr, value):
-        """ set_all_tracks(self, attr, value)
+        """Set the passed attribute of all tracks in the set to the passed value.
 
-            o attr      An attribute of the Track class
+        Arguments:
+            - attr    - An attribute of the Track class.
+            - value   - The value to set that attribute.
 
-            o value     The value to set that attribute
-
-            Set the passed attribute of all tracks in the set to the
-            passed value
+        set_all_tracks(self, attr, value)
         """
         for track in self.tracks.values():
             if hasattr(track, attr):          # If the feature has the attribute
@@ -248,6 +123,8 @@ class Diagram(object):
              fragment_size=None, track_size=None, circular=None,
              circle_core=None, cross_track_links=None):
         """Draw the diagram, with passed parameters overriding existing attributes.
+
+        gdd.draw(format='circular')
         """
         # Pass the parameters to the drawer objects that will build the
         # diagrams.  At the moment, we detect overrides with an or in the
@@ -281,35 +158,36 @@ class Diagram(object):
         self.drawing = drawer.drawing  # Get the completed drawing
 
     def write(self, filename='test1.ps', output='PS', dpi=72):
-        """ write(self, filename='test1.ps', output='PS', dpi=72)
-
-            o filename      String indicating the name of the output file,
-                            or a handle to write to.
-
-            o output        String indicating output format, one of PS, PDF,
-                            SVG, or provided the ReportLab renderPM module is
-                            installed, one of the bitmap formats JPG, BMP,
-                            GIF, PNG, TIFF or TIFF.  The format can be given
-                            in upper or lower case.
-
-            o dpi           Resolution (dots per inch) for bitmap formats.
-
-            Write the completed drawing out to a file in a prescribed format
-
+        """Writes the drawn diagram to a specified file, in a specified format.
+
+        Arguments:
+            - filename   - a string indicating the name of the output file,
+              or a handle to write to.
+            - output     - a string indicating output format, one of PS, PDF,
+              SVG, or provided the ReportLab renderPM module is installed, one
+              of the bitmap formats JPG, BMP, GIF, PNG, TIFF or TIFF.  The
+              format can be given in upper or lower case.
+            - dpi        - an integer. Resolution (dots per inch) for bitmap formats.
+
+        Returns:
             No return value.
+
+        write(self, filename='test1.ps', output='PS', dpi=72)
         """
         return _write(self.drawing, filename, output, dpi=dpi)
 
     def write_to_string(self, output='PS', dpi=72):
         """Returns a byte string containing the diagram in the requested format.
 
-            o output        String indicating output format, one of PS, PDF,
-                            SVG, JPG, BMP, GIF, PNG, TIFF or TIFF (as
-                            specified for the write method).
-
-            o dpi           Resolution (dots per inch) for bitmap formats.
+        Arguments:
+            - output    - a string indicating output format, one of PS, PDF,
+              SVG, JPG, BMP, GIF, PNG, TIFF or TIFF (as specified for the write
+              method).
+            - dpi       - Resolution (dots per inch) for bitmap formats.
 
-            Return the completed drawing as a bytes string in a prescribed format
+        Returns:
+            Return the completed drawing as a bytes string in a prescribed
+            format.
         """
         # The ReportLab drawToString method, which this function used to call,
         # just used a cStringIO or StringIO handle with the drawToFile method.
@@ -324,14 +202,17 @@ class Diagram(object):
         return handle.getvalue()
 
     def add_track(self, track, track_level):
-        """ add_track(self, track, track_level)
+        """Adds a Track object to the diagram.
 
-            o track         Track object to draw
+        It also accepts instructions to place it at a particular level on the
+        diagram.
 
-            o track_level   Int, the level at which the track will be drawn
-                            (above an arbitrary baseline)
+        Arguments:
+            - track          - Track object to draw.
+            - track_level    - an integer. The level at which the track will be
+              drawn (above an arbitrary baseline).
 
-            Add a pre-existing Track to the diagram at a given level
+        add_track(self, track, track_level)
         """
         if track is None:
             raise ValueError("Must specify track")
@@ -348,13 +229,15 @@ class Diagram(object):
         self.tracks[track_level].track_level = track_level
 
     def new_track(self, track_level, **args):
-        """ new_track(self, track_level) -> Track
+        """Add a new Track to the diagram at a given level.
 
-            o track_level   Int, the level at which the track will be drawn
-                            (above an arbitrary baseline)
+        The track is returned for further user manipulation.
 
-            Add a new Track to the diagram at a given level and returns it for
-            further user manipulation.
+        Arguments:
+            - track_level   - an integer. The level at which the track will be
+              drawn (above an arbitrary baseline).
+
+        new_track(self, track_level)
         """
         newtrack = Track()
         for key in args:
@@ -372,44 +255,48 @@ class Diagram(object):
         return newtrack
 
     def del_track(self, track_level):
-        """ del_track(self, track_level)
+        """Removes the track to be drawn at a particular level on the diagram.
 
-            o track_level   Int, the level of the track on the diagram to delete
+        Arguments:
+            - track_level   - an integer. The level of the track on the diagram
+              to delete.
 
-            Remove the track at the passed level from the diagram
+        del_track(self, track_level)
         """
         del self.tracks[track_level]
 
     def get_tracks(self):
-        """ get_tracks(self) -> list
+        """Returns a list of the tracks contained in the diagram.
 
-            Returns a list of the tracks contained in the diagram
+        get_tracks(self)
         """
         return list(self.tracks.values())
 
     def move_track(self, from_level, to_level):
-        """ move_track(self, from_level, to_level)
-
-            o from_level    Int, the level at which the track to be moved is
-                            found
+        """Moves a track from one level on the diagram to another.
 
-            o to_level      Int, the level to move the track to
+        Arguments:
+            - from_level   - an integer. The level at which the track to be
+              moved is found.
+            - to_level     - an integer. The level to move the track to.
 
-            Moves a track from one level on the diagram to another
+        move_track(self, from_level, to_level)
         """
         aux = self.tracks[from_level]
         del self.tracks[from_level]
         self.add_track(aux, to_level)
 
     def renumber_tracks(self, low=1, step=1):
-        """ renumber_tracks(self, low=1, step=1)
+        """Renumbers all tracks consecutively.
 
-            o low       Int, the track number to start from
+        Optionally from a passed lowest number.
 
-            o step      Int, the track interval for separation of tracks
+        Arguments:
+            - low     - an integer. The track number to start from.
+            - step    - an integer. The track interval for separation of
+              tracks.
 
-            Reassigns all the tracks to run consecutively from the lowest
-            value (low)
+        renumber_tracks(self, low=1, step=1)
         """
         track = low                 # Start numbering from here
         levels = self.get_levels()
@@ -422,46 +309,49 @@ class Diagram(object):
         self.tracks = conversion   # Replace old set of levels with new set
 
     def get_levels(self):
-        """ get_levels(self) -> [int, int, ...]
+        """Return a sorted list of levels occupied by tracks in the diagram.
 
-            Return a sorted list of levels occupied by tracks in the diagram
+        get_levels(self)
         """
         return sorted(self.tracks)
 
     def get_drawn_levels(self):
-        """ get_drawn_levels(self) -> [int, int, ...]
+        """Return a sorted list of levels occupied by tracks.
 
-            Return a sorted list of levels occupied by tracks that are not
-            explicitly hidden
+        These tracks are not explicitly hidden.
+
+        get_drawn_levels(self)
         """
         return sorted(key for key in self.tracks if not self.tracks[key].hide)
 
     def range(self):
-        """ range(self) -> (int, int)
+        """Returns lowest and highest base numbers from track features.
+
+        Returned type is a tuple.
 
-            Returns the lowest and highest base (or mark) numbers containd in
-            track features as a tuple
+        range(self)
         """
         lows, highs = [], []
         for track in self.tracks.values():  # Get ranges for each track
             low, high = track.range()
             lows.append(low)
             highs.append(high)
-        return (min(lows), max(highs))      # Return extremes from all tracks
+        return min(lows), max(highs)      # Return extremes from all tracks
 
     def __getitem__(self, key):
-        """ __getitem__(self, key) -> Track
+        """Returns the track contained at the level of the passed key.
 
-            o key       The id of a track in the diagram
+        Arguments:
+            - key    - The id of a track in the diagram.
 
-            Return the Track object with the passed id
+        __getitem__(self, key)
         """
         return self.tracks[key]
 
     def __str__(self):
-        """ __str__(self) -> ""
+        """Returns a formatted string describing the diagram.
 
-            Returns a formatted string with information about the diagram
+        __str__(self)
         """
         outstr = ["\n<%s: %s>" % (self.__class__, self.name)]
         outstr.append("%d tracks" % len(self.tracks))
diff --git a/Bio/Graphics/GenomeDiagram/_FeatureSet.py b/Bio/Graphics/GenomeDiagram/_FeatureSet.py
index 38a2aea..1add356 100644
--- a/Bio/Graphics/GenomeDiagram/_FeatureSet.py
+++ b/Bio/Graphics/GenomeDiagram/_FeatureSet.py
@@ -22,29 +22,14 @@ For drawing capabilities, this module uses reportlab to draw and write
 the diagram: http://www.reportlab.com
 """
 
-# -----------------------------------------------------------------------------
-# IMPORTS
-
-# ReportLab
 from __future__ import print_function
 
-from reportlab.pdfbase import _fontdata
-from reportlab.lib import colors
-
 # GenomeDiagram
 from ._Feature import Feature
 
 # Builtins
 import re
 
-__docformat__ = "restructuredtext en"
-
-# ------------------------------------------------------------------------------
-# CLASSES
-
-# ------------------------------------------------------------
-# FeatureSet
-
 
 class FeatureSet(object):
     """FeatureSet object."""
@@ -122,7 +107,7 @@ class FeatureSet(object):
         #    self.set_all_feature("color",value)
 
     def get_features(self, attribute=None, value=None, comparator=None):
-        """Retreive features.
+        """Retrieve features.
 
         Arguments:
          - attribute: String, attribute of a Feature object
diff --git a/Bio/Graphics/GenomeDiagram/_LinearDrawer.py b/Bio/Graphics/GenomeDiagram/_LinearDrawer.py
index 4fbc741..6a4cfec 100644
--- a/Bio/Graphics/GenomeDiagram/_LinearDrawer.py
+++ b/Bio/Graphics/GenomeDiagram/_LinearDrawer.py
@@ -121,7 +121,7 @@ class LinearDrawer(AbstractDrawer):
 
         Attributes:
 
-        o tracklines    Boolean for whether to draw lines dilineating tracks
+        o tracklines    Boolean for whether to draw lines delineating tracks
 
         o pagesize      Tuple describing the size of the page in pixels
 
@@ -356,7 +356,7 @@ class LinearDrawer(AbstractDrawer):
         for track in range(bot_track, top_track + 1):  # for all track numbers to 'draw'
             try:
                 trackheight = self._parent[track].height    # Get track height
-            except:
+            except Exception:  # TODO: IndexError?
                 trackheight = 1                             # ...or default to 1
             trackunit_sum += trackheight    # increment total track unit height
             trackunits[track] = (heightholder, heightholder + trackheight)
diff --git a/Bio/Graphics/GenomeDiagram/_Track.py b/Bio/Graphics/GenomeDiagram/_Track.py
index d89807b..7245fdc 100644
--- a/Bio/Graphics/GenomeDiagram/_Track.py
+++ b/Bio/Graphics/GenomeDiagram/_Track.py
@@ -102,7 +102,7 @@ class Track(object):
                         as numerals.  Setting this to 'SInt' invokes SI
                         unit-like multiples, such as Mbp, Kbp and so on.
 
-        o scale_color  colors.Color to draw the elemnts of the scale
+        o scale_color  colors.Color to draw the elements of the scale
 
         o scale_font    String describing the font to use for the scale labels
 
@@ -178,7 +178,7 @@ class Track(object):
 
             o scale     Boolean, 1 if a scale is to be drawn on the track
 
-            o scale_color  colors.Color to draw the elemnts of the scale
+            o scale_color  colors.Color to draw the elements of the scale
                            (overridden by backwards compatible argument with UK
                            spelling, colour).
 
@@ -347,7 +347,7 @@ class Track(object):
         """
         if not verbose:             # Return the short description
             return "%s" % self      # Use __str__ method instead
-        else:                       # Return the long desription
+        else:                       # Return the long description
             outstr = ["\n<%s: %s>" % (self.__class__, self.name)]
             outstr.append("%d sets" % len(self._sets))
             for key in self._sets:
diff --git a/Bio/Graphics/GenomeDiagram/__init__.py b/Bio/Graphics/GenomeDiagram/__init__.py
index 5d11602..9014ea7 100644
--- a/Bio/Graphics/GenomeDiagram/__init__.py
+++ b/Bio/Graphics/GenomeDiagram/__init__.py
@@ -9,6 +9,8 @@
 #                L.Pritchard at scri.ac.uk
 # #############################################################################
 
+"""GenomeDiagram module integrated into Biopython."""
+
 # Local imports, to make these classes available directly under the
 # Bio.Graphics.GenomeDiagram namespace:
 
@@ -19,6 +21,6 @@ from ._GraphSet import GraphSet
 from ._CrossLink import CrossLink
 
 # Not (currently) made public,
-# from _Colors import ColorTranslator
-# from _Feature import Feature
-# from _Graph import GraphData
+# from ._Colors import ColorTranslator
+# from ._Feature import Feature
+# from ._Graph import GraphData
diff --git a/Bio/Graphics/KGML_vis.py b/Bio/Graphics/KGML_vis.py
index e214d65..0219a7a 100644
--- a/Bio/Graphics/KGML_vis.py
+++ b/Bio/Graphics/KGML_vis.py
@@ -10,12 +10,9 @@ from __future__ import print_function
 
 import os
 import tempfile
-from itertools import chain
 from io import BytesIO
 
-from reportlab.lib import pagesizes
 from reportlab.lib import colors
-from reportlab.lib.utils import ImageReader
 from reportlab.graphics.shapes import *
 from reportlab.pdfgen import canvas
 
diff --git a/Bio/Graphics/__init__.py b/Bio/Graphics/__init__.py
index a104069..19818b9 100644
--- a/Bio/Graphics/__init__.py
+++ b/Bio/Graphics/__init__.py
@@ -17,7 +17,6 @@ except ImportError:
         "to use Bio.Graphics. You can find ReportLab at "
         "http://www.reportlab.com/software/opensource/")
 
-__docformat__ = "restructuredtext en"
 
 # The following code is to allow all the Bio.Graphics
 # code to deal with the different ReportLab renderers
diff --git a/Bio/HMM/DynamicProgramming.py b/Bio/HMM/DynamicProgramming.py
index f49bb0c..d48c4a3 100644
--- a/Bio/HMM/DynamicProgramming.py
+++ b/Bio/HMM/DynamicProgramming.py
@@ -269,7 +269,7 @@ class ScaledDPAlgorithms(AbstractDPAlgorithms):
                                                        cur_state)]
             state_pos_sum += prev_forward * cur_trans_prob
 
-        # if we have the possiblity of having a transition
+        # if we have the possibility of having a transition
         # return the recursion value
         if have_transition:
             return (scale_emission_prob * state_pos_sum)
diff --git a/Bio/HMM/Trainer.py b/Bio/HMM/Trainer.py
index 8b432c4..696571d 100644
--- a/Bio/HMM/Trainer.py
+++ b/Bio/HMM/Trainer.py
@@ -23,8 +23,6 @@ import math
 # local stuff
 from .DynamicProgramming import ScaledDPAlgorithms
 
-__docformat__ = "restructuredtext en"
-
 
 class TrainingSequence(object):
     """Hold a training sequence with emissions and optionally, a state path.
diff --git a/Bio/Index.py b/Bio/Index.py
index 22108d8..3d2b492 100644
--- a/Bio/Index.py
+++ b/Bio/Index.py
@@ -14,10 +14,12 @@ _ShelveIndex    An Index class based on the shelve module.
 _InMemoryIndex  An in-memory Index class.
 
 """
+
 import os
 import array
 import shelve
 
+
 try:
     import cPickle as pickle  # Only available under Python 2
 except ImportError:
@@ -51,13 +53,13 @@ class _ShelveIndex(dict):
                         os.unlink(file)
                 raise Exception("open a new shelf")
             self.data = shelve.open(indexname, flag='r')
-        except:
+        except Exception:  # TODO: Which exception?
             # No database exists.
             self.data = shelve.open(indexname, flag='n')
             self.data[self.__version_key] = self.__version
         else:
             # Check to make sure the database is the correct version.
-            version = self.data.get(self.__version_key, None)
+            version = self.data.get(self.__version_key)
             if version is None:
                 raise IOError("Unrecognized index format")
             elif version != self.__version:
diff --git a/Bio/KDTree/KDTree.py b/Bio/KDTree/KDTree.py
index 5762a53..cb83a45 100644
--- a/Bio/KDTree/KDTree.py
+++ b/Bio/KDTree/KDTree.py
@@ -20,8 +20,6 @@ from numpy import random
 
 from Bio.KDTree import _CKDTree
 
-__docformat__ = "restructuredtext en"
-
 
 def _dist(p, q):
     diff = p - q
diff --git a/Bio/KDTree/__init__.py b/Bio/KDTree/__init__.py
index 0ffd69e..56646bb 100644
--- a/Bio/KDTree/__init__.py
+++ b/Bio/KDTree/__init__.py
@@ -14,5 +14,3 @@ Otfried Schwarzkopf).
 """
 
 from .KDTree import KDTree
-
-__docformat__ = "restructuredtext en"
diff --git a/Bio/KEGG/Enzyme/__init__.py b/Bio/KEGG/Enzyme/__init__.py
index 61d6672..ff6d3ad 100644
--- a/Bio/KEGG/Enzyme/__init__.py
+++ b/Bio/KEGG/Enzyme/__init__.py
@@ -308,6 +308,35 @@ def parse(handle):
             record.sysname.append(data.strip(";"))
 
 
+def read(handle):
+    """Parse a KEGG Enzyme file with exactly one entry.
+
+    If the handle contains no records, or more than one record,
+    an exception is raised.  For example:
+
+    >>> with open("KEGG/enzyme.new") as handle:
+    ...     record = read(handle)
+    ...     print("%s %s" % (record.entry, record.name[0]))
+    ...
+    6.2.1.25 benzoate---CoA ligase
+    """
+
+    iterator = parse(handle)
+    try:
+        first = next(iterator)
+    except StopIteration:
+        first = None
+    if first is None:
+        raise ValueError("No records found in handle")
+    try:
+        second = next(iterator)
+    except StopIteration:
+        second = None
+    if second is not None:
+        raise ValueError("More than one record found in handle")
+    return first
+
+
 if __name__ == "__main__":
     from Bio._utils import run_doctest
     run_doctest()
diff --git a/Bio/KEGG/KGML/KGML_parser.py b/Bio/KEGG/KGML/KGML_parser.py
index 8c38115..c15596c 100644
--- a/Bio/KEGG/KGML/KGML_parser.py
+++ b/Bio/KEGG/KGML/KGML_parser.py
@@ -30,8 +30,6 @@ from Bio._py3k import StringIO
 
 from Bio.KEGG.KGML.KGML_pathway import *
 
-__docformat__ = "restructuredtext en"
-
 
 def read(handle, debug=0):
     """Parses a single KEGG Pathway from given file handle.
diff --git a/Bio/KEGG/KGML/KGML_pathway.py b/Bio/KEGG/KGML/KGML_pathway.py
index a07b769..ccdc439 100644
--- a/Bio/KEGG/KGML/KGML_pathway.py
+++ b/Bio/KEGG/KGML/KGML_pathway.py
@@ -28,8 +28,6 @@ import xml.etree.ElementTree as ET
 
 from Bio._py3k import _is_int_or_long, _as_string
 
-__docformat__ = "restructuredtext en"
-
 
 # Pathway
 class Pathway(object):
diff --git a/Bio/KEGG/REST.py b/Bio/KEGG/REST.py
index b5129bf..df5ce2b 100644
--- a/Bio/KEGG/REST.py
+++ b/Bio/KEGG/REST.py
@@ -28,6 +28,7 @@ Nucleic Acids Res. 28, 29-34 (2000).
 """
 
 from Bio._py3k import urlopen as _urlopen
+from Bio._py3k import _binary_to_string_handle
 
 
 def _q(op, arg1, arg2=None, arg3=None):
@@ -38,7 +39,12 @@ def _q(op, arg1, arg2=None, arg3=None):
         args = "%s/%s/%s" % (op, arg1, arg2)
     else:
         args = "%s/%s" % (op, arg1)
-    return _urlopen(URL % (args))
+    resp = _urlopen(URL % (args))
+
+    if "image" == arg2:
+        return resp
+
+    return _binary_to_string_handle(resp)
 
 
 # http://www.kegg.jp/kegg/rest/keggapi.html
diff --git a/Bio/KEGG/__init__.py b/Bio/KEGG/__init__.py
index 880e3c3..d413c3a 100644
--- a/Bio/KEGG/__init__.py
+++ b/Bio/KEGG/__init__.py
@@ -3,8 +3,7 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-"""
-This module provides code to work with data from the KEGG database.
+"""Code to work with data from the KEGG database.
 
 References:
 
@@ -13,6 +12,8 @@ Nucleic Acids Res. 28, 29-34 (2000).
 
 URL: http://www.genome.ad.jp/kegg/
 """
+
+
 KEGG_ITEM_LENGTH = 12
 KEGG_LINE_LENGTH = 80
 KEGG_DATA_LENGTH = KEGG_LINE_LENGTH - KEGG_ITEM_LENGTH
diff --git a/Bio/MarkovModel.py b/Bio/MarkovModel.py
index 3e6d6b8..632f88b 100644
--- a/Bio/MarkovModel.py
+++ b/Bio/MarkovModel.py
@@ -8,7 +8,6 @@ This is an implementation of a state-emitting MarkovModel.  I am using
 terminology similar to Manning and Schutze.
 
 
-
 Functions:
 train_bw        Train a markov model using the Baum-Welch algorithm.
 train_visible   Train a visible markov model using MLE.
@@ -23,6 +22,7 @@ MarkovModel     Holds the description of a markov model
 
 import numpy
 
+
 try:
     logaddexp = numpy.logaddexp
 except AttributeError:
diff --git a/Bio/Medline/__init__.py b/Bio/Medline/__init__.py
index 76cb0fd..25b5ae0 100644
--- a/Bio/Medline/__init__.py
+++ b/Bio/Medline/__init__.py
@@ -13,8 +13,6 @@ Functions:
  - parse            Allows you to iterate over a bunch of Medline records
 """
 
-__docformat__ = "restructuredtext en"
-
 
 class Record(dict):
     """A dictionary holding information from a Medline record.
@@ -112,7 +110,7 @@ def parse(handle):
         from Bio import Medline
         with open("mymedlinefile") as handle:
             records = Medline.parse(handle)
-            for record in record:
+            for record in records:
                 print(record['TI'])
 
     """
diff --git a/Bio/Motif/Applications/__init__.py b/Bio/Motif/Applications/__init__.py
deleted file mode 100644
index 8f08069..0000000
--- a/Bio/Motif/Applications/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright 2009 by Bartek Wilczynski.  All rights reserved.
-# Revisions copyright 2009-2013 by Peter Cock.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-"""Motif command line tool wrappers (DEPRECATED, see Bio.motifs instead)."""
-from Bio.motifs.applications import AlignAceCommandline
-from Bio.motifs.applications import CompareAceCommandline
-from Bio.motifs.applications import XXmotifCommandline
diff --git a/Bio/Motif/Parsers/AlignAce.py b/Bio/Motif/Parsers/AlignAce.py
deleted file mode 100644
index 40f6c6a..0000000
--- a/Bio/Motif/Parsers/AlignAce.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2003 by Bartek Wilczynski.  All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-
-"""Parsing AlignACE files: AlignAceParser
-"""
-
-from Bio.Motif import Motif
-from Bio.Alphabet import IUPAC
-from Bio.Seq import Seq
-
-
-class Record(object):
-    def __init__(self):
-        self.motifs = []
-        self.current_motif = None
-        self.param_dict = None
-
-
-def read(handle):
-    """read(handle)"""
-    record = Record()
-    record.ver = next(handle)
-    record.cmd_line = next(handle)
-    for line in handle:
-        if line.strip() == "":
-            pass
-        elif line[:4] == "Para":
-            record.param_dict = {}
-        elif line[0] == "#":
-            seq_name = line.split("\t")[1]
-            record.seq_dict.append(seq_name)
-        elif "=" in line:
-            par_name = line.split("=")[0].strip()
-            par_value = line.split("=")[1].strip()
-            record.param_dict[par_name] = par_value
-        elif line[:5] == "Input":
-            record.seq_dict = []
-        elif line[:5] == "Motif":
-            record.current_motif = Motif()
-            record.motifs.append(record.current_motif)
-            record.current_motif.alphabet = IUPAC.unambiguous_dna
-        elif line[:3] == "MAP":
-            record.current_motif.score = float(line.split()[-1])
-        elif len(line.split("\t")) == 4:
-            seq = Seq(line.split("\t")[0], IUPAC.unambiguous_dna)
-            record.current_motif.add_instance(seq)
-        elif "*" in line:
-            record.current_motif.set_mask(line.strip("\n\c"))
-        else:
-            raise ValueError(line)
-    return record
diff --git a/Bio/Motif/Parsers/MAST.py b/Bio/Motif/Parsers/MAST.py
deleted file mode 100644
index d38def9..0000000
--- a/Bio/Motif/Parsers/MAST.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright 2008 by Bartek Wilczynski.
-# Adapted from Bio.MEME.Parser by Jason A. Hackney.  All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-
-from Bio.Alphabet import IUPAC
-from Bio.Motif.Parsers.MEME import MEMEMotif
-
-
-class Record(object):
-    """The class for holding the results from a MAST run.
-
-    A MAST.Record holds data about matches between motifs and sequences.
-    The motifs held by the Record are objects of the class MEMEMotif.
-
-    Methods:
-    get_motif_by_name (motif_name): returns a MEMEMotif with the given
-    name.
-    """
-
-    def __init__(self):
-        self.sequences = []
-        self.version = ""
-        self.database = ""
-        self.diagrams = {}
-        self.alphabet = None
-        self.motifs = []
-
-    def get_motif_by_name(self, name):
-        for m in self.motifs:
-            if m.name == name:
-                return m
-
-
-def read(handle):
-    """read(handle)"""
-    record = Record()
-    __read_version(record, handle)
-    __read_database_and_motifs(record, handle)
-    __read_section_i(record, handle)
-    __read_section_ii(record, handle)
-    __read_section_iii(record, handle)
-    return record
-
-
-# Everything below is private
-
-
-def __read_version(record, handle):
-    for line in handle:
-        if "MAST version" in line:
-            break
-    else:
-        raise ValueError("Improper input file. Does not begin with a line with 'MAST version'")
-    record.version = line.strip().split()[2]
-
-
-def __read_database_and_motifs(record, handle):
-    for line in handle:
-        if line.startswith('DATABASE AND MOTIFS'):
-            break
-    line = next(handle)
-    if not line.startswith('****'):
-        raise ValueError("Line does not start with '****':\n%s" % line)
-    line = next(handle)
-    if 'DATABASE' not in line:
-        raise ValueError("Line does not contain 'DATABASE':\n%s" % line)
-    words = line.strip().split()
-    record.database = words[1]
-    if words[2] == '(nucleotide)':
-        record.alphabet = IUPAC.unambiguous_dna
-    elif words[2] == '(peptide)':
-        record.alphabet = IUPAC.protein
-    for line in handle:
-        if 'MOTIF WIDTH' in line:
-            break
-    line = next(handle)
-    if '----' not in line:
-        raise ValueError("Line does not contain '----':\n%s" % line)
-    for line in handle:
-        if not line.strip():
-            break
-        words = line.strip().split()
-        motif = MEMEMotif()
-        motif.alphabet = record.alphabet
-        motif.name = words[0]
-        motif.length = int(words[1])
-        # motif.add_instance(words[2])
-        record.motifs.append(motif)
-
-
-def __read_section_i(record, handle):
-    for line in handle:
-        if line.startswith('SECTION I:'):
-            break
-    for line in handle:
-        if line.startswith('SEQUENCE NAME'):
-            break
-    line = next(handle)
-    if not line.startswith('---'):
-        raise ValueError("Line does not start with '---':\n%s" % line)
-    for line in handle:
-        if not line.strip():
-            break
-        else:
-            sequence, description_evalue_length = line.split(None, 1)
-            record.sequences.append(sequence)
-    line = next(handle)
-    if not line.startswith('****'):
-        raise ValueError("Line does not start with '****':\n%s" % line)
-
-
-def __read_section_ii(record, handle):
-    for line in handle:
-        if line.startswith('SECTION II:'):
-            break
-    for line in handle:
-        if line.startswith('SEQUENCE NAME'):
-            break
-    line = next(handle)
-    if not line.startswith('---'):
-        raise ValueError("Line does not start with '---':\n%s" % line)
-    for line in handle:
-        if not line.strip():
-            break
-        elif line.startswith(" "):
-            diagram = line.strip()
-            record.diagrams[sequence] += diagram
-        else:
-            sequence, pvalue, diagram = line.split()
-            record.diagrams[sequence] = diagram
-    line = next(handle)
-    if not line.startswith('****'):
-        raise ValueError("Line does not start with '****':\n%s" % line)
-
-
-def __read_section_iii(record, handle):
-    for line in handle:
-        if line.startswith('SECTION III:'):
-            break
-    for line in handle:
-        if line.startswith('****'):
-            break
-    for line in handle:
-        if line.startswith('*****'):
-            break
-    for line in handle:
-        if line.strip():
-            break
diff --git a/Bio/Motif/Parsers/MEME.py b/Bio/Motif/Parsers/MEME.py
deleted file mode 100644
index 36f8092..0000000
--- a/Bio/Motif/Parsers/MEME.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright 2008 by Bartek Wilczynski
-# Adapted from  Bio.MEME.Parser by Jason A. Hackney.  All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-
-from __future__ import print_function
-
-from Bio.Alphabet import IUPAC
-from Bio import Seq
-import re
-from math import sqrt
-import sys
-from Bio.Motif import Motif
-
-
-def read(handle):
-    """Parses the text output of the MEME program into MEME.Record object.
-
-    Example:
-
-    >>> from Bio.Motif.Parsers import MEME
-    >>> with open("meme.output.txt") as f:
-    ...     record = MEME.read(f)
-    >>> for motif in record.motifs:
-    ...     for instance in motif.instances:
-    ...         print(instance.motif_name, instance.sequence_name, instance.strand, instance.pvalue)
-
-    """
-    record = MEMERecord()
-    __read_version(record, handle)
-    __read_datafile(record, handle)
-    __read_alphabet(record, handle)
-    __read_sequence_names(record, handle)
-    __read_command(record, handle)
-    for line in handle:
-        if line.startswith('MOTIF  1'):
-            break
-    else:
-        raise ValueError('Unexpected end of stream')
-    while True:
-        motif = __create_motif(line)
-        motif.alphabet = record.alphabet
-        record.motifs.append(motif)
-        __read_motif_name(motif, handle)
-        __read_motif_sequences(motif, handle, 'revcomp' in record.command)
-        __skip_unused_lines(handle)
-        try:
-            line = next(handle)
-        except StopIteration:
-            raise ValueError('Unexpected end of stream: Expected to find new motif, or the summary of motifs')
-        if line.startswith("SUMMARY OF MOTIFS"):
-            break
-        if not line.startswith('MOTIF'):
-            raise ValueError("Line does not start with 'MOTIF':\n%s" % line)
-    return record
-
-
-class MEMEMotif (Motif):
-    """A subclass of Motif used in parsing MEME (and MAST) output.
-
-    This sublcass defines functions and data specific to MEME motifs.
-    This includes the evalue for a motif and the PSSM of the motif.
-
-    Methods:
-    add_instance_from_values (name = 'default', pvalue = 1, sequence = 'ATA', start = 0, strand = +): create a new instance of the motif with the specified values.
-    add_to_pssm (position): add a new position to the pssm. The position should be a list of nucleotide/amino acid frequencies
-    add_to_logodds (position): add a new position to the log odds matrix. The position should be a tuple of log odds values for the nucleotide/amino acid at that position.
-    compare_motifs (other_motif): returns the maximum correlation between this motif and other_motif
-    """
-    def __init__(self):
-        Motif.__init__(self)
-        self.evalue = 0.0
-
-    def _numoccurrences(self, number):
-        if isinstance(number, int):
-            self.num_occurrences = number
-        else:
-            number = int(number)
-            self.num_occurrences = number
-
-    def get_instance_by_name(self, name):
-        for i in self.instances:
-            if i.sequence_name == name:
-                return i
-        return None
-
-    def add_instance_from_values(self, name='default', pvalue=1, sequence='ATA', start=0, strand='+'):
-        inst = MEMEInstance(sequence, self.alphabet)
-        inst._pvalue(pvalue)
-        inst._seqname(name)
-        inst._start(start)
-        inst._strand(strand)
-        if self.length:
-            inst._length(self.length)
-        else:
-            inst._length(len(sequence))
-        if self.name:
-            inst._motifname(self.name)
-        self.add_instance(inst)
-
-    def _evalue(self, evalue):
-        if isinstance(evalue, float):
-            self.evalue = evalue
-        else:
-            evalue = float(evalue)
-            self.evalue = evalue
-
-
-class MEMEInstance(Seq.Seq):
-    """A class describing the instances of a MEME motif, and the data thereof.
-    """
-    def __init__(self, *args, **kwds):
-        Seq.Seq.__init__(self, *args, **kwds)
-        self.sequence_name = ""
-        self.start = 0
-        self.pvalue = 1.0
-        self.strand = 0
-        self.length = 0
-        self.motif_name = ""
-
-    def _seqname(self, name):
-        self.sequence_name = name
-
-    def _motifname(self, name):
-        self.motif_name = name
-
-    def _start(self, start):
-        start = int(start)
-        self.start = start
-
-    def _pvalue(self, pval):
-        pval = float(pval)
-        self.pvalue = pval
-
-    def _score(self, score):
-        score = float(score)
-        self.score = score
-
-    def _strand(self, strand):
-        self.strand = strand
-
-    def _length(self, length):
-        self.length = length
-
-
-class MEMERecord(object):
-    """A class for holding the results of a MEME run.
-
-    A MEMERecord is an object that holds the results from running
-    MEME. It implements no methods of its own.
-
-    """
-    def __init__(self):
-        """__init__(self)"""
-        self.motifs = []
-        self.version = ""
-        self.datafile = ""
-        self.command = ""
-        self.alphabet = None
-        self.sequence_names = []
-
-    def get_motif_by_name(self, name):
-        for m in self.motifs:
-            if m.name == name:
-                return m
-
-
-# Everything below is private
-
-
-def __read_version(record, handle):
-    for line in handle:
-        if line.startswith('MEME version'):
-            break
-    else:
-        raise ValueError("Improper input file. File should contain a line starting MEME version.")
-    line = line.strip()
-    ls = line.split()
-    record.version = ls[2]
-
-
-def __read_datafile(record, handle):
-    for line in handle:
-        if line.startswith('TRAINING SET'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: 'TRAINING SET' not found.")
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '****'")
-    if not line.startswith('****'):
-        raise ValueError("Line does not start with '****':\n%s" % line)
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'DATAFILE'")
-    if not line.startswith('DATAFILE'):
-        raise ValueError("Line does not start with 'DATAFILE':\n%s" % line)
-    line = line.strip()
-    line = line.replace('DATAFILE= ', '')
-    record.datafile = line
-
-
-def __read_alphabet(record, handle):
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'ALPHABET'")
-    if not line.startswith('ALPHABET'):
-        raise ValueError("Line does not start with 'ALPHABET':\n%s" % line)
-    line = line.strip()
-    line = line.replace('ALPHABET= ', '')
-    if line == 'ACGT':
-        al = IUPAC.unambiguous_dna
-    else:
-        al = IUPAC.protein
-    record.alphabet = al
-
-
-def __read_sequence_names(record, handle):
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'Sequence name'")
-    if not line.startswith('Sequence name'):
-        raise ValueError("Line does not start with 'Sequence name':\n%s" % line)
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '----'")
-    if not line.startswith('----'):
-        raise ValueError("Line does not start with '----':\n%s" % line)
-    for line in handle:
-        if line.startswith('***'):
-            break
-        line = line.strip()
-        ls = line.split()
-        record.sequence_names.append(ls[0])
-        if len(ls) == 6:
-            record.sequence_names.append(ls[3])
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '***'")
-
-
-def __read_command(record, handle):
-    for line in handle:
-        if line.startswith('command:'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'command'")
-    line = line.strip()
-    line = line.replace('command: ', '')
-    record.command = line
-
-
-def __create_motif(line):
-    line = line[5:].strip()
-    ls = line.split()
-    motif = MEMEMotif()
-    motif.length = int(ls[3])
-    motif._numoccurrences(ls[6])
-    motif._evalue(ls[12])
-    return motif
-
-
-def __read_motif_name(motif, handle):
-    for line in handle:
-        if 'sorted by position p-value' in line:
-            break
-    else:
-        raise ValueError('Unexpected end of stream: Failed to find motif name')
-    line = line.strip()
-    ls = line.split()
-    name = " ".join(ls[0:2])
-    motif.name = name
-
-
-def __read_motif_sequences(motif, handle, rv):
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError('Unexpected end of stream: Failed to find motif sequences')
-    if not line.startswith('---'):
-        raise ValueError("Line does not start with '---':\n%s" % line)
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'Sequence name'")
-    if not line.startswith('Sequence name'):
-        raise ValueError("Line does not start with 'Sequence name':\n%s" % line)
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError('Unexpected end of stream: Failed to find motif sequences')
-    if not line.startswith('---'):
-        raise ValueError("Line does not start with '---':\n%s" % line)
-    for line in handle:
-        if line.startswith('---'):
-            break
-        line = line.strip()
-        ls = line.split()
-        if rv:
-            # seq = Seq.Seq(ls[5], record.alphabet)
-            motif.add_instance_from_values(name=ls[0], sequence=ls[5], start=ls[2], pvalue=ls[3], strand=ls[1])
-        else:
-            # seq = Seq.Seq(ls[4], record.alphabet)
-            motif.add_instance_from_values(name=ls[0], sequence=ls[4], start=ls[1], pvalue=ls[2])
-    else:
-        raise ValueError('Unexpected end of stream')
-
-
-def __skip_unused_lines(handle):
-    for line in handle:
-        if line.startswith('log-odds matrix'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'log-odds matrix'")
-    for line in handle:
-        if line.startswith('---'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '---'")
-    for line in handle:
-        if line.startswith('letter-probability matrix'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'letter-probability matrix'")
-    for line in handle:
-        if line.startswith('---'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '---'")
-    for line in handle:
-        if line.startswith('Time'):
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with 'Time'")
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError('Unexpected end of stream: Expected to find blank line')
-    if line.strip():
-        raise ValueError("Expected blank line, but got:\n%s" % line)
-    try:
-        line = next(handle)
-    except StopIteration:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '***'")
-    if not line.startswith('***'):
-        raise ValueError("Line does not start with '***':\n%s" % line)
-    for line in handle:
-        if line.strip():
-            break
-    else:
-        raise ValueError("Unexpected end of stream: Expected to find line starting with '***'")
-    if not line.startswith('***'):
-        raise ValueError("Line does not start with '***':\n%s" % line)
diff --git a/Bio/Motif/Parsers/__init__.py b/Bio/Motif/Parsers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/Bio/Motif/Thresholds.py b/Bio/Motif/Thresholds.py
deleted file mode 100644
index 103f089..0000000
--- a/Bio/Motif/Thresholds.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright 2008 by Norbert Dojer.  All rights reserved.
-# Adapted by Bartek Wilczynski.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-"""Approximate calculation of appropriate thresholds for motif finding
-"""
-import math
-import random
-
-
-class ScoreDistribution(object):
-    """ Class representing approximate score distribution for a given motif.
-
-    Utilizes a dynamic programming approch to calculate the distribution of
-    scores with a predefined precision. Provides a number of methods for calculating
-    thresholds for motif occurences.
-    """
-    def __init__(self, motif, precision=10 ** 3):
-        self.min_score = min(0.0, motif.min_score())
-        self.interval = max(0.0, motif.max_score()) - self.min_score
-        self.n_points = precision * motif.length
-        self.step = self.interval / (self.n_points - 1)
-        self.mo_density = [0.0] * self.n_points
-        self.mo_density[-self._index_diff(self.min_score)] = 1.0
-        self.bg_density = [0.0] * self.n_points
-        self.bg_density[-self._index_diff(self.min_score)] = 1.0
-        self.ic = motif.ic()
-        for lo, mo in zip(motif.log_odds(), motif.pwm()):
-            self.modify(lo, mo, motif.background)
-
-    def _index_diff(self, x, y=0.0):
-        return int((x - y + 0.5 * self.step) // self.step)
-
-    def _add(self, i, j):
-        return max(0, min(self.n_points - 1, i + j))
-
-    def modify(self, scores, mo_probs, bg_probs):
-        mo_new = [0.0] * self.n_points
-        bg_new = [0.0] * self.n_points
-        for k, v in scores.items():
-            d = self._index_diff(v)
-            for i in range(self.n_points):
-                mo_new[self._add(i, d)] += self.mo_density[i] * mo_probs[k]
-                bg_new[self._add(i, d)] += self.bg_density[i] * bg_probs[k]
-        self.mo_density = mo_new
-        self.bg_density = bg_new
-
-    def threshold_fpr(self, fpr):
-        """
-        Approximate the log-odds threshold which makes the type I error (false positive rate).
-        """
-        i = self.n_points
-        prob = 0.0
-        while prob < fpr:
-            i -= 1
-            prob += self.bg_density[i]
-        return self.min_score + i * self.step
-
-    def threshold_fnr(self, fnr):
-        """
-        Approximate the log-odds threshold which makes the type II error (false negative rate).
-        """
-        i = -1
-        prob = 0.0
-        while prob < fnr:
-            i += 1
-            prob += self.mo_density[i]
-        return self.min_score + i * self.step
-
-    def threshold_balanced(self, rate_proportion=1.0, return_rate=False):
-        """
-        Approximate the log-odds threshold which makes FNR equal to FPR times rate_proportion
-        """
-        i = self.n_points
-        fpr = 0.0
-        fnr = 1.0
-        while fpr * rate_proportion < fnr:
-            i -= 1
-            fpr += self.bg_density[i]
-            fnr -= self.mo_density[i]
-        if return_rate:
-            return self.min_score + i * self.step, fpr
-        else:
-            return self.min_score + i * self.step
-
-    def threshold_patser(self):
-        """Threshold selection mimicking the behaviour of patser (Hertz, Stormo 1999) software.
-
-        It selects such a threshold that the log(fpr)=-ic(M)
-        note: the actual patser software uses natural logarithms instead of log_2, so the numbers
-        are not directly comparable.
-        """
-        return self.threshold_fpr(fpr=2 ** -self.ic)
diff --git a/Bio/Motif/_Motif.py b/Bio/Motif/_Motif.py
deleted file mode 100644
index 2bd2990..0000000
--- a/Bio/Motif/_Motif.py
+++ /dev/null
@@ -1,800 +0,0 @@
-# Copyright 2003-2009 by Bartek Wilczynski.  All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-"""Implementation of sequence motifs (PRIVATE).
-"""
-
-from __future__ import print_function
-
-from Bio._py3k import range
-
-from Bio.Seq import Seq
-from Bio.SubsMat import FreqTable
-from Bio.Alphabet import IUPAC
-import math
-import random
-
-
-__docformat__ = "restructuredtext en"
-
-
-class Motif(object):
-    """
-    A class representing sequence motifs.
-    """
-    def __init__(self, alphabet=IUPAC.unambiguous_dna):
-        self.instances = []
-        self.has_instances = False
-        self.counts = {}
-        self.has_counts = False
-        self.mask = []
-        self._pwm_is_current = False
-        self._pwm = []
-        self._log_odds_is_current = False
-        self._log_odds = []
-        self.alphabet = alphabet
-        self.length = None
-        self.background = dict((n, 1.0 / len(self.alphabet.letters))
-                               for n in self.alphabet.letters)
-        self.beta = 1.0
-        self.info = None
-        self.name = ""
-
-    def _check_length(self, len):
-        # TODO - Change parameter name (len clashes with built in function)?
-        if self.length is None:
-            self.length = len
-        elif self.length != len:
-            raise ValueError("You can't change the length of the motif "
-                             "%r %r %r" % (self.length, self.instances, len))
-
-    def _check_alphabet(self, alphabet):
-        if self.alphabet is None:
-            self.alphabet = alphabet
-        elif self.alphabet != alphabet:
-                raise ValueError("Wrong Alphabet")
-
-    def add_instance(self, instance):
-        """
-        adds new instance to the motif
-        """
-        self._check_alphabet(instance.alphabet)
-        self._check_length(len(instance))
-        if self.has_counts:
-            for i in range(self.length):
-                let = instance[i]
-                self.counts[let][i] += 1
-
-        if self.has_instances or not self.has_counts:
-            self.instances.append(instance)
-            self.has_instances = True
-
-        self._pwm_is_current = False
-        self._log_odds_is_current = False
-
-    def set_mask(self, mask):
-        """
-        sets the mask for the motif
-
-        The mask should be a string containing asterisks in the position of significant columns and spaces in other columns
-        """
-        self._check_length(len(mask))
-        self.mask = []
-        for char in mask:
-            if char == "*":
-                self.mask.append(1)
-            elif char == " ":
-                self.mask.append(0)
-            else:
-                raise ValueError("Mask should contain only '*' or ' ' and not a '%s'" % char)
-
-    def pwm(self, laplace=True):
-        """
-        returns the PWM computed for the set of instances
-
-        if laplace=True (default), pseudocounts equal to self.background multiplied by self.beta are added to all positions.
-        """
-        if self._pwm_is_current:
-            return self._pwm
-        # we need to compute new pwm
-        self._pwm = []
-        for i in range(self.length):
-            dict = {}
-            # filling the dict with 0's
-            for letter in self.alphabet.letters:
-                if laplace:
-                    dict[letter] = self.beta * self.background[letter]
-                else:
-                    dict[letter] = 0.0
-            if self.has_counts:
-                # taking the raw counts
-                for letter in self.alphabet.letters:
-                    dict[letter] += self.counts[letter][i]
-            elif self.has_instances:
-                # counting the occurences of letters in instances
-                for seq in self.instances:
-                    # dict[seq[i]]=dict[seq[i]]+1
-                    try:
-                        dict[seq[i]] += 1
-                    except KeyError:  # we need to ignore non-alphabet letters
-                        pass
-            self._pwm.append(FreqTable.FreqTable(dict, FreqTable.COUNT, self.alphabet))
-        self._pwm_is_current = 1
-        return self._pwm
-
-    def log_odds(self, laplace=True):
-        """
-        returns the logg odds matrix computed for the set of instances
-        """
-
-        if self._log_odds_is_current:
-            return self._log_odds
-        # we need to compute new pwm
-        self._log_odds = []
-        pwm = self.pwm(laplace)
-        for i in range(self.length):
-            d = {}
-            for a in self.alphabet.letters:
-                    d[a] = math.log(pwm[i][a] / self.background[a], 2)
-            self._log_odds.append(d)
-        self._log_odds_is_current = 1
-        return self._log_odds
-
-    def ic(self):
-        """Method returning the information content of a motif.
-        """
-        res = 0
-        pwm = self.pwm()
-        for i in range(self.length):
-            res += 2
-            for a in self.alphabet.letters:
-                if pwm[i][a] != 0:
-                    res += pwm[i][a] * math.log(pwm[i][a], 2)
-        return res
-
-    def exp_score(self, st_dev=False):
-        """
-        Computes expected score of motif's instance and its standard deviation
-        """
-        exs = 0.0
-        var = 0.0
-        pwm = self.pwm()
-        for i in range(self.length):
-            ex1 = 0.0
-            ex2 = 0.0
-            for a in self.alphabet.letters:
-                if pwm[i][a] != 0:
-                    ex1 += pwm[i][a] * (math.log(pwm[i][a], 2) - math.log(self.background[a], 2))
-                    ex2 += pwm[i][a] * (math.log(pwm[i][a], 2) - math.log(self.background[a], 2)) ** 2
-            exs += ex1
-            var += ex2 - ex1 ** 2
-        if st_dev:
-            return exs, math.sqrt(var)
-        else:
-            return exs
-
-    def search_instances(self, sequence):
-        """
-        a generator function, returning found positions of instances of the motif in a given sequence
-        """
-        if not self.has_instances:
-            raise ValueError("This motif has no instances")
-        for pos in range(0, len(sequence) - self.length + 1):
-            for instance in self.instances:
-                if str(instance) == str(sequence[pos:pos + self.length]):
-                    yield (pos, instance)
-                    break  # no other instance will fit (we don't want to return multiple hits)
-
-    def score_hit(self, sequence, position, normalized=0, masked=0):
-        """
-        give the pwm score for a given position
-        """
-        lo = self.log_odds()
-        score = 0.0
-        for pos in range(self.length):
-            a = sequence[position + pos]
-            if not masked or self.mask[pos]:
-                try:
-                    score += lo[pos][a]
-                except:
-                    pass
-        if normalized:
-            if not masked:
-                score /= self.length
-            else:
-                score /= len([x for x in self.mask if x])
-        return score
-
-    def search_pwm(self, sequence, normalized=0, masked=0, threshold=0.0, both=True):
-        """
-        a generator function, returning found hits in a given sequence with the pwm score higher than the threshold
-        """
-        if both:
-            rc = self.reverse_complement()
-
-        sequence = str(sequence).upper()
-        for pos in range(0, len(sequence) - self.length + 1):
-            score = self.score_hit(sequence, pos, normalized, masked)
-            if score > threshold:
-                yield (pos, score)
-            if both:
-                rev_score = rc.score_hit(sequence, pos, normalized, masked)
-                if rev_score > threshold:
-                    yield (-pos, rev_score)
-
-    def dist_pearson(self, motif, masked=0):
-        """
-        return the similarity score based on pearson correlation for the given motif against self.
-
-        We use the Pearson's correlation of the respective probabilities.
-        """
-
-        if self.alphabet != motif.alphabet:
-            raise ValueError("Cannot compare motifs with different alphabets")
-
-        max_p = -2
-        for offset in range(-self.length + 1, motif.length):
-            if offset < 0:
-                p = self.dist_pearson_at(motif, -offset)
-            else:  # offset>=0
-                p = motif.dist_pearson_at(self, offset)
-
-            if max_p < p:
-                max_p = p
-                max_o = -offset
-        return 1 - max_p, max_o
-
-    def dist_pearson_at(self, motif, offset):
-        sxx = 0  # \sum x^2
-        sxy = 0  # \sum x \cdot y
-        sx = 0  # \sum x
-        sy = 0  # \sum y
-        syy = 0  # \sum x^2
-        norm = max(self.length, offset + motif.length)
-
-        for pos in range(max(self.length, offset + motif.length)):
-            for l in self.alphabet.letters:
-                xi = self[pos][l]
-                yi = motif[pos - offset][l]
-                sx = sx + xi
-                sy = sy + yi
-                sxx = sxx + xi * xi
-                syy = syy + yi * yi
-                sxy = sxy + xi * yi
-
-        norm *= len(self.alphabet.letters)
-        s1 = (sxy - sx * sy * 1.0 / norm)
-        s2 = (norm * sxx - sx * sx * 1.0) * (norm * syy - sy * sy * 1.0)
-        return s1 / math.sqrt(s2)
-
-    def dist_product(self, other):
-        """
-        A similarity measure taking into account a product probability of generating overlaping instances of two motifs
-        """
-        max_p = 0.0
-        for offset in range(-self.length + 1, other.length):
-            if offset < 0:
-                p = self.dist_product_at(other, -offset)
-            else:  # offset>=0
-                p = other.dist_product_at(self, offset)
-            if max_p < p:
-                max_p = p
-                max_o = -offset
-        return 1 - max_p / self.dist_product_at(self, 0), max_o
-
-    def dist_product_at(self, other, offset):
-        s = 0
-        for i in range(max(self.length, offset + other.length)):
-            f1 = self[i]
-            f2 = other[i - offset]
-            for n, b in self.background.items():
-                s += b * f1[n] * f2[n]
-        return s / i
-
-    def dist_dpq(self, other):
-        r"""Calculates the DPQ distance measure between motifs.
-
-        It is calculated as a maximal value of DPQ formula (shown using LaTeX
-        markup, familiar to mathematicians)::
-
-            \sqrt{\sum_{i=1}^{alignment.len()} \sum_{k=1}^alphabet.len() \
-            \{ m1[i].freq(alphabet[k])*log_2(m1[i].freq(alphabet[k])/m2[i].freq(alphabet[k])) +
-               m2[i].freq(alphabet[k])*log_2(m2[i].freq(alphabet[k])/m1[i].freq(alphabet[k]))
-            }
-
-        over possible non-spaced alignemts of two motifs.  See this reference:
-
-        D. M Endres and J. E Schindelin, "A new metric for probability
-        distributions", IEEE transactions on Information Theory 49, no. 7
-        (July 2003): 1858-1860.
-        """
-
-        min_d = float("inf")
-        min_o = -1
-        d_s = []
-        for offset in range(-self.length + 1, other.length):
-            # print("%2.3d"%offset)
-            if offset < 0:
-                d = self.dist_dpq_at(other, -offset)
-                overlap = self.length + offset
-            else:  # offset>=0
-                d = other.dist_dpq_at(self, offset)
-                overlap = other.length - offset
-            overlap = min(self.length, other.length, overlap)
-            out = self.length + other.length - 2 * overlap
-            # print("%f %f %f" % (d,1.0*(overlap+out)/overlap,d*(overlap+out)/overlap))
-            # d = d/(2*overlap)
-            d = (d / (out + overlap)) * (2 * overlap + out) / (2 * overlap)
-            # print(d)
-            d_s.append((offset, d))
-            if min_d > d:
-                min_d = d
-                min_o = -offset
-        return min_d, min_o  # ,d_s
-
-    def dist_dpq_at(self, other, offset):
-        """
-        calculates the dist_dpq measure with a given offset.
-
-        offset should satisfy 0<=offset<=len(self)
-        """
-        def dpq(f1, f2, alpha):
-            s = 0
-            for n in alpha.letters:
-                avg = (f1[n] + f2[n]) / 2
-                s += f1[n] * math.log(f1[n] / avg, 2) + f2[n] * math.log(f2[n] / avg, 2)
-            return math.sqrt(s)
-
-        s = 0
-        for i in range(max(self.length, offset + other.length)):
-            f1 = self[i]
-            f2 = other[i - offset]
-            s += dpq(f1, f2, self.alphabet)
-        return s
-
-    def _read(self, stream):
-        """Reads the motif from the stream (in AlignAce format).
-
-        the self.alphabet variable must be set beforehand.
-        If the last line contains asterisks it is used for setting mask
-        """
-
-        while True:
-            ln = stream.readline()
-            if "*" in ln:
-                self.set_mask(ln.strip("\n\c"))
-                break
-            self.add_instance(Seq(ln.strip(), self.alphabet))
-
-    def __str__(self, masked=False):
-        """ string representation of a motif.
-        """
-        str = "".join(str(inst) + "\n" for inst in self.instances)
-
-        if masked:
-            for i in range(self.length):
-                if self.mask[i]:
-                    str += "*"
-                else:
-                    str += " "
-            str += "\n"
-        return str
-
-    def __len__(self):
-        """return the length of a motif
-
-        Please use this method (i.e. invoke len(m)) instead of refering to the m.length directly.
-        """
-        if self.length is None:
-            return 0
-        else:
-            return self.length
-
-    def _write(self, stream):
-        """
-        writes the motif to the stream
-        """
-
-        stream.write(self.__str__())
-
-    def _to_fasta(self):
-        """
-        FASTA representation of motif
-        """
-        if not self.has_instances:
-            self.make_instances_from_counts()
-        return "".join(">instance%d\n%s\n" % (i, inst) for i, inst in enumerate(self.instances))
-
-    def reverse_complement(self):
-        """
-        Gives the reverse complement of the motif
-        """
-        res = Motif()
-        if self.has_instances:
-            for i in self.instances:
-                res.add_instance(i.reverse_complement())
-        else:  # has counts
-            res.has_counts = True
-            res.counts["A"] = self.counts["T"][:]
-            res.counts["T"] = self.counts["A"][:]
-            res.counts["G"] = self.counts["C"][:]
-            res.counts["C"] = self.counts["G"][:]
-            res.counts["A"].reverse()
-            res.counts["C"].reverse()
-            res.counts["G"].reverse()
-            res.counts["T"].reverse()
-            res.length = self.length
-        res.mask = self.mask
-        return res
-
-    def _from_jaspar_pfm(self, stream, make_instances=False):
-        """
-        reads the motif from Jaspar .pfm file
-
-        The instances are fake, but the pwm is accurate.
-        """
-        return self._from_horiz_matrix(stream, letters="ACGT", make_instances=make_instances)
-
-    def _from_vert_matrix(self, stream, letters=None, make_instances=False):
-        """reads a vertical count matrix from stream and fill in the counts.
-        """
-
-        self.counts = {}
-        self.has_counts = True
-        if letters is None:
-            letters = self.alphabet.letters
-        self.length = 0
-        for i in letters:
-            self.counts[i] = []
-        for ln in stream.readlines():
-            rec = [float(x) for x in ln.strip().split()]
-            for k, v in zip(letters, rec):
-                self.counts[k].append(v)
-            self.length += 1
-        self.set_mask("*" * self.length)
-        if make_instances is True:
-            self.make_instances_from_counts()
-        return self
-
-    def _from_horiz_matrix(self, stream, letters=None, make_instances=False):
-        """reads a horizontal count matrix from stream and fill in the counts.
-        """
-        if letters is None:
-            letters = self.alphabet.letters
-        self.counts = {}
-        self.has_counts = True
-
-        for i in letters:
-            ln = stream.readline().strip().split()
-            # if there is a letter in the beginning, ignore it
-            if ln[0] == i:
-                ln = ln[1:]
-            # print(ln)
-            try:
-                self.counts[i] = [int(x) for x in ln]
-            except ValueError:  # not integers
-                self.counts[i] = [float(x) for x in ln]
-            # print(counts[i])
-
-        s = sum(self.counts[nuc][0] for nuc in letters)
-        l = len(self.counts[letters[0]])
-        self.length = l
-        self.set_mask("*" * l)
-        if make_instances is True:
-            self.make_instances_from_counts()
-        return self
-
-    def make_instances_from_counts(self):
-        """Creates "fake" instances for a motif created from a count matrix.
-
-        In case the sums of counts are different for different columnes, the
-        shorter columns are padded with background.
-        """
-        alpha = "".join(self.alphabet.letters)
-        # col[i] is a column taken from aligned motif instances
-        col = []
-        self.has_instances = True
-        self.instances = []
-        s = sum(self.counts[nuc][0] for nuc in self.alphabet.letters)
-        for i in range(self.length):
-            col.append("")
-            for n in self.alphabet.letters:
-                col[i] = col[i] + n * (self.counts[n][i])
-            if len(col[i]) < s:
-                print("WARNING, column too short %i %i" % (len(col[i]), s))
-                col[i] += (alpha * s)[:(s - len(col[i]))]
-            # print("column %i, %s" % (i, col[i]))
-        # iterate over instances
-        for i in range(s):
-            inst = ""  # start with empty seq
-            for j in range(self.length):  # iterate over positions
-                inst += col[j][i]
-            # print("%i %s" % (i,inst)
-            inst = Seq(inst, self.alphabet)
-            self.add_instance(inst)
-        return self.instances
-
-    def make_counts_from_instances(self):
-        """Creates the count matrix for a motif with instances.
-
-        """
-        # make strings for "columns" of motifs
-        # col[i] is a column taken from aligned motif instances
-        counts = {}
-        for a in self.alphabet.letters:
-            counts[a] = []
-        self.has_counts = True
-        s = len(self.instances)
-        for i in range(self.length):
-            ci = dict((a, 0) for a in self.alphabet.letters)
-            for inst in self.instances:
-                ci[inst[i]] += 1
-            for a in self.alphabet.letters:
-                counts[a].append(ci[a])
-        self.counts = counts
-        return counts
-
-    def _from_jaspar_sites(self, stream):
-        """
-        reads the motif from Jaspar .sites file
-
-        The instances and pwm are OK.
-        """
-        while True:
-            ln = stream.readline()  # read the header "$>...."
-            if ln == "" or ln[0] != ">":
-                break
-
-            ln = stream.readline().strip()  # read the actual sequence
-            i = 0
-            while ln[i] == ln[i].lower():
-                i += 1
-            inst = ""
-            while i < len(ln) and ln[i] == ln[i].upper():
-                inst += ln[i]
-                i += 1
-            inst = Seq(inst, self.alphabet)
-            self.add_instance(inst)
-
-        self.set_mask("*" * len(inst))
-        return self
-
-    def __getitem__(self, index):
-        """Returns the probability distribution over symbols at a given position, padding with background.
-
-        If the requested index is out of bounds, the returned distribution comes from background.
-        """
-        if index in range(self.length):
-            return self.pwm()[index]
-        else:
-            return self.background
-
-    def consensus(self):
-        """Returns the consensus sequence of a motif.
-        """
-        res = ""
-        for i in range(self.length):
-            max_f = 0
-            max_n = "X"
-            for n in sorted(self[i]):
-                if self[i][n] > max_f:
-                    max_f = self[i][n]
-                    max_n = n
-            res += max_n
-        return Seq(res, self.alphabet)
-
-    def anticonsensus(self):
-        """returns the least probable pattern to be generated from this motif.
-        """
-        res = ""
-        for i in range(self.length):
-            min_f = 10.0
-            min_n = "X"
-            for n in sorted(self[i]):
-                if self[i][n] < min_f:
-                    min_f = self[i][n]
-                    min_n = n
-            res += min_n
-        return Seq(res, self.alphabet)
-
-    def max_score(self):
-        """Maximal possible score for this motif.
-
-        returns the score computed for the consensus sequence.
-        """
-        return self.score_hit(self.consensus(), 0)
-
-    def min_score(self):
-        """Minimal possible score for this motif.
-
-        returns the score computed for the anticonsensus sequence.
-        """
-        return self.score_hit(self.anticonsensus(), 0)
-
-    def weblogo(self, fname, format="PNG", **kwds):
-        """
-        uses the Berkeley weblogo service to download and save a weblogo of itself
-
-        requires an internet connection.
-        The parameters from ``**kwds`` are passed directly to the weblogo server.
-        """
-        from Bio._py3k import urlopen, urlencode, Request
-
-        al = self._to_fasta()
-        url = 'http://weblogo.berkeley.edu/logo.cgi'
-        values = {'sequence': al,
-                  'format': format,
-                  'logowidth': '18',
-                  'logoheight': '5',
-                  'logounits': 'cm',
-                  'kind': 'AUTO',
-                  'firstnum': "1",
-                  'command': 'Create Logo',
-                  'smallsamplecorrection': "on",
-                  'symbolsperline': 32,
-                  'res': '96',
-                  'res_units': 'ppi',
-                  'antialias': 'on',
-                  'title': '',
-                  'barbits': '',
-                  'xaxis': 'on',
-                  'xaxis_label': '',
-                  'yaxis': 'on',
-                  'yaxis_label': '',
-                  'showends': 'on',
-                  'shrink': '0.5',
-                  'fineprint': 'on',
-                  'ticbits': '1',
-                  'colorscheme': 'DEFAULT',
-                  'color1': 'green',
-                  'color2': 'blue',
-                  'color3': 'red',
-                  'color4': 'black',
-                  'color5': 'purple',
-                  'color6': 'orange',
-                  'color1': 'black',
-                  }
-        for k, v in kwds.items():
-            values[k] = str(v)
-
-        data = urlencode(values)
-        req = Request(url, data)
-        response = urlopen(req)
-        with open(fname, "w") as f:
-            im = response.read()
-            f.write(im)
-
-    def _to_transfac(self):
-        """Write the representation of a motif in TRANSFAC format
-        """
-        res = "XX\nTY Motif\n"  # header
-        try:
-            res += "ID %s\n" % self.name
-        except:
-            pass
-        res += "BF undef\nP0"
-        for a in self.alphabet.letters:
-            res += " %s" % a
-        res += "\n"
-        if not self.has_counts:
-            self.make_counts_from_instances()
-        for i in range(self.length):
-            if i < 9:
-                res += "0%d" % (i + 1)
-            else:
-                res += "%d" % (i + 1)
-            for a in self.alphabet.letters:
-                res += " %d" % self.counts[a][i]
-            res += "\n"
-        res += "XX\n"
-        return res
-
-    def _to_vertical_matrix(self, letters=None):
-        """Return string representation of the motif as a matrix.
-        """
-        if letters is None:
-            letters = self.alphabet.letters
-        self._pwm_is_current = False
-        pwm = self.pwm(laplace=False)
-        res = ""
-        for i in range(self.length):
-            res += "\t".join(str(pwm[i][a]) for a in letters)
-            res += "\n"
-        return res
-
-    def _to_horizontal_matrix(self, letters=None, normalized=True):
-        """Return string representation of the motif as a matrix.
-        """
-        if letters is None:
-            letters = self.alphabet.letters
-        res = ""
-        if normalized:  # output PWM
-            self._pwm_is_current = False
-            mat = self.pwm(laplace=False)
-            for a in letters:
-                res += "\t".join(str(mat[i][a]) for i in range(self.length))
-                res += "\n"
-        else:  # output counts
-            if not self.has_counts:
-                self.make_counts_from_instances()
-            mat = self.counts
-            for a in letters:
-                res += "\t".join(str(mat[a][i]) for i in range(self.length))
-                res += "\n"
-        return res
-
-    def _to_jaspar_pfm(self):
-        """Returns the pfm representation of the motif
-        """
-        return self._to_horizontal_matrix(normalized=False, letters="ACGT")
-
-    def format(self, format):
-        """Returns a string representation of the Motif in a given format
-
-        Currently supported fromats:
-         - jaspar-pfm : JASPAR Position Frequency Matrix
-         - transfac : TRANSFAC like files
-         - fasta : FASTA file with instances
-        """
-
-        formatters = {
-            "jaspar-pfm": self._to_jaspar_pfm,
-            "transfac": self._to_transfac,
-            "fasta": self._to_fasta,
-            }
-
-        try:
-            return formatters[format]()
-        except KeyError:
-            raise ValueError("Wrong format type")
-
-    def scanPWM(self, seq):
-        """Matrix of log-odds scores for a nucleotide sequence.
-
-        scans a nucleotide sequence and returns the matrix of log-odds
-        scores for all positions.
-
-        - the result is a one-dimensional list or numpy array
-        - the sequence can only be a DNA sequence
-        - the search is performed only on one strand
-        """
-        # TODO - Code itself tolerates ambiguous bases (as NaN).
-        if not isinstance(self.alphabet, IUPAC.IUPACUnambiguousDNA):
-            raise ValueError("PSSM has wrong alphabet: %s - Use only with DNA motifs"
-                                 % self.alphabet)
-        if not isinstance(seq.alphabet, IUPAC.IUPACUnambiguousDNA):
-            raise ValueError("Sequence has wrong alphabet: %r - Use only with DNA sequences"
-                                 % sequence.alphabet)
-
-        seq = str(seq)
-
-        # check if the fast C code can be used
-        try:
-            import _pwm
-        except ImportError:
-            # use the slower Python code otherwise
-            return self._pwm_calculate(seq)
-
-        # get the log-odds matrix into a proper shape
-        # (each row contains sorted (ACGT) log-odds values)
-        logodds = [[y[1] for y in sorted(x.items())] for x in self.log_odds()]
-        return _pwm.calculate(seq, logodds)
-
-    def _pwm_calculate(self, sequence):
-        logodds = self.log_odds()
-        m = len(logodds)
-        s = len(sequence)
-        n = s - m + 1
-        result = [None] * n
-        for i in range(n):
-            score = 0.0
-            for j in range(m):
-                c = sequence[i + j]
-                temp = logodds[j].get(c)
-                if temp is None:
-                    break
-                score += temp
-            else:
-                result[i] = score
-        return result
diff --git a/Bio/Motif/__init__.py b/Bio/Motif/__init__.py
deleted file mode 100644
index d146ced..0000000
--- a/Bio/Motif/__init__.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# Copyright 2003-2009 by Bartek Wilczynski.  All rights reserved.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-"""Tools for sequence motif analysis (DEPRECATED, see Bio.motifs instead).
-
-This module (Bio.Motif) has been deprecated and will be removed in a
-future release of release of Biopython. Please use the new module
-Bio.motifs instead.
-
-This contains the core Motif class containing various I/O methods as
-well as methods for motif comparisons and motif searching in sequences.
-It also inlcudes functionality for parsing AlignACE and MEME programs.
-"""
-
-from __future__ import print_function
-
-import warnings
-from Bio import BiopythonDeprecationWarning
-warnings.warn("The module Bio.Motif has been deprecated and will be "
-              "removed in a future release of Biopython. Instead "
-              "please use the new module Bio.motifs instead. Please "
-              "be aware that though the functionality of Bio.Motif "
-              "is retained (and extended) in Bio.motifs, usage may "
-              "be different.",
-              BiopythonDeprecationWarning)
-
-
-from Bio.Motif._Motif import Motif
-from Bio.Motif.Parsers.AlignAce import read as _AlignAce_read
-from Bio.Motif.Parsers.MEME import read as _MEME_read
-from Bio.Motif.Thresholds import ScoreDistribution
-
-__docformat__ = "restructuredtext en"
-
-_parsers = {"AlignAce": _AlignAce_read,
-            "MEME": _MEME_read,
-            }
-
-
-def _from_pfm(handle):
-    return Motif()._from_jaspar_pfm(handle)
-
-
-def _from_sites(handle):
-    return Motif()._from_jaspar_sites(handle)
-
-_readers = {"jaspar-pfm": _from_pfm,
-            "jaspar-sites": _from_sites
-            }
-
-
-def parse(handle, format):
-    """Parses an output file of motif finding programs.
-
-    Currently supported formats:
-
-        - AlignAce
-        - MEME
-
-    You can also use single-motif formats, although the Bio.Motif.read()
-    function is simpler to use in this situation.
-
-        - jaspar-pfm
-        - jaspar-sites
-
-    For example:
-
-    >>> from Bio import Motif
-    >>> with open("Motif/alignace.out") as handle:
-    ...     for motif in Motif.parse(handle, "AlignAce"):
-    ...         print(motif.consensus())
-    ...
-    TCTACGATTGAG
-    CTGCACCTAGCTACGAGTGAG
-    GTGCCCTAAGCATACTAGGCG
-    GCCACTAGCAGAGCAGGGGGC
-    CGACTCAGAGGTT
-    CCACGCTAAGAGAAGTGCCGGAG
-    GCACGTCCCTGAGCA
-    GTCCATCGCAAAGCGTGGGGC
-    GAGATCAGAGGGCCG
-    TGGACGCGGGG
-    GACCAGAGCCTCGCATGGGGG
-    AGCGCGCGTG
-    GCCGGTTGCTGTTCATTAGG
-    ACCGACGGCAGCTAAAAGGG
-    GACGCCGGGGAT
-    CGACTCGCGCTTACAAGG
-    """
-    try:
-        parser = _parsers[format]
-
-    except KeyError:
-        try:  # not a true parser, try reader formats
-            reader = _readers[format]
-        except:
-            raise ValueError("Wrong parser format")
-        else:  # we have a proper reader
-            yield reader(handle)
-    else:  # we have a proper reader
-        for m in parser(handle).motifs:
-            yield m
-
-
-def read(handle, format):
-    """Reads a motif from a handle using a specified file-format.
-
-    This supports the same formats as Bio.Motif.parse(), but
-    only for files containing exactly one record.  For example,
-    reading a pfm file:
-
-    >>> from Bio import Motif
-    >>> with open("Motif/SRF.pfm") as handle:
-    ...     motif = Motif.read(handle, "jaspar-pfm")
-    ...
-    >>> motif.consensus()
-    Seq('GCCCATATATGG', IUPACUnambiguousDNA())
-
-    Or a single-motif MEME file,
-
-    >>> from Bio import Motif
-    >>> with open("Motif/meme.out") as handle:
-    ...     motif =  Motif.read(handle, "MEME")
-    ...
-    >>> motif.consensus()
-    Seq('CTCAATCGTA', IUPACUnambiguousDNA())
-
-    If the handle contains no records, or more than one record,
-    an exception is raised:
-
-    >>> from Bio import Motif
-    >>> with open("Motif/alignace.out") as handle:
-    ...     motif = Motif.read(handle, "AlignAce")
-    ...
-    Traceback (most recent call last):
-        ...
-    ValueError: More than one motif found in handle
-
-    If however you want the first record from a file containing
-    multiple records this function would raise an exception (as
-    shown in the example above).  Instead use:
-
-    >>> from Bio import Motif
-    >>> with open("Motif/alignace.out") as handle:
-    ...    motif = next(Motif.parse(handle, "AlignAce"))
-    ...
-    >>> motif.consensus()
-    Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-
-    Use the Bio.Motif.parse(handle, format) function if you want
-    to read multiple records from the handle.
-    """
-    iterator = parse(handle, format)
-    try:
-        first = next(iterator)
-    except StopIteration:
-        first = None
-    if first is None:
-        raise ValueError("No motifs found in handle")
-    try:
-        second = next(iterator)
-    except StopIteration:
-        second = None
-    if second is not None:
-        raise ValueError("More than one motif found in handle")
-    return first
-
-
-if __name__ == "__main__":
-    from Bio._utils import run_doctest
-    run_doctest(verbose=0)
diff --git a/Bio/Motif/_pwm.c b/Bio/Motif/_pwm.c
deleted file mode 100644
index 8e36f22..0000000
--- a/Bio/Motif/_pwm.c
+++ /dev/null
@@ -1,166 +0,0 @@
-#include <Python.h>
-#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#include "numpy/arrayobject.h"
-
-
-
-static PyObject*
-calculate(const char sequence[], int s, PyObject* matrix, npy_intp m)
-{
-    npy_intp n = s - m + 1;
-    npy_intp i, j;
-    char c;
-    double score;
-    int ok;
-    PyObject* result;
-    PyArrayObject* array;
-    float* p;
-    npy_intp shape = (npy_intp)n;
-    float nan = 0.0;
-    nan /= nan;
-    if ((int)shape!=n)
-    {
-        PyErr_SetString(PyExc_ValueError, "integer overflow");
-        return NULL;
-    }
-    result = PyArray_SimpleNew(1, &shape, NPY_FLOAT32);
-    if (!result)
-    {
-        PyErr_SetString(PyExc_MemoryError, "failed to create output data");
-        return NULL;
-    }
-    p = PyArray_DATA((PyArrayObject*)result);
-    array = (PyArrayObject*)matrix;
-    for (i = 0; i < n; i++)
-    {
-        score = 0.0;
-        ok = 1;
-        for (j = 0; j < m; j++)
-        {
-            c = sequence[i+j];
-            switch (c)
-            {
-                case 'A':
-                case 'a':
-                    score += *((double*)PyArray_GETPTR2(array, j, 0)); break;
-                case 'C':
-                case 'c':
-                    score += *((double*)PyArray_GETPTR2(array, j, 1)); break;
-                case 'G':
-                case 'g':
-                    score += *((double*)PyArray_GETPTR2(array, j, 2)); break;
-                case 'T':
-                case 't':
-                    score += *((double*)PyArray_GETPTR2(array, j, 3)); break;
-                default:
-                    ok = 0;
-            }
-        }
-        if (ok) *p = (float)score;
-        else *p = nan;
-        p++;
-    }
-    return result;
-}
-
-static char calculate__doc__[] =
-"    calculate(sequence, pwm) -> array of score values\n"
-"\n"
-"This function calculates the position-weight matrix scores for all\n"
-"positions along the sequence, and returns them as a Numerical Python\n"
-"array.\n";
-
-static PyObject*
-py_calculate(PyObject* self, PyObject* args, PyObject* keywords)
-{
-    const char* sequence;
-    PyObject* matrix = NULL;
-    static char* kwlist[] = {"sequence", "matrix", NULL};
-    npy_intp m;
-    int s;
-    PyObject* result;
-    PyArrayObject* array;
-    if(!PyArg_ParseTupleAndKeywords(args, keywords, "s#O&", kwlist,
-                                    &sequence,
-                                    &s,
-                                    PyArray_Converter,
-                                    &matrix)) return NULL;
-
-    array = (PyArrayObject*) matrix;
-    if (PyArray_TYPE(array) != NPY_DOUBLE)
-    {
-        PyErr_SetString(PyExc_ValueError,
-            "position-weight matrix should contain floating-point values");
-        result = NULL;
-    }
-    else if (PyArray_NDIM(array) != 2) /* Checking number of dimensions */
-    {
-        result = PyErr_Format(PyExc_ValueError,
-            "position-weight matrix has incorrect rank (%d expected 2)",
-            PyArray_NDIM(array));
-    }
-    else if(PyArray_DIM(array, 1) != 4)
-    {
-        result = PyErr_Format(PyExc_ValueError,
-            "position-weight matrix should have four columns (%" NPY_INTP_FMT
-            " columns found)", PyArray_DIM(array, 1));
-    }
-    else
-    {
-        m = PyArray_DIM(array, 0);
-        result = calculate(sequence, s, matrix, m);
-    }
-    Py_DECREF(matrix);
-    return result;
-}
-
-
-static struct PyMethodDef methods[] = {
-   {"calculate", (PyCFunction)py_calculate, METH_VARARGS | METH_KEYWORDS, calculate__doc__},
-   {NULL,          NULL, 0, NULL} /* sentinel */
-};
-
-
-#if PY_MAJOR_VERSION >= 3
-
-static struct PyModuleDef moduledef = {
-        PyModuleDef_HEAD_INIT,
-        "_pwm",
-        "Fast calculations involving position-weight matrices",
-        -1,
-        methods,
-        NULL,
-        NULL,
-        NULL,
-        NULL
-};
-
-PyObject*
-PyInit__pwm(void)
-
-#else
-
-void init_pwm(void)
-#endif
-{
-  PyObject *m;
-
-  import_array();
-
-#if PY_MAJOR_VERSION >= 3
-  m = PyModule_Create(&moduledef);
-  if (m==NULL) return NULL;
-#else
-  m = Py_InitModule4("_pwm",
-                     methods,
-                     "Fast calculations involving position-weight matrices",
-                     NULL,
-                     PYTHON_API_VERSION);
-  if (m==NULL) return;
-#endif
-
-  if (PyErr_Occurred()) Py_FatalError("can't initialize module _pwm");
-#if PY_MAJOR_VERSION >= 3
-    return m;
-#endif
-}
diff --git a/Bio/NMR/NOEtools.py b/Bio/NMR/NOEtools.py
index bab5b1e..7e87454 100644
--- a/Bio/NMR/NOEtools.py
+++ b/Bio/NMR/NOEtools.py
@@ -12,8 +12,6 @@ input assignment peaklist.
 
 from . import xpktools
 
-__docformat__ = "restructuredtext en"
-
 
 def predictNOE(peaklist, originNuc, detectedNuc, originResNum, toResNum):
     """Predict the i->j NOE position based on self peak (diagonal) assignments
diff --git a/Bio/NMR/xpktools.py b/Bio/NMR/xpktools.py
index 7485f05..9f71e7a 100644
--- a/Bio/NMR/xpktools.py
+++ b/Bio/NMR/xpktools.py
@@ -9,7 +9,6 @@ from __future__ import print_function
 
 import sys
 
-__docformat__ = "restructuredtext en"
 
 HEADERLEN = 6
 
diff --git a/Bio/NaiveBayes.py b/Bio/NaiveBayes.py
index cf35338..3956722 100644
--- a/Bio/NaiveBayes.py
+++ b/Bio/NaiveBayes.py
@@ -12,17 +12,17 @@ the Bayes assumption that the features are independent.  Although this
 is hardly ever true, the classifier works well enough in practice.
 
 Glossary:
-observation    A feature vector of discrete data.
-class          A possible classification for an observation.
+    - observation - A feature vector of discrete data.
+    - class       - A possible classification for an observation.
 
 
 Classes:
-NaiveBayes     Holds information for a naive Bayes classifier.
+    - NaiveBayes - Holds information for a naive Bayes classifier.
 
 Functions:
-train          Train a new naive Bayes classifier.
-calculate      Calculate the probabilities of each class, given an observation.
-classify       Classify an observation into a class.
+    - train     - Train a new naive Bayes classifier.
+    - calculate - Calculate the probabilities of each class, given an observation.
+    - classify  - Classify an observation into a class.
 
 """
 
@@ -42,11 +42,11 @@ def _contents(items):
 class NaiveBayes(object):
     """Holds information for a NaiveBayes classifier.
 
-    Members:
-    classes         List of the possible classes of data.
-    p_conditional   CLASS x DIM array of dicts of value -> P(value|class,dim)
-    p_prior         List of the prior probabilities for every class.
-    dimensionality  Dimensionality of the data.
+    Attributes:
+        - classes        - List of the possible classes of data.
+        - p_conditional  - CLASS x DIM array of dicts of value -> ``P(value|class,dim)``
+        - p_prior        - List of the prior probabilities for every class.
+        - dimensionality - Dimensionality of the data.
 
     """
     def __init__(self):
@@ -56,16 +56,17 @@ class NaiveBayes(object):
         self.dimensionality = None
 
 
-def calculate(nb, observation, scale=0):
-    """calculate(nb, observation[, scale]) -> probability dict
+def calculate(nb, observation, scale=False):
+    """Calculate ``log P(class|observation)`` for each class.
 
-    Calculate log P(class|observation) for each class.  nb is a NaiveBayes
-    classifier that has been trained.  observation is a list representing
-    the observed data.  scale is whether the probability should be
-    scaled by P(observation).  By default, no scaling is done.  The return
-    value is a dictionary where the keys is the class and the value is the
-    log probability of the class.
+        - nb          - A NaiveBayes classifier that has been trained.
+        - observation - A list representing the observed data.
+        - scale       - Boolean to indicate whether the probability should be
+          scaled by ``P(observation)``.  By default, no scaling is done.
 
+    Returns:
+        A dictionary where the keys is the class and the value is the log
+        probability of the class.
     """
     # P(class|observation) = P(observation|class)*P(class)/P(observation)
     # Taking the log:
@@ -73,8 +74,8 @@ def calculate(nb, observation, scale=0):
 
     # Make sure the observation has the right dimensionality.
     if len(observation) != nb.dimensionality:
-        raise ValueError("observation in %d dimension, but classifier in %d"
-                         % (len(observation), nb.dimensionality))
+        raise ValueError("observation in {0} dimension, but classifier in {1}".format(len(observation),
+                                                                                      nb.dimensionality))
 
     # Calculate log P(observation|class) for every class.
     n = len(nb.classes)
@@ -107,13 +108,13 @@ def calculate(nb, observation, scale=0):
 
 
 def classify(nb, observation):
-    """classify(nb, observation) -> class
+    """ Classify an observation into a class.
 
-    Classify an observation into a class.
+    ``classify(nb, observation) -> class``
 
     """
     # The class is the one with the highest probability.
-    probs = calculate(nb, observation, scale=0)
+    probs = calculate(nb, observation, scale=False)
     max_prob = max_class = None
     for klass in nb.classes:
         if max_prob is None or probs[klass] > max_prob:
@@ -122,14 +123,16 @@ def classify(nb, observation):
 
 
 def train(training_set, results, priors=None, typecode=None):
-    """train(training_set, results[, priors]) -> NaiveBayes
+    """ Train a naive bayes classifier on a training set.
 
-    Train a naive bayes classifier on a training set.  training_set is a
-    list of observations.  results is a list of the class assignments
-    for each observation.  Thus, training_set and results must be the same
-    length.  priors is an optional dictionary specifying the prior
-    probabilities for each type of result.  If not specified, the priors
-    will be estimated from the training results.
+    ``train(training_set, results[, priors]) -> NaiveBayes``
+
+        - training_set - List of observations.
+        - results      - List of the class assignments for each observation.
+          Thus, training_set and results must be the same length.
+        - priors       - Optional dictionary specifying the prior probabilities
+          for each type of result.  If not specified, the priors will be
+          estimated from the training results.
 
     """
     if not len(training_set):
@@ -179,7 +182,7 @@ def train(training_set, results, priors=None, typecode=None):
     for i in range(len(results)):
         klass, obs = results[i], training_set[i]
         observations[c2i[klass]].append(obs)
-    # Now make the observations Numeric matrics.
+    # Now make the observations Numeric matrix.
     for i in range(len(observations)):
         # XXX typecode must be specified!
         observations[i] = numpy.asarray(observations[i], typecode)
@@ -200,38 +203,3 @@ def train(training_set, results, priors=None, typecode=None):
             # Estimate P(value|class,dim)
             nb.p_conditional[i][j] = _contents(values)
     return nb
-
-if __name__ == "__main__":
-    # Car data from example 'Naive Bayes Classifier example' by Eric Meisner November 22, 2003
-    # http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
-    xcar = [
-        ['Red',    'Sports', 'Domestic'],
-        ['Red',    'Sports', 'Domestic'],
-        ['Red',    'Sports', 'Domestic'],
-        ['Yellow', 'Sports', 'Domestic'],
-        ['Yellow', 'Sports', 'Imported'],
-        ['Yellow', 'SUV',    'Imported'],
-        ['Yellow', 'SUV',    'Imported'],
-        ['Yellow', 'SUV',    'Domestic'],
-        ['Red',    'SUV',    'Imported'],
-        ['Red',    'Sports', 'Imported']
-    ]
-
-    ycar = [
-        'Yes',
-        'No',
-        'Yes',
-        'No',
-        'Yes',
-        'No',
-        'Yes',
-        'No',
-        'No',
-        'Yes'
-    ]
-
-    carmodel = train(xcar, ycar)
-    carresult = classify(carmodel, ['Red', 'Sports', 'Domestic'])
-    print('Is Yes? %s' % carresult)
-    carresult = classify(carmodel, ['Red', 'SUV', 'Domestic'])
-    print('Is No? %s' % carresult)
diff --git a/Bio/NeuralNetwork/Gene/Motif.py b/Bio/NeuralNetwork/Gene/Motif.py
index 8a13866..647cd4d 100644
--- a/Bio/NeuralNetwork/Gene/Motif.py
+++ b/Bio/NeuralNetwork/Gene/Motif.py
@@ -9,7 +9,7 @@ Representing DNA (or RNA or proteins) in a neural network can be difficult
 since input sequences can have different lengths. One way to get around
 this problem is to deal with sequences by finding common motifs, and counting
 the number of times those motifs occur in a sequence. This information can
-then be used for creating the neural networks, with occurances of motifs
+then be used for creating the neural networks, with occurrences of motifs
 going into the network instead of raw sequence data.
 """
 # biopython
@@ -207,8 +207,8 @@ class MotifCoder(object):
         # otherwise we'll just return 0 for everything
         if max_count > 0:
             for motif in seq_motifs:
-                seq_motifs[motif] = (float(seq_motifs[motif] - min_count)
-                                     / float(max_count))
+                seq_motifs[motif] = (float(seq_motifs[motif] - min_count) /
+                                     float(max_count))
 
         # return the relative motif counts in the specified order
         motif_amounts = []
diff --git a/Bio/NeuralNetwork/Gene/Pattern.py b/Bio/NeuralNetwork/Gene/Pattern.py
index 5ade29d..2909f63 100644
--- a/Bio/NeuralNetwork/Gene/Pattern.py
+++ b/Bio/NeuralNetwork/Gene/Pattern.py
@@ -17,13 +17,11 @@ import random
 from Bio.Alphabet import _verify_alphabet
 from Bio.Seq import Seq, MutableSeq
 
-__docformat__ = "restructuredtext en"
-
 
 class PatternIO(object):
     """Allow reading and writing of patterns to files.
 
-    This just defines a simple persistance class for patterns, making
+    This just defines a simple persistence class for patterns, making
     it easy to write them to a file and read 'em back.
     """
     def __init__(self, alphabet=None):
@@ -50,7 +48,7 @@ class PatternIO(object):
         """
         for pattern in pattern_list:
             # deal with signatures, concatentate them with the separator
-            if isinstance(pattern, list) or isinstance(pattern, tuple):
+            if isinstance(pattern, (list, tuple)):
                 string_pattern = self.separator.join(pattern)
             # deal with the normal cases
             else:
diff --git a/Bio/NeuralNetwork/Gene/Schema.py b/Bio/NeuralNetwork/Gene/Schema.py
index 8d10335..f8853ac 100644
--- a/Bio/NeuralNetwork/Gene/Schema.py
+++ b/Bio/NeuralNetwork/Gene/Schema.py
@@ -38,8 +38,6 @@ from Bio.GA.Repair.Stabilizing import AmbiguousRepair
 from Bio.GA.Selection.Tournament import TournamentSelection
 from Bio.GA.Selection.Diversity import DiversitySelection
 
-__docformat__ = "restructuredtext en"
-
 
 class Schema(object):
     """Deal with motifs that have ambiguity characters in it.
@@ -129,7 +127,7 @@ class Schema(object):
         """Return all non-overlapping motif matches in the query string.
 
         This utilizes the regular expression findall function, and will
-        return a list of all non-overlapping occurances in query that
+        return a list of all non-overlapping occurrences in query that
         match the ambiguous motif.
         """
         try:
@@ -292,8 +290,8 @@ class DifferentialSchemaFitness(object):
     def calculate_fitness(self, genome):
         """Calculate the fitness for a given schema.
 
-        Fitness is specified by the number of occurances of the schema in
-        the positive sequences minus the number of occurances in the
+        Fitness is specified by the number of occurrences of the schema in
+        the positive sequences minus the number of occurrences in the
         negative examples.
 
         This fitness is then modified by multiplying by the length of the
diff --git a/Bio/NeuralNetwork/Gene/Signature.py b/Bio/NeuralNetwork/Gene/Signature.py
index 418aa2d..97730e8 100644
--- a/Bio/NeuralNetwork/Gene/Signature.py
+++ b/Bio/NeuralNetwork/Gene/Signature.py
@@ -213,8 +213,8 @@ class SignatureCoder(object):
         # otherwise we'll just return 0 for everything
         if max_count > 0:
             for sig in sequence_sigs:
-                sequence_sigs[sig] = (float(sequence_sigs[sig] - min_count)
-                                      / float(max_count))
+                sequence_sigs[sig] = (float(sequence_sigs[sig] - min_count) /
+                                      float(max_count))
 
         # return the relative signature info in the specified order
         sig_amounts = []
diff --git a/Bio/NeuralNetwork/Training.py b/Bio/NeuralNetwork/Training.py
index fe7fa9e..6c26410 100644
--- a/Bio/NeuralNetwork/Training.py
+++ b/Bio/NeuralNetwork/Training.py
@@ -3,17 +3,15 @@
 # as part of this package.
 #
 
-"""Provide classes for dealing with Training Neural Networks.
-"""
+"""Provide classes for dealing with Training Neural Networks."""
+
 # standard modules
 import random
 
 
 class TrainingExample(object):
-    """Hold inputs and outputs of a training example.
+    """Hold inputs and outputs of a training example."""
 
-    XXX Do I really need this?
-    """
     def __init__(self, inputs, outputs, name=""):
         self.name = name
         self.inputs = inputs
diff --git a/Bio/Nexus/.project b/Bio/Nexus/.project
new file mode 100644
index 0000000..8bfeb52
--- /dev/null
+++ b/Bio/Nexus/.project
@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>Nexus</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.python.pydev.PyDevBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.python.pydev.pythonNature</nature>
+	</natures>
+</projectDescription>
diff --git a/Bio/Nexus/Nexus.py b/Bio/Nexus/Nexus.py
index d05fcb1..f4231f7 100644
--- a/Bio/Nexus/Nexus.py
+++ b/Bio/Nexus/Nexus.py
@@ -1,4 +1,5 @@
 # Copyright 2005-2008 by Frank Kauff & Cymon J. Cox. All rights reserved.
+#           2014-2015 by Joe Cora (standard data)
 # This code is part of the Biopython distribution and governed by its
 # license. Please see the LICENSE file that should have been included
 # as part of this package.
@@ -26,9 +27,9 @@ from Bio.Alphabet import IUPAC
 from Bio.Data import IUPACData
 from Bio.Seq import Seq
 
-from .Trees import Tree
+from Bio.Nexus.StandardData import StandardData
+from Bio.Nexus.Trees import Tree
 
-__docformat__ = "restructuredtext en"
 
 INTERLEAVE = 70
 SPECIAL_COMMANDS = ['charstatelabels', 'charlabels', 'taxlabels', 'taxset',
@@ -119,18 +120,20 @@ class CharBuffer(object):
 
         This deals with single and double quotes, whitespace and punctuation.
         """
-
         word = []
         quoted = False
-        first = self.next_nonwhitespace()                   # get first character
-        if not first:                                       # return empty if only whitespace left
+        # get first character
+        first = self.next_nonwhitespace()
+        if not first:
+            # return empty if only whitespace left
             return None
         word.append(first)
-        if first == "'":                                    # word starts with a quote
+        if first == "'":
             quoted = "'"
         elif first == '"':
             quoted = '"'
-        elif first in PUNCTUATION:                          # if it's punctuation, return immediately
+        elif first in PUNCTUATION:
+            # if it's non-quote punctuation, return immediately
             return first
         while True:
             c = self.peek()
@@ -141,7 +144,8 @@ class CharBuffer(object):
                 elif quoted:                                # second single quote ends word
                     break
             elif quoted:
-                word.append(next(self))                              # if quoted, then add anything
+                # if quoted, then add anything
+                word.append(next(self))
             elif not c or c in PUNCTUATION or c in WHITESPACE:
                 # if not quoted and special character, stop
                 break
@@ -159,7 +163,6 @@ class StepMatrix(object):
 
     See Wheeler (1990), Cladistics 6:269-275.
     """
-
     def __init__(self, symbols, gap):
         self.data = {}
         self.symbols = sorted(symbols)
@@ -245,14 +248,11 @@ def quotestrip(word):
     return word
 
 
-def get_start_end(sequence, skiplist=None):
+def get_start_end(sequence, skiplist=('-', '?')):
     """Return position of first and last character which is not in skiplist.
 
     Skiplist defaults to ['-','?'].
     """
-    if skiplist is None:
-        skiplist = ["-", "?"]
-
     length = len(sequence)
     if length == 0:
         return None, None
@@ -281,9 +281,13 @@ def _make_unique(l):
 def _unique_label(previous_labels, label):
     """Returns a unique name if label is already in previous_labels."""
     while label in previous_labels:
-        if label.split('.')[-1].startswith('copy'):
-            label = '.'.join(label.split('.')[:-1]) \
-                  + '.copy' + str(eval('0' + label.split('.')[-1][4:]) + 1)
+        label_split = label.split('.')
+        if label_split[-1].startswith('copy'):
+            copy_num = 1
+            if label_split[-1] != "copy":
+                copy_num = int(label_split[-1][4:]) + 1
+            new_label = "%s.copy%s" % ('.'.join(label_split[:-1]), copy_num)
+            label = new_label
         else:
             label += '.copy'
     return label
@@ -367,13 +371,21 @@ def combine(matrices):
         m_only = [t for t in m.taxlabels if t not in both]
         for t in both:
             # concatenate sequences and unify gap and missing character symbols
-            combined.matrix[t] += Seq(str(m.matrix[t]).replace(m.gap, combined.gap).replace(m.missing, combined.missing), combined.alphabet)
+            combined.matrix[t] += Seq(str(m.matrix[t])
+                                      .replace(m.gap, combined.gap)
+                                      .replace(m.missing, combined.missing),
+                                      combined.alphabet)
         # replace date of missing taxa with symbol for missing data
         for t in combined_only:
-            combined.matrix[t] += Seq(combined.missing * m.nchar, combined.alphabet)
+            combined.matrix[t] += Seq(combined.missing * m.nchar,
+                                      combined.alphabet)
         for t in m_only:
-            combined.matrix[t] = Seq(combined.missing * combined.nchar, combined.alphabet) + \
-                Seq(str(m.matrix[t]).replace(m.gap, combined.gap).replace(m.missing, combined.missing), combined.alphabet)
+            combined.matrix[t] = Seq(combined.missing * combined.nchar,
+                                     combined.alphabet) + \
+                                Seq(str(m.matrix[t])
+                                    .replace(m.gap, combined.gap)
+                                    .replace(m.missing, combined.missing),
+                                    combined.alphabet)
         combined.taxlabels.extend(m_only)    # new taxon list
         for cn, cs in m.charsets.items():  # adjust character sets for new matrix
             combined.charsets['%s.%s' % (n, cn)] = [x + combined.nchar for x in cs]
@@ -614,7 +626,7 @@ class Nexus(object):
                 file_contents = fp.read()
                 self.filename = getattr(fp, 'name', 'Unknown_nexus_file')
         except (TypeError, IOError, AttributeError):
-            # 2 Assume we have a string from a fh.read()
+            # 2. Assume we have a string from a fh.read()
             if isinstance(input, basestring):
                 file_contents = input
                 self.filename = 'input_string'
@@ -630,7 +642,7 @@ class Nexus(object):
             try:
                 if cl[:6].upper() == '#NEXUS':
                     commandlines[i] = cl[6:].strip()
-            except:
+            except IndexError:
                 pass
         # now loop through blocks (we parse only data in known blocks, thus ignoring non-block commands
         nexus_block_gen = self._get_nexus_block(commandlines)
@@ -674,10 +686,10 @@ class Nexus(object):
 
     def _parse_nexus_block(self, title, contents):
         """Parse a known Nexus Block (PRIVATE)."""
-        # attached the structered block representation
+        # attached the structured block representation
         self._apply_block_structure(title, contents)
         # now check for taxa,characters,data blocks. If this stuff is defined more than once
-        # the later occurences will override the previous ones.
+        # the later occurrences will override the previous ones.
         block = self.structured[-1]
         for line in block.commandlines:
             try:
@@ -706,15 +718,17 @@ class Nexus(object):
             self.respectcase = True
         # adjust symbols to for respectcase
         if 'symbols' in options:
-            self.symbols = options['symbols']
+            self.symbols = ''.join(options['symbols'].split())
             if (self.symbols.startswith('"') and self.symbols.endswith('"')) or\
-            (self.symbols.startswith("'") and self.symbols.endswith("'")):
-                self.symbols = self.symbols[1:-1].replace(' ', '')
+                (self.symbols.startswith("'") and self.symbols.endswith("'")):
+                self.symbols = self.symbols[1:-1]
             if not self.respectcase:
-                self.symbols = self.symbols.lower() + self.symbols.upper()
-                self.symbols = list(set(self.symbols))
+                self.symbols = list(self.symbols.upper())
+                # self.symbols = self.symbols.lower() + self.symbols.upper()
+                # self.symbols = list(set(self.symbols))
         if 'datatype' in options:
             self.datatype = options['datatype'].lower()
+
             if self.datatype == 'dna' or self.datatype == 'nucleotide':
                 self.alphabet = IUPAC.IUPACAmbiguousDNA()  # fresh instance!
                 self.ambiguous_values = IUPACData.ambiguous_dna_values.copy()
@@ -726,16 +740,29 @@ class Nexus(object):
             elif self.datatype == 'protein':
                 # TODO - Should this not be ExtendedIUPACProtein?
                 self.alphabet = IUPAC.IUPACProtein()  # fresh instance
-                self.ambiguous_values = {'B': 'DN', 'Z': 'EQ', 'X': IUPACData.protein_letters}
+                self.ambiguous_values = {'B': 'DN',
+                                         'Z': 'EQ',
+                                         'X': IUPACData.protein_letters}
                 # that's how PAUP handles it
                 self.unambiguous_letters = IUPACData.protein_letters + '*'  # stop-codon
             elif self.datatype == 'standard':
-                raise NexusError('Datatype standard is not yet supported.')
-                # self.alphabet = None
-                # self.ambiguous_values = {}
-                # if not self.symbols:
-                #    self.symbols = '01' # if nothing else defined, then 0 and 1 are the default states
-                # self.unambiguous_letters = self.symbols
+                self.alphabet = None
+                self.ambiguous_values = {}
+                if not self.symbols:
+                    # PARSER BUG ##
+                    # This error arises when symbols are absent or when
+                    # whitespace is located within the SYMBOLS command values.
+                    # The Nexus parser quits reading the SYMBOLS line upon
+                    # finding a whitespace character.
+                    raise NexusError(
+                        "Symbols must be defined when using standard datatype. "
+                        "Please remove any whitespace (spaces, tabs, etc.) "
+                        "between values for symbols as this confuses the Nexus "
+                        "parser.")
+
+                self.unambiguous_letters = ''.join(self.symbols)
+                if not self.respectcase:
+                    self.unambiguous_letters += self.unambiguous_letters.lower()
             else:
                 raise NexusError('Unsupported datatype: ' + self.datatype)
             self.valid_characters = ''.join(self.ambiguous_values) + self.unambiguous_letters
@@ -754,9 +781,9 @@ class Nexus(object):
             if self.missing not in self.ambiguous_values:
                 self.ambiguous_values[self.missing] = self.unambiguous_letters + self.gap
             self.ambiguous_values[self.gap] = self.gap
-        elif self.datatype == 'standard':
-            if not self.symbols:
-                self.symbols = ['1', '0']
+        # elif self.datatype == 'standard':
+        #    if not self.symbols:
+        #        self.symbols = ['0', '1']
         if 'missing' in options:
             self.missing = options['missing'][0]
         if 'gap' in options:
@@ -831,8 +858,65 @@ class Nexus(object):
                 raise NexusError('Missing \',\' in line %s.' % options)
 
     def _charstatelabels(self, options):
-        # warning: charstatelabels supports only charlabels-syntax!
-        self._charlabels(options)
+        self.charlabels = {}
+        self.statelabels = {}
+        opts = CharBuffer(options)
+
+        # Make sure symbols are defined
+        if not self.symbols:
+            raise NexusError(
+                'Symbols must be defined when using character states')
+
+        while True:
+            # get id and character name
+            w = opts.next_word()
+
+            # McClade saves and reads charlabel-lists with terminal comma?!
+            if w is None:
+                break
+
+            identifier = self._resolve(w, set_type=CHARSET)
+            character = quotestrip(opts.next_word())
+
+            self.charlabels[identifier] = character
+            self.statelabels[identifier] = []
+
+            # check for comma, slash or end of command
+            c = opts.next_nonwhitespace()
+
+            if c is None:
+                break
+            elif c != ',':
+                # Check if states are defined, otherwise report error
+                if c != '/':
+                    raise NexusError('Missing \',\' in line %s.' % options)
+
+                # Get the first state
+                state = quotestrip(opts.next_word())
+
+                if state is None:
+                    raise NexusError(
+                        'Missing character state in line %s.' % options)
+
+                while True:
+                    # Make sure current state does not exceed number of
+                    # available symbols
+                    if len(self.statelabels[identifier]) > len(self.symbols):
+                        raise NexusError(
+                            'Character states exceed number of available symbols in line %s.' % options)
+
+                    # Add the character state to the statelabels
+                    self.statelabels[identifier].append(state)
+
+                    # Check for another state or comma to end states (last
+                    # character should not have comma at end of states - but
+                    # we'll ignore)
+                    state = quotestrip(opts.next_word())
+
+                    if state is None:
+                        return
+                    elif state is ',':
+                        break
 
     def _statelabels(self, options):
         # self.charlabels = options
@@ -861,7 +945,6 @@ class Nexus(object):
                     break
             # count the taxa and check for interleaved matrix
             taxcount += 1
-            # print taxcount
             if taxcount > self.ntax:
                 if not self.interleave:
                     raise NexusError('Too many taxa in matrix - should matrix be interleaved?')
@@ -875,7 +958,6 @@ class Nexus(object):
             chars = ''
             if self.interleave:
                 # interleaved matrix
-                # print 'In interleave'
                 if l:
                     chars = ''.join(l.split())
                 else:
@@ -886,22 +968,42 @@ class Nexus(object):
                 while len(chars) < self.nchar:
                     l = next(lineiter)
                     chars += ''.join(l.split())
-            iupac_seq = Seq(_replace_parenthesized_ambigs(chars, self.rev_ambiguous_values), self.alphabet)
-            # first taxon has the reference sequence if matchhar is used
-            if taxcount == 1:
-                refseq = iupac_seq
+
+            # Reformat sequence for non-standard datatypes
+            if self.datatype != 'standard':
+                iupac_seq = Seq(_replace_parenthesized_ambigs(
+                    chars, self.rev_ambiguous_values), self.alphabet)
+                # first taxon has the reference sequence if matchhar is used
+                if taxcount == 1:
+                    refseq = iupac_seq
+                else:
+                    if self.matchchar:
+                        while True:
+                            p = str(iupac_seq).find(self.matchchar)
+                            if p == -1:
+                                break
+                            iupac_seq = Seq(str(iupac_seq)[:p] + refseq[
+                                            p] + str(iupac_seq)[p + 1:], self.alphabet)
+
+                # Check for invalid characters
+                for i, c in enumerate(str(iupac_seq)):
+                    if c not in self.valid_characters and c != self.gap and c != self.missing:
+                        raise NexusError("Taxon %s: Illegal character %s in sequence %s "
+                                         "(check dimensions/interleaving)" % (id, c, iupac_seq))
             else:
-                if self.matchchar:
-                    while True:
-                        p = str(iupac_seq).find(self.matchchar)
-                        if p == -1:
-                            break
-                        iupac_seq = Seq(str(iupac_seq)[:p] + refseq[p] + str(iupac_seq)[p + 1:], self.alphabet)
-            # check for invalid characters
-            for c in str(iupac_seq):
-                if c not in self.valid_characters and c != self.gap and c != self.missing:
-                    raise NexusError("Taxon %s: Illegal character %s in sequence %s "
-                                     "(check dimensions/interleaving)" % (id, c, iupac_seq))
+                iupac_seq = StandardData(chars)
+
+                # Check for invalid characters
+                for i, c in enumerate(iupac_seq):
+                    # Go through each coding for each character
+                    for coding in c['d']:
+                        if (coding not in self.valid_characters and
+                            coding != self.gap and coding != self.missing):
+                            raise NexusError("Taxon %s: Illegal character %s "
+                                             "in sequence %s "
+                                             "(check dimensions/interleaving)"
+                                             % (id, coding, iupac_seq))
+
             # add sequence to matrix
             if first_matrix_block:
                 self.unaltered_taxlabels.append(id)
@@ -946,7 +1048,7 @@ class Nexus(object):
                     raise NexusError('Missing \',\' in line %s.' % options)
             except NexusError:
                 raise
-            except:
+            except Exception:  # TODO: ValueError?
                 raise NexusError('Format error in line %s.' % options)
 
     def _utree(self, options):
@@ -979,7 +1081,8 @@ class Nexus(object):
                 rooted = False
             elif special == 'W':
                 weight = float(value)
-        tree = Tree(name=name, weight=weight, rooted=rooted, tree=opts.rest().strip())
+        tree = Tree(name=name, weight=weight, rooted=rooted,
+                    tree=opts.rest().strip())
         # if there's an active translation table, translate
         if self.translate:
             for n in tree.get_terminals():
@@ -1056,8 +1159,9 @@ class Nexus(object):
         name = self._name_n_vector(opts)
         if not name:
             raise NexusError('Formatting error in charpartition: %s ' % options)
-        # now collect thesubbpartitions and parse them
-        # subpartitons separated by commas - which unfortunately could be part of a quoted identifier...
+        # now collect the subpartitions and parse them
+        # subpartitions separated by commas - which unfortunately could be part
+        # of a quoted identifier...
         sub = ''
         while True:
             w = next(opts)
@@ -1146,9 +1250,11 @@ class Nexus(object):
                             taxrange = self.taxlabels[start:end + 1]
                             plain_list.extend(taxrange)
                     else:
-                        if isinstance(start, list):           # start was the name of charset or taxset
+                        if isinstance(start, list):
+                            # start was the name of charset or taxset
                             plain_list.extend(start)
-                        else:                           # start was an ordinary identifier
+                        else:
+                            # start was an ordinary identifier
                             plain_list.append(start)
             except NexusError:
                 raise
@@ -1229,7 +1335,7 @@ class Nexus(object):
         pass
 
     def write_nexus_data_partitions(self, matrix=None, filename=None, blocksize=None,
-                                    interleave=False, exclude=[], delete=[],
+                                    interleave=False, exclude=(), delete=(),
                                     charpartition=None, comment='', mrbayes=False):
         """Writes a nexus file for each partition in charpartition.
 
@@ -1246,7 +1352,7 @@ class Nexus(object):
         if charpartition:
             pfilenames = {}
             for p in charpartition:
-                total_exclude = [] + exclude
+                total_exclude = list(exclude)
                 total_exclude.extend(c for c in range(self.nchar) if c not in charpartition[p])
                 total_exclude = _make_unique(total_exclude)
                 pcomment = comment + '\nPartition: ' + p + '\n'
@@ -1267,7 +1373,7 @@ class Nexus(object):
                                   comment=comment, append_sets=False, mrbayes=mrbayes)
             return fn
 
-    def write_nexus_data(self, filename=None, matrix=None, exclude=[], delete=[],
+    def write_nexus_data(self, filename=None, matrix=None, exclude=(), delete=(),
                          blocksize=None, interleave=False, interleave_by_partition=False,
                          comment=None, omit_NEXUS=False, append_sets=True, mrbayes=False,
                          codons_block=True):
@@ -1301,7 +1407,8 @@ class Nexus(object):
                 raise NexusError('Unknown partition: %r' % interleave_by_partition)
             else:
                 partition = self.charpartitions[interleave_by_partition]
-                # we need to sort the partition names by starting position before we exclude characters
+                # we need to sort the partition names by starting position
+                # before we exclude characters
                 names = _sort_keys_by_values(partition)
                 newpartition = {}
                 for p in partition:
@@ -1342,9 +1449,9 @@ class Nexus(object):
             if self.charlabels:
                 newcharlabels = self._adjust_charlabels(exclude=exclude)
                 clkeys = sorted(newcharlabels)
-                fh.write('charlabels '
-                         + ', '.join("%s %s" % (k + 1, safename(newcharlabels[k])) for k in clkeys)
-                         + ';\n')
+                fh.write('charlabels ' +
+                         ', '.join("%s %s" % (k + 1, safename(newcharlabels[k])) for k in clkeys) +
+                         ';\n')
             fh.write('matrix\n')
             if not blocksize:
                 if interleave:
@@ -1354,7 +1461,8 @@ class Nexus(object):
             # delete deleted taxa and ecxclude excluded characters...
             namelength = max(len(safename(t, mrbayes=mrbayes)) for t in undelete)
             if interleave_by_partition:
-                # interleave by partitions, but adjust partitions with regard to excluded characters
+                # interleave by partitions, but adjust partitions with regard
+                # to excluded characters
                 seek = 0
                 for p in names:
                     fh.write('[%s: %s]\n' % (interleave_by_partition, p))
@@ -1391,7 +1499,7 @@ class Nexus(object):
                     fh.write(self.append_sets(exclude=exclude, delete=delete, mrbayes=mrbayes))
         return filename
 
-    def append_sets(self, exclude=[], delete=[], mrbayes=False, include_codons=True, codons_only=False):
+    def append_sets(self, exclude=(), delete=(), mrbayes=False, include_codons=True, codons_only=False):
         """Returns a sets block."""
         if not self.charsets and not self.taxsets and not self.charpartitions:
             return ''
@@ -1493,7 +1601,7 @@ class Nexus(object):
                 fh.write('%s %s\n' % (safename(taxon), str(self.matrix[taxon])))
         return filename
 
-    def constant(self, matrix=None, delete=[], exclude=[]):
+    def constant(self, matrix=None, delete=(), exclude=()):
         """Return a list with all constant characters."""
         if not matrix:
             matrix = self.matrix
@@ -1512,14 +1620,16 @@ class Nexus(object):
                 # print '%d (paup=%d)' % (site[0],site[0]+1),
                 seqsite = matrix[taxon][site[0]].upper()
                 # print seqsite,'checked against',site[1],'\t',
-                if seqsite == self.missing \
-                or (seqsite == self.gap and self.options['gapmode'].lower() == 'missing') \
-                or seqsite == site[1]:
+                if (seqsite == self.missing or
+                    (seqsite == self.gap and
+                     self.options['gapmode'].lower() == 'missing') or
+                    seqsite == site[1]):
                     # missing or same as before  -> ok
                     newconstant.append(site)
-                elif seqsite in site[1] \
-                or site[1] == self.missing \
-                or (self.options['gapmode'].lower() == 'missing' and site[1] == self.gap):
+                elif (seqsite in site[1] or
+                      site[1] == self.missing or
+                      (self.options['gapmode'].lower() == 'missing' and
+                       site[1] == self.gap)):
                     # subset of an ambig or only missing in previous -> take subset
                     newconstant.append((site[0], self.ambiguous_values.get(seqsite, seqsite)))
                 elif seqsite in self.ambiguous_values:
@@ -1536,7 +1646,7 @@ class Nexus(object):
         cpos = [s[0] for s in constant]
         return cpos
 
-    def cstatus(self, site, delete=[], narrow=True):
+    def cstatus(self, site, delete=(), narrow=True):
         """Summarize character.
 
         narrow=True:  paup-mode (a c ? --> ac; ? ? ? --> ?)
@@ -1560,7 +1670,7 @@ class Nexus(object):
         cstatus.sort()
         return cstatus
 
-    def weighted_stepmatrix(self, name='your_name_here', exclude=[], delete=[]):
+    def weighted_stepmatrix(self, name='your_name_here', exclude=(), delete=()):
         """Calculates a stepmatrix for weighted parsimony.
 
         See Wheeler (1990), Cladistics 6:269-275 and
@@ -1574,7 +1684,7 @@ class Nexus(object):
                     m.add(b1.upper(), b2.upper(), 1)
         return m.transformation().weighting().smprint(name=name)
 
-    def crop_matrix(self, matrix=None, delete=[], exclude=[]):
+    def crop_matrix(self, matrix=None, delete=(), exclude=()):
         """Return a matrix without deleted taxa and excluded characters."""
         if not matrix:
             matrix = self.matrix
@@ -1595,7 +1705,7 @@ class Nexus(object):
         else:
             return dict((t, matrix[t]) for t in self.taxlabels if t in matrix and t not in delete)
 
-    def bootstrap(self, matrix=None, delete=[], exclude=[]):
+    def bootstrap(self, matrix=None, delete=(), exclude=()):
         """Return a bootstrapped matrix."""
         if not matrix:
             matrix = self.matrix
@@ -1611,7 +1721,8 @@ class Nexus(object):
             alphabet = matrix[list(matrix.keys())[0]].alphabet
         else:
             sitesm = list(zip(*[cm[t] for t in undelete]))
-        bootstrapsitesm = [sitesm[random.randint(0, len(sitesm) - 1)] for i in range(len(sitesm))]
+        bootstrapsitesm = [sitesm[random.randint(0, len(sitesm) - 1)]
+                           for i in range(len(sitesm))]
         bootstrapseqs = [''.join(x) for x in zip(*bootstrapsitesm)]
         if seqobjects:
             bootstrapseqs = [Seq(s, alphabet) for s in bootstrapseqs]
@@ -1675,7 +1786,8 @@ class Nexus(object):
         sitesm = list(zip(*[str(self.matrix[t]) for t in self.taxlabels]))
         sitesm[pos:pos] = [['-'] * len(self.taxlabels)] * n
         mapped = [''.join(x) for x in zip(*sitesm)]
-        listed = [(taxon, Seq(mapped[i], self.alphabet)) for i, taxon in enumerate(self.taxlabels)]
+        listed = [(taxon, Seq(mapped[i], self.alphabet))
+                  for i, taxon in enumerate(self.taxlabels)]
         self.matrix = dict(listed)
         self.nchar += n
         # now adjust character sets
@@ -1770,3 +1882,70 @@ else:
         # nexus file under normal circumstances)
         commandlines = _adjust_lines(decommented.split(chr(7)))
         return commandlines
+
+
+# Test code from command line (python Nexus.py)
+if __name__ == '__main__':
+    print()
+    print()
+    print('=== NEXUS PARSING TEST ===')
+    print()
+
+    # 1. Check basic tree file with TREES and TAXA block
+    print('Testing file "bats.nex": TREES and TAXA')
+    nexus1 = Nexus()
+    try:
+        nexus1.read('../../Tests/Nexus/bats.nex')
+    except Exception as nexus_error:
+        raise Exception(nexus_error.message)
+
+    # 2. Check simple sequence data file with DATA and CODONS block
+    print('Testing file "codonposset.nex": DATA and CODONS')
+    nexus2 = Nexus()
+    try:
+        nexus2.read('../../Tests/Nexus/codonposset.nex')
+    except Exception as nexus_error:
+        raise Exception(nexus_error.message)
+
+    # 3. Check sequence data file with DATA, SETS, TREES and an unknown block
+    print('Testing file "test_Nexus_input.nex": DATA, SETS, TREES and unknown')
+    nexus3 = Nexus()
+    try:
+        nexus3.read('../../Tests/Nexus/test_Nexus_input.nex')
+    except Exception as nexus_error:
+        raise Exception(nexus_error.message)
+
+    # 4. Check simple multi-state character data file with TAXA and CHARACTERS
+    # block
+    print('Testing file "vSysLab_Ganaspidium_multistate.nex": TAXA, CHARACTERS (multi-state)')
+    nexus4 = Nexus()
+    try:
+        nexus4.read('../../Tests/Nexus/vSysLab_Ganaspidium_multistate.nex')
+    except Exception as nexus_error:
+        raise Exception(nexus_error.message)
+
+    # 5. Check character data file with TAXA and CHARACTERS block with more
+    # than 9 codings and a character without states
+    print('Testing file "vSysLab_Heptascelio_no-states_10+chars.nex": TAXA, CHARACTERS (stateless and 10+ codings)')
+    nexus5 = Nexus()
+    try:
+        nexus5.read('../../Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex')
+    except Exception as nexus_error:
+        raise Exception(nexus_error.message)
+
+    # TODO: Not supported
+    # 6. Check character data file with TAXA and two CHARACTERS blocks (one with continuous characters)
+    # print('Testing file "vSysLab_Oreiscelio_discrete+continuous.nex": TAXA, 2 CHARACTERS (discrete and continuous)')
+    #
+    # nexus6 = Nexus()
+    #
+    # try:
+    #    nexus6.read('../../Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex')
+    # except Exception as nexus_error:
+    #    raise Exception(nexus_error.message)
+
+    # Made it here, so success
+    print()
+    print('Successfully completed test suite.')
+    print()
+    print()
diff --git a/Bio/Nexus/Nodes.py b/Bio/Nexus/Nodes.py
index a1b3b3f..70c46a9 100644
--- a/Bio/Nexus/Nodes.py
+++ b/Bio/Nexus/Nodes.py
@@ -2,17 +2,16 @@
 # This code is part of the Biopython distribution and governed by its
 # license. Please see the LICENSE file that should have been included
 # as part of this package.
-#
-# Nodes.py
-#
-# Provides functionality of a linked list.
-# Each node has one (or none) predecessor, and an arbitrary number of successors.
-# Nodes can store arbitrary data in a NodeData class.
-#
-# Subclassed by Nexus.Trees to store phylogenetic trees.
-#
-# Bug reports to Frank Kauff (fkauff at biologie.uni-kl.de)
-#
+"""Linked list functionality for use in Bio.Nexus.
+
+Provides functionality of a linked list.
+Each node has one (or none) predecessor, and an arbitrary number of successors.
+Nodes can store arbitrary data in a NodeData class.
+
+Subclassed by Nexus.Trees to store phylogenetic trees.
+
+Bug reports to Frank Kauff (fkauff at biologie.uni-kl.de)
+"""
 
 
 class ChainException(Exception):
diff --git a/Bio/Nexus/StandardData.py b/Bio/Nexus/StandardData.py
new file mode 100644
index 0000000..3cf7709
--- /dev/null
+++ b/Bio/Nexus/StandardData.py
@@ -0,0 +1,107 @@
+# Copyright 2014 Joe Cora.
+# All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+"""Provides objects to represent NEXUS standard data type matrix coding.
+"""
+from __future__ import print_function
+
+
+class NexusError(Exception):
+    pass
+
+
+class StandardData(object):
+    """Create a StandardData iterable object.
+
+    Each coding specifies t [type] => (std [standard], multi [multistate] or
+    uncer [uncertain]) and d [data]
+    """
+    def __init__(self, data):
+        self._data = []
+        self._current_pos = 0
+
+        # Enforce string data requirement
+        if not isinstance(data, basestring):
+            raise NexusError("The coding data given to a StandardData object should be a string")
+
+        # Transfer each coding to a position within a sequence
+        multi_coding = False
+        uncertain_coding = False
+        coding_list = {'t': 'std', 'd': []}
+
+        for pos, coding in enumerate(data):
+            # Check if in a multiple coded or uncertain character
+            if multi_coding:
+                # End multicoding if close parenthesis
+                if coding == ')':
+                    multi_coding = False
+                else:
+                    # Add current coding to list and advance to next coding
+                    coding_list['d'].append(coding)
+                    continue
+            elif uncertain_coding:
+                # End multicoding if close parenthesis
+                if coding == '}':
+                    uncertain_coding = False
+                else:
+                    # Add current coding to list and advance to next coding
+                    coding_list['d'].append(coding)
+                    continue
+            else:
+                # Check if a multiple coded or uncertain character is starting
+                if coding == '(':
+                    multi_coding = True
+                    coding_list['t'] = 'multi'
+                    continue
+                elif coding == '{':
+                    uncertain_coding = True
+                    coding_list['t'] = 'uncer'
+                    continue
+                elif coding in [')', '}']:
+                    raise NexusError('Improper character "' + coding +
+                                     '" at position ' + pos +
+                                     ' of a coding sequence.')
+                else:
+                    coding_list['d'].append(coding)
+
+            # Add character coding to data
+            self._data.append(coding_list.copy())
+            coding_list = {'t': 'std', 'd': []}
+
+    def __len__(self):
+        """Returns the length of the coding, use len(my_coding)."""
+        return len(self._data)
+
+    def __getitem__(self, arg):
+        return self._data[arg]
+
+    def __iter__(self):
+        return self
+
+    def next(self):
+        try:
+            return_coding = self._data[self._current_pos]
+        except:
+            self._current_pos = 0
+            raise StopIteration
+        else:
+            self._current_pos += 1
+            return return_coding
+
+    def raw(self):
+        """Returns the full coding as a python list."""
+        return self._data
+
+    def __str__(self):
+        """Returns the full coding as a python string, use str(my_coding)."""
+        str_return = ''
+        for coding in self._data:
+            if coding['t'] == 'multi':
+                str_return += '(' + ''.join(coding['d']) + ')'
+            elif coding['t'] == 'uncer':
+                str_return += '{' + ''.join(coding['d']) + '}'
+            else:
+                str_return += coding['d'][0]
+        return str_return
diff --git a/Bio/Nexus/Trees.py b/Bio/Nexus/Trees.py
index c1e7dc5..9d00ff8 100755
--- a/Bio/Nexus/Trees.py
+++ b/Bio/Nexus/Trees.py
@@ -18,6 +18,7 @@ import random
 import sys
 from . import Nodes
 
+
 PRECISION_BRANCHLENGTH = 6
 PRECISION_SUPPORT = 6
 NODECOMMENT_START = '[&'
@@ -318,7 +319,7 @@ class Tree(Nodes.Chain):
                         t = t.replace(' ', '_')
                     try:
                         genus = t.split('_', 1)[0]
-                    except:
+                    except IndexError:
                         genus = 'None'
                     if genus not in genera:
                         genera.append(genus)
diff --git a/Bio/PDB/Chain.py b/Bio/PDB/Chain.py
index 4ed7e3d..74ab570 100644
--- a/Bio/PDB/Chain.py
+++ b/Bio/PDB/Chain.py
@@ -7,8 +7,6 @@
 
 from Bio.PDB.Entity import Entity
 
-__docformat__ = "restructuredtext en"
-
 
 class Chain(Entity):
     def __init__(self, id):
@@ -111,12 +109,12 @@ class Chain(Entity):
         """Return 1 if a residue with given id is present.
 
         The id of a residue is (hetero flag, sequence identifier, insertion code).
-        
+
         If id is an int, it is translated to (" ", id, " ") by the _translate_id
         method.
 
         Arguments:
-        
+
             - id - (string, int, string) or int
         """
         id = self._translate_id(id)
diff --git a/Bio/PDB/DSSP.py b/Bio/PDB/DSSP.py
index 38d2852..152f461 100644
--- a/Bio/PDB/DSSP.py
+++ b/Bio/PDB/DSSP.py
@@ -22,8 +22,6 @@ The DSSP codes for secondary structure used here are:
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"
-
 import re
 from Bio._py3k import StringIO
 import subprocess
@@ -108,13 +106,13 @@ def dssp_dict_from_pdb_file(in_file, DSSP="dssp"):
     p = subprocess.Popen([DSSP, in_file], universal_newlines=True,
                          stdout=subprocess.PIPE, stderr=subprocess.PIPE)
     out, err = p.communicate()
-    
+
     # Alert user for errors
     if err.strip():
         warnings.warn(err)
         if not out.strip():
             raise Exception('DSSP failed to produce an output')
-    
+
     out_dict, keys = _make_dssp_dict(StringIO(out))
     return out_dict, keys
 
@@ -225,7 +223,7 @@ class DSSP(AbstractResiduePropertyMap):
     >>> model = structure[0]
     >>> dssp = DSSP(model, "1MOT.pdb")
     >>> # DSSP data is accessed by a tuple (chain_id, res_id)
-    >>> a_key = list(dssp)[2]
+    >>> a_key = list(dssp.keys())[2]
     >>> # residue object, secondary structure, solvent accessibility,
     >>> # relative accessiblity, phi, psi
     >>> dssp[a_key]
diff --git a/Bio/PDB/Entity.py b/Bio/PDB/Entity.py
index 6f9e94b..de591ad 100644
--- a/Bio/PDB/Entity.py
+++ b/Bio/PDB/Entity.py
@@ -13,6 +13,7 @@ from copy import copy
 
 from Bio.PDB.PDBExceptions import PDBConstructionException
 
+
 class Entity(object):
     """
     Basic container object. Structure, Model, Chain and Residue
@@ -136,14 +137,14 @@ class Entity(object):
         Residue with id (" ", 10, "A")
 
         The Residue id indicates that the residue is not a hetero-residue
-        (or a water) beacuse it has a blank hetero field, that its sequence
+        (or a water) because it has a blank hetero field, that its sequence
         identifier is 10 and its insertion code "A".
         """
         if self.full_id is None:
             entity_id = self.get_id()
             l = [entity_id]
             parent = self.get_parent()
-            while not (parent is None):
+            while parent is not None:
                 entity_id = parent.get_id()
                 l.append(entity_id)
                 parent = parent.get_parent()
diff --git a/Bio/PDB/FragmentMapper.py b/Bio/PDB/FragmentMapper.py
index edad0b0..0bb36b2 100644
--- a/Bio/PDB/FragmentMapper.py
+++ b/Bio/PDB/FragmentMapper.py
@@ -41,7 +41,6 @@ from Bio.PDB.PDBExceptions import PDBException
 from Bio.PDB.PDBParser import PDBParser
 from Bio.PDB.Polypeptide import PPBuilder
 
-__docformat__ = "restructuredtext en"
 
 # fragment file (lib_SIZE_z_LENGTH.txt)
 # SIZE=number of fragments
@@ -233,9 +232,8 @@ class FragmentMapper(object):
     Map polypeptides in a model to lists of representative fragments.
     """
     def __init__(self, model, lsize=20, flength=5, fdir="."):
-        """
-        ::
-        
+        """Create instance of FragmentMapper
+
             @param model: the model that will be mapped
             @type model: L{Model}
 
diff --git a/Bio/PDB/HSExposure.py b/Bio/PDB/HSExposure.py
index 433e966..bbcab07 100644
--- a/Bio/PDB/HSExposure.py
+++ b/Bio/PDB/HSExposure.py
@@ -15,8 +15,6 @@ from Bio.PDB.PDBParser import PDBParser
 from Bio.PDB.Polypeptide import CaPPBuilder, is_aa
 from Bio.PDB.Vector import rotaxis
 
-__docformat__ = "restructuredtext en"
-
 
 class _AbstractHSExposure(AbstractPropertyMap):
     """
diff --git a/Bio/PDB/MMCIFParser.py b/Bio/PDB/MMCIFParser.py
index b4dc778..d673dc9 100644
--- a/Bio/PDB/MMCIFParser.py
+++ b/Bio/PDB/MMCIFParser.py
@@ -3,7 +3,7 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-"""mmCIF parser"""
+"""mmCIF parsers"""
 
 from __future__ import print_function
 
@@ -12,6 +12,7 @@ from string import ascii_letters
 import numpy
 import warnings
 
+from Bio.File import as_handle
 from Bio._py3k import range
 
 from Bio.PDB.MMCIF2Dict import MMCIF2Dict
@@ -21,19 +22,21 @@ from Bio.PDB.PDBExceptions import PDBConstructionWarning
 
 
 class MMCIFParser(object):
-    """Parse a PDB file and return a Structure object."""
+    """Parse a mmCIF file and return a Structure object."""
 
     def __init__(self, structure_builder=None, QUIET=False):
         """Create a PDBParser object.
-        The PDB parser call a number of standard methods in an aggregated
+
+        The mmCIF parser calls a number of standard methods in an aggregated
         StructureBuilder object. Normally this object is instanciated by the
         MMCIParser object itself, but if the user provides his/her own
         StructureBuilder object, the latter is used instead.
+
         Arguments:
          - structure_builder - an optional user implemented StructureBuilder class.
          - QUIET - Evaluated as a Boolean. If true, warnings issued in constructing
            the SMCRA data will be suppressed. If false (DEFAULT), they will be shown.
-           These warnings might be indicative of problems in the PDB file!
+           These warnings might be indicative of problems in the mmCIF file!
         """
         if structure_builder is not None:
             self._structure_builder = structure_builder
@@ -57,8 +60,9 @@ class MMCIFParser(object):
         with warnings.catch_warnings():
             if self.QUIET:
                 warnings.filterwarnings("ignore", category=PDBConstructionWarning)
-        self._mmcif_dict = MMCIF2Dict(filename)
-        self._build_structure(structure_id)
+            self._mmcif_dict = MMCIF2Dict(filename)
+            self._build_structure(structure_id)
+
         return self._structure_builder.get_structure()
 
     # Private methods
@@ -72,7 +76,7 @@ class MMCIFParser(object):
         except KeyError:
             element_list = None
         seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
-        chain_id_list = mmcif_dict["_atom_site.label_asym_id"]
+        chain_id_list = mmcif_dict["_atom_site.auth_asym_id"]
         x_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_x"]]
         y_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_y"]]
         z_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_z"]]
@@ -115,7 +119,7 @@ class MMCIFParser(object):
         # Historically, Biopython PDB parser uses model_id to mean array index
         # so serial_id means the Model ID specified in the file
         current_model_id = -1
-        current_serial_id = 0
+        current_serial_id = -1
         for i in range(0, len(atom_id_list)):
 
             # set the line_counter for 'ATOM' lines only and not
@@ -131,7 +135,7 @@ class MMCIFParser(object):
             altloc = alt_list[i]
             if altloc == ".":
                 altloc = " "
-            resseq = seq_id_list[i]
+            int_resseq = int(seq_id_list[i])
             icode = icode_list[i]
             if icode == "?":
                 icode = " "
@@ -150,6 +154,9 @@ class MMCIFParser(object):
                 hetatm_flag = "H"
             else:
                 hetatm_flag = " "
+
+            resseq = (hetatm_flag, int_resseq, icode)
+
             if serial_list is not None:
                 # model column exists; use it
                 serial_id = serial_list[i]
@@ -167,10 +174,10 @@ class MMCIFParser(object):
             if current_chain_id != chainid:
                 current_chain_id = chainid
                 structure_builder.init_chain(current_chain_id)
+                current_residue_id = None
 
             if current_residue_id != resseq:
                 current_residue_id = resseq
-                int_resseq = int(resseq)
                 structure_builder.init_residue(resname, hetatm_flag, int_resseq, icode)
 
             coord = numpy.array((x, y, z), 'f')
@@ -201,6 +208,220 @@ class MMCIFParser(object):
             pass    # no cell found, so just ignore
 
 
+class FastMMCIFParser(object):
+    """Parse an MMCIF file and return a Structure object."""
+
+    def __init__(self, structure_builder=None, QUIET=False):
+        """Create a FastMMCIFParser object.
+
+        The mmCIF parser calls a number of standard methods in an aggregated
+        StructureBuilder object. Normally this object is instanciated by the
+        parser object itself, but if the user provides his/her own
+        StructureBuilder object, the latter is used instead.
+
+        The main difference between this class and the regular MMCIFParser is
+        that only 'ATOM' and 'HETATM' lines are parsed here. Use if you are
+        interested only in coordinate information.
+
+        Arguments:
+         - structure_builder - an optional user implemented StructureBuilder class.
+         - QUIET - Evaluated as a Boolean. If true, warnings issued in constructing
+           the SMCRA data will be suppressed. If false (DEFAULT), they will be shown.
+           These warnings might be indicative of problems in the mmCIF file!
+        """
+        if structure_builder is not None:
+            self._structure_builder = structure_builder
+        else:
+            self._structure_builder = StructureBuilder()
+
+        self.line_counter = 0
+        self.build_structure = None
+        self.QUIET = bool(QUIET)
+
+    # Public methods
+
+    def get_structure(self, structure_id, filename):
+        """Return the structure.
+
+        Arguments:
+         - structure_id - string, the id that will be used for the structure
+         - filename - name of the mmCIF file OR an open filehandle
+        """
+        with warnings.catch_warnings():
+            if self.QUIET:
+                warnings.filterwarnings("ignore", category=PDBConstructionWarning)
+        with as_handle(filename) as handle:
+            self._build_structure(structure_id, handle)
+
+        return self._structure_builder.get_structure()
+
+    # Private methods
+
+    def _build_structure(self, structure_id, filehandle):
+
+        # Read only _atom_site. and atom_site_anisotrop entries
+        read_atom, read_aniso = False, False
+        _fields, _records = [], []
+        _anisof, _anisors = [], []
+        for line in filehandle:
+            if line.startswith('_atom_site.'):
+                read_atom = True
+                _fields.append(line.strip())
+            elif line.startswith('_atom_site_anisotrop.'):
+                read_aniso = True
+                _anisof.append(line.strip())
+            elif read_atom and line.startswith('#'):
+                read_atom = False
+            elif read_aniso and line.startswith('#'):
+                read_aniso = False
+            elif read_atom:
+                _records.append(line.strip())
+            elif read_aniso:
+                _anisors.append(line.strip())
+
+        # Dumping the shlex module here since this particular
+        # category should be rather straightforward.
+        # Quite a performance boost..
+        _record_tbl = zip(*map(str.split, _records))
+        _anisob_tbl = zip(*map(str.split, _anisors))
+
+        mmcif_dict = dict(zip(_fields, _record_tbl))
+        mmcif_dict.update(dict(zip(_anisof, _anisob_tbl)))
+
+        # Build structure object
+        atom_id_list = mmcif_dict["_atom_site.label_atom_id"]
+        residue_id_list = mmcif_dict["_atom_site.label_comp_id"]
+
+        try:
+            element_list = mmcif_dict["_atom_site.type_symbol"]
+        except KeyError:
+            element_list = None
+
+        seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
+        chain_id_list = mmcif_dict["_atom_site.auth_asym_id"]
+
+        x_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_x"]]
+        y_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_y"]]
+        z_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_z"]]
+        alt_list = mmcif_dict["_atom_site.label_alt_id"]
+        icode_list = mmcif_dict["_atom_site.pdbx_PDB_ins_code"]
+        b_factor_list = mmcif_dict["_atom_site.B_iso_or_equiv"]
+        occupancy_list = mmcif_dict["_atom_site.occupancy"]
+        fieldname_list = mmcif_dict["_atom_site.group_PDB"]
+
+        try:
+            serial_list = [int(n) for n in mmcif_dict["_atom_site.pdbx_PDB_model_num"]]
+        except KeyError:
+            # No model number column
+            serial_list = None
+        except ValueError:
+            # Invalid model number (malformed file)
+            raise PDBConstructionException("Invalid model number")
+
+        try:
+            aniso_u11 = mmcif_dict["_atom_site.aniso_U[1][1]"]
+            aniso_u12 = mmcif_dict["_atom_site.aniso_U[1][2]"]
+            aniso_u13 = mmcif_dict["_atom_site.aniso_U[1][3]"]
+            aniso_u22 = mmcif_dict["_atom_site.aniso_U[2][2]"]
+            aniso_u23 = mmcif_dict["_atom_site.aniso_U[2][3]"]
+            aniso_u33 = mmcif_dict["_atom_site.aniso_U[3][3]"]
+            aniso_flag = 1
+        except KeyError:
+            # no anisotropic B factors
+            aniso_flag = 0
+
+        # if auth_seq_id is present, we use this.
+        # Otherwise label_seq_id is used.
+        if "_atom_site.auth_seq_id" in mmcif_dict:
+            seq_id_list = mmcif_dict["_atom_site.auth_seq_id"]
+        else:
+            seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
+
+        # Now loop over atoms and build the structure
+        current_chain_id = None
+        current_residue_id = None
+        structure_builder = self._structure_builder
+        structure_builder.init_structure(structure_id)
+        structure_builder.init_seg(" ")
+
+        # Historically, Biopython PDB parser uses model_id to mean array index
+        # so serial_id means the Model ID specified in the file
+        current_model_id = -1
+        current_serial_id = -1
+        for i in range(0, len(atom_id_list)):
+
+            # set the line_counter for 'ATOM' lines only and not
+            # as a global line counter found in the PDBParser()
+            # this number should match the '_atom_site.id' index in the MMCIF
+            structure_builder.set_line_counter(i)
+
+            x = x_list[i]
+            y = y_list[i]
+            z = z_list[i]
+            resname = residue_id_list[i]
+            chainid = chain_id_list[i]
+            altloc = alt_list[i]
+            if altloc == ".":
+                altloc = " "
+            int_resseq = int(seq_id_list[i])
+            icode = icode_list[i]
+            if icode == "?":
+                icode = " "
+            name = atom_id_list[i].strip('"')  # Remove occasional " from quoted atom names (e.g. xNA)
+
+            # occupancy & B factor
+            try:
+                tempfactor = float(b_factor_list[i])
+            except ValueError:
+                raise PDBConstructionException("Invalid or missing B factor")
+
+            try:
+                occupancy = float(occupancy_list[i])
+            except ValueError:
+                raise PDBConstructionException("Invalid or missing occupancy")
+
+            fieldname = fieldname_list[i]
+            if fieldname == "HETATM":
+                hetatm_flag = "H"
+            else:
+                hetatm_flag = " "
+
+            resseq = (hetatm_flag, int_resseq, icode)
+
+            if serial_list is not None:
+                # model column exists; use it
+                serial_id = serial_list[i]
+                if current_serial_id != serial_id:
+                    # if serial changes, update it and start new model
+                    current_serial_id = serial_id
+                    current_model_id += 1
+                    structure_builder.init_model(current_model_id, current_serial_id)
+                    current_chain_id = None
+                    current_residue_id = None
+            else:
+                # no explicit model column; initialize single model
+                structure_builder.init_model(current_model_id)
+
+            if current_chain_id != chainid:
+                current_chain_id = chainid
+                structure_builder.init_chain(current_chain_id)
+                current_residue_id = None
+
+            if current_residue_id != resseq:
+                current_residue_id = resseq
+                structure_builder.init_residue(resname, hetatm_flag, int_resseq, icode)
+
+            coord = numpy.array((x, y, z), 'f')
+            element = element_list[i] if element_list else None
+            structure_builder.init_atom(name, coord, tempfactor, occupancy, altloc,
+                name, element=element)
+            if aniso_flag == 1:
+                u = (aniso_u11[i], aniso_u12[i], aniso_u13[i],
+                    aniso_u22[i], aniso_u23[i], aniso_u33[i])
+                mapped_anisou = [float(x) for x in u]
+                anisou_array = numpy.array(mapped_anisou, 'f')
+                structure_builder.set_anisou(anisou_array)
+
 if __name__ == "__main__":
     import sys
 
diff --git a/Bio/PDB/NACCESS.py b/Bio/PDB/NACCESS.py
index c50d18c..7205bd9 100644
--- a/Bio/PDB/NACCESS.py
+++ b/Bio/PDB/NACCESS.py
@@ -26,6 +26,7 @@ import warnings
 from Bio.PDB.PDBIO import PDBIO
 from Bio.PDB.AbstractPropertyMap import AbstractResiduePropertyMap, AbstractAtomPropertyMap
 
+
 def run_naccess(model, pdb_file, probe_size=None, z_slice=None,
                 naccess='naccess', temp_path='/tmp/'):
 
diff --git a/Bio/PDB/NeighborSearch.py b/Bio/PDB/NeighborSearch.py
index 2d09b62..c6ce599 100644
--- a/Bio/PDB/NeighborSearch.py
+++ b/Bio/PDB/NeighborSearch.py
@@ -74,7 +74,7 @@ class NeighborSearch(object):
 
         Return all atoms/residues/chains/models/structures
         that have at least one atom within radius of center.
-        What entitity level is returned (e.g. atoms or residues)
+        What entity level is returned (e.g. atoms or residues)
         is determined by level (A=atoms, R=residues, C=chains,
         M=models, S=structures).
 
diff --git a/Bio/PDB/PDBIO.py b/Bio/PDB/PDBIO.py
index a4febf9..67a088c 100644
--- a/Bio/PDB/PDBIO.py
+++ b/Bio/PDB/PDBIO.py
@@ -10,7 +10,6 @@ from Bio._py3k import basestring
 from Bio.PDB.StructureBuilder import StructureBuilder  # To allow saving of chains, residues, etc..
 from Bio.Data.IUPACData import atom_weights  # Allowed Elements
 
-__docformat__ = "restructuredtext en"
 
 _ATOM_FORMAT_STRING = "%s%5i %-4s%c%3s %c%4i%c   %8.3f%8.3f%8.3f%s%6.2f      %4s%2s%2s\n"
 
@@ -156,7 +155,7 @@ class PDBIO(object):
          - accept_chain(chain)
          - accept_residue(residue)
          - accept_atom(atom)
-                
+
         These methods should return 1 if the entity is to be
         written out, 0 otherwise.
 
@@ -211,8 +210,8 @@ class PDBIO(object):
                     fp.write("TER\n")
             if model_flag and model_residues_written:
                 fp.write("ENDMDL\n")
-            if write_end:
-                fp.write('END\n')
+        if write_end:
+            fp.write('END\n')
         if close_file:
             fp.close()
 
diff --git a/Bio/PDB/PDBList.py b/Bio/PDB/PDBList.py
index 14e8f7c..20f72f0 100644
--- a/Bio/PDB/PDBList.py
+++ b/Bio/PDB/PDBList.py
@@ -31,8 +31,6 @@ import shutil
 from Bio._py3k import urlopen as _urlopen
 from Bio._py3k import urlretrieve as _urlretrieve
 
-__docformat__ = "restructuredtext en"
-
 
 class PDBList(object):
     """
diff --git a/Bio/PDB/PDBParser.py b/Bio/PDB/PDBParser.py
index 23c9df8..a8bf608 100644
--- a/Bio/PDB/PDBParser.py
+++ b/Bio/PDB/PDBParser.py
@@ -78,7 +78,7 @@ class PDBParser(object):
             # Make a StructureBuilder instance (pass id of structure as parameter)
             self.structure_builder.init_structure(id)
 
-            with as_handle(file) as handle:
+            with as_handle(file, mode='rU') as handle:
                 self._parse(handle.readlines())
 
             self.structure_builder.set_header(self.header)
@@ -133,7 +133,7 @@ class PDBParser(object):
         current_residue_id = None
         current_resname = None
         for i in range(0, len(coords_trailer)):
-            line = coords_trailer[i]
+            line = coords_trailer[i].rstrip('\n')
             record_type = line[0:6]
             global_line_counter = self.line_counter + local_line_counter + 1
             structure_builder.set_line_counter(global_line_counter)
@@ -201,7 +201,7 @@ class PDBParser(object):
                                                global_line_counter)
                     bfactor = 0.0  # The PDB use a default of zero if the data is missing
                 segid = line[72:76]
-                element = line[76:78].strip()
+                element = line[76:78].strip().upper()
                 if current_segid != segid:
                     current_segid = segid
                     structure_builder.init_seg(current_segid)
diff --git a/Bio/PDB/QCPSuperimposer/__init__.py b/Bio/PDB/QCPSuperimposer/__init__.py
index 2bc88a5..220a332 100644
--- a/Bio/PDB/QCPSuperimposer/__init__.py
+++ b/Bio/PDB/QCPSuperimposer/__init__.py
@@ -17,6 +17,7 @@ from Bio import BiopythonExperimentalWarning
 from numpy import dot, sqrt, array, matrix, inner, zeros
 from .qcprotmodule import FastCalcRMSDAndRotation
 
+
 warnings.warn('Bio.PDB.QCPSuperimposer is an experimental submodule which may undergo '
               'significant changes prior to its future official release.',
               BiopythonExperimentalWarning)
diff --git a/Bio/PDB/Residue.py b/Bio/PDB/Residue.py
index 6def595..076286e 100644
--- a/Bio/PDB/Residue.py
+++ b/Bio/PDB/Residue.py
@@ -9,6 +9,7 @@
 from Bio.PDB.PDBExceptions import PDBConstructionException
 from Bio.PDB.Entity import Entity, DisorderedEntityWrapper
 
+
 _atom_name_dict = {}
 _atom_name_dict["N"] = 1
 _atom_name_dict["CA"] = 2
diff --git a/Bio/PDB/Selection.py b/Bio/PDB/Selection.py
index 63c484a..5d07ef6 100644
--- a/Bio/PDB/Selection.py
+++ b/Bio/PDB/Selection.py
@@ -54,7 +54,7 @@ def unfold_entities(entity_list, target_level):
         raise PDBException("%s: Not an entity level." % target_level)
     if entity_list == []:
         return []
-    if isinstance(entity_list, Entity) or isinstance(entity_list, Atom):
+    if isinstance(entity_list, (Entity, Atom)):
         entity_list = [entity_list]
 
     level = entity_list[0].get_level()
diff --git a/Bio/PDB/StructureAlignment.py b/Bio/PDB/StructureAlignment.py
index 335d91f..ccf3d51 100644
--- a/Bio/PDB/StructureAlignment.py
+++ b/Bio/PDB/StructureAlignment.py
@@ -14,8 +14,6 @@ from Bio.Data import SCOPData
 from Bio.PDB import Selection
 from Bio.PDB.Polypeptide import is_aa
 
-__docformat__ = "restructuredtext en"
-
 
 class StructureAlignment(object):
     """
@@ -25,7 +23,7 @@ class StructureAlignment(object):
     def __init__(self, fasta_align, m1, m2, si=0, sj=1):
         """
         Attributes:
-    
+
             - fasta_align --- Alignment object
             - m1, m2 --- two models
             - si, sj --- the sequences in the Alignment object that
diff --git a/Bio/PDB/StructureBuilder.py b/Bio/PDB/StructureBuilder.py
index 9132cdd..da38469 100644
--- a/Bio/PDB/StructureBuilder.py
+++ b/Bio/PDB/StructureBuilder.py
@@ -20,8 +20,6 @@ from Bio.PDB.Atom import Atom, DisorderedAtom
 from Bio.PDB.PDBExceptions import PDBConstructionException
 from Bio.PDB.PDBExceptions import PDBConstructionWarning
 
-__docformat__ = "restructuredtext en"
-
 
 class StructureBuilder(object):
     """
@@ -102,7 +100,7 @@ class StructureBuilder(object):
         Initiate a new Residue object.
 
         Arguments:
-        
+
             - resname - string, e.g. "ASN"
             - field - hetero flag, "W" for waters, "H" for
               hetero residues, otherwise blank.
diff --git a/Bio/PDB/__init__.py b/Bio/PDB/__init__.py
index c02bbd5..10f476b 100644
--- a/Bio/PDB/__init__.py
+++ b/Bio/PDB/__init__.py
@@ -14,12 +14,8 @@ Author: Thomas Hamelryck.  Additional code by Kristian Rother.
 # Get a Structure object from a PDB file
 from .PDBParser import PDBParser
 
-try:
-    # Get a Structure object from an mmCIF file
-    from .MMCIFParser import MMCIFParser
-except:
-    # Not compiled I guess
-    pass
+from .MMCIFParser import MMCIFParser
+from .MMCIFParser import FastMMCIFParser
 
 # Download from the PDB
 from .PDBList import PDBList
diff --git a/Bio/PDB/mmCIF/Makefile b/Bio/PDB/mmCIF/Makefile
new file mode 100644
index 0000000..79bd95a
--- /dev/null
+++ b/Bio/PDB/mmCIF/Makefile
@@ -0,0 +1,31 @@
+PYTHON_INC_DIR=-I/usr/include/python2.3/ -I/usr/include/python2.3/Numeric/
+CC=gcc
+LEX=flex
+ 
+all:	MMCIFlexmodule.so mmcif_test
+
+MMCIFlexmodule.so: lex.yy.o MMCIFlexmodule.o
+	$(CC) -fPIC -shared -o $@ $^ -lfl 
+
+MMCIFlexmodule.o: MMCIFlexmodule.c
+	$(CC) -c -fPIC $(PYTHON_INC_DIR) -o $@ $<
+
+#mmcifparser.o: mmcifparser.c
+#	$(CC) -c -fPIC $(PYTHON_INC_DIR)  $<
+
+lex.yy.c: mmcif.lex
+	$(LEX) mmcif.lex 
+
+lex.yy.o: lex.yy.c
+	$(CC) -fPIC -c $<
+
+mmcif_test: mmcif_test.o lex.yy.o
+	$(CC) $^ -lfl -o $@
+
+.PHONY:	links clean veryclean
+
+clean:
+	rm *.o
+
+veryclean: clean
+	- rm *.so lex.yy.c mmcif_test *~
diff --git a/Bio/PDB/mmCIF/__init__.py b/Bio/PDB/mmCIF/__init__.py
new file mode 100644
index 0000000..4adc984
--- /dev/null
+++ b/Bio/PDB/mmCIF/__init__.py
@@ -0,0 +1 @@
+# Python module
diff --git a/Bio/PDB/mmCIF/mmcif.lex b/Bio/PDB/mmCIF/mmcif.lex
new file mode 100644
index 0000000..b7fba51
--- /dev/null
+++ b/Bio/PDB/mmCIF/mmcif.lex
@@ -0,0 +1,62 @@
+%option noyywrap
+COMMENT					#.*\n
+NAME					_[^ \t\n]+
+LOOP					[Ll][Oo][Oo][Pp]_
+DATA					[Dd][Aa][Tt][Aa]_[^ \t\n]+
+FREE_VALUE				[^ \t\n]+
+SINGLE_QUOTE_VALUE		'[^'\n]*'
+DOUBLE_QUOTE_VALUE		\"[^"\n]*\"
+SEMICOLON_VALUE			^;(.*\n[^;])*.*\n;	
+
+/* 
+Number code of the tokens is:
+
+NAME 			(eg. _entity.id)		1
+LOOP 			(loop_)					2
+DATA			(eg. data_something)	3
+SEMICOLON 		(eg. ;value\n;)	 		4	
+DOUBLE QUOTE 	(eg. "value")			5
+SINGLE QUOTE 	(eg. 'value')			6
+FREE 			(eg. value )			7
+
+*/
+
+%%
+
+{COMMENT}					/* ignore */			
+
+{NAME}						{ return 1; }	
+
+{LOOP}						{ return 2; }					
+
+{DATA}						{ return 3;	}
+
+{SEMICOLON_VALUE}			{ return 4; }	
+
+{DOUBLE_QUOTE_VALUE}		{ return 5; }	
+
+{SINGLE_QUOTE_VALUE}		{ return 6; }	
+
+{FREE_VALUE}				{ return 7; }	
+
+[ \t\n]+					/* ignore */
+
+%%					
+
+
+void mmcif_set_file(FILE *fp)
+{
+	yyin=fp;
+}	
+
+int mmcif_get_token(void)
+{
+	extern int yylex(void);
+	return yylex();
+}
+
+char *mmcif_get_string(void)
+{
+	return yytext;
+}	
+
diff --git a/Bio/ParserSupport.py b/Bio/ParserSupport.py
index 225094b..62437f9 100644
--- a/Bio/ParserSupport.py
+++ b/Bio/ParserSupport.py
@@ -10,8 +10,6 @@ Classes:
     - AbstractParser         Base class for parsers.
     - AbstractConsumer       Base class of all Consumers.
     - TaggingConsumer        Consumer that tags output with its event.  For debugging
-    - EventGenerator         Generate Biopython Events from Martel XML output
-      (note that Martel has been removed)
 
 Functions:
 
@@ -25,34 +23,14 @@ Functions:
 
 """
 
+import sys
+from Bio._py3k import StringIO
+
 from Bio import BiopythonDeprecationWarning
 import warnings
 warnings.warn("Bio.ParserSupport is now deprecated will be removed in a "
               "future release of Biopython.", BiopythonDeprecationWarning)
 
-import sys
-try:
-    from types import InstanceType
-except ImportError:
-    # Python 3, see http://bugs.python.org/issue8206
-    InstanceType = object
-from types import MethodType
-
-from Bio._py3k import StringIO
-
-from Bio import File
-
-# XML from python 2.0
-try:
-    from xml.sax import handler
-    xml_support = 1
-except ImportError:
-    sys.stderr.write("Warning: Could not import SAX for dealing with XML.\n" +
-                     "This causes problems with some ParserSupport modules\n")
-    xml_support = 0
-
-__docformat__ = "restructuredtext en"
-
 
 class AbstractParser(object):
     """Base class for other parsers.
@@ -133,139 +111,6 @@ class TaggingConsumer(AbstractConsumer):
         return method
 
 
-# onle use the Event Generator if XML handling is okay
-if xml_support:
-    class EventGenerator(handler.ContentHandler):
-        """Handler to generate events associated with a Martel parsed file.
-
-        This acts like a normal SAX handler, and accepts XML generated by
-        Martel during parsing. These events are then converted into
-        'Biopython events', which can then be caught by a standard
-        biopython consumer.
-
-        Note that Martel is now DEPRECATED.
-        """
-        def __init__(self, consumer, interest_tags, callback_finalizer=None,
-                     exempt_tags=[]):
-            """Initialize to begin catching and firing off events.
-
-            Arguments:
-            o consumer - The consumer that we'll send Biopython events to.
-
-            o interest_tags - A listing of all the tags we are interested in.
-
-            o callback_finalizer - A function to deal with the collected
-            information before passing it on to the consumer. By default
-            the collected information is a list of all of the lines read
-            for a particular tag -- if there are multiple tags in a row
-            like:
-
-            <some_info>Spam<some_info>
-            <some_info>More Spam<some_info>
-
-            In this case the list of information would be:
-
-            ['Spam', 'More Spam']
-
-            This list of lines will be passed to the callback finalizer if
-            it is present. Otherwise the consumer will be called with the
-            list of content information.
-
-            o exempt_tags - A listing of particular tags that are exempt from
-            being processed by the callback_finalizer. This allows you to
-            use a finalizer to deal with most tags, but leave those you don't
-            want touched.
-            """
-            self._consumer = consumer
-            self.interest_tags = interest_tags
-            self._finalizer = callback_finalizer
-            self._exempt_tags = exempt_tags
-
-            # a dictionary of content for each tag of interest
-            # the information for each tag is held as a list of the lines.
-            # This allows us to collect information from multiple tags
-            # in a row, and return it all at once.
-            self.info = {}
-            for tag in self.interest_tags:
-                self.info[tag] = []
-
-            # the previous tag we were collecting information for.
-            # We set a delay in sending info to the consumer so that we can
-            # collect a bunch of tags in a row and append all of the info
-            # together.
-            self._previous_tag = ''
-
-            # the current character information for a tag
-            self._cur_content = []
-            # whether we should be collecting information
-            self._collect_characters = 0
-
-        def startElement(self, name, attrs):
-            """Determine if we should collect characters from this tag.
-            """
-            if name in self.interest_tags:
-                self._collect_characters = 1
-
-        def characters(self, content):
-            """Extract the information if we are interested in it.
-            """
-            if self._collect_characters:
-                self._cur_content.append(content)
-
-        def endElement(self, name):
-            """Send the information to the consumer.
-
-            Once we've got the end element we've collected up all of the
-            character information we need, and we need to send this on to
-            the consumer to do something with it.
-
-            We have a delay of one tag on doing this, so that we can collect
-            all of the info from multiple calls to the same element at once.
-            """
-            # only deal with the tag if it is something we are
-            # interested in and potentially have information for
-            if self._collect_characters:
-                # add all of the information collected inside this tag
-                self.info[name].append("".join(self._cur_content))
-                # reset our information and flags
-                self._cur_content = []
-                self._collect_characters = 0
-
-                # if we are at a new tag, pass on the info from the last tag
-                if self._previous_tag and self._previous_tag != name:
-                    self._make_callback(self._previous_tag)
-
-                # set this tag as the next to be passed
-                self._previous_tag = name
-
-        def _make_callback(self, name):
-            """Call the callback function with the info with the given name.
-            """
-            # strip off whitespace and call the consumer
-            callback_function = getattr(self._consumer, name)
-
-            # --- pass back the information
-            # if there is a finalizer, use that
-            if self._finalizer is not None and name not in self._exempt_tags:
-                info_to_pass = self._finalizer(self.info[name])
-            # otherwise pass back the entire list of information
-            else:
-                info_to_pass = self.info[name]
-
-            callback_function(info_to_pass)
-
-            # reset the information for the tag
-            self.info[name] = []
-
-        def endDocument(self):
-            """Make sure all of our information has been passed.
-
-            This just flushes out any stored tags that need to be passed.
-            """
-            if self._previous_tag:
-                self._make_callback(self._previous_tag)
-
-
 def read_and_call(uhandle, method, **keywds):
     """read_and_call(uhandle, method[, start][, end][, contains][, blank][, has_re])
 
diff --git a/Bio/Pathway/Rep/Graph.py b/Bio/Pathway/Rep/Graph.py
index f325897..9669f6c 100644
--- a/Bio/Pathway/Rep/Graph.py
+++ b/Bio/Pathway/Rep/Graph.py
@@ -11,7 +11,7 @@ from functools import reduce
 class Graph(object):
     """A directed graph abstraction with labeled edges."""
 
-    def __init__(self, nodes=[]):
+    def __init__(self, nodes=()):
         """Initializes a new Graph object."""
         self._adjacency_list = {}    # maps parent -> set of child objects
         for n in nodes:
@@ -119,7 +119,7 @@ class Graph(object):
         for n in self._adjacency_list.keys():
             self._adjacency_list[n] = set(x for x in self._adjacency_list[n]
                                           if x != node)
-        # remove all refering pairs in label map
+        # remove all referring pairs in label map
         for label in list(self._label_map.keys()):  # we're editing this!
             lm = set(x for x in self._label_map[label]
                      if (x[0] != node) and (x[1] != node))
@@ -128,7 +128,7 @@ class Graph(object):
                 self._label_map[label] = lm
             else:
                 del self._label_map[label]
-        # remove all refering entries in edge map
+        # remove all referring entries in edge map
         for edge in list(self._edge_map.keys()):  # we're editing this!
             if edge[0] == node or edge[1] == node:
                 del self._edge_map[edge]
diff --git a/Bio/Pathway/Rep/MultiGraph.py b/Bio/Pathway/Rep/MultiGraph.py
index be3e9c7..37ad546 100644
--- a/Bio/Pathway/Rep/MultiGraph.py
+++ b/Bio/Pathway/Rep/MultiGraph.py
@@ -12,7 +12,7 @@ from functools import reduce
 class MultiGraph(object):
     """A directed multigraph abstraction with labeled edges."""
 
-    def __init__(self, nodes=[]):
+    def __init__(self, nodes=()):
         """Initializes a new MultiGraph object."""
         self._adjacency_list = {}    # maps parent -> set of (child, label) pairs
         for n in nodes:
@@ -114,7 +114,7 @@ class MultiGraph(object):
         for n in self._adjacency_list:
             self._adjacency_list[n] = set(x for x in self._adjacency_list[n]
                                           if x[0] != node)
-        # remove all refering pairs in label map
+        # remove all referring pairs in label map
         for label in list(self._label_map.keys()):  # we're editing this!
             lm = set(x for x in self._label_map[label]
                      if (x[0] != node) and (x[1] != node))
@@ -129,7 +129,7 @@ class MultiGraph(object):
         # hm , this is a multigraph - how should this be implemented?
         raise NotImplementedError("remove_edge is not yet implemented")
 
-# auxilliary graph functions
+# auxiliary graph functions
 
 
 def df_search(graph, root=None):
diff --git a/Bio/Pathway/__init__.py b/Bio/Pathway/__init__.py
index 3eb2d15..369f6de 100644
--- a/Bio/Pathway/__init__.py
+++ b/Bio/Pathway/__init__.py
@@ -34,8 +34,6 @@ from functools import reduce
 
 from Bio.Pathway.Rep.MultiGraph import *
 
-__docformat__ = "restructuredtext en"
-
 
 class Reaction(object):
     """Abstraction for a biochemical transformation.
@@ -57,9 +55,9 @@ class Reaction(object):
 
     Attributes:
 
-        - reactants   -- map of involved species to their stochiometric coefficients:
+        - reactants   -- dict of involved species to their stochiometric coefficients:
           reactants[S] = stochiometric constant for S
-        - catalysts   -- list of tuples of catalysts required for this reaction
+        - catalysts   -- list/tuple of tuples of catalysts required for this reaction
         - reversible  -- true iff reaction is reversible
         - data        -- reference to arbitrary additional data
 
@@ -70,15 +68,18 @@ class Reaction(object):
 
     """
 
-    def __init__(self, reactants={}, catalysts=[],
+    def __init__(self, reactants=None, catalysts=(),
                  reversible=0, data=None):
         """Initializes a new Reaction object."""
         # enforce invariants on reactants:
-        self.reactants = reactants.copy()
-        # loop over original, edit the copy
-        for r, value in reactants.items():
-            if value == 0:
-                del self.reactants[r]
+        if reactants is None:
+            self.reactants = {}
+        else:
+            self.reactants = reactants.copy()
+            # loop over original, edit the copy
+            for r, value in reactants.items():
+                if value == 0:
+                    del self.reactants[r]
         self.catalysts = sorted(set(catalysts))
         self.data = data
         self.reversible = reversible
@@ -160,7 +161,7 @@ class System(object):
     None
     """
 
-    def __init__(self, reactions=[]):
+    def __init__(self, reactions=()):
         """Initializes a new System object."""
         self.__reactions = set(reactions)
 
@@ -262,7 +263,7 @@ class Network(object):
     None
     """
 
-    def __init__(self, species=[]):
+    def __init__(self, species=()):
         """Initializes a new Network object."""
         self.__graph = MultiGraph(species)
 
diff --git a/Bio/Phylo/Applications/_Fasttree.py b/Bio/Phylo/Applications/_Fasttree.py
index 9834e9a..5796f1e 100644
--- a/Bio/Phylo/Applications/_Fasttree.py
+++ b/Bio/Phylo/Applications/_Fasttree.py
@@ -5,7 +5,6 @@
 """Command-line wrapper for tree inference program Fasttree."""
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"
 
 from Bio.Application import _Option, _Switch, _Argument, AbstractCommandline
 
diff --git a/Bio/Phylo/Applications/_Phyml.py b/Bio/Phylo/Applications/_Phyml.py
index 9bff1ea..54e1823 100644
--- a/Bio/Phylo/Applications/_Phyml.py
+++ b/Bio/Phylo/Applications/_Phyml.py
@@ -3,7 +3,6 @@
 # Please see the LICENSE file that should have been included as part of this
 # package.
 """Command-line wrapper for the tree inference program PhyML."""
-__docformat__ = "restructuredtext en"
 
 from Bio._py3k import basestring
 
@@ -105,8 +104,8 @@ class PhymlCommandline(AbstractCommandline):
                     # Amino acid models:
                     'LG', 'WAG', 'JTT', 'MtREV', 'Dayhoff', 'DCMut',
                     'RtREV', 'CpREV', 'VT', 'Blosum62', 'MtMam', 'MtArt',
-                    'HIVw', 'HIVb')
-                    or isinstance(x, int)),
+                    'HIVw', 'HIVb') or
+                    isinstance(x, int)),
                 equate=False,
                 ),
 
@@ -118,11 +117,11 @@ class PhymlCommandline(AbstractCommandline):
                 e : Empirical frequencies, determined as follows :
 
                     - Nucleotide sequences: (Empirical) the equilibrium base
-                      frequencies are estimated by counting the occurence of the
+                      frequencies are estimated by counting the occurrence of the
                       different bases in the alignment.
                     - Amino-acid sequences: (Empirical) the equilibrium
                       amino-acid frequencies are estimated by counting the
-                      occurence of the different amino-acids in the alignment.
+                      occurrence of the different amino-acids in the alignment.
 
                 m : ML/model-based frequencies, determined as follows :
 
diff --git a/Bio/Phylo/Applications/_Raxml.py b/Bio/Phylo/Applications/_Raxml.py
index 048b28c..19d40b4 100644
--- a/Bio/Phylo/Applications/_Raxml.py
+++ b/Bio/Phylo/Applications/_Raxml.py
@@ -10,8 +10,6 @@ should work for any version 7.X (and probably earlier for most options).
 from __future__ import print_function
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"
-
 from Bio.Application import _Option, _Switch, AbstractCommandline
 
 
@@ -370,3 +368,8 @@ class RaxmlCommandline(AbstractCommandline):
         # ENH: enforce -s, -n and -m
         if not self.parsimony_seed:
             self.parsimony_seed = 10000
+
+
+if __name__ == "__main__":
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Phylo/Applications/__init__.py b/Bio/Phylo/Applications/__init__.py
index 3d2b3ff..dd5b82f 100644
--- a/Bio/Phylo/Applications/__init__.py
+++ b/Bio/Phylo/Applications/__init__.py
@@ -3,7 +3,6 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 """Phylogenetics command line tool wrappers."""
-__docformat__ = "restructuredtext en"
 
 from ._Phyml import PhymlCommandline
 from ._Raxml import RaxmlCommandline
diff --git a/Bio/Phylo/BaseTree.py b/Bio/Phylo/BaseTree.py
index 04c6956..362f3a2 100644
--- a/Bio/Phylo/BaseTree.py
+++ b/Bio/Phylo/BaseTree.py
@@ -8,7 +8,6 @@
 All object representations for phylogenetic trees should derive from these base
 classes in order to use the common methods defined on them.
 """
-__docformat__ = "restructuredtext en"
 
 from Bio._py3k import basestring, filter, unicode, zip
 
@@ -20,7 +19,6 @@ import re
 
 from Bio import _utils
 
-
 # NB: On Python 2, repr() and str() are specified to return byte strings, not
 # unicode. On Python 3, it's the opposite. Horrible.
 import sys
@@ -335,8 +333,8 @@ class TreeMixin(object):
         Example
         -------
 
-        >>> from Bio.Phylo.IO import PhyloXMIO
-        >>> phx = PhyloXMLIO.read('phyloxml_examples.xml')
+        >>> from Bio import Phylo
+        >>> phx = Phylo.PhyloXMLIO.read('PhyloXML/phyloxml_examples.xml')
         >>> matches = phx.phylogenies[5].find_elements(code='OCTVU')
         >>> next(matches)
         Taxonomy(code='OCTVU', scientific_name='Octopus vulgaris')
@@ -591,8 +589,13 @@ class TreeMixin(object):
         For example, this will safely collapse nodes with poor bootstrap
         support:
 
-            >>> tree.collapse_all(lambda c: c.confidence is not None and
-            ...                   c.confidence < 70)
+            >>> from Bio import Phylo
+            >>> tree = Phylo.read('PhyloXML/apaf.xml', 'phyloxml')
+            >>> print("Total branch length %0.2f" % tree.total_branch_length())
+            Total branch length 20.44
+            >>> tree.collapse_all(lambda c: c.confidence is not None and c.confidence < 70)
+            >>> print("Total branch length %0.2f" % tree.total_branch_length())
+            Total branch length 21.37
 
         This implementation avoids strange side-effects by using level-order
         traversal and testing all clade properties (versus the target
@@ -600,7 +603,13 @@ class TreeMixin(object):
         specification in the original tree, it will be collapsed.  For example,
         if the condition is:
 
+            >>> from Bio import Phylo
+            >>> tree = Phylo.read('PhyloXML/apaf.xml', 'phyloxml')
+            >>> print("Total branch length %0.2f" % tree.total_branch_length())
+            Total branch length 20.44
             >>> tree.collapse_all(lambda c: c.branch_length < 0.1)
+            >>> print("Total branch length %0.2f" % tree.total_branch_length())
+            Total branch length 21.13
 
         Collapsing a clade's parent node adds the parent's branch length to the
         child, so during the execution of collapse_all, a clade's branch_length
@@ -1023,7 +1032,7 @@ class Clade(TreeElement, TreeMixin):
 
     def __getitem__(self, index):
         """Get clades by index (integer or slice)."""
-        if isinstance(index, int) or isinstance(index, slice):
+        if isinstance(index, (int, slice)):
             return self.clades[index]
         ref = self
         for idx in index:
diff --git a/Bio/Phylo/CDAO.py b/Bio/Phylo/CDAO.py
index 38b45e6..91a79e9 100644
--- a/Bio/Phylo/CDAO.py
+++ b/Bio/Phylo/CDAO.py
@@ -8,7 +8,6 @@
 
 See classes in `Bio.Nexus`: Trees.Tree, Trees.NodeData, and Nodes.Chain.
 """
-__docformat__ = "restructuredtext en"
 
 from Bio.Phylo import BaseTree
 
diff --git a/Bio/Phylo/CDAOIO.py b/Bio/Phylo/CDAOIO.py
index ab3aca1..b9ef6be 100644
--- a/Bio/Phylo/CDAOIO.py
+++ b/Bio/Phylo/CDAOIO.py
@@ -19,8 +19,6 @@ the CDAOIO.Writer can store triples in a triple store instead of serializing
 them to a file.
 """
 
-__docformat__ = "restructuredtext en"
-
 from Bio._py3k import StringIO
 
 from Bio.Phylo import CDAO
@@ -117,7 +115,8 @@ class Parser(object):
         if 'base_uri' in kwargs:
             base_uri = kwargs['base_uri']
         else:
-            base_uri = "file://" + os.path.abspath(self.handle.name)
+            # Windows style slashes cannot be used in an RDF URI
+            base_uri = "file://" + os.path.abspath(self.handle.name).replace("\\", "/")
 
         graph.parse(file=self.handle, publicID=base_uri, format=parse_format)
 
diff --git a/Bio/Phylo/Consensus.py b/Bio/Phylo/Consensus.py
index 9e6f1c9..3d7805b 100644
--- a/Bio/Phylo/Consensus.py
+++ b/Bio/Phylo/Consensus.py
@@ -17,8 +17,6 @@ import itertools
 from ast import literal_eval
 from Bio.Phylo import BaseTree
 
-__docformat__ = "restructuredtext en"
-
 
 class _BitString(str):
     """Helper class for binary string data (PRIVATE).
@@ -114,14 +112,10 @@ class _BitString(str):
     False
     >>> bitstr2.independent(bitstr3)
     False
-    >>> bitstr2.independent(bitstr4)
-    True
     >>> bitstr1.iscompatible(bitstr2)
     True
     >>> bitstr2.iscompatible(bitstr3)
     False
-    >>> bitstr2.iscompatible(bitstr4)
-    True
     """
 
     def __new__(cls, strdata):
diff --git a/Bio/Phylo/NeXML.py b/Bio/Phylo/NeXML.py
index 3b0ca05..60fb873 100644
--- a/Bio/Phylo/NeXML.py
+++ b/Bio/Phylo/NeXML.py
@@ -8,7 +8,6 @@
 
 See classes in `Bio.Nexus`: Trees.Tree, Trees.NodeData, and Nodes.Chain.
 """
-__docformat__ = "restructuredtext en"
 
 from Bio.Phylo import BaseTree
 
diff --git a/Bio/Phylo/NeXMLIO.py b/Bio/Phylo/NeXMLIO.py
index eba2411..d009b04 100644
--- a/Bio/Phylo/NeXMLIO.py
+++ b/Bio/Phylo/NeXMLIO.py
@@ -11,7 +11,6 @@
 See: http://www.nexml.org
 """
 
-__docformat__ = "restructuredtext en"
 
 from Bio._py3k import StringIO
 
@@ -185,8 +184,8 @@ class Parser(object):
                     # with the first node that's not a child of any other nodes
                     rooted = False
                     possible_roots = (node.attrib['id'] for node in nodes
-                                      if node.attrib['id'] in srcs
-                                      and not node.attrib['id'] in tars)
+                                      if node.attrib['id'] in srcs and
+                                      node.attrib['id'] not in tars)
                     root = next(possible_roots)
                 else:
                     rooted = True
diff --git a/Bio/Phylo/Newick.py b/Bio/Phylo/Newick.py
index e5fd8c1..5442a3a 100644
--- a/Bio/Phylo/Newick.py
+++ b/Bio/Phylo/Newick.py
@@ -8,8 +8,6 @@
 See classes in `Bio.Nexus`: Trees.Tree, Trees.NodeData, and Nodes.Chain.
 """
 
-__docformat__ = "restructuredtext en"
-
 from Bio.Phylo import BaseTree
 
 
diff --git a/Bio/Phylo/NewickIO.py b/Bio/Phylo/NewickIO.py
index 1d98ce7..8f2f5b5 100644
--- a/Bio/Phylo/NewickIO.py
+++ b/Bio/Phylo/NewickIO.py
@@ -10,8 +10,6 @@
 See: http://evolution.genetics.washington.edu/phylip/newick_doc.html
 """
 
-__docformat__ = "restructuredtext en"
-
 import re
 from Bio._py3k import StringIO
 
@@ -24,15 +22,15 @@ class NewickError(Exception):
 
 
 tokens = [
-    (r"\(",                                  'open parens'),
-    (r"\)",                                  'close parens'),
-    (r"[^\s\(\)\[\]\'\:\;\,]+",              'unquoted node label'),
-    (r"\:[0-9]*\.?[0-9]+([eE][+-]?[0-9]+)?", 'edge length'),
-    (r"\,",                                  'comma'),
-    (r"\[(\\.|[^\]])*\]",                    'comment'),
-    (r"\'(\\.|[^\'])*\'",                    'quoted node label'),
-    (r"\;",                                  'semicolon'),
-    (r"\n",                                  'newline'),
+    (r"\(",                                       'open parens'),
+    (r"\)",                                       'close parens'),
+    (r"[^\s\(\)\[\]\'\:\;\,]+",                   'unquoted node label'),
+    (r"\:[+-]?[0-9]*\.?[0-9]+([eE][+-]?[0-9]+)?", 'edge length'),
+    (r"\,",                                       'comma'),
+    (r"\[(\\.|[^\]])*\]",                         'comment'),
+    (r"\'(\\.|[^\'])*\'",                         'quoted node label'),
+    (r"\;",                                       'semicolon'),
+    (r"\n",                                       'newline'),
 ]
 tokenizer = re.compile('(%s)' % '|'.join(token[0] for token in tokens))
 token_dict = dict((name, re.compile(token)) for (token, name) in tokens)
@@ -273,8 +271,7 @@ class Writer(object):
                         '\\', '\\\\').replace("'", "\\'")
 
             if clade.is_terminal():    # terminal
-                return (label
-                        + make_info_string(clade, terminal=True))
+                return (label + make_info_string(clade, terminal=True))
             else:
                 subtrees = (newickize(sub) for sub in clade)
                 return '(%s)%s' % (','.join(subtrees),
diff --git a/Bio/Phylo/NexusIO.py b/Bio/Phylo/NexusIO.py
index e9ad09e..cf6b5af 100644
--- a/Bio/Phylo/NexusIO.py
+++ b/Bio/Phylo/NexusIO.py
@@ -4,7 +4,6 @@
 # as part of this package.
 
 """I/O function wrappers for `Bio.Nexus` trees."""
-__docformat__ = "restructuredtext en"
 
 from itertools import chain
 
@@ -25,7 +24,7 @@ End;
 """
 
 # 'index' starts from 1; 'tree' is the Newick tree string
-TREE_TEMPLATE = "Tree tree%(index)d=[&U]%(tree)s"
+TREE_TEMPLATE = "Tree tree%(index)d=%(tree)s"
 
 
 def parse(handle):
diff --git a/Bio/Phylo/PAML/_paml.py b/Bio/Phylo/PAML/_paml.py
index 9304bea..9625171 100644
--- a/Bio/Phylo/PAML/_paml.py
+++ b/Bio/Phylo/PAML/_paml.py
@@ -8,6 +8,7 @@ from __future__ import print_function
 import os
 import subprocess
 
+
 try:
     from os.path import relpath as _relpath
 except ImportError:
@@ -42,14 +43,14 @@ except ImportError:
 
 class PamlError(EnvironmentError):
     """paml has failed. Run with verbose = True to view the error
-message"""
+    message"""
 
 
 class Paml(object):
     """Base class for wrapping PAML commands."""
 
     def __init__(self, alignment=None, working_dir=None,
-                out_file=None):
+                 out_file=None):
         if working_dir is None:
             self.working_dir = os.getcwd()
         else:
@@ -76,7 +77,7 @@ class Paml(object):
         """Set the value of an option.
 
         This function abstracts the options dict to prevent the user from
-        adding options that do not exist or mispelling options.
+        adding options that do not exist or misspelling options.
         """
         for option, value in kwargs.items():
             if option not in self._options:
@@ -142,7 +143,7 @@ class Paml(object):
             else:
                 # To suppress output, redirect it to a pipe to nowhere
                 result_code = subprocess.call([command, self.ctl_file],
-                    stdout=subprocess.PIPE)
+                                              stdout=subprocess.PIPE)
         else:
             if not os.path.exists(ctl_file):
                 raise IOError("The specified control file does not exist.")
@@ -150,14 +151,14 @@ class Paml(object):
                 result_code = subprocess.call([command, ctl_file])
             else:
                 result_code = subprocess.call([command, ctl_file],
-                    stdout=subprocess.PIPE)
+                                              stdout=subprocess.PIPE)
         os.chdir(cwd)
         if result_code > 0:
             # If the program fails for any reason
             raise PamlError(
-            "%s has failed (return code %i). Run with verbose = True to view error message"
-            % (command, result_code))
+                "%s has failed (return code %i). Run with verbose = True to view error message"
+                % (command, result_code))
         if result_code < 0:
             # If the paml process is killed by a signal somehow
             raise EnvironmentError("The %s process was killed (return code %i)."
-                  % (command, result_code))
+                                   % (command, result_code))
diff --git a/Bio/Phylo/PAML/_parse_baseml.py b/Bio/Phylo/PAML/_parse_baseml.py
index 22405d3..edd6973 100644
--- a/Bio/Phylo/PAML/_parse_baseml.py
+++ b/Bio/Phylo/PAML/_parse_baseml.py
@@ -5,6 +5,7 @@
 
 import re
 
+
 line_floats_re = re.compile("-*\d+\.\d+")
 
 
@@ -62,7 +63,7 @@ def parse_parameters(lines, results, num_params):
 
 
 def parse_parameter_list(lines, parameters, num_params):
-    """ Parse the parameters list, which is just an unlabeled list of numeric values.
+    """Parse the parameters list, which is just an unlabeled list of numeric values.
     """
     for line_num in range(len(lines)):
         line = lines[line_num]
@@ -78,7 +79,7 @@ def parse_parameter_list(lines, parameters, num_params):
         if len(line_floats) == num_params:
             parameters["parameter list"] = line.strip()
         # Find SEs. The same format as parameters above is maintained
-        # since there is a correspondance between the SE format and
+        # since there is a correspondence between the SE format and
         # the parameter format.
         # Example match:
         # "SEs for parameters:
@@ -238,7 +239,7 @@ def parse_freqs(lines, parameters):
         elif "(frequency parameters for branches)" in line:
             parameters["nodes"] = {}
             branch_freqs_found = True
-        elif branch_freqs_found is True:
+        elif branch_freqs_found:
             if len(line_floats) > 0:
                 node_res = re.match("Node \#(\d+)", line)
                 node_num = int(node_res.group(1))
diff --git a/Bio/Phylo/PAML/_parse_codeml.py b/Bio/Phylo/PAML/_parse_codeml.py
index 8760987..7b8bca1 100644
--- a/Bio/Phylo/PAML/_parse_codeml.py
+++ b/Bio/Phylo/PAML/_parse_codeml.py
@@ -1,10 +1,11 @@
-# Copyright (C) 2011 by Brandon Invergo (b.invergo at gmail.com)
+# Copyright (C) 2011, 2016 by Brandon Invergo (b.invergo at gmail.com)
 # This code is part of the Biopython distribution and governed by its
 # license. Please see the LICENSE file that should have been included
 # as part of this package.
 
 import re
 
+
 line_floats_re = re.compile("-*\d+\.\d+")
 
 try:
@@ -99,11 +100,12 @@ def parse_nssites(lines, results, multi_models, multi_genes):
         if siteclass_model is None:
             siteclass_model = "one-ratio"
         current_model = {"one-ratio": 0,
-                        "NearlyNeutral": 1,
-                        "PositiveSelection": 2,
-                        "discrete": 3,
-                        "beta": 7,
-                        "beta&w>1": 8}[siteclass_model]
+                         "NearlyNeutral": 1,
+                         "PositiveSelection": 2,
+                         "discrete": 3,
+                         "beta": 7,
+                         "beta&w>1": 8,
+                         "M2a_rel": 22}[siteclass_model]
         if multi_genes:
             genes = results["genes"]
             current_gene = None
@@ -199,7 +201,7 @@ def parse_model(lines, results):
         elif len(line_floats) == num_params and not SEs_flag:
             parameters["parameter list"] = line.strip()
         # Find SEs. The same format as parameters above is maintained
-        # since there is a correspondance between the SE format and
+        # since there is a correspondence between the SE format and
         # the parameter format.
         # Example match:
         # "SEs for parameters:
@@ -256,7 +258,7 @@ def parse_model(lines, results):
             if parameters.get("genes") is None:
                 parameters["genes"] = {}
             parameters["genes"][gene_num] = {"kappa": line_floats[0],
-                                            "omega": line_floats[1]}
+                                             "omega": line_floats[1]}
         # Find dN values.
         # Example match: "tree length for dN:       0.2990"
         elif "tree length for dN" in line and len(line_floats) > 0:
@@ -289,7 +291,7 @@ def parse_model(lines, results):
                 site_classes = parameters.get("site classes")
                 branch_type_no = int(branch_type.group(1))
                 site_classes = parse_clademodelc(branch_type_no, line_floats,
-                        site_classes)
+                                                 site_classes)
                 parameters["site classes"] = site_classes
         # Find the omega values of the foreground branch for each site
         # class in the branch site A model
@@ -344,7 +346,8 @@ def parse_model(lines, results):
 
 
 def parse_siteclass_proportions(line_floats):
-    """For models which have multiple site classes, find the proportion of the alignment assigned to each class.
+    """For models which have multiple site classes, find the proportion of the
+    alignment assigned to each class.
     """
     site_classes = {}
     if len(line_floats) > 0:
@@ -354,7 +357,8 @@ def parse_siteclass_proportions(line_floats):
 
 
 def parse_siteclass_omegas(line, site_classes):
-    """For models which have multiple site classes, find the omega estimated for each class.
+    """For models which have multiple site classes, find the omega estimated
+    for each class.
     """
     # The omega results are tabular with strictly 9 characters per column
     # (1 to 3 digits before the  decimal point and 5 after). This causes
@@ -390,11 +394,9 @@ def parse_branch_site_a(foreground, line_floats, site_classes):
         if site_classes[n].get("branch types") is None:
             site_classes[n]["branch types"] = {}
         if foreground:
-            site_classes[n]["branch types"]["foreground"] =\
-                    line_floats[n]
+            site_classes[n]["branch types"]["foreground"] = line_floats[n]
         else:
-            site_classes[n]["branch types"]["background"] =\
-                    line_floats[n]
+            site_classes[n]["branch types"]["background"] = line_floats[n]
     return site_classes
 
 
@@ -427,11 +429,11 @@ def parse_pairwise(lines, results):
                 pairwise[seq2][seq1] = pairwise[seq1][seq2]
             elif len(line_floats) == 6:
                 pairwise[seq1][seq2] = {"t": line_floats[0],
-                        "S": line_floats[1],
-                        "N": line_floats[2],
-                        "omega": line_floats[3],
-                        "dN": line_floats[4],
-                        "dS": line_floats[5]}
+                                        "S": line_floats[1],
+                                        "N": line_floats[2],
+                                        "omega": line_floats[3],
+                                        "dN": line_floats[4],
+                                        "dS": line_floats[5]}
                 pairwise[seq2][seq1] = pairwise[seq1][seq2]
     if len(pairwise) > 0:
         results["pairwise"] = pairwise
@@ -461,7 +463,7 @@ def parse_distances(lines, results):
         # Parse AA distances (raw or ML), in a lower diagonal matrix
         matrix_row_res = matrix_row_re.match(line)
         if matrix_row_res and (raw_aa_distances_flag or
-                ml_aa_distances_flag):
+                               ml_aa_distances_flag):
             seq_name = matrix_row_res.group(1).strip()
             if seq_name not in sequences:
                 sequences.append(seq_name)
diff --git a/Bio/Phylo/PAML/_parse_yn00.py b/Bio/Phylo/PAML/_parse_yn00.py
index 3d41376..d9f5bc5 100644
--- a/Bio/Phylo/PAML/_parse_yn00.py
+++ b/Bio/Phylo/PAML/_parse_yn00.py
@@ -38,7 +38,7 @@ def parse_yn00(lines, results, sequences):
     """ Parse the Yang & Nielsen (2000) part of the results.
     Yang & Nielsen results are organized in a table with
     each row comprising one pairwise species comparison.
-    Rows are labeled by spequence number rather than by
+    Rows are labeled by sequence number rather than by
     sequence name."""
 
     # Example (header row and first table row):
diff --git a/Bio/Phylo/PAML/baseml.py b/Bio/Phylo/PAML/baseml.py
index ff37c21..d85138e 100644
--- a/Bio/Phylo/PAML/baseml.py
+++ b/Bio/Phylo/PAML/baseml.py
@@ -11,7 +11,7 @@ from . import _parse_baseml
 
 class BasemlError(EnvironmentError):
     """BASEML has failed. Run with verbose = True to view BASEML's error
-message"""
+    message"""
 
 
 class Baseml(Paml):
@@ -32,30 +32,30 @@ class Baseml(Paml):
         self.tree = tree
         self.ctl_file = "baseml.ctl"
         self._options = {"noisy": None,
-                        "verbose": None,
-                        "runmode": None,
-                        "model": None,
-                        "model_options": None,
-                        "Mgene": None,
-                        "ndata": None,
-                        "clock": None,
-                        "fix_kappa": None,
-                        "kappa": None,
-                        "fix_alpha": None,
-                        "alpha": None,
-                        "Malpha": None,
-                        "ncatG": None,
-                        "fix_rho": None,
-                        "rho": None,
-                        "nparK": None,
-                        "nhomo": None,
-                        "getSE": None,
-                        "RateAncestor": None,
-                        "Small_Diff": None,
-                        "cleandata": None,
-                        "icode": None,
-                        "fix_blength": None,
-                        "method": None}
+                         "verbose": None,
+                         "runmode": None,
+                         "model": None,
+                         "model_options": None,
+                         "Mgene": None,
+                         "ndata": None,
+                         "clock": None,
+                         "fix_kappa": None,
+                         "kappa": None,
+                         "fix_alpha": None,
+                         "alpha": None,
+                         "Malpha": None,
+                         "ncatG": None,
+                         "fix_rho": None,
+                         "rho": None,
+                         "nparK": None,
+                         "nhomo": None,
+                         "getSE": None,
+                         "RateAncestor": None,
+                         "Small_Diff": None,
+                         "cleandata": None,
+                         "icode": None,
+                         "fix_blength": None,
+                         "method": None}
 
     def write_ctl_file(self):
         """Dynamically build a BASEML control file from the options.
@@ -145,7 +145,8 @@ class Baseml(Paml):
                 self._options[option] = None
 
     def _set_rel_paths(self):
-        """Convert all file/directory locations to paths relative to the current working directory.
+        """Convert all file/directory locations to paths relative to the current
+        working directory.
 
         BASEML requires that all paths specified in the control file be
         relative to the directory from which it is called rather than
diff --git a/Bio/Phylo/PAML/chi2.py b/Bio/Phylo/PAML/chi2.py
index ffb398f..fcf2ec5 100644
--- a/Bio/Phylo/PAML/chi2.py
+++ b/Bio/Phylo/PAML/chi2.py
@@ -9,8 +9,6 @@
 
 from math import log, exp
 
-__docformat__ = "restructuredtext en"
-
 
 def cdf_chi2(df, stat):
     if df < 1:
@@ -45,9 +43,9 @@ def _ln_gamma_function(alpha):
         x = z
         f = -log(f)
     z = 1 / (x * x)
-    return f + (x - 0.5) * log(x) - x + .918938533204673             \
-          + (((-.000595238095238 * z + .000793650793651) * z - .002777777777778) * z
-               + .083333333333333) / x
+    return f + (x - 0.5) * log(x) - x + .918938533204673 + \
+           (((-.000595238095238 * z + .000793650793651) * z - .002777777777778) * z +
+            .083333333333333) / x
 
 
 def _incomplete_gamma(x, alpha):
@@ -64,7 +62,7 @@ def _incomplete_gamma(x, alpha):
         RATNEST FORTRAN by
         Bhattacharjee GP (1970) The incomplete gamma integral.  Applied Statistics,
         19: 285-287 (AS32)
-        
+
     """
     p = alpha
     g = _ln_gamma_function(alpha)
diff --git a/Bio/Phylo/PAML/codeml.py b/Bio/Phylo/PAML/codeml.py
index cb0ddcf..666478d 100644
--- a/Bio/Phylo/PAML/codeml.py
+++ b/Bio/Phylo/PAML/codeml.py
@@ -13,14 +13,14 @@ from . import _parse_codeml
 
 class CodemlError(EnvironmentError):
     """CODEML has failed. Run with verbose = True to view CODEML's error
-message"""
+    message"""
 
 
 class Codeml(Paml):
     """This class implements an interface to CODEML, part of the PAML package."""
 
     def __init__(self, alignment=None, tree=None, working_dir=None,
-                out_file=None):
+                 out_file=None):
         """Initialize the codeml instance.
 
         The user may optionally pass in strings specifying the locations
@@ -36,34 +36,34 @@ class Codeml(Paml):
         self.tree = tree
         self.ctl_file = "codeml.ctl"
         self._options = {"noisy": None,
-                        "verbose": None,
-                        "runmode": None,
-                        "seqtype": None,
-                        "CodonFreq": None,
-                        "ndata": None,
-                        "clock": None,
-                        "aaDist": None,
-                        "aaRatefile": None,
-                        "model": None,
-                        "NSsites": None,
-                        "icode": None,
-                        "Mgene": None,
-                        "fix_kappa": None,
-                        "kappa": None,
-                        "fix_omega": None,
-                        "omega": None,
-                        "fix_alpha": None,
-                        "alpha": None,
-                        "Malpha": None,
-                        "ncatG": None,
-                        "getSE": None,
-                        "RateAncestor": None,
-                        "Small_Diff": None,
-                        "cleandata": None,
-                        "fix_blength": None,
-                        "method": None,
-                        "rho": None,
-                        "fix_rho": None}
+                         "verbose": None,
+                         "runmode": None,
+                         "seqtype": None,
+                         "CodonFreq": None,
+                         "ndata": None,
+                         "clock": None,
+                         "aaDist": None,
+                         "aaRatefile": None,
+                         "model": None,
+                         "NSsites": None,
+                         "icode": None,
+                         "Mgene": None,
+                         "fix_kappa": None,
+                         "kappa": None,
+                         "fix_omega": None,
+                         "omega": None,
+                         "fix_alpha": None,
+                         "alpha": None,
+                         "Malpha": None,
+                         "ncatG": None,
+                         "getSE": None,
+                         "RateAncestor": None,
+                         "Small_Diff": None,
+                         "cleandata": None,
+                         "fix_blength": None,
+                         "method": None,
+                         "rho": None,
+                         "fix_rho": None}
 
     def write_ctl_file(self):
         """Dynamically build a CODEML control file from the options.
@@ -108,7 +108,7 @@ class Codeml(Paml):
                         if "=" not in uncommented:
                             raise AttributeError(
                                 "Malformed line in control file:\n%r" % line)
-                        (option, value) = uncommented.split("=")
+                        (option, value) = uncommented.split("=", 1)
                         option = option.strip()
                         value = value.strip()
                         if option == "seqfile":
@@ -159,7 +159,8 @@ class Codeml(Paml):
                 print("%s = %s" % (option[0], option[1]))
 
     def _set_rel_paths(self):
-        """Convert all file/directory locations to paths relative to the current working directory.
+        """Convert all file/directory locations to paths relative to the current
+        working directory.
 
         CODEML requires that all paths specified in the control file be
         relative to the directory from which it is called rather than
@@ -169,8 +170,7 @@ class Codeml(Paml):
         if self.tree is not None:
             self._rel_tree = _relpath(self.tree, self.working_dir)
 
-    def run(self, ctl_file=None, verbose=False, command="codeml",
-                parse=True):
+    def run(self, ctl_file=None, verbose=False, command="codeml", parse=True):
         """Run codeml using the current configuration and then parse the results.
 
         Return a process signal so the user can determine if
diff --git a/Bio/Phylo/PAML/yn00.py b/Bio/Phylo/PAML/yn00.py
index 6155ac5..2b8df97 100644
--- a/Bio/Phylo/PAML/yn00.py
+++ b/Bio/Phylo/PAML/yn00.py
@@ -7,20 +7,20 @@ import os.path
 from ._paml import Paml
 from . import _parse_yn00
 
+
 # TODO - Restore use of with statement for closing handles automatically
 # after dropping Python 2.4
 
 
 class Yn00Error(EnvironmentError):
     """yn00 has failed. Run with verbose = True to view yn00's error
-message"""
+    message"""
 
 
 class Yn00(Paml):
     """This class implements an interface to yn00, part of the PAML package."""
 
-    def __init__(self, alignment=None, working_dir=None,
-                out_file=None):
+    def __init__(self, alignment=None, working_dir=None, out_file=None):
         """Initialize the Yn00 instance.
 
         The user may optionally pass in strings specifying the locations
@@ -30,10 +30,10 @@ class Yn00(Paml):
         Paml.__init__(self, alignment, working_dir, out_file)
         self.ctl_file = "yn00.ctl"
         self._options = {"verbose": None,
-                        "icode": None,
-                        "weighting": None,
-                        "commonf3x4": None,
-                        "ndata": None}
+                         "icode": None,
+                         "weighting": None,
+                         "commonf3x4": None,
+                         "ndata": None}
 
     def write_ctl_file(self):
         """Dynamically build a yn00 control file from the options.
@@ -96,8 +96,7 @@ class Yn00(Paml):
             else:
                 self._options[option] = None
 
-    def run(self, ctl_file=None, verbose=False, command="yn00",
-                parse=True):
+    def run(self, ctl_file=None, verbose=False, command="yn00", parse=True):
         Paml.run(self, ctl_file, verbose, command)
         if parse:
             results = read(self.out_file)
@@ -119,13 +118,13 @@ def read(results_file):
             ng86_start = line_num + 1
         elif "(B) Yang & Nielsen (2000) method" in line:
             (results, sequences) = _parse_yn00.parse_ng86(lines[ng86_start:line_num],
-                    results)
+                                                          results)
             yn00_start = line_num + 1
         elif "(C) LWL85, LPB93 & LWLm methods" in line:
             results = _parse_yn00.parse_yn00(lines[yn00_start:line_num], results,
-                    sequences)
+                                             sequences)
             results = _parse_yn00.parse_others(lines[line_num + 1:], results,
-                    sequences)
+                                               sequences)
     if len(results) == 0:
         raise ValueError("Invalid results file.")
     return results
diff --git a/Bio/Phylo/PhyloXML.py b/Bio/Phylo/PhyloXML.py
index 7a17260..273ffbd 100644
--- a/Bio/Phylo/PhyloXML.py
+++ b/Bio/Phylo/PhyloXML.py
@@ -13,8 +13,6 @@ Journal article:
     Han and Zmasek (2009), doi:10.1186/1471-2105-10-356
 """
 
-__docformat__ = "restructuredtext en"
-
 import re
 import warnings
 
@@ -77,7 +75,7 @@ class Phyloxml(PhyloElement):
 
     def __getitem__(self, index):
         """Get a phylogeny by index or name."""
-        if isinstance(index, int) or isinstance(index, slice):
+        if isinstance(index, (int, slice)):
             return self.phylogenies[index]
         if not isinstance(index, basestring):
             raise KeyError("can't use %s as an index" % type(index))
@@ -257,7 +255,7 @@ class Phylogeny(PhyloElement, BaseTree.Tree):
             # Special case: mirror the behavior of _get_confidence
             self.confidences = []
             return
-        if isinstance(value, float) or isinstance(value, int):
+        if isinstance(value, (float, int)):
             value = Confidence(value)
         elif not isinstance(value, Confidence):
             raise ValueError("value must be a number or Confidence instance")
@@ -390,7 +388,7 @@ class Clade(PhyloElement, BaseTree.Clade):
             # Special case: mirror the behavior of _get_confidence
             self.confidences = []
             return
-        if isinstance(value, float) or isinstance(value, int):
+        if isinstance(value, (float, int)):
             value = Confidence(value)
         elif not isinstance(value, Confidence):
             raise ValueError("value must be a number or Confidence instance")
diff --git a/Bio/Phylo/PhyloXMLIO.py b/Bio/Phylo/PhyloXMLIO.py
index b4fc0dd..95e79d3 100644
--- a/Bio/Phylo/PhyloXMLIO.py
+++ b/Bio/Phylo/PhyloXMLIO.py
@@ -16,7 +16,6 @@ About capitalization:
   `Bio.Phylo.read`!), containing a list of Phylogenies (objects derived from
   `BaseTree.Tree`)
 """
-__docformat__ = "restructuredtext en"
 
 import sys
 
@@ -36,6 +35,7 @@ try:
 except ImportError:
     from xml.etree import ElementTree as ElementTree
 
+
 # Recognize the phyloXML namespace when parsing
 # See http://effbot.org/zone/element-namespaces.htm
 NAMESPACES = {
@@ -120,8 +120,7 @@ def write(obj, file, encoding=DEFAULT_ENCODING, indent=True):
 
     if isinstance(obj, PX.Phyloxml):
         pass
-    elif (isinstance(obj, PX.BaseTree.Tree) or
-          isinstance(obj, PX.BaseTree.Clade)):
+    elif isinstance(obj, (PX.BaseTree.Tree, PX.BaseTree.Clade)):
         obj = fix_single(obj).to_phyloxml()
     elif hasattr(obj, '__iter__'):
         obj = PX.Phyloxml({}, phylogenies=(fix_single(t) for t in obj))
diff --git a/Bio/Phylo/TreeConstruction.py b/Bio/Phylo/TreeConstruction.py
index 6f3b657..c975a46 100644
--- a/Bio/Phylo/TreeConstruction.py
+++ b/Bio/Phylo/TreeConstruction.py
@@ -4,7 +4,6 @@
 # as part of this package.
 
 """Classes and methods for tree construction"""
-__docformat__ = "restructuredtext en"
 
 import itertools
 import copy
@@ -103,10 +102,10 @@ class _Matrix(object):
             self.matrix = matrix
         else:
             # check if all elements are numbers
-            if (isinstance(matrix, list)
-                and all(isinstance(l, list) for l in matrix)
-                and all(_is_numeric(n) for n in [item for sublist in matrix
-                                                 for item in sublist])):
+            if (isinstance(matrix, list) and
+                all(isinstance(l, list) for l in matrix) and
+                all(_is_numeric(n) for n in [item for sublist in matrix
+                                             for item in sublist])):
                 # check if the same length with names
                 if len(matrix) == len(names):
                     # check if is lower triangle format
@@ -375,15 +374,15 @@ class DistanceCalculator(object):
 
     # BLAST nucleic acid scoring matrix
     blastn = [[5],
-              [-4,  5],
-              [-4, -4,  5],
-              [-4, -4, -4,  5]]
+              [-4, 5],
+              [-4, -4, 5],
+              [-4, -4, -4, 5]]
 
     # transition/transversion scoring matrix
     trans = [[6],
-             [-5,  6],
-             [-5, -1,  6],
-             [-1, -5, -5,  6]]
+             [-5, 6],
+             [-5, -1, 6],
+             [-1, -5, -5, 6]]
 
     protein_alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L',
                         'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'X', 'Y',
@@ -411,8 +410,8 @@ class DistanceCalculator(object):
             self.scoring_matrix = self._build_protein_matrix(
                 self.protein_matrices[model])
         else:
-            raise ValueError("Model not supported. Available models: "
-                             + ", ".join(self.models))
+            raise ValueError("Model not supported. Available models: " +
+                             ", ".join(self.models))
 
     def _pairwise(self, seq1, seq2):
         """Calculate pairwise distance from two sequences.
@@ -543,8 +542,8 @@ class DistanceTreeConstructor(TreeConstructor):
     methods = ['nj', 'upgma']
 
     def __init__(self, distance_calculator=None, method="nj"):
-        if (distance_calculator is None
-                or isinstance(distance_calculator, DistanceCalculator)):
+        if (distance_calculator is None or
+            isinstance(distance_calculator, DistanceCalculator)):
             self.distance_calculator = distance_calculator
         else:
             raise TypeError("Must provide a DistanceCalculator object.")
@@ -681,8 +680,8 @@ class DistanceTreeConstructor(TreeConstructor):
             inner_clade.clades.append(clade1)
             inner_clade.clades.append(clade2)
             # assign branch length
-            clade1.branch_length = (dm[min_i, min_j] + node_dist[min_i]
-                                    - node_dist[min_j]) / 2.0
+            clade1.branch_length = (dm[min_i, min_j] + node_dist[min_i] -
+                                    node_dist[min_j]) / 2.0
             clade2.branch_length = dm[min_i, min_j] - clade1.branch_length
 
             # update node list
@@ -693,8 +692,8 @@ class DistanceTreeConstructor(TreeConstructor):
             # set the distances of new node at the index of min_j
             for k in range(0, len(dm)):
                 if k != min_i and k != min_j:
-                    dm[min_j, k] = (dm[min_i, k] + dm[min_j, k]
-                                    - dm[min_i, min_j]) / 2.0
+                    dm[min_j, k] = (dm[min_i, k] + dm[min_j, k] -
+                                    dm[min_i, min_j]) / 2.0
 
             dm.names[min_j] = "Inner" + str(inner_count)
             del dm[min_i]
@@ -924,7 +923,7 @@ class ParsimonyScorer(Scorer):
         terms = tree.get_terminals()
         terms.sort(key=lambda term: term.name)
         alignment.sort()
-        if not all([t.name == a.id for t, a in zip(terms, alignment)]):
+        if not all(t.name == a.id for t, a in zip(terms, alignment)):
             raise ValueError(
                 "Taxon names of the input tree should be the same with the alignment.")
         # term_align = dict(zip(terms, alignment))
diff --git a/Bio/Phylo/__init__.py b/Bio/Phylo/__init__.py
index 86a7027..8bf9708 100644
--- a/Bio/Phylo/__init__.py
+++ b/Bio/Phylo/__init__.py
@@ -7,7 +7,6 @@
 
 See also: http://biopython.org/wiki/Phylo
 """
-__docformat__ = "restructuredtext en"
 
 from Bio.Phylo._io import parse, read, write, convert
 from Bio.Phylo._utils import (draw, draw_ascii, draw_graphviz, to_networkx)
diff --git a/Bio/Phylo/_cdao_owl.py b/Bio/Phylo/_cdao_owl.py
index f9b476a..ca386d0 100644
--- a/Bio/Phylo/_cdao_owl.py
+++ b/Bio/Phylo/_cdao_owl.py
@@ -5,6 +5,7 @@
 
 import xml.etree.ElementTree as ET
 
+
 cdao_namespaces = {
     'cdao': 'http://purl.obolibrary.org/obo/cdao.owl#',
     'obo': 'http://purl.obolibrary.org/obo/',
diff --git a/Bio/Phylo/_io.py b/Bio/Phylo/_io.py
index 9cde2f1..e51b1df 100644
--- a/Bio/Phylo/_io.py
+++ b/Bio/Phylo/_io.py
@@ -10,8 +10,6 @@ This API follows the same semantics as Biopython's `SeqIO` and `AlignIO`.
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"
-
 from Bio import File
 from Bio.Phylo import (
     BaseTree,
@@ -76,7 +74,7 @@ def read(file, format, **kwargs):
 
 def write(trees, file, format, **kwargs):
     """Write a sequence of trees to file in the given format."""
-    if isinstance(trees, BaseTree.Tree) or isinstance(trees, BaseTree.Clade):
+    if isinstance(trees, (BaseTree.Tree, BaseTree.Clade)):
         # Passed a single tree instead of an iterable -- that's OK
         trees = [trees]
     with File.as_handle(file, 'w+') as fp:
@@ -84,7 +82,9 @@ def write(trees, file, format, **kwargs):
     return n
 
 
-def convert(in_file, in_format, out_file, out_format, parse_args={}, **kwargs):
+def convert(in_file, in_format, out_file, out_format, parse_args=None, **kwargs):
     """Convert between two tree file formats."""
+    if parse_args is None:
+        parse_args = {}
     trees = parse(in_file, in_format, **parse_args)
     return write(trees, out_file, out_format, **kwargs)
diff --git a/Bio/Phylo/_utils.py b/Bio/Phylo/_utils.py
index 5c06606..6c55a1a 100644
--- a/Bio/Phylo/_utils.py
+++ b/Bio/Phylo/_utils.py
@@ -7,7 +7,6 @@
 
 Third-party libraries are loaded when the corresponding function is called.
 """
-__docformat__ = "restructuredtext en"
 
 import math
 import sys
@@ -225,8 +224,8 @@ def draw_ascii(tree, file=None, column_width=80):
             depths = tree.depths(unit_branch_lengths=True)
         # Potential drawing overflow due to rounding -- 1 char per tree layer
         fudge_margin = int(math.ceil(math.log(len(taxa), 2)))
-        cols_per_branch_unit = ((drawing_width - fudge_margin)
-                                / float(max(depths.values())))
+        cols_per_branch_unit = ((drawing_width - fudge_margin) /
+                                float(max(depths.values())))
         return dict((clade, int(blen * cols_per_branch_unit + 1.0))
                     for clade, blen in depths.items())
 
@@ -280,7 +279,7 @@ def draw_ascii(tree, file=None, column_width=80):
 
 def draw(tree, label_func=str, do_show=True, show_confidence=True,
          # For power users
-         axes=None, branch_labels=None, *args, **kwargs):
+         axes=None, branch_labels=None, label_colors=None, *args, **kwargs):
     """Plot the given tree using matplotlib (or pylab).
 
     The graphic is a rooted tree, drawn with roughly the same algorithm as
@@ -322,6 +321,10 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
             But if you would like to alter the formatting of confidence values,
             or label the branches with something other than confidence, then use
             this option.
+        label_colors : dict or callable
+            A function or a dictionary specifying the color of the tip label.
+            If the tip label can't be found in the dict or label_colors is
+            None, the label will be shown in black.
     """
 
     try:
@@ -365,6 +368,20 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
             "branch_labels must be either a dict or a callable (function)"
         format_branch_label = branch_labels
 
+    # options for displaying label colors.
+    if label_colors:
+        if callable(label_colors):
+            def get_label_color(label):
+                return label_colors(label)
+        else:
+            # label_colors is presumed to be a dict
+            def get_label_color(label):
+                return label_colors.get(label, 'black')
+    else:
+        def get_label_color(label):
+            # if label_colors is not specified, use black
+            return 'black'
+
     # Layout
 
     def get_x_positions(tree):
@@ -419,14 +436,14 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
         Graphical formatting of the lines representing clades in the plot can be
         customized by altering this function.
         """
-        if (use_linecollection is False and orientation == 'horizontal'):
+        if not use_linecollection and orientation == 'horizontal':
             axes.hlines(y_here, x_start, x_here, color=color, lw=lw)
-        elif (use_linecollection is True and orientation == 'horizontal'):
+        elif use_linecollection and orientation == 'horizontal':
             horizontal_linecollections.append(mpcollections.LineCollection(
                 [[(x_start, y_here), (x_here, y_here)]], color=color, lw=lw),)
-        elif (use_linecollection is False and orientation == 'vertical'):
+        elif not use_linecollection and orientation == 'vertical':
             axes.vlines(x_here, y_bot, y_top, color=color)
-        elif (use_linecollection is True and orientation == 'vertical'):
+        elif use_linecollection and orientation == 'vertical':
             vertical_linecollections.append(mpcollections.LineCollection(
                 [[(x_here, y_bot), (x_here, y_top)]], color=color, lw=lw),)
 
@@ -446,7 +463,8 @@ def draw(tree, label_func=str, do_show=True, show_confidence=True,
         label = label_func(clade)
         if label not in (None, clade.__class__.__name__):
             axes.text(x_here, y_here, ' %s' %
-                      label, verticalalignment='center')
+                      label, verticalalignment='center',
+                      color=get_label_color(label))
         # Add label above the branch (optional)
         conf_label = format_branch_label(clade)
         if conf_label:
diff --git a/Bio/PopGen/Async/Local.py b/Bio/PopGen/Async/Local.py
index 893e42d..52d1468 100644
--- a/Bio/PopGen/Async/Local.py
+++ b/Bio/PopGen/Async/Local.py
@@ -3,43 +3,39 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-'''
-Asynchronous local execution.
+"""Asynchronous local execution.
 
 Supports multicore architectures.
-'''
+"""
 
 from Bio.PopGen.Async import Async
 
 import threading
 
-__docformat__ = "restructuredtext en"
-
 
 class Local(Async):
-    '''Execution on Local machine.
-    '''
+    """Execution on Local machine."""
 
     def __init__(self, num_cores=1):
-        '''Constructor.
+        """Constructor.
 
-           parameters:
+        parameters:
 
-             - num_cores - Number of cores (for multiprocessor machines,
-               multiply accordingly)
-        '''
+         - num_cores - Number of cores (for multiprocessor machines,
+           multiply accordingly)
+        """
         Async.__init__(self)
         self.num_cores = num_cores
         self.cores_used = 0
 
     def _run_program(self, id, hook, parameters, input_files):
-        '''Run program.
+        """Run program.
 
-           For parameters, please check Async.run_program.
+        For parameters, please check Async.run_program.
 
-           Either runs a program if a core is available or
-           schedules it.
-        '''
+        Either runs a program if a core is available or
+        schedules it.
+        """
         self.access_ds.acquire()
         self.waiting.append((id, hook, parameters, input_files))
         if self.cores_used < self.num_cores:
@@ -48,13 +44,13 @@ class Local(Async):
         self.access_ds.release()
 
     def start_work(self):
-        '''Starts work.
+        """Starts work.
 
-           Thread initial point.
-           While there are tasks to be done, runs them.
-           The thread dies as soon as there is nothing waiting to be
-           executed.
-        '''
+        Thread initial point.
+        While there are tasks to be done, runs them.
+        The thread dies as soon as there is nothing waiting to be
+        executed.
+        """
         self.access_ds.acquire()
         while (len(self.waiting) > 0):
             id, hook, parameters, input_files = self.waiting[0]
diff --git a/Bio/PopGen/Async/__init__.py b/Bio/PopGen/Async/__init__.py
index 7f8d096..2b9336b 100644
--- a/Bio/PopGen/Async/__init__.py
+++ b/Bio/PopGen/Async/__init__.py
@@ -3,32 +3,26 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-
-'''
-Support for asynchronous execution.
-
-'''
+"""Support for asynchronous execution."""
 
 import os
 import threading
 
-__docformat__ = "restructuredtext en"
-
 
 class Async(object):
-    '''Abstract Asynchronous execution class.
+    """Abstract Asynchronous execution class.
 
-       This is the top abstract class.
-       Concrete classes must implement the _run_program method.
-    '''
+    This is the top abstract class.
+    Concrete classes must implement the _run_program method.
+    """
 
     def __init__(self):
-        '''Async constructor.
+        """Async constructor.
 
-       Initializes the queues, among other things.
-       Of notice, is the access_ds lock for controlling exclusive
-       access to this object.
-        '''
+        Initializes the queues, among other things.
+        Of notice, is the access_ds lock for controlling exclusive
+        access to this object.
+        """
         self.running = {}
         self.waiting = []
         self.done = {}
@@ -37,22 +31,22 @@ class Async(object):
         self.access_ds = threading.Lock()
 
     def run_program(self, program, parameters, input_files):
-        '''Runs a program.
+        """Runs a program.
 
-           Real _run_program to be implemented by concrete classes.
+        Real _run_program to be implemented by concrete classes.
 
-           parameters:
-           program String identifying program.
-           parameters List of String parameters.
-           input_files Hash of Input file descriptors.
+        parameters:
+        program String identifying program.
+        parameters List of String parameters.
+        input_files Hash of Input file descriptors.
 
-           returns:
-           Task Id.
+        returns:
+        Task Id.
 
-           The input_files hash key is the path that is passed
-           to the program. It should always be relative.
-           Value is a stream.
-        '''
+        The input_files hash key is the path that is passed
+        to the program. It should always be relative.
+        Value is a stream.
+        """
         if program in self.hooks:
             self.access_ds.acquire()
             self.id += 1
@@ -70,19 +64,17 @@ class Async(object):
         raise NotImplementedError("This object should be subclassed")
 
     def get_result(self, id):
-        ''' Returns the results for a certain Id, the info for that Id is
-            forgotten.
+        """ Returns results for a certain Id, the info for that Id is forgotten.
 
-            parameters:
-            id Id of the task.
+        parameters:
+        id Id of the task.
 
-            returns:
-            (return_code, output_files) return code and file access
-            object.
+        returns:
+        (return_code, output_files) return code and file access object.
 
-            The output_files hash key is a relative file name, and the value a
-            output stream.
-        '''
+        The output_files hash key is a relative file name, and the value a
+        output stream.
+        """
         self.access_ds.acquire()
         if id in self.done:
             returnCode, fileObject = self.done[id]
@@ -94,15 +86,13 @@ class Async(object):
 
 
 class FileRetriever(object):
-    '''An Abstract Support class to retrieve files.
-    '''
+    """An Abstract Support class to retrieve files."""
 
     def __init__(self):
         self.file_list = []
 
     def get_File_list(self):
-        '''Returns the list of available files.
-        '''
+        """Returns the list of available files."""
         return self.file_list
 
     def get_file(self, name):
@@ -110,8 +100,7 @@ class FileRetriever(object):
 
 
 class DirectoryRetriever(FileRetriever):
-    '''Retrieves a directory content.
-    '''
+    """Retrieves a directory content."""
 
     def __init__(self, directory):
         FileRetriever.__init__(self)
diff --git a/Bio/PopGen/FDist/Async.py b/Bio/PopGen/FDist/Async.py
index 416d16c..4436eb0 100644
--- a/Bio/PopGen/FDist/Async.py
+++ b/Bio/PopGen/FDist/Async.py
@@ -18,8 +18,6 @@ from time import sleep
 from Bio.PopGen.Async import Local
 from Bio.PopGen.FDist.Controller import FDistController
 
-__docformat__ = "restructuredtext en"
-
 
 class FDistAsync(FDistController):
     """Asynchronous FDist execution.
@@ -29,7 +27,7 @@ class FDistAsync(FDistController):
         """Constructor.
 
         Parameters:
-        
+
           - fdist_dir - Where fdist can be found, if = "", then it
               should be on the path.
           - ext - Extension of binary names (e.g. nothing on Unix,
@@ -80,7 +78,7 @@ class SplitFDist(object):
         """Constructor.
 
            Parameters:
-           
+
              - report_fun - Function that is called when a single packet is
                run, it should have a single parameter: Fst.
              - num_thr - Number of desired threads, typically the number
diff --git a/Bio/PopGen/FDist/Controller.py b/Bio/PopGen/FDist/Controller.py
index db444b8..11ef391 100644
--- a/Bio/PopGen/FDist/Controller.py
+++ b/Bio/PopGen/FDist/Controller.py
@@ -19,8 +19,6 @@ from random import randint
 from time import strftime, clock
 # from logging import debug
 
-__docformat__ = "restructuredtext en"
-
 
 def my_float(f):
     # Because of Jython, mostly
@@ -202,7 +200,7 @@ class FDistController(object):
             - try_runs - Number of simulations on the part trying to get
                        Fst correct
             - limit - Interval limit
-            
+
         Other parameters can be seen on run_fdist.
         """
         max_run_fst = 1
diff --git a/Bio/PopGen/FDist/Utils.py b/Bio/PopGen/FDist/Utils.py
index da19ee8..8d76cbd 100644
--- a/Bio/PopGen/FDist/Utils.py
+++ b/Bio/PopGen/FDist/Utils.py
@@ -7,6 +7,7 @@
 from Bio.PopGen.GenePop import FileParser
 import Bio.PopGen.FDist
 
+
 # Quite a few utility functions could be done (like remove pop,
 # add locus, etc...). The recommended strategy is convert back
 # and forth from/to GenePop and use GenePop Utils
diff --git a/Bio/PopGen/FDist/__init__.py b/Bio/PopGen/FDist/__init__.py
index b2add1a..d55c9f5 100644
--- a/Bio/PopGen/FDist/__init__.py
+++ b/Bio/PopGen/FDist/__init__.py
@@ -20,8 +20,6 @@ read             Parses a FDist record (file) into a Record object.
 
 """
 
-__docformat__ = "restructuredtext en"
-
 
 def read(handle):
     """Parses FDist data into a Record object.
diff --git a/Bio/PopGen/GenePop/Controller.py b/Bio/PopGen/GenePop/Controller.py
index 02556f4..a2ee560 100644
--- a/Bio/PopGen/GenePop/Controller.py
+++ b/Bio/PopGen/GenePop/Controller.py
@@ -16,8 +16,6 @@ import tempfile
 
 from Bio.Application import AbstractCommandline, _Argument
 
-__docformat__ = "restructuredtext en"
-
 
 def _gp_float(tok):
     """Gets a float from a token, if it fails, returns the string (PRIVATE)."""
@@ -232,7 +230,9 @@ class GenePopController(object):
                 opts["HWtests"] = "MCMC"
         return opts
 
-    def _run_genepop(self, extensions, option, fname, opts={}):
+    def _run_genepop(self, extensions, option, fname, opts=None):
+        if opts is None:
+            opts = {}
         cwd = os.getcwd()
         temp_dir = tempfile.mkdtemp()
         os.chdir(temp_dir)
diff --git a/Bio/PopGen/GenePop/EasyController.py b/Bio/PopGen/GenePop/EasyController.py
index c352198..ac80da6 100644
--- a/Bio/PopGen/GenePop/EasyController.py
+++ b/Bio/PopGen/GenePop/EasyController.py
@@ -13,8 +13,6 @@ This interface is less efficient than the standard GenePopControler
 from .Controller import GenePopController
 from Bio.PopGen import GenePop
 
-__docformat__ = "restructuredtext en"
-
 
 class EasyController(object):
     def __init__(self, fname, genepop_dir=None):
diff --git a/Bio/PopGen/GenePop/FileParser.py b/Bio/PopGen/GenePop/FileParser.py
index f2cfba8..ebdd948 100644
--- a/Bio/PopGen/GenePop/FileParser.py
+++ b/Bio/PopGen/GenePop/FileParser.py
@@ -23,8 +23,6 @@ Functions:
 """
 from Bio.PopGen.GenePop import get_indiv
 
-__docformat__ = "restructuredtext en"
-
 
 def read(fname):
     """Parses a file containing a GenePop file.
@@ -186,7 +184,7 @@ class FileRecord(object):
             else:
                 self.current_ind += 1
                 indiv_name, allele_list, ignore = get_indiv(line)
-                return (indiv_name, allele_list)
+                return indiv_name, allele_list
         return False
 
     def remove_population(self, pos, fname):
@@ -196,28 +194,67 @@ class FileRecord(object):
            fname - file to be created with population removed
         """
         old_rec = read(self.fname)
-        f = open(fname, "w")
-        f.write(self.comment_line + "\n")
-        for locus in old_rec.loci_list:
-            f.write(locus + "\n")
-        curr_pop = 0
-        l_parser = old_rec.get_individual()
-        start_pop = True
-        while l_parser:
-            if curr_pop == pos:
-                old_rec.skip_population()
-                curr_pop += 1
-            else:
-                if l_parser is True:
+        with open(fname, "w") as f:
+            f.write(self.comment_line + "\n")
+            for locus in old_rec.loci_list:
+                f.write(locus + "\n")
+            curr_pop = 0
+            l_parser = old_rec.get_individual()
+            start_pop = True
+            while l_parser:
+                if curr_pop == pos:
+                    old_rec.skip_population()
                     curr_pop += 1
-                    start_pop = True
                 else:
-                    if start_pop:
-                        f.write("POP\n")
-                        start_pop = False
+                    if l_parser is True:
+                        curr_pop += 1
+                        start_pop = True
+                    else:
+                        if start_pop:
+                            f.write("POP\n")
+                            start_pop = False
+                        name, markers = l_parser
+                        f.write(name + ",")
+                        for marker in markers:
+                            f.write(' ')
+                            for al in marker:
+                                if al is None:
+                                    al = '0'
+                                aStr = str(al)
+                                while len(aStr) < 3:
+                                    aStr = "".join(['0', aStr])
+                                f.write(aStr)
+                        f.write('\n')
+
+                l_parser = old_rec.get_individual()
+
+    def remove_locus_by_position(self, pos, fname):
+        """Removes a locus by position.
+
+           pos - position
+           fname - file to be created with locus removed
+        """
+        old_rec = read(self.fname)
+        with open(fname, "w") as f:
+            f.write(self.comment_line + "\n")
+            loci_list = old_rec.loci_list
+            del loci_list[pos]
+            for locus in loci_list:
+                f.write(locus + "\n")
+            l_parser = old_rec.get_individual()
+            f.write("POP\n")
+            while l_parser:
+                if l_parser is True:
+                    f.write("POP\n")
+                else:
                     name, markers = l_parser
                     f.write(name + ",")
+                    marker_pos = 0
                     for marker in markers:
+                        if marker_pos == pos:
+                            marker_pos += 1
+                            continue
+                        marker_pos += 1
                         f.write(' ')
                         for al in marker:
                             if al is None:
@@ -228,48 +265,7 @@ class FileRecord(object):
                             f.write(aStr)
                     f.write('\n')
 
-            l_parser = old_rec.get_individual()
-        f.close()
-
-    def remove_locus_by_position(self, pos, fname):
-        """Removes a locus by position.
-
-           pos - position
-           fname - file to be created with locus removed
-        """
-        old_rec = read(self.fname)
-        f = open(fname, "w")
-        f.write(self.comment_line + "\n")
-        loci_list = old_rec.loci_list
-        del loci_list[pos]
-        for locus in loci_list:
-            f.write(locus + "\n")
-        l_parser = old_rec.get_individual()
-        f.write("POP\n")
-        while l_parser:
-            if l_parser is True:
-                f.write("POP\n")
-            else:
-                name, markers = l_parser
-                f.write(name + ",")
-                marker_pos = 0
-                for marker in markers:
-                    if marker_pos == pos:
-                        marker_pos += 1
-                        continue
-                    marker_pos += 1
-                    f.write(' ')
-                    for al in marker:
-                        if al is None:
-                            al = '0'
-                        aStr = str(al)
-                        while len(aStr) < 3:
-                            aStr = "".join(['0', aStr])
-                        f.write(aStr)
-                f.write('\n')
-
-            l_parser = old_rec.get_individual()
-        f.close()
+                l_parser = old_rec.get_individual()
 
     def remove_loci_by_position(self, positions, fname):
         """Removes a set of loci by position.
@@ -278,43 +274,42 @@ class FileRecord(object):
            fname - file to be created with locus removed
         """
         old_rec = read(self.fname)
-        f = open(fname, "w")
-        f.write(self.comment_line + "\n")
-        loci_list = old_rec.loci_list
-        positions.sort()
-        positions.reverse()
-        posSet = set()
-        for pos in positions:
-            del loci_list[pos]
-            posSet.add(pos)
-        for locus in loci_list:
-            f.write(locus + "\n")
-        l_parser = old_rec.get_individual()
-        f.write("POP\n")
-        while l_parser:
-            if l_parser is True:
-                f.write("POP\n")
-            else:
-                name, markers = l_parser
-                f.write(name + ",")
-                marker_pos = 0
-                for marker in markers:
-                    if marker_pos in posSet:
+        with open(fname, "w") as f:
+            f.write(self.comment_line + "\n")
+            loci_list = old_rec.loci_list
+            positions.sort()
+            positions.reverse()
+            posSet = set()
+            for pos in positions:
+                del loci_list[pos]
+                posSet.add(pos)
+            for locus in loci_list:
+                f.write(locus + "\n")
+            l_parser = old_rec.get_individual()
+            f.write("POP\n")
+            while l_parser:
+                if l_parser is True:
+                    f.write("POP\n")
+                else:
+                    name, markers = l_parser
+                    f.write(name + ",")
+                    marker_pos = 0
+                    for marker in markers:
+                        if marker_pos in posSet:
+                            marker_pos += 1
+                            continue
                         marker_pos += 1
-                        continue
-                    marker_pos += 1
-                    f.write(' ')
-                    for al in marker:
-                        if al is None:
-                            al = '0'
-                        aStr = str(al)
-                        while len(aStr) < 3:
-                            aStr = "".join(['0', aStr])
-                        f.write(aStr)
-                f.write('\n')
+                        f.write(' ')
+                        for al in marker:
+                            if al is None:
+                                al = '0'
+                            aStr = str(al)
+                            while len(aStr) < 3:
+                                aStr = "".join(['0', aStr])
+                            f.write(aStr)
+                    f.write('\n')
 
-            l_parser = old_rec.get_individual()
-        f.close()
+                l_parser = old_rec.get_individual()
 
     def remove_locus_by_name(self, name, fname):
         """Removes a locus by name.
diff --git a/Bio/PopGen/GenePop/__init__.py b/Bio/PopGen/GenePop/__init__.py
index 717075e..8c6feab 100644
--- a/Bio/PopGen/GenePop/__init__.py
+++ b/Bio/PopGen/GenePop/__init__.py
@@ -21,8 +21,6 @@ Partially inspired on MedLine Code.
 """
 from copy import deepcopy
 
-__docformat__ = "restructuredtext en"
-
 
 def get_indiv(line):
     def int_no_zero(val):
diff --git a/Bio/PopGen/SimCoal/Cache.py b/Bio/PopGen/SimCoal/Cache.py
index 23e7b8b..b975b4e 100644
--- a/Bio/PopGen/SimCoal/Cache.py
+++ b/Bio/PopGen/SimCoal/Cache.py
@@ -1,18 +1,15 @@
 # Copyright 2007 by Tiago Antao <tiagoantao at gmail.com>.  All rights reserved.
 
-"""
-(DEPRECATED)
-This module allows to cache Simcoal2 results, and return on the fly
-in case the calculation was done.
+"""Cache for Simacoal2 results ((DEPRECATED).
 
+This module allows you to cache Simcoal2 results, and return on the fly
+in case the calculation was done.
 """
 
 import os
 import tarfile
 from .Controller import SimCoalController
 
-__docformat__ = "restructuredtext en"
-
 
 class SimCoalCache(object):
     def __init__(self, data_dir, simcoal_dir):
@@ -57,9 +54,9 @@ class SimCoalCache(object):
         tf.close()
 
     def listSimulations(self, ploidy='1'):
-        '''
+        """
            Lists available simulations.
-        '''
+        """
         files = os.listdir(self.cacheDir + os.sep + ploidy)
         sims = []
         for file in files:
@@ -68,13 +65,12 @@ class SimCoalCache(object):
         return sims
 
     def getSimulation(self, sim_name, ploidy='1', parDir=None):
-        '''
-           Makes available a cached simulation.
+        """Makes available a cached simulation.
 
-           @param sim_name simulation name.
+        @param sim_name simulation name.
 
-           This mainly means untaring a file.
-        '''
+        This mainly means untaring a file.
+        """
         if parDir is None:
             parDir = os.sep.join([self.dataDir, 'SimCoal', 'runs'])
         tar_name = os.sep.join([self.cacheDir, ploidy, sim_name +
diff --git a/Bio/PopGen/SimCoal/Controller.py b/Bio/PopGen/SimCoal/Controller.py
index 4f7b52e..a238542 100644
--- a/Bio/PopGen/SimCoal/Controller.py
+++ b/Bio/PopGen/SimCoal/Controller.py
@@ -21,7 +21,7 @@ class SimCoalController(object):
 
         simcoal_dir is the directory where simcoal is.
 
-        The initializer checks for existance and executability of binaries.
+        The initializer checks for existence and executability of binaries.
         """
         self.simcoal_dir = simcoal_dir
         self.os_name = os.name  # remove this?
@@ -199,7 +199,7 @@ class FastSimCoalController(object):
         if not os.access(os.path.join(self.fastsimcoal_dir, self.bin_name), os.X_OK):
             raise IOError("Fastsimcoal not executable")
 
-    def run_fastsimcoal(self, par_file, num_sims, par_dir='.', opts={}):
+    def run_fastsimcoal(self, par_file, num_sims, par_dir='.', opts=None):
         """Executes Fastsimcoal.
 
         par_file is the input parameter file (--ifile) for fastsimcoal.
@@ -207,6 +207,8 @@ class FastSimCoalController(object):
         par_dir is the directory where par_file is and where output will be written.
         opts is a dictionary of additional options to fastsimcoal.
         """
+        if opts is None:
+            opts = {}
         if par_dir is None:
             par_dir = os.sep.join([".", "Fastsimcoal", "runs"])
             if not os.path.exists(par_dir):
diff --git a/Bio/PopGen/SimCoal/Template.py b/Bio/PopGen/SimCoal/Template.py
index b1af549..189afee 100644
--- a/Bio/PopGen/SimCoal/Template.py
+++ b/Bio/PopGen/SimCoal/Template.py
@@ -11,8 +11,6 @@ from functools import reduce
 
 from Bio.PopGen.SimCoal import builtin_tpl_dir
 
-__docformat__ = "restructuredtext en"
-
 
 def exec_template(template):
     executed_template = template
@@ -148,16 +146,15 @@ def generate_model(par_stream, out_prefix, params,
 
 
 def get_demography_template(stream, model, tp_dir=None):
-    '''
-        Gets a demograpy template.
+    """Gets a demograpy template.
 
-        Most probably this model needs to be sent to GenCases.
+    Most probably this model needs to be sent to GenCases.
 
-            - stream - Writable stream.
-            - param  - Template file.
-            - tp_dir - Directory where to find the template, if None
-              use an internal template
-    '''
+    - stream - Writable stream.
+    - param  - Template file.
+    - tp_dir - Directory where to find the template, if None
+               use an internal template
+    """
     if tp_dir is None:
         # Internal Template
         filename = sep.join([builtin_tpl_dir, model + '.par'])
@@ -181,13 +178,12 @@ def _gen_loci(stream, loci):
 
 
 def get_chr_template(stream, chrs):
-    '''
-        Writes a Simcoal2 loci template part.
+    """Writes a Simcoal2 loci template part.
 
-        stream - Writable stream.
-        chr    - Chromosome list.
+    stream - Writable stream.
+    chr    - Chromosome list.
 
-        Current loci list:
+    Current loci list:
 
           - [(chr_repeats,[(marker, (params))])]
 
@@ -195,7 +191,7 @@ def get_chr_template(stream, chrs):
               - marker  --> 'SNP', 'DNA', 'RFLP', 'MICROSAT'
               - params  --> Simcoal2 parameters for markers (list of floats
                 or ints - if to be processed by generate_model)
-    '''
+    """
     num_chrs = reduce(lambda x, y: x + y[0], chrs, 0)
     stream.write('//Number of independent (unlinked) chromosomes, and "chromosome structure" flag:  0 for identical structure across chromosomes, and  1 for different structures on different chromosomes.\n')
     if len(chrs) > 1 or num_chrs == 1:
@@ -213,15 +209,14 @@ def get_chr_template(stream, chrs):
 
 
 def generate_simcoal_from_template(model, chrs, params, out_dir='.', tp_dir=None):
-    '''
-       Writes a complete SimCoal2 template file.
-
-       This joins together get_demography_template and get_chr_template,
-       which are feed into generate_model
-       Please check the three functions for parameters (model from
-       get_demography_template, chrs from get_chr_template and
-       params from generate_model).
-    '''
+    """Writes a complete SimCoal2 template file.
+
+    This joins together get_demography_template and get_chr_template,
+    which are feed into generate_model
+    Please check the three functions for parameters (model from
+    get_demography_template, chrs from get_chr_template and
+    params from generate_model).
+    """
     with open(out_dir + sep + 'tmp.par', 'w') as stream:
         get_demography_template(stream, model, tp_dir)
         get_chr_template(stream, chrs)
diff --git a/Bio/PopGen/SimCoal/__init__.py b/Bio/PopGen/SimCoal/__init__.py
index 7c2709a..7f5bb6b 100644
--- a/Bio/PopGen/SimCoal/__init__.py
+++ b/Bio/PopGen/SimCoal/__init__.py
@@ -3,13 +3,12 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-'''
-SimCoal2 execution module and support functions.
-'''
+"""SimCoal2 execution module and support functions."""
 
 import os
 import sys
 
+
 # This is a workaround to work with the test system
 # In any case the problem is with the test system
 for instance in sys.path:
diff --git a/Bio/PopGen/__init__.py b/Bio/PopGen/__init__.py
index 7c1a735..e763de1 100644
--- a/Bio/PopGen/__init__.py
+++ b/Bio/PopGen/__init__.py
@@ -3,6 +3,4 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-"""
-  PopGen: Population Genetics and Genomics library in Python
-"""
+"""PopGen: Population Genetics and Genomics library in Python."""
diff --git a/Bio/Restriction/PrintFormat.py b/Bio/Restriction/PrintFormat.py
index d2ca85b..1cd8928 100644
--- a/Bio/Restriction/PrintFormat.py
+++ b/Bio/Restriction/PrintFormat.py
@@ -52,8 +52,6 @@ from Bio._py3k import range
 
 from Bio.Restriction import RanaConfig as RanaConf
 
-__docformat__ = "restructuredtext en"
-
 
 class PrintFormat(object):
     """PrintFormat allow the printing of results of restriction analysis."""
@@ -112,7 +110,7 @@ class PrintFormat(object):
         print(self.make_format(ls, title, nc, s1))
         return
 
-    def make_format(self, cut=[], title='', nc=[], s1=''):
+    def make_format(self, cut=(), title='', nc=(), s1=''):
         """PF.make_format(cut, nc, title, s) -> string
 
         Virtual method.
@@ -134,9 +132,9 @@ class PrintFormat(object):
             enzyme2     :   position1, position2, position3.
 
         Arguments:
-         - ls is a list of cutting enzymes.
+         - ls is a tuple or list of cutting enzymes.
          - title is the title.
-         - nc is a list of non cutting enzymes.
+         - nc is a tuple or list of non cutting enzymes.
          - s1 is the sentence before the non cutting enzymes.
         """
         return self._make_list_only(ls, title) + self._make_nocut_only(nc, s1)
@@ -201,22 +199,21 @@ class PrintFormat(object):
         """
         return title + self._make_nocut_only(nc, s1)
 
-    def _make_nocut_only(self, nc, s1, ls=[], title=''):
+    def _make_nocut_only(self, nc, s1, ls=(), title=''):
         """PF._make_nocut_only(nc, s1) -> string.
 
         return a formatted string of the non cutting enzymes.
 
         Arguments:
-         - nc is a list of non cutting enzymes.
+         - nc is a tuple or list of non cutting enzymes.
          - s1 is the sentence before the non cutting enzymes.
         """
         if not nc:
             return s1
-        nc.sort()
         st = ''
         stringsite = s1 or '\n   Enzymes which do not cut the sequence.\n\n'
         Join = ''.join
-        for key in nc:
+        for key in sorted(nc):
             st = Join((st, str.ljust(str(key), self.NameWidth)))
             if len(st) > self.linesize:
                 stringsite = Join((stringsite, st, '\n'))
@@ -224,7 +221,7 @@ class PrintFormat(object):
         stringsite = Join((stringsite, st, '\n'))
         return stringsite
 
-    def _make_list_only(self, ls, title, nc=[], s1=''):
+    def _make_list_only(self, ls, title, nc=(), s1=''):
         """PF._make_list_only(ls, title) -> string.
 
         return a string of form::
@@ -236,7 +233,7 @@ class PrintFormat(object):
             ...
 
         Arguments:
-         - ls is a list of results.
+         - ls is a tuple or list of results.
          - title is a string.
          - Non cutting enzymes are not included.
         """
@@ -244,7 +241,7 @@ class PrintFormat(object):
             return title
         return self.__next_section(ls, title)
 
-    def _make_number_only(self, ls, title, nc=[], s1=''):
+    def _make_number_only(self, ls, title, nc=(), s1=''):
         """PF._make_number_only(ls, title) -> string.
 
         return a string of form::
@@ -267,6 +264,7 @@ class PrintFormat(object):
         """
         if not ls:
             return title
+        # TODO: Use key to sort!
         ls.sort(lambda x, y: cmp(len(x[1]), len(y[1])))
         iterator = iter(ls)
         cur_len = 1
@@ -282,7 +280,7 @@ class PrintFormat(object):
         title += "\n\nenzymes which cut %i times :\n\n" % cur_len
         return self.__next_section(new_sect, title)
 
-    def _make_map_only(self, ls, title, nc=[], s1=''):
+    def _make_map_only(self, ls, title, nc=(), s1=''):
         """PF._make_map_only(ls, title) -> string.
 
         return a string of form::
@@ -324,7 +322,7 @@ class PrintFormat(object):
                     l.append(key)
                 else:
                     remaining.append(key)
-            mapping = remaining 
+            mapping = remaining
         cutloc[x] = mapping
         sequence = str(self.sequence)
         revsequence = str(self.sequence.complement())
@@ -396,7 +394,7 @@ class PrintFormat(object):
         """FP.__next_section(ls, into) -> string.
 
         Arguments:
-         - ls is a list of tuple (string, [int, int]).
+         - ls is a tuple/list of tuple (string, [int, int]).
          - into is a string to which the formatted ls will be added.
 
         Format ls as a string of lines:
@@ -408,12 +406,11 @@ class PrintFormat(object):
         then add the formatted ls to tot
         return tot.
         """
-        ls.sort()
         indentation = '\n' + (self.NameWidth + self.Indent) * ' '
         linesize = self.linesize - self.MaxSize
         pat = re.compile("([\w,\s()]){1,%i}[,\.]" % linesize)
         several, Join = '', ''.join
-        for name, sites in ls:
+        for name, sites in sorted(ls):
             stringsite = ''
             l = Join((', '.join(str(site) for site in sites), '.'))
             if len(l) > linesize:
diff --git a/Bio/Restriction/RanaConfig.py b/Bio/Restriction/RanaConfig.py
index 700c88c..d3043c5 100644
--- a/Bio/Restriction/RanaConfig.py
+++ b/Bio/Restriction/RanaConfig.py
@@ -1,56 +1,58 @@
-#
-#      Restriction Analysis Libraries.
-#      Copyright (C) 2004. Frederic Sohm.
-#
+# Copyright (C) 2004. Frederic Sohm.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
-#
-###############################################################################
-#                   Configuration of the console.
-#
-#   Mainly used  by PrintFormat.PrintFormat
-#
-#   ConsoleWidth : width of the console used default to 80.
-#                   should never be less than 60.
-#   NameWidth    : space attributed to the name in PrintList method.
-#   Indent       : Indent of the second line.
-#   MaxSize      : Maximal size of the sequence (default=6:
-#                                                -> 99 999 bp + 1 trailing ','
-#                  people are unlikely to ask for restriction map of sequences
-#                  bigger than 100.000 bp. This is needed to determine the
-#                  space to be reserved for sites location.
-#
-#                  MaxSize = 5  =>   9.999 bp
-#                  MaxSize = 6  =>  99.999 bp
-#                  MaxSize = 7  => 999.999 bp
-#   example:
-#
-#   <------------ ConsoleWidth --------------->
-#   <- NameWidth ->
-#   EcoRI         :   1, 45, 50, 300, 400, 650,
-#                         700, 1200, 2500.
-#                     <-->
-#                       Indent
-#
+
+"""Configuration of the console.
+
+Mainly used by PrintFormat.PrintFormat
+
+ - ConsoleWidth : width of the console used default to 80.
+                  should never be less than 60.
+ - NameWidth    : space attributed to the name in PrintList method.
+ - Indent       : Indent of the second line.
+ - MaxSize      : Maximal size of the sequence (default=6:
+                  -> 99 999 bp + 1 trailing ','
+                  people are unlikely to ask for restriction map of sequences
+                  bigger than 100.000 bp. This is needed to determine the
+                  space to be reserved for sites location.
+
+                   - MaxSize = 5  =>   9.999 bp
+                   - MaxSize = 6  =>  99.999 bp
+                   - MaxSize = 7  => 999.999 bp
+
+Example::
+
+    <------------ ConsoleWidth --------------->
+    <- NameWidth ->
+    EcoRI         :   1, 45, 50, 300, 400, 650,
+                          700, 1200, 2500.
+                      <-->
+                        Indent
+
+"""
+
+
 ConsoleWidth = 80
 NameWidth = 10
 Indent = 4
 MaxSize = 6
-###############################################################################
-#                   Proxies
+
+# Proxies
+#
+# Enter here the address of your proxy if any.
+# If you don't use proxy use an empty string
 #
-#   Enter here the address of your proxy if any.
-#   If you don't use proxy use an empty string
-#   i.e.
-#   ftp_proxy       =   ''
-#                   -> no proxy
+# Example:
 #
-#   ftp_proxy       =   'http://www.somewhere.something:one_number'
-#                   -> www.somewhere.something is the address of the proxy.
-#                      one_number is the port number.
+#  ftp_proxy       =   ''
+#                  -> no proxy
 #
+#  ftp_proxy       =   'http://www.somewhere.something:one_number'
+#                  -> www.somewhere.something is the address of the proxy.
+#                     one_number is the port number.
 ftp_proxy = ''
+
 ###############################################################################
 #                   Rebase ftp location
 #
@@ -70,12 +72,7 @@ ftp_emb_r = ftp_Rebase + 'pub/rebase/emboss_r.###'
 #   connect as anonymous user (rebase_name) and providing your e-mail address
 #   as password.
 #
-#   Therefore, you need to enter your e-mail address in rebase_password.
-#   The address will not be send to anyone but is necessary to login the
-#   ftp server of rebase when connecting as anonymous user.
-#
-#   Do not forget to enclose the address between "'".
+#   However, a password is not required (any more?) for connecting tho REBASE,
+#   so we don't send our e-mail
 #
 Rebase_name = 'anonymous'
-Rebase_password = ''
-# Rebase_password = 'your_address at somewhere.something'
diff --git a/Bio/Restriction/Restriction.py b/Bio/Restriction/Restriction.py
index 0ced980..14ff5a8 100644
--- a/Bio/Restriction/Restriction.py
+++ b/Bio/Restriction/Restriction.py
@@ -18,8 +18,8 @@ Notes about the diverses class of the restriction enzyme implementation::
         ----------------------------------------------------------------------------
             NoCut, OneCut,TwoCuts   represent the number of double strand cuts
                                     produced by the enzyme.
-                                    they correspond to the 4th field of the rebase
-                                    record emboss_e.NNN.
+                                    they correspond to the 4th field of the
+                                    rebase record emboss_e.NNN.
                     0->NoCut    : the enzyme is not characterised.
                     2->OneCut   : the enzyme produce one double strand cut.
                     4->TwoCuts  : two double strand cuts.
@@ -34,17 +34,17 @@ Notes about the diverses class of the restriction enzyme implementation::
                                     with palindromic sites.
         ----------------------------------------------------------------------------
             Unknown, Blunt,         represent the overhang.
-            Ov5, Ov3                Unknown is here for symetry reasons and
-                                    correspond to enzymes that are not characterised
-                                    in rebase.
+            Ov5, Ov3                Unknown is here for symmetry reasons and
+                                    correspond to enzymes that are not
+                                    characterised in rebase.
         ----------------------------------------------------------------------------
             Defined, Ambiguous,     represent the sequence of the overhang.
             NotDefined
-                                    NotDefined is for enzymes not characterised in
-                                    rebase.
+                                    NotDefined is for enzymes not characterised
+                                    in rebase.
 
-                                    Defined correspond to enzymes that display a
-                                    constant overhang whatever the sequence.
+                                    Defined correspond to enzymes that display
+                                    a constant overhang whatever the sequence.
                                     ex : EcoRI. G^AATTC -> overhang :AATT
                                                 CTTAA^G
 
@@ -59,8 +59,8 @@ Notes about the diverses class of the restriction enzyme implementation::
                                          CTGN^NNNNNCAG
 
                 note : these 3 classes refers to the overhang not the site.
-                   So the enzyme ApoI (RAATTY) is defined even if its restriction
-                   site is ambiguous.
+                   So the enzyme ApoI (RAATTY) is defined even if its
+                   restriction site is ambiguous.
 
                         ApoI R^AATTY -> overhang : AATT -> Defined
                              YTTAA^R
@@ -69,11 +69,12 @@ Notes about the diverses class of the restriction enzyme implementation::
         ----------------------------------------------------------------------------
             Not_available,          as found in rebase file emboss_r.NNN files.
             Commercially_available
-                                    allow the selection of the enzymes according to
-                                    their suppliers to reduce the quantity
-                                    of results.
-                                    Also will allow the implementation of buffer
-                                    compatibility tables. Not implemented yet.
+                                    allow the selection of the enzymes
+                                    according to their suppliers to reduce the
+                                    quantity of results.
+                                    Also will allow the implementation of
+                                    buffer compatibility tables. Not
+                                    implemented yet.
 
                                     the list of suppliers is extracted from
                                     emboss_s.NNN
@@ -98,7 +99,6 @@ from Bio.Restriction.Restriction_Dictionary import suppliers as suppliers_dict
 from Bio.Restriction.RanaConfig import *
 from Bio.Restriction.PrintFormat import PrintFormat
 
-__docformat__ = "restructuredtext en"
 
 # Used to use Bio.Restriction.DNAUtils.check_bases (and expose it under this
 # namespace), but have deprecated that module.
@@ -149,15 +149,17 @@ class FormattedSeq(object):
 
         Retains information about the shape of the molecule linear (default)
         or circular. Restriction sites are search over the edges of circular
-        sequence."""
+        sequence.
+        """
 
     def __init__(self, seq, linear=True):
         """FormattedSeq(seq, [linear=True])-> new FormattedSeq.
 
         seq is either a Bio.Seq, Bio.MutableSeq or a FormattedSeq.
         if seq is a FormattedSeq, linear will have no effect on the
-        shape of the sequence."""
-        if isinstance(seq, Seq) or isinstance(seq, MutableSeq):
+        shape of the sequence.
+        """
+        if isinstance(seq, (Seq, MutableSeq)):
             stringy = str(seq)
             self.lower = stringy.islower()
             # Note this adds a leading space to the sequence (!)
@@ -178,7 +180,8 @@ class FormattedSeq(object):
         return len(self.data) - 1
 
     def __repr__(self):
-        return 'FormattedSeq(%s, linear=%s)' % (repr(self[1:]), repr(self.linear))
+        return 'FormattedSeq(%s, linear=%s)' % (repr(self[1:]),
+                                                repr(self.linear))
 
     def __eq__(self, other):
         if isinstance(other, FormattedSeq):
@@ -224,7 +227,8 @@ class FormattedSeq(object):
         the latter is used with non palindromic sites.
         pattern is the regular expression pattern corresponding to the
         enzyme restriction site.
-        size is the size of the restriction enzyme recognition-site size."""
+        size is the size of the restriction enzyme recognition-site size.
+        """
         if self.is_linear():
             data = self.data
         else:
@@ -240,9 +244,10 @@ class FormattedSeq(object):
 class RestrictionType(type):
     """RestrictionType. Type from which derives all enzyme classes.
 
-    Implement the operator methods."""
+    Implement the operator methods.
+    """
 
-    def __init__(cls, name='', bases=(), dct={}):
+    def __init__(cls, name='', bases=(), dct=None):
         """RE(name, bases, dct) -> RestrictionType instance.
 
         Not intended to be used in normal operation. The enzymes are
@@ -265,7 +270,8 @@ class RestrictionType(type):
         """RE.__add__(other) -> RestrictionBatch().
 
         if other is an enzyme returns a batch of the two enzymes.
-        if other is already a RestrictionBatch add enzyme to it."""
+        if other is already a RestrictionBatch add enzyme to it.
+        """
         if isinstance(other, RestrictionType):
             return RestrictionBatch([cls, other])
         elif isinstance(other, RestrictionBatch):
@@ -366,7 +372,8 @@ class RestrictionType(type):
         """RE >> other -> bool.
 
         neoschizomer : same recognition site, different restriction. -> True
-        all the others :                                             -> False"""
+        all the others :                                             -> False
+        """
         if not isinstance(other, RestrictionType):
             return False
         elif cls.site == other.site and cls.charac != other.charac:
@@ -378,7 +385,8 @@ class RestrictionType(type):
         """a % b -> bool.
 
         Test compatibility of the overhang of a and b.
-        True if a and b have compatible overhang."""
+        True if a and b have compatible overhang.
+        """
         if not isinstance(other, RestrictionType):
             raise TypeError(
                   'expected RestrictionType, got %s instead' % type(other))
@@ -419,7 +427,8 @@ class RestrictionType(type):
 
         sorting order:
                     1. size of the recognition site.
-                    2. if equal size, alphabetical order of the names."""
+                    2. if equal size, alphabetical order of the names.
+        """
         if not isinstance(other, RestrictionType):
             raise NotImplementedError
         elif len(cls) < len(other):
@@ -434,7 +443,8 @@ class RestrictionType(type):
 
         sorting order:
                     1. size of the recognition site.
-                    2. if equal size, alphabetical order of the names."""
+                    2. if equal size, alphabetical order of the names.
+        """
         if not isinstance(other, RestrictionType):
             raise NotImplementedError
         elif len(cls) < len(other):
@@ -450,7 +460,8 @@ class AbstractCut(RestrictionType):
 
     All the methods are classmethod.
 
-    For internal use only. Not meant to be instantiate."""
+    For internal use only. Not meant to be instantiate.
+    """
 
     @classmethod
     def search(cls, dna, linear=True):
@@ -465,7 +476,8 @@ class AbstractCut(RestrictionType):
         will be included.
 
         The positions are the first base of the 3' fragment,
-        i.e. the first base after the position the enzyme will cut. """
+        i.e. the first base after the position the enzyme will cut.
+        """
         #
         #   Separating search from _search allow a (very limited) optimisation
         #   of the search when using a batch of restriction enzymes.
@@ -494,7 +506,8 @@ class AbstractCut(RestrictionType):
         True if other is an isoschizomer of RE.
         False else.
 
-        equischizomer <=> same site, same position of restriction."""
+        equischizomer <=> same site, same position of restriction.
+        """
         return not self != other
 
     @classmethod
@@ -504,7 +517,8 @@ class AbstractCut(RestrictionType):
         True if other is an isoschizomer of RE.
         False else.
 
-        neoschizomer <=> same site, different position of restriction."""
+        neoschizomer <=> same site, different position of restriction.
+        """
         return self >> other
 
     @classmethod
@@ -524,7 +538,8 @@ class AbstractCut(RestrictionType):
         return a tuple of all the isoschizomers of RE.
         if batch is supplied it is used instead of the default AllEnzymes.
 
-        equischizomer <=> same site, same position of restriction."""
+        equischizomer <=> same site, same position of restriction.
+        """
         if not batch:
             batch = AllEnzymes
         r = [x for x in batch if not self != x]
@@ -551,7 +566,8 @@ class AbstractCut(RestrictionType):
         """RE.isoschizomers([batch]) -> list.
 
         return a tuple of all the equischizomers and neoschizomers of RE.
-        if batch is supplied it is used instead of the default AllEnzymes."""
+        if batch is supplied it is used instead of the default AllEnzymes.
+        """
         if not batch:
             batch = AllEnzymes
         r = [x for x in batch if (self >> x) or (not self != x)]
@@ -576,14 +592,15 @@ class NoCut(AbstractCut):
     which the pattern of cut is to complex to be recorded in Rebase
     (ncuts values of 0 in emboss_e.###).
 
-    When using search() with these enzymes the values returned are at the start of
-    the restriction site.
+    When using search() with these enzymes the values returned are at the start
+    of the restriction site.
 
     Their catalyse() method returns a TypeError.
 
     Unknown and NotDefined are also part of the base classes of these enzymes.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def cut_once(self):
@@ -647,7 +664,8 @@ class NoCut(AbstractCut):
             fst3 -> first 3' cut (complementary strand) or None
             scd5 -> second 5' cut (current strand) or None
             scd5 -> second 3' cut (complementary strand) or None
-            site -> recognition site."""
+            site -> recognition site.
+        """
         return None, None, None, None, self.site
 
 
@@ -656,20 +674,23 @@ class OneCut(AbstractCut):
 
     Correspond to ncuts values of 2 in emboss_e.###
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def cut_once(self):
         """RE.cut_once() -> bool.
 
-        True if the enzyme cut the sequence one time on each strand."""
+        True if the enzyme cut the sequence one time on each strand.
+        """
         return True
 
     @classmethod
     def cut_twice(self):
         """RE.cut_twice() -> bool.
 
-        True if the enzyme cut the sequence twice on each strand."""
+        True if the enzyme cut the sequence twice on each strand.
+        """
         return False
 
     @classmethod
@@ -720,7 +741,8 @@ class OneCut(AbstractCut):
             fst3 -> first 3' cut (complementary strand) or None
             scd5 -> second 5' cut (current strand) or None
             scd5 -> second 3' cut (complementary strand) or None
-            site -> recognition site."""
+            site -> recognition site.
+            """
         return self.fst5, self.fst3, None, None, self.site
 
 
@@ -742,7 +764,8 @@ class TwoCuts(AbstractCut):
     def cut_twice(self):
         """RE.cut_twice() -> bool.
 
-        True if the enzyme cut the sequence twice on each strand."""
+        True if the enzyme cut the sequence twice on each strand.
+        """
         return True
 
     @classmethod
@@ -756,7 +779,7 @@ class TwoCuts(AbstractCut):
         _modify returns the real place where the enzyme will cut.
 
         example::
-        
+
             EcoRI pattern : GAATTC
             EcoRI will cut after the G.
             so in the sequence:
@@ -795,33 +818,38 @@ class TwoCuts(AbstractCut):
             fst3 -> first 3' cut (complementary strand) or None
             scd5 -> second 5' cut (current strand) or None
             scd5 -> second 3' cut (complementary strand) or None
-            site -> recognition site."""
+            site -> recognition site.
+        """
         return self.fst5, self.fst3, self.scd5, self.scd3, self.site
 
 
 class Meth_Dep(AbstractCut):
     """Implement the information about methylation.
 
-    Enzymes of this class possess a site which is methylable."""
+    Enzymes of this class possess a site which is methylable.
+    """
 
     @classmethod
     def is_methylable(self):
         """RE.is_methylable() -> bool.
 
-        True if the recognition site is a methylable."""
+        True if the recognition site is a methylable.
+        """
         return True
 
 
 class Meth_Undep(AbstractCut):
     """Implement information about methylation sensitibility.
 
-    Enzymes of this class are not sensible to methylation."""
+    Enzymes of this class are not sensible to methylation.
+    """
 
     @classmethod
     def is_methylable(self):
         """RE.is_methylable() -> bool.
 
-        True if the recognition site is a methylable."""
+        True if the recognition site is a methylable.
+        """
         return False
 
 
@@ -854,7 +882,8 @@ class Palindromic(AbstractCut):
     def is_palindromic(self):
         """RE.is_palindromic() -> bool.
 
-        True if the recognition site is a palindrom."""
+        True if the recognition site is a palindrom.
+        """
         return True
 
 
@@ -895,7 +924,8 @@ class NonPalindromic(AbstractCut):
     def is_palindromic(self):
         """RE.is_palindromic() -> bool.
 
-        True if the recognition site is a palindrom."""
+        True if the recognition site is a palindrom.
+        """
         return False
 
 
@@ -905,7 +935,8 @@ class Unknown(AbstractCut):
 
     These enzymes are also NotDefined and NoCut.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def catalyse(self, dna, linear=True):
@@ -918,7 +949,8 @@ class Unknown(AbstractCut):
         dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
 
         if linear is False, the sequence is considered to be circular and the
-        output will be modified accordingly."""
+        output will be modified accordingly.
+        """
         raise NotImplementedError('%s restriction is unknown.'
                                   % self.__name__)
     catalyze = catalyse
@@ -932,7 +964,8 @@ class Unknown(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_5overhang()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -944,7 +977,8 @@ class Unknown(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -956,21 +990,24 @@ class Unknown(AbstractCut):
         see also:
             RE.is_5overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
     def overhang(self):
         """RE.overhang() -> str. type of overhang of the enzyme.,
 
-        can be "3' overhang", "5' overhang", "blunt", "unknown"   """
+        can be "3' overhang", "5' overhang", "blunt", "unknown"
+        """
         return 'unknown'
 
     @classmethod
     def compatible_end(self):
         """RE.compatible_end() -> list.
 
-        list of all the enzymes that share compatible end with RE."""
+        list of all the enzymes that share compatible end with RE.
+        """
         return []
 
     @classmethod
@@ -979,7 +1016,8 @@ class Unknown(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         return False
 
 
@@ -990,7 +1028,8 @@ class Blunt(AbstractCut):
     The enzyme cuts the + strand and the - strand of the DNA at the same
     place.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def catalyse(self, dna, linear=True):
@@ -1003,7 +1042,8 @@ class Blunt(AbstractCut):
         dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
 
         if linear is False, the sequence is considered to be circular and the
-        output will be modified accordingly."""
+        output will be modified accordingly.
+        """
         r = self.search(dna, linear)
         d = self.dna
         if not r:
@@ -1050,7 +1090,8 @@ class Blunt(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_5overhang()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return True
 
     @classmethod
@@ -1062,7 +1103,8 @@ class Blunt(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1074,21 +1116,24 @@ class Blunt(AbstractCut):
         see also:
             RE.is_5overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
     def overhang(self):
         """RE.overhang() -> str. type of overhang of the enzyme.,
 
-        can be "3' overhang", "5' overhang", "blunt", "unknown"   """
+        can be "3' overhang", "5' overhang", "blunt", "unknown"
+        """
         return 'blunt'
 
     @classmethod
     def compatible_end(self, batch=None):
         """RE.compatible_end() -> list.
 
-        list of all the enzymes that share compatible end with RE."""
+        list of all the enzymes that share compatible end with RE.
+        """
         if not batch:
             batch = AllEnzymes
         r = sorted(x for x in iter(AllEnzymes) if x.is_blunt())
@@ -1100,7 +1145,8 @@ class Blunt(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         return issubclass(other, Blunt)
 
 
@@ -1110,7 +1156,8 @@ class Ov5(AbstractCut):
 
     The enzyme cuts the + strand after the - strand of the DNA.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def catalyse(self, dna, linear=True):
@@ -1123,7 +1170,8 @@ class Ov5(AbstractCut):
         dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
 
         if linear is False, the sequence is considered to be circular and the
-        output will be modified accordingly."""
+        output will be modified accordingly.
+        """
         r = self.search(dna, linear)
         d = self.dna
         if not r:
@@ -1170,7 +1218,8 @@ class Ov5(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_5overhang()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1182,7 +1231,8 @@ class Ov5(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return True
 
     @classmethod
@@ -1194,14 +1244,16 @@ class Ov5(AbstractCut):
         see also:
             RE.is_5overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
     def overhang(self):
         """RE.overhang() -> str. type of overhang of the enzyme.,
 
-        can be "3' overhang", "5' overhang", "blunt", "unknown"   """
+        can be "3' overhang", "5' overhang", "blunt", "unknown"
+        """
         return "5' overhang"
 
     @classmethod
@@ -1211,7 +1263,8 @@ class Ov5(AbstractCut):
         list of all the enzymes that share compatible end with RE."""
         if not batch:
             batch = AllEnzymes
-        r = sorted(x for x in iter(AllEnzymes) if x.is_5overhang() and x % self)
+        r = sorted(x for x in iter(AllEnzymes) if x.is_5overhang() and
+                   x % self)
         return r
 
     @classmethod
@@ -1220,7 +1273,8 @@ class Ov5(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         if issubclass(other, Ov5):
             return self._mod2(other)
         else:
@@ -1233,7 +1287,8 @@ class Ov3(AbstractCut):
 
     The enzyme cuts the - strand after the + strand of the DNA.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def catalyse(self, dna, linear=True):
@@ -1246,7 +1301,8 @@ class Ov3(AbstractCut):
         dna must be a Bio.Seq.Seq instance or a Bio.Seq.MutableSeq instance.
 
         if linear is False, the sequence is considered to be circular and the
-        output will be modified accordingly."""
+        output will be modified accordingly.
+        """
         r = self.search(dna, linear)
         d = self.dna
         if not r:
@@ -1293,7 +1349,8 @@ class Ov3(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_5overhang()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1305,7 +1362,8 @@ class Ov3(AbstractCut):
         see also:
             RE.is_3overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1317,24 +1375,28 @@ class Ov3(AbstractCut):
         see also:
             RE.is_5overhang()
             RE.is_blunt()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return True
 
     @classmethod
     def overhang(self):
         """RE.overhang() -> str. type of overhang of the enzyme.,
 
-        can be "3' overhang", "5' overhang", "blunt", "unknown"   """
+        can be "3' overhang", "5' overhang", "blunt", "unknown"
+        """
         return "3' overhang"
 
     @classmethod
     def compatible_end(self, batch=None):
         """RE.compatible_end() -> list.
 
-        list of all the enzymes that share compatible end with RE."""
+        list of all the enzymes that share compatible end with RE.
+        """
         if not batch:
             batch = AllEnzymes
-        r = sorted(x for x in iter(AllEnzymes) if x.is_3overhang() and x % self)
+        r = sorted(x for x in iter(AllEnzymes) if x.is_3overhang() and
+                   x % self)
         return r
 
     @classmethod
@@ -1343,7 +1405,8 @@ class Ov3(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         #
         #   called by RE._mod1(other) when the one of the enzyme is ambiguous
         #
@@ -1371,8 +1434,9 @@ class Defined(AbstractCut):
 
         for internal use only.
 
-        drop the site that are situated outside the sequence in linear sequence.
-        modify the index for site in circular sequences."""
+        drop the site that are situated outside the sequence in linear
+        sequence. modify the index for site in circular sequences.
+        """
         #
         #   remove or modify the results that are outside the sequence.
         #   This is necessary since after finding the site we add the distance
@@ -1410,7 +1474,8 @@ class Defined(AbstractCut):
 
         see also:
             RE.is_ambiguous()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return True
 
     @classmethod
@@ -1423,7 +1488,8 @@ class Defined(AbstractCut):
 
         see also:
             RE.is_defined()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1435,7 +1501,8 @@ class Defined(AbstractCut):
 
         see also:
             RE.is_defined()
-            RE.is_ambiguous()"""
+            RE.is_ambiguous()
+        """
         return False
 
     @classmethod
@@ -1451,7 +1518,7 @@ class Defined(AbstractCut):
         'G_GTAC^C'
         >>> EcoRV.elucidate()   # blunt
         'GAT^_ATC'
-        >>> SnaI.elucidate()     # NotDefined, cut profile unknown.
+        >>> SnaI.elucidate()    # NotDefined, cut profile unknown.
         '? GTATAC ?'
         >>>
         """
@@ -1482,7 +1549,8 @@ class Defined(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         #
         #   called by RE._mod1(other) when the one of the enzyme is ambiguous
         #
@@ -1504,7 +1572,8 @@ class Ambiguous(AbstractCut):
         Blunt enzymes are always defined. even if there site is GGATCCNNN^_N
         There overhang is always the same : blunt!
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def _drop(self):
@@ -1512,14 +1581,16 @@ class Ambiguous(AbstractCut):
 
         for internal use only.
 
-        drop the site that are situated outside the sequence in linear sequence.
-        modify the index for site in circular sequences."""
+        drop the site that are situated outside the sequence in linear
+        sequence. modify the index for site in circular sequences.
+        """
         length = len(self.dna)
         drop = itertools.dropwhile
         take = itertools.takewhile
         if self.dna.is_linear():
             self.results = [x for x in drop(lambda x: x < 1, self.results)]
-            self.results = [x for x in take(lambda x: x < length, self.results)]
+            self.results = [x for x in take(lambda x: x <
+                                            length, self.results)]
         else:
             for index, location in enumerate(self.results):
                 if location < 1:
@@ -1543,7 +1614,8 @@ class Ambiguous(AbstractCut):
 
         see also:
             RE.is_ambiguous()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1556,7 +1628,8 @@ class Ambiguous(AbstractCut):
 
         see also:
             RE.is_defined()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return True
 
     @classmethod
@@ -1568,7 +1641,8 @@ class Ambiguous(AbstractCut):
 
         see also:
             RE.is_defined()
-            RE.is_ambiguous()"""
+            RE.is_ambiguous()
+        """
         return False
 
     @classmethod
@@ -1577,7 +1651,8 @@ class Ambiguous(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         #
         #   called by RE._mod1(other) when the one of the enzyme is ambiguous
         #
@@ -1633,7 +1708,8 @@ class Ambiguous(AbstractCut):
             elif f3 + length < 0:
                 re = 'N^' * abs(f5) * 'N' + '_' + abs(length + f3) * 'N' + site
             elif f5 > length:
-                re = site + (f5 - length) * 'N' + '^' + (length + f3 - f5) * 'N' + '_N'
+                re = site + (f5 - length) * 'N' + '^' + (length +
+                                                         f3 - f5) * 'N' + '_N'
             else:
                 re = 'N^' + abs(f5) * 'N' + site + f3 * 'N' + '_N'
         elif self.is_blunt():
@@ -1659,9 +1735,11 @@ class Ambiguous(AbstractCut):
             elif f3 > 0:
                 re = site + f3 * 'N' + '_' + (f5 - f3 - length) * 'N' + '^N'
             elif f5 < 0:
-                re = 'N_' + abs(f3 - f5 + length) * 'N' + '^' + abs(f5) * 'N' + site
+                re = 'N_' + abs(f3 - f5 + length) * 'N' + '^' + abs(f5) * 'N' \
+                     + site
             else:
-                re = 'N_' + abs(f3 + length) * 'N' + site + (f5 - length) * 'N' + '^N'
+                re = 'N_' + abs(f3 + length) * 'N' + site + (f5 - length) * \
+                     'N' + '^N'
         return re
 
 
@@ -1671,7 +1749,8 @@ class NotDefined(AbstractCut):
 
     Correspond to NoCut and Unknown.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def _drop(self):
@@ -1679,8 +1758,9 @@ class NotDefined(AbstractCut):
 
         for internal use only.
 
-        drop the site that are situated outside the sequence in linear sequence.
-        modify the index for site in circular sequences."""
+        drop the site that are situated outside the sequence in linear
+        sequence. modify the index for site in circular sequences.
+        """
         if self.dna.is_linear():
             return
         else:
@@ -1707,7 +1787,8 @@ class NotDefined(AbstractCut):
 
         see also:
             RE.is_ambiguous()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1720,7 +1801,8 @@ class NotDefined(AbstractCut):
 
         see also:
             RE.is_defined()
-            RE.is_unknown()"""
+            RE.is_unknown()
+        """
         return False
 
     @classmethod
@@ -1741,9 +1823,11 @@ class NotDefined(AbstractCut):
 
         for internal use only
 
-        test for the compatibility of restriction ending of RE and other."""
+        test for the compatibility of restriction ending of RE and other.
+        """
         #
-        #   Normally we should not arrive here. But well better safe than sorry.
+        #   Normally we should not arrive here. But well better safe than
+        #   sorry.
         #   the overhang is not defined we are compatible with nobody.
         #   could raise an Error may be rather than return quietly.
         #
@@ -1779,34 +1863,38 @@ class Commercially_available(AbstractCut):
     """Implement the methods specific to the enzymes which are commercially
     available.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @classmethod
     def suppliers(self):
         """RE.suppliers() -> print the suppliers of RE."""
-        for s in self.suppliers_dict():
-            print(s + ',')
+        for s in self.suppl:
+            print(suppliers_dict[s][0] + ',')
         return
 
     @classmethod
     def supplier_list(self):
         """RE.supplier_list() -> list.
 
-        list of the supplier names for RE."""
+        list of the supplier names for RE.
+        """
         return [v[0] for k, v in suppliers_dict.items() if k in self.suppl]
 
     @classmethod
     def buffers(self, supplier):
         """RE.buffers(supplier) -> string.
 
-        not implemented yet."""
+        not implemented yet.
+        """
         return
 
     @classmethod
     def is_comm(self):
         """RE.iscomm() -> bool.
 
-        True if RE has suppliers."""
+        True if RE has suppliers.
+        """
         return True
 
 
@@ -1814,7 +1902,8 @@ class Not_available(AbstractCut):
     """Implement the methods specific to the enzymes which are not commercially
     available.
 
-    Internal use only. Not meant to be instantiated."""
+    Internal use only. Not meant to be instantiated.
+    """
 
     @staticmethod
     def suppliers():
@@ -1825,21 +1914,24 @@ class Not_available(AbstractCut):
     def supplier_list(self):
         """RE.supplier_list() -> list.
 
-        list of the supplier names for RE."""
+        list of the supplier names for RE.
+        """
         return []
 
     @classmethod
     def buffers(self, supplier):
         """RE.buffers(supplier) -> string.
 
-        not implemented yet."""
+        not implemented yet.
+        """
         raise TypeError("Enzyme not commercially available.")
 
     @classmethod
     def is_comm(self):
         """RE.iscomm() -> bool.
 
-        True if RE has suppliers."""
+        True if RE has suppliers.
+        """
         return False
 
 
@@ -1852,7 +1944,7 @@ class Not_available(AbstractCut):
 
 class RestrictionBatch(set):
 
-    def __init__(self, first=[], suppliers=[]):
+    def __init__(self, first=(), suppliers=()):
         """RestrictionBatch([sequence]) -> new RestrictionBatch."""
         first = [self.format(x) for x in first]
         first += [eval(x) for n in suppliers for x in suppliers_dict[n][1]]
@@ -1889,7 +1981,8 @@ class RestrictionBatch(set):
         if add is True and enzyme is not in B add enzyme to B.
         if add is False (which is the default) only return enzyme.
         if enzyme is not a RestrictionType or can not be evaluated to
-        a RestrictionType, raise a ValueError."""
+        a RestrictionType, raise a ValueError.
+        """
         e = self.format(enzyme)
         if e in self:
             return e
@@ -1904,7 +1997,8 @@ class RestrictionBatch(set):
         """B.lambdasplit(func) -> RestrictionBatch .
 
         the new batch will contains only the enzymes for which
-        func return True."""
+        func return True.
+        """
         d = [x for x in filter(func, self)]
         new = RestrictionBatch()
         new._data = dict(zip(d, [True] * len(d)))
@@ -1916,7 +2010,8 @@ class RestrictionBatch(set):
         letter represents the suppliers as defined in the dictionary
         RestrictionDictionary.suppliers
         return None.
-        raise a KeyError if letter is not a supplier code."""
+        raise a KeyError if letter is not a supplier code.
+        """
         supplier = suppliers_dict[letter]
         self.suppliers.append(letter)
         for x in supplier[1]:
@@ -1927,7 +2022,8 @@ class RestrictionBatch(set):
         """B.current_suppliers() -> add a new set of enzyme to B.
 
         return a sorted list of the suppliers which have been used to
-        create the batch."""
+        create the batch.
+        """
         suppl_list = sorted(suppliers_dict[x][0] for x in self.suppliers)
         return suppl_list
 
@@ -1943,12 +2039,14 @@ class RestrictionBatch(set):
         return new
 
     def remove(self, other):
-        """B.remove(other) -> remove other from B if other is a RestrictionType.
+        """B.remove(other) -> remove other from B if other is a
+        RestrictionType.
 
-        Safe set.remove method. Verify that other is a RestrictionType or can be
-        evaluated to a RestrictionType.
+        Safe set.remove method. Verify that other is a RestrictionType or can
+        be evaluated to a RestrictionType.
         raise a ValueError if other can not be evaluated to a RestrictionType.
-        raise a KeyError if other is not in B."""
+        raise a KeyError if other is not in B.
+        """
         return set.remove(self, self.format(other))
 
     def add(self, other):
@@ -1970,7 +2068,8 @@ class RestrictionBatch(set):
 
         if y is a RestrictionType return y
         if y can be evaluated to a RestrictionType return eval(y)
-        raise a Value Error in all other case."""
+        raise a Value Error in all other case.
+        """
         try:
             if isinstance(y, RestrictionType):
                 return y
@@ -1985,15 +2084,17 @@ class RestrictionBatch(set):
     def is_restriction(self, y):
         """B.is_restriction(y) -> bool.
 
-        True is y or eval(y) is a RestrictionType."""
-        return isinstance(y, RestrictionType) or \
-               isinstance(eval(str(y)), RestrictionType)
+        True is y or eval(y) is a RestrictionType.
+        """
+        return (isinstance(y, RestrictionType) or
+                isinstance(eval(str(y)), RestrictionType))
 
     def split(self, *classes, **bool):
         """B.split(class, [class.__name__ = True]) -> new RestrictionBatch.
 
         it works but it is slow, so it has really an interest when splitting
-        over multiple conditions."""
+        over multiple conditions.
+        """
         def splittest(element):
             for klass in classes:
                 b = bool.get(klass.__name__, True)
@@ -2015,21 +2116,24 @@ class RestrictionBatch(set):
     def elements(self):
         """B.elements() -> tuple.
 
-        give all the names of the enzymes in B sorted alphabetically."""
+        give all the names of the enzymes in B sorted alphabetically.
+        """
         l = sorted(str(e) for e in self)
         return l
 
     def as_string(self):
         """B.as_string() -> list.
 
-        return a list of the name of the elements of B."""
+        return a list of the name of the elements of B.
+        """
         return [str(e) for e in self]
 
     @classmethod
     def suppl_codes(self):
         """B.suppl_codes() -> dict
 
-        letter code for the suppliers"""
+        letter code for the suppliers
+        """
         supply = dict((k, v[0]) for k, v in suppliers_dict.items())
         return supply
 
@@ -2171,7 +2275,8 @@ class Analysis(RestrictionBatch, PrintFormat):
         Changing one of these parameters here might not give the results
         you expect. In which case, you can settle back to a 80 columns shell
         or try to change self.Cmodulo and self.PrefWidth in PrintFormat until
-        you get it right."""
+        you get it right.
+        """
         for k, v in what.items():
             if k in ('NameWidth', 'ConsoleWidth'):
                 setattr(self, k, v)
@@ -2199,13 +2304,15 @@ class Analysis(RestrictionBatch, PrintFormat):
     def full(self, linear=True):
         """A.full() -> dict.
 
-        Full Restriction Map of the sequence."""
+        Full Restriction Map of the sequence.
+        """
         return self.mapping
 
     def blunt(self, dct=None):
         """A.blunt([dct]) -> dict.
 
-        Only the enzymes which have a 3'overhang restriction site."""
+        Only the enzymes which have a 3'overhang restriction site.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items() if k.is_blunt())
@@ -2213,7 +2320,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def overhang5(self, dct=None):
         """A.overhang5([dct]) -> dict.
 
-        Only the enzymes which have a 5' overhang restriction site."""
+        Only the enzymes which have a 5' overhang restriction site.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items() if k.is_5overhang())
@@ -2221,7 +2329,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def overhang3(self, dct=None):
         """A.Overhang3([dct]) -> dict.
 
-        Only the enzymes which have a 3'overhang restriction site."""
+        Only the enzymes which have a 3'overhang restriction site.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items() if k.is_3overhang())
@@ -2229,7 +2338,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def defined(self, dct=None):
         """A.defined([dct]) -> dict.
 
-        Only the enzymes that have a defined restriction site in Rebase."""
+        Only the enzymes that have a defined restriction site in Rebase.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items() if k.is_defined())
@@ -2237,7 +2347,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def with_sites(self, dct=None):
         """A.with_sites([dct]) -> dict.
 
-        Enzymes which have at least one site in the sequence."""
+        Enzymes which have at least one site in the sequence.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items() if v)
@@ -2245,7 +2356,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def without_site(self, dct=None):
         """A.without_site([dct]) -> dict.
 
-        Enzymes which have no site in the sequence."""
+        Enzymes which have no site in the sequence.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items() if not v)
@@ -2253,7 +2365,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def with_N_sites(self, N, dct=None):
         """A.With_N_Sites(N [, dct]) -> dict.
 
-        Enzymes which cut N times the sequence."""
+        Enzymes which cut N times the sequence.
+        """
         if not dct:
             dct = self.mapping
         return dict((k, v) for k, v in dct.items()if len(v) == N)
@@ -2266,7 +2379,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def with_name(self, names, dct=None):
         """A.with_name(list_of_names [, dct]) ->
 
-         Limit the search to the enzymes named in list_of_names."""
+         Limit the search to the enzymes named in list_of_names.
+         """
         for i, enzyme in enumerate(names):
             if enzyme not in AllEnzymes:
                 print("no data for the enzyme: %s" % name)
@@ -2278,7 +2392,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def with_site_size(self, site_size, dct=None):
         """A.with_site_size(site_size [, dct]) ->
 
-         Limit the search to the enzymes whose site is of size <site_size>."""
+         Limit the search to the enzymes whose site is of size <site_size>.
+         """
         sites = [name for name in self if name.size == site_size]
         if not dct:
             return RestrictionBatch(sites).search(self.sequence)
@@ -2287,7 +2402,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def only_between(self, start, end, dct=None):
         """A.only_between(start, end[, dct]) -> dict.
 
-        Enzymes that cut the sequence only in between start and end."""
+        Enzymes that cut the sequence only in between start and end.
+        """
         start, end, test = self._boundaries(start, end)
         if not dct:
             dct = self.mapping
@@ -2308,7 +2424,8 @@ class Analysis(RestrictionBatch, PrintFormat):
         """A.between(start, end [, dct]) -> dict.
 
         Enzymes that cut the sequence at least in between start and end.
-        They may cut outside as well."""
+        They may cut outside as well.
+        """
         start, end, test = self._boundaries(start, end)
         d = {}
         if not dct:
@@ -2325,7 +2442,8 @@ class Analysis(RestrictionBatch, PrintFormat):
         """A.show_only_between(start, end [, dct]) -> dict.
 
         Enzymes that cut the sequence outside of the region
-        in between start and end but do not cut inside."""
+        in between start and end but do not cut inside.
+        """
         d = []
         if start <= end:
             d = [(k, [vv for vv in v if start <= vv <= end])
@@ -2339,7 +2457,8 @@ class Analysis(RestrictionBatch, PrintFormat):
         """A.only_outside(start, end [, dct]) -> dict.
 
         Enzymes that cut the sequence outside of the region
-        in between start and end but do not cut inside."""
+        in between start and end but do not cut inside.
+        """
         start, end, test = self._boundaries(start, end)
         if not dct:
             dct = self.mapping
@@ -2360,7 +2479,8 @@ class Analysis(RestrictionBatch, PrintFormat):
         """A.outside((start, end [, dct]) -> dict.
 
         Enzymes that cut outside the region in between start and end.
-        No test is made to know if they cut or not inside this region."""
+        No test is made to know if they cut or not inside this region.
+        """
         start, end, test = self._boundaries(start, end)
         if not dct:
             dct = self.mapping
@@ -2377,7 +2497,8 @@ class Analysis(RestrictionBatch, PrintFormat):
     def do_not_cut(self, start, end, dct=None):
         """A.do_not_cut(start, end [, dct]) -> dict.
 
-        Enzymes that do not cut the region in between start and end."""
+        Enzymes that do not cut the region in between start and end.
+        """
         if not dct:
             dct = self.mapping
         d = self.without_site()
@@ -2403,8 +2524,8 @@ class Analysis(RestrictionBatch, PrintFormat):
 #   which provide a very efficient layout for the class themselves mostly
 #   alleviating the need of if/else loops in the class methods.
 #
-#   It is essential to run Restriction with doc string optimisation (-OO switch)
-#   as the doc string of 660 classes take a lot of processing.
+#   It is essential to run Restriction with doc string optimisation (-OO
+#   switch) as the doc string of 660 classes take a lot of processing.
 #
 CommOnly = RestrictionBatch()    # commercial enzymes
 NonComm = RestrictionBatch()     # not available commercially
@@ -2462,5 +2583,6 @@ except NameError:
     # Scoping changed in Python 3, the variable isn't leaked
     pass
 locals().update(dict(zip(names, AllEnzymes)))
-__all__ = ['FormattedSeq', 'Analysis', 'RestrictionBatch', 'AllEnzymes', 'CommOnly', 'NonComm'] + names
+__all__ = ['FormattedSeq', 'Analysis', 'RestrictionBatch', 'AllEnzymes',
+           'CommOnly', 'NonComm'] + names
 del k, enzymes, TYPE, bases, names
diff --git a/Bio/Restriction/Restriction_Dictionary.py b/Bio/Restriction/Restriction_Dictionary.py
index 99c1098..8f06e1c 100644
--- a/Bio/Restriction/Restriction_Dictionary.py
+++ b/Bio/Restriction/Restriction_Dictionary.py
@@ -1,4 +1,3 @@
-
 #!/usr/bin/env python
 #
 #      Restriction Analysis Libraries.
@@ -15,11 +14,10 @@
 # The following dictionaries used to be defined in one go, but that does
 # not work on Jython due to JVM limitations. Therefore we break this up
 # into steps, using temporary functions to avoid the JVM limits.
-
+#
+# Used REBASE emboss files version 605 (2016).
 
 rest_dict = {}
-
-
 def _temp():
     return {
         'compsite': '(?P<AanI>TTATAA)',
@@ -35,14 +33,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'TTATAA'),
         'ovhgseq': '',
     }
 rest_dict['AanI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AarI>CACCTGC)|(?P<AarI_as>GCAGGTG)',
@@ -58,14 +55,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (11, 8, None, None, 'CACCTGC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['AarI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AasI>GAC......GTC)',
@@ -81,14 +77,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, -7, None, None, 'GACNNNNNNGTC'),
         'ovhgseq': 'NN',
     }
 rest_dict['AasI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AatII>GACGTC)',
@@ -104,13 +99,56 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F', 'I', 'K', 'M', 'N', 'R'),
+        'suppl': ('B', 'I', 'K', 'M', 'N', 'V'),
         'scd5': None,
         'charac': (5, -5, None, None, 'GACGTC'),
         'ovhgseq': 'ACGT',
     }
 rest_dict['AatII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Aba6411II>C[AG][AG]TAAG)|(?P<Aba6411II_as>CTTA[CT][CT]G)',
+        'results': None,
+        'site': 'CRRTAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CRRTAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Aba6411II'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<AbaCIII>CTATCA[ACG])|(?P<AbaCIII_as>[CGT]TGATAG)',
+        'results': None,
+        'site': 'CTATCAV',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CTATCAV'),
+        'ovhgseq': None,
+    }
+rest_dict['AbaCIII'] = _temp()
 
 def _temp():
     return {
@@ -134,6 +172,27 @@ def _temp():
     }
 rest_dict['AbaSI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<AbaUMB2I>[CT]CCG[CG][CG])|(?P<AbaUMB2I_as>[CG][CG]CGG[AG])',
+        'results': None,
+        'site': 'YCCGSS',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 512,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'YCCGSS'),
+        'ovhgseq': None,
+    }
+rest_dict['AbaUMB2I'] = _temp()
 
 def _temp():
     return {
@@ -157,7 +216,6 @@ def _temp():
     }
 rest_dict['AbsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Acc16I>TGCGCA)',
@@ -173,14 +231,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': ('I', 'V'),
         'scd5': None,
         'charac': (3, -3, None, None, 'TGCGCA'),
         'ovhgseq': '',
     }
 rest_dict['Acc16I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Acc36I>ACCTGC)|(?P<Acc36I_as>GCAGGT)',
@@ -203,7 +260,6 @@ def _temp():
     }
 rest_dict['Acc36I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Acc65I>GGTACC)',
@@ -219,14 +275,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F', 'I', 'N', 'R'),
+        'suppl': ('B', 'I', 'N', 'V'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGTACC'),
         'ovhgseq': 'GTAC',
     }
 rest_dict['Acc65I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AccB1I>GG[CT][AG]CC)',
@@ -242,14 +297,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': ('I', 'V'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGYRCC'),
         'ovhgseq': 'GYRC',
     }
 rest_dict['AccB1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AccB7I>CCA.....TGG)',
@@ -265,14 +319,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': ('I', 'V'),
         'scd5': None,
         'charac': (7, -7, None, None, 'CCANNNNNTGG'),
         'ovhgseq': 'NNN',
     }
 rest_dict['AccB7I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AccBSI>CCGCTC)|(?P<AccBSI_as>GAGCGG)',
@@ -288,14 +341,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': ('I', 'V'),
         'scd5': None,
         'charac': (3, -3, None, None, 'CCGCTC'),
         'ovhgseq': '',
     }
 rest_dict['AccBSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AccI>GT[AC][GT]AC)',
@@ -311,14 +363,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'X'),
+        'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GTMKAC'),
         'ovhgseq': 'MK',
     }
 rest_dict['AccI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AccII>CGCG)',
@@ -341,7 +392,6 @@ def _temp():
     }
 rest_dict['AccII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AccIII>TCCGGA)',
@@ -364,7 +414,6 @@ def _temp():
     }
 rest_dict['AccIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AceIII>CAGCTC)|(?P<AceIII_as>GAGCTG)',
@@ -387,6 +436,27 @@ def _temp():
     }
 rest_dict['AceIII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<AchA6III>AGCCAG)|(?P<AchA6III_as>CTGGCT)',
+        'results': None,
+        'site': 'AGCCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'AGCCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['AchA6III'] = _temp()
 
 def _temp():
     return {
@@ -410,7 +480,6 @@ def _temp():
     }
 rest_dict['AciI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AclI>AACGTT)',
@@ -426,14 +495,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('I', 'N'),
+        'suppl': ('I', 'N', 'V'),
         'scd5': None,
         'charac': (2, -2, None, None, 'AACGTT'),
         'ovhgseq': 'CG',
     }
 rest_dict['AclI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AclWI>GGATC)|(?P<AclWI_as>GATCC)',
@@ -456,6 +524,27 @@ def _temp():
     }
 rest_dict['AclWI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Aco12261II>CC[AG]GAG)|(?P<Aco12261II_as>CTC[CT]GG)',
+        'results': None,
+        'site': 'CCRGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCRGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Aco12261II'] = _temp()
 
 def _temp():
     return {
@@ -479,7 +568,6 @@ def _temp():
     }
 rest_dict['AcoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AcsI>[AG]AATT[CT])',
@@ -495,14 +583,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': ('I', 'V'),
         'scd5': None,
         'charac': (1, -1, None, None, 'RAATTY'),
         'ovhgseq': 'AATT',
     }
 rest_dict['AcsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AcuI>CTGAAG)|(?P<AcuI_as>CTTCAG)',
@@ -525,7 +612,6 @@ def _temp():
     }
 rest_dict['AcuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AcvI>CACGTG)',
@@ -548,7 +634,6 @@ def _temp():
     }
 rest_dict['AcvI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AcyI>G[AG]CG[CT]C)',
@@ -571,7 +656,6 @@ def _temp():
     }
 rest_dict['AcyI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AdeI>CAC...GTG)',
@@ -587,14 +671,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (6, -6, None, None, 'CACNNNGTG'),
         'ovhgseq': 'NNN',
     }
 rest_dict['AdeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AfaI>GTAC)',
@@ -617,7 +700,6 @@ def _temp():
     }
 rest_dict['AfaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AfeI>AGCGCT)',
@@ -640,7 +722,6 @@ def _temp():
     }
 rest_dict['AfeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AfiI>CC.......GG)',
@@ -663,7 +744,6 @@ def _temp():
     }
 rest_dict['AfiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AflII>CTTAAG)',
@@ -686,7 +766,6 @@ def _temp():
     }
 rest_dict['AflII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AflIII>AC[AG][CT]GT)',
@@ -709,7 +788,6 @@ def _temp():
     }
 rest_dict['AflIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AgeI>ACCGGT)',
@@ -732,7 +810,6 @@ def _temp():
     }
 rest_dict['AgeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AgsI>TT[CG]AA)',
@@ -755,7 +832,6 @@ def _temp():
     }
 rest_dict['AgsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AhaIII>TTTAAA)',
@@ -778,7 +854,6 @@ def _temp():
     }
 rest_dict['AhaIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AhdI>GAC.....GTC)',
@@ -801,7 +876,6 @@ def _temp():
     }
 rest_dict['AhdI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AhlI>ACTAGT)',
@@ -824,6 +898,27 @@ def _temp():
     }
 rest_dict['AhlI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<AhyYL17I>[CT]AA[AC]GAG)|(?P<AhyYL17I_as>CTC[GT]TT[AG])',
+        'results': None,
+        'site': 'YAAMGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'YAAMGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['AhyYL17I'] = _temp()
 
 def _temp():
     return {
@@ -840,14 +935,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'CACGTC'),
         'ovhgseq': '',
     }
 rest_dict['AjiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AjnI>CC[AT]GG)',
@@ -870,7 +964,6 @@ def _temp():
     }
 rest_dict['AjnI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AjuI>GAA.......TTGG)|(?P<AjuI_as>CCAA.......TTC)',
@@ -886,14 +979,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 5,
         'scd3': 6,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': 25,
         'charac': (-7, -26, 25, 6, 'GAANNNNNNNTTGG'),
         'ovhgseq': 'NNNNN',
     }
 rest_dict['AjuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AleI>CAC....GTG)',
@@ -916,7 +1008,6 @@ def _temp():
     }
 rest_dict['AleI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AlfI>GCA......TGC)',
@@ -932,14 +1023,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': 10,
-        'suppl': ('F',),
+        'suppl': (),
         'scd5': 24,
         'charac': (-10, -24, 24, 10, 'GCANNNNNNTGC'),
         'ovhgseq': 'NN',
     }
 rest_dict['AlfI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AloI>GAAC......TCC)|(?P<AloI_as>GGA......GTTC)',
@@ -955,14 +1045,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 5,
         'scd3': 7,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': 25,
         'charac': (-7, -25, 25, 7, 'GAACNNNNNNTCC'),
         'ovhgseq': 'NNNNN',
     }
 rest_dict['AloI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AluBI>AGCT)',
@@ -985,7 +1074,6 @@ def _temp():
     }
 rest_dict['AluBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AluI>AGCT)',
@@ -1001,14 +1089,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (2, -2, None, None, 'AGCT'),
         'ovhgseq': '',
     }
 rest_dict['AluI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Alw21I>G[AT]GC[AT]C)',
@@ -1024,14 +1111,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GWGCWC'),
         'ovhgseq': 'WGCW',
     }
 rest_dict['Alw21I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Alw26I>GTCTC)|(?P<Alw26I_as>GAGAC)',
@@ -1047,14 +1133,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (6, 5, None, None, 'GTCTC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['Alw26I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Alw44I>GTGCAC)',
@@ -1070,14 +1155,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F', 'J'),
+        'suppl': ('B', 'J'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GTGCAC'),
         'ovhgseq': 'TGCA',
     }
 rest_dict['Alw44I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AlwFI>GAAA[CT].....[AG]TG)|(?P<AlwFI_as>CA[CT].....[AG]TTTC)',
@@ -1100,7 +1184,6 @@ def _temp():
     }
 rest_dict['AlwFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AlwI>GGATC)|(?P<AlwI_as>GATCC)',
@@ -1123,7 +1206,6 @@ def _temp():
     }
 rest_dict['AlwI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AlwNI>CAG...CTG)',
@@ -1146,7 +1228,6 @@ def _temp():
     }
 rest_dict['AlwNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ama87I>C[CT]CG[AG]G)',
@@ -1169,7 +1250,6 @@ def _temp():
     }
 rest_dict['Ama87I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Aor13HI>TCCGGA)',
@@ -1192,7 +1272,6 @@ def _temp():
     }
 rest_dict['Aor13HI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Aor51HI>AGCGCT)',
@@ -1215,7 +1294,6 @@ def _temp():
     }
 rest_dict['Aor51HI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AoxI>GGCC)',
@@ -1231,14 +1309,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': (),
+        'suppl': ('I',),
         'scd5': None,
         'charac': (0, 0, None, None, 'GGCC'),
         'ovhgseq': 'GGCC',
     }
 rest_dict['AoxI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ApaBI>GCA.....TGC)',
@@ -1261,7 +1338,6 @@ def _temp():
     }
 rest_dict['ApaBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ApaI>GGGCCC)',
@@ -1277,14 +1353,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('B', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (5, -5, None, None, 'GGGCCC'),
         'ovhgseq': 'GGCC',
     }
 rest_dict['ApaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ApaLI>GTGCAC)',
@@ -1300,14 +1375,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('C', 'K', 'N', 'U'),
+        'suppl': ('C', 'K', 'N'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GTGCAC'),
         'ovhgseq': 'TGCA',
     }
 rest_dict['ApaLI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ApeKI>GC[AT]GC)',
@@ -1330,7 +1404,6 @@ def _temp():
     }
 rest_dict['ApeKI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ApoI>[AG]AATT[CT])',
@@ -1353,7 +1426,6 @@ def _temp():
     }
 rest_dict['ApoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ApyPI>ATCGAC)|(?P<ApyPI_as>GTCGAT)',
@@ -1376,7 +1448,6 @@ def _temp():
     }
 rest_dict['ApyPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AquII>GCCG.AC)|(?P<AquII_as>GT.CGGC)',
@@ -1399,7 +1470,6 @@ def _temp():
     }
 rest_dict['AquII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AquIII>GAGGAG)|(?P<AquIII_as>CTCCTC)',
@@ -1422,7 +1492,6 @@ def _temp():
     }
 rest_dict['AquIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AquIV>G[AG]GGAAG)|(?P<AquIV_as>CTTCC[CT]C)',
@@ -1445,7 +1514,6 @@ def _temp():
     }
 rest_dict['AquIV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ArsI>GAC......TT[CT]G)|(?P<ArsI_as>C[AG]AA......GTC)',
@@ -1468,7 +1536,6 @@ def _temp():
     }
 rest_dict['ArsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AscI>GGCGCGCC)',
@@ -1491,7 +1558,6 @@ def _temp():
     }
 rest_dict['AscI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AseI>ATTAAT)',
@@ -1507,14 +1573,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('J', 'N', 'O'),
+        'suppl': ('J', 'N'),
         'scd5': None,
         'charac': (2, -2, None, None, 'ATTAAT'),
         'ovhgseq': 'TA',
     }
 rest_dict['AseI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Asi256I>GATC)',
@@ -1537,7 +1602,6 @@ def _temp():
     }
 rest_dict['Asi256I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AsiGI>ACCGGT)',
@@ -1560,7 +1624,6 @@ def _temp():
     }
 rest_dict['AsiGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AsiSI>GCGATCGC)',
@@ -1583,7 +1646,6 @@ def _temp():
     }
 rest_dict['AsiSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Asp700I>GAA....TTC)',
@@ -1606,7 +1668,6 @@ def _temp():
     }
 rest_dict['Asp700I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Asp718I>GGTACC)',
@@ -1629,7 +1690,6 @@ def _temp():
     }
 rest_dict['Asp718I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AspA2I>CCTAGG)',
@@ -1652,7 +1712,6 @@ def _temp():
     }
 rest_dict['AspA2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AspBHI>[CT][CG]C.[CG])|(?P<AspBHI_as>[CG].G[CG][AG])',
@@ -1675,6 +1734,49 @@ def _temp():
     }
 rest_dict['AspBHI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<AspDUT2V>G.GCAAC)|(?P<AspDUT2V_as>GTTGC.C)',
+        'results': None,
+        'site': 'GNGCAAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GNGCAAC'),
+        'ovhgseq': None,
+    }
+rest_dict['AspDUT2V'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<AspJHL3II>CGCCCAG)|(?P<AspJHL3II_as>CTGGGCG)',
+        'results': None,
+        'site': 'CGCCCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CGCCCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['AspJHL3II'] = _temp()
 
 def _temp():
     return {
@@ -1698,7 +1800,6 @@ def _temp():
     }
 rest_dict['AspLEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AspS9I>GG.CC)',
@@ -1721,29 +1822,27 @@ def _temp():
     }
 rest_dict['AspS9I'] = _temp()
 
-
 def _temp():
     return {
-        'compsite': '(?P<AssI>AGTACT)',
+        'compsite': '(?P<Asu14238IV>CGT[AG]AC)|(?P<Asu14238IV_as>GT[CT]ACG)',
         'results': None,
-        'site': 'AGTACT',
+        'site': 'CGTRAC',
         'substrat': 'DNA',
-        'fst3': -3,
-        'fst5': 3,
-        'freq': 4096,
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
         'size': 6,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': 0,
+        'ovhg': None,
         'scd3': None,
-        'suppl': ('U',),
+        'suppl': (),
         'scd5': None,
-        'charac': (3, -3, None, None, 'AGTACT'),
-        'ovhgseq': '',
+        'charac': (None, None, None, None, 'CGTRAC'),
+        'ovhgseq': None,
     }
-rest_dict['AssI'] = _temp()
-
+rest_dict['Asu14238IV'] = _temp()
 
 def _temp():
     return {
@@ -1767,7 +1866,6 @@ def _temp():
     }
 rest_dict['AsuC2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AsuHPI>GGTGA)|(?P<AsuHPI_as>TCACC)',
@@ -1790,7 +1888,6 @@ def _temp():
     }
 rest_dict['AsuHPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AsuI>GG.CC)',
@@ -1813,7 +1910,6 @@ def _temp():
     }
 rest_dict['AsuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AsuII>TTCGAA)',
@@ -1836,7 +1932,6 @@ def _temp():
     }
 rest_dict['AsuII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AsuNHI>GCTAGC)',
@@ -1859,7 +1954,6 @@ def _temp():
     }
 rest_dict['AsuNHI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AvaI>C[CT]CG[AG]G)',
@@ -1875,14 +1969,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('J', 'N', 'Q', 'R', 'U', 'X'),
+        'suppl': ('J', 'N', 'Q', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CYCGRG'),
         'ovhgseq': 'YCGR',
     }
 rest_dict['AvaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AvaII>GG[AT]CC)',
@@ -1898,14 +1991,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('J', 'N', 'R', 'X', 'Y'),
+        'suppl': ('J', 'N', 'R', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGWCC'),
         'ovhgseq': 'GWC',
     }
 rest_dict['AvaII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AvaIII>ATGCAT)',
@@ -1928,7 +2020,6 @@ def _temp():
     }
 rest_dict['AvaIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<AvrII>CCTAGG)',
@@ -1951,17 +2042,38 @@ def _temp():
     }
 rest_dict['AvrII'] = _temp()
 
-
 def _temp():
     return {
-        'compsite': '(?P<AxyI>CCT.AGG)',
+        'compsite': '(?P<Awo1030IV>GCC[AG]AG)|(?P<Awo1030IV_as>CT[CT]GGC)',
         'results': None,
-        'site': 'CCTNAGG',
+        'site': 'GCCRAG',
         'substrat': 'DNA',
-        'fst3': -2,
-        'fst5': 2,
-        'freq': 4096,
-        'size': 7,
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GCCRAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Awo1030IV'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<AxyI>CCT.AGG)',
+        'results': None,
+        'site': 'CCTNAGG',
+        'substrat': 'DNA',
+        'fst3': -2,
+        'fst5': 2,
+        'freq': 4096,
+        'size': 7,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
@@ -1974,7 +2086,6 @@ def _temp():
     }
 rest_dict['AxyI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BaeGI>G[GT]GC[AC]C)',
@@ -1997,7 +2108,6 @@ def _temp():
     }
 rest_dict['BaeGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BaeI>AC....GTA[CT]C)|(?P<BaeI_as>G[AG]TAC....GT)',
@@ -2020,6 +2130,27 @@ def _temp():
     }
 rest_dict['BaeI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Bag18758I>CCCGAG)|(?P<Bag18758I_as>CTCGGG)',
+        'results': None,
+        'site': 'CCCGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCCGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Bag18758I'] = _temp()
 
 def _temp():
     return {
@@ -2043,7 +2174,6 @@ def _temp():
     }
 rest_dict['BalI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BamHI>GGATCC)',
@@ -2059,14 +2189,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGATCC'),
         'ovhgseq': 'GATC',
     }
 rest_dict['BamHI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BanI>GG[CT][AG]CC)',
@@ -2082,14 +2211,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('N', 'R', 'U'),
+        'suppl': ('N', 'R'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGYRCC'),
         'ovhgseq': 'GYRC',
     }
 rest_dict['BanI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BanII>G[AG]GC[CT]C)',
@@ -2112,6 +2240,27 @@ def _temp():
     }
 rest_dict['BanII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BanLI>[AG]TCAGG)|(?P<BanLI_as>CCTGA[CT])',
+        'results': None,
+        'site': 'RTCAGG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'RTCAGG'),
+        'ovhgseq': None,
+    }
+rest_dict['BanLI'] = _temp()
 
 def _temp():
     return {
@@ -2135,30 +2284,6 @@ def _temp():
     }
 rest_dict['BarI'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<BasI>CCA.....TGG)',
-        'results': None,
-        'site': 'CCANNNNNTGG',
-        'substrat': 'DNA',
-        'fst3': -7,
-        'fst5': 7,
-        'freq': 4096,
-        'size': 11,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': 3,
-        'scd3': None,
-        'suppl': ('U',),
-        'scd5': None,
-        'charac': (7, -7, None, None, 'CCANNNNNTGG'),
-        'ovhgseq': 'NNN',
-    }
-rest_dict['BasI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<BauI>CACGAG)|(?P<BauI_as>CTCGTG)',
@@ -2174,14 +2299,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CACGAG'),
         'ovhgseq': 'ACGA',
     }
 rest_dict['BauI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bbr7I>GAAGAC)|(?P<Bbr7I_as>GTCTTC)',
@@ -2204,7 +2328,6 @@ def _temp():
     }
 rest_dict['Bbr7I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BbrPI>CACGTG)',
@@ -2227,7 +2350,6 @@ def _temp():
     }
 rest_dict['BbrPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BbsI>GAAGAC)|(?P<BbsI_as>GTCTTC)',
@@ -2250,7 +2372,6 @@ def _temp():
     }
 rest_dict['BbsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bbv12I>G[AT]GC[AT]C)',
@@ -2273,7 +2394,6 @@ def _temp():
     }
 rest_dict['Bbv12I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BbvCI>CCTCAGC)|(?P<BbvCI_as>GCTGAGG)',
@@ -2296,7 +2416,6 @@ def _temp():
     }
 rest_dict['BbvCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BbvI>GCAGC)|(?P<BbvI_as>GCTGC)',
@@ -2319,7 +2438,6 @@ def _temp():
     }
 rest_dict['BbvI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BbvII>GAAGAC)|(?P<BbvII_as>GTCTTC)',
@@ -2342,7 +2460,6 @@ def _temp():
     }
 rest_dict['BbvII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BccI>CCATC)|(?P<BccI_as>GATGG)',
@@ -2365,6 +2482,27 @@ def _temp():
     }
 rest_dict['BccI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Bce3081I>TAGGAG)|(?P<Bce3081I_as>CTCCTA)',
+        'results': None,
+        'site': 'TAGGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'TAGGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Bce3081I'] = _temp()
 
 def _temp():
     return {
@@ -2388,7 +2526,6 @@ def _temp():
     }
 rest_dict['Bce83I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BceAI>ACGGC)|(?P<BceAI_as>GCCGT)',
@@ -2411,6 +2548,27 @@ def _temp():
     }
 rest_dict['BceAI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BceSIV>GCAGC)|(?P<BceSIV_as>GCTGC)',
+        'results': None,
+        'site': 'GCAGC',
+        'substrat': 'DNA',
+        'fst3': -10,
+        'fst5': -7,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -2,
+        'scd3': 11,
+        'suppl': (),
+        'scd5': 14,
+        'charac': (-7, -10, 14, 11, 'GCAGC'),
+        'ovhgseq': 'NN',
+    }
+rest_dict['BceSIV'] = _temp()
 
 def _temp():
     return {
@@ -2434,7 +2592,6 @@ def _temp():
     }
 rest_dict['BcefI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BcgI>CGA......TGC)|(?P<BcgI_as>GCA......TCG)',
@@ -2457,7 +2614,6 @@ def _temp():
     }
 rest_dict['BcgI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BciT130I>CC[AT]GG)',
@@ -2480,7 +2636,6 @@ def _temp():
     }
 rest_dict['BciT130I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BciVI>GTATCC)|(?P<BciVI_as>GGATAC)',
@@ -2503,7 +2658,6 @@ def _temp():
     }
 rest_dict['BciVI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BclI>TGATCA)',
@@ -2519,14 +2673,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('C', 'F', 'J', 'M', 'N', 'O', 'R', 'S', 'U', 'Y'),
+        'suppl': ('B', 'C', 'J', 'M', 'N', 'O', 'R', 'S'),
         'scd5': None,
         'charac': (1, -1, None, None, 'TGATCA'),
         'ovhgseq': 'GATC',
     }
 rest_dict['BclI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BcnI>CC[CG]GG)',
@@ -2542,14 +2695,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCSGG'),
         'ovhgseq': 'S',
     }
 rest_dict['BcnI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BcoDI>GTCTC)|(?P<BcoDI_as>GAGAC)',
@@ -2572,7 +2724,6 @@ def _temp():
     }
 rest_dict['BcoDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BcuI>ACTAGT)',
@@ -2588,14 +2739,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'ACTAGT'),
         'ovhgseq': 'CTAG',
     }
 rest_dict['BcuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BdaI>TGA......TCA)',
@@ -2618,7 +2768,6 @@ def _temp():
     }
 rest_dict['BdaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BetI>[AT]CCGG[AT])',
@@ -2641,7 +2790,6 @@ def _temp():
     }
 rest_dict['BetI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfaI>CTAG)',
@@ -2664,6 +2812,27 @@ def _temp():
     }
 rest_dict['BfaI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BfaSII>GA.GGAG)|(?P<BfaSII_as>CTCC.TC)',
+        'results': None,
+        'site': 'GANGGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GANGGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['BfaSII'] = _temp()
 
 def _temp():
     return {
@@ -2687,7 +2856,6 @@ def _temp():
     }
 rest_dict['BfiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfmI>CT[AG][CT]AG)',
@@ -2703,14 +2871,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTRYAG'),
         'ovhgseq': 'TRYA',
     }
 rest_dict['BfmI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfoI>[AG]GCGC[CT])',
@@ -2726,14 +2893,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'RGCGCY'),
         'ovhgseq': 'GCGC',
     }
 rest_dict['BfoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfrI>CTTAAG)',
@@ -2756,7 +2922,6 @@ def _temp():
     }
 rest_dict['BfrI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfuAI>ACCTGC)|(?P<BfuAI_as>GCAGGT)',
@@ -2779,7 +2944,6 @@ def _temp():
     }
 rest_dict['BfuAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfuCI>GATC)',
@@ -2802,7 +2966,6 @@ def _temp():
     }
 rest_dict['BfuCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BfuI>GTATCC)|(?P<BfuI_as>GGATAC)',
@@ -2818,14 +2981,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (12, 5, None, None, 'GTATCC'),
         'ovhgseq': 'N',
     }
 rest_dict['BfuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BglI>GCC.....GGC)',
@@ -2841,14 +3003,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('C', 'F', 'I', 'J', 'K', 'N', 'O', 'Q', 'R', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'N', 'O', 'Q', 'R', 'V', 'X'),
         'scd5': None,
         'charac': (7, -7, None, None, 'GCCNNNNNGGC'),
         'ovhgseq': 'NNN',
     }
 rest_dict['BglI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BglII>AGATCT)',
@@ -2864,14 +3025,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'AGATCT'),
         'ovhgseq': 'GATC',
     }
 rest_dict['BglII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BinI>GGATC)|(?P<BinI_as>GATCC)',
@@ -2894,7 +3054,6 @@ def _temp():
     }
 rest_dict['BinI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BisI>GC.GC)',
@@ -2917,7 +3076,6 @@ def _temp():
     }
 rest_dict['BisI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BlnI>CCTAGG)',
@@ -2940,6 +3098,27 @@ def _temp():
     }
 rest_dict['BlnI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BloAII>GAGGAC)|(?P<BloAII_as>GTCCTC)',
+        'results': None,
+        'site': 'GAGGAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GAGGAC'),
+        'ovhgseq': None,
+    }
+rest_dict['BloAII'] = _temp()
 
 def _temp():
     return {
@@ -2963,7 +3142,6 @@ def _temp():
     }
 rest_dict['BlpI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BlsI>GC.GC)',
@@ -2986,7 +3164,6 @@ def _temp():
     }
 rest_dict['BlsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmcAI>AGTACT)',
@@ -3009,7 +3186,6 @@ def _temp():
     }
 rest_dict['BmcAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bme1390I>CC.GG)',
@@ -3025,14 +3201,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCNGG'),
         'ovhgseq': 'N',
     }
 rest_dict['Bme1390I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bme18I>GG[AT]CC)',
@@ -3055,7 +3230,6 @@ def _temp():
     }
 rest_dict['Bme18I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmeDI>C)|(?P<BmeDI_as>G)',
@@ -3078,7 +3252,6 @@ def _temp():
     }
 rest_dict['BmeDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmeRI>GAC.....GTC)',
@@ -3101,7 +3274,6 @@ def _temp():
     }
 rest_dict['BmeRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmeT110I>C[CT]CG[AG]G)',
@@ -3124,7 +3296,6 @@ def _temp():
     }
 rest_dict['BmeT110I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmgBI>CACGTC)|(?P<BmgBI_as>GACGTG)',
@@ -3147,7 +3318,6 @@ def _temp():
     }
 rest_dict['BmgBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmgI>G[GT]GCCC)|(?P<BmgI_as>GGGC[AC]C)',
@@ -3170,7 +3340,6 @@ def _temp():
     }
 rest_dict['BmgI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmgT120I>GG.CC)',
@@ -3193,7 +3362,6 @@ def _temp():
     }
 rest_dict['BmgT120I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmiI>GG..CC)',
@@ -3216,7 +3384,6 @@ def _temp():
     }
 rest_dict['BmiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmrFI>CC.GG)',
@@ -3239,7 +3406,6 @@ def _temp():
     }
 rest_dict['BmrFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmrI>ACTGGG)|(?P<BmrI_as>CCCAGT)',
@@ -3262,7 +3428,6 @@ def _temp():
     }
 rest_dict['BmrI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmsI>GCATC)|(?P<BmsI_as>GATGC)',
@@ -3278,14 +3443,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (10, 9, None, None, 'GCATC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['BmsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmtI>GCTAGC)',
@@ -3308,7 +3472,6 @@ def _temp():
     }
 rest_dict['BmtI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BmuI>ACTGGG)|(?P<BmuI_as>CCCAGT)',
@@ -3331,7 +3494,6 @@ def _temp():
     }
 rest_dict['BmuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BoxI>GAC....GTC)',
@@ -3347,14 +3509,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GACNNNNGTC'),
         'ovhgseq': '',
     }
 rest_dict['BoxI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BpiI>GAAGAC)|(?P<BpiI_as>GTCTTC)',
@@ -3370,14 +3531,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (8, 6, None, None, 'GAAGAC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['BpiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BplI>GAG.....CTC)',
@@ -3393,14 +3553,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 5,
         'scd3': 8,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': 24,
         'charac': (-8, -24, 24, 8, 'GAGNNNNNCTC'),
         'ovhgseq': 'NNNNN',
     }
 rest_dict['BplI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BpmI>CTGGAG)|(?P<BpmI_as>CTCCAG)',
@@ -3423,7 +3582,6 @@ def _temp():
     }
 rest_dict['BpmI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bpu10I>CCT.AGC)|(?P<Bpu10I_as>GCT.AGG)',
@@ -3439,14 +3597,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F', 'I', 'N', 'V'),
+        'suppl': ('B', 'I', 'N', 'V'),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCTNAGC'),
         'ovhgseq': 'TNA',
     }
 rest_dict['Bpu10I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bpu1102I>GCT.AGC)',
@@ -3462,14 +3619,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GCTNAGC'),
         'ovhgseq': 'TNA',
     }
 rest_dict['Bpu1102I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bpu14I>TTCGAA)',
@@ -3492,7 +3648,6 @@ def _temp():
     }
 rest_dict['Bpu14I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BpuEI>CTTGAG)|(?P<BpuEI_as>CTCAAG)',
@@ -3515,7 +3670,6 @@ def _temp():
     }
 rest_dict['BpuEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BpuMI>CC[CG]GG)',
@@ -3538,30 +3692,6 @@ def _temp():
     }
 rest_dict['BpuMI'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<BpvUI>CGATCG)',
-        'results': None,
-        'site': 'CGATCG',
-        'substrat': 'DNA',
-        'fst3': -4,
-        'fst5': 4,
-        'freq': 4096,
-        'size': 6,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': 2,
-        'scd3': None,
-        'suppl': ('V',),
-        'scd5': None,
-        'charac': (4, -4, None, None, 'CGATCG'),
-        'ovhgseq': 'AT',
-    }
-rest_dict['BpvUI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<Bsa29I>ATCGAT)',
@@ -3584,7 +3714,6 @@ def _temp():
     }
 rest_dict['Bsa29I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaAI>[CT]ACGT[AG])',
@@ -3607,7 +3736,6 @@ def _temp():
     }
 rest_dict['BsaAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaBI>GAT....ATC)',
@@ -3630,7 +3758,6 @@ def _temp():
     }
 rest_dict['BsaBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaHI>G[AG]CG[CT]C)',
@@ -3653,7 +3780,6 @@ def _temp():
     }
 rest_dict['BsaHI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaI>GGTCTC)|(?P<BsaI_as>GAGACC)',
@@ -3676,7 +3802,6 @@ def _temp():
     }
 rest_dict['BsaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaJI>CC..GG)',
@@ -3699,7 +3824,6 @@ def _temp():
     }
 rest_dict['BsaJI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaWI>[AT]CCGG[AT])',
@@ -3722,7 +3846,6 @@ def _temp():
     }
 rest_dict['BsaWI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsaXI>AC.....CTCC)|(?P<BsaXI_as>GGAG.....GT)',
@@ -3745,7 +3868,6 @@ def _temp():
     }
 rest_dict['BsaXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsbI>CAACAC)|(?P<BsbI_as>GTGTTG)',
@@ -3768,7 +3890,6 @@ def _temp():
     }
 rest_dict['BsbI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsc4I>CC.......GG)',
@@ -3791,7 +3912,6 @@ def _temp():
     }
 rest_dict['Bsc4I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BscAI>GCATC)|(?P<BscAI_as>GATGC)',
@@ -3814,7 +3934,6 @@ def _temp():
     }
 rest_dict['BscAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BscGI>CCCGT)|(?P<BscGI_as>ACGGG)',
@@ -3837,7 +3956,6 @@ def _temp():
     }
 rest_dict['BscGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bse118I>[AG]CCGG[CT])',
@@ -3860,7 +3978,6 @@ def _temp():
     }
 rest_dict['Bse118I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bse1I>ACTGG)|(?P<Bse1I_as>CCAGT)',
@@ -3883,7 +4000,6 @@ def _temp():
     }
 rest_dict['Bse1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bse21I>CCT.AGG)',
@@ -3906,7 +4022,6 @@ def _temp():
     }
 rest_dict['Bse21I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bse3DI>GCAATG)|(?P<Bse3DI_as>CATTGC)',
@@ -3929,7 +4044,6 @@ def _temp():
     }
 rest_dict['Bse3DI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bse8I>GAT....ATC)',
@@ -3952,7 +4066,6 @@ def _temp():
     }
 rest_dict['Bse8I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseAI>TCCGGA)',
@@ -3968,14 +4081,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('C', 'M'),
+        'suppl': ('C',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TCCGGA'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['BseAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseBI>CC[AT]GG)',
@@ -3998,7 +4110,6 @@ def _temp():
     }
 rest_dict['BseBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseCI>ATCGAT)',
@@ -4021,7 +4132,6 @@ def _temp():
     }
 rest_dict['BseCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseDI>CC..GG)',
@@ -4037,14 +4147,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCNNGG'),
         'ovhgseq': 'CNNG',
     }
 rest_dict['BseDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseGI>GGATG)|(?P<BseGI_as>CATCC)',
@@ -4060,14 +4169,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, 0, None, None, 'GGATG'),
         'ovhgseq': 'NN',
     }
 rest_dict['BseGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseJI>GAT....ATC)',
@@ -4083,14 +4191,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GATNNNNATC'),
         'ovhgseq': '',
     }
 rest_dict['BseJI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseLI>CC.......GG)',
@@ -4106,14 +4213,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, -7, None, None, 'CCNNNNNNNGG'),
         'ovhgseq': 'NNN',
     }
 rest_dict['BseLI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseMI>GCAATG)|(?P<BseMI_as>CATTGC)',
@@ -4129,14 +4235,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (8, 0, None, None, 'GCAATG'),
         'ovhgseq': 'NN',
     }
 rest_dict['BseMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseMII>CTCAG)|(?P<BseMII_as>CTGAG)',
@@ -4152,14 +4257,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (15, 8, None, None, 'CTCAG'),
         'ovhgseq': 'NN',
     }
 rest_dict['BseMII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseNI>ACTGG)|(?P<BseNI_as>CCAGT)',
@@ -4175,14 +4279,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (6, -1, None, None, 'ACTGG'),
         'ovhgseq': 'GN',
     }
 rest_dict['BseNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsePI>GCGCGC)',
@@ -4205,7 +4308,6 @@ def _temp():
     }
 rest_dict['BsePI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseRI>GAGGAG)|(?P<BseRI_as>CTCCTC)',
@@ -4228,7 +4330,6 @@ def _temp():
     }
 rest_dict['BseRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseSI>G[GT]GC[AC]C)',
@@ -4244,14 +4345,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GKGCMC'),
         'ovhgseq': 'KGCM',
     }
 rest_dict['BseSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseX3I>CGGCCG)',
@@ -4274,7 +4374,6 @@ def _temp():
     }
 rest_dict['BseX3I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseXI>GCAGC)|(?P<BseXI_as>GCTGC)',
@@ -4290,14 +4389,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (13, 12, None, None, 'GCAGC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['BseXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BseYI>CCCAGC)|(?P<BseYI_as>GCTGGG)',
@@ -4320,7 +4418,6 @@ def _temp():
     }
 rest_dict['BseYI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsgI>GTGCAG)|(?P<BsgI_as>CTGCAC)',
@@ -4343,7 +4440,6 @@ def _temp():
     }
 rest_dict['BsgI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsh1236I>CGCG)',
@@ -4359,14 +4455,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CGCG'),
         'ovhgseq': '',
     }
 rest_dict['Bsh1236I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsh1285I>CG[AG][CT]CG)',
@@ -4382,14 +4477,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'CGRYCG'),
         'ovhgseq': 'RY',
     }
 rest_dict['Bsh1285I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BshFI>GGCC)',
@@ -4412,7 +4506,6 @@ def _temp():
     }
 rest_dict['BshFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BshNI>GG[CT][AG]CC)',
@@ -4428,14 +4521,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGYRCC'),
         'ovhgseq': 'GYRC',
     }
 rest_dict['BshNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BshTI>ACCGGT)',
@@ -4451,14 +4543,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'F'),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'ACCGGT'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['BshTI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BshVI>ATCGAT)',
@@ -4481,7 +4572,6 @@ def _temp():
     }
 rest_dict['BshVI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiEI>CG[AG][CT]CG)',
@@ -4504,7 +4594,6 @@ def _temp():
     }
 rest_dict['BsiEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiHKAI>G[AT]GC[AT]C)',
@@ -4527,7 +4616,6 @@ def _temp():
     }
 rest_dict['BsiHKAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiHKCI>C[CT]CG[AG]G)',
@@ -4550,7 +4638,6 @@ def _temp():
     }
 rest_dict['BsiHKCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiI>CACGAG)|(?P<BsiI_as>CTCGTG)',
@@ -4573,7 +4660,6 @@ def _temp():
     }
 rest_dict['BsiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiSI>CCGG)',
@@ -4589,14 +4675,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('C',),
+        'suppl': ('C', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCGG'),
         'ovhgseq': 'CG',
     }
 rest_dict['BsiSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiWI>CGTACG)',
@@ -4619,7 +4704,6 @@ def _temp():
     }
 rest_dict['BsiWI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsiYI>CC.......GG)',
@@ -4642,7 +4726,6 @@ def _temp():
     }
 rest_dict['BsiYI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BslFI>GGGAC)|(?P<BslFI_as>GTCCC)',
@@ -4665,7 +4748,6 @@ def _temp():
     }
 rest_dict['BslFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BslI>CC.......GG)',
@@ -4688,7 +4770,6 @@ def _temp():
     }
 rest_dict['BslI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsmAI>GTCTC)|(?P<BsmAI_as>GAGAC)',
@@ -4711,7 +4792,6 @@ def _temp():
     }
 rest_dict['BsmAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsmBI>CGTCTC)|(?P<BsmBI_as>GAGACG)',
@@ -4734,7 +4814,6 @@ def _temp():
     }
 rest_dict['BsmBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsmFI>GGGAC)|(?P<BsmFI_as>GTCCC)',
@@ -4757,7 +4836,6 @@ def _temp():
     }
 rest_dict['BsmFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsmI>GAATGC)|(?P<BsmI_as>GCATTC)',
@@ -4780,7 +4858,6 @@ def _temp():
     }
 rest_dict['BsmI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsnI>GGCC)',
@@ -4803,7 +4880,6 @@ def _temp():
     }
 rest_dict['BsnI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bso31I>GGTCTC)|(?P<Bso31I_as>GAGACC)',
@@ -4826,7 +4902,6 @@ def _temp():
     }
 rest_dict['Bso31I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsoBI>C[CT]CG[AG]G)',
@@ -4849,7 +4924,6 @@ def _temp():
     }
 rest_dict['BsoBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp119I>TTCGAA)',
@@ -4865,14 +4939,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'TTCGAA'),
         'ovhgseq': 'CG',
     }
 rest_dict['Bsp119I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp120I>GGGCCC)',
@@ -4888,14 +4961,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGGCCC'),
         'ovhgseq': 'GGCC',
     }
 rest_dict['Bsp120I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp1286I>G[AGT]GC[ACT]C)',
@@ -4918,7 +4990,6 @@ def _temp():
     }
 rest_dict['Bsp1286I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp13I>TCCGGA)',
@@ -4941,7 +5012,6 @@ def _temp():
     }
 rest_dict['Bsp13I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp1407I>TGTACA)',
@@ -4957,14 +5027,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (1, -1, None, None, 'TGTACA'),
         'ovhgseq': 'GTAC',
     }
 rest_dict['Bsp1407I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp143I>GATC)',
@@ -4980,14 +5049,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (0, 0, None, None, 'GATC'),
         'ovhgseq': 'GATC',
     }
 rest_dict['Bsp143I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp1720I>GCT.AGC)',
@@ -5010,7 +5078,6 @@ def _temp():
     }
 rest_dict['Bsp1720I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp19I>CCATGG)',
@@ -5033,7 +5100,6 @@ def _temp():
     }
 rest_dict['Bsp19I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsp24I>GAC......TGG)|(?P<Bsp24I_as>CCA......GTC)',
@@ -5056,6 +5122,27 @@ def _temp():
     }
 rest_dict['Bsp24I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Bsp3004IV>CCGCAT)|(?P<Bsp3004IV_as>ATGCGG)',
+        'results': None,
+        'site': 'CCGCAT',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCGCAT'),
+        'ovhgseq': None,
+    }
+rest_dict['Bsp3004IV'] = _temp()
 
 def _temp():
     return {
@@ -5072,14 +5159,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'TCGCGA'),
         'ovhgseq': '',
     }
 rest_dict['Bsp68I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspACI>CCGC)|(?P<BspACI_as>GCGG)',
@@ -5102,6 +5188,27 @@ def _temp():
     }
 rest_dict['BspACI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BspANI>GGCC)',
+        'results': None,
+        'site': 'GGCC',
+        'substrat': 'DNA',
+        'fst3': -2,
+        'fst5': 2,
+        'freq': 256,
+        'size': 4,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 0,
+        'scd3': None,
+        'suppl': ('X',),
+        'scd5': None,
+        'charac': (2, -2, None, None, 'GGCC'),
+        'ovhgseq': '',
+    }
+rest_dict['BspANI'] = _temp()
 
 def _temp():
     return {
@@ -5125,7 +5232,6 @@ def _temp():
     }
 rest_dict['BspCNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspD6I>GACTC)|(?P<BspD6I_as>GAGTC)',
@@ -5148,7 +5254,6 @@ def _temp():
     }
 rest_dict['BspD6I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspDI>ATCGAT)',
@@ -5171,7 +5276,6 @@ def _temp():
     }
 rest_dict['BspDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspEI>TCCGGA)',
@@ -5194,7 +5298,6 @@ def _temp():
     }
 rest_dict['BspEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspFNI>CGCG)',
@@ -5217,7 +5320,6 @@ def _temp():
     }
 rest_dict['BspFNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspGI>CTGGAC)|(?P<BspGI_as>GTCCAG)',
@@ -5240,7 +5342,6 @@ def _temp():
     }
 rest_dict['BspGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspHI>TCATGA)',
@@ -5263,7 +5364,6 @@ def _temp():
     }
 rest_dict['BspHI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspLI>GG..CC)',
@@ -5279,14 +5379,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'GGNNCC'),
         'ovhgseq': '',
     }
 rest_dict['BspLI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspLU11I>ACATGT)',
@@ -5309,6 +5408,27 @@ def _temp():
     }
 rest_dict['BspLU11I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BspMAI>CTGCAG)',
+        'results': None,
+        'site': 'CTGCAG',
+        'substrat': 'DNA',
+        'fst3': -5,
+        'fst5': 5,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 4,
+        'scd3': None,
+        'suppl': ('X',),
+        'scd5': None,
+        'charac': (5, -5, None, None, 'CTGCAG'),
+        'ovhgseq': 'TGCA',
+    }
+rest_dict['BspMAI'] = _temp()
 
 def _temp():
     return {
@@ -5332,7 +5452,6 @@ def _temp():
     }
 rest_dict['BspMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspMII>TCCGGA)',
@@ -5355,7 +5474,6 @@ def _temp():
     }
 rest_dict['BspMII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspNCI>CCAGA)|(?P<BspNCI_as>TCTGG)',
@@ -5378,7 +5496,6 @@ def _temp():
     }
 rest_dict['BspNCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspOI>GCTAGC)',
@@ -5394,14 +5511,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GCTAGC'),
         'ovhgseq': 'CTAG',
     }
 rest_dict['BspOI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspPI>GGATC)|(?P<BspPI_as>GATCC)',
@@ -5417,14 +5533,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (9, 5, None, None, 'GGATC'),
         'ovhgseq': 'N',
     }
 rest_dict['BspPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspQI>GCTCTTC)|(?P<BspQI_as>GAAGAGC)',
@@ -5447,7 +5562,6 @@ def _temp():
     }
 rest_dict['BspQI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspT104I>TTCGAA)',
@@ -5470,7 +5584,6 @@ def _temp():
     }
 rest_dict['BspT104I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspT107I>GG[CT][AG]CC)',
@@ -5493,7 +5606,6 @@ def _temp():
     }
 rest_dict['BspT107I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BspTI>CTTAAG)',
@@ -5509,13 +5621,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTTAAG'),
         'ovhgseq': 'TTAA',
     }
 rest_dict['BspTI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BspTNI>GGTCTC)|(?P<BspTNI_as>GAGACC)',
+        'results': None,
+        'site': 'GGTCTC',
+        'substrat': 'DNA',
+        'fst3': 5,
+        'fst5': 7,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -4,
+        'scd3': None,
+        'suppl': ('X',),
+        'scd5': None,
+        'charac': (7, 5, None, None, 'GGTCTC'),
+        'ovhgseq': 'NNNN',
+    }
+rest_dict['BspTNI'] = _temp()
 
 def _temp():
     return {
@@ -5539,7 +5672,6 @@ def _temp():
     }
 rest_dict['BsrBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsrDI>GCAATG)|(?P<BsrDI_as>CATTGC)',
@@ -5562,7 +5694,6 @@ def _temp():
     }
 rest_dict['BsrDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsrFI>[AG]CCGG[CT])',
@@ -5585,7 +5716,6 @@ def _temp():
     }
 rest_dict['BsrFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsrGI>TGTACA)',
@@ -5608,7 +5738,6 @@ def _temp():
     }
 rest_dict['BsrGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsrI>ACTGG)|(?P<BsrI_as>CCAGT)',
@@ -5631,30 +5760,6 @@ def _temp():
     }
 rest_dict['BsrI'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<BsrSI>ACTGG)|(?P<BsrSI_as>CCAGT)',
-        'results': None,
-        'site': 'ACTGG',
-        'substrat': 'DNA',
-        'fst3': -1,
-        'fst5': 6,
-        'freq': 1024,
-        'size': 5,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': 2,
-        'scd3': None,
-        'suppl': ('R',),
-        'scd5': None,
-        'charac': (6, -1, None, None, 'ACTGG'),
-        'ovhgseq': 'GN',
-    }
-rest_dict['BsrSI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<BssAI>[AG]CCGG[CT])',
@@ -5677,7 +5782,6 @@ def _temp():
     }
 rest_dict['BssAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BssECI>CC..GG)',
@@ -5700,7 +5804,6 @@ def _temp():
     }
 rest_dict['BssECI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BssHII>GCGCGC)',
@@ -5716,37 +5819,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('J', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
+        'suppl': ('J', 'K', 'M', 'N', 'Q', 'R', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GCGCGC'),
         'ovhgseq': 'CGCG',
     }
 rest_dict['BssHII'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<BssKI>CC.GG)',
-        'results': None,
-        'site': 'CCNGG',
-        'substrat': 'DNA',
-        'fst3': 0,
-        'fst5': 0,
-        'freq': 256,
-        'size': 5,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': -5,
-        'scd3': None,
-        'suppl': ('N',),
-        'scd5': None,
-        'charac': (0, 0, None, None, 'CCNGG'),
-        'ovhgseq': 'CCNGG',
-    }
-rest_dict['BssKI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<BssMI>GATC)',
@@ -5769,7 +5848,6 @@ def _temp():
     }
 rest_dict['BssMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BssNAI>GTATAC)',
@@ -5792,7 +5870,6 @@ def _temp():
     }
 rest_dict['BssNAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BssNI>G[AG]CG[CT]C)',
@@ -5815,7 +5892,6 @@ def _temp():
     }
 rest_dict['BssNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BssSI>CACGAG)|(?P<BssSI_as>CTCGTG)',
@@ -5838,7 +5914,6 @@ def _temp():
     }
 rest_dict['BssSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BssT1I>CC[AT][AT]GG)',
@@ -5861,7 +5936,6 @@ def _temp():
     }
 rest_dict['BssT1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bst1107I>GTATAC)',
@@ -5877,14 +5951,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (3, -3, None, None, 'GTATAC'),
         'ovhgseq': '',
     }
 rest_dict['Bst1107I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bst2BI>CACGAG)|(?P<Bst2BI_as>CTCGTG)',
@@ -5900,14 +5973,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('I', 'V'),
+        'suppl': ('I',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CACGAG'),
         'ovhgseq': 'ACGA',
     }
 rest_dict['Bst2BI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bst2UI>CC[AT]GG)',
@@ -5930,7 +6002,6 @@ def _temp():
     }
 rest_dict['Bst2UI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bst4CI>AC.GT)',
@@ -5953,7 +6024,6 @@ def _temp():
     }
 rest_dict['Bst4CI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bst6I>CTCTTC)|(?P<Bst6I_as>GAAGAG)',
@@ -5976,7 +6046,6 @@ def _temp():
     }
 rest_dict['Bst6I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstACI>G[AG]CG[CT]C)',
@@ -5999,7 +6068,6 @@ def _temp():
     }
 rest_dict['BstACI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstAFI>CTTAAG)',
@@ -6022,7 +6090,6 @@ def _temp():
     }
 rest_dict['BstAFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstAPI>GCA.....TGC)',
@@ -6045,7 +6112,6 @@ def _temp():
     }
 rest_dict['BstAPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstAUI>TGTACA)',
@@ -6068,7 +6134,6 @@ def _temp():
     }
 rest_dict['BstAUI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstBAI>[CT]ACGT[AG])',
@@ -6091,7 +6156,6 @@ def _temp():
     }
 rest_dict['BstBAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstBI>TTCGAA)',
@@ -6114,7 +6178,6 @@ def _temp():
     }
 rest_dict['BstBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstC8I>GC..GC)',
@@ -6137,7 +6200,6 @@ def _temp():
     }
 rest_dict['BstC8I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstDEI>CT.AG)',
@@ -6160,7 +6222,6 @@ def _temp():
     }
 rest_dict['BstDEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstDSI>CC[AG][CT]GG)',
@@ -6183,7 +6244,6 @@ def _temp():
     }
 rest_dict['BstDSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstEII>GGT.ACC)',
@@ -6199,14 +6259,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('C', 'J', 'N', 'R', 'S', 'U'),
+        'suppl': ('C', 'J', 'N', 'R', 'S'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGTNACC'),
         'ovhgseq': 'GTNAC',
     }
 rest_dict['BstEII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstENI>CCT.....AGG)',
@@ -6229,7 +6288,6 @@ def _temp():
     }
 rest_dict['BstENI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstF5I>GGATG)|(?P<BstF5I_as>CATCC)',
@@ -6252,7 +6310,6 @@ def _temp():
     }
 rest_dict['BstF5I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstFNI>CGCG)',
@@ -6275,7 +6332,6 @@ def _temp():
     }
 rest_dict['BstFNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstH2I>[AG]GCGC[CT])',
@@ -6298,7 +6354,6 @@ def _temp():
     }
 rest_dict['BstH2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstHHI>GCGC)',
@@ -6321,7 +6376,6 @@ def _temp():
     }
 rest_dict['BstHHI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstKTI>GATC)',
@@ -6344,7 +6398,6 @@ def _temp():
     }
 rest_dict['BstKTI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstMAI>GTCTC)|(?P<BstMAI_as>GAGAC)',
@@ -6367,7 +6420,6 @@ def _temp():
     }
 rest_dict['BstMAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstMBI>GATC)',
@@ -6390,7 +6442,6 @@ def _temp():
     }
 rest_dict['BstMBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstMCI>CG[AG][CT]CG)',
@@ -6413,7 +6464,6 @@ def _temp():
     }
 rest_dict['BstMCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstMWI>GC.......GC)',
@@ -6436,7 +6486,6 @@ def _temp():
     }
 rest_dict['BstMWI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstNI>CC[AT]GG)',
@@ -6459,7 +6508,6 @@ def _temp():
     }
 rest_dict['BstNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstNSI>[AG]CATG[CT])',
@@ -6475,37 +6523,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': ('I', 'V'),
         'scd5': None,
         'charac': (5, -5, None, None, 'RCATGY'),
         'ovhgseq': 'CATG',
     }
 rest_dict['BstNSI'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<BstOI>CC[AT]GG)',
-        'results': None,
-        'site': 'CCWGG',
-        'substrat': 'DNA',
-        'fst3': -2,
-        'fst5': 2,
-        'freq': 512,
-        'size': 5,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': -1,
-        'scd3': None,
-        'suppl': ('R',),
-        'scd5': None,
-        'charac': (2, -2, None, None, 'CCWGG'),
-        'ovhgseq': 'W',
-    }
-rest_dict['BstOI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<BstPAI>GAC....GTC)',
@@ -6528,7 +6552,6 @@ def _temp():
     }
 rest_dict['BstPAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstPI>GGT.ACC)',
@@ -6551,7 +6574,6 @@ def _temp():
     }
 rest_dict['BstPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstSCI>CC.GG)',
@@ -6574,7 +6596,6 @@ def _temp():
     }
 rest_dict['BstSCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstSFI>CT[AG][CT]AG)',
@@ -6597,7 +6618,6 @@ def _temp():
     }
 rest_dict['BstSFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstSLI>G[GT]GC[AC]C)',
@@ -6620,7 +6640,6 @@ def _temp():
     }
 rest_dict['BstSLI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstSNI>TACGTA)',
@@ -6643,7 +6662,6 @@ def _temp():
     }
 rest_dict['BstSNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstUI>CGCG)',
@@ -6666,7 +6684,6 @@ def _temp():
     }
 rest_dict['BstUI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstV1I>GCAGC)|(?P<BstV1I_as>GCTGC)',
@@ -6689,7 +6706,6 @@ def _temp():
     }
 rest_dict['BstV1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstV2I>GAAGAC)|(?P<BstV2I_as>GTCTTC)',
@@ -6712,7 +6728,6 @@ def _temp():
     }
 rest_dict['BstV2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstX2I>[AG]GATC[CT])',
@@ -6735,7 +6750,6 @@ def _temp():
     }
 rest_dict['BstX2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstXI>CCA......TGG)',
@@ -6751,14 +6765,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'V', 'X'),
+        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (8, -8, None, None, 'CCANNNNNNTGG'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['BstXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstYI>[AG]GATC[CT])',
@@ -6781,7 +6794,6 @@ def _temp():
     }
 rest_dict['BstYI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstZ17I>GTATAC)',
@@ -6804,7 +6816,6 @@ def _temp():
     }
 rest_dict['BstZ17I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BstZI>CGGCCG)',
@@ -6827,7 +6838,6 @@ def _temp():
     }
 rest_dict['BstZI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsu15I>ATCGAT)',
@@ -6843,14 +6853,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'ATCGAT'),
         'ovhgseq': 'CG',
     }
 rest_dict['Bsu15I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Bsu36I>CCT.AGG)',
@@ -6866,13 +6875,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('N', 'R'),
+        'suppl': ('N',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCTNAGG'),
         'ovhgseq': 'TNA',
     }
 rest_dict['Bsu36I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Bsu7003I>GACGAGC)|(?P<Bsu7003I_as>GCTCGTC)',
+        'results': None,
+        'site': 'GACGAGC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GACGAGC'),
+        'ovhgseq': None,
+    }
+rest_dict['Bsu7003I'] = _temp()
 
 def _temp():
     return {
@@ -6896,7 +6926,6 @@ def _temp():
     }
 rest_dict['BsuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BsuRI>GGCC)',
@@ -6912,13 +6941,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F', 'I'),
+        'suppl': ('B', 'I'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GGCC'),
         'ovhgseq': '',
     }
 rest_dict['BsuRI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<BsuTUI>ATCGAT)',
+        'results': None,
+        'site': 'ATCGAT',
+        'substrat': 'DNA',
+        'fst3': -2,
+        'fst5': 2,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -2,
+        'scd3': None,
+        'suppl': ('X',),
+        'scd5': None,
+        'charac': (2, -2, None, None, 'ATCGAT'),
+        'ovhgseq': 'CG',
+    }
+rest_dict['BsuTUI'] = _temp()
 
 def _temp():
     return {
@@ -6942,7 +6992,6 @@ def _temp():
     }
 rest_dict['BtgI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BtgZI>GCGATG)|(?P<BtgZI_as>CATCGC)',
@@ -6965,7 +7014,6 @@ def _temp():
     }
 rest_dict['BtgZI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BthCI>GC.GC)',
@@ -6988,7 +7036,6 @@ def _temp():
     }
 rest_dict['BthCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BtrI>CACGTC)|(?P<BtrI_as>GACGTG)',
@@ -7011,7 +7058,6 @@ def _temp():
     }
 rest_dict['BtrI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BtsCI>GGATG)|(?P<BtsCI_as>CATCC)',
@@ -7034,7 +7080,6 @@ def _temp():
     }
 rest_dict['BtsCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BtsI>GCAGTG)|(?P<BtsI_as>CACTGC)',
@@ -7057,7 +7102,6 @@ def _temp():
     }
 rest_dict['BtsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BtsIMutI>CAGTG)|(?P<BtsIMutI_as>CACTG)',
@@ -7080,7 +7124,6 @@ def _temp():
     }
 rest_dict['BtsIMutI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BtuMI>TCGCGA)',
@@ -7103,7 +7146,6 @@ def _temp():
     }
 rest_dict['BtuMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<BveI>ACCTGC)|(?P<BveI_as>GCAGGT)',
@@ -7119,14 +7161,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (10, 8, None, None, 'ACCTGC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['BveI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cac8I>GC..GC)',
@@ -7149,7 +7190,6 @@ def _temp():
     }
 rest_dict['Cac8I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CaiI>CAG...CTG)',
@@ -7165,13 +7205,78 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (6, -6, None, None, 'CAGNNNCTG'),
         'ovhgseq': 'NNN',
     }
 rest_dict['CaiI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Cal14237I>GGTTAG)|(?P<Cal14237I_as>CTAACC)',
+        'results': None,
+        'site': 'GGTTAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GGTTAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Cal14237I'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<CalB3II>G[AG]TT[AG]AG)|(?P<CalB3II_as>CT[CT]AA[CT]C)',
+        'results': None,
+        'site': 'GRTTRAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GRTTRAG'),
+        'ovhgseq': None,
+    }
+rest_dict['CalB3II'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Cau10061II>GTTAAT)|(?P<Cau10061II_as>ATTAAC)',
+        'results': None,
+        'site': 'GTTAAT',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GTTAAT'),
+        'ovhgseq': None,
+    }
+rest_dict['Cau10061II'] = _temp()
 
 def _temp():
     return {
@@ -7195,6 +7300,49 @@ def _temp():
     }
 rest_dict['CauII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Cba13II>AGGAAT)|(?P<Cba13II_as>ATTCCT)',
+        'results': None,
+        'site': 'AGGAAT',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'AGGAAT'),
+        'ovhgseq': None,
+    }
+rest_dict['Cba13II'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Cba16038I>CCT.A[CT].C)|(?P<Cba16038I_as>G.[AG]T.AGG)',
+        'results': None,
+        'site': 'CCTNAYNC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 8,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCTNAYNC'),
+        'ovhgseq': None,
+    }
+rest_dict['Cba16038I'] = _temp()
 
 def _temp():
     return {
@@ -7218,7 +7366,6 @@ def _temp():
     }
 rest_dict['CchII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CchIII>CCCAAG)|(?P<CchIII_as>CTTGGG)',
@@ -7241,7 +7388,6 @@ def _temp():
     }
 rest_dict['CchIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CciI>TCATGA)',
@@ -7264,7 +7410,6 @@ def _temp():
     }
 rest_dict['CciI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CciNI>GCGGCCGC)',
@@ -7287,12 +7432,33 @@ def _temp():
     }
 rest_dict['CciNI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<CcrNAIII>CGACCAG)|(?P<CcrNAIII_as>CTGGTCG)',
+        'results': None,
+        'site': 'CGACCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CGACCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['CcrNAIII'] = _temp()
 
 def _temp():
     return {
-        'compsite': '(?P<Cdi630V>CAAAAA)|(?P<Cdi630V_as>TTTTTG)',
+        'compsite': '(?P<Cdi11397I>GCGCAG)|(?P<Cdi11397I_as>CTGCGC)',
         'results': None,
-        'site': 'CAAAAA',
+        'site': 'GCGCAG',
         'substrat': 'DNA',
         'fst3': None,
         'fst5': None,
@@ -7305,11 +7471,10 @@ def _temp():
         'scd3': None,
         'suppl': (),
         'scd5': None,
-        'charac': (None, None, None, None, 'CAAAAA'),
+        'charac': (None, None, None, None, 'GCGCAG'),
         'ovhgseq': None,
     }
-rest_dict['Cdi630V'] = _temp()
-
+rest_dict['Cdi11397I'] = _temp()
 
 def _temp():
     return {
@@ -7333,7 +7498,6 @@ def _temp():
     }
 rest_dict['CdiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CdpI>GCGGAG)|(?P<CdpI_as>CTCCGC)',
@@ -7356,6 +7520,27 @@ def _temp():
     }
 rest_dict['CdpI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Cdu23823II>GTGAAG)|(?P<Cdu23823II_as>CTTCAC)',
+        'results': None,
+        'site': 'GTGAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GTGAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Cdu23823II'] = _temp()
 
 def _temp():
     return {
@@ -7379,7 +7564,6 @@ def _temp():
     }
 rest_dict['CfoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cfr10I>[AG]CCGG[CT])',
@@ -7395,14 +7579,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (1, -1, None, None, 'RCCGGY'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['Cfr10I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cfr13I>GG.CC)',
@@ -7418,14 +7601,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGNCC'),
         'ovhgseq': 'GNC',
     }
 rest_dict['Cfr13I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cfr42I>CCGCGG)',
@@ -7441,14 +7623,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'CCGCGG'),
         'ovhgseq': 'GC',
     }
 rest_dict['Cfr42I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cfr9I>CCCGGG)',
@@ -7464,14 +7645,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCCGGG'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['Cfr9I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CfrI>[CT]GGCC[AG])',
@@ -7494,7 +7674,6 @@ def _temp():
     }
 rest_dict['CfrI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cgl13032I>GGCGCA)|(?P<Cgl13032I_as>TGCGCC)',
@@ -7517,7 +7696,6 @@ def _temp():
     }
 rest_dict['Cgl13032I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Cgl13032II>ACGA[CGT]GG)|(?P<Cgl13032II_as>CC[ACG]TCGT)',
@@ -7540,7 +7718,6 @@ def _temp():
     }
 rest_dict['Cgl13032II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ChaI>GATC)',
@@ -7563,7 +7740,6 @@ def _temp():
     }
 rest_dict['ChaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjeFIII>GCAAGG)|(?P<CjeFIII_as>CCTTGC)',
@@ -7586,7 +7762,6 @@ def _temp():
     }
 rest_dict['CjeFIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjeFV>GG[AG]CA)|(?P<CjeFV_as>TG[CT]CC)',
@@ -7609,7 +7784,6 @@ def _temp():
     }
 rest_dict['CjeFV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjeI>CCA......GT)|(?P<CjeI_as>AC......TGG)',
@@ -7632,7 +7806,6 @@ def _temp():
     }
 rest_dict['CjeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjeNII>GAG.....GT)|(?P<CjeNII_as>AC.....CTC)',
@@ -7655,7 +7828,6 @@ def _temp():
     }
 rest_dict['CjeNII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjeNIII>G[GT]AA[CT]G)|(?P<CjeNIII_as>C[AG]TT[AC]C)',
@@ -7678,7 +7850,6 @@ def _temp():
     }
 rest_dict['CjeNIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjeP659IV>CAC.......GAA)|(?P<CjeP659IV_as>TTC.......GTG)',
@@ -7701,7 +7872,6 @@ def _temp():
     }
 rest_dict['CjeP659IV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjePI>CCA.......TC)|(?P<CjePI_as>GA.......TGG)',
@@ -7724,7 +7894,6 @@ def _temp():
     }
 rest_dict['CjePI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjuI>CA[CT].....[AG]TG)',
@@ -7747,7 +7916,6 @@ def _temp():
     }
 rest_dict['CjuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CjuII>CA[CT].....CTC)|(?P<CjuII_as>GAG.....[AG]TG)',
@@ -7770,6 +7938,27 @@ def _temp():
     }
 rest_dict['CjuII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Cla11845III>GCGAA)|(?P<Cla11845III_as>TTCGC)',
+        'results': None,
+        'site': 'GCGAA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GCGAA'),
+        'ovhgseq': None,
+    }
+rest_dict['Cla11845III'] = _temp()
 
 def _temp():
     return {
@@ -7786,13 +7975,56 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('B', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'X'),
+        'suppl': ('B', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'ATCGAT'),
         'ovhgseq': 'CG',
     }
 rest_dict['ClaI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Cly7489II>AAAAG[AG]G)|(?P<Cly7489II_as>C[CT]CTTTT)',
+        'results': None,
+        'site': 'AAAAGRG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'AAAAGRG'),
+        'ovhgseq': None,
+    }
+rest_dict['Cly7489II'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Cma23826I>CGGAAG)|(?P<Cma23826I_as>CTTCCG)',
+        'results': None,
+        'site': 'CGGAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CGGAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Cma23826I'] = _temp()
 
 def _temp():
     return {
@@ -7809,14 +8041,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (2, -2, None, None, 'CGGWCCG'),
         'ovhgseq': 'GWC',
     }
 rest_dict['CpoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CseI>GACGC)|(?P<CseI_as>GCGTC)',
@@ -7832,14 +8063,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (10, 10, None, None, 'GACGC'),
         'ovhgseq': 'NNNNN',
     }
 rest_dict['CseI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CsiI>ACC[AT]GGT)',
@@ -7855,37 +8085,57 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'ACCWGGT'),
         'ovhgseq': 'CCWGG',
     }
 rest_dict['CsiI'] = _temp()
 
-
 def _temp():
     return {
-        'compsite': '(?P<Csp6I>GTAC)',
+        'compsite': '(?P<Csp2014I>GGAGGC)|(?P<Csp2014I_as>GCCTCC)',
         'results': None,
-        'site': 'GTAC',
+        'site': 'GGAGGC',
         'substrat': 'DNA',
-        'fst3': -1,
-        'fst5': 1,
-        'freq': 256,
-        'size': 4,
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GGAGGC'),
+        'ovhgseq': None,
+    }
+rest_dict['Csp2014I'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Csp6I>GTAC)',
+        'results': None,
+        'site': 'GTAC',
+        'substrat': 'DNA',
+        'fst3': -1,
+        'fst5': 1,
+        'freq': 256,
+        'size': 4,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GTAC'),
         'ovhgseq': 'TA',
     }
 rest_dict['Csp6I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CspAI>ACCGGT)',
@@ -7908,7 +8158,6 @@ def _temp():
     }
 rest_dict['CspAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CspCI>CAA.....GTGG)|(?P<CspCI_as>CCAC.....TTG)',
@@ -7931,7 +8180,6 @@ def _temp():
     }
 rest_dict['CspCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CspI>CGG[AT]CCG)',
@@ -7954,7 +8202,6 @@ def _temp():
     }
 rest_dict['CspI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CstMI>AAGGAG)|(?P<CstMI_as>CTCCTT)',
@@ -7977,7 +8224,6 @@ def _temp():
     }
 rest_dict['CstMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CviAII>CATG)',
@@ -8000,7 +8246,6 @@ def _temp():
     }
 rest_dict['CviAII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CviJI>[AG]GC[CT])',
@@ -8023,7 +8268,6 @@ def _temp():
     }
 rest_dict['CviJI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CviKI_1>[AG]GC[CT])',
@@ -8046,7 +8290,6 @@ def _temp():
     }
 rest_dict['CviKI_1'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CviQI>GTAC)',
@@ -8069,7 +8312,6 @@ def _temp():
     }
 rest_dict['CviQI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<CviRI>TGCA)',
@@ -8092,7 +8334,6 @@ def _temp():
     }
 rest_dict['CviRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DdeI>CT.AG)',
@@ -8115,7 +8356,6 @@ def _temp():
     }
 rest_dict['DdeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DinI>GGCGCC)',
@@ -8138,7 +8378,6 @@ def _temp():
     }
 rest_dict['DinI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DpnI>GATC)',
@@ -8154,14 +8393,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'E', 'F', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
+        'suppl': ('B', 'E', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GATC'),
         'ovhgseq': '',
     }
 rest_dict['DpnI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DpnII>GATC)',
@@ -8184,7 +8422,6 @@ def _temp():
     }
 rest_dict['DpnII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DraI>TTTAAA)',
@@ -8200,14 +8437,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'TTTAAA'),
         'ovhgseq': '',
     }
 rest_dict['DraI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DraII>[AG]GG.CC[CT])',
@@ -8230,7 +8466,6 @@ def _temp():
     }
 rest_dict['DraII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DraIII>CAC...GTG)',
@@ -8253,7 +8488,6 @@ def _temp():
     }
 rest_dict['DraIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DraRI>CAAG.AC)|(?P<DraRI_as>GT.CTTG)',
@@ -8276,7 +8510,6 @@ def _temp():
     }
 rest_dict['DraRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DrdI>GAC......GTC)',
@@ -8299,7 +8532,6 @@ def _temp():
     }
 rest_dict['DrdI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DrdII>GAACCA)|(?P<DrdII_as>TGGTTC)',
@@ -8322,7 +8554,6 @@ def _temp():
     }
 rest_dict['DrdII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DriI>GAC.....GTC)',
@@ -8345,7 +8576,6 @@ def _temp():
     }
 rest_dict['DriI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DsaI>CC[AG][CT]GG)',
@@ -8368,7 +8598,6 @@ def _temp():
     }
 rest_dict['DsaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<DseDI>GAC......GTC)',
@@ -8391,7 +8620,6 @@ def _temp():
     }
 rest_dict['DseDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EaeI>[CT]GGCC[AG])',
@@ -8414,7 +8642,6 @@ def _temp():
     }
 rest_dict['EaeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EagI>CGGCCG)',
@@ -8437,7 +8664,6 @@ def _temp():
     }
 rest_dict['EagI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eam1104I>CTCTTC)|(?P<Eam1104I_as>GAAGAG)',
@@ -8453,14 +8679,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, 4, None, None, 'CTCTTC'),
         'ovhgseq': 'NNN',
     }
 rest_dict['Eam1104I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eam1105I>GAC.....GTC)',
@@ -8476,14 +8701,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 1,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (6, -6, None, None, 'GACNNNNNGTC'),
         'ovhgseq': 'N',
     }
 rest_dict['Eam1105I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EarI>CTCTTC)|(?P<EarI_as>GAAGAG)',
@@ -8506,7 +8730,6 @@ def _temp():
     }
 rest_dict['EarI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EciI>GGCGGA)|(?P<EciI_as>TCCGCC)',
@@ -8529,7 +8752,6 @@ def _temp():
     }
 rest_dict['EciI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ecl136II>GAGCTC)',
@@ -8545,13 +8767,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'GAGCTC'),
         'ovhgseq': '',
     }
 rest_dict['Ecl136II'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Ecl35734I>GAAA[CT]TC)|(?P<Ecl35734I_as>GA[AG]TTTC)',
+        'results': None,
+        'site': 'GAAAYTC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GAAAYTC'),
+        'ovhgseq': None,
+    }
+rest_dict['Ecl35734I'] = _temp()
 
 def _temp():
     return {
@@ -8568,14 +8811,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('M', 'S'),
+        'suppl': ('S',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CGGCCG'),
         'ovhgseq': 'GGCC',
     }
 rest_dict['EclXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco105I>TACGTA)',
@@ -8591,14 +8833,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'TACGTA'),
         'ovhgseq': '',
     }
 rest_dict['Eco105I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco130I>CC[AT][AT]GG)',
@@ -8614,14 +8855,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCWWGG'),
         'ovhgseq': 'CWWG',
     }
 rest_dict['Eco130I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco147I>AGGCCT)',
@@ -8637,14 +8877,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'AGGCCT'),
         'ovhgseq': '',
     }
 rest_dict['Eco147I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco24I>G[AG]GC[CT]C)',
@@ -8660,14 +8899,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GRGCYC'),
         'ovhgseq': 'RGCY',
     }
 rest_dict['Eco24I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco31I>GGTCTC)|(?P<Eco31I_as>GAGACC)',
@@ -8683,14 +8921,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, 5, None, None, 'GGTCTC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['Eco31I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco32I>GATATC)',
@@ -8706,14 +8943,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'GATATC'),
         'ovhgseq': '',
     }
 rest_dict['Eco32I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco47I>GG[AT]CC)',
@@ -8729,14 +8965,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGWCC'),
         'ovhgseq': 'GWC',
     }
 rest_dict['Eco47I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco47III>AGCGCT)',
@@ -8752,14 +8987,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F', 'M', 'R'),
+        'suppl': ('B', 'M', 'R'),
         'scd5': None,
         'charac': (3, -3, None, None, 'AGCGCT'),
         'ovhgseq': '',
     }
 rest_dict['Eco47III'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco52I>CGGCCG)',
@@ -8775,14 +9009,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CGGCCG'),
         'ovhgseq': 'GGCC',
     }
 rest_dict['Eco52I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco53kI>GAGCTC)',
@@ -8805,7 +9038,6 @@ def _temp():
     }
 rest_dict['Eco53kI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco57I>CTGAAG)|(?P<Eco57I_as>CTTCAG)',
@@ -8821,14 +9053,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (22, 14, None, None, 'CTGAAG'),
         'ovhgseq': 'NN',
     }
 rest_dict['Eco57I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco57MI>CTG[AG]AG)|(?P<Eco57MI_as>CT[CT]CAG)',
@@ -8851,7 +9082,6 @@ def _temp():
     }
 rest_dict['Eco57MI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco72I>CACGTG)',
@@ -8867,14 +9097,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'CACGTG'),
         'ovhgseq': '',
     }
 rest_dict['Eco72I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco81I>CCT.AGG)',
@@ -8890,14 +9119,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCTNAGG'),
         'ovhgseq': 'TNA',
     }
 rest_dict['Eco81I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco88I>C[CT]CG[AG]G)',
@@ -8913,14 +9141,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CYCGRG'),
         'ovhgseq': 'YCGR',
     }
 rest_dict['Eco88I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Eco91I>GGT.ACC)',
@@ -8936,13 +9163,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGTNACC'),
         'ovhgseq': 'GTNAC',
     }
 rest_dict['Eco91I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<EcoE1140I>ACC[CT]AC)|(?P<EcoE1140I_as>GT[AG]GGT)',
+        'results': None,
+        'site': 'ACCYAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'ACCYAC'),
+        'ovhgseq': None,
+    }
+rest_dict['EcoE1140I'] = _temp()
 
 def _temp():
     return {
@@ -8966,7 +9214,6 @@ def _temp():
     }
 rest_dict['EcoHI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoICRI>GAGCTC)',
@@ -8989,6 +9236,27 @@ def _temp():
     }
 rest_dict['EcoICRI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<EcoMII>CA.CATC)|(?P<EcoMII_as>GATG.TG)',
+        'results': None,
+        'site': 'CANCATC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CANCATC'),
+        'ovhgseq': None,
+    }
+rest_dict['EcoMII'] = _temp()
 
 def _temp():
     return {
@@ -9012,7 +9280,6 @@ def _temp():
     }
 rest_dict['EcoNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoO109I>[AG]GG.CC[CT])',
@@ -9028,14 +9295,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F', 'J', 'K', 'N'),
+        'suppl': ('B', 'J', 'K', 'N'),
         'scd5': None,
         'charac': (2, -2, None, None, 'RGGNCCY'),
         'ovhgseq': 'GNC',
     }
 rest_dict['EcoO109I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoO65I>GGT.ACC)',
@@ -9058,7 +9324,6 @@ def _temp():
     }
 rest_dict['EcoO65I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoRI>GAATTC)',
@@ -9074,14 +9339,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GAATTC'),
         'ovhgseq': 'AATT',
     }
 rest_dict['EcoRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoRII>CC[AT]GG)',
@@ -9097,14 +9361,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('F', 'J'),
+        'suppl': ('B', 'J'),
         'scd5': None,
         'charac': (0, 0, None, None, 'CCWGG'),
         'ovhgseq': 'CCWGG',
     }
 rest_dict['EcoRII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoRV>GATATC)',
@@ -9120,14 +9383,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'GATATC'),
         'ovhgseq': '',
     }
 rest_dict['EcoRV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoT14I>CC[AT][AT]GG)',
@@ -9150,7 +9412,6 @@ def _temp():
     }
 rest_dict['EcoT14I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoT22I>ATGCAT)',
@@ -9173,7 +9434,6 @@ def _temp():
     }
 rest_dict['EcoT22I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EcoT38I>G[AG]GC[CT]C)',
@@ -9196,7 +9456,6 @@ def _temp():
     }
 rest_dict['EcoT38I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EgeI>GGCGCC)',
@@ -9219,7 +9478,6 @@ def _temp():
     }
 rest_dict['EgeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EheI>GGCGCC)',
@@ -9235,14 +9493,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'GGCGCC'),
         'ovhgseq': '',
     }
 rest_dict['EheI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ErhI>CC[AT][AT]GG)',
@@ -9258,14 +9515,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('I', 'V'),
+        'suppl': ('I',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCWWGG'),
         'ovhgseq': 'CWWG',
     }
 rest_dict['ErhI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EsaBC3I>TCGA)',
@@ -9288,7 +9544,6 @@ def _temp():
     }
 rest_dict['EsaBC3I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EsaSSI>GACCAC)|(?P<EsaSSI_as>GTGGTC)',
@@ -9311,6 +9566,27 @@ def _temp():
     }
 rest_dict['EsaSSI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Esp3007I>CAGAAG)|(?P<Esp3007I_as>CTTCTG)',
+        'results': None,
+        'site': 'CAGAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CAGAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Esp3007I'] = _temp()
 
 def _temp():
     return {
@@ -9327,14 +9603,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, 5, None, None, 'CGTCTC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['Esp3I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<EspI>GCT.AGC)',
@@ -9357,7 +9632,6 @@ def _temp():
     }
 rest_dict['EspI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FaeI>CATG)',
@@ -9380,7 +9654,6 @@ def _temp():
     }
 rest_dict['FaeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FaiI>[CT]AT[AG])',
@@ -9403,7 +9676,6 @@ def _temp():
     }
 rest_dict['FaiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FalI>AAG.....CTT)',
@@ -9426,7 +9698,6 @@ def _temp():
     }
 rest_dict['FalI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FaqI>GGGAC)|(?P<FaqI_as>GTCCC)',
@@ -9442,14 +9713,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (15, 14, None, None, 'GGGAC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['FaqI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FatI>CATG)',
@@ -9472,7 +9742,6 @@ def _temp():
     }
 rest_dict['FatI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FauI>CCCGC)|(?P<FauI_as>GCGGG)',
@@ -9495,7 +9764,6 @@ def _temp():
     }
 rest_dict['FauI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FauNDI>CATATG)',
@@ -9518,7 +9786,6 @@ def _temp():
     }
 rest_dict['FauNDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FbaI>TGATCA)',
@@ -9541,7 +9808,6 @@ def _temp():
     }
 rest_dict['FbaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FblI>GT[AC][GT]AC)',
@@ -9564,7 +9830,6 @@ def _temp():
     }
 rest_dict['FblI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FinI>GGGAC)|(?P<FinI_as>GTCCC)',
@@ -9587,7 +9852,6 @@ def _temp():
     }
 rest_dict['FinI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FmuI>GG.CC)',
@@ -9610,7 +9874,6 @@ def _temp():
     }
 rest_dict['FmuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Fnu4HI>GC.GC)',
@@ -9633,7 +9896,6 @@ def _temp():
     }
 rest_dict['Fnu4HI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FnuDII>CGCG)',
@@ -9656,7 +9918,6 @@ def _temp():
     }
 rest_dict['FnuDII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FokI>GGATG)|(?P<FokI_as>CATCC)',
@@ -9672,14 +9933,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('I', 'J', 'K', 'M', 'N', 'V', 'X'),
+        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (14, 13, None, None, 'GGATG'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['FokI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FriOI>G[AG]GC[CT]C)',
@@ -9702,7 +9962,6 @@ def _temp():
     }
 rest_dict['FriOI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FseI>GGCCGGCC)',
@@ -9725,7 +9984,6 @@ def _temp():
     }
 rest_dict['FseI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Fsp4HI>GC.GC)',
@@ -9748,7 +10006,6 @@ def _temp():
     }
 rest_dict['Fsp4HI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FspAI>[AG]TGCGCA[CT])',
@@ -9764,14 +10021,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'RTGCGCAY'),
         'ovhgseq': '',
     }
 rest_dict['FspAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FspBI>CTAG)',
@@ -9787,14 +10043,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTAG'),
         'ovhgseq': 'TA',
     }
 rest_dict['FspBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FspEI>CC)|(?P<FspEI_as>GG)',
@@ -9817,7 +10072,6 @@ def _temp():
     }
 rest_dict['FspEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<FspI>TGCGCA)',
@@ -9840,6 +10094,27 @@ def _temp():
     }
 rest_dict['FspI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<FtnUV>GAAACA)|(?P<FtnUV_as>TGTTTC)',
+        'results': None,
+        'site': 'GAAACA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GAAACA'),
+        'ovhgseq': None,
+    }
+rest_dict['FtnUV'] = _temp()
 
 def _temp():
     return {
@@ -9863,6 +10138,27 @@ def _temp():
     }
 rest_dict['GauT27I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Gba708II>ATGCAC)|(?P<Gba708II_as>GTGCAT)',
+        'results': None,
+        'site': 'ATGCAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'ATGCAC'),
+        'ovhgseq': None,
+    }
+rest_dict['Gba708II'] = _temp()
 
 def _temp():
     return {
@@ -9886,7 +10182,6 @@ def _temp():
     }
 rest_dict['GdiII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<GlaI>GCGC)',
@@ -9909,7 +10204,6 @@ def _temp():
     }
 rest_dict['GlaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<GluI>GC.GC)',
@@ -9932,7 +10226,6 @@ def _temp():
     }
 rest_dict['GluI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<GsaI>CCCAGC)|(?P<GsaI_as>GCTGGG)',
@@ -9955,7 +10248,6 @@ def _temp():
     }
 rest_dict['GsaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<GsuI>CTGGAG)|(?P<GsuI_as>CTCCAG)',
@@ -9971,14 +10263,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (22, 14, None, None, 'CTGGAG'),
         'ovhgseq': 'NN',
     }
 rest_dict['GsuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HaeI>[AT]GGCC[AT])',
@@ -10001,7 +10292,6 @@ def _temp():
     }
 rest_dict['HaeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HaeII>[AG]GCGC[CT])',
@@ -10024,7 +10314,6 @@ def _temp():
     }
 rest_dict['HaeII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HaeIII>GGCC)',
@@ -10040,14 +10329,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GGCC'),
         'ovhgseq': '',
     }
 rest_dict['HaeIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HapII>CCGG)',
@@ -10070,7 +10358,6 @@ def _temp():
     }
 rest_dict['HapII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HauII>TGGCCA)',
@@ -10093,6 +10380,27 @@ def _temp():
     }
 rest_dict['HauII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<HbaII>GCCCAG)|(?P<HbaII_as>CTGGGC)',
+        'results': None,
+        'site': 'GCCCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GCCCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['HbaII'] = _temp()
 
 def _temp():
     return {
@@ -10116,7 +10424,6 @@ def _temp():
     }
 rest_dict['HgaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HgiAI>G[AT]GC[AT]C)',
@@ -10139,7 +10446,6 @@ def _temp():
     }
 rest_dict['HgiAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HgiCI>GG[CT][AG]CC)',
@@ -10162,7 +10468,6 @@ def _temp():
     }
 rest_dict['HgiCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HgiEII>ACC......GGT)',
@@ -10185,7 +10490,6 @@ def _temp():
     }
 rest_dict['HgiEII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HgiJII>G[AG]GC[CT]C)',
@@ -10208,7 +10512,6 @@ def _temp():
     }
 rest_dict['HgiJII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HhaI>GCGC)',
@@ -10224,14 +10527,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('B', 'F', 'J', 'K', 'N', 'Q', 'R', 'U', 'X', 'Y'),
+        'suppl': ('B', 'J', 'K', 'N', 'Q', 'R', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'GCGC'),
         'ovhgseq': 'CG',
     }
 rest_dict['HhaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hin1I>G[AG]CG[CT]C)',
@@ -10247,14 +10549,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GRCGYC'),
         'ovhgseq': 'CG',
     }
 rest_dict['Hin1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hin1II>CATG)',
@@ -10270,14 +10571,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'CATG'),
         'ovhgseq': 'CATG',
     }
 rest_dict['Hin1II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hin4I>GA[CT].....[ACG]TC)|(?P<Hin4I_as>GA[CGT].....[AG]TC)',
@@ -10300,7 +10600,6 @@ def _temp():
     }
 rest_dict['Hin4I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hin4II>CCTTC)|(?P<Hin4II_as>GAAGG)',
@@ -10323,7 +10622,6 @@ def _temp():
     }
 rest_dict['Hin4II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hin6I>GCGC)',
@@ -10339,14 +10637,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GCGC'),
         'ovhgseq': 'CG',
     }
 rest_dict['Hin6I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HinP1I>GCGC)',
@@ -10369,7 +10666,6 @@ def _temp():
     }
 rest_dict['HinP1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HincII>GT[CT][AG]AC)',
@@ -10385,14 +10681,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'F', 'J', 'K', 'N', 'O', 'Q', 'R', 'U', 'X', 'Y'),
+        'suppl': ('B', 'J', 'K', 'N', 'O', 'Q', 'R', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'GTYRAC'),
         'ovhgseq': '',
     }
 rest_dict['HincII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HindII>GT[CT][AG]AC)',
@@ -10415,7 +10710,6 @@ def _temp():
     }
 rest_dict['HindII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HindIII>AAGCTT)',
@@ -10431,14 +10725,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'AAGCTT'),
         'ovhgseq': 'AGCT',
     }
 rest_dict['HindIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HinfI>GA.TC)',
@@ -10454,14 +10747,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GANTC'),
         'ovhgseq': 'ANT',
     }
 rest_dict['HinfI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpaI>GTTAAC)',
@@ -10477,14 +10769,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'GTTAAC'),
         'ovhgseq': '',
     }
 rest_dict['HpaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpaII>CCGG)',
@@ -10500,14 +10791,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F', 'I', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+        'suppl': ('B', 'I', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCGG'),
         'ovhgseq': 'CG',
     }
 rest_dict['HpaII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HphI>GGTGA)|(?P<HphI_as>TCACC)',
@@ -10523,14 +10813,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 1,
         'scd3': None,
-        'suppl': ('F', 'N'),
+        'suppl': ('B', 'N'),
         'scd5': None,
         'charac': (13, 7, None, None, 'GGTGA'),
         'ovhgseq': 'N',
     }
 rest_dict['HphI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy166II>GT..AC)',
@@ -10553,7 +10842,6 @@ def _temp():
     }
 rest_dict['Hpy166II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy178III>TC..GA)',
@@ -10576,7 +10864,6 @@ def _temp():
     }
 rest_dict['Hpy178III'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy188I>TC.GA)',
@@ -10599,7 +10886,6 @@ def _temp():
     }
 rest_dict['Hpy188I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy188III>TC..GA)',
@@ -10622,7 +10908,6 @@ def _temp():
     }
 rest_dict['Hpy188III'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy8I>GT..AC)',
@@ -10638,14 +10923,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'GTNNAC'),
         'ovhgseq': '',
     }
 rest_dict['Hpy8I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy99I>CG[AT]CG)',
@@ -10668,7 +10952,6 @@ def _temp():
     }
 rest_dict['Hpy99I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy99XIII>GCCTA)|(?P<Hpy99XIII_as>TAGGC)',
@@ -10691,7 +10974,6 @@ def _temp():
     }
 rest_dict['Hpy99XIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hpy99XIV>GG[AT]TAA)|(?P<Hpy99XIV_as>TTA[AT]CC)',
@@ -10714,6 +10996,49 @@ def _temp():
     }
 rest_dict['Hpy99XIV'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Hpy99XIV_mut1>GG[AT]C.A)|(?P<Hpy99XIV_mut1_as>T.G[AT]CC)',
+        'results': None,
+        'site': 'GGWCNA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 512,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GGWCNA'),
+        'ovhgseq': None,
+    }
+rest_dict['Hpy99XIV_mut1'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Hpy99XXII>TCA......T[AG]G)|(?P<Hpy99XXII_as>C[CT]A......TGA)',
+        'results': None,
+        'site': 'TCANNNNNNTRG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 12,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'TCANNNNNNTRG'),
+        'ovhgseq': None,
+    }
+rest_dict['Hpy99XXII'] = _temp()
 
 def _temp():
     return {
@@ -10737,6 +11062,71 @@ def _temp():
     }
 rest_dict['HpyAV'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<HpyAXIV>GCGTA)|(?P<HpyAXIV_as>TACGC)',
+        'results': None,
+        'site': 'GCGTA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GCGTA'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyAXIV'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<HpyAXVI_mut1>C[AG]TTAA)|(?P<HpyAXVI_mut1_as>TTAA[CT]G)',
+        'results': None,
+        'site': 'CRTTAA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CRTTAA'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyAXVI_mut1'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<HpyAXVI_mut2>C[AG]TC.A)|(?P<HpyAXVI_mut2_as>T.GA[CT]G)',
+        'results': None,
+        'site': 'CRTCNA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 512,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CRTCNA'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyAXVI_mut2'] = _temp()
 
 def _temp():
     return {
@@ -10760,7 +11150,6 @@ def _temp():
     }
 rest_dict['HpyCH4III'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpyCH4IV>ACGT)',
@@ -10783,7 +11172,6 @@ def _temp():
     }
 rest_dict['HpyCH4IV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpyCH4V>TGCA)',
@@ -10806,7 +11194,6 @@ def _temp():
     }
 rest_dict['HpyCH4V'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpyF10VI>GC.......GC)',
@@ -10822,14 +11209,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, -7, None, None, 'GCNNNNNNNGC'),
         'ovhgseq': 'NNN',
     }
 rest_dict['HpyF10VI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpyF3I>CT.AG)',
@@ -10845,14 +11231,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTNAG'),
         'ovhgseq': 'TNA',
     }
 rest_dict['HpyF3I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HpySE526I>ACGT)',
@@ -10875,6 +11260,93 @@ def _temp():
     }
 rest_dict['HpySE526I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<HpyUM032XIII>C[CT]A.......T[AG]G)',
+        'results': None,
+        'site': 'CYANNNNNNNTRG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 13,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CYANNNNNNNTRG'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyUM032XIII'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<HpyUM032XIII_mut1>C[CT]A.......TTC)|(?P<HpyUM032XIII_mut1_as>GAA.......T[AG]G)',
+        'results': None,
+        'site': 'CYANNNNNNNTTC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 13,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CYANNNNNNNTTC'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyUM032XIII_mut1'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<HpyUM032XIV>GAAAG)|(?P<HpyUM032XIV_as>CTTTC)',
+        'results': None,
+        'site': 'GAAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GAAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyUM032XIV'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<HpyUM037X>T.GG.AG)|(?P<HpyUM037X_as>CT.CC.A)',
+        'results': None,
+        'site': 'TNGGNAG|GTGGNAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'TNGGNAG|GTGGNAG'),
+        'ovhgseq': None,
+    }
+rest_dict['HpyUM037X'] = _temp()
 
 def _temp():
     return {
@@ -10898,7 +11370,6 @@ def _temp():
     }
 rest_dict['Hsp92I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Hsp92II>CATG)',
@@ -10921,7 +11392,6 @@ def _temp():
     }
 rest_dict['Hsp92II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<HspAI>GCGC)',
@@ -10944,7 +11414,6 @@ def _temp():
     }
 rest_dict['HspAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Jma19592I>GTAT.AC)|(?P<Jma19592I_as>GT.ATAC)',
@@ -10967,6 +11436,27 @@ def _temp():
     }
 rest_dict['Jma19592I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Jma19592II>G[AG]GC[AG]AC)|(?P<Jma19592II_as>GT[CT]GC[CT]C)',
+        'results': None,
+        'site': 'GRGCRAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GRGCRAC'),
+        'ovhgseq': None,
+    }
+rest_dict['Jma19592II'] = _temp()
 
 def _temp():
     return {
@@ -10990,7 +11480,6 @@ def _temp():
     }
 rest_dict['KasI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<KflI>GGG[AT]CCC)',
@@ -11006,14 +11495,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'GGGWCCC'),
         'ovhgseq': 'GWC',
     }
 rest_dict['KflI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Kpn2I>TCCGGA)',
@@ -11029,14 +11517,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TCCGGA'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['Kpn2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<KpnI>GGTACC)',
@@ -11052,13 +11539,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (5, -5, None, None, 'GGTACC'),
         'ovhgseq': 'GTAC',
     }
 rest_dict['KpnI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<KpnNIH30III>GTTC.AC)|(?P<KpnNIH30III_as>GT.GAAC)',
+        'results': None,
+        'site': 'GTTCNAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GTTCNAC'),
+        'ovhgseq': None,
+    }
+rest_dict['KpnNIH30III'] = _temp()
 
 def _temp():
     return {
@@ -11082,7 +11590,6 @@ def _temp():
     }
 rest_dict['KroI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ksp22I>TGATCA)',
@@ -11105,7 +11612,6 @@ def _temp():
     }
 rest_dict['Ksp22I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ksp632I>CTCTTC)|(?P<Ksp632I_as>GAAGAG)',
@@ -11128,7 +11634,6 @@ def _temp():
     }
 rest_dict['Ksp632I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<KspAI>GTTAAC)',
@@ -11142,84 +11647,168 @@ def _temp():
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': 0,
+        'ovhg': 0,
+        'scd3': None,
+        'suppl': ('B',),
+        'scd5': None,
+        'charac': (3, -3, None, None, 'GTTAAC'),
+        'ovhgseq': '',
+    }
+rest_dict['KspAI'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<KspI>CCGCGG)',
+        'results': None,
+        'site': 'CCGCGG',
+        'substrat': 'DNA',
+        'fst3': -4,
+        'fst5': 4,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 2,
+        'scd3': None,
+        'suppl': ('M', 'S'),
+        'scd5': None,
+        'charac': (4, -4, None, None, 'CCGCGG'),
+        'ovhgseq': 'GC',
+    }
+rest_dict['KspI'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Kzo9I>GATC)',
+        'results': None,
+        'site': 'GATC',
+        'substrat': 'DNA',
+        'fst3': 0,
+        'fst5': 0,
+        'freq': 256,
+        'size': 4,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -4,
+        'scd3': None,
+        'suppl': ('I',),
+        'scd5': None,
+        'charac': (0, 0, None, None, 'GATC'),
+        'ovhgseq': 'GATC',
+    }
+rest_dict['Kzo9I'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Lba2029III>C[CT]AAA.G)|(?P<Lba2029III_as>C.TTT[AG]G)',
+        'results': None,
+        'site': 'CYAAANG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CYAAANG'),
+        'ovhgseq': None,
+    }
+rest_dict['Lba2029III'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<LguI>GCTCTTC)|(?P<LguI_as>GAAGAGC)',
+        'results': None,
+        'site': 'GCTCTTC',
+        'substrat': 'DNA',
+        'fst3': 4,
+        'fst5': 8,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
-        'charac': (3, -3, None, None, 'GTTAAC'),
-        'ovhgseq': '',
+        'charac': (8, 4, None, None, 'GCTCTTC'),
+        'ovhgseq': 'NNN',
     }
-rest_dict['KspAI'] = _temp()
-
+rest_dict['LguI'] = _temp()
 
 def _temp():
     return {
-        'compsite': '(?P<KspI>CCGCGG)',
+        'compsite': '(?P<LmnI>GCTCC)|(?P<LmnI_as>GGAGC)',
         'results': None,
-        'site': 'CCGCGG',
+        'site': 'GCTCC',
         'substrat': 'DNA',
-        'fst3': -4,
-        'fst5': 4,
-        'freq': 4096,
-        'size': 6,
+        'fst3': -1,
+        'fst5': 6,
+        'freq': 1024,
+        'size': 5,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('M', 'S'),
+        'suppl': ('I',),
         'scd5': None,
-        'charac': (4, -4, None, None, 'CCGCGG'),
-        'ovhgseq': 'GC',
+        'charac': (6, -1, None, None, 'GCTCC'),
+        'ovhgseq': 'CN',
     }
-rest_dict['KspI'] = _temp()
-
+rest_dict['LmnI'] = _temp()
 
 def _temp():
     return {
-        'compsite': '(?P<Kzo9I>GATC)',
+        'compsite': '(?P<Lmo370I>AGCGCCG)|(?P<Lmo370I_as>CGGCGCT)',
         'results': None,
-        'site': 'GATC',
+        'site': 'AGCGCCG',
         'substrat': 'DNA',
-        'fst3': 0,
-        'fst5': 0,
-        'freq': 256,
-        'size': 4,
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': -4,
+        'ovhg': None,
         'scd3': None,
-        'suppl': ('I',),
+        'suppl': (),
         'scd5': None,
-        'charac': (0, 0, None, None, 'GATC'),
-        'ovhgseq': 'GATC',
+        'charac': (None, None, None, None, 'AGCGCCG'),
+        'ovhgseq': None,
     }
-rest_dict['Kzo9I'] = _temp()
-
+rest_dict['Lmo370I'] = _temp()
 
 def _temp():
     return {
-        'compsite': '(?P<LguI>GCTCTTC)|(?P<LguI_as>GAAGAGC)',
+        'compsite': '(?P<Lmo911II>TAG[AG]AG)|(?P<Lmo911II_as>CT[CT]CTA)',
         'results': None,
-        'site': 'GCTCTTC',
+        'site': 'TAGRAG',
         'substrat': 'DNA',
-        'fst3': 4,
-        'fst5': 8,
-        'freq': 16384,
-        'size': 7,
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': -3,
+        'ovhg': None,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': (),
         'scd5': None,
-        'charac': (8, 4, None, None, 'GCTCTTC'),
-        'ovhgseq': 'NNN',
+        'charac': (None, None, None, None, 'TAGRAG'),
+        'ovhgseq': None,
     }
-rest_dict['LguI'] = _temp()
-
+rest_dict['Lmo911II'] = _temp()
 
 def _temp():
     return {
@@ -11243,7 +11832,6 @@ def _temp():
     }
 rest_dict['LpnI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<LpnPI>CC[AGT]G)|(?P<LpnPI_as>C[ACT]GG)',
@@ -11266,7 +11854,6 @@ def _temp():
     }
 rest_dict['LpnPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Lsp1109I>GCAGC)|(?P<Lsp1109I_as>GCTGC)',
@@ -11282,13 +11869,56 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (13, 12, None, None, 'GCAGC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['Lsp1109I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Lsp48III>AGCACC)|(?P<Lsp48III_as>GGTGCT)',
+        'results': None,
+        'site': 'AGCACC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'AGCACC'),
+        'ovhgseq': None,
+    }
+rest_dict['Lsp48III'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Lsp6406VI>C[AG]AGCAC)|(?P<Lsp6406VI_as>GTGCT[CT]G)',
+        'results': None,
+        'site': 'CRAGCAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CRAGCAC'),
+        'ovhgseq': None,
+    }
+rest_dict['Lsp6406VI'] = _temp()
 
 def _temp():
     return {
@@ -11305,14 +11935,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (10, 9, None, None, 'GCATC'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['LweI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MabI>ACC[AT]GGT)',
@@ -11335,7 +11964,6 @@ def _temp():
     }
 rest_dict['MabI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MaeI>CTAG)',
@@ -11358,7 +11986,6 @@ def _temp():
     }
 rest_dict['MaeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MaeII>ACGT)',
@@ -11381,7 +12008,6 @@ def _temp():
     }
 rest_dict['MaeII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MaeIII>GT.AC)',
@@ -11404,7 +12030,6 @@ def _temp():
     }
 rest_dict['MaeIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MalI>GATC)',
@@ -11427,7 +12052,6 @@ def _temp():
     }
 rest_dict['MalI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MaqI>C[AG]TTGAC)|(?P<MaqI_as>GTCAA[CT]G)',
@@ -11450,7 +12074,6 @@ def _temp():
     }
 rest_dict['MaqI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MauBI>CGCGCGCG)',
@@ -11466,13 +12089,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CGCGCGCG'),
         'ovhgseq': 'CGCG',
     }
 rest_dict['MauBI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Mba11I>AGGCGA)|(?P<Mba11I_as>TCGCCT)',
+        'results': None,
+        'site': 'AGGCGA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'AGGCGA'),
+        'ovhgseq': None,
+    }
+rest_dict['Mba11I'] = _temp()
 
 def _temp():
     return {
@@ -11489,14 +12133,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'CCGCTC'),
         'ovhgseq': '',
     }
 rest_dict['MbiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MboI>GATC)',
@@ -11512,14 +12155,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'K', 'N', 'Q', 'R', 'U', 'X', 'Y'),
+        'suppl': ('B', 'C', 'K', 'N', 'Q', 'R', 'X', 'Y'),
         'scd5': None,
         'charac': (0, 0, None, None, 'GATC'),
         'ovhgseq': 'GATC',
     }
 rest_dict['MboI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MboII>GAAGA)|(?P<MboII_as>TCTTC)',
@@ -11535,14 +12177,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 1,
         'scd3': None,
-        'suppl': ('F', 'I', 'J', 'K', 'N', 'Q', 'R', 'V', 'X'),
+        'suppl': ('B', 'I', 'J', 'K', 'N', 'Q', 'R', 'V', 'X'),
         'scd5': None,
         'charac': (13, 7, None, None, 'GAAGA'),
         'ovhgseq': 'N',
     }
 rest_dict['MboII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<McaTI>GCGCGC)',
@@ -11565,6 +12206,27 @@ def _temp():
     }
 rest_dict['McaTI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Mcr10I>GAAG.....CTC)|(?P<Mcr10I_as>GAG.....CTTC)',
+        'results': None,
+        'site': 'GAAGNNNNNCTC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 12,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GAAGNNNNNCTC'),
+        'ovhgseq': None,
+    }
+rest_dict['Mcr10I'] = _temp()
 
 def _temp():
     return {
@@ -11588,7 +12250,6 @@ def _temp():
     }
 rest_dict['McrI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MfeI>CAATTG)',
@@ -11611,7 +12272,6 @@ def _temp():
     }
 rest_dict['MfeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MflI>[AG]GATC[CT])',
@@ -11634,7 +12294,6 @@ def _temp():
     }
 rest_dict['MflI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MhlI>G[AGT]GC[ACT]C)',
@@ -11657,7 +12316,6 @@ def _temp():
     }
 rest_dict['MhlI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MjaIV>GT..AC)',
@@ -11680,7 +12338,6 @@ def _temp():
     }
 rest_dict['MjaIV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MkaDII>GAGA[CT]GT)|(?P<MkaDII_as>AC[AG]TCTC)',
@@ -11703,7 +12360,6 @@ def _temp():
     }
 rest_dict['MkaDII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MlsI>TGGCCA)',
@@ -11719,14 +12375,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'TGGCCA'),
         'ovhgseq': '',
     }
 rest_dict['MlsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MluCI>AATT)',
@@ -11749,7 +12404,6 @@ def _temp():
     }
 rest_dict['MluCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MluI>ACGCGT)',
@@ -11765,14 +12419,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'U', 'V', 'X'),
+        'suppl': ('B', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'ACGCGT'),
         'ovhgseq': 'CGCG',
     }
 rest_dict['MluI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MluNI>TGGCCA)',
@@ -11795,7 +12448,6 @@ def _temp():
     }
 rest_dict['MluNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Mly113I>GGCGCC)',
@@ -11818,7 +12470,6 @@ def _temp():
     }
 rest_dict['Mly113I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MlyI>GAGTC)|(?P<MlyI_as>GACTC)',
@@ -11841,7 +12492,6 @@ def _temp():
     }
 rest_dict['MlyI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MmeI>TCC[AG]AC)|(?P<MmeI_as>GT[CT]GGA)',
@@ -11857,14 +12507,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('N',),
+        'suppl': ('N', 'X'),
         'scd5': None,
         'charac': (26, 18, None, None, 'TCCRAC'),
         'ovhgseq': 'NN',
     }
 rest_dict['MmeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MnlI>CCTC)|(?P<MnlI_as>GAGG)',
@@ -11880,13 +12529,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 1,
         'scd3': None,
-        'suppl': ('F', 'I', 'N', 'Q', 'V', 'X'),
+        'suppl': ('B', 'I', 'N', 'Q', 'V', 'X'),
         'scd5': None,
         'charac': (11, 6, None, None, 'CCTC'),
         'ovhgseq': 'N',
     }
 rest_dict['MnlI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Mox20I>TGGCCA)',
+        'results': None,
+        'site': 'TGGCCA',
+        'substrat': 'DNA',
+        'fst3': -3,
+        'fst5': 3,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 0,
+        'scd3': None,
+        'suppl': ('I',),
+        'scd5': None,
+        'charac': (3, -3, None, None, 'TGGCCA'),
+        'ovhgseq': '',
+    }
+rest_dict['Mox20I'] = _temp()
 
 def _temp():
     return {
@@ -11903,14 +12573,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'ATGCAT'),
         'ovhgseq': 'TGCA',
     }
 rest_dict['Mph1103I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MreI>CGCCGGCG)',
@@ -11926,14 +12595,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CGCCGGCG'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['MreI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MroI>TCCGGA)',
@@ -11956,7 +12624,6 @@ def _temp():
     }
 rest_dict['MroI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MroNI>GCCGGC)',
@@ -11979,7 +12646,6 @@ def _temp():
     }
 rest_dict['MroNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MroXI>GAA....TTC)',
@@ -12002,7 +12668,6 @@ def _temp():
     }
 rest_dict['MroXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MscI>TGGCCA)',
@@ -12025,7 +12690,6 @@ def _temp():
     }
 rest_dict['MscI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MseI>TTAA)',
@@ -12041,14 +12705,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('B', 'N'),
+        'suppl': ('N',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TTAA'),
         'ovhgseq': 'TA',
     }
 rest_dict['MseI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MslI>CA[CT]....[AG]TG)',
@@ -12071,7 +12734,6 @@ def _temp():
     }
 rest_dict['MslI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Msp20I>TGGCCA)',
@@ -12087,14 +12749,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('I', 'V'),
+        'suppl': ('V',),
         'scd5': None,
         'charac': (3, -3, None, None, 'TGGCCA'),
         'ovhgseq': '',
     }
 rest_dict['Msp20I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MspA1I>C[AC]GC[GT]G)',
@@ -12117,7 +12778,6 @@ def _temp():
     }
 rest_dict['MspA1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MspCI>CTTAAG)',
@@ -12140,7 +12800,6 @@ def _temp():
     }
 rest_dict['MspCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MspI>CCGG)',
@@ -12156,13 +12815,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F', 'I', 'J', 'K', 'N', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'I', 'J', 'K', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCGG'),
         'ovhgseq': 'CG',
     }
 rest_dict['MspI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<MspI7II>ACG[AG]AG)|(?P<MspI7II_as>CT[CT]CGT)',
+        'results': None,
+        'site': 'ACGRAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'ACGRAG'),
+        'ovhgseq': None,
+    }
+rest_dict['MspI7II'] = _temp()
 
 def _temp():
     return {
@@ -12186,7 +12866,6 @@ def _temp():
     }
 rest_dict['MspJI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MspR9I>CC.GG)',
@@ -12207,54 +12886,95 @@ def _temp():
         'charac': (2, -2, None, None, 'CCNGG'),
         'ovhgseq': 'N',
     }
-rest_dict['MspR9I'] = _temp()
-
+rest_dict['MspR9I'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<MssI>GTTTAAAC)',
+        'results': None,
+        'site': 'GTTTAAAC',
+        'substrat': 'DNA',
+        'fst3': -4,
+        'fst5': 4,
+        'freq': 65536,
+        'size': 8,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 0,
+        'scd3': None,
+        'suppl': ('B',),
+        'scd5': None,
+        'charac': (4, -4, None, None, 'GTTTAAAC'),
+        'ovhgseq': '',
+    }
+rest_dict['MssI'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<MstI>TGCGCA)',
+        'results': None,
+        'site': 'TGCGCA',
+        'substrat': 'DNA',
+        'fst3': -3,
+        'fst5': 3,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 0,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (3, -3, None, None, 'TGCGCA'),
+        'ovhgseq': '',
+    }
+rest_dict['MstI'] = _temp()
 
 def _temp():
     return {
-        'compsite': '(?P<MssI>GTTTAAAC)',
+        'compsite': '(?P<MteI>GCGC.GCGC)',
         'results': None,
-        'site': 'GTTTAAAC',
+        'site': 'GCGCNGCGC',
         'substrat': 'DNA',
         'fst3': -4,
         'fst5': 4,
         'freq': 65536,
-        'size': 8,
+        'size': 9,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': 0,
+        'ovhg': -1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('I',),
         'scd5': None,
-        'charac': (4, -4, None, None, 'GTTTAAAC'),
-        'ovhgseq': '',
+        'charac': (4, -4, None, None, 'GCGCNGCGC'),
+        'ovhgseq': 'N',
     }
-rest_dict['MssI'] = _temp()
-
+rest_dict['MteI'] = _temp()
 
 def _temp():
     return {
-        'compsite': '(?P<MstI>TGCGCA)',
+        'compsite': '(?P<MtuHN878II>CACGCAG)|(?P<MtuHN878II_as>CTGCGTG)',
         'results': None,
-        'site': 'TGCGCA',
+        'site': 'CACGCAG',
         'substrat': 'DNA',
-        'fst3': -3,
-        'fst5': 3,
-        'freq': 4096,
-        'size': 6,
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': 0,
+        'ovhg': None,
         'scd3': None,
         'suppl': (),
         'scd5': None,
-        'charac': (3, -3, None, None, 'TGCGCA'),
-        'ovhgseq': '',
+        'charac': (None, None, None, None, 'CACGCAG'),
+        'ovhgseq': None,
     }
-rest_dict['MstI'] = _temp()
-
+rest_dict['MtuHN878II'] = _temp()
 
 def _temp():
     return {
@@ -12271,14 +12991,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F', 'K', 'M'),
+        'suppl': ('B', 'K', 'M'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CAATTG'),
         'ovhgseq': 'AATT',
     }
 rest_dict['MunI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Mva1269I>GAATGC)|(?P<Mva1269I_as>GCATTC)',
@@ -12294,14 +13013,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, -1, None, None, 'GAATGC'),
         'ovhgseq': 'CN',
     }
 rest_dict['Mva1269I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MvaI>CC[AT]GG)',
@@ -12317,14 +13035,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F', 'M', 'S'),
+        'suppl': ('B', 'M', 'S'),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCWGG'),
         'ovhgseq': 'W',
     }
 rest_dict['MvaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<MvnI>CGCG)',
@@ -12347,30 +13064,6 @@ def _temp():
     }
 rest_dict['MvnI'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<MvrI>CGATCG)',
-        'results': None,
-        'site': 'CGATCG',
-        'substrat': 'DNA',
-        'fst3': -4,
-        'fst5': 4,
-        'freq': 4096,
-        'size': 6,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': 2,
-        'scd3': None,
-        'suppl': ('U',),
-        'scd5': None,
-        'charac': (4, -4, None, None, 'CGATCG'),
-        'ovhgseq': 'AT',
-    }
-rest_dict['MvrI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<MwoI>GC.......GC)',
@@ -12393,7 +13086,6 @@ def _temp():
     }
 rest_dict['MwoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NaeI>GCCGGC)',
@@ -12409,13 +13101,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('C', 'K', 'N', 'U'),
+        'suppl': ('C', 'K', 'N'),
         'scd5': None,
         'charac': (3, -3, None, None, 'GCCGGC'),
         'ovhgseq': '',
     }
 rest_dict['NaeI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Nal45188II>ACCAGC)|(?P<Nal45188II_as>GCTGGT)',
+        'results': None,
+        'site': 'ACCAGC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'ACCAGC'),
+        'ovhgseq': None,
+    }
+rest_dict['Nal45188II'] = _temp()
 
 def _temp():
     return {
@@ -12432,14 +13145,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('J', 'M', 'N', 'Q', 'R', 'U', 'X'),
+        'suppl': ('J', 'M', 'N', 'Q', 'R', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GGCGCC'),
         'ovhgseq': 'CG',
     }
 rest_dict['NarI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NciI>CC[CG]GG)',
@@ -12462,7 +13174,6 @@ def _temp():
     }
 rest_dict['NciI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NcoI>CCATGG)',
@@ -12478,14 +13189,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCATGG'),
         'ovhgseq': 'CATG',
     }
 rest_dict['NcoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NdeI>CATATG)',
@@ -12501,14 +13211,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('B', 'F', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'CATATG'),
         'ovhgseq': 'TA',
     }
 rest_dict['NdeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NdeII>GATC)',
@@ -12531,6 +13240,27 @@ def _temp():
     }
 rest_dict['NdeII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<NgoAVII>GCCGC)|(?P<NgoAVII_as>GCGGC)',
+        'results': None,
+        'site': 'GCCGC',
+        'substrat': 'DNA',
+        'fst3': 7,
+        'fst5': 12,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 0,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (12, 7, None, None, 'GCCGC'),
+        'ovhgseq': '',
+    }
+rest_dict['NgoAVII'] = _temp()
 
 def _temp():
     return {
@@ -12554,7 +13284,6 @@ def _temp():
     }
 rest_dict['NgoAVIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NgoMIV>GCCGGC)',
@@ -12577,7 +13306,6 @@ def _temp():
     }
 rest_dict['NgoMIV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NhaXI>CAAG[AG]AG)|(?P<NhaXI_as>CT[CT]CTTG)',
@@ -12600,7 +13328,6 @@ def _temp():
     }
 rest_dict['NhaXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NheI>GCTAGC)',
@@ -12616,13 +13343,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X'),
+        'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GCTAGC'),
         'ovhgseq': 'CTAG',
     }
 rest_dict['NheI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<NhoI>GC[AT]GC)',
+        'results': None,
+        'site': 'GCWGC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 512,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GCWGC'),
+        'ovhgseq': None,
+    }
+rest_dict['NhoI'] = _temp()
 
 def _temp():
     return {
@@ -12646,7 +13394,6 @@ def _temp():
     }
 rest_dict['NlaCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NlaIII>CATG)',
@@ -12669,7 +13416,6 @@ def _temp():
     }
 rest_dict['NlaIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NlaIV>GG..CC)',
@@ -12692,7 +13438,6 @@ def _temp():
     }
 rest_dict['NlaIV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Nli3877I>C[CT]CG[AG]G)',
@@ -12715,7 +13460,6 @@ def _temp():
     }
 rest_dict['Nli3877I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NmeAIII>GCCGAG)|(?P<NmeAIII_as>CTCGGC)',
@@ -12738,7 +13482,6 @@ def _temp():
     }
 rest_dict['NmeAIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NmeDI>[AG]CCGG[CT])',
@@ -12761,7 +13504,6 @@ def _temp():
     }
 rest_dict['NmeDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NmuCI>GT[CG]AC)',
@@ -12777,14 +13519,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (0, 0, None, None, 'GTSAC'),
         'ovhgseq': 'GTSAC',
     }
 rest_dict['NmuCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NotI>GCGGCCGC)',
@@ -12800,14 +13541,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GCGGCCGC'),
         'ovhgseq': 'GGCC',
     }
 rest_dict['NotI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NruI>TCGCGA)',
@@ -12823,14 +13563,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'U', 'X'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'TCGCGA'),
         'ovhgseq': '',
     }
 rest_dict['NruI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NsbI>TGCGCA)',
@@ -12846,14 +13585,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (3, -3, None, None, 'TGCGCA'),
         'ovhgseq': '',
     }
 rest_dict['NsbI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NsiI>ATGCAT)',
@@ -12869,14 +13607,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('J', 'M', 'N', 'Q', 'R', 'S', 'U', 'X'),
+        'suppl': ('J', 'M', 'N', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (5, -5, None, None, 'ATGCAT'),
         'ovhgseq': 'TGCA',
     }
 rest_dict['NsiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NspBII>C[AC]GC[GT]G)',
@@ -12899,7 +13636,6 @@ def _temp():
     }
 rest_dict['NspBII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NspI>[AG]CATG[CT])',
@@ -12922,7 +13658,6 @@ def _temp():
     }
 rest_dict['NspI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<NspV>TTCGAA)',
@@ -12945,7 +13680,6 @@ def _temp():
     }
 rest_dict['NspV'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<OliI>CAC....GTG)',
@@ -12961,13 +13695,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'CACNNNNGTG'),
         'ovhgseq': '',
     }
 rest_dict['OliI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<OspHL35III>[CT]AGGAG)|(?P<OspHL35III_as>CTCCT[AG])',
+        'results': None,
+        'site': 'YAGGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'YAGGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['OspHL35III'] = _temp()
 
 def _temp():
     return {
@@ -12991,6 +13746,27 @@ def _temp():
     }
 rest_dict['PabI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pac19842II>CCTTGA)|(?P<Pac19842II_as>TCAAGG)',
+        'results': None,
+        'site': 'CCTTGA',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCTTGA'),
+        'ovhgseq': None,
+    }
+rest_dict['Pac19842II'] = _temp()
 
 def _temp():
     return {
@@ -13007,14 +13783,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F', 'N', 'O'),
+        'suppl': ('B', 'N', 'O'),
         'scd5': None,
         'charac': (5, -5, None, None, 'TTAATTAA'),
         'ovhgseq': 'AT',
     }
 rest_dict['PacI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PaeI>GCATGC)',
@@ -13030,14 +13805,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GCATGC'),
         'ovhgseq': 'CATG',
     }
 rest_dict['PaeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PaeR7I>CTCGAG)',
@@ -13060,7 +13834,6 @@ def _temp():
     }
 rest_dict['PaeR7I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PagI>TCATGA)',
@@ -13076,13 +13849,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TCATGA'),
         'ovhgseq': 'CATG',
     }
 rest_dict['PagI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pal408I>CC[AG]TGAG)|(?P<Pal408I_as>CTCA[CT]GG)',
+        'results': None,
+        'site': 'CCRTGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCRTGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Pal408I'] = _temp()
 
 def _temp():
     return {
@@ -13106,7 +13900,6 @@ def _temp():
     }
 rest_dict['PalAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PasI>CCC[AT]GGG)',
@@ -13122,14 +13915,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CCCWGGG'),
         'ovhgseq': 'CWG',
     }
 rest_dict['PasI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PauI>GCGCGC)',
@@ -13145,14 +13937,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GCGCGC'),
         'ovhgseq': 'CGCG',
     }
 rest_dict['PauI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PceI>AGGCCT)',
@@ -13175,7 +13966,6 @@ def _temp():
     }
 rest_dict['PceI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PciI>ACATGT)',
@@ -13198,7 +13988,6 @@ def _temp():
     }
 rest_dict['PciI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PciSI>GCTCTTC)|(?P<PciSI_as>GAAGAGC)',
@@ -13221,7 +14010,6 @@ def _temp():
     }
 rest_dict['PciSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PcsI>[AT]CG.......CG[AT])',
@@ -13244,7 +14032,6 @@ def _temp():
     }
 rest_dict['PcsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PctI>GAATGC)|(?P<PctI_as>GCATTC)',
@@ -13267,6 +14054,27 @@ def _temp():
     }
 rest_dict['PctI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pdi8503III>CCGG.AG)|(?P<Pdi8503III_as>CT.CCGG)',
+        'results': None,
+        'site': 'CCGGNAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CCGGNAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Pdi8503III'] = _temp()
 
 def _temp():
     return {
@@ -13283,14 +14091,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'GCCGGC'),
         'ovhgseq': '',
     }
 rest_dict['PdiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PdmI>GAA....TTC)',
@@ -13306,13 +14113,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GAANNNNTTC'),
         'ovhgseq': '',
     }
 rest_dict['PdmI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pdu1735I>CACCAC)|(?P<Pdu1735I_as>GTGGTG)',
+        'results': None,
+        'site': 'CACCAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CACCAC'),
+        'ovhgseq': None,
+    }
+rest_dict['Pdu1735I'] = _temp()
 
 def _temp():
     return {
@@ -13336,7 +14164,6 @@ def _temp():
     }
 rest_dict['PenI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PfeI>GA[AT]TC)',
@@ -13352,14 +14179,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GAWTC'),
         'ovhgseq': 'AWT',
     }
 rest_dict['PfeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Pfl1108I>TCGTAG)|(?P<Pfl1108I_as>CTACGA)',
@@ -13382,7 +14208,6 @@ def _temp():
     }
 rest_dict['Pfl1108I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Pfl23II>CGTACG)',
@@ -13398,14 +14223,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CGTACG'),
         'ovhgseq': 'GTAC',
     }
 rest_dict['Pfl23II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PflFI>GAC...GTC)',
@@ -13428,7 +14252,6 @@ def _temp():
     }
 rest_dict['PflFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PflMI>CCA.....TGG)',
@@ -13451,7 +14274,6 @@ def _temp():
     }
 rest_dict['PflMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PfoI>TCC.GGA)',
@@ -13467,14 +14289,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -5,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TCCNGGA'),
         'ovhgseq': 'CCNGG',
     }
 rest_dict['PfoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PinAI>ACCGGT)',
@@ -13490,13 +14311,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('M', 'Q', 'X'),
+        'suppl': ('Q', 'X'),
+        'scd5': None,
+        'charac': (1, -1, None, None, 'ACCGGT'),
+        'ovhgseq': 'CCGG',
+    }
+rest_dict['PinAI'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<PkrI>GC.GC)',
+        'results': None,
+        'site': 'GCNGC',
+        'substrat': 'DNA',
+        'fst3': -3,
+        'fst5': 3,
+        'freq': 256,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 1,
+        'scd3': None,
+        'suppl': ('I',),
         'scd5': None,
-        'charac': (1, -1, None, None, 'ACCGGT'),
-        'ovhgseq': 'CCGG',
+        'charac': (3, -3, None, None, 'GCNGC'),
+        'ovhgseq': 'N',
     }
-rest_dict['PinAI'] = _temp()
-
+rest_dict['PkrI'] = _temp()
 
 def _temp():
     return {
@@ -13520,7 +14362,6 @@ def _temp():
     }
 rest_dict['PlaDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ple19I>CGATCG)',
@@ -13543,7 +14384,6 @@ def _temp():
     }
 rest_dict['Ple19I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PleI>GAGTC)|(?P<PleI_as>GACTC)',
@@ -13566,6 +14406,27 @@ def _temp():
     }
 rest_dict['PleI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<PliMI>CGCCGAC)|(?P<PliMI_as>GTCGGCG)',
+        'results': None,
+        'site': 'CGCCGAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CGCCGAC'),
+        'ovhgseq': None,
+    }
+rest_dict['PliMI'] = _temp()
 
 def _temp():
     return {
@@ -13589,7 +14450,6 @@ def _temp():
     }
 rest_dict['PluTI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PmaCI>CACGTG)',
@@ -13612,6 +14472,27 @@ def _temp():
     }
 rest_dict['PmaCI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pme5II>GACGAG)|(?P<Pme5II_as>CTCGTC)',
+        'results': None,
+        'site': 'GACGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GACGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Pme5II'] = _temp()
 
 def _temp():
     return {
@@ -13635,6 +14516,27 @@ def _temp():
     }
 rest_dict['PmeI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<PmeS132I>GACGAG)|(?P<PmeS132I_as>CTCGTC)',
+        'results': None,
+        'site': 'GACGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GACGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['PmeS132I'] = _temp()
 
 def _temp():
     return {
@@ -13658,7 +14560,6 @@ def _temp():
     }
 rest_dict['PmlI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PpiI>GAAC.....CTC)|(?P<PpiI_as>GAG.....GTTC)',
@@ -13681,7 +14582,6 @@ def _temp():
     }
 rest_dict['PpiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PpsI>GAGTC)|(?P<PpsI_as>GACTC)',
@@ -13704,7 +14604,6 @@ def _temp():
     }
 rest_dict['PpsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ppu10I>ATGCAT)',
@@ -13727,7 +14626,6 @@ def _temp():
     }
 rest_dict['Ppu10I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Ppu21I>[CT]ACGT[AG])',
@@ -13743,14 +14641,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'YACGTR'),
         'ovhgseq': '',
     }
 rest_dict['Ppu21I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PpuMI>[AG]GG[AT]CC[CT])',
@@ -13773,7 +14670,6 @@ def _temp():
     }
 rest_dict['PpuMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PscI>ACATGT)',
@@ -13789,13 +14685,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'ACATGT'),
         'ovhgseq': 'CATG',
     }
 rest_dict['PscI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pse18267I>[AG]CCGAAG)|(?P<Pse18267I_as>CTTCGG[CT])',
+        'results': None,
+        'site': 'RCCGAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'RCCGAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Pse18267I'] = _temp()
 
 def _temp():
     return {
@@ -13819,7 +14736,6 @@ def _temp():
     }
 rest_dict['PshAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PshBI>ATTAAT)',
@@ -13842,7 +14758,6 @@ def _temp():
     }
 rest_dict['PshBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PsiI>TTATAA)',
@@ -13865,6 +14780,27 @@ def _temp():
     }
 rest_dict['PsiI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Psp0357II>GCGAAG)|(?P<Psp0357II_as>CTTCGC)',
+        'results': None,
+        'site': 'GCGAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GCGAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Psp0357II'] = _temp()
 
 def _temp():
     return {
@@ -13888,6 +14824,27 @@ def _temp():
     }
 rest_dict['Psp03I'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Psp10HII>G[AG]AGCAG)|(?P<Psp10HII_as>CTGCT[CT]C)',
+        'results': None,
+        'site': 'GRAGCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GRAGCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Psp10HII'] = _temp()
 
 def _temp():
     return {
@@ -13911,7 +14868,6 @@ def _temp():
     }
 rest_dict['Psp124BI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Psp1406I>AACGTT)',
@@ -13927,14 +14883,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (2, -2, None, None, 'AACGTT'),
         'ovhgseq': 'CG',
     }
 rest_dict['Psp1406I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Psp5II>[AG]GG[AT]CC[CT])',
@@ -13950,14 +14905,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'RGGWCCY'),
         'ovhgseq': 'GWC',
     }
 rest_dict['Psp5II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Psp6I>CC[AT]GG)',
@@ -13980,7 +14934,6 @@ def _temp():
     }
 rest_dict['Psp6I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspCI>CACGTG)',
@@ -14003,7 +14956,6 @@ def _temp():
     }
 rest_dict['PspCI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspEI>GGT.ACC)',
@@ -14026,6 +14978,27 @@ def _temp():
     }
 rest_dict['PspEI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<PspFI>CCCAGC)|(?P<PspFI_as>GCTGGG)',
+        'results': None,
+        'site': 'CCCAGC',
+        'substrat': 'DNA',
+        'fst3': -1,
+        'fst5': 1,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -4,
+        'scd3': None,
+        'suppl': ('B',),
+        'scd5': None,
+        'charac': (1, -1, None, None, 'CCCAGC'),
+        'ovhgseq': 'CCAG',
+    }
+rest_dict['PspFI'] = _temp()
 
 def _temp():
     return {
@@ -14049,7 +15022,6 @@ def _temp():
     }
 rest_dict['PspGI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspLI>CGTACG)',
@@ -14072,7 +15044,6 @@ def _temp():
     }
 rest_dict['PspLI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspN4I>GG..CC)',
@@ -14095,7 +15066,6 @@ def _temp():
     }
 rest_dict['PspN4I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspOMI>GGGCCC)',
@@ -14118,7 +15088,6 @@ def _temp():
     }
 rest_dict['PspOMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspOMII>CGCCCA[AG])|(?P<PspOMII_as>[CT]TGGGCG)',
@@ -14141,7 +15110,6 @@ def _temp():
     }
 rest_dict['PspOMII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspPI>GG.CC)',
@@ -14164,7 +15132,6 @@ def _temp():
     }
 rest_dict['PspPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspPPI>[AG]GG[AT]CC[CT])',
@@ -14187,7 +15154,6 @@ def _temp():
     }
 rest_dict['PspPPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspPRI>CC[CT]CAG)|(?P<PspPRI_as>CTG[AG]GG)',
@@ -14210,7 +15176,6 @@ def _temp():
     }
 rest_dict['PspPRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PspXI>[ACG]CTCGAG[CGT])',
@@ -14233,7 +15198,6 @@ def _temp():
     }
 rest_dict['PspXI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PsrI>GAAC......TAC)|(?P<PsrI_as>GTA......GTTC)',
@@ -14256,7 +15220,6 @@ def _temp():
     }
 rest_dict['PsrI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PssI>[AG]GG.CC[CT])',
@@ -14279,6 +15242,27 @@ def _temp():
     }
 rest_dict['PssI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Pst14472I>C.[CT]ACAC)|(?P<Pst14472I_as>GTGT[AG].G)',
+        'results': None,
+        'site': 'CNYACAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 2048,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CNYACAC'),
+        'ovhgseq': None,
+    }
+rest_dict['Pst14472I'] = _temp()
 
 def _temp():
     return {
@@ -14295,14 +15279,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (5, -5, None, None, 'CTGCAG'),
         'ovhgseq': 'TGCA',
     }
 rest_dict['PstI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PstNI>CAG...CTG)',
@@ -14325,6 +15308,27 @@ def _temp():
     }
 rest_dict['PstNI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<PsuGI>[CGT][CGT]CG[AGT])|(?P<PsuGI_as>[ACT]CG[ACG][ACG])',
+        'results': None,
+        'site': 'BBCGD',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'BBCGD'),
+        'ovhgseq': None,
+    }
+rest_dict['PsuGI'] = _temp()
 
 def _temp():
     return {
@@ -14341,14 +15345,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'RGATCY'),
         'ovhgseq': 'GATC',
     }
 rest_dict['PsuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PsyI>GAC...GTC)',
@@ -14364,14 +15367,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'GACNNNGTC'),
         'ovhgseq': 'N',
     }
 rest_dict['PsyI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PteI>GCGCGC)',
@@ -14387,14 +15389,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GCGCGC'),
         'ovhgseq': 'CGCG',
     }
 rest_dict['PteI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PvuI>CGATCG)',
@@ -14410,14 +15411,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('B', 'F', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (4, -4, None, None, 'CGATCG'),
         'ovhgseq': 'AT',
     }
 rest_dict['PvuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<PvuII>CAGCTG)',
@@ -14433,36 +15433,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'CAGCTG'),
         'ovhgseq': '',
     }
 rest_dict['PvuII'] = _temp()
 
-
 def _temp():
     return {
-        'compsite': '(?P<R2_BceSIV>GCAGC)|(?P<R2_BceSIV_as>GCTGC)',
+        'compsite': '(?P<Rba2021I>CACGAG[ACT])|(?P<Rba2021I_as>[AGT]CTCGTG)',
         'results': None,
-        'site': 'GCAGC',
+        'site': 'CACGAGH',
         'substrat': 'DNA',
-        'fst3': -10,
-        'fst5': -7,
-        'freq': 1024,
-        'size': 5,
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': -2,
-        'scd3': 11,
+        'ovhg': None,
+        'scd3': None,
         'suppl': (),
-        'scd5': 14,
-        'charac': (-7, -10, 14, 11, 'GCAGC'),
-        'ovhgseq': 'NN',
+        'scd5': None,
+        'charac': (None, None, None, None, 'CACGAGH'),
+        'ovhgseq': None,
     }
-rest_dict['R2_BceSIV'] = _temp()
-
+rest_dict['Rba2021I'] = _temp()
 
 def _temp():
     return {
@@ -14486,7 +15484,6 @@ def _temp():
     }
 rest_dict['RceI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RdeGBI>CCGCAG)|(?P<RdeGBI_as>CTGCGG)',
@@ -14509,7 +15506,6 @@ def _temp():
     }
 rest_dict['RdeGBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RdeGBII>ACCCAG)|(?P<RdeGBII_as>CTGGGT)',
@@ -14532,7 +15528,6 @@ def _temp():
     }
 rest_dict['RdeGBII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RdeGBIII>TG[AG][CT]CA)',
@@ -14555,7 +15550,6 @@ def _temp():
     }
 rest_dict['RdeGBIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RflFIII>CGCCAG)|(?P<RflFIII_as>CTGGCG)',
@@ -14578,7 +15572,6 @@ def _temp():
     }
 rest_dict['RflFIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RgaI>GCGATCGC)',
@@ -14601,7 +15594,6 @@ def _temp():
     }
 rest_dict['RgaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RigI>GGCCGGCC)',
@@ -14624,7 +15616,6 @@ def _temp():
     }
 rest_dict['RigI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RlaI>[ACG]C[AT])|(?P<RlaI_as>[AT]G[CGT])',
@@ -14647,7 +15638,6 @@ def _temp():
     }
 rest_dict['RlaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RleAI>CCCACA)|(?P<RleAI_as>TGTGGG)',
@@ -14670,7 +15660,6 @@ def _temp():
     }
 rest_dict['RleAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RpaB5I>CG[AG]GGAC)|(?P<RpaB5I_as>GTCC[CT]CG)',
@@ -14693,7 +15682,6 @@ def _temp():
     }
 rest_dict['RpaB5I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RpaBI>CCCGCAG)|(?P<RpaBI_as>CTGCGGG)',
@@ -14716,7 +15704,6 @@ def _temp():
     }
 rest_dict['RpaBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RpaI>GT[CT]GGAG)|(?P<RpaI_as>CTCC[AG]AC)',
@@ -14739,7 +15726,6 @@ def _temp():
     }
 rest_dict['RpaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RpaTI>G[AG]TGGAG)|(?P<RpaTI_as>CTCCA[CT]C)',
@@ -14762,7 +15748,6 @@ def _temp():
     }
 rest_dict['RpaTI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RruI>TCGCGA)',
@@ -14778,14 +15763,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'TCGCGA'),
         'ovhgseq': '',
     }
 rest_dict['RruI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RsaI>GTAC)',
@@ -14801,14 +15785,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('C', 'F', 'I', 'J', 'M', 'N', 'Q', 'R', 'S', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'M', 'N', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (2, -2, None, None, 'GTAC'),
         'ovhgseq': '',
     }
 rest_dict['RsaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RsaNI>GTAC)',
@@ -14831,7 +15814,6 @@ def _temp():
     }
 rest_dict['RsaNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RseI>CA[CT]....[AG]TG)',
@@ -14847,14 +15829,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'CAYNNNNRTG'),
         'ovhgseq': '',
     }
 rest_dict['RseI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Rsr2I>CGG[AT]CCG)',
@@ -14877,7 +15858,6 @@ def _temp():
     }
 rest_dict['Rsr2I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<RsrII>CGG[AT]CCG)',
@@ -14900,7 +15880,6 @@ def _temp():
     }
 rest_dict['RsrII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SacI>GAGCTC)',
@@ -14916,36 +15895,56 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('B', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X'),
+        'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (5, -5, None, None, 'GAGCTC'),
         'ovhgseq': 'AGCT',
     }
 rest_dict['SacI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SacII>CCGCGG)',
         'results': None,
-        'site': 'CCGCGG',
+        'site': 'CCGCGG',
+        'substrat': 'DNA',
+        'fst3': -4,
+        'fst5': 4,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 2,
+        'scd3': None,
+        'suppl': ('B', 'J', 'K', 'N', 'O', 'Q', 'R', 'X'),
+        'scd5': None,
+        'charac': (4, -4, None, None, 'CCGCGG'),
+        'ovhgseq': 'GC',
+    }
+rest_dict['SacII'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Saf8902III>CAAT.AG)|(?P<Saf8902III_as>CT.ATTG)',
+        'results': None,
+        'site': 'CAATNAG',
         'substrat': 'DNA',
-        'fst3': -4,
-        'fst5': 4,
+        'fst3': None,
+        'fst5': None,
         'freq': 4096,
-        'size': 6,
+        'size': 7,
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': 2,
+        'ovhg': None,
         'scd3': None,
-        'suppl': ('B', 'J', 'K', 'N', 'O', 'Q', 'R', 'X'),
+        'suppl': (),
         'scd5': None,
-        'charac': (4, -4, None, None, 'CCGCGG'),
-        'ovhgseq': 'GC',
+        'charac': (None, None, None, None, 'CAATNAG'),
+        'ovhgseq': None,
     }
-rest_dict['SacII'] = _temp()
-
+rest_dict['Saf8902III'] = _temp()
 
 def _temp():
     return {
@@ -14962,14 +15961,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GTCGAC'),
         'ovhgseq': 'TCGA',
     }
 rest_dict['SalI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SanDI>GGG[AT]CCC)',
@@ -14992,7 +15990,6 @@ def _temp():
     }
 rest_dict['SanDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SapI>GCTCTTC)|(?P<SapI_as>GAAGAGC)',
@@ -15015,7 +16012,6 @@ def _temp():
     }
 rest_dict['SapI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SaqAI>TTAA)',
@@ -15031,14 +16027,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TTAA'),
         'ovhgseq': 'TA',
     }
 rest_dict['SaqAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SatI>GC.GC)',
@@ -15054,14 +16049,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'GCNGC'),
         'ovhgseq': 'N',
     }
 rest_dict['SatI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sau3AI>GATC)',
@@ -15077,14 +16071,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('C', 'J', 'K', 'M', 'N', 'R', 'S', 'U'),
+        'suppl': ('C', 'J', 'K', 'M', 'N', 'R', 'S'),
         'scd5': None,
         'charac': (0, 0, None, None, 'GATC'),
         'ovhgseq': 'GATC',
     }
 rest_dict['Sau3AI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sau96I>GG.CC)',
@@ -15100,14 +16093,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -3,
         'scd3': None,
-        'suppl': ('J', 'N', 'U'),
+        'suppl': ('J', 'N'),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGNCC'),
         'ovhgseq': 'GNC',
     }
 rest_dict['Sau96I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SauI>CCT.AGG)',
@@ -15130,7 +16122,6 @@ def _temp():
     }
 rest_dict['SauI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SbfI>CCTGCAGG)',
@@ -15153,6 +16144,27 @@ def _temp():
     }
 rest_dict['SbfI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Sbo46I>TGAAC)|(?P<Sbo46I_as>GTTCA)',
+        'results': None,
+        'site': 'TGAAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'TGAAC'),
+        'ovhgseq': None,
+    }
+rest_dict['Sbo46I'] = _temp()
 
 def _temp():
     return {
@@ -15169,14 +16181,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
+        'suppl': ('B', 'C', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'AGTACT'),
         'ovhgseq': '',
     }
 rest_dict['ScaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SchI>GAGTC)|(?P<SchI_as>GACTC)',
@@ -15192,14 +16203,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (10, 5, None, None, 'GAGTC'),
         'ovhgseq': '',
     }
 rest_dict['SchI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SciI>CTCGAG)',
@@ -15222,7 +16232,6 @@ def _temp():
     }
 rest_dict['SciI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ScrFI>CC.GG)',
@@ -15245,7 +16254,6 @@ def _temp():
     }
 rest_dict['ScrFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SdaI>CCTGCAGG)',
@@ -15261,14 +16269,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (6, -6, None, None, 'CCTGCAGG'),
         'ovhgseq': 'TGCA',
     }
 rest_dict['SdaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SdeAI>CAG[AG]AG)|(?P<SdeAI_as>CT[CT]CTG)',
@@ -15291,7 +16298,6 @@ def _temp():
     }
 rest_dict['SdeAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SdeOSI>GAC....[AG]TGA)|(?P<SdeOSI_as>TCA[CT]....GTC)',
@@ -15314,7 +16320,6 @@ def _temp():
     }
 rest_dict['SdeOSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SduI>G[AGT]GC[ACT]C)',
@@ -15330,14 +16335,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GDGCHC'),
         'ovhgseq': 'DGCH',
     }
 rest_dict['SduI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SecI>CC..GG)',
@@ -15360,7 +16364,6 @@ def _temp():
     }
 rest_dict['SecI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SelI>CGCG)',
@@ -15383,6 +16386,71 @@ def _temp():
     }
 rest_dict['SelI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Sen13311III>GATCAG)|(?P<Sen13311III_as>CTGATC)',
+        'results': None,
+        'site': 'GATCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GATCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Sen13311III'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Sen1736II>GATCAG)|(?P<Sen1736II_as>CTGATC)',
+        'results': None,
+        'site': 'GATCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GATCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Sen1736II'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<SenTFIV>GATCAG)|(?P<SenTFIV_as>CTGATC)',
+        'results': None,
+        'site': 'GATCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GATCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['SenTFIV'] = _temp()
 
 def _temp():
     return {
@@ -15406,7 +16474,6 @@ def _temp():
     }
 rest_dict['SetI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SexAI>ACC[AT]GGT)',
@@ -15429,7 +16496,6 @@ def _temp():
     }
 rest_dict['SexAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfaAI>GCGATCGC)',
@@ -15445,14 +16511,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'GCGATCGC'),
         'ovhgseq': 'AT',
     }
 rest_dict['SfaAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfaNI>GCATC)|(?P<SfaNI_as>GATGC)',
@@ -15475,7 +16540,6 @@ def _temp():
     }
 rest_dict['SfaNI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfcI>CT[AG][CT]AG)',
@@ -15498,7 +16562,6 @@ def _temp():
     }
 rest_dict['SfcI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfeI>CT[AG][CT]AG)',
@@ -15521,7 +16584,6 @@ def _temp():
     }
 rest_dict['SfeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfiI>GGCC.....GGCC)',
@@ -15537,14 +16599,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (8, -8, None, None, 'GGCCNNNNNGGCC'),
         'ovhgseq': 'NNN',
     }
 rest_dict['SfiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfoI>GGCGCC)',
@@ -15567,7 +16628,6 @@ def _temp():
     }
 rest_dict['SfoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sfr274I>CTCGAG)',
@@ -15590,7 +16650,6 @@ def _temp():
     }
 rest_dict['Sfr274I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sfr303I>CCGCGG)',
@@ -15613,7 +16672,6 @@ def _temp():
     }
 rest_dict['Sfr303I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SfuI>TTCGAA)',
@@ -15636,7 +16694,6 @@ def _temp():
     }
 rest_dict['SfuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgeI>C..G)',
@@ -15652,14 +16709,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (13, 13, None, None, 'CNNG'),
         'ovhgseq': 'NNNN',
     }
 rest_dict['SgeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgfI>GCGATCGC)',
@@ -15682,7 +16738,6 @@ def _temp():
     }
 rest_dict['SgfI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgrAI>C[AG]CCGG[CT]G)',
@@ -15705,7 +16760,6 @@ def _temp():
     }
 rest_dict['SgrAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgrBI>CCGCGG)',
@@ -15728,7 +16782,6 @@ def _temp():
     }
 rest_dict['SgrBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgrDI>CGTCGACG)',
@@ -15744,14 +16797,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'CGTCGACG'),
         'ovhgseq': 'TCGA',
     }
 rest_dict['SgrDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgrTI>CC[AGT][CG])|(?P<SgrTI_as>[CG][ACT]GG)',
@@ -15774,7 +16826,6 @@ def _temp():
     }
 rest_dict['SgrTI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SgsI>GGCGCGCC)',
@@ -15790,14 +16841,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'GGCGCGCC'),
         'ovhgseq': 'CGCG',
     }
 rest_dict['SgsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SimI>GGGTC)|(?P<SimI_as>GACCC)',
@@ -15820,6 +16870,27 @@ def _temp():
     }
 rest_dict['SimI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<SinI>GG[AT]CC)',
+        'results': None,
+        'site': 'GGWCC',
+        'substrat': 'DNA',
+        'fst3': -1,
+        'fst5': 1,
+        'freq': 512,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -3,
+        'scd3': None,
+        'suppl': ('X',),
+        'scd5': None,
+        'charac': (1, -1, None, None, 'GGWCC'),
+        'ovhgseq': 'GWC',
+    }
+rest_dict['SinI'] = _temp()
 
 def _temp():
     return {
@@ -15836,14 +16907,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('C',),
+        'suppl': ('C', 'Y'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTCGAG'),
         'ovhgseq': 'TCGA',
     }
 rest_dict['SlaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SmaI>CCCGGG)',
@@ -15859,14 +16929,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'V', 'X', 'Y'),
         'scd5': None,
         'charac': (3, -3, None, None, 'CCCGGG'),
         'ovhgseq': '',
     }
 rest_dict['SmaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SmiI>ATTTAAAT)',
@@ -15882,14 +16951,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('F', 'I', 'K', 'V'),
+        'suppl': ('B', 'I', 'K', 'V'),
         'scd5': None,
         'charac': (4, -4, None, None, 'ATTTAAAT'),
         'ovhgseq': '',
     }
 rest_dict['SmiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SmiMI>CA[CT]....[AG]TG)',
@@ -15912,7 +16980,6 @@ def _temp():
     }
 rest_dict['SmiMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SmlI>CT[CT][AG]AG)',
@@ -15935,7 +17002,6 @@ def _temp():
     }
 rest_dict['SmlI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SmoI>CT[CT][AG]AG)',
@@ -15951,14 +17017,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTYRAG'),
         'ovhgseq': 'TYRA',
     }
 rest_dict['SmoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SnaBI>TACGTA)',
@@ -15974,14 +17039,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('C', 'K', 'M', 'N', 'R', 'U'),
+        'suppl': ('C', 'K', 'M', 'N', 'R'),
         'scd5': None,
         'charac': (3, -3, None, None, 'TACGTA'),
         'ovhgseq': '',
     }
 rest_dict['SnaBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SnaI>GTATAC)',
@@ -16004,7 +17068,6 @@ def _temp():
     }
 rest_dict['SnaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sno506I>GGCCGAG)|(?P<Sno506I_as>CTCGGCC)',
@@ -16027,7 +17090,6 @@ def _temp():
     }
 rest_dict['Sno506I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SpeI>ACTAGT)',
@@ -16043,14 +17105,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X'),
+        'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'ACTAGT'),
         'ovhgseq': 'CTAG',
     }
 rest_dict['SpeI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SphI>GCATGC)',
@@ -16073,7 +17134,6 @@ def _temp():
     }
 rest_dict['SphI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SplI>CGTACG)',
@@ -16096,6 +17156,27 @@ def _temp():
     }
 rest_dict['SplI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<SpnRII>TCGAG)|(?P<SpnRII_as>CTCGA)',
+        'results': None,
+        'site': 'TCGAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 1024,
+        'size': 5,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'TCGAG'),
+        'ovhgseq': None,
+    }
+rest_dict['SpnRII'] = _temp()
 
 def _temp():
     return {
@@ -16119,7 +17200,6 @@ def _temp():
     }
 rest_dict['SpoDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SrfI>GCCCGGGC)',
@@ -16135,14 +17215,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': (),
+        'suppl': ('N',),
         'scd5': None,
         'charac': (4, -4, None, None, 'GCCCGGGC'),
         'ovhgseq': '',
     }
 rest_dict['SrfI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sse232I>CGCCGGCG)',
@@ -16165,7 +17244,6 @@ def _temp():
     }
 rest_dict['Sse232I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sse8387I>CCTGCAGG)',
@@ -16188,7 +17266,6 @@ def _temp():
     }
 rest_dict['Sse8387I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sse8647I>AGG[AT]CCT)',
@@ -16211,7 +17288,6 @@ def _temp():
     }
 rest_dict['Sse8647I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sse9I>AATT)',
@@ -16234,7 +17310,6 @@ def _temp():
     }
 rest_dict['Sse9I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SseBI>AGGCCT)',
@@ -16257,7 +17332,6 @@ def _temp():
     }
 rest_dict['SseBI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SsiI>CCGC)|(?P<SsiI_as>GCGG)',
@@ -16273,13 +17347,56 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCGC'),
         'ovhgseq': 'CG',
     }
 rest_dict['SsiI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Ssp6803IV>GAAGGC)|(?P<Ssp6803IV_as>GCCTTC)',
+        'results': None,
+        'site': 'GAAGGC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GAAGGC'),
+        'ovhgseq': None,
+    }
+rest_dict['Ssp6803IV'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Ssp714II>CGCAGCG)|(?P<Ssp714II_as>CGCTGCG)',
+        'results': None,
+        'site': 'CGCAGCG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CGCAGCG'),
+        'ovhgseq': None,
+    }
+rest_dict['Ssp714II'] = _temp()
 
 def _temp():
     return {
@@ -16303,7 +17420,6 @@ def _temp():
     }
 rest_dict['SspD5I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SspDI>GGCGCC)',
@@ -16319,14 +17435,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'GGCGCC'),
         'ovhgseq': 'GCGC',
     }
 rest_dict['SspDI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SspI>AATATT)',
@@ -16342,13 +17457,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'N', 'Q', 'R', 'V', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'AATATT'),
         'ovhgseq': '',
     }
 rest_dict['SspI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<SspMI>CTAG)',
+        'results': None,
+        'site': 'CTAG',
+        'substrat': 'DNA',
+        'fst3': -1,
+        'fst5': 1,
+        'freq': 256,
+        'size': 4,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': -2,
+        'scd3': None,
+        'suppl': ('I',),
+        'scd5': None,
+        'charac': (1, -1, None, None, 'CTAG'),
+        'ovhgseq': 'TA',
+    }
+rest_dict['SspMI'] = _temp()
 
 def _temp():
     return {
@@ -16372,7 +17508,6 @@ def _temp():
     }
 rest_dict['SstE37I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SstI>GAGCTC)',
@@ -16395,7 +17530,6 @@ def _temp():
     }
 rest_dict['SstI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Sth132I>CCCG)|(?P<Sth132I_as>CGGG)',
@@ -16409,15 +17543,36 @@ def _temp():
         'opt_temp': 37,
         'dna': None,
         'inact_temp': 65,
-        'ovhg': -4,
+        'ovhg': -4,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (8, 8, None, None, 'CCCG'),
+        'ovhgseq': 'NNNN',
+    }
+rest_dict['Sth132I'] = _temp()
+
+def _temp():
+    return {
+        'compsite': '(?P<Sth20745III>GGACGAC)|(?P<Sth20745III_as>GTCGTCC)',
+        'results': None,
+        'site': 'GGACGAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 16384,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
         'scd3': None,
         'suppl': (),
         'scd5': None,
-        'charac': (8, 8, None, None, 'CCCG'),
-        'ovhgseq': 'NNNN',
+        'charac': (None, None, None, None, 'GGACGAC'),
+        'ovhgseq': None,
     }
-rest_dict['Sth132I'] = _temp()
-
+rest_dict['Sth20745III'] = _temp()
 
 def _temp():
     return {
@@ -16441,30 +17596,6 @@ def _temp():
     }
 rest_dict['Sth302II'] = _temp()
 
-
-def _temp():
-    return {
-        'compsite': '(?P<StrI>CTCGAG)',
-        'results': None,
-        'site': 'CTCGAG',
-        'substrat': 'DNA',
-        'fst3': -1,
-        'fst5': 1,
-        'freq': 4096,
-        'size': 6,
-        'opt_temp': 37,
-        'dna': None,
-        'inact_temp': 65,
-        'ovhg': -4,
-        'scd3': None,
-        'suppl': ('U',),
-        'scd5': None,
-        'charac': (1, -1, None, None, 'CTCGAG'),
-        'ovhgseq': 'TCGA',
-    }
-rest_dict['StrI'] = _temp()
-
-
 def _temp():
     return {
         'compsite': '(?P<StsI>GGATG)|(?P<StsI_as>CATCC)',
@@ -16487,7 +17618,6 @@ def _temp():
     }
 rest_dict['StsI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<StuI>AGGCCT)',
@@ -16503,13 +17633,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'U', 'X'),
+        'suppl': ('B', 'J', 'K', 'M', 'N', 'Q', 'R', 'X'),
         'scd5': None,
         'charac': (3, -3, None, None, 'AGGCCT'),
         'ovhgseq': '',
     }
 rest_dict['StuI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Sty13348III>GATCAG)|(?P<Sty13348III_as>CTGATC)',
+        'results': None,
+        'site': 'GATCAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GATCAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Sty13348III'] = _temp()
 
 def _temp():
     return {
@@ -16533,7 +17684,6 @@ def _temp():
     }
 rest_dict['StyD4I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<StyI>CC[AT][AT]GG)',
@@ -16556,7 +17706,6 @@ def _temp():
     }
 rest_dict['StyI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<SwaI>ATTTAAAT)',
@@ -16579,7 +17728,6 @@ def _temp():
     }
 rest_dict['SwaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TaaI>AC.GT)',
@@ -16595,14 +17743,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (3, -3, None, None, 'ACNGT'),
         'ovhgseq': 'N',
     }
 rest_dict['TaaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TaiI>ACGT)',
@@ -16618,14 +17765,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'ACGT'),
         'ovhgseq': 'ACGT',
     }
 rest_dict['TaiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TaqI>TCGA)',
@@ -16641,14 +17787,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'TCGA'),
         'ovhgseq': 'CG',
     }
 rest_dict['TaqI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TaqII>GACCGA)|(?P<TaqII_as>TCGGTC)',
@@ -16671,6 +17816,27 @@ def _temp():
     }
 rest_dict['TaqII'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<TaqIII>CACCCA)|(?P<TaqIII_as>TGGGTG)',
+        'results': None,
+        'site': 'CACCCA',
+        'substrat': 'DNA',
+        'fst3': 9,
+        'fst5': 17,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': 2,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (17, 9, None, None, 'CACCCA'),
+        'ovhgseq': 'NN',
+    }
+rest_dict['TaqIII'] = _temp()
 
 def _temp():
     return {
@@ -16687,14 +17853,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (0, 0, None, None, 'AATT'),
         'ovhgseq': 'AATT',
     }
 rest_dict['TasI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TatI>[AT]GTAC[AT])',
@@ -16710,14 +17875,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'WGTACW'),
         'ovhgseq': 'GTAC',
     }
 rest_dict['TatI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TauI>GC[CG]GC)',
@@ -16733,14 +17897,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (4, -4, None, None, 'GCSGC'),
         'ovhgseq': 'CSG',
     }
 rest_dict['TauI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TfiI>GA[AT]TC)',
@@ -16763,7 +17926,6 @@ def _temp():
     }
 rest_dict['TfiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Tru1I>TTAA)',
@@ -16779,14 +17941,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'TTAA'),
         'ovhgseq': 'TA',
     }
 rest_dict['Tru1I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Tru9I>TTAA)',
@@ -16809,7 +17970,6 @@ def _temp():
     }
 rest_dict['Tru9I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TscAI>CA[CG]TG)',
@@ -16825,14 +17985,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 10,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (7, -7, None, None, 'CASTG'),
         'ovhgseq': 'NNCASTGNN',
     }
 rest_dict['TscAI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TseFI>GT[CG]AC)',
@@ -16855,7 +18014,6 @@ def _temp():
     }
 rest_dict['TseFI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TseI>GC[AT]GC)',
@@ -16878,7 +18036,6 @@ def _temp():
     }
 rest_dict['TseI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TsoI>TA[AG]CCA)|(?P<TsoI_as>TGG[CT]TA)',
@@ -16901,7 +18058,6 @@ def _temp():
     }
 rest_dict['TsoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Tsp45I>GT[CG]AC)',
@@ -16924,7 +18080,6 @@ def _temp():
     }
 rest_dict['Tsp45I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Tsp4CI>AC.GT)',
@@ -16947,6 +18102,27 @@ def _temp():
     }
 rest_dict['Tsp4CI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<TspARh3I>G[AG]ACGAC)|(?P<TspARh3I_as>GTCGT[CT]C)',
+        'results': None,
+        'site': 'GRACGAC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 8192,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'GRACGAC'),
+        'ovhgseq': None,
+    }
+rest_dict['TspARh3I'] = _temp()
 
 def _temp():
     return {
@@ -16970,7 +18146,6 @@ def _temp():
     }
 rest_dict['TspDTI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TspEI>AATT)',
@@ -16993,7 +18168,6 @@ def _temp():
     }
 rest_dict['TspEI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TspGWI>ACGGA)|(?P<TspGWI_as>TCCGT)',
@@ -17016,7 +18190,6 @@ def _temp():
     }
 rest_dict['TspGWI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TspMI>CCCGGG)',
@@ -17039,7 +18212,6 @@ def _temp():
     }
 rest_dict['TspMI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TspRI>CA[CG]TG)',
@@ -17062,7 +18234,6 @@ def _temp():
     }
 rest_dict['TspRI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TssI>GAG...CTC)',
@@ -17085,7 +18256,6 @@ def _temp():
     }
 rest_dict['TssI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TstI>CAC......TCC)|(?P<TstI_as>GGA......GTG)',
@@ -17108,7 +18278,6 @@ def _temp():
     }
 rest_dict['TstI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<TsuI>GCGAC)|(?P<TsuI_as>GTCGC)',
@@ -17131,7 +18300,6 @@ def _temp():
     }
 rest_dict['TsuI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Tth111I>GAC...GTC)',
@@ -17154,7 +18322,6 @@ def _temp():
     }
 rest_dict['Tth111I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Tth111II>CAA[AG]CA)|(?P<Tth111II_as>TG[CT]TTG)',
@@ -17177,7 +18344,6 @@ def _temp():
     }
 rest_dict['Tth111II'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UbaF11I>TCGTA)|(?P<UbaF11I_as>TACGA)',
@@ -17200,7 +18366,6 @@ def _temp():
     }
 rest_dict['UbaF11I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UbaF12I>CTAC...GTC)|(?P<UbaF12I_as>GAC...GTAG)',
@@ -17223,7 +18388,6 @@ def _temp():
     }
 rest_dict['UbaF12I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UbaF13I>GAG......CTGG)|(?P<UbaF13I_as>CCAG......CTC)',
@@ -17246,7 +18410,6 @@ def _temp():
     }
 rest_dict['UbaF13I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UbaF14I>CCA.....TCG)|(?P<UbaF14I_as>CGA.....TGG)',
@@ -17269,7 +18432,6 @@ def _temp():
     }
 rest_dict['UbaF14I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UbaF9I>TAC.....[AG]TGT)|(?P<UbaF9I_as>ACA[CT].....GTA)',
@@ -17292,7 +18454,6 @@ def _temp():
     }
 rest_dict['UbaF9I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UbaPI>CGAACG)|(?P<UbaPI_as>CGTTCG)',
@@ -17315,7 +18476,6 @@ def _temp():
     }
 rest_dict['UbaPI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UcoMSI>GAGCTC)',
@@ -17338,7 +18498,6 @@ def _temp():
     }
 rest_dict['UcoMSI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<UnbI>GG.CC)',
@@ -17361,7 +18520,6 @@ def _temp():
     }
 rest_dict['UnbI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Van91I>CCA.....TGG)',
@@ -17377,14 +18535,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 3,
         'scd3': None,
-        'suppl': ('F', 'K'),
+        'suppl': ('B', 'K'),
         'scd5': None,
         'charac': (7, -7, None, None, 'CCANNNNNTGG'),
         'ovhgseq': 'NNN',
     }
 rest_dict['Van91I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Vha464I>CTTAAG)',
@@ -17407,7 +18564,6 @@ def _temp():
     }
 rest_dict['Vha464I'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<VneI>GTGCAC)',
@@ -17430,7 +18586,6 @@ def _temp():
     }
 rest_dict['VneI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<VpaK11AI>GG[AT]CC)',
@@ -17453,7 +18608,6 @@ def _temp():
     }
 rest_dict['VpaK11AI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<VpaK11BI>GG[AT]CC)',
@@ -17476,7 +18630,6 @@ def _temp():
     }
 rest_dict['VpaK11BI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<VspI>ATTAAT)',
@@ -17492,13 +18645,34 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F', 'I', 'R', 'V'),
+        'suppl': ('B', 'I', 'R', 'V'),
         'scd5': None,
         'charac': (2, -2, None, None, 'ATTAAT'),
         'ovhgseq': 'TA',
     }
 rest_dict['VspI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Vtu19109I>CAC[AG]A[CT]C)|(?P<Vtu19109I_as>G[AG]T[CT]GTG)',
+        'results': None,
+        'site': 'CACRAYC',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 7,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CACRAYC'),
+        'ovhgseq': None,
+    }
+rest_dict['Vtu19109I'] = _temp()
 
 def _temp():
     return {
@@ -17522,7 +18696,6 @@ def _temp():
     }
 rest_dict['WviI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XagI>CCT.....AGG)',
@@ -17538,14 +18711,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -1,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'CCTNNNNNAGG'),
         'ovhgseq': 'N',
     }
 rest_dict['XagI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XapI>[AG]AATT[CT])',
@@ -17561,14 +18733,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'RAATTY'),
         'ovhgseq': 'AATT',
     }
 rest_dict['XapI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XbaI>TCTAGA)',
@@ -17584,14 +18755,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'C', 'F', 'I', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'V', 'X', 'Y'),
+        'suppl': ('B', 'C', 'I', 'J', 'K', 'M', 'N', 'Q', 'R', 'S', 'V', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'TCTAGA'),
         'ovhgseq': 'CTAG',
     }
 rest_dict['XbaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XceI>[AG]CATG[CT])',
@@ -17607,14 +18777,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (5, -5, None, None, 'RCATGY'),
         'ovhgseq': 'CATG',
     }
 rest_dict['XceI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XcmI>CCA.........TGG)',
@@ -17637,7 +18806,6 @@ def _temp():
     }
 rest_dict['XcmI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XhoI>CTCGAG)',
@@ -17653,14 +18821,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('B', 'F', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'U', 'X', 'Y'),
+        'suppl': ('B', 'J', 'K', 'M', 'N', 'O', 'Q', 'R', 'S', 'X'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CTCGAG'),
         'ovhgseq': 'TCGA',
     }
 rest_dict['XhoI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XhoII>[AG]GATC[CT])',
@@ -17676,14 +18843,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('R',),
+        'suppl': (),
         'scd5': None,
         'charac': (1, -1, None, None, 'RGATCY'),
         'ovhgseq': 'GATC',
     }
 rest_dict['XhoII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XmaI>CCCGGG)',
@@ -17699,14 +18865,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('I', 'N', 'R', 'U', 'V'),
+        'suppl': ('I', 'N', 'R', 'V'),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCCGGG'),
         'ovhgseq': 'CCGG',
     }
 rest_dict['XmaI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XmaIII>CGGCCG)',
@@ -17729,7 +18894,6 @@ def _temp():
     }
 rest_dict['XmaIII'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XmaJI>CCTAGG)',
@@ -17745,14 +18909,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -4,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (1, -1, None, None, 'CCTAGG'),
         'ovhgseq': 'CTAG',
     }
 rest_dict['XmaJI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XmiI>GT[AC][GT]AC)',
@@ -17768,14 +18931,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': -2,
         'scd3': None,
-        'suppl': ('F',),
+        'suppl': ('B',),
         'scd5': None,
         'charac': (2, -2, None, None, 'GTMKAC'),
         'ovhgseq': 'MK',
     }
 rest_dict['XmiI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XmnI>GAA....TTC)',
@@ -17791,14 +18953,13 @@ def _temp():
         'inact_temp': 65,
         'ovhg': 0,
         'scd3': None,
-        'suppl': ('N', 'R', 'U'),
+        'suppl': ('N', 'R'),
         'scd5': None,
         'charac': (5, -5, None, None, 'GAANNNNTTC'),
         'ovhgseq': '',
     }
 rest_dict['XmnI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<XspI>CTAG)',
@@ -17821,7 +18982,6 @@ def _temp():
     }
 rest_dict['XspI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<YkrI>C)|(?P<YkrI_as>G)',
@@ -17844,6 +19004,27 @@ def _temp():
     }
 rest_dict['YkrI'] = _temp()
 
+def _temp():
+    return {
+        'compsite': '(?P<Yps3606I>CGGAAG)|(?P<Yps3606I_as>CTTCCG)',
+        'results': None,
+        'site': 'CGGAAG',
+        'substrat': 'DNA',
+        'fst3': None,
+        'fst5': None,
+        'freq': 4096,
+        'size': 6,
+        'opt_temp': 37,
+        'dna': None,
+        'inact_temp': 65,
+        'ovhg': None,
+        'scd3': None,
+        'suppl': (),
+        'scd5': None,
+        'charac': (None, None, None, None, 'CGGAAG'),
+        'ovhgseq': None,
+    }
+rest_dict['Yps3606I'] = _temp()
 
 def _temp():
     return {
@@ -17867,7 +19048,6 @@ def _temp():
     }
 rest_dict['ZraI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<ZrmI>AGTACT)',
@@ -17890,7 +19070,6 @@ def _temp():
     }
 rest_dict['ZrmI'] = _temp()
 
-
 def _temp():
     return {
         'compsite': '(?P<Zsp2I>ATGCAT)',
@@ -17914,24 +19093,20 @@ def _temp():
 rest_dict['Zsp2I'] = _temp()
 
 suppliers = {}
-
-
 def _temp():
     return (
         'Life Technologies',
-        ['MluI', 'SacI', 'SalI', 'BshTI', 'NcoI', 'ClaI', 'DraI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'HapII', 'BglII', 'SacII', 'BmeT110I', 'MseI', 'NruI', 'HincII', 'XbaI', 'MboI', 'AluI', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'HaeIII', 'AccI', 'SspI', 'ApaI', 'EcoT22I', 'ScaI', 'BalI', 'AfaI', 'NotI', 'HindIII', 'BamHI', 'HpaI', 'HhaI'],
+        ['BshTI', 'MluI', 'HpaII', 'MreI', 'BclI', 'SacI', 'PauI', 'BglI', 'SalI', 'MspI', 'ScaI', 'Bsu15I', 'Mva1269I', 'Bsp68I', 'LweI', 'SmiI', 'PteI', 'TscAI', 'NcoI', 'PsyI', 'BseJI', 'ClaI', 'MauBI', 'CaiI', 'Eco24I', 'CseI', 'Eco47III', 'Eco91I', 'DraI', 'BseXI', 'BstXI', 'RruI', 'Esp3I', 'BseSI', 'Cfr9I', 'AarI', 'OliI', 'PvuI', 'BspOI', 'DpnI', 'Hin6I', 'Van91I', 'Bst1107I', 'Bme1390I', 'BveI', 'Psp5II', 'TaqI', 'Eco52I', 'GsuI', 'KpnI', 'SspDI', 'SsiI', 'RseI', 'MlsI', 'NdeI',  [...]
     )
 suppliers['B'] = _temp()
 
-
 def _temp():
     return (
         'Minotech Biotechnology',
-        ['SgrBI', 'BclI', 'BglI', 'SalI', 'PspPI', 'SnaBI', 'BstEII', 'NcoI', 'BshFI', 'AsuII', 'BssAI', 'BseAI', 'TaqI', 'KpnI', 'BglII', 'NaeI', 'BseBI', 'NruI', 'SlaI', 'RsaI', 'BsiSI', 'XbaI', 'Sau3AI', 'MboI', 'AluI', 'SseBI', 'SmaI', 'NheI', 'SstI', 'SphI', 'PvuII', 'ApaLI', 'HinfI', 'MspCI', 'EcoRV', 'EcoRI', 'BseCI', 'PstI', 'SfiI', 'SspI', 'CspAI', 'ScaI', 'NotI', 'HindIII', 'BamHI', 'HpaI', 'StyI'],
+        ['SgrBI', 'BclI', 'BglI', 'SalI', 'PspPI', 'ScaI', 'SnaBI', 'BstEII', 'NcoI', 'BshFI', 'AsuII', 'BssAI', 'BseAI', 'TaqI', 'KpnI', 'BglII', 'NaeI', 'BseBI', 'NruI', 'RsaI', 'BsiSI', 'XbaI', 'MboI', 'AluI', 'SlaI', 'SseBI', 'SmaI', 'NheI', 'SstI', 'Sau3AI', 'SphI', 'PvuII', 'ApaLI', 'HinfI', 'MspCI', 'HindIII', 'EcoRV', 'EcoRI', 'BseCI', 'PstI', 'SfiI', 'SspI', 'CspAI', 'NotI', 'BamHI', 'HpaI', 'StyI'],
     )
 suppliers['C'] = _temp()
 
-
 def _temp():
     return (
         'Agilent Technologies',
@@ -17939,121 +19114,91 @@ def _temp():
     )
 suppliers['E'] = _temp()
 
-
-def _temp():
-    return (
-        'Thermo Scientific Fermentas',
-        ['MluI', 'CseI', 'HpaII', 'Cfr9I', 'MreI', 'BclI', 'SacI', 'PauI', 'BglI', 'SalI', 'MspI', 'Bsu15I', 'Mva1269I', 'Bsp68I', 'LweI', 'SmiI', 'PteI', 'BshTI', 'TscAI', 'NcoI', 'PsyI', 'BseJI', 'MauBI', 'Eco24I', 'Eco47III', 'Eco91I', 'DraI', 'BseXI', 'BstXI', 'RruI', 'Esp3I', 'BseSI', 'AdeI', 'AarI', 'RseI', 'PvuI', 'BspOI', 'DpnI', 'Hin6I', 'Van91I', 'Bst1107I', 'Bme1390I', 'BveI', 'Psp5II', 'TaqI', 'Eco52I', 'KpnI', 'SspDI', 'SsiI', 'MlsI', 'NdeI', 'Cfr13I', 'MboII', 'SdaI', 'BmsI [...]
-    )
-suppliers['F'] = _temp()
-
-
 def _temp():
     return (
         'SibEnzyme Ltd.',
-        ['AsuNHI', 'AgsI', 'BstSFI', 'MluI', 'CciI', 'BstHHI', 'HpaII', 'AhlI', 'KroI', 'PspN4I', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BisI', 'BmtI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'AbsI', 'FauNDI', 'BstMWI', 'AclWI', 'DraI', 'Bst2UI', 'AluBI', 'PsrI', 'BstACI', 'BstXI', 'BstDEI', 'GluI', 'AcoI', 'XmaI', 'BstF5I', 'BstENI', 'BssECI', 'FalI', 'EgeI', 'Ama87I', 'BstDSI', 'BstV2I', 'AjnI', 'AspLEI', 'Pa [...]
+        ['AsuNHI', 'AgsI', 'MluI', 'CciI', 'BstHHI', 'HpaII', 'AhlI', 'KroI', 'PspN4I', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BisI', 'BmtI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BstSFI', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'AbsI', 'FauNDI', 'BstMWI', 'LmnI', 'AclWI', 'DraI', 'Bst2UI', 'AluBI', 'PsrI', 'BstACI', 'BstXI', 'BstDEI', 'GluI', 'AcoI', 'XmaI', 'BstF5I', 'BstMBI', 'BstENI', 'BssECI', 'FalI', 'EgeI', 'Ama87I', 'BstDSI', 'BstV2I', 'Aj [...]
     )
 suppliers['I'] = _temp()
 
-
 def _temp():
     return (
         'Nippon Gene Co., Ltd.',
-        ['BssHII', 'AxyI', 'MluI', 'BclI', 'SacI', 'EcoT38I', 'BglI', 'SalI', 'MspI', 'BstEII', 'NcoI', 'DraI', 'BstXI', 'SwaI', 'AvaI', 'TaqI', 'AseI', 'KpnI', 'Sau96I', 'HaeII', 'NdeI', 'MboII', 'AflII', 'BglII', 'AccII', 'SacII', 'NruI', 'NarI', 'RsaI', 'HincII', 'XbaI', 'Sau3AI', 'AluI', 'ScrFI', 'EcoRII', 'SmaI', 'NheI', 'StuI', 'SphI', 'NciI', 'FspI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'EcoO109I', 'EcoRV', 'EcoRI', 'XhoI', 'Bsp1286I', 'AccIII', 'PstI', 'BsmI', 'Alw44I', 'SfiI', 'Hae [...]
+        ['BssHII', 'AxyI', 'MluI', 'BclI', 'SacI', 'EcoT38I', 'BglI', 'SalI', 'MspI', 'ScaI', 'BstEII', 'NcoI', 'DraI', 'BstXI', 'SwaI', 'AvaI', 'TaqI', 'AseI', 'KpnI', 'Sau96I', 'HaeII', 'NdeI', 'MboII', 'AflII', 'BglII', 'AccII', 'SacII', 'NruI', 'NarI', 'RsaI', 'HincII', 'XbaI', 'AluI', 'ScrFI', 'EcoRII', 'SmaI', 'NheI', 'StuI', 'Sau3AI', 'SphI', 'FspI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'NciI', 'EcoO109I', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'Bsp1286I', 'AccIII', 'AvaII', 'PstI', 'B [...]
     )
 suppliers['J'] = _temp()
 
-
 def _temp():
     return (
         'Takara Bio Inc.',
-        ['BssHII', 'MluI', 'BspT107I', 'SacI', 'XspI', 'BglI', 'SalI', 'MspI', 'BstPI', 'BanII', 'PmaCI', 'SnaBI', 'SmiI', 'BmgT120I', 'NcoI', 'ClaI', 'DraI', 'BstXI', 'PshAI', 'PvuI', 'DpnI', 'Van91I', 'Bst1107I', 'TaqI', 'EaeI', 'Eco52I', 'BspT104I', 'KpnI', 'HaeII', 'EcoO65I', 'NdeI', 'HapII', 'MboII', 'AflII', 'EcoT14I', 'BglII', 'NaeI', 'AccII', 'SacII', 'BmeT110I', 'Aor51HI', 'Bsp1407I', 'NruI', 'Sse8387I', 'CpoI', 'HincII', 'XbaI', 'Sau3AI', 'Cfr10I', 'MboI', 'AluI', 'BcnI', 'SmaI [...]
+        ['BssHII', 'PshBI', 'MluI', 'BspT107I', 'SacI', 'XspI', 'BglI', 'SalI', 'MspI', 'BstPI', 'ScaI', 'BanII', 'PmaCI', 'SnaBI', 'SmiI', 'BmgT120I', 'NcoI', 'ClaI', 'DraI', 'BstXI', 'PshAI', 'PvuI', 'DpnI', 'Van91I', 'Bst1107I', 'TaqI', 'EaeI', 'Eco52I', 'BspT104I', 'KpnI', 'HaeII', 'EcoO65I', 'NdeI', 'HapII', 'MboII', 'AflII', 'EcoT14I', 'BglII', 'NaeI', 'AccII', 'SacII', 'BmeT110I', 'Aor51HI', 'Bsp1407I', 'NruI', 'Sse8387I', 'CpoI', 'HincII', 'XbaI', 'MboI', 'AluI', 'BcnI', 'SmaI',  [...]
     )
 suppliers['K'] = _temp()
 
-
 def _temp():
     return (
         'Roche Applied Science',
-        ['BssHII', 'MluI', 'BclI', 'SacI', 'SalI', 'Asp718I', 'SnaBI', 'NcoI', 'ClaI', 'Eco47III', 'DraI', 'BstXI', 'SwaI', 'PvuI', 'BseAI', 'DpnI', 'TaqI', 'SexAI', 'KpnI', 'NdeI', 'PinAI', 'BglII', 'MaeI', 'NruI', 'MvaI', 'NarI', 'RsaI', 'MaeII', 'AflIII', 'XbaI', 'Sau3AI', 'MvnI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'BbrPI', 'SphI', 'MaeIII', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'DraIII', 'MluNI', 'EcoRV', 'AatII', 'EcoRI', 'XhoI', 'MunI', 'EclXI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'Ha [...]
+        ['BssHII', 'MluI', 'BclI', 'SacI', 'SalI', 'Asp718I', 'ScaI', 'SnaBI', 'NcoI', 'ClaI', 'Eco47III', 'DraI', 'BstXI', 'SwaI', 'PvuI', 'DpnI', 'TaqI', 'SexAI', 'KpnI', 'NdeI', 'BglII', 'MaeI', 'NruI', 'MvaI', 'NarI', 'RsaI', 'MaeII', 'AflIII', 'XbaI', 'MvnI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'Sau3AI', 'BbrPI', 'SphI', 'MaeIII', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'DraIII', 'MluNI', 'HindIII', 'EcoRV', 'AatII', 'EcoRI', 'XhoI', 'MunI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'HaeIII', ' [...]
     )
 suppliers['M'] = _temp()
 
-
 def _temp():
     return (
         'New England Biolabs',
-        ['BssHII', 'EciI', 'BsrFI', 'DpnII', 'AlwI', 'MluI', 'NgoMIV', 'HpaII', 'TspMI', 'BclI', 'MlyI', 'BsaWI', 'SacI', 'MwoI', 'BfaI', 'DrdI', 'BmgBI', 'BglI', 'SalI', 'MspI', 'BanII', 'MslI', 'BmtI', 'PspXI', 'BsaBI', 'SnaBI', 'BstEII', 'TspRI', 'NcoI', 'MspA1I', 'BtgI', 'ClaI', 'BsaI', 'BsrBI', 'AlwNI', 'XmnI', 'DraI', 'Hpy166II', 'Hpy99I', 'StyD4I', 'BstXI', 'PspGI', 'BsiHKAI', 'BsoBI', 'BlpI', 'PshAI', 'XmaI', 'BtsIMutI', 'SwaI', 'AvaI', 'PvuI', 'DpnI', 'CspCI', 'PflFI', 'BpuEI',  [...]
+        ['BssHII', 'EciI', 'BsrFI', 'DpnII', 'AlwI', 'MluI', 'AlwNI', 'NgoMIV', 'HpaII', 'TspMI', 'BclI', 'MlyI', 'BsaWI', 'SacI', 'MwoI', 'BfaI', 'DrdI', 'BmgBI', 'BglI', 'SalI', 'MspI', 'ScaI', 'BanII', 'MslI', 'BmtI', 'PspXI', 'BsaBI', 'SnaBI', 'BstEII', 'TspRI', 'NcoI', 'MspA1I', 'BtgI', 'ClaI', 'BsaI', 'BsrBI', 'XmnI', 'DraI', 'Hpy166II', 'Hpy99I', 'StyD4I', 'BstXI', 'PspGI', 'BsiHKAI', 'BsoBI', 'BlpI', 'PshAI', 'XmaI', 'BtsIMutI', 'SwaI', 'AvaI', 'PvuI', 'DpnI', 'CspCI', 'PflFI', ' [...]
     )
 suppliers['N'] = _temp()
 
-
 def _temp():
     return (
         'Toyobo Biochemicals',
-        ['MluI', 'BclI', 'SacI', 'BglI', 'SalI', 'NcoI', 'PvuI', 'DpnI', 'AseI', 'KpnI', 'BglII', 'SacII', 'HincII', 'XbaI', 'AluI', 'MscI', 'SmaI', 'NheI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'SfiI', 'HaeIII', 'ScaI', 'PacI', 'DdeI', 'NotI', 'MroI', 'HindIII', 'BamHI'],
+        ['MluI', 'BclI', 'SacI', 'BglI', 'SalI', 'ScaI', 'NcoI', 'PvuI', 'DpnI', 'KpnI', 'BglII', 'SacII', 'HincII', 'AluI', 'MscI', 'SmaI', 'NheI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'SfiI', 'HaeIII', 'PacI', 'DdeI', 'NotI', 'MroI', 'BamHI'],
     )
 suppliers['O'] = _temp()
 
-
 def _temp():
     return (
         'Molecular Biology Resources - CHIMERx',
-        ['BssHII', 'MluI', 'HpaII', 'SacI', 'BglI', 'SalI', 'MspI', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'PinAI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'BsiHKCI', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'PstI', 'SfiI', 'HaeIII', 'AccI', 'SspI', 'Tth111I', 'NsiI', 'ApaI', 'ScaI', 'TspDTI', 'BalI', 'DdeI' [...]
+        ['BssHII', 'MluI', 'HpaII', 'SacI', 'BglI', 'SalI', 'MspI', 'ScaI', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'BsiHKCI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'PinAI', 'PstI', 'SfiI', 'Tth111I', 'HaeIII', 'AccI', 'SspI', 'NsiI', 'ApaI', 'TspDTI', 'Ba [...]
     )
 suppliers['Q'] = _temp()
 
-
 def _temp():
     return (
         'Promega Corporation',
-        ['BssHII', 'MluI', 'HpaII', 'BclI', 'SacI', 'BglI', 'SalI', 'BstOI', 'MspI', 'SnaBI', 'BstEII', 'NcoI', 'MspA1I', 'ClaI', 'XmnI', 'Eco47III', 'DraI', 'BstXI', 'XmaI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'HaeII', 'NdeI', 'MboII', 'BglII', 'SacII', 'Acc65I', 'NruI', 'CspI', 'NarI', 'Bsu36I', 'RsaI', 'HincII', 'XbaI', 'BsrSI', 'Sau3AI', 'MboI', 'BanI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'SphI', 'NciI', 'PvuII', 'SpeI', 'HinfI', 'SgfI', 'EcoRV', 'AatII', 'EcoRI', 'XhoI', 'Ac [...]
+        ['BssHII', 'MluI', 'HpaII', 'BclI', 'SacI', 'BglI', 'SalI', 'MspI', 'ScaI', 'SnaBI', 'BstEII', 'NcoI', 'MspA1I', 'ClaI', 'XmnI', 'Eco47III', 'DraI', 'BstXI', 'XmaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'HaeII', 'NdeI', 'MboII', 'BglII', 'SacII', 'NruI', 'CspI', 'NarI', 'RsaI', 'Hsp92II', 'HincII', 'XbaI', 'MboI', 'BanI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'StuI', 'Sau3AI', 'SphI', 'PvuII', 'SpeI', 'HinfI', 'NciI', 'HindIII', 'SgfI', 'EcoRV', 'EcoRI', 'XhoI', 'AccIII', 'AvaII', 'PstI', ' [...]
     )
 suppliers['R'] = _temp()
 
-
 def _temp():
     return (
         'Sigma Chemical Corporation',
-        ['BssHII', 'HpaII', 'BclI', 'SacI', 'SalI', 'MspI', 'BstEII', 'NcoI', 'ClaI', 'DraI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'BglII', 'MvaI', 'RsaI', 'XbaI', 'Sau3AI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'SphI', 'PvuII', 'SpeI', 'EcoRV', 'EcoRI', 'XhoI', 'EclXI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'HaeIII', 'AccI', 'SspI', 'NsiI', 'ApaI', 'ScaI', 'KspI', 'DdeI', 'NotI', 'HindIII', 'BamHI', 'HpaI'],
+        ['HpaII', 'BclI', 'SacI', 'SalI', 'MspI', 'ScaI', 'BstEII', 'NcoI', 'ClaI', 'DraI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'BglII', 'MvaI', 'RsaI', 'XbaI', 'AluI', 'CfoI', 'SmaI', 'NheI', 'Sau3AI', 'SphI', 'PvuII', 'SpeI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'EclXI', 'PstI', 'BsmI', 'SfiI', 'BlnI', 'HaeIII', 'AccI', 'NsiI', 'ApaI', 'KspI', 'DdeI', 'NotI', 'BamHI', 'HpaI'],
     )
 suppliers['S'] = _temp()
 
-
-def _temp():
-    return (
-        'Bangalore Genei',
-        ['MluI', 'HpaII', 'BclI', 'SacI', 'BglI', 'SalI', 'MspI', 'SnaBI', 'BstEII', 'NcoI', 'ClaI', 'XmnI', 'DraI', 'XmaI', 'AvaI', 'PvuI', 'AssI', 'TaqI', 'KpnI', 'Sau96I', 'NdeI', 'BglII', 'NaeI', 'NruI', 'NarI', 'HincII', 'XbaI', 'Sau3AI', 'StrI', 'MboI', 'BanI', 'AluI', 'SmaI', 'BasI', 'NheI', 'StuI', 'PvuII', 'ApaLI', 'SpeI', 'HinfI', 'MvrI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'SfiI', 'HaeIII', 'AccI', 'SspI', 'NsiI', 'ApaI', 'NotI', 'HindIII', 'BamHI', 'HpaI', 'HhaI'],
-    )
-suppliers['U'] = _temp()
-
-
 def _temp():
     return (
         'Vivantis Technologies',
-        ['BssMI', 'AsuNHI', 'MluI', 'BstHHI', 'HpaII', 'AhlI', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BmtI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'BmcAI', 'FauNDI', 'DraI', 'Bst2UI', 'Vha464I', 'BstXI', 'BstDEI', 'XmaI', 'BstF5I', 'BpvUI', 'BstENI', 'Ama87I', 'BstDSI', 'BstV2I', 'AspLEI', 'Zsp2I', 'DseDI', 'BstAUI', 'Bpu14I', 'TaqI', 'KpnI', 'BstSNI', 'MboII', 'BmrFI', 'BmeRI', 'BseX3I', 'Bpu10I', 'Rsr2I', 'BtuMI', ' [...]
+        ['BssMI', 'AsuNHI', 'MluI', 'BstHHI', 'HpaII', 'AhlI', 'BglI', 'SalI', 'PspEI', 'MspI', 'VneI', 'BstH2I', 'BmtI', 'AsiGI', 'CciNI', 'Sfr274I', 'SmiI', 'Ksp22I', 'BssT1I', 'MspA1I', 'Bsp19I', 'Bse1I', 'AspS9I', 'BmcAI', 'FauNDI', 'DraI', 'Bst2UI', 'Vha464I', 'BstXI', 'BstDEI', 'XmaI', 'BstF5I', 'BstMBI', 'BstENI', 'Ama87I', 'BstDSI', 'BstV2I', 'Zsp2I', 'DseDI', 'BstAUI', 'Bpu14I', 'TaqI', 'KpnI', 'BstSNI', 'AclI', 'MboII', 'BmrFI', 'BglII', 'AcsI', 'BstNSI', 'BmeRI', 'BseX3I', 'Bp [...]
     )
 suppliers['V'] = _temp()
 
-
 def _temp():
     return (
         'EURx Ltd.',
-        ['BssHII', 'MluI', 'HpaII', 'SacI', 'BglI', 'SalI', 'MspI', 'BanII', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'KpnI', 'NdeI', 'PinAI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'BsiHKCI', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'PstI', 'SfiI', 'HaeIII', 'AccI', 'SspI', 'Tth111I', 'NsiI', 'ApaI', 'ScaI', 'TspDTI [...]
+        ['BssHII', 'MluI', 'HpaII', 'BspTNI', 'SacI', 'BglI', 'SalI', 'MspI', 'ScaI', 'BanII', 'NcoI', 'ClaI', 'CviJI', 'DraI', 'BstXI', 'AcvI', 'AvaI', 'PvuI', 'DpnI', 'TaqI', 'SinI', 'KpnI', 'NdeI', 'MboII', 'BglII', 'SacII', 'NruI', 'NarI', 'TaqII', 'RsaI', 'HincII', 'XbaI', 'BspANI', 'TspGWI', 'MboI', 'AluI', 'RsrII', 'SmaI', 'NheI', 'StuI', 'SphI', 'PvuII', 'FokI', 'SpeI', 'HinfI', 'BsiHKCI', 'HindIII', 'EcoRV', 'EcoRI', 'XhoI', 'MnlI', 'AvaII', 'PinAI', 'PstI', 'BspMAI', 'SfiI', 'T [...]
     )
 suppliers['X'] = _temp()
 
-
 def _temp():
     return (
         'SinaClon BioScience Co.',
-        ['BclI', 'BglI', 'SalI', 'MspI', 'NcoI', 'DraI', 'PvuI', 'TaqI', 'KpnI', 'NdeI', 'BglII', 'RsaI', 'HincII', 'XbaI', 'MboI', 'AluI', 'SmaI', 'PvuII', 'HinfI', 'EcoRV', 'EcoRI', 'XhoI', 'PstI', 'HaeIII', 'NotI', 'HindIII', 'BamHI', 'AvaII', 'HhaI'],
+        ['NcoI', 'BstXI', 'KpnI', 'RsaI', 'BsiSI', 'MboI', 'AluI', 'SlaI', 'SmaI', 'FokI', 'HinfI', 'HindIII', 'EcoRI', 'BamHI'],
     )
 suppliers['Y'] = _temp()
 
 typedict = {}
-
-
 def _temp():
     return (
         ('Palindromic', 'TwoCuts', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18061,7 +19206,6 @@ def _temp():
     )
 typedict['type130'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'TwoCuts', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18069,7 +19213,6 @@ def _temp():
     )
 typedict['type132'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18077,63 +19220,62 @@ def _temp():
     )
 typedict['type142'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['FalI', 'BplI', 'AlfI'],
+        ['FalI', 'BplI'],
     )
 typedict['type143'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['BdaI'],
+        ['BdaI', 'AlfI'],
     )
 typedict['type144'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['SpoDI', 'Cgl13032I', 'Cdi630V', 'EsaSSI', 'RflFIII', 'Cgl13032II', 'CjeFV', 'BscGI', 'Sno506I', 'Hpy99XIV', 'RdeGBI', 'Hpy99XIII', 'CjeFIII', 'GauT27I', 'DrdII', 'NhaXI', 'RpaTI', 'MkaDII', 'Jma19592I', 'CjeNII'],
+        ['Sen13311III', 'SpoDI', 'OspHL35III', 'Cgl13032I', 'Pal408I', 'EsaSSI', 'Pse18267I', 'HpyUM032XIV', 'Hpy99XIV_mut1', 'Cdu23823II', 'RflFIII', 'Pdi8503III', 'Pme5II', 'Cgl13032II', 'Yps3606I', 'CjeFV', 'Bsu7003I', 'Psp10HII', 'Lsp48III', 'HpyAXVI_mut1', 'BscGI', 'Bsp3004IV', 'SpnRII', 'EcoE1140I', 'PliMI', 'Ecl35734I', 'Pac19842II', 'Sno506I', 'Hpy99XIV', 'Aco12261II', 'AspJHL3II', 'Sth20745III', 'Cdi11397I', 'Mba11I', 'TspARh3I', 'RdeGBI', 'Jma19592II', 'Vtu19109I', 'CjeFIII', ' [...]
     )
 typedict['type146'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['UbaF14I', 'CjeP659IV', 'UbaPI', 'CjuII', 'AlwFI', 'BspGI', 'Pfl1108I', 'UbaF13I', 'RlaI', 'PenI', 'UbaF12I', 'BspNCI', 'TsuI', 'UbaF9I', 'FinI', 'UbaF11I', 'BmgI'],
+        ['UbaF14I', 'CjeP659IV', 'PsuGI', 'UbaPI', 'CjuII', 'AlwFI', 'BspGI', 'Pfl1108I', 'UbaF13I', 'AbaUMB2I', 'RlaI', 'PenI', 'UbaF12I', 'BspNCI', 'TsuI', 'UbaF9I', 'FinI', 'UbaF11I', 'BmgI'],
     )
 typedict['type148'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['MjaIV'],
+        ['MjaIV', 'HpyUM032XIII'],
     )
 typedict['type2'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['MlyI', 'BsrBI'],
+        ['MlyI', 'BmgBI', 'BsrBI'],
     )
 typedict['type209'] = _temp()
 
+def _temp():
+    return (
+        ('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
+        ['NgoAVII'],
+    )
+typedict['type210'] = _temp()
 
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['BmgBI', 'SchI', 'BtrI', 'AjiI', 'AccBSI', 'MbiI'],
+        ['SchI', 'BtrI', 'AjiI', 'AccBSI', 'MbiI'],
     )
 typedict['type211'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18141,7 +19283,6 @@ def _temp():
     )
 typedict['type212'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18149,15 +19290,13 @@ def _temp():
     )
 typedict['type221'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['SsiI', 'Bst2BI', 'BseYI', 'BauI'],
+        ['SsiI', 'Bst2BI', 'PspFI', 'BseYI', 'BauI'],
     )
 typedict['type223'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18165,39 +19304,34 @@ def _temp():
     )
 typedict['type224'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['AlwI', 'Esp3I', 'Bpu10I', 'Lsp1109I', 'BfuAI', 'BbvI', 'HgaI', 'BspMI', 'BccI', 'FokI', 'Alw26I', 'Eco31I', 'BceAI', 'BsmAI', 'BsmFI', 'FauI', 'BsmBI', 'BtgZI'],
+        ['AlwI', 'Esp3I', 'Bpu10I', 'Lsp1109I', 'BfuAI', 'BbvI', 'HgaI', 'BspMI', 'BccI', 'BcoDI', 'FokI', 'Alw26I', 'Eco31I', 'BceAI', 'BsmAI', 'BsmFI', 'FauI', 'BsmBI', 'BtgZI'],
     )
 typedict['type225'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['StsI'],
+        ['StsI', 'BscAI'],
     )
 typedict['type226'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['CseI', 'LweI', 'BsaI', 'AclWI', 'BseXI', 'BstV2I', 'AarI', 'BveI', 'BmsI', 'BspQI', 'MspJI', 'BstV1I', 'Bso31I', 'PleI', 'FaqI', 'FspEI', 'BcoDI', 'PpsI', 'Eam1104I', 'Acc36I', 'SapI', 'BspPI', 'BslFI', 'LguI', 'PciSI', 'Bst6I', 'BpiI', 'LpnPI', 'BstMAI', 'EarI', 'BbsI', 'SfaNI'],
+        ['BspTNI', 'LweI', 'BsaI', 'CseI', 'AclWI', 'BseXI', 'BstV2I', 'AarI', 'BveI', 'BmsI', 'BspQI', 'MspJI', 'BstV1I', 'Bso31I', 'PleI', 'FaqI', 'FspEI', 'PpsI', 'Eam1104I', 'Acc36I', 'SapI', 'BspPI', 'BslFI', 'LguI', 'PciSI', 'Bst6I', 'BpiI', 'LpnPI', 'BstMAI', 'EarI', 'BbsI', 'SfaNI'],
     )
 typedict['type227'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['SgrTI', 'Sth132I', 'BbvII', 'BinI', 'AspBHI', 'Bbr7I', 'BscAI', 'BspD6I', 'BcefI', 'AceIII', 'Ksp632I'],
+        ['SgrTI', 'Sth132I', 'BbvII', 'BinI', 'AspBHI', 'Bbr7I', 'BspD6I', 'BcefI', 'AceIII', 'Ksp632I'],
     )
 typedict['type228'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18205,7 +19339,6 @@ def _temp():
     )
 typedict['type235'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18213,39 +19346,34 @@ def _temp():
     )
 typedict['type237'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['RpaBI', 'DraRI', 'SdeAI', 'RceI', 'WviI', 'BfiI', 'CstMI', 'PspOMII', 'CchII', 'PlaDI', 'SstE37I', 'RpaB5I', 'MaqI', 'CdpI', 'CchIII', 'Tth111II', 'CjeNIII', 'NlaCI', 'AquII', 'AquIV', 'ApyPI', 'RpaI', 'PspPRI', 'AquIII', 'RdeGBII'],
+        ['MaqI', 'RpaBI', 'DraRI', 'SdeAI', 'RceI', 'WviI', 'BfiI', 'CstMI', 'PspOMII', 'TaqIII', 'CchII', 'PlaDI', 'SstE37I', 'RpaB5I', 'CdpI', 'CchIII', 'Tth111II', 'CjeNIII', 'BsbI', 'NlaCI', 'AquII', 'AquIV', 'ApyPI', 'RpaI', 'PspPRI', 'AquIII', 'RdeGBII'],
     )
 typedict['type238'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['EciI', 'Mva1269I', 'Bse1I', 'BtsIMutI', 'BtsCI', 'BsuI', 'AsuHPI', 'BsrSI', 'BseGI', 'Bse3DI', 'GsuI', 'BsrDI', 'AbaSI', 'BciVI', 'BtsI', 'BfuI', 'BsmI', 'BseMI', 'TspDTI', 'BmuI', 'PctI', 'BseNI'],
+        ['EciI', 'Mva1269I', 'Bse1I', 'LmnI', 'BtsIMutI', 'GsuI', 'BtsCI', 'BsuI', 'AsuHPI', 'AbaSI', 'BseGI', 'Bse3DI', 'BsrDI', 'BciVI', 'BtsI', 'BfuI', 'BsmI', 'BseMI', 'TspDTI', 'BmuI', 'PctI', 'BseNI'],
     )
 typedict['type239'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['BmeDI', 'Bce83I', 'Hin4II', 'TsoI', 'Eco57MI', 'BsbI', 'YkrI', 'RleAI'],
+        ['BmeDI', 'Bce83I', 'Hin4II', 'TsoI', 'Eco57MI', 'YkrI', 'RleAI'],
     )
 typedict['type240'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'TwoCuts', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['R2_BceSIV'],
+        ['BceSIV'],
     )
 typedict['type274'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18253,7 +19381,6 @@ def _temp():
     )
 typedict['type285'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18261,7 +19388,6 @@ def _temp():
     )
 typedict['type286'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18269,7 +19395,6 @@ def _temp():
     )
 typedict['type287'] = _temp()
 
-
 def _temp():
     return (
         ('NonPalindromic', 'TwoCuts', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18277,23 +19402,20 @@ def _temp():
     )
 typedict['type288'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'NoCut', 'Unknown', 'NotDefined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['CjuI', 'AvaIII', 'TssI', 'SnaI', 'HgiEII'],
+        ['NhoI', 'CjuI', 'AvaIII', 'TssI', 'SnaI', 'HgiEII'],
     )
 typedict['type4'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['SnaBI', 'MspA1I', 'CviJI', 'DraI', 'PshAI', 'SwaI', 'NaeI', 'RsaI', 'HincII', 'BstC8I', 'PmlI', 'AluI', 'Hpy8I', 'SmaI', 'FspI', 'PvuII', 'BsuRI', 'EcoRV', 'BstUI', 'HaeIII', 'SspI', 'ScaI', 'BalI', 'NlaIV', 'HpaI', 'Cac8I', 'HindII', 'BsaAI'],
+        ['ScaI', 'SnaBI', 'MspA1I', 'CviJI', 'DraI', 'AluBI', 'PshAI', 'SwaI', 'NaeI', 'SfoI', 'RsaI', 'HincII', 'BstC8I', 'PmlI', 'AluI', 'Hpy8I', 'SmaI', 'FspI', 'PvuII', 'BsuRI', 'EcoRV', 'BstUI', 'HaeIII', 'SspI', 'BalI', 'NlaIV', 'HpaI', 'Cac8I', 'HindII', 'BsaAI'],
     )
 typedict['type65'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18301,63 +19423,55 @@ def _temp():
     )
 typedict['type66'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['PspN4I', 'MslI', 'Bsp68I', 'PmaCI', 'BsaBI', 'SmiI', 'BseJI', 'BshFI', 'BmcAI', 'XmnI', 'Eco47III', 'Hpy166II', 'AluBI', 'AcvI', 'RruI', 'EgeI', 'RseI', 'DpnI', 'AssI', 'Bst1107I', 'BstSNI', 'MlsI', 'AccII', 'BtuMI', 'Aor51HI', 'NruI', 'SmiMI', 'Bsh1236I', 'MalI', 'MssI', 'BsnI', 'SfoI', 'FspAI', 'BmiI', 'BoxI', 'BssNAI', 'GlaI', 'MvnI', 'ZraI', 'Eco72I', 'MscI', 'SseBI', 'Ppu21I', 'StuI', 'BbrPI', 'Eco53kI', 'EheI', 'BstPAI', 'Ecl136II', 'Acc16I', 'MluNI', 'PsiI', 'MroXI', 'Zr [...]
+        ['PspN4I', 'MslI', 'Bsp68I', 'PmaCI', 'BsaBI', 'SmiI', 'BseJI', 'BshFI', 'BmcAI', 'XmnI', 'Eco47III', 'Hpy166II', 'AcvI', 'RruI', 'EgeI', 'OliI', 'DpnI', 'Bst1107I', 'BstSNI', 'RseI', 'MlsI', 'AccII', 'BtuMI', 'Aor51HI', 'NruI', 'Bsh1236I', 'MalI', 'MssI', 'BsnI', 'FspAI', 'BmiI', 'BoxI', 'BssNAI', 'BspANI', 'GlaI', 'MvnI', 'ZraI', 'Eco72I', 'MscI', 'SseBI', 'SmiMI', 'Ppu21I', 'StuI', 'BbrPI', 'Eco53kI', 'EheI', 'BstPAI', 'Ecl136II', 'Acc16I', 'MluNI', 'PsiI', 'MroXI', 'ZrmI', 'D [...]
     )
 typedict['type67'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Blunt', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['NspBII', 'HaeI', 'SciI', 'LpnI', 'SrfI', 'AhaIII', 'Sth302II', 'MstI'],
+        ['NspBII', 'HaeI', 'SciI', 'LpnI', 'AhaIII', 'Sth302II', 'MstI'],
     )
 typedict['type68'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['BssHII', 'BsrFI', 'DpnII', 'MluI', 'NgoMIV', 'HpaII', 'Cfr9I', 'BclI', 'BsaWI', 'SalI', 'MspI', 'Bsu15I', 'NcoI', 'ClaI', 'XmaI', 'TaqI', 'EaeI', 'AseI', 'AclI', 'ApoI', 'HpyCH4IV', 'NdeI', 'HapII', 'BglII', 'MseI', 'BsaHI', 'XbaI', 'Sau3AI', 'Cfr10I', 'MboI', 'CviQI', 'CviAII', 'MfeI', 'ApaLI', 'HinP1I', 'HspAI', 'EcoRI', 'XhoI', 'BseCI', 'MunI', 'EagI', 'AscI', 'VspI', 'KasI', 'Kpn2I', 'BspHI', 'BstYI', 'AgeI', 'BfuCI', 'XhoII', 'Sse9I', 'NotI', 'HindIII', 'FatI', 'BamHI', 'P [...]
+        ['BssHII', 'BsrFI', 'DpnII', 'MluI', 'NgoMIV', 'HpaII', 'TspMI', 'BclI', 'BsaWI', 'SalI', 'MspI', 'Bsu15I', 'NcoI', 'ClaI', 'XmaI', 'Cfr9I', 'TaqI', 'EaeI', 'AseI', 'AclI', 'ApoI', 'HpyCH4IV', 'NdeI', 'HapII', 'MseI', 'BsaHI', 'XbaI', 'MboI', 'CviQI', 'NheI', 'Sau3AI', 'CviAII', 'MfeI', 'ApaLI', 'Cfr10I', 'HinP1I', 'HspAI', 'HindIII', 'EcoRI', 'XhoI', 'BseCI', 'MunI', 'EagI', 'AscI', 'VspI', 'KasI', 'Kpn2I', 'SgrAI', 'BspHI', 'BstYI', 'AgeI', 'BfuCI', 'Sse9I', 'NotI', 'FatI', 'Ba [...]
     )
 typedict['type77'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['XmaIII', 'CfrI'],
+        ['XmaIII', 'CfrI', 'XhoII'],
     )
 typedict['type78'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['BssMI', 'AsuNHI', 'CciI', 'TspMI', 'MreI', 'AhlI', 'XspI', 'KroI', 'BfaI', 'PauI', 'Asp718I', 'VneI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'PteI', 'Ksp22I', 'BshTI', 'Bsp19I', 'MauBI', 'AbsI', 'AsuII', 'FauNDI', 'Vha464I', 'BstACI', 'AcoI', 'BssAI', 'BseAI', 'Hin6I', 'PalAI', 'BstAUI', 'Bpu14I', 'Eco52I', 'BspT104I', 'SspDI', 'PinAI', 'AflII', 'TasI', 'AcsI', 'BseX3I', 'FspBI', 'RsaNI', 'Acc65I', 'AvrII', 'MaeI', 'XapI', 'PscI', 'Bsp1407I', 'PciI', 'Bse118I', 'SlaI', 'NarI', ' [...]
+        ['BssMI', 'AsuNHI', 'PshBI', 'BshTI', 'CciI', 'MreI', 'AhlI', 'XspI', 'KroI', 'BfaI', 'PauI', 'Asp718I', 'VneI', 'PspXI', 'AsiGI', 'CciNI', 'Sfr274I', 'PteI', 'Ksp22I', 'Bsp19I', 'MauBI', 'AbsI', 'AsuII', 'FauNDI', 'Vha464I', 'BstACI', 'AcoI', 'BstMBI', 'BssAI', 'BseAI', 'Hin6I', 'PalAI', 'BstAUI', 'Bpu14I', 'Eco52I', 'BspT104I', 'SspDI', 'AflII', 'BglII', 'TasI', 'AcsI', 'BseX3I', 'FspBI', 'RsaNI', 'Acc65I', 'AvrII', 'MaeI', 'XapI', 'PscI', 'Bsp1407I', 'SspMI', 'PciI', 'Bse118I' [...]
     )
 typedict['type79'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['SelI', 'BspLU11I', 'TspEI', 'Asi256I', 'Ppu10I', 'Sse232I', 'BetI', 'SplI', 'AoxI', 'BspMII'],
+        ['SelI', 'BspLU11I', 'SplI', 'TspEI', 'Asi256I', 'Ppu10I', 'Sse232I', 'BetI', 'BspMII'],
     )
 typedict['type80'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['PspPI', 'AspS9I', 'StyD4I', 'PspGI', 'BsoBI', 'BlpI', 'BssECI', 'AjnI', 'AvaI', 'SexAI', 'Sau96I', 'BstNI', 'Cfr13I', 'TseI', 'MvaI', 'BssKI', 'AflIII', 'TfiI', 'BanI', 'RsrII', 'BcnI', 'ScrFI', 'EcoRII', 'EcoNI', 'NciI', 'ApeKI', 'HinfI', 'Fsp4HI', 'EcoO109I', 'PpuMI', 'AccI', 'Tth111I', 'DdeI', 'AvaII', 'BsaJI', 'Fnu4HI', 'Tsp45I'],
+        ['PspPI', 'AspS9I', 'StyD4I', 'PspGI', 'BsoBI', 'BlpI', 'BssECI', 'AjnI', 'AvaI', 'SexAI', 'SinI', 'Sau96I', 'BstNI', 'Cfr13I', 'TseI', 'MvaI', 'AflIII', 'TfiI', 'BanI', 'RsrII', 'BcnI', 'ScrFI', 'EcoRII', 'EcoNI', 'ApeKI', 'HinfI', 'NciI', 'Fsp4HI', 'EcoO109I', 'PpuMI', 'AvaII', 'Tth111I', 'AccI', 'DdeI', 'BsaJI', 'Fnu4HI', 'Tsp45I'],
     )
 typedict['type81'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18365,23 +19479,20 @@ def _temp():
     )
 typedict['type82'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['AxyI', 'BstSFI', 'BspT107I', 'BstOI', 'PspEI', 'BisI', 'BstPI', 'BstEII', 'BmgT120I', 'BssT1I', 'PsyI', 'BtgI', 'Eco91I', 'Bst2UI', 'BstDEI', 'GluI', 'BstENI', 'Ama87I', 'BstDSI', 'PflFI', 'Bme1390I', 'Psp5II', 'EcoO65I', 'BmrFI', 'EcoT14I', 'PspPPI', 'SmlI', 'BseBI', 'PfoI', 'BshNI', 'Rsr2I', 'BmeT110I', 'PasI', 'TseFI', 'CpoI', 'Eco130I', 'CspI', 'BfmI', 'Eco47I', 'Bsu36I', 'Bme18I', 'MabI', 'Hpy188III', 'Bse21I', 'BstSCI', 'BpuMI', 'SgeI', 'SmoI', 'Bsp1720I', 'MaeIII', 'ErhI [...]
+        ['AxyI', 'BspT107I', 'PspEI', 'BisI', 'BstPI', 'BstSFI', 'BstEII', 'BmgT120I', 'BssT1I', 'PsyI', 'BtgI', 'Eco91I', 'Bst2UI', 'BstDEI', 'GluI', 'BstENI', 'Ama87I', 'BstDSI', 'PflFI', 'Bme1390I', 'Psp5II', 'EcoO65I', 'BmrFI', 'EcoT14I', 'PspPPI', 'SmlI', 'BseBI', 'PfoI', 'BshNI', 'Rsr2I', 'BmeT110I', 'PasI', 'TseFI', 'CpoI', 'Eco130I', 'CspI', 'BfmI', 'Eco47I', 'Bsu36I', 'Bme18I', 'MabI', 'Hpy188III', 'Bse21I', 'BstSCI', 'BpuMI', 'SgeI', 'XagI', 'SmoI', 'Bsp1720I', 'MaeIII', 'ErhI' [...]
     )
 typedict['type83'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov5', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
-        ['SanDI', 'DsaI', 'DraII', 'SauI', 'UnbI', 'VpaK11AI', 'SfeI', 'CauII', 'Hpy178III', 'AsuI', 'SecI', 'EspI', 'Sse8647I'],
+        ['SanDI', 'DsaI', 'SauI', 'DraII', 'UnbI', 'VpaK11AI', 'SfeI', 'CauII', 'Hpy178III', 'AsuI', 'SecI', 'EspI', 'Sse8647I'],
     )
 typedict['type84'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
@@ -18389,7 +19500,6 @@ def _temp():
     )
 typedict['type89'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18397,15 +19507,13 @@ def _temp():
     )
 typedict['type90'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['SgrBI', 'BstHHI', 'BstH2I', 'BmtI', 'BpvUI', 'PvuI', 'BspOI', 'AspLEI', 'Zsp2I', 'FaeI', 'SdaI', 'BstNSI', 'Ple19I', 'Hin1II', 'Sse8387I', 'TaiI', 'Psp124BI', 'Sfr303I', 'Mph1103I', 'CfoI', 'PaeI', 'BstKTI', 'SstI', 'SphI', 'MvrI', 'SfaAI', 'RgaI', 'SgfI', 'SbfI', 'XceI', 'NsiI', 'EcoT22I', 'PacI', 'KspI', 'Hsp92II', 'BfoI', 'RigI'],
+        ['SgrBI', 'BstHHI', 'BstH2I', 'BmtI', 'PvuI', 'BspOI', 'Zsp2I', 'FaeI', 'SdaI', 'BstNSI', 'Ple19I', 'Hin1II', 'Sse8387I', 'TaiI', 'Hsp92II', 'Psp124BI', 'Sfr303I', 'Mph1103I', 'CfoI', 'PaeI', 'BstKTI', 'SstI', 'AspLEI', 'SphI', 'SfaAI', 'RgaI', 'SgfI', 'SbfI', 'BspMAI', 'XceI', 'NsiI', 'EcoT22I', 'PacI', 'KspI', 'BfoI', 'RigI'],
     )
 typedict['type91'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Defined', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18413,15 +19521,13 @@ def _temp():
     )
 typedict['type92'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['MwoI', 'EcoT38I', 'BglI', 'BanII', 'TspRI', 'Hpy99I', 'BstXI', 'BsiHKAI', 'BaeGI', 'Hpy188I', 'Bsc4I', 'DraIII', 'Bsp1286I', 'AhdI', 'SfiI', 'XcmI', 'BslI'],
+        ['AgsI', 'MwoI', 'EcoT38I', 'BanII', 'TspRI', 'Hpy99I', 'BstXI', 'Bsc4I', 'BaeGI', 'Hpy188I', 'DraIII', 'Bsp1286I', 'AhdI', 'SfiI', 'XcmI', 'BslI'],
     )
 typedict['type93'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Dep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18429,15 +19535,13 @@ def _temp():
     )
 typedict['type94'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Commercially_available', 'AbstractCut', 'RestrictionType'),
-        ['AgsI', 'DrdI', 'TscAI', 'BstMWI', 'Eco24I', 'AlwNI', 'BseSI', 'AdeI', 'Van91I', 'DseDI', 'SetI', 'BmeRI', 'TaaI', 'BstMCI', 'HpyF10VI', 'Bst4CI', 'SduI', 'Alw21I', 'AccB7I', 'BasI', 'BsiEI', 'BseLI', 'FriOI', 'AasI', 'Eam1105I', 'TauI', 'AfiI', 'CaiI', 'Bbv12I', 'BstAPI', 'HpyCH4III', 'BstSLI', 'BlsI', 'PcsI', 'PstNI', 'MhlI', 'DriI', 'Bsh1285I', 'PflMI'],
+        ['AlwNI', 'DrdI', 'BglI', 'TscAI', 'CaiI', 'BstMWI', 'Eco24I', 'BsiHKAI', 'BseSI', 'Van91I', 'DseDI', 'SetI', 'BmeRI', 'TaaI', 'BstMCI', 'HpyF10VI', 'Bst4CI', 'SduI', 'Alw21I', 'AccB7I', 'AdeI', 'BsiEI', 'BseLI', 'FriOI', 'AasI', 'Eam1105I', 'TauI', 'AfiI', 'Bbv12I', 'BstAPI', 'HpyCH4III', 'BstSLI', 'BlsI', 'PcsI', 'PstNI', 'PkrI', 'MhlI', 'DriI', 'Bsh1285I', 'PflMI'],
     )
 typedict['type95'] = _temp()
 
-
 def _temp():
     return (
         ('Palindromic', 'OneCut', 'Ov3', 'Ambiguous', 'Meth_Undep', 'Not_available', 'AbstractCut', 'RestrictionType'),
@@ -18446,3 +19550,4 @@ def _temp():
 typedict['type96'] = _temp()
 
 del _temp
+
diff --git a/Bio/Restriction/__init__.py b/Bio/Restriction/__init__.py
index 26f5ae0..02dd01b 100644
--- a/Bio/Restriction/__init__.py
+++ b/Bio/Restriction/__init__.py
@@ -13,7 +13,7 @@ Example:
 
     >>> from Rana.fts import fts    #
     >>> from Rana.Vector import *   # Just a way to get a sequence.
-    >>> from Bio.Seq import Seq     # Use your prefered method here.
+    >>> from Bio.Seq import Seq     # Use your preferred method here.
     >>> pbr = fts(pBR322)           #
     >>> seq = Seq(str(pbr))         #
     >>>
@@ -56,7 +56,6 @@ Example:
 
 from Bio.Restriction.Restriction import *
 
-__docformat__ = "restructuredtext en"
 
 #
 #   OK can't put the following code in Bio.Restriction.__init__ unless
diff --git a/Bio/SCOP/Raf.py b/Bio/SCOP/Raf.py
index 5e6d9de..eac25aa 100644
--- a/Bio/SCOP/Raf.py
+++ b/Bio/SCOP/Raf.py
@@ -36,8 +36,6 @@ from Bio.Data.SCOPData import protein_letters_3to1
 
 from Bio.SCOP.Residues import Residues
 
-__docformat__ = "restructuredtext en"
-
 
 def normalize_letters(one_letter_code):
     """Convert RAF one-letter amino acid codes into IUPAC standard codes.
@@ -285,9 +283,9 @@ class SeqMap(object):
             # for k in resFound:
             #    del resSet[k]
             # print(resSet)
-
-            raise RuntimeError('I could not find at least one ATOM or HETATM'
-                   + ' record for each and every residue in this sequence map.')
+            raise RuntimeError("Could not find at least one ATOM or HETATM"
+                               " record for each and every residue in this"
+                               " sequence map.")
 
 
 class Res(object):
diff --git a/Bio/SCOP/Residues.py b/Bio/SCOP/Residues.py
index d3a17c2..9805a00 100644
--- a/Bio/SCOP/Residues.py
+++ b/Bio/SCOP/Residues.py
@@ -5,10 +5,10 @@
 
 # Gavin E. Crooks 2001-11-03
 # Minor extensions, some bug fixes, and major changes to the interface
+"""A collection of residues from a PDB structure."""
 
 import re
 
-"""A collection of residues from a PDB structure."""
 
 _pdbid_re = re.compile(r"^(\w\w\w\w)(?:$|\s+|_)(.*)")
 _fragment_re = re.compile(r"\(?(\w:)?(-?\w*)-?(-?\w*)\)?(.*)")
diff --git a/Bio/SCOP/__init__.py b/Bio/SCOP/__init__.py
index 3b3313d..d2849c4 100644
--- a/Bio/SCOP/__init__.py
+++ b/Bio/SCOP/__init__.py
@@ -60,7 +60,6 @@ from . import Residues
 from Bio import SeqIO
 from Bio.Seq import Seq
 
-__docformat__ = "restructuredtext en"
 
 nodeCodeDict = {'cl': 'class', 'cf': 'fold', 'sf': 'superfamily',
                 'fa': 'family', 'dm': 'protein', 'sp': 'species', 'px': 'domain'}
@@ -319,7 +318,7 @@ class Scop(object):
 
     def write_des(self, handle):
         """Build a DES SCOP parsable file from this object"""
-        # Origional SCOP file is not ordered?
+        # Original SCOP file is not ordered?
         for n in sorted(self._sunidDict.values(), key=lambda n: n.sunid):
             if n != self.root:
                 handle.write(str(n.toDesRecord()))
@@ -574,7 +573,7 @@ class Node(object):
             return self.scop.getNodeBySunid(self.parent)
 
     def getDescendents(self, node_type):
-        """ Return a list of all decendent nodes of the given type. Node type can a
+        """ Return a list of all descendant nodes of the given type. Node type can a
         two letter code or longer description. e.g. 'fa' or 'family'
         """
         if node_type in _nodetype_to_code:
@@ -698,8 +697,8 @@ class Astral(object):
         """
 
         if astral_file is None and dir_path is None and db_handle is None:
-            raise RuntimeError("Need either file handle, or (dir_path + "
-                       + "version) or database handle to construct Astral")
+            raise RuntimeError("Need either file handle, or (dir_path + version),"
+                               " or database handle to construct Astral")
         if not scop:
             raise RuntimeError("Must provide a Scop instance to construct")
 
@@ -872,8 +871,8 @@ def search(pdb=None, key=None, sid=None, disp=None, dir=None, loc=None,
     return _open(cgi, variables)
 
 
-def _open(cgi, params={}, get=1):
-    """_open(cgi, params={}, get=1) -> UndoHandle
+def _open(cgi, params=None, get=1):
+    """Open a hnadle to SCOP, returns an UndoHandle
 
     Open a handle to SCOP.  cgi is the URL for the cgi script to access.
     params is a dictionary with the options to pass to it.  get is a boolean
@@ -884,6 +883,8 @@ def _open(cgi, params={}, get=1):
     from Bio._py3k import urlopen, urlencode
 
     # Open a handle to SCOP.
+    if params is None:
+        params = {}
     options = urlencode(params)
     if get:  # do a GET
         if options:
diff --git a/Bio/SVDSuperimposer/__init__.py b/Bio/SVDSuperimposer/__init__.py
index f14c112..a6af2ff 100644
--- a/Bio/SVDSuperimposer/__init__.py
+++ b/Bio/SVDSuperimposer/__init__.py
@@ -2,7 +2,8 @@
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
-"""
+"""Align on protein structure onto another using SVD alignment.
+
 SVDSuperimposer finds the best rotation and translation to put
 two point sets on top of each other (minimizing the RMSD). This is
 eg. useful to superimpose crystal structures. SVD stands for singular
@@ -16,7 +17,8 @@ from numpy.linalg import svd, det
 
 
 class SVDSuperimposer(object):
-    """
+    """Class to run SVD alignment,
+
     SVDSuperimposer finds the best rotation and translation to put
     two point sets on top of each other (minimizing the RMSD). This is
     eg. useful to superimpose crystal structures.
@@ -44,7 +46,7 @@ class SVDSuperimposer(object):
         self.init_rms = None
 
     def _rms(self, coords1, coords2):
-        "Return rms deviations between coords1 and coords2."
+        """Return rms deviations between coords1 and coords2."""
         diff = coords1 - coords2
         l = coords1.shape[0]
         return sqrt(sum(sum(diff * diff)) / l)
@@ -52,12 +54,12 @@ class SVDSuperimposer(object):
     # Public methods
 
     def set(self, reference_coords, coords):
-        """
-        Set the coordinates to be superimposed.
+        """Set the coordinates to be superimposed.
+
         coords will be put on top of reference_coords.
 
-        o reference_coords: an NxDIM array
-        o coords: an NxDIM array
+        - reference_coords: an NxDIM array
+        - coords: an NxDIM array
 
         DIM is the dimension of the points, N is the number
         of points to be superimposed.
@@ -74,7 +76,7 @@ class SVDSuperimposer(object):
         self.n = n[0]
 
     def run(self):
-        "Superimpose the coordinate sets."
+        """Superimpose the coordinate sets."""
         if self.coords is None or self.reference_coords is None:
             raise Exception("No coordinates set.")
         coords = self.coords
@@ -95,7 +97,7 @@ class SVDSuperimposer(object):
         self.tran = av2 - dot(av1, self.rot)
 
     def get_transformed(self):
-        "Get the transformed coordinate set."
+        """Get the transformed coordinate set."""
         if self.coords is None or self.reference_coords is None:
             raise Exception("No coordinates set.")
         if self.rot is None:
@@ -105,13 +107,13 @@ class SVDSuperimposer(object):
         return self.transformed_coords
 
     def get_rotran(self):
-        "Right multiplying rotation matrix and translation."
+        """Right multiplying rotation matrix and translation."""
         if self.rot is None:
             raise Exception("Nothing superimposed yet.")
         return self.rot, self.tran
 
     def get_init_rms(self):
-        "Root mean square deviation of untransformed coordinates."
+        """Root mean square deviation of untransformed coordinates."""
         if self.coords is None:
             raise Exception("No coordinates set yet.")
         if self.init_rms is None:
@@ -119,7 +121,7 @@ class SVDSuperimposer(object):
         return self.init_rms
 
     def get_rms(self):
-        "Root mean square deviation of superimposed coordinates."
+        """Root mean square deviation of superimposed coordinates."""
         if self.rms is None:
             transformed_coords = self.get_transformed()
             self.rms = self._rms(transformed_coords, self.reference_coords)
diff --git a/Bio/SearchIO/BlastIO/__init__.py b/Bio/SearchIO/BlastIO/__init__.py
index d616d6f..6fd05a3 100644
--- a/Bio/SearchIO/BlastIO/__init__.py
+++ b/Bio/SearchIO/BlastIO/__init__.py
@@ -402,8 +402,6 @@ from .blast_tab import BlastTabParser, BlastTabIndexer, BlastTabWriter
 from .blast_xml import BlastXmlParser, BlastXmlIndexer, BlastXmlWriter
 from .blast_text import BlastTextParser
 
-__docformat__ = "restructuredtext en"
-
 
 # if not used as a module, run the doctest
 if __name__ == "__main__":
diff --git a/Bio/SearchIO/BlastIO/blast_tab.py b/Bio/SearchIO/BlastIO/blast_tab.py
index cd37f4b..be45202 100644
--- a/Bio/SearchIO/BlastIO/blast_tab.py
+++ b/Bio/SearchIO/BlastIO/blast_tab.py
@@ -16,8 +16,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
 
 __all__ = ['BlastTabIndexer', 'BlastTabParser', 'BlastTabWriter']
 
-__docformat__ = "restructuredtext en"
-
 
 # longname-shortname map
 # maps the column names shown in a commented output to its short name
@@ -602,7 +600,7 @@ class BlastTabIndexer(SearchIndexer):
                 break
 
     def get_raw(self, offset):
-        """Returns the raw string of a QueryResult object from the given offset."""
+        """Returns the raw bytes string of a QueryResult object from the given offset."""
         if self._kwargs['comments']:
             getfunc = self._get_raw_qresult_commented
         else:
@@ -611,7 +609,7 @@ class BlastTabIndexer(SearchIndexer):
         return getfunc(offset)
 
     def _get_raw_qresult(self, offset):
-        """Returns the raw string of a single QueryResult from a noncommented file."""
+        """Returns the raw bytes string of a single QueryResult from a noncommented file."""
         handle = self._handle
         handle.seek(offset)
         qresult_raw = _as_bytes('')
@@ -638,7 +636,7 @@ class BlastTabIndexer(SearchIndexer):
         return qresult_raw
 
     def _get_raw_qresult_commented(self, offset):
-        """Returns the raw string of a single QueryResult from a commented file."""
+        """Returns the bytes raw string of a single QueryResult from a commented file."""
         handle = self._handle
         handle.seek(offset)
         qresult_raw = _as_bytes('')
diff --git a/Bio/SearchIO/BlastIO/blast_text.py b/Bio/SearchIO/BlastIO/blast_text.py
index cbcdec9..832c440 100644
--- a/Bio/SearchIO/BlastIO/blast_text.py
+++ b/Bio/SearchIO/BlastIO/blast_text.py
@@ -22,8 +22,6 @@ with warnings.catch_warnings():
 
 __all__ = ['BlastTextParser']
 
-__docformat__ = "restructuredtext en"
-
 
 class BlastTextParser(object):
 
diff --git a/Bio/SearchIO/BlastIO/blast_xml.py b/Bio/SearchIO/BlastIO/blast_xml.py
index 0ee785d..f4555f6 100644
--- a/Bio/SearchIO/BlastIO/blast_xml.py
+++ b/Bio/SearchIO/BlastIO/blast_xml.py
@@ -14,7 +14,6 @@ from xml.sax.saxutils import XMLGenerator, escape
 
 from Bio import BiopythonParserWarning
 
-
 # For speed try to use cElementTree rather than ElementTree
 try:
     if (3, 0) <= sys.version_info[:2] <= (3, 1):
@@ -27,18 +26,15 @@ except ImportError:
     from xml.etree import ElementTree as ElementTree
 
 
-from Bio._py3k import _as_bytes, _bytes_to_string, unicode
-_empty_bytes_string = _as_bytes("")
-
 from Bio.Alphabet import generic_dna, generic_protein
 from Bio.SearchIO._index import SearchIndexer
 from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
 
+from Bio._py3k import _as_bytes, _bytes_to_string, unicode
+_empty_bytes_string = _as_bytes("")
 
 __all__ = ['BlastXmlParser', 'BlastXmlIndexer', 'BlastXmlWriter']
 
-__docformat__ = "restructuredtext en"
-
 
 # element - optional qresult attribute name mapping
 _ELEM_QRESULT_OPT = {
@@ -199,8 +195,8 @@ def _extract_ids_and_descs(concat_str):
 
     # create a list of lists, each list containing an ID and description
     # or just an ID, if description is not present
-    id_desc_pairs = [re.split(_RE_ID_DESC_PATTERN, x, 1) \
-            for x in re.split(_RE_ID_DESC_PAIRS_PATTERN, concat_str)]
+    id_desc_pairs = [re.split(_RE_ID_DESC_PATTERN, x, 1)
+                     for x in re.split(_RE_ID_DESC_PAIRS_PATTERN, concat_str)]
     # make sure empty descriptions are added as empty strings
     # also, we return lists for compatibility reasons between Py2 and Py3
     add_descs = lambda x: x if len(x) == 2 else x + [""]
@@ -409,7 +405,6 @@ class BlastXmlParser(object):
             else:
                 blast_hit_id = ''
 
-
             # combine primary ID and defline first before splitting
             full_id_desc = hit_id + ' ' + hit_desc
             id_descs = _extract_ids_and_descs(full_id_desc)
@@ -612,6 +607,7 @@ class BlastXmlIndexer(SearchIndexer):
         return next(iter(generator))
 
     def get_raw(self, offset):
+        """Return the raw record from the file as a bytes string."""
         qend_mark = self.qend_mark
         handle = self._handle
         handle.seek(offset)
@@ -654,7 +650,7 @@ class _BlastXmlGenerator(XMLGenerator):
                 '<!DOCTYPE BlastOutput PUBLIC "-//NCBI//NCBI BlastOutput/EN" '
                 '"http://www.ncbi.nlm.nih.gov/dtd/NCBI_BlastOutput.dtd">\n')
 
-    def startElement(self, name, attrs={}, children=False):
+    def startElement(self, name, attrs=None, children=False):
         """Starts an XML element.
 
         :param name: element name
@@ -665,6 +661,8 @@ class _BlastXmlGenerator(XMLGenerator):
         :type children: bool
 
         """
+        if attrs is None:
+            attrs = {}
         self.ignorableWhitespace(self._indent * self._level)
         XMLGenerator.startElement(self, name, attrs)
 
@@ -673,7 +671,7 @@ class _BlastXmlGenerator(XMLGenerator):
         XMLGenerator.endElement(self, name)
         self.write(u'\n')
 
-    def startParent(self, name, attrs={}):
+    def startParent(self, name, attrs=None):
         """Starts an XML element which has children.
 
         :param name: element name
@@ -682,6 +680,8 @@ class _BlastXmlGenerator(XMLGenerator):
         :type attrs: dictionary {string: object}
 
         """
+        if attrs is None:
+            attrs = {}
         self.startElement(name, attrs, children=True)
         self._level += self._increment
         self.write(u'\n')
@@ -747,7 +747,7 @@ class BlastXmlWriter(object):
         return self.qresult_counter, self.hit_counter, self.hsp_counter, \
             self.frag_counter
 
-    def _write_elem_block(self, block_name, map_name, obj, opt_dict={}):
+    def _write_elem_block(self, block_name, map_name, obj, opt_dict=None):
         """Writes sibling XML elements.
 
         :param block_name: common element name prefix
@@ -760,6 +760,8 @@ class BlastXmlWriter(object):
         :type opt_dict: dictionary {string: string}
 
         """
+        if opt_dict is None:
+            opt_dict = {}
         for elem, attr in _WRITE_MAPS[map_name]:
             elem = block_name + elem
             try:
diff --git a/Bio/SearchIO/BlatIO.py b/Bio/SearchIO/BlatIO.py
index ac2fac9..e2314a7 100644
--- a/Bio/SearchIO/BlatIO.py
+++ b/Bio/SearchIO/BlatIO.py
@@ -192,8 +192,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
 
 __all__ = ['BlatPslParser', 'BlatPslIndexer', 'BlatPslWriter']
 
-__docformat__ = "restructuredtext en"
-
 
 # precompile regex patterns
 _PTR_ROW_CHECK = r'^\d+\s+\d+\s+\d+\s+\d+'
@@ -567,7 +565,7 @@ class BlatPslIndexer(SearchIndexer):
                 break
 
     def get_raw(self, offset):
-        """Returns the raw string of a QueryResult object from the given offset."""
+        """Returns raw bytes string of a QueryResult object from the given offset."""
         handle = self._handle
         handle.seek(offset)
         query_id_idx = 9
diff --git a/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py b/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py
index 956936c..da8edf8 100644
--- a/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py
+++ b/Bio/SearchIO/ExonerateIO/exonerate_vulgar.py
@@ -179,7 +179,7 @@ class ExonerateVulgarIndexer(_BaseExonerateIndexer):
         return id.group(1)
 
     def get_raw(self, offset):
-        """Returns the raw string of a QueryResult object from the given offset."""
+        """Returns the raw bytes string of a QueryResult object from the given offset."""
         handle = self._handle
         handle.seek(offset)
         qresult_key = None
diff --git a/Bio/SearchIO/FastaIO.py b/Bio/SearchIO/FastaIO.py
index 6e49976..e0d88dd 100644
--- a/Bio/SearchIO/FastaIO.py
+++ b/Bio/SearchIO/FastaIO.py
@@ -116,8 +116,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
 
 __all__ = ['FastaM10Parser', 'FastaM10Indexer']
 
-__docformat__ = "restructuredtext en"
-
 
 # precompile regex patterns
 # regex for program name
@@ -153,7 +151,7 @@ _STATE_HIT_BLOCK = 2
 _STATE_CONS_BLOCK = 3
 
 
-def _set_qresult_hits(qresult, hit_rows=[]):
+def _set_qresult_hits(qresult, hit_rows=()):
     """Helper function for appending Hits without alignments into QueryResults."""
     for hit_row in hit_rows:
         hit_id, remainder = hit_row.split(' ', 1)
@@ -536,14 +534,15 @@ class FastaM10Indexer(SearchIndexer):
                 start_offset = end_offset - len(line)
             # yield whenever we encounter a new query or at the end of the file
             if qresult_key is not None:
-                if (not peekline.startswith(query_mark)
-                        and query_mark in peekline) or not line:
+                if (not peekline.startswith(query_mark) and
+                    query_mark in peekline) or not line:
                     yield qresult_key, start_offset, end_offset - start_offset
                     if not line:
                         break
                     start_offset = end_offset
 
     def get_raw(self, offset):
+        """Return the raw record from the file as a bytes string."""
         handle = self._handle
         qresult_raw = _as_bytes('')
         query_mark = _as_bytes('>>>')
diff --git a/Bio/SearchIO/HmmerIO/__init__.py b/Bio/SearchIO/HmmerIO/__init__.py
index 3bb9b3d..bf9c6ea 100644
--- a/Bio/SearchIO/HmmerIO/__init__.py
+++ b/Bio/SearchIO/HmmerIO/__init__.py
@@ -242,6 +242,8 @@ The following SearchIO objects attributes are provided:
 |                 | evalue                  | hit-level e-value                |
 |                 +-------------------------+----------------------------------+
 |                 | id                      | hit sequence ID                  |
+|                 +-------------------------+----------------------------------+
+|                 | seq_len                 | length of hit sequence or HMM    |
 +-----------------+-------------------------+----------------------------------+
 | HSP             | acc_avg                 | expected accuracy per alignment  |
 |                 |                         | residue (acc column)             |
@@ -291,9 +293,6 @@ from .hmmer3_text import Hmmer3TextParser, Hmmer3TextIndexer
 from .hmmer3_tab import Hmmer3TabParser, Hmmer3TabIndexer, Hmmer3TabWriter
 
 
-__docformat__ = "restructuredtext en"
-
-
 # if not used as a module, run the doctest
 if __name__ == "__main__":
     from Bio._utils import run_doctest
diff --git a/Bio/SearchIO/HmmerIO/_base.py b/Bio/SearchIO/HmmerIO/_base.py
index c8e279b..bd396d0 100644
--- a/Bio/SearchIO/HmmerIO/_base.py
+++ b/Bio/SearchIO/HmmerIO/_base.py
@@ -9,9 +9,6 @@ from Bio._py3k import _as_bytes
 from Bio.SearchIO._index import SearchIndexer
 
 
-__docformat__ = "restructuredtext en"
-
-
 class _BaseHmmerTextIndexer(SearchIndexer):
 
     """Base indexer class for HMMER plain text output."""
@@ -21,6 +18,7 @@ class _BaseHmmerTextIndexer(SearchIndexer):
         self._preamble = _as_bytes('')
 
     def get_raw(self, offset):
+        """Return the raw record from the file as a bytes string."""
         handle = self._handle
         qresult_raw = _as_bytes('')
 
diff --git a/Bio/SearchIO/HmmerIO/hmmer2_text.py b/Bio/SearchIO/HmmerIO/hmmer2_text.py
index 32e8af2..54e20e2 100644
--- a/Bio/SearchIO/HmmerIO/hmmer2_text.py
+++ b/Bio/SearchIO/HmmerIO/hmmer2_text.py
@@ -16,7 +16,6 @@ from ._base import _BaseHmmerTextIndexer
 
 __all__ = ['Hmmer2TextParser', 'Hmmer2TextIndexer']
 
-__docformat__ = "restructuredtext en"
 
 _HSP_ALIGN_LINE = re.compile(r'(\S+):\s+domain (\d+) of (\d+)')
 
diff --git a/Bio/SearchIO/HmmerIO/hmmer3_domtab.py b/Bio/SearchIO/HmmerIO/hmmer3_domtab.py
index 03265ad..de3ce51 100644
--- a/Bio/SearchIO/HmmerIO/hmmer3_domtab.py
+++ b/Bio/SearchIO/HmmerIO/hmmer3_domtab.py
@@ -13,9 +13,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
 from .hmmer3_tab import Hmmer3TabParser, Hmmer3TabIndexer
 
 
-__docformat__ = "restructuredtext en"
-
-
 class Hmmer3DomtabParser(Hmmer3TabParser):
 
     """Base hmmer3-domtab iterator."""
diff --git a/Bio/SearchIO/HmmerIO/hmmer3_tab.py b/Bio/SearchIO/HmmerIO/hmmer3_tab.py
index 720b50a..44e0062 100644
--- a/Bio/SearchIO/HmmerIO/hmmer3_tab.py
+++ b/Bio/SearchIO/HmmerIO/hmmer3_tab.py
@@ -15,8 +15,6 @@ from Bio.SearchIO._model import QueryResult, Hit, HSP, HSPFragment
 
 __all__ = ['Hmmer3TabParser', 'Hmmer3TabIndexer', 'Hmmer3TabWriter']
 
-__docformat__ = "restructuredtext en"
-
 
 class Hmmer3TabParser(object):
 
@@ -199,7 +197,7 @@ class Hmmer3TabIndexer(SearchIndexer):
                 break
 
     def get_raw(self, offset):
-        """Returns the raw string of a QueryResult object from the given offset."""
+        """Returns the raw bytes string of a QueryResult object from the given offset."""
         handle = self._handle
         handle.seek(offset)
         query_id_idx = self._query_id_idx
diff --git a/Bio/SearchIO/HmmerIO/hmmer3_text.py b/Bio/SearchIO/HmmerIO/hmmer3_text.py
index 1652d45..a49478e 100644
--- a/Bio/SearchIO/HmmerIO/hmmer3_text.py
+++ b/Bio/SearchIO/HmmerIO/hmmer3_text.py
@@ -16,8 +16,6 @@ from ._base import _BaseHmmerTextIndexer
 
 __all__ = ['Hmmer3TextParser', 'Hmmer3TextIndexer']
 
-__docformat__ = "restructuredtext en"
-
 
 # precompile regex patterns for faster processing
 # regex for program name capture
@@ -219,8 +217,8 @@ class Hmmer3TextParser(object):
     def _create_hits(self, hit_attrs, qid, qdesc):
         """Parses a HMMER3 hsp block, beginning with the hsp table."""
         # read through until the beginning of the hsp block
-        self._read_until(lambda line: line.startswith('Internal pipeline')
-                or line.startswith('>>'))
+        self._read_until(lambda line: line.startswith('Internal pipeline') or
+                         line.startswith('>>'))
 
         # start parsing the hsp block
         hit_list = []
diff --git a/Bio/SearchIO/__init__.py b/Bio/SearchIO/__init__.py
index bb73f9b..78cbd01 100755
--- a/Bio/SearchIO/__init__.py
+++ b/Bio/SearchIO/__init__.py
@@ -213,8 +213,6 @@ warnings.warn('Bio.SearchIO is an experimental submodule which may undergo '
 
 __all__ = ['read', 'parse', 'to_dict', 'index', 'index_db', 'write', 'convert']
 
-__docformat__ = "restructuredtext en"
-
 
 # dictionary of supported formats for parse() and read()
 _ITERATOR_MAP = {
diff --git a/Bio/SearchIO/_index.py b/Bio/SearchIO/_index.py
index b6d272b..613cdde 100644
--- a/Bio/SearchIO/_index.py
+++ b/Bio/SearchIO/_index.py
@@ -12,9 +12,6 @@ from Bio import bgzf
 from Bio.File import _IndexedSeqFileProxy, _open_for_random_access
 
 
-__docformat__ = "restructuredtext en"
-
-
 class SearchIndexer(_IndexedSeqFileProxy):
     """Base class for file format specific random access.
 
diff --git a/Bio/SearchIO/_model/__init__.py b/Bio/SearchIO/_model/__init__.py
index ebfa545..ecd90cc 100644
--- a/Bio/SearchIO/_model/__init__.py
+++ b/Bio/SearchIO/_model/__init__.py
@@ -51,8 +51,6 @@ from .hsp import HSP, HSPFragment
 
 __all__ = ['QueryResult', 'Hit', 'HSP', 'HSPFragment']
 
-__docformat__ = "restructuredtext en"
-
 
 # if not used as a module, run the doctest
 if __name__ == "__main__":
diff --git a/Bio/SearchIO/_model/_base.py b/Bio/SearchIO/_model/_base.py
index 8c08a35..260ecdb 100644
--- a/Bio/SearchIO/_model/_base.py
+++ b/Bio/SearchIO/_model/_base.py
@@ -9,9 +9,6 @@
 from Bio._utils import getattr_str, trim_str
 
 
-__docformat__ = "restructuredtext en"
-
-
 class _BaseSearchObject(object):
 
     """Abstract class for SearchIO objects."""
diff --git a/Bio/SearchIO/_model/hit.py b/Bio/SearchIO/_model/hit.py
index c27b7a3..006322a 100644
--- a/Bio/SearchIO/_model/hit.py
+++ b/Bio/SearchIO/_model/hit.py
@@ -18,9 +18,6 @@ from ._base import _BaseSearchObject
 from .hsp import HSP
 
 
-__docformat__ = "restructuredtext en"
-
-
 class Hit(_BaseSearchObject):
 
     """Class representing a single database hit of a search result.
@@ -107,7 +104,7 @@ class Hit(_BaseSearchObject):
     # from this one
     _NON_STICKY_ATTRS = ('_items', )
 
-    def __init__(self, hsps=[], id=None, query_id=None):
+    def __init__(self, hsps=(), id=None, query_id=None):
         """Initializes a Hit object.
 
         :param hsps: HSP objects contained in the Hit object
@@ -129,6 +126,8 @@ class Hit(_BaseSearchObject):
         self._description_alt = []
         self._query_description = None
 
+        # TODO - Move this into the for look below in case
+        # hsps is a single use iterator?
         for attr in ('query_id', 'query_description', 'hit_id',
                 'hit_description'):
             # HACK: setting the if clause to '> 1' allows for empty hit objects.
diff --git a/Bio/SearchIO/_model/hsp.py b/Bio/SearchIO/_model/hsp.py
index 8f282b4..e87e617 100644
--- a/Bio/SearchIO/_model/hsp.py
+++ b/Bio/SearchIO/_model/hsp.py
@@ -24,9 +24,6 @@ from Bio.SearchIO._utils import singleitem, allitems, fullcascade, \
 from ._base import _BaseHSP
 
 
-__docformat__ = "restructuredtext en"
-
-
 class HSP(_BaseHSP):
 
     """Class representing high-scoring region(s) between query and hit.
@@ -253,7 +250,7 @@ class HSP(_BaseHSP):
     # from this one
     _NON_STICKY_ATTRS = ('_items', )
 
-    def __init__(self, fragments=[]):
+    def __init__(self, fragments=()):
         """Initializes an HSP object.
 
         :param fragments: fragments contained in the HSP object
@@ -269,6 +266,8 @@ class HSP(_BaseHSP):
         if not fragments:
             raise ValueError("HSP objects must have at least one HSPFragment "
                     "object.")
+        # TODO - Move this into the for look in case hsps is a single use
+        # iterable?
         # check that all fragments contain the same IDs, descriptions, alphabet
         for attr in ('query_id', 'query_description', 'hit_id',
                 'hit_description', 'alphabet'):
diff --git a/Bio/SearchIO/_model/query.py b/Bio/SearchIO/_model/query.py
index 4101b31..0839998 100644
--- a/Bio/SearchIO/_model/query.py
+++ b/Bio/SearchIO/_model/query.py
@@ -21,9 +21,6 @@ from ._base import _BaseSearchObject
 from .hit import Hit
 
 
-__docformat__ = "restructuredtext en"
-
-
 class QueryResult(_BaseSearchObject):
 
     """Class representing search results from a single query.
@@ -188,7 +185,7 @@ class QueryResult(_BaseSearchObject):
     # from this one
     _NON_STICKY_ATTRS = ('_items', '__alt_hit_ids', )
 
-    def __init__(self, hits=[], id=None,
+    def __init__(self, hits=(), id=None,
             hit_key_function=lambda hit: hit.id):
         """Initializes a QueryResult object.
 
@@ -497,7 +494,7 @@ class QueryResult(_BaseSearchObject):
         else:
             hit_key = hit.id
 
-        if hit_key not in self and all([pid not in self for pid in hit.id_all[1:]]):
+        if hit_key not in self and all(pid not in self for pid in hit.id_all[1:]):
             self[hit_key] = hit
         else:
             raise ValueError("The ID or alternative IDs of Hit %r exists in "
diff --git a/Bio/Seq.py b/Bio/Seq.py
index ae3e42f..1474c74 100644
--- a/Bio/Seq.py
+++ b/Bio/Seq.py
@@ -1,6 +1,6 @@
 # Copyright 2000-2002 Brad Chapman.
 # Copyright 2004-2005 by M de Hoon.
-# Copyright 2007-2014 by Peter Cock.
+# Copyright 2007-2015 by Peter Cock.
 # All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -17,8 +17,6 @@ See also the Seq_ wiki and the chapter in our tutorial:
 """
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 import string  # for maketrans only
 import array
 import sys
@@ -881,7 +879,7 @@ class Seq(object):
         return Seq(str(self).replace("U", "T").replace("u", "t"), alphabet)
 
     def translate(self, table="Standard", stop_symbol="*", to_stop=False,
-                  cds=False):
+                  cds=False, gap=None):
         """Turns a nucleotide sequence into a protein sequence. New Seq object.
 
         This method will translate DNA or RNA sequences, and those with a
@@ -908,6 +906,8 @@ class Seq(object):
               single in frame stop codon at the end (this will be excluded
               from the protein sequence, regardless of the to_stop option).
               If these tests fail, an exception is raised.
+            - gap - Single character string to denote symbol used for gaps.
+              It will try to guess the gap character from the alphabet.
 
         e.g. Using the standard table:
 
@@ -949,12 +949,24 @@ class Seq(object):
         >>> coding_dna2.translate(to_stop=True)
         Seq('LAIVMGR', ExtendedIUPACProtein())
 
+        When translating gapped sequences, the gap character is inferred from
+        the alphabet:
+
+        >>> from Bio.Alphabet import Gapped
+        >>> coding_dna3 = Seq("GTG---GCCATT", Gapped(IUPAC.unambiguous_dna))
+        >>> coding_dna3.translate()
+        Seq('V-AI', Gapped(ExtendedIUPACProtein(), '-'))
+
+        It is possible to pass the gap character when the alphabet is missing:
+
+        >>> coding_dna4 = Seq("GTG---GCCATT")
+        >>> coding_dna4.translate(gap='-')
+        Seq('V-AI', Gapped(ExtendedIUPACProtein(), '-'))
+
         NOTE - Ambiguous codons like "TAN" or "NNN" could be an amino acid
         or a stop codon.  These are translated as "X".  Any invalid codon
         (e.g. "TA?" or "T-A") will throw a TranslationError.
 
-        NOTE - Does NOT support gapped sequences.
-
         NOTE - This does NOT behave like the python string's translate
         method.  For that use str(my_seq).translate(...) instead.
         """
@@ -1000,13 +1012,26 @@ class Seq(object):
                 # The same table can be used for RNA or DNA (we use this for
                 # translating strings).
                 codon_table = CodonTable.ambiguous_generic_by_id[table_id]
-        protein = _translate_str(str(self), codon_table,
-                                 stop_symbol, to_stop, cds)
-        if stop_symbol in protein:
-            alphabet = Alphabet.HasStopCodon(codon_table.protein_alphabet,
-                                             stop_symbol=stop_symbol)
+
+        # Deal with gaps for translation
+        if hasattr(self.alphabet, "gap_char"):
+            if not gap:
+                gap = self.alphabet.gap_char
+            elif gap != self.alphabet.gap_char:
+                raise ValueError("Gap {0!r} does not match {1!r} from alphabet".format(
+                    gap, self.alphabet.gap_char))
+
+        protein = _translate_str(str(self), codon_table, stop_symbol, to_stop,
+                                 cds, gap=gap)
+
+        if gap and gap in protein:
+            alphabet = Alphabet.Gapped(codon_table.protein_alphabet, gap)
         else:
             alphabet = codon_table.protein_alphabet
+
+        if stop_symbol in protein:
+            alphabet = Alphabet.HasStopCodon(alphabet, stop_symbol)
+
         return Seq(protein, alphabet)
 
     def ungap(self, gap=None):
@@ -1108,7 +1133,7 @@ class UnknownSeq(Seq):
     NNNNN
 
     However, this is rather wasteful of memory (especially for large
-    sequences), which is where this class is most usefull:
+    sequences), which is where this class is most useful:
 
     >>> unk_five = UnknownSeq(5)
     >>> unk_five
@@ -1871,11 +1896,15 @@ class MutableSeq(object):
         Because str(my_seq) will give you the full sequence as a python string,
         there is often no need to make an explicit conversion.  For example,
 
-        print("ID={%s}, sequence={%s}" % (my_name, my_seq))
+        >>> my_seq = Seq("ATCGTG")
+        >>> my_name = "seq_1"
+        >>> print("ID={%s}, sequence={%s}" % (my_name, my_seq))
+        ID={seq_1}, sequence={ATCGTG}
 
         On Biopython 1.44 or older you would have to have done this:
 
-        print("ID={%s}, sequence={%s}" % (my_name, my_seq.tostring()))
+        >>> print("ID={%s}, sequence={%s}" % (my_name, my_seq.tostring()))
+        ID={seq_1}, sequence={ATCGTG}
         """
         from Bio import BiopythonDeprecationWarning
         warnings.warn("This method is obsolete; please use str(my_seq) "
@@ -1949,7 +1978,7 @@ def back_transcribe(rna):
 
 
 def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
-                   cds=False, pos_stop="X"):
+                   cds=False, pos_stop="X", gap=None):
     """Helper function to translate a nucleotide string (PRIVATE).
 
     Arguments:
@@ -1968,6 +1997,8 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
           single in frame stop codon at the end (this will be excluded
           from the protein sequence, regardless of the to_stop option).
           If these tests fail, an exception is raised.
+        - gap - Single character string to denote symbol used for gaps.
+          Defaults to None.
 
     Returns a string.
 
@@ -1988,7 +2019,7 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
        ...
     TranslationError: Codon 'TA?' is invalid
 
-    In a change to older verions of Biopython, partial codons are now
+    In a change to older versions of Biopython, partial codons are now
     always regarded as an error (previously only checked if cds=True)
     and will trigger a warning (likely to become an exception in a
     future release).
@@ -2034,17 +2065,21 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
         n -= 6
         amino_acids = ["M"]
     elif n % 3 != 0:
-        from Bio import BiopythonWarning
         warnings.warn("Partial codon, len(sequence) not a multiple of three. "
                       "Explicitly trim the sequence or add trailing N before "
                       "translation. This may become an error in future.",
                       BiopythonWarning)
+    if gap is not None:
+        if not isinstance(gap, basestring):
+            raise TypeError("Gap character should be a single character string.")
+        elif len(gap) > 1:
+            raise ValueError("Gap character should be a single character string.")
+
     for i in range(0, n - n % 3, 3):
         codon = sequence[i:i + 3]
         try:
             amino_acids.append(forward_table[codon])
         except (KeyError, CodonTable.TranslationError):
-            # TODO? Treat "---" as a special case (gapped translation)
             if codon in table.stop_codons:
                 if cds:
                     raise CodonTable.TranslationError(
@@ -2055,6 +2090,9 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
             elif valid_letters.issuperset(set(codon)):
                 # Possible stop codon (e.g. NNN or TAN)
                 amino_acids.append(pos_stop)
+            elif gap is not None and codon == gap * 3:
+                # Gapped translation
+                amino_acids.append(gap)
             else:
                 raise CodonTable.TranslationError(
                     "Codon '{0}' is invalid".format(codon))
@@ -2062,7 +2100,7 @@ def _translate_str(sequence, table, stop_symbol="*", to_stop=False,
 
 
 def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
-              cds=False):
+              cds=False, gap=None):
     """Translate a nucleotide sequence into amino acids.
 
     If given a string, returns a new string object. Given a Seq or
@@ -2088,6 +2126,8 @@ def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
           single in frame stop codon at the end (this will be excluded
           from the protein sequence, regardless of the to_stop option).
           If these tests fail, an exception is raised.
+        - gap - Single character string to denote symbol used for gaps.
+          Defaults to None.
 
     A simple string example using the default (standard) genetic code:
 
@@ -2126,8 +2166,6 @@ def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
     or a stop codon.  These are translated as "X".  Any invalid codon
     (e.g. "TA?" or "T-A") will throw a TranslationError.
 
-    NOTE - Does NOT support gapped sequences.
-
     It will however translate either DNA or RNA.
     """
     if isinstance(sequence, Seq):
@@ -2146,7 +2184,8 @@ def translate(sequence, table="Standard", stop_symbol="*", to_stop=False,
                 codon_table = table
             else:
                 raise ValueError('Bad table argument')
-        return _translate_str(sequence, codon_table, stop_symbol, to_stop, cds)
+        return _translate_str(sequence, codon_table, stop_symbol, to_stop, cds,
+                              gap=gap)
 
 
 def reverse_complement(sequence):
diff --git a/Bio/SeqFeature.py b/Bio/SeqFeature.py
index f990c26..3c6c751 100644
--- a/Bio/SeqFeature.py
+++ b/Bio/SeqFeature.py
@@ -44,8 +44,8 @@ classes:
     - CompoundLocation - Collection of FeatureLocation objects (for joins etc).
 
     - ExactPosition - Specify the position as being exact.
-    - WithinPosition - Specify a position occuring within some range.
-    - BetweenPosition - Specify a position occuring between a range (OBSOLETE?).
+    - WithinPosition - Specify a position occurring within some range.
+    - BetweenPosition - Specify a position occurring between a range (OBSOLETE?).
     - BeforePosition - Specify the position as being found before some base.
     - AfterPosition - Specify the position as being found after some base.
     - OneOfPosition - Specify a position where the location can be multiple positions.
@@ -54,9 +54,9 @@ classes:
 
 from __future__ import print_function
 
-from Bio.Seq import MutableSeq, reverse_complement
+from Bio._py3k import _is_int_or_long
 
-__docformat__ = "restructuredtext en"
+from Bio.Seq import MutableSeq, reverse_complement
 
 
 class SeqFeature(object):
@@ -88,8 +88,8 @@ class SeqFeature(object):
           values.
         - sub_features - Obsolete list of additional SeqFeatures which was
           used for holding compound locations (e.g. joins in GenBank/EMBL).
-          This is now superceded by a CompoundFeatureLocation as the location,
-          and should not be used (DEPRECATED).
+          This is now superceded by a CompoundLocation as the location, and
+          should not be used (DEPRECATED).
     """
 
     def __init__(self, location=None, type='', location_operator='',
@@ -545,13 +545,27 @@ class Reference(object):
         return "%s(title=%s, ...)" % (self.__class__.__name__,
                                       repr(self.title))
 
+    def __eq__(self, other):
+        """Check if two Reference objects should be considered equal
+
+        Note that the location is not compared, as __eq__ for the
+        FeatureLocation class is not defined.
+        """
+        return self.authors == other.authors and \
+            self.consrtm == other.consrtm and \
+            self.title == other.title and \
+            self.journal == other.journal and \
+            self.medline_id == other.medline_id and \
+            self.pubmed_id == other.pubmed_id and \
+            self.comment == other.comment
+
 
 # --- Handling feature locations
 
 class FeatureLocation(object):
     """Specify the location of a feature along a sequence.
 
-    The FeatureLocation is used for simple continous features, which can
+    The FeatureLocation is used for simple continuous features, which can
     be described as running from a start position to and end position
     (optionally with a strand and reference information).  More complex
     locations made up from several non-continuous parts (e.g. a coding
@@ -670,13 +684,13 @@ class FeatureLocation(object):
         # TODO - Check 0 <= start <= end (<= length of reference)
         if isinstance(start, AbstractPosition):
             self._start = start
-        elif isinstance(start, int) or isinstance(start, long):
+        elif _is_int_or_long(start):
             self._start = ExactPosition(start)
         else:
             raise TypeError("start=%r %s" % (start, type(start)))
         if isinstance(end, AbstractPosition):
             self._end = end
-        elif isinstance(end, int) or isinstance(end, long):
+        elif _is_int_or_long(end):
             self._end = ExactPosition(end)
         else:
             raise TypeError("end=%r %s" % (end, type(end)))
@@ -896,9 +910,9 @@ class FeatureLocation(object):
     def parts(self):
         """Read only list of parts (always one, the Feature Location).
 
-        This is a convience property allowing you to write code handling
+        This is a convenience property allowing you to write code handling
         both simple FeatureLocation objects (with one part) and more complex
-        CompoundLocation objects (with multiple parts) interchangably.
+        CompoundLocation objects (with multiple parts) interchangeably.
         """
         return [self]
 
@@ -1513,7 +1527,7 @@ class WithinPosition(int, AbstractPosition):
     def __new__(cls, position, left, right):
         assert position == left or position == right, \
             "WithinPosition: %r should match left %r or right %r" \
-            (position, left, right)
+            % (position, left, right)
         obj = int.__new__(cls, position)
         obj._left = left
         obj._right = right
diff --git a/Bio/SeqIO/AbiIO.py b/Bio/SeqIO/AbiIO.py
index cd4ec24..efeac1d 100644
--- a/Bio/SeqIO/AbiIO.py
+++ b/Bio/SeqIO/AbiIO.py
@@ -1,5 +1,5 @@
 # Copyright 2011 by Wibowo Arindrarto (w.arindrarto at gmail.com)
-# Revisions copyright 2011, 2014 by Peter Cock.
+# Revisions copyright 2011-2016 by Peter Cock.
 # This code is part of the Biopython distribution and governed by its
 # license. Please see the LICENSE file that should have been included
 # as part of this package.
@@ -14,7 +14,6 @@ http://www.appliedbiosystem.com/support/software_community/ABIF_File_Format.pdf
 
 """
 
-__docformat__ = "restructuredtext en"
 
 import datetime
 import struct
@@ -170,9 +169,9 @@ _INSTRUMENT_SPECIFIC_TAGS['general'] = {
     'User1': 'Name of user who created the plate (optional)',
 }
 
-# No instrument specific tags
-#_INSTRUMENT_SPECIFIC_TAGS['abi_prism_3100/3100-Avant'] = {
-#}
+#  No instrument specific tags
+# _INSTRUMENT_SPECIFIC_TAGS['abi_prism_3100/3100-Avant'] = {
+# }
 
 _INSTRUMENT_SPECIFIC_TAGS['abi_3130/3130xl'] = {
     'CTOw1': 'Container owner',
@@ -405,7 +404,7 @@ def AbiIterator(handle, alphabet=None, trim=False):
     # use the file name as SeqRecord.name if available
     try:
         file_name = basename(handle.name).replace('.ab1', '')
-    except:
+    except AttributeError:
         file_name = ""
     record = SeqRecord(Seq(seq, alphabet),
                        id=sample_id, name=file_name,
diff --git a/Bio/SeqIO/AceIO.py b/Bio/SeqIO/AceIO.py
index c63cbfb..389606c 100644
--- a/Bio/SeqIO/AceIO.py
+++ b/Bio/SeqIO/AceIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock.  All rights reserved.
+# Copyright 2008-2015 by Peter Cock.  All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -18,8 +18,6 @@ from Bio.SeqRecord import SeqRecord
 from Bio.Alphabet import generic_nucleotide, generic_dna, generic_rna, Gapped
 from Bio.Sequencing import Ace
 
-__docformat__ = "restructuredtext en"
-
 
 def AceIterator(handle):
     """Returns SeqRecord objects from an ACE file.
@@ -97,8 +95,8 @@ def AceIterator(handle):
 
         # Consensus base quality (BQ lines).  Note that any gaps (originally
         # as * characters) in the consensus do not get a quality entry, so
-        # we assign a quality of None (zero would be missleading as there may
-        # be excelent support for having a gap here).
+        # we assign a quality of None (zero would be misleading as there may
+        # be excellent support for having a gap here).
         quals = []
         i = 0
         for base in consensus_seq:
diff --git a/Bio/SeqIO/FastaIO.py b/Bio/SeqIO/FastaIO.py
index 4e2589e..d279f18 100644
--- a/Bio/SeqIO/FastaIO.py
+++ b/Bio/SeqIO/FastaIO.py
@@ -1,4 +1,4 @@
-# Copyright 2006-2014 by Peter Cock.  All rights reserved.
+# Copyright 2006-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -18,8 +18,6 @@ from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio.SeqIO.Interfaces import SequentialSequenceWriter
 
-__docformat__ = "restructuredtext en"
-
 
 def SimpleFastaParser(handle):
     """Generator function to iterate over Fasta records (as string tuples).
@@ -137,7 +135,7 @@ class FastaWriter(SequentialSequenceWriter):
     def __init__(self, handle, wrap=60, record2title=None):
         """Create a Fasta writer.
 
-        Arguements:
+        Arguments:
 
          - handle - Handle to an output file, e.g. as returned
            by open(filename, "w")
@@ -213,71 +211,5 @@ class FastaWriter(SequentialSequenceWriter):
             self.handle.write(data + "\n")
 
 if __name__ == "__main__":
-    print("Running quick self test")
-
-    import os
-    from Bio.Alphabet import generic_protein, generic_nucleotide
-
-    # Download the files from here:
-    # ftp://ftp.ncbi.nlm.nih.gov/genomes/Bacteria/Nanoarchaeum_equitans
-    fna_filename = "NC_005213.fna"
-    faa_filename = "NC_005213.faa"
-
-    def genbank_name_function(text):
-        text, descr = text.split(None, 1)
-        id = text.split("|")[3]
-        name = id.split(".", 1)[0]
-        return id, name, descr
-
-    def print_record(record):
-        # See also bug 2057
-        # http://bugzilla.open-bio.org/show_bug.cgi?id=2057
-        print("ID:" + record.id)
-        print("Name:" + record.name)
-        print("Descr:" + record.description)
-        print(record.seq)
-        for feature in record.annotations:
-            print('/%s=%s' % (feature, record.annotations[feature]))
-        if record.dbxrefs:
-            print("Database cross references:")
-            for x in record.dbxrefs:
-                print(" - %s" % x)
-
-    if os.path.isfile(fna_filename):
-        print("--------")
-        print("FastaIterator (single sequence)")
-        with open(fna_filename, "r") as h:
-            iterator = FastaIterator(h, alphabet=generic_nucleotide,
-                                     title2ids=genbank_name_function)
-        count = 0
-        for record in iterator:
-            count += 1
-            print_record(record)
-        assert count == 1
-        print(str(record.__class__))
-
-    if os.path.isfile(faa_filename):
-        print("--------")
-        print("FastaIterator (multiple sequences)")
-        with open(faa_filename, "r") as h:
-            iterator = FastaIterator(h, alphabet=generic_protein,
-                                     title2ids=genbank_name_function)
-        count = 0
-        for record in iterator:
-            count += 1
-            print_record(record)
-            break
-        assert count > 0
-        print(str(record.__class__))
-
-    from Bio._py3k import StringIO
-    print("--------")
-    print("FastaIterator (empty input file)")
-    # Just to make sure no errors happen
-    iterator = FastaIterator(StringIO(""))
-    count = 0
-    for record in iterator:
-        count += 1
-    assert count == 0
-
-    print("Done")
+    from Bio._utils import run_doctest
+    run_doctest(verbose=0)
diff --git a/Bio/SeqIO/IgIO.py b/Bio/SeqIO/IgIO.py
index 5cdda57..7a1380f 100644
--- a/Bio/SeqIO/IgIO.py
+++ b/Bio/SeqIO/IgIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock.  All rights reserved.
+# Copyright 2008-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -18,8 +18,6 @@ from Bio.Alphabet import single_letter_alphabet
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 
-__docformat__ = "restructuredtext en"
-
 
 def IgIterator(handle, alphabet=single_letter_alphabet):
     """Iterate over IntelliGenetics records (as SeqRecord objects).
@@ -34,6 +32,31 @@ def IgIterator(handle, alphabet=single_letter_alphabet):
     start with a semi-colon) are recorded as a single string with embedded
     new line characters in the SeqRecord's annotations dictionary under the
     key 'comment'.
+
+    Example:
+
+    >>> with open("IntelliGenetics/TAT_mase_nuc.txt") as handle:
+    ...     for record in IgIterator(handle):
+    ...         print("%s length %i" % (record.id, len(record)))
+    ...
+    A_U455 length 303
+    B_HXB2R length 306
+    C_UG268A length 267
+    D_ELI length 309
+    F_BZ163A length 309
+    O_ANT70 length 342
+    O_MVP5180 length 348
+    CPZGAB length 309
+    CPZANT length 309
+    A_ROD length 390
+    B_EHOA length 420
+    D_MM251 length 390
+    STM_STM length 387
+    VER_AGM3 length 354
+    GRI_AGM677 length 264
+    SAB_SAB1C length 219
+    SYK_SYK length 330
+
     """
     # Skip any file header text before the first record (;; lines)
     while True:
@@ -47,7 +70,7 @@ def IgIterator(handle, alphabet=single_letter_alphabet):
         # Now iterate over the records
         if line[0] != ";":
             raise ValueError(
-                "Records should start with ';' and not:\n%s" % repr(line))
+                "Records should start with ';' and not:\n%r" % line)
 
         # Try and agree with SeqRecord convention from the GenBank parser,
         # (and followed in the SwissProt parser) which stores the comments
@@ -88,19 +111,5 @@ def IgIterator(handle, alphabet=single_letter_alphabet):
     assert not line
 
 if __name__ == "__main__":
-    print("Running quick self test")
-
-    import os
-    path = "../../Tests/IntelliGenetics/"
-    if os.path.isdir(path):
-        for filename in os.listdir(path):
-            if os.path.splitext(filename)[-1] == ".txt":
-                print("")
-                print(filename)
-                print("-" * len(filename))
-                with open(os.path.join(path, filename)) as handle:
-                    for record in IgIterator(handle):
-                        print("%s %i" % (record.id, len(record)))
-        print("Done")
-    else:
-        print("Could not find input files")
+    from Bio._utils import run_doctest
+    run_doctest(verbose=0)
diff --git a/Bio/SeqIO/InsdcIO.py b/Bio/SeqIO/InsdcIO.py
index 33fc993..43af8dd 100644
--- a/Bio/SeqIO/InsdcIO.py
+++ b/Bio/SeqIO/InsdcIO.py
@@ -1,4 +1,4 @@
-# Copyright 2007-2011 by Peter Cock.  All rights reserved.
+# Copyright 2007-2016 by Peter Cock.  All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -39,13 +39,12 @@ from Bio import BiopythonWarning
 from Bio.Seq import UnknownSeq
 from Bio.GenBank.Scanner import GenBankScanner, EmblScanner, _ImgtScanner
 from Bio import Alphabet
-from .Interfaces import SequentialSequenceWriter
+from Bio.SeqIO.Interfaces import SequentialSequenceWriter
 from Bio import SeqFeature
 
 from Bio._py3k import _is_int_or_long
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"
 
 # NOTE
 # ====
@@ -62,7 +61,35 @@ def GenBankIterator(handle):
     a single SeqRecord with associated annotation and features.
 
     Note that for genomes or chromosomes, there is typically only
-    one record."""
+    one record.
+
+    This gets called internally by Bio.SeqIO for the GenBank file format:
+
+    >>> from Bio import SeqIO
+    >>> for record in SeqIO.parse("GenBank/cor6_6.gb", "gb"):
+    ...     print(record.id)
+    ...
+    X55053.1
+    X62281.1
+    M81224.1
+    AJ237582.1
+    L31939.1
+    AF297471.1
+
+    Equivalently,
+
+    >>> with open("GenBank/cor6_6.gb") as handle:
+    ...     for record in GenBankIterator(handle):
+    ...         print(record.id)
+    ...
+    X55053.1
+    X62281.1
+    M81224.1
+    AJ237582.1
+    L31939.1
+    AF297471.1
+
+    """
     # This calls a generator function:
     return GenBankScanner(debug=0).parse_records(handle)
 
@@ -74,7 +101,41 @@ def EmblIterator(handle):
     a single SeqRecord with associated annotation and features.
 
     Note that for genomes or chromosomes, there is typically only
-    one record."""
+    one record.
+
+    This gets called internally by Bio.SeqIO for the EMBL file format:
+
+    >>> from Bio import SeqIO
+    >>> for record in SeqIO.parse("EMBL/epo_prt_selection.embl", "embl"):
+    ...     print(record.id)
+    ...
+    A00022.1
+    A00028.1
+    A00031.1
+    A00034.1
+    A00060.1
+    A00071.1
+    A00072.1
+    A00078.1
+    CQ797900.1
+
+    Equivalently,
+
+    >>> with open("EMBL/epo_prt_selection.embl") as handle:
+    ...     for record in EmblIterator(handle):
+    ...         print(record.id)
+    ...
+    A00022.1
+    A00028.1
+    A00031.1
+    A00034.1
+    A00060.1
+    A00071.1
+    A00072.1
+    A00078.1
+    CQ797900.1
+
+    """
     # This calls a generator function:
     return EmblScanner(debug=0).parse_records(handle)
 
@@ -183,8 +244,8 @@ def _insdc_location_string_ignoring_strand_and_subfeatures(location, rec_length)
             # Treat the unknown end position as an AfterPosition
             return "%s%s..>%i" \
                 % (ref,
-                   _insdc_feature_position_string(location.start),
-                   location.nofuzzy_start)
+                   _insdc_feature_position_string(location.start, +1),
+                   location.nofuzzy_start + 1)
     else:
         # Typical case, e.g. 12..15 gets mapped to 11:15
         return ref \
@@ -350,7 +411,7 @@ class _InsdcWriter(SequentialSequenceWriter):
         # Now the qualifiers...
         for key in sorted(feature.qualifiers.keys()):
             values = feature.qualifiers[key]
-            if isinstance(values, list) or isinstance(values, tuple):
+            if isinstance(values, (list, tuple)):
                 for value in values:
                     self._write_feature_qualifier(key, value)
             else:
@@ -408,7 +469,7 @@ class _InsdcWriter(SequentialSequenceWriter):
         # It would need the addition of the comma splitting logic...
         # are there any other cases where that would be sensible?
         contig = record.annotations.get("contig", "")
-        if isinstance(contig, list) or isinstance(contig, tuple):
+        if isinstance(contig, (list, tuple)):
             contig = "".join(contig)
         contig = self.clean(contig)
         answer = []
@@ -433,8 +494,12 @@ class GenBankWriter(_InsdcWriter):
         """Used in the 'header' of each GenBank record."""
         assert len(tag) < self.HEADER_WIDTH
         if len(text) > self.MAX_WIDTH - self.HEADER_WIDTH:
-            warnings.warn("Annotation %r too long for %r line" % (text, tag),
-                          BiopythonWarning)
+            if tag:
+                warnings.warn("Annotation %r too long for %r line" % (text, tag),
+                              BiopythonWarning)
+            else:
+                # Can't give such a precise warning
+                warnings.warn("Annotation %r too long" % text, BiopythonWarning)
         self.handle.write("%s%s\n" % (tag.ljust(self.HEADER_WIDTH),
                                       text.replace("\n", " ")))
 
@@ -683,7 +748,7 @@ class GenBankWriter(_InsdcWriter):
         comment = record.annotations["comment"]
         if isinstance(comment, basestring):
             lines = comment.split("\n")
-        elif isinstance(comment, list) or isinstance(comment, tuple):
+        elif isinstance(comment, (list, tuple)):
             lines = comment
         else:
             raise ValueError("Could not understand comment annotation")
@@ -866,8 +931,8 @@ class EmblWriter(_InsdcWriter):
                 index = LETTERS_PER_LINE * line_number + \
                     LETTERS_PER_BLOCK * block
                 handle.write((" %s" % data[index:index + LETTERS_PER_BLOCK]))
-            handle.write(str((line_number + 1)
-                             * LETTERS_PER_LINE).rjust(POSITION_PADDING))
+            handle.write(str((line_number + 1) *
+                             LETTERS_PER_LINE).rjust(POSITION_PADDING))
             handle.write("\n")
         if seq_len % LETTERS_PER_LINE:
             # Final (partial) line
@@ -1049,7 +1114,7 @@ class EmblWriter(_InsdcWriter):
         comment = record.annotations["comment"]
         if isinstance(comment, basestring):
             lines = comment.split("\n")
-        elif isinstance(comment, list) or isinstance(comment, tuple):
+        elif isinstance(comment, (list, tuple)):
             lines = comment
         else:
             raise ValueError("Could not understand comment annotation")
@@ -1067,7 +1132,30 @@ class EmblWriter(_InsdcWriter):
         self._write_the_first_lines(record)
 
         # PR line (0 or 1 lines only), project identifier
-        for xref in record.dbxrefs:
+        #
+        # Assuming can't use 2 lines, we should prefer newer GenBank
+        # DBLINK BioProject:... entries over the older GenBank DBLINK
+        # Project:... lines.
+        #
+        # In either case, seems EMBL usess just "PR    Project:..."
+        # regardless of the type of ID (old numeric only, or new
+        # with alpha prefix), e.g. for CP002497 NCBI now uses:
+        #
+        # DBLINK      BioProject: PRJNA60715
+        #             BioSample: SAMN03081426
+        #
+        # While EMBL uses:
+        #
+        # XX
+        # PR   Project:PRJNA60715;
+        # XX
+        #
+        # Sorting ensures (new) BioProject:... is before old Project:...
+        for xref in sorted(record.dbxrefs):
+            if xref.startswith("BioProject:"):
+                self._write_single_line("PR", xref[3:] + ";")
+                handle.write("XX\n")
+                break
             if xref.startswith("Project:"):
                 self._write_single_line("PR", xref + ";")
                 handle.write("XX\n")
@@ -1119,140 +1207,5 @@ class ImgtWriter(EmblWriter):
     FEATURE_HEADER = "FH   Key                 Location/Qualifiers\n"
 
 if __name__ == "__main__":
-    print("Quick self test")
-    import os
-    from Bio._py3k import StringIO
-
-    def compare_record(old, new):
-        if old.id != new.id and old.name != new.name:
-            raise ValueError("'%s' or '%s' vs '%s' or '%s' records"
-                             % (old.id, old.name, new.id, new.name))
-        if len(old.seq) != len(new.seq):
-            raise ValueError("%i vs %i" % (len(old.seq), len(new.seq)))
-        if str(old.seq).upper() != str(new.seq).upper():
-            if len(old.seq) < 200:
-                raise ValueError("'%s' vs '%s'" % (old.seq, new.seq))
-            else:
-                raise ValueError(
-                    "'%s...' vs '%s...'" % (old.seq[:100], new.seq[:100]))
-        if old.features and new.features:
-            return compare_features(old.features, new.features)
-        # Just insist on at least one word in common:
-        if (old.description or new.description) \
-                and not set(old.description.split()).intersection(new.description.split()):
-            raise ValueError("%s versus %s"
-                             % (repr(old.description), repr(new.description)))
-        # TODO - check annotation
-        if "contig" in old.annotations:
-            assert old.annotations["contig"] == \
-                new.annotations["contig"]
-        return True
-
-    def compare_records(old_list, new_list):
-        """Check two lists of SeqRecords agree, raises a ValueError if mismatch."""
-        if len(old_list) != len(new_list):
-            raise ValueError(
-                "%i vs %i records" % (len(old_list), len(new_list)))
-        for old, new in zip(old_list, new_list):
-            if not compare_record(old, new):
-                return False
-        return True
-
-    def compare_feature(old, new, ignore_sub_features=False):
-        """Check two SeqFeatures agree."""
-        if old.type != new.type:
-            raise ValueError("Type %s versus %s" % (old.type, new.type))
-        if old.location.nofuzzy_start != new.location.nofuzzy_start \
-                or old.location.nofuzzy_end != new.location.nofuzzy_end:
-            raise ValueError("%s versus %s:\n%s\nvs:\n%s"
-                             % (old.location, new.location, str(old), str(new)))
-        if old.strand != new.strand:
-            raise ValueError(
-                "Different strand:\n%s\nvs:\n%s" % (str(old), str(new)))
-        if old.location.start != new.location.start:
-            raise ValueError("Start %s versus %s:\n%s\nvs:\n%s"
-                             % (old.location.start, new.location.start, str(old), str(new)))
-        if old.location.end != new.location.end:
-            raise ValueError("End %s versus %s:\n%s\nvs:\n%s"
-                             % (old.location.end, new.location.end, str(old), str(new)))
-        if not ignore_sub_features:
-            if len(old.sub_features) != len(new.sub_features):
-                raise ValueError("Different sub features")
-            for a, b in zip(old.sub_features, new.sub_features):
-                if not compare_feature(a, b):
-                    return False
-        # This only checks key shared qualifiers
-        # Would a white list be easier?
-        # for key in ["name", "gene", "translation", "codon_table", "codon_start", "locus_tag"]:
-        for key in set(old.qualifiers).intersection(new.qualifiers):
-            if key in ["db_xref", "protein_id", "product", "note"]:
-                # EMBL and GenBank files are use different references/notes/etc
-                continue
-            if old.qualifiers[key] != new.qualifiers[key]:
-                raise ValueError("Qualifier mis-match for %s:\n%s\n%s"
-                                 % (key, old.qualifiers[key], new.qualifiers[key]))
-        return True
-
-    def compare_features(old_list, new_list, ignore_sub_features=False):
-        """Check two lists of SeqFeatures agree, raises a ValueError if mismatch."""
-        if len(old_list) != len(new_list):
-            raise ValueError(
-                "%i vs %i features" % (len(old_list), len(new_list)))
-        for old, new in zip(old_list, new_list):
-            # This assumes they are in the same order
-            if not compare_feature(old, new, ignore_sub_features):
-                return False
-        return True
-
-    def check_genbank_writer(records):
-        handle = StringIO()
-        GenBankWriter(handle).write_file(records)
-        handle.seek(0)
-
-        records2 = list(GenBankIterator(handle))
-        assert compare_records(records, records2)
-
-    def check_embl_writer(records):
-        handle = StringIO()
-        try:
-            EmblWriter(handle).write_file(records)
-        except ValueError as err:
-            print(err)
-            return
-        handle.seek(0)
-
-        records2 = list(EmblIterator(handle))
-        assert compare_records(records, records2)
-
-    for filename in os.listdir("../../Tests/GenBank"):
-        if not filename.endswith(".gbk") and not filename.endswith(".gb"):
-            continue
-        print(filename)
-
-        with open("../../Tests/GenBank/%s" % filename) as handle:
-            records = list(GenBankIterator(handle))
-
-        check_genbank_writer(records)
-        check_embl_writer(records)
-
-    for filename in os.listdir("../../Tests/EMBL"):
-        if not filename.endswith(".embl"):
-            continue
-        print(filename)
-
-        with open("../../Tests/EMBL/%s" % filename) as handle:
-            records = list(EmblIterator(handle))
-
-        check_genbank_writer(records)
-        check_embl_writer(records)
-
-    from Bio import SeqIO
-    for filename in os.listdir("../../Tests/SwissProt"):
-        if not filename.startswith("sp"):
-            continue
-        print(filename)
-
-        with open("../../Tests/SwissProt/%s" % filename) as handle:
-            records = list(SeqIO.parse(handle, "swiss"))
-
-        check_genbank_writer(records)
+    from Bio._utils import run_doctest
+    run_doctest(verbose=0)
diff --git a/Bio/SeqIO/Interfaces.py b/Bio/SeqIO/Interfaces.py
index 8e76930..6478837 100644
--- a/Bio/SeqIO/Interfaces.py
+++ b/Bio/SeqIO/Interfaces.py
@@ -16,8 +16,6 @@ from Bio.Alphabet import generic_alphabet
 from Bio.Seq import Seq, MutableSeq
 from Bio.SeqRecord import SeqRecord
 
-__docformat__ = "restructuredtext en"
-
 
 class SequenceIterator(object):
     """Base class for building SeqRecord iterators.
diff --git a/Bio/SeqIO/PdbIO.py b/Bio/SeqIO/PdbIO.py
index 6066fbf..4286b4a 100644
--- a/Bio/SeqIO/PdbIO.py
+++ b/Bio/SeqIO/PdbIO.py
@@ -5,13 +5,12 @@
 import collections
 import warnings
 
+from Bio import BiopythonWarning
 from Bio.Alphabet import generic_protein
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio.Data.SCOPData import protein_letters_3to1
 
-__docformat__ = "restructuredtext en"
-
 
 def PdbSeqresIterator(handle):
     """Returns SeqRecord objects for each chain in a PDB file.
@@ -22,6 +21,30 @@ def PdbSeqresIterator(handle):
     Specifically, these PDB records are handled: DBREF, SEQADV, SEQRES, MODRES
 
     See: http://www.wwpdb.org/documentation/format23/sect3.html
+
+    This gets called internally via Bio.SeqIO for the SEQRES based interpretation
+    of the PDB file format:
+
+    >>> from Bio import SeqIO
+    >>> for record in SeqIO.parse("PDB/1A8O.pdb", "pdb-seqres"):
+    ...     print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+    ...     print(record.dbxrefs)
+    ...
+    Record id 1A8O:A, chain A
+    ['UNP:P12497', 'UNP:POL_HV1N5']
+
+    Equivalently,
+
+    >>> with open("PDB/1A8O.pdb") as handle:
+    ...     for record in PdbSeqresIterator(handle):
+    ...         print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+    ...         print(record.dbxrefs)
+    ...
+    Record id 1A8O:A, chain A
+    ['UNP:P12497', 'UNP:POL_HV1N5']
+
+    Note the chain is recorded in the annotations dictionary, and any PDB DBREF
+    lines are recorded in the database cross-references list.
     """
     # Late-binding import to avoid circular dependency on SeqIO in Bio.SeqUtils
     from Bio.SeqUtils import seq1
@@ -120,7 +143,27 @@ def PdbAtomIterator(handle):
     This function uses the Bio.PDB module to do most of the hard work. The
     annotation information could be improved but this extra parsing should be
     done in parse_pdb_header, not this module.
+
+    This gets called internally via Bio.SeqIO for the atom based interpretation
+    of the PDB file format:
+
+    >>> from Bio import SeqIO
+    >>> for record in SeqIO.parse("PDB/1A8O.pdb", "pdb-atom"):
+    ...     print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+    ...
+    Record id 1A8O:A, chain A
+
+    Equivalently,
+
+    >>> with open("PDB/1A8O.pdb") as handle:
+    ...     for record in PdbAtomIterator(handle):
+    ...         print("Record id %s, chain %s" % (record.id, record.annotations["chain"]))
+    ...
+    Record id 1A8O:A, chain A
+
     """
+    # TODO - Add record.annotations to the doctest, esp the residues (not working?)
+
     # Only import PDB when needed, to avoid/delay NumPy dependency in SeqIO
     from Bio.PDB import PDBParser
     from Bio.SeqUtils import seq1
@@ -140,7 +183,8 @@ def PdbAtomIterator(handle):
     if firstline.startswith("HEADER"):
         pdb_id = firstline[62:66]
     else:
-        warnings.warn("First line is not a 'HEADER'; can't determine PDB ID")
+        warnings.warn("First line is not a 'HEADER'; can't determine PDB ID. "
+                      "Line: %r" % firstline, BiopythonWarning)
         pdb_id = '????'
 
     struct = PDBParser().get_structure(pdb_id, undo_handle)
@@ -171,7 +215,7 @@ def PdbAtomIterator(handle):
                     res_out.append('X' * gapsize)
                 else:
                     warnings.warn("Ignoring out-of-order residues after a gap",
-                                  UserWarning)
+                                  BiopythonWarning)
                     # Keep the normal part, drop the out-of-order segment
                     # (presumably modified or hetatm residues, e.g. 3BEG)
                     res_out.extend(restype(x) for x in residues[prev_idx:i])
@@ -209,11 +253,5 @@ def PdbAtomIterator(handle):
 
 
 if __name__ == '__main__':
-    # Test
-    import sys
-    from Bio import SeqIO
-    for fname in sys.argv[1:]:
-        for parser in (PdbSeqresIterator, PdbAtomIterator):
-            with open(fname) as handle:
-                records = parser(handle)
-                SeqIO.write(records, sys.stdout, 'fasta')
+    from Bio._utils import run_doctest
+    run_doctest(verbose=0)
diff --git a/Bio/SeqIO/PhdIO.py b/Bio/SeqIO/PhdIO.py
index 5de62e1..92771e2 100644
--- a/Bio/SeqIO/PhdIO.py
+++ b/Bio/SeqIO/PhdIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock.  All rights reserved.
+# Copyright 2008-2016 by Peter Cock.  All rights reserved.
 # Revisions copyright 2009 by Cymon J. Cox.  All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
@@ -59,8 +59,6 @@ from Bio.Sequencing import Phd
 from Bio.SeqIO.Interfaces import SequentialSequenceWriter
 from Bio.SeqIO import QualityIO
 
-__docformat__ = "restructuredtext en"
-
 
 def PhdIterator(handle):
     """Returns SeqRecord objects from a PHD file.
@@ -106,7 +104,7 @@ class PhdWriter(SequentialSequenceWriter):
         # This method returns the 'phred_quality' scores or converted
         # 'solexa_quality' scores if present, else raises a value error
         phred_qualities = QualityIO._get_phred_quality(record)
-        peak_locations = record.letter_annotations.get("peak_location", None)
+        peak_locations = record.letter_annotations.get("peak_location")
         assert len(record.seq) == len(phred_qualities), "Number of " + \
             "phd quality scores does not match length of sequence"
         if peak_locations:
@@ -123,14 +121,14 @@ class PhdWriter(SequentialSequenceWriter):
         for annot in [k.lower() for k in Phd.CKEYWORDS]:
             value = None
             if annot == "trim":
-                if record.annotations.get("trim", None):
+                if record.annotations.get("trim"):
                     value = "%s %s %.4f" % record.annotations["trim"]
             elif annot == "trace_peak_area_ratio":
-                if record.annotations.get("trace_peak_area_ratio", None):
+                if record.annotations.get("trace_peak_area_ratio"):
                     value = "%.4f" % record.annotations[
                         "trace_peak_area_ratio"]
             else:
-                value = record.annotations.get(annot, None)
+                value = record.annotations.get(annot)
             if value or value == 0:
                 self.handle.write("%s: %s\n" % (annot.upper(), value))
 
diff --git a/Bio/SeqIO/PirIO.py b/Bio/SeqIO/PirIO.py
index 1ae5676..7fd58c9 100644
--- a/Bio/SeqIO/PirIO.py
+++ b/Bio/SeqIO/PirIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2009 by Peter Cock.  All rights reserved.
+# Copyright 2008-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -93,7 +93,6 @@ from Bio.Alphabet import single_letter_alphabet, generic_protein, \
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 
-__docformat__ = "restructuredtext en"
 
 _pir_alphabets = {"P1": generic_protein,
                   "F1": generic_protein,
@@ -121,6 +120,19 @@ def PirIterator(handle):
 
     Note that use of title2ids matches that of Bio.Fasta.SequenceParser
     but the defaults are slightly different.
+
+    Example:
+
+    >>> with open("NBRF/DMB_prot.pir") as handle:
+    ...    for record in PirIterator(handle):
+    ...        print("%s length %i" % (record.id, len(record)))
+    HLA:HLA00489 length 263
+    HLA:HLA00490 length 94
+    HLA:HLA00491 length 94
+    HLA:HLA00492 length 80
+    HLA:HLA00493 length 175
+    HLA:HLA01083 length 188
+
     """
     # Skip any text before the first record (e.g. blank lines, comments)
     while True:
@@ -171,22 +183,5 @@ def PirIterator(handle):
     assert False, "Should not reach this line"
 
 if __name__ == "__main__":
-    print("Running quick self test")
-
-    import os
-
-    for name in ["clustalw", "DMA_nuc", "DMB_prot", "B_nuc", "Cw_prot"]:
-        print(name)
-        filename = "../../Tests/NBRF/%s.pir" % name
-        if not os.path.isfile(filename):
-            print("Missing %s" % filename)
-            continue
-
-        records = list(PirIterator(open(filename)))
-        count = 0
-        for record in records:
-            count += 1
-            parts = record.description.split()
-            if "bases," in parts:
-                assert len(record) == int(parts[parts.index("bases,") - 1])
-        print("Could read %s (%i records)" % (name, count))
+    from Bio._utils import run_doctest
+    run_doctest(verbose=0)
diff --git a/Bio/SeqIO/QualityIO.py b/Bio/SeqIO/QualityIO.py
index f6754ee..93dac24 100644
--- a/Bio/SeqIO/QualityIO.py
+++ b/Bio/SeqIO/QualityIO.py
@@ -1,4 +1,4 @@
-# Copyright 2009-2010 by Peter Cock.  All rights reserved.
+# Copyright 2009-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -259,7 +259,7 @@ using ("fastq" or "fastq-sanger" for the Sanger standard using PHRED values,
 for the more recent variant), as this cannot be detected reliably
 automatically.
 
-To illustrate this problem, let's consider an artifical example:
+To illustrate this problem, let's consider an artificial example:
 
 >>> from Bio.Seq import Seq
 >>> from Bio.Alphabet import generic_dna
@@ -363,8 +363,6 @@ are approximately equal.
 """
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from Bio.Alphabet import single_letter_alphabet
 from Bio.Seq import Seq, UnknownSeq
 from Bio.SeqRecord import SeqRecord
@@ -469,8 +467,8 @@ def solexa_quality_from_phred(phred_quality):
         # Special case, map to -5 as discussed in the docstring
         return -5.0
     else:
-        raise ValueError("PHRED qualities must be positive (or zero), not %s"
-                         % repr(phred_quality))
+        raise ValueError("PHRED qualities must be positive (or zero), not %r"
+                         % phred_quality)
 
 
 def phred_quality_from_solexa(solexa_quality):
@@ -515,8 +513,8 @@ def phred_quality_from_solexa(solexa_quality):
         # Assume None is used as some kind of NULL or NA value; return None
         return None
     if solexa_quality < -5:
-        warnings.warn("Solexa quality less than -5 passed, %s"
-                      % repr(solexa_quality), BiopythonWarning)
+        warnings.warn("Solexa quality less than -5 passed, %r" % solexa_quality,
+                      BiopythonWarning)
     return 10 * log(10 ** (solexa_quality / 10.0) + 1, 10)
 
 
diff --git a/Bio/SeqIO/SeqXmlIO.py b/Bio/SeqIO/SeqXmlIO.py
index 31c45bd..3ea72c9 100644
--- a/Bio/SeqIO/SeqXmlIO.py
+++ b/Bio/SeqIO/SeqXmlIO.py
@@ -134,7 +134,7 @@ class SeqXmlIterator(XMLRecordIterator):
         if "name" not in attr_dict:
             raise ValueError("Malformed property element.")
 
-        value = attr_dict.get("value", None)
+        value = attr_dict.get("value")
 
         if attr_dict["name"] not in record.annotations:
             record.annotations[attr_dict["name"]] = value
@@ -418,25 +418,3 @@ class SeqXmlWriter(SequentialSequenceWriter):
                     self.xml_generator.startElement(
                         "property", AttributesImpl(attr))
                     self.xml_generator.endElement("property")
-
-if __name__ == "__main__":
-    print("Running quick self test")
-
-    from Bio import SeqIO
-    import sys
-
-    with open("Tests/SeqXML/protein_example.xml", "r") as fileHandle:
-        records = list(SeqIO.parse(fileHandle, "seqxml"))
-
-    from Bio._py3k import StringIO
-    stringHandle = StringIO()
-
-    SeqIO.write(records, stringHandle, "seqxml")
-    SeqIO.write(records, sys.stdout, "seqxml")
-    print("")
-
-    stringHandle.seek(0)
-    records = list(SeqIO.parse(stringHandle, "seqxml"))
-
-    SeqIO.write(records, sys.stdout, "seqxml")
-    print("")
diff --git a/Bio/SeqIO/SffIO.py b/Bio/SeqIO/SffIO.py
index 353c6ba..b78f6cd 100644
--- a/Bio/SeqIO/SffIO.py
+++ b/Bio/SeqIO/SffIO.py
@@ -1,4 +1,4 @@
-# Copyright 2009-2010 by Peter Cock.  All rights reserved.
+# Copyright 2009-2016 by Peter Cock.  All rights reserved.
 # Based on code contributed and copyright 2009 by Jose Blanca (COMAV-UPV).
 #
 # This code is part of the Biopython distribution and governed by its
@@ -251,8 +251,6 @@ _srt = b".srt"
 _mft = b".mft"
 _flag = b"\xff"
 
-__docformat__ = "restructuredtext en"
-
 
 def _check_mode(handle):
     """Ensure handle not opened in text mode.
@@ -321,7 +319,7 @@ def _sff_file_header(handle):
     data = handle.read(31)
     if not data:
         raise ValueError("Empty file.")
-    elif len(data) < 13:
+    elif len(data) < 31:
         raise ValueError("File too small to hold a valid SFF header.")
     magic_number, ver0, ver1, ver2, ver3, index_offset, index_length, \
         number_of_reads, header_length, key_length, number_of_flows_per_read, \
@@ -330,8 +328,7 @@ def _sff_file_header(handle):
         # Probably user error, calling Bio.SeqIO.parse() twice!
         raise ValueError("Handle seems to be at SFF index block, not start")
     if magic_number != _sff:  # 779314790
-        raise ValueError("SFF file did not start '.sff', but %s"
-                         % repr(magic_number))
+        raise ValueError("SFF file did not start '.sff', but %r" % magic_number)
     if (ver0, ver1, ver2, ver3) != (0, 0, 0, 1):
         raise ValueError("Unsupported SFF version in header, %i.%i.%i.%i"
                          % (ver0, ver1, ver2, ver3))
@@ -400,8 +397,8 @@ def _sff_do_slow_index(handle):
             clip_qual_right, clip_adapter_left, clip_adapter_right \
             = struct.unpack(read_header_fmt, data)
         if read_header_length < 10 or read_header_length % 8 != 0:
-            raise ValueError("Malformed read header, says length is %i:\n%s"
-                             % (read_header_length, repr(data)))
+            raise ValueError("Malformed read header, says length is %i:\n%r"
+                             % (read_header_length, data))
         # now the name and any padding (remainder of header)
         name = _bytes_to_string(handle.read(name_length))
         padding = read_header_length - read_header_size - name_length
@@ -459,8 +456,8 @@ def _sff_find_roche_index(handle):
         raise ValueError("Premature end of file? Expected index of size %i at offest %i, found nothing"
                          % (index_length, index_offset))
     if len(data) < fmt_size:
-        raise ValueError("Premature end of file? Expected index of size %i at offest %i, found %s"
-                         % (index_length, index_offset, repr(data)))
+        raise ValueError("Premature end of file? Expected index of size %i at offest %i, found %r"
+                         % (index_length, index_offset, data))
     magic_number, ver0, ver1, ver2, ver3 = struct.unpack(fmt, data)
     if magic_number == _mft:  # 778921588
         # Roche 454 manifest index
@@ -500,8 +497,8 @@ def _sff_find_roche_index(handle):
         raise ValueError("Hash table style indexes (.hsh) in SFF files are "
                          "not (yet) supported")
     else:
-        raise ValueError("Unknown magic number %s in SFF index header:\n%s"
-                         % (repr(magic_number), repr(data)))
+        raise ValueError("Unknown magic number %r in SFF index header:\n%r"
+                         % (magic_number, data))
 
 
 def ReadRocheXmlManifest(handle):
@@ -509,7 +506,7 @@ def ReadRocheXmlManifest(handle):
 
     The SFF file format allows for multiple different index blocks, and Roche
     took advantage of this to define their own index block which also embeds
-    an XML manifest string. This is not a publically documented extension to
+    an XML manifest string. This is not a publicly documented extension to
     the SFF file format, this was reverse engineered.
 
     The handle should be to an SFF file opened in binary mode. This function
@@ -1311,77 +1308,14 @@ if __name__ == "__main__":
     filename = "../../Tests/Roche/E3MFGYR02_random_10_reads.sff"
     with open(filename, "rb") as handle:
         metadata = ReadRocheXmlManifest(handle)
-    with open(filename, "rb") as handle:
-        index1 = sorted(_sff_read_roche_index(handle))
-    with open(filename, "rb") as handle:
-        index2 = sorted(_sff_do_slow_index(handle))
-    assert index1 == index2
-    with open(filename, "rb") as handle:
-        assert len(index1) == len(list(SffIterator(handle)))
-    from Bio._py3k import StringIO
+
     from io import BytesIO
-    with open(filename, "rb") as handle:
-        assert len(index1) == len(list(SffIterator(BytesIO(handle.read()))))
-
-    if sys.platform != "win32" and sys.version_info[0] < 3:
-        # Can be lazy and treat as binary...
-        with open(filename, "r") as handle:
-            assert len(index1) == len(list(SffIterator(handle)))
-        with open(filename) as handle:
-            index2 = sorted(_sff_read_roche_index(handle))
-        assert index1 == index2
-        with open(filename, "r") as handle:
-            index2 = sorted(_sff_do_slow_index(handle))
-        assert index1 == index2
-        with open(filename, "r") as handle:
-            assert len(index1) == len(list(SffIterator(handle)))
-        with open(filename, "r") as handle:
-            assert len(index1) == len(list(SffIterator(BytesIO(handle.read()))))
 
     with open(filename, "rb") as handle:
         sff = list(SffIterator(handle))
-
-    with open("../../Tests/Roche/E3MFGYR02_alt_index_at_end.sff", "rb") as handle:
-        sff2 = list(SffIterator(handle))
-    assert len(sff) == len(sff2)
-    for old, new in zip(sff, sff2):
-        assert old.id == new.id
-        assert str(old.seq) == str(new.seq)
-
-    with open("../../Tests/Roche/E3MFGYR02_alt_index_at_start.sff", "rb") as handle:
-        sff2 = list(SffIterator(handle))
-    assert len(sff) == len(sff2)
-    for old, new in zip(sff, sff2):
-        assert old.id == new.id
-        assert str(old.seq) == str(new.seq)
-
-    with open("../../Tests/Roche/E3MFGYR02_alt_index_in_middle.sff", "rb") as handle:
-        sff2 = list(SffIterator(handle))
-    assert len(sff) == len(sff2)
-    for old, new in zip(sff, sff2):
-        assert old.id == new.id
-        assert str(old.seq) == str(new.seq)
-
-    with open("../../Tests/Roche/E3MFGYR02_index_at_start.sff", "rb") as handle:
-        sff2 = list(SffIterator(handle))
-    assert len(sff) == len(sff2)
-    for old, new in zip(sff, sff2):
-        assert old.id == new.id
-        assert str(old.seq) == str(new.seq)
-
-    with open("../../Tests/Roche/E3MFGYR02_index_in_middle.sff", "rb") as handle:
-        sff2 = list(SffIterator(handle))
-    assert len(sff) == len(sff2)
-    for old, new in zip(sff, sff2):
-        assert old.id == new.id
-        assert str(old.seq) == str(new.seq)
-
     with open(filename, "rb") as handle:
         sff_trim = list(SffIterator(handle, trim=True))
 
-    with open(filename, "rb") as handle:
-        print(ReadRocheXmlManifest(handle))
-
     from Bio import SeqIO
     filename = "../../Tests/Roche/E3MFGYR02_random_10_reads_no_trim.fasta"
     fasta_no_trim = list(SeqIO.parse(filename, "fasta"))
diff --git a/Bio/SeqIO/SwissIO.py b/Bio/SeqIO/SwissIO.py
index 62d6208..ce4b2c6 100644
--- a/Bio/SeqIO/SwissIO.py
+++ b/Bio/SeqIO/SwissIO.py
@@ -23,8 +23,6 @@ from Bio import Alphabet
 from Bio import SeqFeature
 from Bio import SwissProt
 
-__docformat__ = "restructuredtext en"
-
 
 def _make_position(location_string, offset=0):
     """Turn a Swiss location position into a SeqFeature position object (PRIVATE).
@@ -79,6 +77,9 @@ def SwissIterator(handle):
 
     For consistency with BioPerl and EMBOSS we call this the "swiss"
     format. See also the SeqIO support for "uniprot-xml" format.
+
+    Rather than calling it directly, you are expected to use this
+    parser via Bio.SeqIO.parse(..., format="swiss") instead.
     """
     swiss_records = SwissProt.parse(handle)
     for swiss_record in swiss_records:
@@ -143,24 +144,3 @@ def SwissIterator(handle):
         if swiss_record.keywords:
             record.annotations['keywords'] = swiss_record.keywords
         yield record
-
-if __name__ == "__main__":
-    print("Quick self test...")
-
-    example_filename = "../../Tests/SwissProt/sp008"
-
-    import os
-    if not os.path.isfile(example_filename):
-        print("Missing test file %s" % example_filename)
-    else:
-        # Try parsing it!
-        with open(example_filename) as handle:
-            records = SwissIterator(handle)
-            for record in records:
-                print(record.name)
-                print(record.id)
-                print(record.annotations['keywords'])
-                print(repr(record.annotations['organism']))
-                print(str(record.seq)[:20] + "...")
-                for f in record.features:
-                    print(f)
diff --git a/Bio/SeqIO/TabIO.py b/Bio/SeqIO/TabIO.py
index 4e7e646..8deb96e 100644
--- a/Bio/SeqIO/TabIO.py
+++ b/Bio/SeqIO/TabIO.py
@@ -1,4 +1,4 @@
-# Copyright 2008-2010 by Peter Cock.  All rights reserved.
+# Copyright 2008-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -38,8 +38,6 @@ from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
 from Bio.SeqIO.Interfaces import SequentialSequenceWriter
 
-__docformat__ = "restructuredtext en"
-
 
 def TabIterator(handle, alphabet=single_letter_alphabet):
     """Iterates over tab separated lines (as SeqRecord objects).
@@ -55,6 +53,23 @@ def TabIterator(handle, alphabet=single_letter_alphabet):
     any spaces within the text) and the second field is the sequence.
 
     Any blank lines are ignored.
+
+    Example:
+
+    >>> with open("GenBank/NC_005816.tsv") as handle:
+    ...     for record in TabIterator(handle):
+    ...         print("%s length %i" % (record.id, len(record)))
+    gi|45478712|ref|NP_995567.1| length 340
+    gi|45478713|ref|NP_995568.1| length 260
+    gi|45478714|ref|NP_995569.1| length 64
+    gi|45478715|ref|NP_995570.1| length 123
+    gi|45478716|ref|NP_995571.1| length 145
+    gi|45478717|ref|NP_995572.1| length 357
+    gi|45478718|ref|NP_995573.1| length 138
+    gi|45478719|ref|NP_995574.1| length 312
+    gi|45478720|ref|NP_995575.1| length 99
+    gi|45478721|ref|NP_995576.1| length 90
+
     """
     for line in handle:
         try:
@@ -64,8 +79,8 @@ def TabIterator(handle, alphabet=single_letter_alphabet):
                 # It's a blank line, ignore it
                 continue
             raise ValueError("Each line should have one tab separating the" +
-                             " title and sequence, this line has %i tabs: %s"
-                             % (line.count("\t"), repr(line)))
+                             " title and sequence, this line has %i tabs: %r"
+                             % (line.count("\t"), line))
         title = title.strip()
         seq = seq.strip()  # removes the trailing new line
         yield SeqRecord(Seq(seq, alphabet),
@@ -98,20 +113,5 @@ class TabWriter(SequentialSequenceWriter):
 
 
 if __name__ == "__main__":
-    print("Running quick self test")
-    from Bio._py3k import StringIO
-
-    # This example has a trailing blank line which should be ignored
-    handle = StringIO("Alpha\tAAAAAAA\nBeta\tCCCCCCC\n\n")
-    records = list(TabIterator(handle))
-    assert len(records) == 2
-
-    handle = StringIO("Alpha\tAAAAAAA\tExtra\nBeta\tCCCCCCC\n")
-    try:
-        records = list(TabIterator(handle))
-        assert False, "Should have reject this invalid example!"
-    except ValueError:
-        # Good!
-        pass
-
-    print("Done")
+    from Bio._utils import run_doctest
+    run_doctest(verbose=0)
diff --git a/Bio/SeqIO/UniprotIO.py b/Bio/SeqIO/UniprotIO.py
index ea07fbb..504a3e6 100644
--- a/Bio/SeqIO/UniprotIO.py
+++ b/Bio/SeqIO/UniprotIO.py
@@ -1,5 +1,5 @@
 # Copyright 2010 by Andrea Pierleoni
-# Revisions copyright 2010 by Peter Cock
+# Revisions copyright 2010, 2016 by Peter Cock
 # All rights reserved.
 #
 # This code is part of the Biopython distribution and governed by its
@@ -23,8 +23,6 @@ from Bio import Alphabet
 from Bio.SeqRecord import SeqRecord
 from Bio._py3k import StringIO
 
-__docformat__ = "restructuredtext en"
-
 
 # For speed try to use cElementTree rather than ElementTree
 try:
@@ -273,7 +271,7 @@ class Parser(object):
                         else:
                             start = int(list(loc_element.getiterator(NS + 'begin'))[0].attrib['position']) - 1
                             end = int(list(loc_element.getiterator(NS + 'end'))[0].attrib['position'])
-                    except:  # undefined positions or erroneously mapped
+                    except ValueError:  # undefined positions or erroneously mapped
                         pass
                 mass = element.attrib['mass']
                 method = element.attrib['method']
@@ -361,8 +359,8 @@ class Parser(object):
                             for person_element in cit_element:
                                 authors.append(person_element.attrib['name'])
                         elif cit_element.tag == NS + 'dbReference':
-                            self.ParsedSeqRecord.dbxrefs.append(cit_element.attrib['type']
-                                                                + ':' + cit_element.attrib['id'])
+                            self.ParsedSeqRecord.dbxrefs.append(cit_element.attrib['type'] +
+                                                                ':' + cit_element.attrib['id'])
                             if cit_element.attrib['type'] == 'PubMed':
                                 reference.pubmed_id = cit_element.attrib['id']
                             elif ref_element.attrib['type'] == 'MEDLINE':
@@ -439,7 +437,7 @@ class Parser(object):
                 else:
                     try:
                         feature.qualifiers[feature_element.tag.replace(NS, '')] = feature_element.text
-                    except:
+                    except Exception:  # TODO - Which exceptions?
                         pass  # skip unparsable tag
             self.ParsedSeqRecord.features.append(feature)
 
diff --git a/Bio/SeqIO/__init__.py b/Bio/SeqIO/__init__.py
index e5e8696..058f4fc 100644
--- a/Bio/SeqIO/__init__.py
+++ b/Bio/SeqIO/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2006-2010 by Peter Cock.  All rights reserved.
+# Copyright 2006-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -313,8 +313,6 @@ making up each alignment as SeqRecords.
 from __future__ import print_function
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"  # not just plaintext
-
 # TODO
 # - define policy on reading aligned sequences with gaps in
 #   (e.g. - and . characters) including how the alphabet interacts
@@ -325,40 +323,37 @@ __docformat__ = "restructuredtext en"  # not just plaintext
 #
 # - MSF multiple alignment format, aka GCG, aka PileUp format (*.msf)
 #   http://www.bioperl.org/wiki/MSF_multiple_alignment_format
-
-"""
-FAO BioPython Developers
-------------------------
-The way I envision this SeqIO system working as that for any sequence file
-format we have an iterator that returns SeqRecord objects.
-
-This also applies to interlaced file formats (like clustal - although that
-is now handled via Bio.AlignIO instead) where the file cannot be read record
-by record.  You should still return an iterator, even if the implementation
-could just as easily return a list.
-
-These file format specific sequence iterators may be implemented as:
-    - Classes which take a handle for __init__ and provide the __iter__ method
-    - Functions that take a handle, and return an iterator object
-    - Generator functions that take a handle, and yield SeqRecord objects
-
-It is then trivial to turn this iterator into a list of SeqRecord objects,
-an in memory dictionary, or a multiple sequence alignment object.
-
-For building the dictionary by default the id property of each SeqRecord is
-used as the key.  You should always populate the id property, and it should
-be unique in most cases. For some file formats the accession number is a good
-choice.  If the file itself contains ambiguous identifiers, don't try and
-dis-ambiguate them - return them as is.
-
-When adding a new file format, please use the same lower case format name
-as BioPerl, or if they have not defined one, try the names used by EMBOSS.
-
-See also http://biopython.org/wiki/SeqIO_dev
-
---Peter
-"""
-
+#
+# FAO BioPython Developers
+# ------------------------
+# The way I envision this SeqIO system working as that for any sequence file
+# format we have an iterator that returns SeqRecord objects.
+#
+# This also applies to interlaced file formats (like clustal - although that
+# is now handled via Bio.AlignIO instead) where the file cannot be read record
+# by record.  You should still return an iterator, even if the implementation
+# could just as easily return a list.
+#
+# These file format specific sequence iterators may be implemented as:
+#    - Classes which take a handle for __init__ and provide the __iter__ method
+#    - Functions that take a handle, and return an iterator object
+#    - Generator functions that take a handle, and yield SeqRecord objects
+#
+# It is then trivial to turn this iterator into a list of SeqRecord objects,
+# an in memory dictionary, or a multiple sequence alignment object.
+#
+# For building the dictionary by default the id property of each SeqRecord is
+# used as the key.  You should always populate the id property, and it should
+# be unique in most cases. For some file formats the accession number is a good
+# choice.  If the file itself contains ambiguous identifiers, don't try and
+# dis-ambiguate them - return them as is.
+#
+# When adding a new file format, please use the same lower case format name
+# as BioPerl, or if they have not defined one, try the names used by EMBOSS.
+#
+# See also http://biopython.org/wiki/SeqIO_dev
+#
+# --Peter
 
 from Bio.File import as_handle
 from Bio.SeqRecord import SeqRecord
@@ -488,7 +483,7 @@ def write(sequences, handle, format):
             alignment_count = AlignIO.write([alignment], fp, format)
             assert alignment_count == 1, \
                 "Internal error - the underlying writer " \
-                " should have returned 1, not %s" % repr(alignment_count)
+                " should have returned 1, not %r" % alignment_count
             count = len(alignment)
             del alignment_count, alignment
         elif format in _FormatToIterator or format in AlignIO._FormatToIterator:
@@ -498,8 +493,8 @@ def write(sequences, handle, format):
             raise ValueError("Unknown format '%s'" % format)
 
         assert isinstance(count, int), "Internal error - the underlying %s " \
-            "writer should have returned the record count, not %s" \
-            % (format, repr(count))
+            "writer should have returned the record count, not %r" \
+            % (format, count)
 
     return count
 
@@ -578,7 +573,7 @@ def parse(handle, format, alphabet=None):
         raise ValueError("Format string '%s' should be lower case" % format)
     if alphabet is not None and not (isinstance(alphabet, Alphabet) or
                                      isinstance(alphabet, AlphabetEncoder)):
-        raise ValueError("Invalid alphabet, %s" % repr(alphabet))
+        raise ValueError("Invalid alphabet, %r" % alphabet)
 
     with as_handle(handle, mode) as fp:
         # Map the file format to a sequence iterator:
@@ -613,9 +608,9 @@ def _force_alphabet(record_iterator, alphabet):
             record.seq.alphabet = alphabet
             yield record
         else:
-            raise ValueError("Specified alphabet %s clashes with "
-                             "that determined from the file, %s"
-                             % (repr(alphabet), repr(record.seq.alphabet)))
+            raise ValueError("Specified alphabet %r clashes with "
+                             "that determined from the file, %r"
+                             % (alphabet, record.seq.alphabet))
 
 
 def read(handle, format, alphabet=None):
@@ -857,7 +852,7 @@ def index(filename, format, alphabet=None, key_function=None):
         raise ValueError("Format string '%s' should be lower case" % format)
     if alphabet is not None and not (isinstance(alphabet, Alphabet) or
                                      isinstance(alphabet, AlphabetEncoder)):
-        raise ValueError("Invalid alphabet, %s" % repr(alphabet))
+        raise ValueError("Invalid alphabet, %r" % alphabet)
 
     # Map the file format to a sequence iterator:
     from ._index import _FormatToRandomAccess  # Lazy import
@@ -937,7 +932,7 @@ def index_db(index_filename, filenames=None, format=None, alphabet=None,
         raise ValueError("Format string '%s' should be lower case" % format)
     if alphabet is not None and not (isinstance(alphabet, Alphabet) or
                                      isinstance(alphabet, AlphabetEncoder)):
-        raise ValueError("Invalid alphabet, %s" % repr(alphabet))
+        raise ValueError("Invalid alphabet, %r" % alphabet)
 
     # Map the file format to a sequence iterator:
     from ._index import _FormatToRandomAccess  # Lazy import
diff --git a/Bio/SeqIO/_convert.py b/Bio/SeqIO/_convert.py
index a0b1813..cc5f183 100644
--- a/Bio/SeqIO/_convert.py
+++ b/Bio/SeqIO/_convert.py
@@ -1,4 +1,4 @@
-# Copyright 2009 by Peter Cock.  All rights reserved.
+# Copyright 2009-2016 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -24,11 +24,10 @@ means that using Bio.SeqIO.convert() may be faster, as well as more convenient.
 All these file format specific optimisations are handled by this (private) module.
 """
 
+from Bio import BiopythonWarning
 from Bio import SeqIO
 # NOTE - Lots of lazy imports further on...
 
-__docformat__ = "restructuredtext en"
-
 
 def _genbank_convert_fasta(in_handle, out_handle, alphabet=None):
     """Fast GenBank to FASTA (PRIVATE)."""
@@ -79,7 +78,7 @@ def _fastq_generic2(in_handle, out_handle, mapping, truncate_char, truncate_msg)
         if truncate_char in qual:
             qual = qual.replace(truncate_char, chr(126))
             import warnings
-            warnings.warn(truncate_msg)
+            warnings.warn(truncate_msg, BiopythonWarning)
         out_handle.write("@%s\n%s\n+\n%s\n" % (title, seq, qual))
     return count
 
@@ -94,9 +93,9 @@ def _fastq_sanger_convert_fastq_sanger(in_handle, out_handle, alphabet=None):
     conversion.
     """
     # Map unexpected chars to null
-    mapping = "".join([chr(0) for ascii in range(0, 33)]
-                      + [chr(ascii) for ascii in range(33, 127)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 33)] +
+                      [chr(ascii) for ascii in range(33, 127)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
@@ -110,9 +109,9 @@ def _fastq_solexa_convert_fastq_solexa(in_handle, out_handle, alphabet=None):
     conversion.
     """
     # Map unexpected chars to null
-    mapping = "".join([chr(0) for ascii in range(0, 59)]
-                      + [chr(ascii) for ascii in range(59, 127)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 59)] +
+                      [chr(ascii) for ascii in range(59, 127)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
@@ -126,9 +125,9 @@ def _fastq_illumina_convert_fastq_illumina(in_handle, out_handle, alphabet=None)
     conversion.
     """
     # Map unexpected chars to null
-    mapping = "".join([chr(0) for ascii in range(0, 64)]
-                      + [chr(ascii) for ascii in range(64, 127)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 64)] +
+                      [chr(ascii) for ascii in range(64, 127)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
@@ -140,9 +139,9 @@ def _fastq_illumina_convert_fastq_sanger(in_handle, out_handle, alphabet=None):
     conversion.
     """
     # Map unexpected chars to null
-    mapping = "".join([chr(0) for ascii in range(0, 64)]
-                      + [chr(33 + q) for q in range(0, 62 + 1)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 64)] +
+                      [chr(33 + q) for q in range(0, 62 + 1)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
@@ -156,10 +155,10 @@ def _fastq_sanger_convert_fastq_illumina(in_handle, out_handle, alphabet=None):
     """
     # Map unexpected chars to null
     trunc_char = chr(1)
-    mapping = "".join([chr(0) for ascii in range(0, 33)]
-                      + [chr(64 + q) for q in range(0, 62 + 1)]
-                      + [trunc_char for ascii in range(96, 127)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 33)] +
+                      [chr(64 + q) for q in range(0, 62 + 1)] +
+                      [trunc_char for ascii in range(96, 127)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic2(in_handle, out_handle, mapping, trunc_char,
                            "Data loss - max PHRED quality 62 in Illumina 1.3+ FASTQ")
@@ -173,10 +172,10 @@ def _fastq_solexa_convert_fastq_sanger(in_handle, out_handle, alphabet=None):
     """
     # Map unexpected chars to null
     from Bio.SeqIO.QualityIO import phred_quality_from_solexa
-    mapping = "".join([chr(0) for ascii in range(0, 59)]
-                      + [chr(33 + int(round(phred_quality_from_solexa(q))))
-                         for q in range(-5, 62 + 1)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 59)] +
+                      [chr(33 + int(round(phred_quality_from_solexa(q))))
+                       for q in range(-5, 62 + 1)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
@@ -191,11 +190,11 @@ def _fastq_sanger_convert_fastq_solexa(in_handle, out_handle, alphabet=None):
     # Map unexpected chars to null
     from Bio.SeqIO.QualityIO import solexa_quality_from_phred
     trunc_char = chr(1)
-    mapping = "".join([chr(0) for ascii in range(0, 33)]
-                      + [chr(64 + int(round(solexa_quality_from_phred(q))))
-                         for q in range(0, 62 + 1)]
-                      + [trunc_char for ascii in range(96, 127)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 33)] +
+                      [chr(64 + int(round(solexa_quality_from_phred(q))))
+                       for q in range(0, 62 + 1)] +
+                      [trunc_char for ascii in range(96, 127)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic2(in_handle, out_handle, mapping, trunc_char,
                            "Data loss - max Solexa quality 62 in Solexa FASTQ")
@@ -209,10 +208,10 @@ def _fastq_solexa_convert_fastq_illumina(in_handle, out_handle, alphabet=None):
     """
     # Map unexpected chars to null
     from Bio.SeqIO.QualityIO import phred_quality_from_solexa
-    mapping = "".join([chr(0) for ascii in range(0, 59)]
-                      + [chr(64 + int(round(phred_quality_from_solexa(q))))
-                         for q in range(-5, 62 + 1)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 59)] +
+                      [chr(64 + int(round(phred_quality_from_solexa(q))))
+                       for q in range(-5, 62 + 1)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
@@ -225,11 +224,10 @@ def _fastq_illumina_convert_fastq_solexa(in_handle, out_handle, alphabet=None):
     """
     # Map unexpected chars to null
     from Bio.SeqIO.QualityIO import solexa_quality_from_phred
-    trunc_char = chr(1)
-    mapping = "".join([chr(0) for ascii in range(0, 64)]
-                      + [chr(64 + int(round(solexa_quality_from_phred(q))))
-                         for q in range(0, 62 + 1)]
-                      + [chr(0) for ascii in range(127, 256)])
+    mapping = "".join([chr(0) for ascii in range(0, 64)] +
+                      [chr(64 + int(round(solexa_quality_from_phred(q))))
+                       for q in range(0, 62 + 1)] +
+                      [chr(0) for ascii in range(127, 256)])
     assert len(mapping) == 256
     return _fastq_generic(in_handle, out_handle, mapping)
 
diff --git a/Bio/SeqIO/_index.py b/Bio/SeqIO/_index.py
index 2a523c6..3e5eeb9 100644
--- a/Bio/SeqIO/_index.py
+++ b/Bio/SeqIO/_index.py
@@ -33,8 +33,6 @@ from Bio import SeqIO
 from Bio import Alphabet
 from Bio.File import _IndexedSeqFileProxy, _open_for_random_access
 
-__docformat__ = "restructuredtext en"
-
 
 class SeqFileRandomAccess(_IndexedSeqFileProxy):
     def __init__(self, filename, format, alphabet):
@@ -143,6 +141,7 @@ class SffRandomAccess(SeqFileRandomAccess):
                                                 self._alphabet)
 
     def get_raw(self, offset):
+        """Return the raw record from the file as a bytes string."""
         handle = self._handle
         handle.seek(offset)
         return SeqIO.SffIO._sff_read_raw_record(handle, self._flows_per_read)
@@ -176,7 +175,6 @@ class SequentialSeqFileRandomAccess(SeqFileRandomAccess):
                   "phd": "BEGIN_SEQUENCE",
                   "pir": ">..;",
                   "qual": ">",
-                  "qual": ">",
                   "swiss": "ID ",
                   "uniprot-xml": "<entry ",
                   }[format]
@@ -214,7 +212,7 @@ class SequentialSeqFileRandomAccess(SeqFileRandomAccess):
         assert not line, repr(line)
 
     def get_raw(self, offset):
-        """Similar to the get method, but returns the record as a raw string."""
+        """Return the raw record from the file as a bytes string."""
         # For non-trivial file formats this must be over-ridden in the subclass
         handle = self._handle
         marker_re = self._marker_re
@@ -417,7 +415,7 @@ class UniprotRandomAccess(SequentialSeqFileRandomAccess):
         assert not line, repr(line)
 
     def get_raw(self, offset):
-        """Similar to the get method, but returns the record as a raw string."""
+        """Return the raw record from the file as a bytes string."""
         handle = self._handle
         marker_re = self._marker_re
         end_entry_marker = _as_bytes("</entry>")
@@ -482,6 +480,7 @@ class IntelliGeneticsRandomAccess(SeqFileRandomAccess):
                 break
 
     def get_raw(self, offset):
+        """Return the raw record from the file as a bytes string."""
         handle = self._handle
         handle.seek(offset)
         marker_re = self._marker_re
@@ -520,7 +519,7 @@ class TabRandomAccess(SeqFileRandomAccess):
                 yield _bytes_to_string(key), start_offset, len(line)
 
     def get_raw(self, offset):
-        """Like the get method, but returns the record as a raw string."""
+        """Return the raw record from the file as a bytes string."""
         handle = self._handle
         handle.seek(offset)
         return handle.readline()
@@ -591,7 +590,7 @@ class FastqRandomAccess(SeqFileRandomAccess):
         # print("EOF")
 
     def get_raw(self, offset):
-        """Similar to the get method, but returns the record as a raw string."""
+        """Return the raw record from the file as a bytes string."""
         # TODO - Refactor this and the __init__ method to reduce code duplication?
         handle = self._handle
         handle.seek(offset)
diff --git a/Bio/SeqRecord.py b/Bio/SeqRecord.py
index 91e72d5..eba7d6e 100644
--- a/Bio/SeqRecord.py
+++ b/Bio/SeqRecord.py
@@ -10,13 +10,13 @@
 
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"  # Simple markup to show doctests nicely
-
 # NEEDS TO BE SYNCH WITH THE REST OF BIOPYTHON AND BIOPERL
 # In particular, the SeqRecord and BioSQL.BioSeq.DBSeqRecord classes
 # need to be in sync (this is the BioSQL "Database SeqRecord", see
 # also BioSQL.BioSeq.DBSeq which is the "Database Seq" class)
 
+_NO_SEQRECORD_COMPARISON = "SeqRecord comparison is deliberately not implemented. Explicitly compare the attributes of interest."
+
 
 class _RestrictedDict(dict):
     """Dict which only allows sequences of given length as values (PRIVATE).
@@ -357,8 +357,8 @@ class SeqRecord(object):
         Number of features: 1
         Per letter annotation for: secondary_structure
         Seq('MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEE...YLR', IUPACProtein())
-        >>> print(rec.letter_annotations["secondary_structure"])
-          S  SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT  
+        >>> rec.letter_annotations["secondary_structure"]
+        '  S  SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT  '
         >>> print(rec.features[0].location)
         [20:21]
 
@@ -373,8 +373,8 @@ class SeqRecord(object):
         Number of features: 1
         Per letter annotation for: secondary_structure
         Seq('RTLLMAGVSHDLRTPLTRIRLATEMMSEQD', IUPACProtein())
-        >>> print(sub.letter_annotations["secondary_structure"])
-        HHHHHTTTHHHHHHHHHHHHHHHHHHHHHH
+        >>> sub.letter_annotations["secondary_structure"]
+        'HHHHHTTTHHHHHHHHHHHHHHHHHHHHHH'
         >>> print(sub.features[0].location)
         [9:10]
 
@@ -710,6 +710,27 @@ class SeqRecord(object):
         """
         return len(self.seq)
 
+    def __lt__(self, other):
+        raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+    def __le___(self, other):
+        raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+    def __eq__(self, other):
+        raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+    def __ne__(self, other):
+        raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+    def __gt__(self, other):
+        raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+    def __ge__(self, other):
+        raise NotImplementedError(_NO_SEQRECORD_COMPARISON)
+
+    # Note Python 3 does not use __cmp__ and there is no need to
+    # define __cmp__ on Python 2 as have all of  _lt__ etc defined.
+
     # Python 3:
     def __bool__(self):
         """Boolean value of an instance of this class (True).
diff --git a/Bio/SeqUtils/CheckSum.py b/Bio/SeqUtils/CheckSum.py
index dc9e90f..e35e4a3 100644
--- a/Bio/SeqUtils/CheckSum.py
+++ b/Bio/SeqUtils/CheckSum.py
@@ -15,11 +15,18 @@ from __future__ import print_function
 from binascii import crc32 as _crc32
 from Bio._py3k import _as_bytes
 
-__docformat__ = "restructuredtext en"
-
 
 def crc32(seq):
-    """Returns the crc32 checksum for a sequence (string or Seq object)."""
+    """Returns the crc32 checksum for a sequence (string or Seq object).
+
+    Note that the case is important:
+
+    >>> crc32("ACGTACGTACGT")
+    20049947
+    >>> crc32("acgtACGTacgt")
+    1688586483
+
+    """
     # NOTE - On Python 2 returns a signed int, on Python 3 it is unsigned
     # Docs suggest should use crc32(x) & 0xffffffff for consistency.
     # TODO - Should we return crc32(x) & 0xffffffff here?
@@ -52,7 +59,16 @@ _table_h = _init_table_h()
 
 
 def crc64(s):
-    """Returns the crc64 checksum for a sequence (string or Seq object)."""
+    """Returns the crc64 checksum for a sequence (string or Seq object).
+
+    Note that the case is important:
+
+    >>> crc64("ACGTACGTACGT")
+    'CRC-C4FBB762C4A87EBD'
+    >>> crc64("acgtACGTacgt")
+    'CRC-DA4509DC64A87EBD'
+
+    """
     crcl = 0
     crch = 0
     for c in s:
@@ -77,6 +93,12 @@ def gcg(seq):
     with the help of John Lenton, Pablo Ziliani, and Gabriel Genellina.
 
     All sequences are converted to uppercase.
+
+    >>> gcg("ACGTACGTACGT")
+    5688
+    >>> gcg("acgtACGTacgt")
+    5688
+
     """
     try:
         # Assume its a Seq object
@@ -100,6 +122,13 @@ def seguid(seq):
     returns the SEGUID string (A SEquence Globally Unique IDentifier).
     seq type = str.
 
+    Note that the case is not important:
+
+    >>> seguid("ACGTACGTACGT")
+    'If6HIvcnRSQDVNiAoefAzySc6i4'
+    >>> seguid("acgtACGTacgt")
+    'If6HIvcnRSQDVNiAoefAzySc6i4'
+
     For more information about SEGUID, see:
     http://bioinformatics.anl.gov/seguid/
     DOI: 10.1002/pmic.200600032
@@ -124,20 +153,5 @@ def seguid(seq):
 
 
 if __name__ == "__main__":
-    print("Quick self test")
-
-    str_light_chain_one = "QSALTQPASVSGSPGQSITISCTGTSSDVGSYNLVSWYQQHPGK" \
-                    + "APKLMIYEGSKRPSGVSNRFSGSKSGNTASLTISGLQAEDEADY" \
-                    + "YCSSYAGSSTLVFGGGTKLTVL"
-
-    str_light_chain_two = "QSALTQPASVSGSPGQSITISCTGTSSDVGSYNLVSWYQQHPGK" \
-                    + "APKLMIYEGSKRPSGVSNRFSGSKSGNTASLTISGLQAEDEADY" \
-                    + "YCCSYAGSSTWVFGGGTKLTVL"
-
-    assert crc64(str_light_chain_one) == crc64(str_light_chain_two)
-    assert 'CRC-44CAAD88706CC153' == crc64(str_light_chain_one)
-
-    assert 'BpBeDdcNUYNsdk46JoJdw7Pd3BI' == seguid(str_light_chain_one)
-    assert 'X5XEaayob1nZLOc7eVT9qyczarY' == seguid(str_light_chain_two)
-
-    print("Done")
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/SeqUtils/CodonUsage.py b/Bio/SeqUtils/CodonUsage.py
index c123e67..e1a85f1 100644
--- a/Bio/SeqUtils/CodonUsage.py
+++ b/Bio/SeqUtils/CodonUsage.py
@@ -9,8 +9,6 @@ import math
 from .CodonUsageIndices import SharpEcoliIndex
 from Bio import SeqIO  # To parse a FASTA file
 
-__docformat__ = "restructuredtext en"
-
 
 CodonsDict = {'TTT': 0, 'TTC': 0, 'TTA': 0, 'TTG': 0, 'CTT': 0,
 'CTC': 0, 'CTA': 0, 'CTG': 0, 'ATT': 0, 'ATC': 0,
diff --git a/Bio/SeqUtils/CodonUsageIndices.py b/Bio/SeqUtils/CodonUsageIndices.py
index 5f55e3e..23026f1 100644
--- a/Bio/SeqUtils/CodonUsageIndices.py
+++ b/Bio/SeqUtils/CodonUsageIndices.py
@@ -7,8 +7,6 @@ Currently this module only defines a single codon adaption index from
 Sharp & Li, Nucleic Acids Res. 1987.
 """
 
-__docformat__ = "restructuredtext en"
-
 
 SharpEcoliIndex = {
 'GCA': 0.586, 'GCC': 0.122, 'GCG': 0.424, 'GCT': 1, 'AGA': 0.004, 'AGG': 0.002, 'CGA': 0.004,
diff --git a/Bio/SeqUtils/IsoelectricPoint.py b/Bio/SeqUtils/IsoelectricPoint.py
index a63d220..0c72c59 100644
--- a/Bio/SeqUtils/IsoelectricPoint.py
+++ b/Bio/SeqUtils/IsoelectricPoint.py
@@ -20,7 +20,6 @@ http://fields.scripps.edu/DTASelect/20010710-pI-Algorithm.pdf
 
 """
 
-__docformat__ = "restructuredtext en"
 
 positive_pKs = {'Nterm': 7.5, 'K': 10.0, 'R': 12.0, 'H': 5.98}
 negative_pKs = {'Cterm': 3.55, 'D': 4.05, 'E': 4.45, 'C': 9.0, 'Y': 10.0}
diff --git a/Bio/SeqUtils/MeltingTemp.py b/Bio/SeqUtils/MeltingTemp.py
index a953a05..9e4496a 100644
--- a/Bio/SeqUtils/MeltingTemp.py
+++ b/Bio/SeqUtils/MeltingTemp.py
@@ -25,14 +25,14 @@ with default parameters gives (essentially) the same results.
 
 General parameters for most Tm methods:
  - seq -- A Biopython sequence object or a string.
- - check -- Checks if the sequence is valid for the given method (default=True).
-   In general, whitespaces and non-base characters are removed and
+ - check -- Checks if the sequence is valid for the given method (default=
+   True). In general, whitespaces and non-base characters are removed and
    characters are converted to uppercase. RNA will be backtranscribed.
- - strict - Do not allow base characters or neighbor duplex keys (e.g. 'AT/NA').
-   that could not or not unambigiously be evaluated for the respective
-   method (default=True). Note that W (= A or T) and S (= C or G) are
-   not ambiguous for Tm_Wallace and Tm_GC. If 'False', average values
-   (if applicable) will be used.
+ - strict -- Do not allow base characters or neighbor duplex keys (e.g.
+   'AT/NA') that could not or not unambigiously be evaluated for the respective
+   method (default=True). Note that W (= A or T) and S (= C or G) are not
+   ambiguous for Tm_Wallace and Tm_GC. If 'False', average values (if
+   applicable) will be used.
 
 This module is not able to detect self-complementary and it will not use
 alignment tools to align an oligonucleotide sequence to its target sequence.
@@ -106,21 +106,38 @@ to von Ahsen et al. (2001) or Owczarzy et al. (2008) (for Tm_NN and Tm_GC):
 
 Dangling ends and mismatches, e.g.::
 
-    Oligo:     CGTTCCaAAGATGTGGGCATGAGCTTAC         CGTTCCaAAGATGTGGGCATGAGCTTAC
-               ::::::X:::::::::::::::::::::   or    ::::::X:::::::::::::::::::::
-    Template:  GCAAGGcTTCTACACCCGTACTCGAATG        TGCAAGGcTTCTACACCCGTACTCGAATGC
+    Oligo:     CGTTCCaAAGATGTGGGCATGAGCTTAC       CGTTCCaAAGATGTGGGCATGAGCTTAC
+               ::::::X:::::::::::::::::::::  or   ::::::X:::::::::::::::::::::
+    Template:  GCAAGGcTTCTACACCCGTACTCGAATG      TGCAAGGcTTCTACACCCGTACTCGAATGC
 
 Here:
 
     >>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC'))
     60.32
     >>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC',
-    ...                    c_seq='GCAAGGCTTCTACACCCGTACTCGAATG'))
+    ...                    c_seq='GCAAGGcTTCTACACCCGTACTCGAATG'))
     55.39
     >>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC', shift=1,
-    ...                   c_seq='TGCAAGGCTTCTACACCCGTACTCGAATGC'))
+    ...                   c_seq='TGCAAGGcTTCTACACCCGTACTCGAATGC'))
     55.69
 
+The same for RNA:
+
+    >>> print('%0.2f' % mt.Tm_NN('CGUUCCAAAGAUGUGGGCAUGAGCUUAC',
+    ...                   c_seq='UGCAAGGcUUCUACACCCGUACUCGAAUGC',
+    ...                   shift=1, nn_table=mt.RNA_NN3,
+    ...                   de_table=mt.RNA_DE1))
+    73.00
+
+Note, that thermodynamic data are not available for all kind of mismatches,
+e.g. most double mismatches or terminal mismaches combined with danglind ends:
+
+    >>> print('%0.2f' % mt.Tm_NN('CGTTCCAAAGATGTGGGCATGAGCTTAC',
+    ...                   c_seq='TtCAAGGcTTCTACACCCGTACTCGAATGC',
+    ...                   shift=1))
+    Traceback (most recent call last):
+    ValueError: no thermodynamic data for neighbors '.C/TT' available
+
 Make your own tables, or update/extend existing tables. E.g., add values for
 locked nucleotides. Here, 'locked A' (and its complement) should be represented
 by '1':
@@ -145,7 +162,6 @@ import warnings
 from Bio import SeqUtils, Seq
 from Bio import BiopythonWarning
 
-__docformat__ = "restructuredtext en"
 
 # Thermodynamic lookup tables (dictionaries):
 # Enthalpy (dH) and entropy (dS) values for nearest neighbors and initiation
@@ -333,30 +349,30 @@ DNA_DE1 = {
 # Dangling ends table (RNA)
 # Turner & Mathews (2010), Nucl Acids Res 38: D280-D282
 RNA_DE1 = {
-    'AA/T.': (-4.9, -13.2), 'AC/T.': (-0.9, -1.3), 'AG/T.': (-5.5, -15.1),
-    'AT/T.': (-2.3, -5.5),
-    'CA/G.': (-9.0, -23.5), 'CC/G.': (-4.1, -10.6), 'CG/G.': (-8.6, -22.2),
-    'CT/G.': (-7.5, -20.31),
-    'GA/C.': (-7.4, -20.3), 'GC/C.': (-2.8, -7.7), 'GG/C.': (-6.4, -16.4),
-    'GT/C.': (-3.6, -9.7),
-    'GA/T.': (-4.9, -13.2), 'GC/T.': (-0.9, -1.3), 'GG/T.': (-5.5, -15.1),
-    'GT/T.': (-2.3, -5.5),
-    'TA/A.': (-5.7, -16.1), 'TC/A.': (-0.7, -1.9), 'TG/A.': (-5.8, -16.4),
-    'TT/A.': (-2.2, -6.8),
-    'TA/G.': (-5.7, -16.1), 'TC/G.': (-0.7, -1.9), 'TG/G.': (-5.8, -16.4),
-    'TT/G.': (-2.2, -6.8),
-    'A./TA': (-0.5, -0.6), 'A./TC': (6.9, 22.6), 'A./TG': (0.6, 2.6),
-    'A./TT': (0.6, 2.6),
-    'C./GA': (-1.6, -4.5), 'C./GC': (0.7, 3.2), 'C./GG': (-4.6, -14.8),
-    'C./GT': (-0.4, -1.3),
-    'G./CA': (-2.4, -6.1), 'G./CC': (3.3, 11.6), 'G./CG': (0.8, 3.2),
-    'G./CT': (-1.4, -4.2),
-    'G./TA': (-0.5, -0.6), 'G./TC': (6.9, 22.6), 'G./TG': (0.6, 2.6),
-    'G./TT': (0.6, 2.6),
-    'T./AA': (1.6, 6.1), 'T./AC': (2.2, 8.1), 'T./AG': (0.7, 3.5),
-    'T./AT': (3.1, 10.6),
-    'T./GA': (1.6, 6.1), 'T./GC': (2.2, 8.1), 'T./GG': (0.7, 3.5),
-    'T./GT': (3.1, 10.6)}
+    '.T/AA': (-4.9, -13.2), '.T/CA': (-0.9, -1.3), '.T/GA': (-5.5, -15.1),
+    '.T/TA': (-2.3, -5.5),
+    '.G/AC': (-9.0, -23.5), '.G/CC': (-4.1, -10.6), '.G/GC': (-8.6, -22.2),
+    '.G/TC': (-7.5, -20.31),
+    '.C/AG': (-7.4, -20.3), '.C/CG': (-2.8, -7.7), '.C/GG': (-6.4, -16.4),
+    '.C/TG': (-3.6, -9.7),
+    '.T/AG': (-4.9, -13.2), '.T/CG': (-0.9, -1.3), '.T/GG': (-5.5, -15.1),
+    '.T/TG': (-2.3, -5.5),
+    '.A/AT': (-5.7, -16.1), '.A/CT': (-0.7, -1.9), '.A/GT': (-5.8, -16.4),
+    '.A/TT': (-2.2, -6.8),
+    '.G/AT': (-5.7, -16.1), '.G/CT': (-0.7, -1.9), '.G/GT': (-5.8, -16.4),
+    '.G/TT': (-2.2, -6.8),
+    'AT/.A': (-0.5, -0.6), 'CT/.A': (6.9, 22.6), 'GT/.A': (0.6, 2.6),
+    'TT/.A': (0.6, 2.6),
+    'AG/.C': (-1.6, -4.5), 'CG/.C': (0.7, 3.2), 'GG/.C': (-4.6, -14.8),
+    'TG/.C': (-0.4, -1.3),
+    'AC/.G': (-2.4, -6.1), 'CC/.G': (3.3, 11.6), 'GC/.G': (0.8, 3.2),
+    'TC/.G': (-1.4, -4.2),
+    'AT/.G': (-0.5, -0.6), 'CT/.G': (6.9, 22.6), 'GT/.G': (0.6, 2.6),
+    'TT/.G': (0.6, 2.6),
+    'AA/.T': (1.6, 6.1), 'CA/.T': (2.2, 8.1), 'GA/.T': (0.7, 3.5),
+    'TA/.T': (3.1, 10.6),
+    'AG/.T': (1.6, 6.1), 'CG/.T': (2.2, 8.1), 'GG/.T': (0.7, 3.5),
+    'TG/.T': (3.1, 10.6)}
 
 
 def make_table(oldtable=None, values=None):
@@ -395,13 +411,13 @@ def make_table(oldtable=None, values=None):
 
 
 def _check(seq, method):
-    """Return a sequence which fullfils the requirements of the given method (PRIVATE).
+    """Return a sequence which fullfils the requirements of the given method.
 
     All Tm methods in this package require the sequence in uppercase format.
     Most methods make use of the length of the sequence (directly or
     indirectly), which can only be expressed as len(seq) if the sequence does
     not contain whitespaces and other non-base characters. RNA sequences are
-    backtranscribed to DNA. This method is private.
+    backtranscribed to DNA. This method is PRIVATE.
 
     Arguments:
     seq: The sequence as given by the user (passed as string).
@@ -436,8 +452,8 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
      - methods 6+7: Tm(new) = 1/(1/Tm(old) + corr)
 
     Parameters:
-     - Na, K, Tris, Mg, dNTPS: Millimolar concentration of respective ion. To have
-       a simple 'salt correction', just pass Na. If any of K, Tris, Mg and
+     - Na, K, Tris, Mg, dNTPS: Millimolar concentration of respective ion. To
+       have a simple 'salt correction', just pass Na. If any of K, Tris, Mg and
        dNTPS is non-zero, a 'sodium-equivalent' concentration is calculated
        according to von Ahsen et al. (2001, Clin Chem 47: 1956-1961):
        [Na_eq] = [Na+] + [K+] + [Tris]/2 + 120*([Mg2+] - [dNTPs])^0.5
@@ -486,11 +502,10 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
     Mon = Na + K + Tris / 2.0  # Note: all these values are millimolar
     mg = Mg * 1e-3             # Lowercase ions (mg, mon, dntps) are molar
     # Na equivalent according to von Ahsen et al. (2001):
-    if sum((K, Mg, Tris, dNTPs)) > 0 and not method == 7:
-        if dNTPs < Mg:
-            # dNTPs bind Mg2+ strongly. If [dNTPs] is larger or equal than
-            # [Mg2+], free Mg2+ is considered not to be relevant.
-            Mon += 120 * math.sqrt(Mg - dNTPs)
+    if sum((K, Mg, Tris, dNTPs)) > 0 and not method == 7 and dNTPs < Mg:
+        # dNTPs bind Mg2+ strongly. If [dNTPs] is larger or equal than
+        # [Mg2+], free Mg2+ is considered not to be relevant.
+        Mon += 120 * math.sqrt(Mg - dNTPs)
     mon = Mon * 1e-3
     # Note: math.log = ln(), math.log10 = log()
     if method in range(1, 7) and not mon:
@@ -507,7 +522,7 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
     if method == 5:
         corr = 0.368 * (len(seq) - 1) * math.log(mon)
     if method == 6:
-        corr = (4.29 * SeqUtils.GC(seq) / 100 - 3.95) * 1e-5 * math.log(mon) + \
+        corr = (4.29 * SeqUtils.GC(seq) / 100 - 3.95) * 1e-5 * math.log(mon) +\
             9.40e-6 * math.log(mon) ** 2
     if method == 7:
         a, b, c, d = 3.92, -0.911, 6.26, 1.42
@@ -517,7 +532,8 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
             ka = 3e4  # Dissociation constant for Mg:dNTP
             # Free Mg2+ calculation:
             mg = (-(ka * dntps - ka * mg + 1.0) +
-                  math.sqrt((ka * dntps - ka * mg + 1.0) ** 2 + 4.0 * ka * mg)) / (2.0 * ka)
+                  math.sqrt((ka * dntps - ka * mg + 1.0) ** 2 +
+                            4.0 * ka * mg)) / (2.0 * ka)
         if Mon > 0:
             R = math.sqrt(mg) / mon
             if R < 0.22:
@@ -538,20 +554,20 @@ def salt_correction(Na=0, K=0, Tris=0, Mg=0, dNTPs=0, method=1, seq=None):
     return corr
 
 
-def chem_correction(Tm, DMSO=0, fmd=0, DMSOfactor=0.75, fmdfactor=0.65,
-                    fmdmethod=1, GC=None):
+def chem_correction(melting_temp, DMSO=0, fmd=0, DMSOfactor=0.75,
+                    fmdfactor=0.65, fmdmethod=1, GC=None):
     """Correct a given Tm for DMSO and formamide.
 
     Please note that these corrections are +/- rough approximations.
 
     Arguments:
-     - Tm: Melting temperature.
+     - melting_temp: Melting temperature.
      - DMSO: Percent DMSO.
-     - fmd: Formamide concentration in %(fmdmethod=1) or in molar (fmdmethod=2).
+     - fmd: Formamide concentration in %(fmdmethod=1) or molar (fmdmethod=2).
      - DMSOfactor: How much should Tm decreases per percent DMSO. Default=0.65
        (von Ahsen et al. 2001). Other published values are 0.5, 0.6 and 0.675.
-     - fmdfactor: How much should Tm decrease per percent formamide. Default=0.65.
-       Several papers report factors between 0.6 and 0.72.
+     - fmdfactor: How much should Tm decrease per percent formamide.
+       Default=0.65. Several papers report factors between 0.6 and 0.72.
      - fmdmethod:
 
          1. Tm = Tm - factor(%formamide) (Default)
@@ -577,19 +593,21 @@ def chem_correction(Tm, DMSO=0, fmd=0, DMSOfactor=0.75, fmdfactor=0.65,
 
     """
     if DMSO:
-        Tm -= DMSOfactor * DMSO
+        melting_temp -= DMSOfactor * DMSO
     if fmd:
         # McConaughy et al. (1969), Biochemistry 8: 3289-3295
         if fmdmethod == 1:
-            Tm -= fmdfactor * fmd                 # Note: fmd is percent
+            # Note: Here fmd is given in percent
+            melting_temp -= fmdfactor * fmd
         # Blake & Delcourt (1996), Nucl Acids Res 11: 2095-2103
         if fmdmethod == 2:
             if GC is None or GC < 0:
                 raise ValueError('\'GC\' is missing or negative')
-            Tm += (0.453 * (GC / 100.0) - 2.88) * fmd   # Note: fmd is molar
+            # Note: Here fmd is given in molar
+            melting_temp += (0.453 * (GC / 100.0) - 2.88) * fmd
         if fmdmethod not in (1, 2):
             raise ValueError('\'fmdmethod\' must be 1 or 2')
-    return Tm
+    return melting_temp
 
 
 def Tm_Wallace(seq, check=True, strict=True):
@@ -618,7 +636,7 @@ def Tm_Wallace(seq, check=True, strict=True):
     if check:
         seq = _check(seq, 'Tm_Wallace')
 
-    Tm = 2 * (sum(map(seq.count, ('A', 'T', 'W')))) + \
+    melting_temp = 2 * (sum(map(seq.count, ('A', 'T', 'W')))) + \
         4 * (sum(map(seq.count, ('C', 'G', 'S'))))
 
     # Intermediate values for ambiguous positions:
@@ -629,8 +647,8 @@ def Tm_Wallace(seq, check=True, strict=True):
         raise ValueError('ambiguous bases B, D, H, K, M, N, R, V, Y not ' +
                          'allowed when strict=True')
     else:
-        Tm += tmp
-    return Tm
+        melting_temp += tmp
+    return melting_temp
 
 
 def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
@@ -655,17 +673,20 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
            (Marmur & Doty 1962, J Mol Biol 5: 109-118; Chester & Marshak 1993),
            Anal Biochem 209: 284-290)
         2. Tm = 81.5 + 0.41(%GC) - 675/N - %mismatch
-           'QuikChange' formula. Recommended (by the manufacturer) for the design
-           of primers for QuikChange mutagenesis.
+           'QuikChange' formula. Recommended (by the manufacturer) for the
+           design of primers for QuikChange mutagenesis.
         3. Tm = 81.5 + 0.41(%GC) - 675/N + 16.6 x log[Na+]
-           (Marmur & Doty 1962, J Mol Biol 5: 109-118; Schildkraut & Lifson 1965,
-           Biopolymers 3: 195-208)
-        4. Tm = 81.5 + 0.41(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - %mismatch
+           (Marmur & Doty 1962, J Mol Biol 5: 109-118; Schildkraut & Lifson
+           1965, Biopolymers 3: 195-208)
+        4. Tm = 81.5 + 0.41(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x
+           [Na+])) - %mismatch
            (Wetmur 1991, Crit Rev Biochem Mol Biol 126: 227-259). This is the
            standard formula in approximative mode of MELTING 4.3.
-        5. Tm = 78 + 0.7(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - %mismatch
+        5. Tm = 78 + 0.7(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+]))
+           - %mismatch
            (Wetmur 1991, Crit Rev Biochem Mol Biol 126: 227-259). For RNA.
-        6. Tm = 67 + 0.8(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+])) - %mismatch
+        6. Tm = 67 + 0.8(%GC) - 500/N + 16.6 x log([Na+]/(1.0 + 0.7 x [Na+]))
+           - %mismatch
            (Wetmur 1991, Crit Rev Biochem Mol Biol 126: 227-259). For RNA/DNA
            hybrids.
         7. Tm = 81.5 + 0.41(%GC) - 600/N + 16.6 x log[Na+]
@@ -676,12 +697,12 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
 
      - userset: Tuple of four values for A, B, C, and D. Usersets override
        valuesets.
-     - Na, K, Tris, Mg, dNTPs: Concentration of the respective ions [mM]. If any
-       of K, Tris, Mg and dNTPS is non-zero, a 'sodium-equivalent'
+     - Na, K, Tris, Mg, dNTPs: Concentration of the respective ions [mM]. If
+       any of K, Tris, Mg and dNTPS is non-zero, a 'sodium-equivalent'
        concentration is calculated and used for salt correction (von Ahsen et
        al., 2001).
-     - saltcorr: Type of salt correction (see method salt_correction). Default=5.
-       0 or None means no salt correction.
+     - saltcorr: Type of salt correction (see method salt_correction).
+       Default=5. 0 or None means no salt correction.
      - mismatch: If 'True' (default) every 'X' in the sequence is counted as
        mismatch.
 
@@ -692,7 +713,7 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
     seq = str(seq)
     if check:
         seq = _check(seq, 'Tm_GC')
-    pGC = SeqUtils.GC(seq)
+    percent_gc = SeqUtils.GC(seq)
     # Ambiguous bases: add 0.5, 0.67 or 0.33% depending on G+C probability:
     tmp = sum(map(seq.count, ('K', 'M', 'N', 'R', 'Y'))) * 50.0 / len(seq) + \
         sum(map(seq.count, ('B', 'V'))) * 66.67 / len(seq) + \
@@ -701,7 +722,7 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
         raise ValueError('ambiguous bases B, D, H, K, M, N, R, V, Y not ' +
                          'allowed when strict=True')
     else:
-        pGC += tmp
+        percent_gc += tmp
     if userset:
         A, B, C, D = userset
     else:
@@ -732,13 +753,26 @@ def Tm_GC(seq, check=True, strict=True, valueset=7, userset=None, Na=50, K=0,
     if valueset > 8:
         raise ValueError('allowed values for parameter \'valueset\' are 0-8.')
 
-    Tm = A + B * pGC - C / (len(seq) * 1.0)
+    melting_temp = A + B * percent_gc - C / (len(seq) * 1.0)
     if saltcorr:
-        Tm += salt_correction(Na=Na, K=K, Tris=Tris, Mg=Mg, dNTPs=dNTPs,
-                              seq=seq, method=saltcorr)
+        melting_temp += salt_correction(Na=Na, K=K, Tris=Tris, Mg=Mg,
+                                        dNTPs=dNTPs, seq=seq, method=saltcorr)
     if mismatch:
-        Tm -= D * (seq.count('X') * 100.0 / len(seq))
-    return Tm
+        melting_temp -= D * (seq.count('X') * 100.0 / len(seq))
+    return melting_temp
+
+
+def _key_error(neighbors, strict):
+    """Throw an error or a warning if there is no data for the neighbors."""
+    # We haven't found the key in the tables
+    if strict:
+        raise ValueError('no thermodynamic data for neighbors \'' + neighbors +
+                         '\' available')
+    else:
+        warnings.warn('no themodynamic data for neighbors \'' + neighbors +
+                      '\' available. Calculation will be wrong',
+                      BiopythonWarning)
+        return
 
 
 def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
@@ -748,14 +782,14 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
     """Return the Tm using nearest neighbor thermodynamics.
 
     Arguments:
-     - seq: The primer/probe sequence as string or Biopython sequence object. For
-       RNA/DNA hybridizations seq must be the RNA sequence.
+     - seq: The primer/probe sequence as string or Biopython sequence object.
+       For RNA/DNA hybridizations seq must be the RNA sequence.
      - c_seq: Complementary sequence. The sequence of the template/target in
        3'->5' direction. c_seq is necessary for mismatch correction and
        dangling-ends correction. Both corrections will automatically be
        applied if mismatches or dangling ends are present. Default=None.
-     - shift: Shift of the primer/probe sequence on the template/target sequence,
-       e.g.::
+     - shift: Shift of the primer/probe sequence on the template/target
+       sequence, e.g.::
 
                            shift=0       shift=1        shift= -1
         Primer (seq):      5' ATGC...    5'  ATGC...    5' ATGC...
@@ -783,8 +817,8 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
 
        Use the module's maketable method to make a new table or to update one
        one of the implemented tables.
-     - tmm_table: Thermodynamic values for terminal mismatches. Default: DNA_TMM1
-       (SantaLucia & Peyret, 2001)
+     - tmm_table: Thermodynamic values for terminal mismatches.
+       Default: DNA_TMM1 (SantaLucia & Peyret, 2001)
      - imm_table: Thermodynamic values for internal mismatches, may include
        insosine mismatches. Default: DNA_IMM1 (Allawi & SantaLucia, 1997-1998;
        Peyret et al., 1999; Watkins & SantaLucia, 2005)
@@ -793,18 +827,18 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
         - DNA_DE1: for DNA. Values from Bommarito et al. (2000). Default
         - RNA_DE1: for RNA. Values from Turner & Mathews (2010)
 
-     - dnac1: Concentration of the higher concentrated strand [nM]. Typically this
-       will be the primer (for PCR) or the probe. Default=25.
-     - dnac2: Concentration of the lower concentrated strand [nM]. In PCR this is
-       the template strand which concentration is typically very low and may
+     - dnac1: Concentration of the higher concentrated strand [nM]. Typically
+       this will be the primer (for PCR) or the probe. Default=25.
+     - dnac2: Concentration of the lower concentrated strand [nM]. In PCR this
+       is the template strand which concentration is typically very low and may
        be ignored (dnac2=0). In oligo/oligo hybridization experiments, dnac1
        equals dnac1. Default=25.
        MELTING and Primer3Plus use k = [Oligo(Total)]/4 by default. To mimic
        this behaviour, you have to divide [Oligo(Total)] by 2 and assign this
        concentration to dnac1 and dnac2. E.g., Total oligo concentration of
        50 nM in Primer3Plus means dnac1=25, dnac2=25.
-     - selfcomp: Is the sequence self-complementary? Default=False. If 'True' the
-       primer is thought binding to itself, thus dnac2 is not considered.
+     - selfcomp: Is the sequence self-complementary? Default=False. If 'True'
+       the primer is thought binding to itself, thus dnac2 is not considered.
      - Na, K, Tris, Mg, dNTPs: See method 'Tm_GC' for details. Defaults: Na=50,
        K=0, Tris=0, Mg=0, dNTPs=0.
      - saltcorr: See method 'Tm_GC'. Default=5. 0 means no salt correction.
@@ -819,57 +853,63 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
     if check:
         seq = _check(seq, 'Tm_NN')
         c_seq = _check(c_seq, 'Tm_NN')
-    tmpseq = seq
+    tmp_seq = seq
     tmp_cseq = c_seq
-    deltaH = 0
-    deltaS = 0
-    dH = 0  # Names for indexes
-    dS = 1  # 0 and 1
+    delta_h = 0
+    delta_s = 0
+    d_h = 0  # Names for indexes
+    d_s = 1  # 0 and 1
 
     # Dangling ends?
     if shift or len(seq) != len(c_seq):
         # Align both sequences using the shift parameter
         if shift > 0:
-            tmpseq = '.' * shift + seq
+            tmp_seq = '.' * shift + seq
         if shift < 0:
             tmp_cseq = '.' * abs(shift) + c_seq
-        if len(tmp_cseq) > len(tmpseq):
-            tmpseq += (len(tmp_cseq) - len(tmpseq)) * '.'
-        if len(tmp_cseq) < len(tmpseq):
-            tmp_cseq += (len(tmpseq) - len(tmp_cseq)) * '.'
+        if len(tmp_cseq) > len(tmp_seq):
+            tmp_seq += (len(tmp_cseq) - len(tmp_seq)) * '.'
+        if len(tmp_cseq) < len(tmp_seq):
+            tmp_cseq += (len(tmp_seq) - len(tmp_cseq)) * '.'
         # Remove 'over-dangling' ends
-        while tmpseq.startswith('..') or tmp_cseq.startswith('..'):
-            tmpseq = tmpseq[1:]
+        while tmp_seq.startswith('..') or tmp_cseq.startswith('..'):
+            tmp_seq = tmp_seq[1:]
             tmp_cseq = tmp_cseq[1:]
-        while tmpseq.endswith('..') or tmp_cseq.endswith('..'):
-            tmpseq = tmpseq[:-1]
+        while tmp_seq.endswith('..') or tmp_cseq.endswith('..'):
+            tmp_seq = tmp_seq[:-1]
             tmp_cseq = tmp_cseq[:-1]
         # Now for the dangling ends
-        if tmpseq.startswith('.') or tmp_cseq.startswith('.'):
-            left_de = tmpseq[:2] + '/' + tmp_cseq[:2]
-            deltaH += de_table[left_de][dH]
-            deltaS += de_table[left_de][dS]
-            tmpseq = tmpseq[1:]
+        if tmp_seq.startswith('.') or tmp_cseq.startswith('.'):
+            left_de = tmp_seq[:2] + '/' + tmp_cseq[:2]
+            try:
+                delta_h += de_table[left_de][d_h]
+                delta_s += de_table[left_de][d_s]
+            except KeyError:
+                _key_error(left_de, strict)
+            tmp_seq = tmp_seq[1:]
             tmp_cseq = tmp_cseq[1:]
-        if tmpseq.endswith('.') or tmp_cseq.endswith('.'):
-            right_de = tmp_cseq[-2:][::-1] + '/' + tmpseq[-2:][::-1]
-            deltaH += de_table[right_de][dH]
-            deltaS += de_table[right_de][dS]
-            tmpseq = tmpseq[:-1]
+        if tmp_seq.endswith('.') or tmp_cseq.endswith('.'):
+            right_de = tmp_cseq[-2:][::-1] + '/' + tmp_seq[-2:][::-1]
+            try:
+                delta_h += de_table[right_de][d_h]
+                delta_s += de_table[right_de][d_s]
+            except KeyError:
+                _key_error(right_de, strict)
+            tmp_seq = tmp_seq[:-1]
             tmp_cseq = tmp_cseq[:-1]
 
     # Now for terminal mismatches
-    left_tmm = tmp_cseq[:2][::-1] + '/' + tmpseq[:2][::-1]
+    left_tmm = tmp_cseq[:2][::-1] + '/' + tmp_seq[:2][::-1]
     if left_tmm in tmm_table:
-        deltaH += tmm_table[left_tmm][dH]
-        deltaS += tmm_table[left_tmm][dS]
-        tmpseq = tmpseq[1:]
+        delta_h += tmm_table[left_tmm][d_h]
+        delta_s += tmm_table[left_tmm][d_s]
+        tmp_seq = tmp_seq[1:]
         tmp_cseq = tmp_cseq[1:]
-    right_tmm = tmpseq[-2:] + '/' + tmp_cseq[-2:]
+    right_tmm = tmp_seq[-2:] + '/' + tmp_cseq[-2:]
     if right_tmm in tmm_table:
-        deltaH += tmm_table[right_tmm][dH]
-        deltaS += tmm_table[right_tmm][dS]
-        tmpseq = tmpseq[:-1]
+        delta_h += tmm_table[right_tmm][d_h]
+        delta_s += tmm_table[right_tmm][d_s]
+        tmp_seq = tmp_seq[:-1]
         tmp_cseq = tmp_cseq[:-1]
 
     # Now everything 'unusual' at the ends is handled and removed and we can
@@ -877,84 +917,77 @@ def Tm_NN(seq, check=True, strict=True, c_seq=None, shift=0, nn_table=DNA_NN3,
     # One or several of the following initiation types may apply:
 
     # Type: General initiation value
-    deltaH += nn_table['init'][dH]
-    deltaS += nn_table['init'][dS]
+    delta_h += nn_table['init'][d_h]
+    delta_s += nn_table['init'][d_s]
 
     # Type: Duplex with no (allA/T) or at least one (oneG/C) GC pair
     if SeqUtils.GC(seq) == 0:
-        deltaH += nn_table['init_allA/T'][dH]
-        deltaS += nn_table['init_allA/T'][dS]
+        delta_h += nn_table['init_allA/T'][d_h]
+        delta_s += nn_table['init_allA/T'][d_s]
     else:
-        deltaH += nn_table['init_oneG/C'][dH]
-        deltaS += nn_table['init_oneG/C'][dS]
+        delta_h += nn_table['init_oneG/C'][d_h]
+        delta_s += nn_table['init_oneG/C'][d_s]
 
     # Type: Penalty if 5' end is T
     if seq.startswith('T'):
-        deltaH += nn_table['init_5T/A'][dH]
-        deltaS += nn_table['init_5T/A'][dS]
+        delta_h += nn_table['init_5T/A'][d_h]
+        delta_s += nn_table['init_5T/A'][d_s]
     if seq.endswith('A'):
-        deltaH += nn_table['init_5T/A'][dH]
-        deltaS += nn_table['init_5T/A'][dS]
+        delta_h += nn_table['init_5T/A'][d_h]
+        delta_s += nn_table['init_5T/A'][d_s]
 
     # Type: Different values for G/C or A/T terminal basepairs
     ends = seq[0] + seq[-1]
     AT = ends.count('A') + ends.count('T')
     GC = ends.count('G') + ends.count('C')
-    deltaH += nn_table['init_A/T'][dH] * AT
-    deltaS += nn_table['init_A/T'][dS] * AT
-    deltaH += nn_table['init_G/C'][dH] * GC
-    deltaS += nn_table['init_G/C'][dS] * GC
+    delta_h += nn_table['init_A/T'][d_h] * AT
+    delta_s += nn_table['init_A/T'][d_s] * AT
+    delta_h += nn_table['init_G/C'][d_h] * GC
+    delta_s += nn_table['init_G/C'][d_s] * GC
 
     # Finally, the 'zipping'
-    for basenumber in range(len(tmpseq) - 1):
-        neighbors = tmpseq[basenumber:basenumber + 2] + '/' + \
+    for basenumber in range(len(tmp_seq) - 1):
+        neighbors = tmp_seq[basenumber:basenumber + 2] + '/' + \
             tmp_cseq[basenumber:basenumber + 2]
         if neighbors in imm_table:
-            deltaH += imm_table[neighbors][dH]
-            deltaS += imm_table[neighbors][dS]
+            delta_h += imm_table[neighbors][d_h]
+            delta_s += imm_table[neighbors][d_s]
         elif neighbors[::-1] in imm_table:
-            deltaH += imm_table[neighbors[::-1]][dH]
-            deltaS += imm_table[neighbors[::-1]][dS]
+            delta_h += imm_table[neighbors[::-1]][d_h]
+            delta_s += imm_table[neighbors[::-1]][d_s]
         elif neighbors in nn_table:
-            deltaH += nn_table[neighbors][dH]
-            deltaS += nn_table[neighbors][dS]
+            delta_h += nn_table[neighbors][d_h]
+            delta_s += nn_table[neighbors][d_s]
         elif neighbors[::-1] in nn_table:
-            deltaH += nn_table[neighbors[::-1]][dH]
-            deltaS += nn_table[neighbors[::-1]][dS]
+            delta_h += nn_table[neighbors[::-1]][d_h]
+            delta_s += nn_table[neighbors[::-1]][d_s]
         else:
             # We haven't found the key...
-            if strict:
-                raise ValueError('no data for neighbors \'' + neighbors + '\'')
-            else:
-                warnings.warn('no data for neighbors \'' + neighbors +
-                              '\'. Calculation will be wrong',
-                              BiopythonWarning)
+            _key_error(neighbors, strict)
 
     k = (dnac1 - (dnac2 / 2.0)) * 1e-9
     if selfcomp:
         k = dnac1 * 1e-9
-        deltaH += nn_table['sym'][dH]
-        deltaS += nn_table['sym'][dS]
+        delta_h += nn_table['sym'][d_h]
+        delta_s += nn_table['sym'][d_s]
     R = 1.987  # universal gas constant in Cal/degrees C*Mol
     if saltcorr:
         corr = salt_correction(Na=Na, K=K, Tris=Tris, Mg=Mg, dNTPs=dNTPs,
                                method=saltcorr, seq=seq)
     if saltcorr == 5:
-        deltaS += corr
-    Tm = (1000 * deltaH) / (deltaS + (R * (math.log(k)))) - 273.15
+        delta_s += corr
+    melting_temp = (1000 * delta_h) / (delta_s + (R * (math.log(k)))) - 273.15
     if saltcorr in (1, 2, 3, 4):
-        Tm += corr
+        melting_temp += corr
     if saltcorr in (6, 7):
         # Tm = 1/(1/Tm + corr)
-        Tm = (1 / (1 / (Tm + 273.15) + corr) - 273.15)
-
-    return Tm
+        melting_temp = (1 / (1 / (melting_temp + 273.15) + corr) - 273.15)
 
-__docformat__ = "restructuredtext en"
+    return melting_temp
 
 
 def Tm_staluc(s, dnac=50, saltc=50, rna=0):
-    """Returns DNA/DNA tm using nearest neighbor thermodynamics (OBSOLETE).
+    """Returns DNA/DNA Tm using nearest neighbor thermodynamics (OBSOLETE).
 
     This method may be depreceated in the future. Use Tm_NN instead. Tm_NN
     with default values gives the same result as Tm_staluc.
@@ -996,7 +1029,7 @@ def Tm_staluc(s, dnac=50, saltc=50, rna=0):
         return Tm_NN(s, dnac1=dnac / 2.0, dnac2=dnac / 2.0, Na=saltc,
                      nn_table=RNA_NN2)
     else:
-        raise ValueError("rna=%r not supported" % rna)
+        raise ValueError("rna={0} not supported".format(rna))
 
 
 def _test():
diff --git a/Bio/SeqUtils/ProtParam.py b/Bio/SeqUtils/ProtParam.py
index 5d91f40..be36ec5 100644
--- a/Bio/SeqUtils/ProtParam.py
+++ b/Bio/SeqUtils/ProtParam.py
@@ -28,8 +28,6 @@ from Bio.Alphabet import IUPAC
 from Bio.Data import IUPACData
 from Bio.SeqUtils import molecular_weight
 
-__docformat__ = "restructuredtext en"
-
 
 class ProteinAnalysis(object):
     """Class containing methods for protein analysis.
diff --git a/Bio/SeqUtils/ProtParamData.py b/Bio/SeqUtils/ProtParamData.py
index e018a47..51071c4 100644
--- a/Bio/SeqUtils/ProtParamData.py
+++ b/Bio/SeqUtils/ProtParamData.py
@@ -1,7 +1,5 @@
 """This module contains indices to be used with ProtParam."""
 
-__docformat__ = "restructuredtext en"
-
 
 # Kyte & Doolittle index of hydrophobicity
 kd = {'A': 1.8, 'R':-4.5, 'N':-3.5, 'D':-3.5, 'C': 2.5,
@@ -39,7 +37,7 @@ ja = {'A': 0.28, 'R':-1.14, 'N':-0.55, 'D':-0.52, 'C': 0.97,
       'S':-0.19, 'T':-0.32, 'W': 0.29, 'Y':-0.15, 'V': 0.60}
 
 
-# A two dimentional dictionary for calculating the instability index.
+# A two dimensional dictionary for calculating the instability index.
 # Guruprasad K., Reddy B.V.B., Pandit M.W.    Protein Engineering 4:155-161(1990).
 # It is based on dipeptide values therefore the vale for the dipeptide DG is DIWV['D']['G'].
 DIWV = {'A': {'A': 1.0, 'C': 44.94, 'E': 1.0, 'D': -7.49,
diff --git a/Bio/SeqUtils/__init__.py b/Bio/SeqUtils/__init__.py
index 61ffe38..59b25f5 100644
--- a/Bio/SeqUtils/__init__.py
+++ b/Bio/SeqUtils/__init__.py
@@ -3,7 +3,7 @@
 # thomas at cbs.dtu.dk, Cecilia.Alsmark at ebc.uu.se
 # Copyright 2001 by Thomas Sicheritz-Ponten and Cecilia Alsmark.
 # Revisions copyright 2014 by Markus Piotrowski.
-# Revisions copyright 2014 by Peter Cock.
+# Revisions copyright 2014-2016 by Peter Cock.
 # All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -21,7 +21,6 @@ from Bio import Alphabet
 from Bio.Alphabet import IUPAC
 from Bio.Data import IUPACData
 
-__docformat__ = "restructuredtext en"
 
 ######################################
 # DNA
@@ -81,7 +80,7 @@ def GC123(seq):
         try:
             n = d['G'][i] + d['C'][i] + d['T'][i] + d['A'][i]
             gc[i] = (d['G'][i] + d['C'][i]) * 100.0 / n
-        except:
+        except Exception:  # TODO - ValueError?
             gc[i] = 0
 
         gcall = gcall + d['G'][i] + d['C'][i]
@@ -209,18 +208,19 @@ def nt_search(seq, subseq):
 # {{{
 
 
-def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
+def seq3(seq, custom_map=None, undef_code='Xaa'):
     """Turn a one letter code protein sequence into one with three letter codes.
 
-    The single input argument 'seq' should be a protein sequence using single
-    letter codes, either as a python string or as a Seq or MutableSeq object.
+    The single required input argument 'seq' should be a protein sequence using
+    single letter codes, either as a python string or as a Seq or MutableSeq
+    object.
 
     This function returns the amino acid sequence as a string using the three
     letter amino acid codes. Output follows the IUPAC standard (including
     ambiguous characters B for "Asx", J for "Xle" and X for "Xaa", and also U
     for "Sel" and O for "Pyl") plus "Ter" for a terminator given as an asterisk.
     Any unknown character (including possible gap characters), is changed into
-    'Xaa'.
+    'Xaa' by default.
 
     e.g.
 
@@ -229,7 +229,7 @@ def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
     'MetAlaIleValMetGlyArgTrpLysGlyAlaArgTer'
 
     You can set a custom translation of the codon termination code using the
-    "custom_map" argument, e.g.
+    dictionary "custom_map" argument (which defaults to {'*': 'Ter'}), e.g.
 
     >>> seq3("MAIVMGRWKGAR*", custom_map={"*": "***"})
     'MetAlaIleValMetGlyArgTrpLysGlyAlaArg***'
@@ -247,6 +247,8 @@ def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
 
     This function was inspired by BioPerl's seq3.
     """
+    if custom_map is None:
+        custom_map = {'*': 'Ter'}
     # not doing .update() on IUPACData dict with custom_map dict
     # to preserve its initial state (may be imported in other modules)
     threecode = dict(list(IUPACData.protein_letters_1to3_extended.items()) +
@@ -256,18 +258,19 @@ def seq3(seq, custom_map={'*': 'Ter'}, undef_code='Xaa'):
     return ''.join(threecode.get(aa, undef_code) for aa in seq)
 
 
-def seq1(seq, custom_map={'Ter': '*'}, undef_code='X'):
+def seq1(seq, custom_map=None, undef_code='X'):
     """Turns a three-letter code protein sequence into one with single letter codes.
 
-    The single input argument 'seq' should be a protein sequence using three-
-    letter codes, either as a python string or as a Seq or MutableSeq object.
+    The single required input argument 'seq' should be a protein sequence
+    using three-letter codes, either as a python string or as a Seq or
+    MutableSeq object.
 
     This function returns the amino acid sequence as a string using the one
     letter amino acid codes. Output follows the IUPAC standard (including
     ambiguous characters "B" for "Asx", "J" for "Xle", "X" for "Xaa", "U" for
     "Sel", and "O" for "Pyl") plus "*" for a terminator given the "Ter" code.
-    Any unknown character (including possible gap characters), is changed into
-    '-'.
+    Any unknown character (including possible gap characters), is changed
+    into '-' by default.
 
     e.g.
 
@@ -282,7 +285,7 @@ def seq1(seq, custom_map={'Ter': '*'}, undef_code='X'):
     'MAIVMGRWKGAR*'
 
     You can set a custom translation of the codon termination code using the
-    "custom_map" argument, e.g.
+    dictionary "custom_map" argument (defaulting to {'Ter': '*'}), e.g.
 
     >>> seq1("MetAlaIleValMetGlyArgTrpLysGlyAlaArg***", custom_map={"***": "*"})
     'MAIVMGRWKGAR*'
@@ -299,6 +302,8 @@ def seq1(seq, custom_map={'Ter': '*'}, undef_code='X'):
     'MAIVMGRWKGAXXR*'
 
     """
+    if custom_map is None:
+        custom_map = {'Ter': '*'}
     # reverse map of threecode
     # upper() on all keys to enable caps-insensitive input seq handling
     onecode = dict((k.upper(), v) for k, v in
@@ -376,7 +381,7 @@ def molecular_weight(seq, seq_type=None, double_stranded=False, circular=False,
 
     # Find the alphabet type
     tmp_type = ''
-    if isinstance(seq, Seq) or isinstance(seq, MutableSeq):
+    if isinstance(seq, (Seq, MutableSeq)):
         base_alphabet = Alphabet._get_base_alphabet(seq.alphabet)
         if isinstance(base_alphabet, Alphabet.DNAAlphabet):
             tmp_type = 'DNA'
@@ -516,56 +521,6 @@ def six_frame_translations(seq, genetic_code=1):
 
 # }}}
 
-######################################
-# FASTA file utilities
-######################
-# {{{
-
-
-def quick_FASTA_reader(file):
-    """Simple FASTA reader, returning a list of string tuples (DEPRECATED).
-
-    The single argument 'file' should be the filename of a FASTA format file.
-    This function will open and read in the entire file, constructing a list
-    of all the records, each held as a tuple of strings (the sequence name or
-    title, and its sequence).
-
-    >>> seqs = quick_FASTA_reader("Fasta/dups.fasta")
-    >>> for title, sequence in seqs:
-    ...     print("%s %s" % (title, sequence))
-    alpha ACGTA
-    beta CGTC
-    gamma CCGCC
-    alpha (again - this is a duplicate entry to test the indexing code) ACGTA
-    delta CGCGC
-
-    This function was is fast, but because it returns the data as a single in
-    memory list, is unsuitable for large files where an iterator approach is
-    preferable.
-
-    You are generally encouraged to use Bio.SeqIO.parse(handle, "fasta") which
-    allows you to iterate over the records one by one (avoiding having all the
-    records in memory at once).  Using Bio.SeqIO also makes it easy to switch
-    between different input file formats.  However, please note that rather
-    than simple strings, Bio.SeqIO uses SeqRecord objects for each record.
-
-    If you want to use simple strings, use the function SimpleFastaParser
-    added to Bio.SeqIO.FastaIO in Biopython 1.61 instead.
-    """
-    import warnings
-    from Bio import BiopythonDeprecationWarning
-    warnings.warn("The quick_FASTA_reader has been deprecated and will be "
-                  "removed in a future release of Biopython. Please try "
-                  "function SimpleFastaParser from Bio.SeqIO.FastaIO "
-                  "instead.", BiopythonDeprecationWarning)
-    from Bio.SeqIO.FastaIO import SimpleFastaParser
-    with open(file) as handle:
-        entries = list(SimpleFastaParser(handle))
-    return entries
-
-
-# }}}
-
 
 def _test():
     """Run the module's doctests (PRIVATE)."""
diff --git a/Bio/SeqUtils/lcc.py b/Bio/SeqUtils/lcc.py
index 4c51c22..c47e404 100644
--- a/Bio/SeqUtils/lcc.py
+++ b/Bio/SeqUtils/lcc.py
@@ -7,8 +7,6 @@
 
 import math
 
-__docformat__ = "restructuredtext en"
-
 
 def lcc_mult(seq, wsize):
     """Local Composition Complexity (LCC) values over sliding window.
@@ -146,21 +144,21 @@ def lcc_simp(seq):
         term_a = 0
         # Check to avoid calculating the log of 0.
     else:
-        term_a = ((upper.count('A')) / float(wsize)) * ((math.log((upper.count('A'))
-                                                                  / float(wsize))) / l2)
+        term_a = ((upper.count('A')) / float(wsize)) * \
+                 ((math.log((upper.count('A')) / float(wsize))) / l2)
     if 'C' not in seq:
         term_c = 0
     else:
-        term_c = ((upper.count('C')) / float(wsize)) * ((math.log((upper.count('C'))
-                                                                  / float(wsize))) / l2)
+        term_c = ((upper.count('C')) / float(wsize)) * \
+                 ((math.log((upper.count('C')) / float(wsize))) / l2)
     if 'T' not in seq:
         term_t = 0
     else:
-        term_t = ((upper.count('T')) / float(wsize)) * ((math.log((upper.count('T'))
-                                                                  / float(wsize))) / l2)
+        term_t = ((upper.count('T')) / float(wsize)) * \
+                 ((math.log((upper.count('T')) / float(wsize))) / l2)
     if 'G' not in seq:
         term_g = 0
     else:
-        term_g = ((upper.count('G')) / float(wsize)) * ((math.log((upper.count('G'))
-                                                                  / float(wsize))) / l2)
+        term_g = ((upper.count('G')) / float(wsize)) * \
+                 ((math.log((upper.count('G')) / float(wsize))) / l2)
     return -(term_a + term_c + term_t + term_g)
diff --git a/Bio/Sequencing/Ace.py b/Bio/Sequencing/Ace.py
index fbbf32e..60a72ef 100644
--- a/Bio/Sequencing/Ace.py
+++ b/Bio/Sequencing/Ace.py
@@ -58,8 +58,6 @@ are needed, the 'read' function rather than the 'parse' function might be more a
 from __future__ import print_function
 from Bio._py3k import zip
 
-__docformat__ = "restructuredtext en"
-
 
 class rd(object):
     """RD (reads), store a read with its name, sequence etc.
diff --git a/Bio/Sequencing/Applications/_Novoalign.py b/Bio/Sequencing/Applications/_Novoalign.py
index f22c44c..5a17fe1 100644
--- a/Bio/Sequencing/Applications/_Novoalign.py
+++ b/Bio/Sequencing/Applications/_Novoalign.py
@@ -176,12 +176,6 @@ class NovoalignCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running Novoalign doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Sequencing/Applications/__init__.py b/Bio/Sequencing/Applications/__init__.py
index e458cf8..754c91f 100644
--- a/Bio/Sequencing/Applications/__init__.py
+++ b/Bio/Sequencing/Applications/__init__.py
@@ -16,6 +16,7 @@ from ._samtools import SamtoolsMpileupCommandline, SamtoolsPhaseCommandline
 from ._samtools import SamtoolsReheaderCommandline, SamtoolsRmdupCommandline
 from ._samtools import SamtoolsSortCommandline, SamtoolsTargetcutCommandline
 
+
 # Make this explicit, then they show up in the API docs
 __all__ = ["BwaIndexCommandline",
            "BwaAlignCommandline",
diff --git a/Bio/Sequencing/Applications/_bwa.py b/Bio/Sequencing/Applications/_bwa.py
index a3eaa78..aa08659 100644
--- a/Bio/Sequencing/Applications/_bwa.py
+++ b/Bio/Sequencing/Applications/_bwa.py
@@ -9,8 +9,6 @@
 from __future__ import print_function
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"
-
 from Bio.Application import _Option, _Argument, _Switch, AbstractCommandline
 from Bio.Application import _StaticArgument
 
@@ -379,13 +377,6 @@ class BwaBwaswCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-        """Run the module's doctests (PRIVATE)."""
-        print("Running modules doctests...")
-        import doctest
-        doctest.testmod()
-        print("Done")
-
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Sequencing/Applications/_samtools.py b/Bio/Sequencing/Applications/_samtools.py
index 6ac1591..910f88a 100644
--- a/Bio/Sequencing/Applications/_samtools.py
+++ b/Bio/Sequencing/Applications/_samtools.py
@@ -4,7 +4,7 @@
 # are missing from this wrapper
 
 from __future__ import print_function
-__docformat__ = "restructuredtext en"
+
 from Bio.Application import _Option, _Argument, _Switch
 from Bio.Application import AbstractCommandline, _ArgumentList
 from Bio.Application import _StaticArgument
@@ -731,12 +731,6 @@ class SamtoolsPhaseCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-        """Run the module's doctests (PRIVATE)."""
-        print("Running modules doctests...")
-        import doctest
-        doctest.testmod()
-        print("Done")
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/Sequencing/Phd.py b/Bio/Sequencing/Phd.py
index 8527825..9ae91a9 100644
--- a/Bio/Sequencing/Phd.py
+++ b/Bio/Sequencing/Phd.py
@@ -19,7 +19,6 @@ internally.  This will give SeqRecord objects for each contig sequence.
 from Bio import Seq
 from Bio.Alphabet import generic_dna
 
-__docformat__ = "restructuredtext en"
 
 CKEYWORDS = ['CHROMAT_FILE', 'ABI_THUMBPRINT', 'PHRED_VERSION', 'CALL_METHOD',
         'QUALITY_LEVELS', 'TIME', 'TRACE_ARRAY_MIN_INDEX', 'TRACE_ARRAY_MAX_INDEX',
diff --git a/Bio/Statistics/lowess.py b/Bio/Statistics/lowess.py
index 569ef1e..18d513a 100644
--- a/Bio/Statistics/lowess.py
+++ b/Bio/Statistics/lowess.py
@@ -25,6 +25,7 @@ from Bio._py3k import range
 
 import numpy
 
+
 try:
     from Bio.Cluster import median
     # The function median in Bio.Cluster is faster than the function median
diff --git a/Bio/SubsMat/FreqTable.py b/Bio/SubsMat/FreqTable.py
index f2eefb8..077cda5 100644
--- a/Bio/SubsMat/FreqTable.py
+++ b/Bio/SubsMat/FreqTable.py
@@ -1,56 +1,53 @@
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
-#
+# Copyright Iddo Friedberg idoerg at cc.huji.ac.il
+"""A class to handle frequency tables
+
+Methods to read a letter frequency or a letter count file:
+Example files for a DNA alphabet:
+
+A count file (whitespace separated):
+
+A  50
+C  37
+G  23
+T  58
+
+The same info as a frequency file:
+
+A 0.2976
+C 0.2202
+G 0.1369
+T 0.3452
+
+Functions:
+  read_count(f): read a count file from stream f. Then convert to
+  frequencies
+  read_freq(f): read a frequency data file from stream f. Of course, we then
+  don't have the counts, but it is usually the letter frquencies which are
+  interesting.
+
+Methods:
+  (all internal)
+Attributes:
+  alphabet: The IUPAC alphabet set (or any other) whose letters you are
+  using. Common sets are: IUPAC.protein (20-letter protein),
+  IUPAC.unambiguous_dna (4-letter DNA). See Bio/alphabet for more.
+  data: frequency dictionary.
+  count: count dictionary. Empty if no counts are provided.
+
+Example of use:
+  >>> from SubsMat import FreqTable
+  >>> ftab = FreqTable.FreqTable(my_frequency_dictionary,FreqTable.FREQ)
+  >>> ftab = FreqTable.FreqTable(my_count_dictionary,FreqTable.COUNT)
+  >>> ftab = FreqTable.read_count(open('myDNACountFile'))
+
+"""
 
 from Bio import Alphabet
 COUNT = 1
 FREQ = 2
-##################################################################
-# A class to handle frequency tables
-# Copyright Iddo Friedberg idoerg at cc.huji.ac.il
-# Biopython (http://biopython.org) license applies
-# Methods to read a letter frequency or a letter count file:
-# Example files for a DNA alphabet:
-#
-# A count file (whitespace separated):
-#
-# A  50
-# C  37
-# G  23
-# T  58
-#
-# The same info as a frequency file:
-#
-# A 0.2976
-# C 0.2202
-# G 0.1369
-# T 0.3452
-#
-# Functions:
-#   read_count(f): read a count file from stream f. Then convert to
-#   frequencies
-#   read_freq(f): read a frequency data file from stream f. Of course, we then
-#   don't have the counts, but it is usually the letter frquencies which are
-#   interesting.
-#
-# Methods:
-#   (all internal)
-# Attributes:
-#   alphabet: The IUPAC alphabet set (or any other) whose letters you are
-#   using. Common sets are: IUPAC.protein (20-letter protein),
-#   IUPAC.unambiguous_dna (4-letter DNA). See Bio/alphabet for more.
-#   data: frequency dictionary.
-#   count: count dictionary. Empty if no counts are provided.
-#
-# Example of use:
-#   >>> from SubsMat import FreqTable
-#   >>> ftab = FreqTable.FreqTable(my_frequency_dictionary,FreqTable.FREQ)
-#   >>> ftab = FreqTable.FreqTable(my_count_dictionary,FreqTable.COUNT)
-#   >>> ftab = FreqTable.read_count(open('myDNACountFile'))
-#
-#
-##################################################################
 
 
 class FreqTable(dict):
diff --git a/Bio/SubsMat/MatrixInfo.py b/Bio/SubsMat/MatrixInfo.py
index 9e94058..e0e7f4d 100644
--- a/Bio/SubsMat/MatrixInfo.py
+++ b/Bio/SubsMat/MatrixInfo.py
@@ -13,7 +13,6 @@ You can view an archive copy of this webpage from 1999 here;
 https://web.archive.org/web/19991014010917/http://www.embl-heidelberg.de/%7Evogt/matrices/mlist1.html
 """
 
-__docformat__ = "restructuredtext en"
 
 # a list of all available substitution matrices
 available_matrices = ['benner6', 'benner22', 'benner74', 'blosum100',
@@ -28,69 +27,69 @@ available_matrices = ['benner6', 'benner22', 'benner74', 'blosum100',
                       'pam90', 'rao', 'risler', 'structure'
                      ]
 
-#NOTE - The only reason we define temp functions rather than assigning
-#the dictionaries directly to variables is to work around a JVM
-#limitation on code size if running under Jython. The temp functions
-#have an underscore to indicate they are private, and are in any case
-#deleted after use (partly to ensure we don't use any of them twice).
+# NOTE - The only reason we define temp functions rather than assigning
+# the dictionaries directly to variables is to work around a JVM
+# limitation on code size if running under Jython. The temp functions
+# have an underscore to indicate they are private, and are in any case
+# deleted after use (partly to ensure we don't use any of them twice).
 
 
 # http://www.embl-heidelberg.de/~vogt/matrices/benner6.cmp
 def _temp():
     return {
-('W', 'F') : -1.6, ('L', 'R') : -3.2, ('I', 'I') : 4.4, ('Q', 'Q') : 5.3,
-('W', 'N') : -4.4, ('V', 'I') : 3.9, ('H', 'T') : -1.7, ('H', 'P') : -0.4,
-('W', 'V') : -4.8, ('Q', 'E') : 2.1, ('W', 'R') : 2.0, ('Q', 'A') : -1.7,
-('H', 'H') : 6.1, ('H', 'D') : 0.1, ('L', 'N') : -3.4, ('Y', 'M') : -3.6,
-('Y', 'I') : -3.3, ('Y', 'E') : -4.1, ('E', 'S') : -1.2, ('Y', 'A') : -4.0,
-('Y', 'Y') : 9.5, ('T', 'C') : -1.5, ('E', 'C') : -4.7, ('Y', 'Q') : -1.4,
-('E', 'G') : 0.5, ('V', 'A') : 0.7, ('C', 'C') : 12.1, ('M', 'R') : -3.0,
-('P', 'T') : 0.6, ('V', 'E') : -3.0, ('P', 'P') : 6.5, ('I', 'T') : 0.7,
-('K', 'S') : -1.2, ('R', 'G') : -0.1, ('I', 'P') : -2.0, ('R', 'C') : -0.4,
-('A', 'T') : 1.7, ('K', 'K') : 5.6, ('A', 'P') : 1.1, ('V', 'M') : 3.3,
-('I', 'D') : -4.2, ('K', 'C') : -2.8, ('K', 'G') : -1.4, ('R', 'S') : -0.9,
-('F', 'Q') : -4.4, ('F', 'A') : -3.2, ('V', 'V') : 4.0, ('M', 'N') : -2.5,
-('F', 'E') : -6.7, ('D', 'N') : 2.5, ('F', 'I') : 0.0, ('F', 'M') : -0.1,
-('M', 'S') : -1.3, ('S', 'S') : 2.1, ('L', 'Q') : -2.4, ('W', 'E') : -5.6,
-('W', 'A') : -4.3, ('W', 'M') : -4.4, ('H', 'S') : -0.9, ('W', 'I') : -5.0,
-('S', 'C') : 0.9, ('L', 'A') : -1.3, ('L', 'E') : -5.0, ('W', 'Q') : -2.6,
-('H', 'G') : -2.1, ('Q', 'N') : 0.1, ('H', 'C') : -1.2, ('L', 'M') : -2.9,
-('W', 'Y') : -0.3, ('Y', 'N') : -0.9, ('E', 'P') : -2.6, ('Y', 'F') : 5.6,
-('E', 'T') : -1.6, ('A', 'A') : 2.5, ('I', 'N') : -2.5, ('G', 'A') : 0.8,
-('Y', 'V') : -3.8, ('E', 'D') : 4.4, ('W', 'H') : -2.8, ('Y', 'R') : -2.6,
-('M', 'Q') : -3.1, ('P', 'S') : 1.4, ('R', 'H') : 1.8, ('A', 'C') : -1.7,
-('R', 'D') : -1.5, ('K', 'P') : -2.3, ('L', 'D') : -5.3, ('K', 'T') : -1.1,
-('V', 'N') : -2.4, ('M', 'A') : -0.2, ('K', 'H') : 0.9, ('V', 'R') : -3.7,
-('P', 'C') : -2.7, ('M', 'E') : -4.1, ('A', 'S') : 1.4, ('T', 'T') : 2.4,
-('R', 'T') : -1.3, ('I', 'G') : -3.4, ('R', 'P') : -1.3, ('K', 'D') : -0.2,
-('I', 'C') : -3.6, ('F', 'R') : -4.9, ('F', 'V') : -0.5, ('L', 'C') : -3.8,
-('F', 'F') : 8.3, ('D', 'A') : -0.6, ('F', 'N') : -3.5, ('W', 'D') : -6.3,
-('L', 'P') : -0.2, ('Q', 'S') : -1.4, ('N', 'C') : -1.6, ('N', 'G') : -0.1,
-('H', 'N') : 1.4, ('W', 'T') : -2.6, ('Q', 'G') : -1.6, ('W', 'P') : -4.8,
-('Q', 'C') : -3.2, ('N', 'S') : 1.2, ('L', 'H') : -2.2, ('L', 'L') : 4.8,
-('G', 'T') : -0.5, ('M', 'M') : 4.8, ('G', 'P') : -1.7, ('Y', 'K') : -4.0,
-('Y', 'G') : -4.9, ('Y', 'C') : 2.6, ('E', 'A') : -0.7, ('E', 'E') : 5.2,
-('Y', 'S') : -1.8, ('M', 'P') : -1.8, ('V', 'C') : -3.1, ('M', 'T') : 0.6,
-('V', 'G') : -2.3, ('R', 'E') : -0.4, ('V', 'K') : -3.8, ('K', 'Q') : 2.5,
-('R', 'A') : -1.7, ('I', 'R') : -3.8, ('N', 'A') : 0.0, ('V', 'S') : -0.9,
-('M', 'D') : -4.3, ('M', 'H') : -3.4, ('K', 'A') : -1.9, ('R', 'Q') : 2.5,
-('K', 'E') : 0.9, ('F', 'S') : -1.8, ('I', 'K') : -3.8, ('D', 'P') : -2.8,
-('D', 'T') : -1.2, ('I', 'M') : 4.0, ('F', 'C') : -0.1, ('W', 'L') : -3.0,
-('F', 'G') : -5.7, ('F', 'K') : -6.3, ('F', 'T') : -2.4, ('D', 'D') : 5.2,
-('Q', 'T') : -1.7, ('W', 'G') : -1.7, ('Q', 'P') : 0.1, ('W', 'C') : 1.6,
-('W', 'K') : -1.4, ('H', 'Q') : 3.2, ('Q', 'D') : 0.6, ('W', 'W') : 14.7,
-('V', 'L') : 1.9, ('L', 'G') : -4.6, ('W', 'S') : -2.9, ('L', 'K') : -4.1,
-('N', 'P') : -1.1, ('H', 'E') : -0.2, ('N', 'T') : 0.5, ('H', 'A') : -2.1,
-('Y', 'L') : -1.6, ('Y', 'H') : 4.4, ('G', 'S') : 0.8, ('Y', 'D') : -2.3,
-('V', 'Q') : -3.5, ('L', 'T') : -0.4, ('G', 'G') : 5.8, ('G', 'C') : -1.3,
-('E', 'N') : 1.1, ('Y', 'T') : -3.4, ('Y', 'P') : -3.8, ('R', 'N') : -0.1,
-('V', 'D') : -3.3, ('K', 'R') : 4.3, ('V', 'H') : -3.8, ('I', 'Q') : -3.8,
-('V', 'P') : -1.6, ('M', 'C') : -3.7, ('K', 'N') : 1.0, ('V', 'T') : 0.6,
-('M', 'G') : -3.7, ('T', 'S') : 1.5, ('I', 'E') : -4.1, ('M', 'K') : -2.9,
-('I', 'A') : 0.1, ('N', 'N') : 3.6, ('R', 'R') : 5.1, ('F', 'P') : -3.2,
-('L', 'I') : 2.4, ('I', 'S') : -1.2, ('D', 'S') : -0.4, ('L', 'S') : -1.5,
-('I', 'H') : -3.7, ('F', 'D') : -5.7, ('D', 'C') : -3.7, ('F', 'H') : 0.1,
-('D', 'G') : 0.8, ('F', 'L') : 2.4
+('W', 'F'): -1.6, ('L', 'R'): -3.2, ('I', 'I'): 4.4, ('Q', 'Q'): 5.3,
+('W', 'N'): -4.4, ('V', 'I'): 3.9, ('H', 'T'): -1.7, ('H', 'P'): -0.4,
+('W', 'V'): -4.8, ('Q', 'E'): 2.1, ('W', 'R'): 2.0, ('Q', 'A'): -1.7,
+('H', 'H'): 6.1, ('H', 'D'): 0.1, ('L', 'N'): -3.4, ('Y', 'M'): -3.6,
+('Y', 'I'): -3.3, ('Y', 'E'): -4.1, ('E', 'S'): -1.2, ('Y', 'A'): -4.0,
+('Y', 'Y'): 9.5, ('T', 'C'): -1.5, ('E', 'C'): -4.7, ('Y', 'Q'): -1.4,
+('E', 'G'): 0.5, ('V', 'A'): 0.7, ('C', 'C'): 12.1, ('M', 'R'): -3.0,
+('P', 'T'): 0.6, ('V', 'E'): -3.0, ('P', 'P'): 6.5, ('I', 'T'): 0.7,
+('K', 'S'): -1.2, ('R', 'G'): -0.1, ('I', 'P'): -2.0, ('R', 'C'): -0.4,
+('A', 'T'): 1.7, ('K', 'K'): 5.6, ('A', 'P'): 1.1, ('V', 'M'): 3.3,
+('I', 'D'): -4.2, ('K', 'C'): -2.8, ('K', 'G'): -1.4, ('R', 'S'): -0.9,
+('F', 'Q'): -4.4, ('F', 'A'): -3.2, ('V', 'V'): 4.0, ('M', 'N'): -2.5,
+('F', 'E'): -6.7, ('D', 'N'): 2.5, ('F', 'I'): 0.0, ('F', 'M'): -0.1,
+('M', 'S'): -1.3, ('S', 'S'): 2.1, ('L', 'Q'): -2.4, ('W', 'E'): -5.6,
+('W', 'A'): -4.3, ('W', 'M'): -4.4, ('H', 'S'): -0.9, ('W', 'I'): -5.0,
+('S', 'C'): 0.9, ('L', 'A'): -1.3, ('L', 'E'): -5.0, ('W', 'Q'): -2.6,
+('H', 'G'): -2.1, ('Q', 'N'): 0.1, ('H', 'C'): -1.2, ('L', 'M'): -2.9,
+('W', 'Y'): -0.3, ('Y', 'N'): -0.9, ('E', 'P'): -2.6, ('Y', 'F'): 5.6,
+('E', 'T'): -1.6, ('A', 'A'): 2.5, ('I', 'N'): -2.5, ('G', 'A'): 0.8,
+('Y', 'V'): -3.8, ('E', 'D'): 4.4, ('W', 'H'): -2.8, ('Y', 'R'): -2.6,
+('M', 'Q'): -3.1, ('P', 'S'): 1.4, ('R', 'H'): 1.8, ('A', 'C'): -1.7,
+('R', 'D'): -1.5, ('K', 'P'): -2.3, ('L', 'D'): -5.3, ('K', 'T'): -1.1,
+('V', 'N'): -2.4, ('M', 'A'): -0.2, ('K', 'H'): 0.9, ('V', 'R'): -3.7,
+('P', 'C'): -2.7, ('M', 'E'): -4.1, ('A', 'S'): 1.4, ('T', 'T'): 2.4,
+('R', 'T'): -1.3, ('I', 'G'): -3.4, ('R', 'P'): -1.3, ('K', 'D'): -0.2,
+('I', 'C'): -3.6, ('F', 'R'): -4.9, ('F', 'V'): -0.5, ('L', 'C'): -3.8,
+('F', 'F'): 8.3, ('D', 'A'): -0.6, ('F', 'N'): -3.5, ('W', 'D'): -6.3,
+('L', 'P'): -0.2, ('Q', 'S'): -1.4, ('N', 'C'): -1.6, ('N', 'G'): -0.1,
+('H', 'N'): 1.4, ('W', 'T'): -2.6, ('Q', 'G'): -1.6, ('W', 'P'): -4.8,
+('Q', 'C'): -3.2, ('N', 'S'): 1.2, ('L', 'H'): -2.2, ('L', 'L'): 4.8,
+('G', 'T'): -0.5, ('M', 'M'): 4.8, ('G', 'P'): -1.7, ('Y', 'K'): -4.0,
+('Y', 'G'): -4.9, ('Y', 'C'): 2.6, ('E', 'A'): -0.7, ('E', 'E'): 5.2,
+('Y', 'S'): -1.8, ('M', 'P'): -1.8, ('V', 'C'): -3.1, ('M', 'T'): 0.6,
+('V', 'G'): -2.3, ('R', 'E'): -0.4, ('V', 'K'): -3.8, ('K', 'Q'): 2.5,
+('R', 'A'): -1.7, ('I', 'R'): -3.8, ('N', 'A'): 0.0, ('V', 'S'): -0.9,
+('M', 'D'): -4.3, ('M', 'H'): -3.4, ('K', 'A'): -1.9, ('R', 'Q'): 2.5,
+('K', 'E'): 0.9, ('F', 'S'): -1.8, ('I', 'K'): -3.8, ('D', 'P'): -2.8,
+('D', 'T'): -1.2, ('I', 'M'): 4.0, ('F', 'C'): -0.1, ('W', 'L'): -3.0,
+('F', 'G'): -5.7, ('F', 'K'): -6.3, ('F', 'T'): -2.4, ('D', 'D'): 5.2,
+('Q', 'T'): -1.7, ('W', 'G'): -1.7, ('Q', 'P'): 0.1, ('W', 'C'): 1.6,
+('W', 'K'): -1.4, ('H', 'Q'): 3.2, ('Q', 'D'): 0.6, ('W', 'W'): 14.7,
+('V', 'L'): 1.9, ('L', 'G'): -4.6, ('W', 'S'): -2.9, ('L', 'K'): -4.1,
+('N', 'P'): -1.1, ('H', 'E'): -0.2, ('N', 'T'): 0.5, ('H', 'A'): -2.1,
+('Y', 'L'): -1.6, ('Y', 'H'): 4.4, ('G', 'S'): 0.8, ('Y', 'D'): -2.3,
+('V', 'Q'): -3.5, ('L', 'T'): -0.4, ('G', 'G'): 5.8, ('G', 'C'): -1.3,
+('E', 'N'): 1.1, ('Y', 'T'): -3.4, ('Y', 'P'): -3.8, ('R', 'N'): -0.1,
+('V', 'D'): -3.3, ('K', 'R'): 4.3, ('V', 'H'): -3.8, ('I', 'Q'): -3.8,
+('V', 'P'): -1.6, ('M', 'C'): -3.7, ('K', 'N'): 1.0, ('V', 'T'): 0.6,
+('M', 'G'): -3.7, ('T', 'S'): 1.5, ('I', 'E'): -4.1, ('M', 'K'): -2.9,
+('I', 'A'): 0.1, ('N', 'N'): 3.6, ('R', 'R'): 5.1, ('F', 'P'): -3.2,
+('L', 'I'): 2.4, ('I', 'S'): -1.2, ('D', 'S'): -0.4, ('L', 'S'): -1.5,
+('I', 'H'): -3.7, ('F', 'D'): -5.7, ('D', 'C'): -3.7, ('F', 'H'): 0.1,
+('D', 'G'): 0.8, ('F', 'L'): 2.4
 }
 benner6 = _temp()
 
@@ -98,59 +97,59 @@ benner6 = _temp()
 # http://www.embl-heidelberg.de/~vogt/matrices/benner22.cmp
 def _temp():
     return {
-('W', 'F') : 0.5, ('L', 'R') : -2.9, ('I', 'I') : 4.2, ('Q', 'Q') : 4.2,
-('W', 'N') : -5.2, ('V', 'I') : 3.6, ('H', 'T') : -1.1, ('H', 'P') : -0.4,
-('W', 'V') : -4.5, ('Q', 'E') : 1.7, ('W', 'R') : -1.1, ('Q', 'A') : -0.9,
-('H', 'H') : 6.1, ('H', 'D') : 0.3, ('L', 'N') : -3.5, ('Y', 'M') : -1.8,
-('Y', 'I') : -2.2, ('Y', 'E') : -4.0, ('E', 'S') : -0.5, ('Y', 'A') : -3.5,
-('Y', 'Y') : 9.0, ('T', 'C') : -1.1, ('E', 'C') : -4.3, ('Y', 'Q') : -1.9,
-('E', 'G') : 0.5, ('V', 'A') : 0.4, ('C', 'C') : 12.6, ('M', 'R') : -2.1,
-('P', 'T') : 0.4, ('V', 'E') : -2.7, ('P', 'P') : 7.0, ('I', 'T') : 0.3,
-('K', 'S') : -0.4, ('R', 'G') : -0.7, ('I', 'P') : -2.3, ('R', 'C') : -1.6,
-('A', 'T') : 1.4, ('K', 'K') : 4.4, ('A', 'P') : 0.8, ('V', 'M') : 2.5,
-('I', 'D') : -4.0, ('K', 'C') : -3.3, ('K', 'G') : -1.0, ('R', 'S') : -0.5,
-('F', 'Q') : -3.6, ('F', 'A') : -3.1, ('V', 'V') : 3.7, ('M', 'N') : -2.6,
-('F', 'E') : -5.7, ('D', 'N') : 2.4, ('F', 'I') : 0.5, ('F', 'M') : 0.7,
-('M', 'S') : -1.5, ('S', 'S') : 2.0, ('L', 'Q') : -2.0, ('W', 'E') : -6.3,
-('W', 'A') : -5.5, ('W', 'M') : -2.8, ('H', 'S') : -0.5, ('W', 'I') : -4.4,
-('S', 'C') : 0.3, ('L', 'A') : -1.7, ('L', 'E') : -4.4, ('W', 'Q') : -3.3,
-('H', 'G') : -2.0, ('Q', 'N') : 0.5, ('H', 'C') : -1.5, ('L', 'M') : 3.2,
-('W', 'Y') : 1.5, ('Y', 'N') : -1.2, ('E', 'P') : -1.7, ('Y', 'F') : 5.9,
-('E', 'T') : -0.9, ('A', 'A') : 2.5, ('I', 'N') : -2.7, ('G', 'A') : 0.8,
-('Y', 'V') : -2.6, ('E', 'D') : 3.9, ('W', 'H') : -2.7, ('Y', 'R') : -2.7,
-('M', 'Q') : -1.7, ('P', 'S') : 1.1, ('R', 'H') : 1.5, ('A', 'C') : -1.2,
-('R', 'D') : -1.0, ('K', 'P') : -1.6, ('L', 'D') : -4.9, ('K', 'T') : -0.4,
-('V', 'N') : -2.3, ('M', 'A') : -0.8, ('K', 'H') : 0.8, ('V', 'R') : -2.9,
-('P', 'C') : -3.1, ('M', 'E') : -3.4, ('A', 'S') : 1.3, ('T', 'T') : 2.5,
-('R', 'T') : -0.7, ('I', 'G') : -3.8, ('R', 'P') : -1.2, ('K', 'D') : 0.2,
-('I', 'C') : -2.4, ('F', 'R') : -4.3, ('F', 'V') : -0.1, ('L', 'C') : -2.6,
-('F', 'F') : 7.7, ('D', 'A') : -0.2, ('F', 'N') : -3.5, ('W', 'D') : -6.4,
-('L', 'P') : -1.3, ('Q', 'S') : -0.6, ('N', 'C') : -1.9, ('N', 'G') : 0.4,
-('H', 'N') : 1.4, ('W', 'T') : -4.5, ('Q', 'G') : -1.4, ('W', 'P') : -5.8,
-('Q', 'C') : -3.3, ('N', 'S') : 1.1, ('L', 'H') : -2.1, ('L', 'L') : 4.6,
-('G', 'T') : -0.7, ('M', 'M') : 4.9, ('G', 'P') : -1.8, ('Y', 'K') : -3.6,
-('Y', 'G') : -4.8, ('Y', 'C') : 0.6, ('E', 'A') : -0.3, ('E', 'E') : 4.6,
-('Y', 'S') : -1.9, ('M', 'P') : -2.0, ('V', 'C') : -1.7, ('M', 'T') : 0.1,
-('V', 'G') : -2.5, ('R', 'E') : -0.1, ('V', 'K') : -2.7, ('K', 'Q') : 2.2,
-('R', 'A') : -1.2, ('I', 'R') : -3.2, ('N', 'A') : 0.0, ('V', 'S') : -0.9,
-('M', 'D') : -3.9, ('M', 'H') : -2.4, ('K', 'A') : -1.0, ('R', 'Q') : 2.2,
-('K', 'E') : 1.0, ('F', 'S') : -2.2, ('I', 'K') : -3.0, ('D', 'P') : -1.8,
-('D', 'T') : -0.7, ('I', 'M') : 3.1, ('F', 'C') : -0.1, ('W', 'L') : -1.8,
-('F', 'G') : -5.8, ('F', 'K') : -5.1, ('F', 'T') : -2.6, ('D', 'D') : 4.8,
-('Q', 'T') : -0.7, ('W', 'G') : -4.5, ('Q', 'P') : -0.1, ('W', 'C') : 0.5,
-('W', 'K') : -3.7, ('H', 'Q') : 2.4, ('Q', 'D') : 0.6, ('W', 'W') : 15.7,
-('V', 'L') : 2.0, ('L', 'G') : -4.9, ('W', 'S') : -3.9, ('L', 'K') : -3.3,
-('N', 'P') : -1.1, ('H', 'E') : -0.2, ('N', 'T') : 0.5, ('H', 'A') : -1.6,
-('Y', 'L') : -0.7, ('Y', 'H') : 3.7, ('G', 'S') : 0.6, ('Y', 'D') : -3.0,
-('V', 'Q') : -2.4, ('L', 'T') : -1.0, ('G', 'G') : 6.2, ('G', 'C') : -1.7,
-('E', 'N') : 1.2, ('Y', 'T') : -3.0, ('Y', 'P') : -3.5, ('R', 'N') : 0.4,
-('V', 'D') : -3.0, ('K', 'R') : 3.9, ('V', 'H') : -3.0, ('I', 'Q') : -2.7,
-('V', 'P') : -1.7, ('M', 'C') : -2.5, ('K', 'N') : 1.0, ('V', 'T') : 0.4,
-('M', 'G') : -3.8, ('T', 'S') : 1.5, ('I', 'E') : -3.6, ('M', 'K') : -2.0,
-('I', 'A') : -0.4, ('N', 'N') : 3.3, ('R', 'R') : 5.0, ('F', 'P') : -3.4,
-('L', 'I') : 2.7, ('I', 'S') : -1.4, ('D', 'S') : 0.1, ('L', 'S') : -2.1,
-('I', 'H') : -3.2, ('F', 'D') : -5.4, ('D', 'C') : -3.7, ('F', 'H') : 0.3,
-('D', 'G') : 0.7, ('F', 'L') : 2.2
+('W', 'F'): 0.5, ('L', 'R'): -2.9, ('I', 'I'): 4.2, ('Q', 'Q'): 4.2,
+('W', 'N'): -5.2, ('V', 'I'): 3.6, ('H', 'T'): -1.1, ('H', 'P'): -0.4,
+('W', 'V'): -4.5, ('Q', 'E'): 1.7, ('W', 'R'): -1.1, ('Q', 'A'): -0.9,
+('H', 'H'): 6.1, ('H', 'D'): 0.3, ('L', 'N'): -3.5, ('Y', 'M'): -1.8,
+('Y', 'I'): -2.2, ('Y', 'E'): -4.0, ('E', 'S'): -0.5, ('Y', 'A'): -3.5,
+('Y', 'Y'): 9.0, ('T', 'C'): -1.1, ('E', 'C'): -4.3, ('Y', 'Q'): -1.9,
+('E', 'G'): 0.5, ('V', 'A'): 0.4, ('C', 'C'): 12.6, ('M', 'R'): -2.1,
+('P', 'T'): 0.4, ('V', 'E'): -2.7, ('P', 'P'): 7.0, ('I', 'T'): 0.3,
+('K', 'S'): -0.4, ('R', 'G'): -0.7, ('I', 'P'): -2.3, ('R', 'C'): -1.6,
+('A', 'T'): 1.4, ('K', 'K'): 4.4, ('A', 'P'): 0.8, ('V', 'M'): 2.5,
+('I', 'D'): -4.0, ('K', 'C'): -3.3, ('K', 'G'): -1.0, ('R', 'S'): -0.5,
+('F', 'Q'): -3.6, ('F', 'A'): -3.1, ('V', 'V'): 3.7, ('M', 'N'): -2.6,
+('F', 'E'): -5.7, ('D', 'N'): 2.4, ('F', 'I'): 0.5, ('F', 'M'): 0.7,
+('M', 'S'): -1.5, ('S', 'S'): 2.0, ('L', 'Q'): -2.0, ('W', 'E'): -6.3,
+('W', 'A'): -5.5, ('W', 'M'): -2.8, ('H', 'S'): -0.5, ('W', 'I'): -4.4,
+('S', 'C'): 0.3, ('L', 'A'): -1.7, ('L', 'E'): -4.4, ('W', 'Q'): -3.3,
+('H', 'G'): -2.0, ('Q', 'N'): 0.5, ('H', 'C'): -1.5, ('L', 'M'): 3.2,
+('W', 'Y'): 1.5, ('Y', 'N'): -1.2, ('E', 'P'): -1.7, ('Y', 'F'): 5.9,
+('E', 'T'): -0.9, ('A', 'A'): 2.5, ('I', 'N'): -2.7, ('G', 'A'): 0.8,
+('Y', 'V'): -2.6, ('E', 'D'): 3.9, ('W', 'H'): -2.7, ('Y', 'R'): -2.7,
+('M', 'Q'): -1.7, ('P', 'S'): 1.1, ('R', 'H'): 1.5, ('A', 'C'): -1.2,
+('R', 'D'): -1.0, ('K', 'P'): -1.6, ('L', 'D'): -4.9, ('K', 'T'): -0.4,
+('V', 'N'): -2.3, ('M', 'A'): -0.8, ('K', 'H'): 0.8, ('V', 'R'): -2.9,
+('P', 'C'): -3.1, ('M', 'E'): -3.4, ('A', 'S'): 1.3, ('T', 'T'): 2.5,
+('R', 'T'): -0.7, ('I', 'G'): -3.8, ('R', 'P'): -1.2, ('K', 'D'): 0.2,
+('I', 'C'): -2.4, ('F', 'R'): -4.3, ('F', 'V'): -0.1, ('L', 'C'): -2.6,
+('F', 'F'): 7.7, ('D', 'A'): -0.2, ('F', 'N'): -3.5, ('W', 'D'): -6.4,
+('L', 'P'): -1.3, ('Q', 'S'): -0.6, ('N', 'C'): -1.9, ('N', 'G'): 0.4,
+('H', 'N'): 1.4, ('W', 'T'): -4.5, ('Q', 'G'): -1.4, ('W', 'P'): -5.8,
+('Q', 'C'): -3.3, ('N', 'S'): 1.1, ('L', 'H'): -2.1, ('L', 'L'): 4.6,
+('G', 'T'): -0.7, ('M', 'M'): 4.9, ('G', 'P'): -1.8, ('Y', 'K'): -3.6,
+('Y', 'G'): -4.8, ('Y', 'C'): 0.6, ('E', 'A'): -0.3, ('E', 'E'): 4.6,
+('Y', 'S'): -1.9, ('M', 'P'): -2.0, ('V', 'C'): -1.7, ('M', 'T'): 0.1,
+('V', 'G'): -2.5, ('R', 'E'): -0.1, ('V', 'K'): -2.7, ('K', 'Q'): 2.2,
+('R', 'A'): -1.2, ('I', 'R'): -3.2, ('N', 'A'): 0.0, ('V', 'S'): -0.9,
+('M', 'D'): -3.9, ('M', 'H'): -2.4, ('K', 'A'): -1.0, ('R', 'Q'): 2.2,
+('K', 'E'): 1.0, ('F', 'S'): -2.2, ('I', 'K'): -3.0, ('D', 'P'): -1.8,
+('D', 'T'): -0.7, ('I', 'M'): 3.1, ('F', 'C'): -0.1, ('W', 'L'): -1.8,
+('F', 'G'): -5.8, ('F', 'K'): -5.1, ('F', 'T'): -2.6, ('D', 'D'): 4.8,
+('Q', 'T'): -0.7, ('W', 'G'): -4.5, ('Q', 'P'): -0.1, ('W', 'C'): 0.5,
+('W', 'K'): -3.7, ('H', 'Q'): 2.4, ('Q', 'D'): 0.6, ('W', 'W'): 15.7,
+('V', 'L'): 2.0, ('L', 'G'): -4.9, ('W', 'S'): -3.9, ('L', 'K'): -3.3,
+('N', 'P'): -1.1, ('H', 'E'): -0.2, ('N', 'T'): 0.5, ('H', 'A'): -1.6,
+('Y', 'L'): -0.7, ('Y', 'H'): 3.7, ('G', 'S'): 0.6, ('Y', 'D'): -3.0,
+('V', 'Q'): -2.4, ('L', 'T'): -1.0, ('G', 'G'): 6.2, ('G', 'C'): -1.7,
+('E', 'N'): 1.2, ('Y', 'T'): -3.0, ('Y', 'P'): -3.5, ('R', 'N'): 0.4,
+('V', 'D'): -3.0, ('K', 'R'): 3.9, ('V', 'H'): -3.0, ('I', 'Q'): -2.7,
+('V', 'P'): -1.7, ('M', 'C'): -2.5, ('K', 'N'): 1.0, ('V', 'T'): 0.4,
+('M', 'G'): -3.8, ('T', 'S'): 1.5, ('I', 'E'): -3.6, ('M', 'K'): -2.0,
+('I', 'A'): -0.4, ('N', 'N'): 3.3, ('R', 'R'): 5.0, ('F', 'P'): -3.4,
+('L', 'I'): 2.7, ('I', 'S'): -1.4, ('D', 'S'): 0.1, ('L', 'S'): -2.1,
+('I', 'H'): -3.2, ('F', 'D'): -5.4, ('D', 'C'): -3.7, ('F', 'H'): 0.3,
+('D', 'G'): 0.7, ('F', 'L'): 2.2
 }
 benner22 = _temp()
 del _temp
@@ -160,59 +159,59 @@ assert benner6 != benner22
 # http://www.embl-heidelberg.de/~vogt/matrices/benner74.cmp
 def _temp():
     return {
-('W', 'F') : 3.0, ('L', 'R') : -2.4, ('I', 'I') : 4.0, ('Q', 'Q') : 3.0,
-('W', 'N') : -4.0, ('V', 'I') : 3.2, ('H', 'T') : -0.5, ('H', 'P') : -1.0,
-('W', 'V') : -2.9, ('Q', 'E') : 1.7, ('W', 'R') : -1.6, ('Q', 'A') : -0.3,
-('H', 'H') : 6.1, ('H', 'D') : 0.4, ('L', 'N') : -3.1, ('Y', 'M') : -0.5,
-('Y', 'I') : -1.0, ('Y', 'E') : -3.0, ('E', 'S') : 0.1, ('Y', 'A') : -2.6,
-('Y', 'Y') : 8.1, ('T', 'C') : -0.6, ('E', 'C') : -3.2, ('Y', 'Q') : -1.8,
-('E', 'G') : -0.5, ('V', 'A') : 0.1, ('C', 'C') : 11.8, ('M', 'R') : -1.8,
-('P', 'T') : 0.1, ('V', 'E') : -2.1, ('P', 'P') : 7.5, ('I', 'T') : -0.3,
-('K', 'S') : 0.0, ('R', 'G') : -1.0, ('I', 'P') : -2.6, ('R', 'C') : -2.2,
-('A', 'T') : 0.7, ('K', 'K') : 3.4, ('A', 'P') : 0.4, ('V', 'M') : 1.8,
-('I', 'D') : -3.9, ('K', 'C') : -2.9, ('K', 'G') : -1.1, ('R', 'S') : -0.2,
-('F', 'Q') : -2.8, ('F', 'A') : -2.6, ('V', 'V') : 3.4, ('M', 'N') : -2.2,
-('F', 'E') : -4.3, ('D', 'N') : 2.2, ('F', 'I') : 0.9, ('F', 'M') : 1.3,
-('M', 'S') : -1.4, ('S', 'S') : 2.1, ('L', 'Q') : -1.7, ('W', 'E') : -4.7,
-('W', 'A') : -4.1, ('W', 'M') : -1.3, ('H', 'S') : -0.3, ('W', 'I') : -2.3,
-('S', 'C') : 0.1, ('L', 'A') : -1.4, ('L', 'E') : -3.1, ('W', 'Q') : -2.8,
-('H', 'G') : -1.6, ('Q', 'N') : 0.7, ('H', 'C') : -1.3, ('L', 'M') : 2.9,
-('W', 'Y') : 3.6, ('Y', 'N') : -1.4, ('E', 'P') : -0.7, ('Y', 'F') : 5.3,
-('E', 'T') : -0.2, ('A', 'A') : 2.4, ('I', 'N') : -2.8, ('G', 'A') : 0.6,
-('Y', 'V') : -1.4, ('E', 'D') : 2.9, ('W', 'H') : -1.0, ('Y', 'R') : -2.0,
-('M', 'Q') : -1.0, ('P', 'S') : 0.5, ('R', 'H') : 1.0, ('A', 'C') : 0.3,
-('R', 'D') : -0.5, ('K', 'P') : -0.8, ('L', 'D') : -4.2, ('K', 'T') : 0.1,
-('V', 'N') : -2.2, ('M', 'A') : -0.8, ('K', 'H') : 0.6, ('V', 'R') : -2.2,
-('P', 'C') : -3.1, ('M', 'E') : -2.2, ('A', 'S') : 1.1, ('T', 'T') : 2.5,
-('R', 'T') : -0.3, ('I', 'G') : -4.3, ('R', 'P') : -0.1, ('K', 'D') : 0.4,
-('I', 'C') : -1.2, ('F', 'R') : -3.5, ('F', 'V') : 0.1, ('L', 'C') : -1.6,
-('F', 'F') : 7.2, ('D', 'A') : -0.3, ('F', 'N') : -3.2, ('W', 'D') : -5.5,
-('L', 'P') : -2.2, ('Q', 'S') : 0.1, ('N', 'C') : -1.8, ('N', 'G') : 0.4,
-('H', 'N') : 1.2, ('W', 'T') : -3.7, ('Q', 'G') : -1.1, ('W', 'P') : -5.2,
-('Q', 'C') : -2.6, ('N', 'S') : 0.9, ('L', 'H') : -1.9, ('L', 'L') : 4.2,
-('G', 'T') : -1.0, ('M', 'M') : 4.5, ('G', 'P') : -1.7, ('Y', 'K') : -2.4,
-('Y', 'G') : -4.3, ('Y', 'C') : -0.4, ('E', 'A') : -0.1, ('E', 'E') : 3.7,
-('Y', 'S') : -1.9, ('M', 'P') : -2.4, ('V', 'C') : -0.2, ('M', 'T') : -0.4,
-('V', 'G') : -3.1, ('R', 'E') : 0.3, ('V', 'K') : -1.9, ('K', 'Q') : 1.7,
-('R', 'A') : -0.8, ('I', 'R') : -2.6, ('N', 'A') : -0.2, ('V', 'S') : -1.0,
-('M', 'D') : -3.2, ('M', 'H') : -1.5, ('K', 'A') : -0.4, ('R', 'Q') : 1.6,
-('K', 'E') : 1.2, ('F', 'S') : -2.6, ('I', 'K') : -2.3, ('D', 'P') : -1.0,
-('D', 'T') : -0.2, ('I', 'M') : 2.6, ('F', 'C') : -0.7, ('W', 'L') : -0.9,
-('F', 'G') : -5.4, ('F', 'K') : -3.6, ('F', 'T') : -2.2, ('D', 'D') : 4.8,
-('Q', 'T') : -0.1, ('W', 'G') : -4.1, ('Q', 'P') : -0.2, ('W', 'C') : -0.9,
-('W', 'K') : -3.6, ('H', 'Q') : 1.4, ('Q', 'D') : 0.8, ('W', 'W') : 14.7,
-('V', 'L') : 1.9, ('L', 'G') : -4.6, ('W', 'S') : -3.4, ('L', 'K') : -2.4,
-('N', 'P') : -1.0, ('H', 'E') : 0.2, ('N', 'T') : 0.4, ('H', 'A') : -1.0,
-('Y', 'L') : -0.1, ('Y', 'H') : 2.5, ('G', 'S') : 0.4, ('Y', 'D') : -2.8,
-('V', 'Q') : -1.7, ('L', 'T') : -1.1, ('G', 'G') : 6.6, ('G', 'C') : -2.0,
-('E', 'N') : 1.0, ('Y', 'T') : -2.1, ('Y', 'P') : -3.4, ('R', 'N') : 0.3,
-('V', 'D') : -2.9, ('K', 'R') : 2.9, ('V', 'H') : -2.1, ('I', 'Q') : -2.0,
-('V', 'P') : -1.9, ('M', 'C') : -1.2, ('K', 'N') : 0.9, ('V', 'T') : 0.2,
-('M', 'G') : -3.5, ('T', 'S') : 1.4, ('I', 'E') : -2.9, ('M', 'K') : -1.5,
-('I', 'A') : -0.8, ('N', 'N') : 3.6, ('R', 'R') : 4.8, ('F', 'P') : -3.8,
-('L', 'I') : 2.8, ('I', 'S') : -1.8, ('D', 'S') : 0.4, ('L', 'S') : -2.2,
-('I', 'H') : -2.3, ('F', 'D') : -4.7, ('D', 'C') : -3.2, ('F', 'H') : 0.0,
-('D', 'G') : 0.2, ('F', 'L') : 2.1
+('W', 'F'): 3.0, ('L', 'R'): -2.4, ('I', 'I'): 4.0, ('Q', 'Q'): 3.0,
+('W', 'N'): -4.0, ('V', 'I'): 3.2, ('H', 'T'): -0.5, ('H', 'P'): -1.0,
+('W', 'V'): -2.9, ('Q', 'E'): 1.7, ('W', 'R'): -1.6, ('Q', 'A'): -0.3,
+('H', 'H'): 6.1, ('H', 'D'): 0.4, ('L', 'N'): -3.1, ('Y', 'M'): -0.5,
+('Y', 'I'): -1.0, ('Y', 'E'): -3.0, ('E', 'S'): 0.1, ('Y', 'A'): -2.6,
+('Y', 'Y'): 8.1, ('T', 'C'): -0.6, ('E', 'C'): -3.2, ('Y', 'Q'): -1.8,
+('E', 'G'): -0.5, ('V', 'A'): 0.1, ('C', 'C'): 11.8, ('M', 'R'): -1.8,
+('P', 'T'): 0.1, ('V', 'E'): -2.1, ('P', 'P'): 7.5, ('I', 'T'): -0.3,
+('K', 'S'): 0.0, ('R', 'G'): -1.0, ('I', 'P'): -2.6, ('R', 'C'): -2.2,
+('A', 'T'): 0.7, ('K', 'K'): 3.4, ('A', 'P'): 0.4, ('V', 'M'): 1.8,
+('I', 'D'): -3.9, ('K', 'C'): -2.9, ('K', 'G'): -1.1, ('R', 'S'): -0.2,
+('F', 'Q'): -2.8, ('F', 'A'): -2.6, ('V', 'V'): 3.4, ('M', 'N'): -2.2,
+('F', 'E'): -4.3, ('D', 'N'): 2.2, ('F', 'I'): 0.9, ('F', 'M'): 1.3,
+('M', 'S'): -1.4, ('S', 'S'): 2.1, ('L', 'Q'): -1.7, ('W', 'E'): -4.7,
+('W', 'A'): -4.1, ('W', 'M'): -1.3, ('H', 'S'): -0.3, ('W', 'I'): -2.3,
+('S', 'C'): 0.1, ('L', 'A'): -1.4, ('L', 'E'): -3.1, ('W', 'Q'): -2.8,
+('H', 'G'): -1.6, ('Q', 'N'): 0.7, ('H', 'C'): -1.3, ('L', 'M'): 2.9,
+('W', 'Y'): 3.6, ('Y', 'N'): -1.4, ('E', 'P'): -0.7, ('Y', 'F'): 5.3,
+('E', 'T'): -0.2, ('A', 'A'): 2.4, ('I', 'N'): -2.8, ('G', 'A'): 0.6,
+('Y', 'V'): -1.4, ('E', 'D'): 2.9, ('W', 'H'): -1.0, ('Y', 'R'): -2.0,
+('M', 'Q'): -1.0, ('P', 'S'): 0.5, ('R', 'H'): 1.0, ('A', 'C'): 0.3,
+('R', 'D'): -0.5, ('K', 'P'): -0.8, ('L', 'D'): -4.2, ('K', 'T'): 0.1,
+('V', 'N'): -2.2, ('M', 'A'): -0.8, ('K', 'H'): 0.6, ('V', 'R'): -2.2,
+('P', 'C'): -3.1, ('M', 'E'): -2.2, ('A', 'S'): 1.1, ('T', 'T'): 2.5,
+('R', 'T'): -0.3, ('I', 'G'): -4.3, ('R', 'P'): -0.1, ('K', 'D'): 0.4,
+('I', 'C'): -1.2, ('F', 'R'): -3.5, ('F', 'V'): 0.1, ('L', 'C'): -1.6,
+('F', 'F'): 7.2, ('D', 'A'): -0.3, ('F', 'N'): -3.2, ('W', 'D'): -5.5,
+('L', 'P'): -2.2, ('Q', 'S'): 0.1, ('N', 'C'): -1.8, ('N', 'G'): 0.4,
+('H', 'N'): 1.2, ('W', 'T'): -3.7, ('Q', 'G'): -1.1, ('W', 'P'): -5.2,
+('Q', 'C'): -2.6, ('N', 'S'): 0.9, ('L', 'H'): -1.9, ('L', 'L'): 4.2,
+('G', 'T'): -1.0, ('M', 'M'): 4.5, ('G', 'P'): -1.7, ('Y', 'K'): -2.4,
+('Y', 'G'): -4.3, ('Y', 'C'): -0.4, ('E', 'A'): -0.1, ('E', 'E'): 3.7,
+('Y', 'S'): -1.9, ('M', 'P'): -2.4, ('V', 'C'): -0.2, ('M', 'T'): -0.4,
+('V', 'G'): -3.1, ('R', 'E'): 0.3, ('V', 'K'): -1.9, ('K', 'Q'): 1.7,
+('R', 'A'): -0.8, ('I', 'R'): -2.6, ('N', 'A'): -0.2, ('V', 'S'): -1.0,
+('M', 'D'): -3.2, ('M', 'H'): -1.5, ('K', 'A'): -0.4, ('R', 'Q'): 1.6,
+('K', 'E'): 1.2, ('F', 'S'): -2.6, ('I', 'K'): -2.3, ('D', 'P'): -1.0,
+('D', 'T'): -0.2, ('I', 'M'): 2.6, ('F', 'C'): -0.7, ('W', 'L'): -0.9,
+('F', 'G'): -5.4, ('F', 'K'): -3.6, ('F', 'T'): -2.2, ('D', 'D'): 4.8,
+('Q', 'T'): -0.1, ('W', 'G'): -4.1, ('Q', 'P'): -0.2, ('W', 'C'): -0.9,
+('W', 'K'): -3.6, ('H', 'Q'): 1.4, ('Q', 'D'): 0.8, ('W', 'W'): 14.7,
+('V', 'L'): 1.9, ('L', 'G'): -4.6, ('W', 'S'): -3.4, ('L', 'K'): -2.4,
+('N', 'P'): -1.0, ('H', 'E'): 0.2, ('N', 'T'): 0.4, ('H', 'A'): -1.0,
+('Y', 'L'): -0.1, ('Y', 'H'): 2.5, ('G', 'S'): 0.4, ('Y', 'D'): -2.8,
+('V', 'Q'): -1.7, ('L', 'T'): -1.1, ('G', 'G'): 6.6, ('G', 'C'): -2.0,
+('E', 'N'): 1.0, ('Y', 'T'): -2.1, ('Y', 'P'): -3.4, ('R', 'N'): 0.3,
+('V', 'D'): -2.9, ('K', 'R'): 2.9, ('V', 'H'): -2.1, ('I', 'Q'): -2.0,
+('V', 'P'): -1.9, ('M', 'C'): -1.2, ('K', 'N'): 0.9, ('V', 'T'): 0.2,
+('M', 'G'): -3.5, ('T', 'S'): 1.4, ('I', 'E'): -2.9, ('M', 'K'): -1.5,
+('I', 'A'): -0.8, ('N', 'N'): 3.6, ('R', 'R'): 4.8, ('F', 'P'): -3.8,
+('L', 'I'): 2.8, ('I', 'S'): -1.8, ('D', 'S'): 0.4, ('L', 'S'): -2.2,
+('I', 'H'): -2.3, ('F', 'D'): -4.7, ('D', 'C'): -3.2, ('F', 'H'): 0.0,
+('D', 'G'): 0.2, ('F', 'L'): 2.1
 }
 benner74 = _temp()
 del _temp
@@ -221,75 +220,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum100.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -4, ('S', 'P') : -2, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -4, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 9, ('S', 'H') : -2,
-('H', 'D') : -2, ('L', 'N') : -5, ('W', 'A') : -4, ('Y', 'M') : -3,
-('G', 'R') : -4, ('Y', 'I') : -3, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -4, ('V', 'D') : -5, ('B', 'S') : -1, ('Y', 'Y') : 8,
-('G', 'N') : -2, ('E', 'C') : -6, ('Y', 'Q') : -3, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : -1, ('P', 'P') : 8, ('V', 'I') : 2, ('V', 'S') : -3,
-('Z', 'P') : -3, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -3, ('I', 'H') : -5, ('I', 'D') : -6,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -3, ('M', 'N') : -4,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -4, ('X', 'L') : -2,
-('T', 'M') : -2, ('Z', 'C') : -6, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -3, ('Z', 'K') : 0, ('F', 'A') : -4,
-('Z', 'W') : -4, ('F', 'E') : -5, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -5, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 6, ('L', 'Q') : -3, ('W', 'E') : -5, ('Q', 'R') : 0,
-('N', 'N') : 7, ('W', 'M') : -3, ('Q', 'C') : -5, ('W', 'I') : -4,
-('S', 'C') : -2, ('L', 'A') : -3, ('S', 'G') : -1, ('L', 'E') : -5,
-('W', 'Q') : -3, ('H', 'G') : -4, ('S', 'K') : -1, ('Q', 'N') : -1,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -4,
-('G', 'A') : -1, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : -1, ('T', 'I') : -2, ('C', 'D') : -5, ('V', 'F') : -2,
-('T', 'A') : -1, ('T', 'P') : -3, ('B', 'P') : -3, ('T', 'E') : -2,
-('V', 'N') : -4, ('P', 'G') : -4, ('M', 'A') : -2, ('K', 'H') : -2,
-('V', 'R') : -4, ('P', 'C') : -5, ('M', 'E') : -4, ('K', 'L') : -4,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -2, ('I', 'G') : -6,
-('P', 'K') : -2, ('M', 'M') : 8, ('K', 'D') : -2, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -4, ('X', 'K') : -2, ('Q', 'D') : -2,
-('X', 'G') : -3, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : -1,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -4,
-('B', 'D') : 4, ('D', 'A') : -3, ('S', 'L') : -4, ('X', 'S') : -1,
-('F', 'N') : -5, ('S', 'R') : -2, ('W', 'D') : -7, ('V', 'Y') : -3,
-('W', 'L') : -4, ('H', 'R') : -1, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -5, ('T', 'T') : 6, ('S', 'F') : -3, ('W', 'P') : -6,
-('L', 'D') : -6, ('B', 'I') : -5, ('L', 'H') : -4, ('S', 'N') : 0,
-('B', 'T') : -2, ('L', 'L') : 5, ('Y', 'K') : -4, ('E', 'Q') : 1,
-('Y', 'G') : -6, ('Z', 'S') : -1, ('Y', 'C') : -4, ('G', 'D') : -3,
-('B', 'V') : -5, ('E', 'A') : -2, ('Y', 'W') : 1, ('E', 'E') : 6,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -3,
-('P', 'R') : -3, ('V', 'G') : -5, ('T', 'L') : -3, ('V', 'K') : -4,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -5, ('I', 'N') : -5, ('K', 'I') : -4, ('M', 'D') : -5,
-('V', 'W') : -4, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -4,
-('K', 'A') : -2, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 5,
-('X', 'N') : -2, ('Z', 'A') : -2, ('Z', 'M') : -3, ('X', 'F') : -3,
-('K', 'C') : -5, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 3, ('X', 'Z') : -2, ('F', 'G') : -5,
-('B', 'E') : 0, ('X', 'V') : -2, ('F', 'K') : -4, ('B', 'A') : -3,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -5, ('Z', 'F') : -5,
-('S', 'Q') : -1, ('W', 'C') : -5, ('W', 'K') : -5, ('H', 'Q') : 0,
-('L', 'C') : -3, ('W', 'N') : -6, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -4,
-('H', 'A') : -3, ('S', 'M') : -3, ('Y', 'L') : -3, ('Y', 'H') : 1,
-('Y', 'D') : -5, ('E', 'R') : -2, ('X', 'P') : -3, ('G', 'G') : 6,
-('G', 'C') : -5, ('E', 'N') : -1, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : -2, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -5, ('T', 'G') : -3, ('I', 'Q') : -4, ('Z', 'T') : -2,
-('C', 'R') : -5, ('V', 'P') : -4, ('P', 'E') : -3, ('M', 'C') : -3,
-('K', 'N') : -1, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -5,
-('T', 'S') : 1, ('I', 'E') : -5, ('P', 'M') : -4, ('M', 'K') : -2,
-('I', 'A') : -3, ('P', 'I') : -4, ('R', 'R') : 7, ('X', 'M') : -2,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 1, ('X', 'E') : -2,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -3, ('Z', 'R') : -1, ('F', 'H') : -2,
-('B', 'F') : -5, ('F', 'L') : 0, ('X', 'Q') : -2, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -4, ('S', 'P'): -2, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -4, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 9, ('S', 'H'): -2,
+('H', 'D'): -2, ('L', 'N'): -5, ('W', 'A'): -4, ('Y', 'M'): -3,
+('G', 'R'): -4, ('Y', 'I'): -3, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -4, ('V', 'D'): -5, ('B', 'S'): -1, ('Y', 'Y'): 8,
+('G', 'N'): -2, ('E', 'C'): -6, ('Y', 'Q'): -3, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): -1, ('P', 'P'): 8, ('V', 'I'): 2, ('V', 'S'): -3,
+('Z', 'P'): -3, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -3, ('I', 'H'): -5, ('I', 'D'): -6,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -3, ('M', 'N'): -4,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -4, ('X', 'L'): -2,
+('T', 'M'): -2, ('Z', 'C'): -6, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -3, ('Z', 'K'): 0, ('F', 'A'): -4,
+('Z', 'W'): -4, ('F', 'E'): -5, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -5, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 6, ('L', 'Q'): -3, ('W', 'E'): -5, ('Q', 'R'): 0,
+('N', 'N'): 7, ('W', 'M'): -3, ('Q', 'C'): -5, ('W', 'I'): -4,
+('S', 'C'): -2, ('L', 'A'): -3, ('S', 'G'): -1, ('L', 'E'): -5,
+('W', 'Q'): -3, ('H', 'G'): -4, ('S', 'K'): -1, ('Q', 'N'): -1,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -4,
+('G', 'A'): -1, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): -1, ('T', 'I'): -2, ('C', 'D'): -5, ('V', 'F'): -2,
+('T', 'A'): -1, ('T', 'P'): -3, ('B', 'P'): -3, ('T', 'E'): -2,
+('V', 'N'): -4, ('P', 'G'): -4, ('M', 'A'): -2, ('K', 'H'): -2,
+('V', 'R'): -4, ('P', 'C'): -5, ('M', 'E'): -4, ('K', 'L'): -4,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -2, ('I', 'G'): -6,
+('P', 'K'): -2, ('M', 'M'): 8, ('K', 'D'): -2, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -4, ('X', 'K'): -2, ('Q', 'D'): -2,
+('X', 'G'): -3, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): -1,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -4,
+('B', 'D'): 4, ('D', 'A'): -3, ('S', 'L'): -4, ('X', 'S'): -1,
+('F', 'N'): -5, ('S', 'R'): -2, ('W', 'D'): -7, ('V', 'Y'): -3,
+('W', 'L'): -4, ('H', 'R'): -1, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -5, ('T', 'T'): 6, ('S', 'F'): -3, ('W', 'P'): -6,
+('L', 'D'): -6, ('B', 'I'): -5, ('L', 'H'): -4, ('S', 'N'): 0,
+('B', 'T'): -2, ('L', 'L'): 5, ('Y', 'K'): -4, ('E', 'Q'): 1,
+('Y', 'G'): -6, ('Z', 'S'): -1, ('Y', 'C'): -4, ('G', 'D'): -3,
+('B', 'V'): -5, ('E', 'A'): -2, ('Y', 'W'): 1, ('E', 'E'): 6,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -3,
+('P', 'R'): -3, ('V', 'G'): -5, ('T', 'L'): -3, ('V', 'K'): -4,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -5, ('I', 'N'): -5, ('K', 'I'): -4, ('M', 'D'): -5,
+('V', 'W'): -4, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -4,
+('K', 'A'): -2, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 5,
+('X', 'N'): -2, ('Z', 'A'): -2, ('Z', 'M'): -3, ('X', 'F'): -3,
+('K', 'C'): -5, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 3, ('X', 'Z'): -2, ('F', 'G'): -5,
+('B', 'E'): 0, ('X', 'V'): -2, ('F', 'K'): -4, ('B', 'A'): -3,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -5, ('Z', 'F'): -5,
+('S', 'Q'): -1, ('W', 'C'): -5, ('W', 'K'): -5, ('H', 'Q'): 0,
+('L', 'C'): -3, ('W', 'N'): -6, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -4,
+('H', 'A'): -3, ('S', 'M'): -3, ('Y', 'L'): -3, ('Y', 'H'): 1,
+('Y', 'D'): -5, ('E', 'R'): -2, ('X', 'P'): -3, ('G', 'G'): 6,
+('G', 'C'): -5, ('E', 'N'): -1, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): -2, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -5, ('T', 'G'): -3, ('I', 'Q'): -4, ('Z', 'T'): -2,
+('C', 'R'): -5, ('V', 'P'): -4, ('P', 'E'): -3, ('M', 'C'): -3,
+('K', 'N'): -1, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -5,
+('T', 'S'): 1, ('I', 'E'): -5, ('P', 'M'): -4, ('M', 'K'): -2,
+('I', 'A'): -3, ('P', 'I'): -4, ('R', 'R'): 7, ('X', 'M'): -2,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 1, ('X', 'E'): -2,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -3, ('Z', 'R'): -1, ('F', 'H'): -2,
+('B', 'F'): -5, ('F', 'L'): 0, ('X', 'Q'): -2, ('B', 'B'): 4
 }
 blosum100 = _temp()
 del _temp
@@ -298,75 +297,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum30.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 1,
-('Q', 'Q') : 8, ('N', 'A') : 0, ('Z', 'Y') : -2, ('W', 'R') : 0,
-('Q', 'A') : 1, ('S', 'D') : 0, ('H', 'H') : 14, ('S', 'H') : -1,
-('H', 'D') : -2, ('L', 'N') : -2, ('W', 'A') : -5, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -4, ('V', 'D') : -2, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : 0, ('E', 'C') : 1, ('Y', 'Q') : -1, ('Z', 'Z') : 4,
-('V', 'A') : 1, ('C', 'C') : 17, ('M', 'R') : 0, ('V', 'E') : -3,
-('T', 'N') : 1, ('P', 'P') : 11, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 0, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 4, ('P', 'D') : -1, ('I', 'H') : -2, ('I', 'D') : -4,
-('T', 'R') : -3, ('P', 'L') : -3, ('K', 'G') : -1, ('M', 'N') : 0,
-('P', 'H') : 1, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : 0,
-('T', 'M') : 0, ('Z', 'C') : 0, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -1, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : 0, ('X', 'T') : 0,
-('F', 'M') : -2, ('B', 'C') : -2, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -1, ('Q', 'R') : 3,
-('N', 'N') : 8, ('W', 'M') : -3, ('Q', 'C') : -2, ('W', 'I') : -3,
-('S', 'C') : -2, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -1,
-('W', 'Q') : -1, ('H', 'G') : -3, ('S', 'K') : 0, ('Q', 'N') : -1,
-('N', 'R') : -2, ('H', 'C') : -5, ('Y', 'N') : -4, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -3, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 1, ('E', 'D') : 1, ('Y', 'R') : 0,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -3, ('V', 'F') : 1,
-('T', 'A') : 1, ('T', 'P') : 0, ('B', 'P') : -2, ('T', 'E') : -2,
-('V', 'N') : -2, ('P', 'G') : -1, ('M', 'A') : 1, ('K', 'H') : -2,
-('V', 'R') : -1, ('P', 'C') : -3, ('M', 'E') : -1, ('K', 'L') : -2,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : 0, ('I', 'G') : -1,
-('P', 'K') : 1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -1, ('X', 'K') : 0, ('Q', 'D') : -1,
-('X', 'G') : -1, ('Z', 'L') : -1, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -1, ('B', 'H') : -2, ('F', 'F') : 10, ('X', 'W') : -2,
-('B', 'D') : 5, ('D', 'A') : 0, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -1, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : 1,
-('W', 'L') : -2, ('H', 'R') : -1, ('W', 'H') : -5, ('H', 'N') : -1,
-('W', 'T') : -5, ('T', 'T') : 5, ('S', 'F') : -1, ('W', 'P') : -3,
-('L', 'D') : -1, ('B', 'I') : -2, ('L', 'H') : -1, ('S', 'N') : 0,
-('B', 'T') : 0, ('L', 'L') : 4, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : -1, ('Y', 'C') : -6, ('G', 'D') : -1,
-('B', 'V') : -2, ('E', 'A') : 0, ('Y', 'W') : 5, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -1, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -1, ('V', 'G') : -3, ('T', 'L') : 0, ('V', 'K') : -2,
-('K', 'Q') : 0, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : 0, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 20, ('M', 'H') : 2, ('P', 'N') : -3,
-('K', 'A') : 0, ('M', 'L') : 2, ('K', 'E') : 2, ('Z', 'E') : 5,
-('X', 'N') : 0, ('Z', 'A') : 0, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : -1, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : 0, ('F', 'G') : -3,
-('B', 'E') : 0, ('X', 'V') : 0, ('F', 'K') : -1, ('B', 'A') : 0,
-('X', 'R') : -1, ('D', 'D') : 9, ('W', 'G') : 1, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -2, ('W', 'K') : -2, ('H', 'Q') : 0,
-('L', 'C') : 0, ('W', 'N') : -7, ('S', 'A') : 1, ('L', 'G') : -2,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -1,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : 3, ('Y', 'H') : 0,
-('Y', 'D') : -1, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 8,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -1, ('Y', 'P') : -2,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : 0, ('T', 'C') : -2,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -2, ('V', 'P') : -4, ('P', 'E') : 1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 6, ('P', 'A') : -1, ('M', 'G') : -2,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -4, ('M', 'K') : 2,
-('I', 'A') : 0, ('P', 'I') : -3, ('R', 'R') : 8, ('X', 'M') : 0,
-('L', 'I') : 2, ('X', 'I') : 0, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : -1, ('X', 'A') : 0, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -3,
-('B', 'F') : -3, ('F', 'L') : 2, ('X', 'Q') : 0, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 1,
+('Q', 'Q'): 8, ('N', 'A'): 0, ('Z', 'Y'): -2, ('W', 'R'): 0,
+('Q', 'A'): 1, ('S', 'D'): 0, ('H', 'H'): 14, ('S', 'H'): -1,
+('H', 'D'): -2, ('L', 'N'): -2, ('W', 'A'): -5, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -4, ('V', 'D'): -2, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): 0, ('E', 'C'): 1, ('Y', 'Q'): -1, ('Z', 'Z'): 4,
+('V', 'A'): 1, ('C', 'C'): 17, ('M', 'R'): 0, ('V', 'E'): -3,
+('T', 'N'): 1, ('P', 'P'): 11, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 0, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 4, ('P', 'D'): -1, ('I', 'H'): -2, ('I', 'D'): -4,
+('T', 'R'): -3, ('P', 'L'): -3, ('K', 'G'): -1, ('M', 'N'): 0,
+('P', 'H'): 1, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): 0,
+('T', 'M'): 0, ('Z', 'C'): 0, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -1, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): 0, ('X', 'T'): 0,
+('F', 'M'): -2, ('B', 'C'): -2, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -1, ('Q', 'R'): 3,
+('N', 'N'): 8, ('W', 'M'): -3, ('Q', 'C'): -2, ('W', 'I'): -3,
+('S', 'C'): -2, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -1,
+('W', 'Q'): -1, ('H', 'G'): -3, ('S', 'K'): 0, ('Q', 'N'): -1,
+('N', 'R'): -2, ('H', 'C'): -5, ('Y', 'N'): -4, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -3, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 1, ('E', 'D'): 1, ('Y', 'R'): 0,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -3, ('V', 'F'): 1,
+('T', 'A'): 1, ('T', 'P'): 0, ('B', 'P'): -2, ('T', 'E'): -2,
+('V', 'N'): -2, ('P', 'G'): -1, ('M', 'A'): 1, ('K', 'H'): -2,
+('V', 'R'): -1, ('P', 'C'): -3, ('M', 'E'): -1, ('K', 'L'): -2,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): 0, ('I', 'G'): -1,
+('P', 'K'): 1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -1, ('X', 'K'): 0, ('Q', 'D'): -1,
+('X', 'G'): -1, ('Z', 'L'): -1, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -1, ('B', 'H'): -2, ('F', 'F'): 10, ('X', 'W'): -2,
+('B', 'D'): 5, ('D', 'A'): 0, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -1, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): 1,
+('W', 'L'): -2, ('H', 'R'): -1, ('W', 'H'): -5, ('H', 'N'): -1,
+('W', 'T'): -5, ('T', 'T'): 5, ('S', 'F'): -1, ('W', 'P'): -3,
+('L', 'D'): -1, ('B', 'I'): -2, ('L', 'H'): -1, ('S', 'N'): 0,
+('B', 'T'): 0, ('L', 'L'): 4, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): -1, ('Y', 'C'): -6, ('G', 'D'): -1,
+('B', 'V'): -2, ('E', 'A'): 0, ('Y', 'W'): 5, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -1, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -1, ('V', 'G'): -3, ('T', 'L'): 0, ('V', 'K'): -2,
+('K', 'Q'): 0, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): 0, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 20, ('M', 'H'): 2, ('P', 'N'): -3,
+('K', 'A'): 0, ('M', 'L'): 2, ('K', 'E'): 2, ('Z', 'E'): 5,
+('X', 'N'): 0, ('Z', 'A'): 0, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): -1, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): 0, ('F', 'G'): -3,
+('B', 'E'): 0, ('X', 'V'): 0, ('F', 'K'): -1, ('B', 'A'): 0,
+('X', 'R'): -1, ('D', 'D'): 9, ('W', 'G'): 1, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -2, ('W', 'K'): -2, ('H', 'Q'): 0,
+('L', 'C'): 0, ('W', 'N'): -7, ('S', 'A'): 1, ('L', 'G'): -2,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -1,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): 3, ('Y', 'H'): 0,
+('Y', 'D'): -1, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 8,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -1, ('Y', 'P'): -2,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): 0, ('T', 'C'): -2,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -2, ('V', 'P'): -4, ('P', 'E'): 1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 6, ('P', 'A'): -1, ('M', 'G'): -2,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -4, ('M', 'K'): 2,
+('I', 'A'): 0, ('P', 'I'): -3, ('R', 'R'): 8, ('X', 'M'): 0,
+('L', 'I'): 2, ('X', 'I'): 0, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): -1, ('X', 'A'): 0, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -3,
+('B', 'F'): -3, ('F', 'L'): 2, ('X', 'Q'): 0, ('B', 'B'): 5
 }
 blosum30 = _temp()
 del _temp
@@ -375,75 +374,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum35.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -2, ('V', 'T') : 1,
-('Q', 'Q') : 7, ('N', 'A') : -1, ('Z', 'Y') : -1, ('W', 'R') : 0,
-('Q', 'A') : 0, ('S', 'D') : -1, ('H', 'H') : 12, ('S', 'H') : -1,
-('H', 'D') : 0, ('L', 'N') : -2, ('W', 'A') : -2, ('Y', 'M') : 0,
-('G', 'R') : -2, ('Y', 'I') : 0, ('Y', 'E') : -1, ('B', 'Y') : -2,
-('Y', 'A') : -1, ('V', 'D') : -2, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 1, ('E', 'C') : -1, ('Y', 'Q') : 0, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 15, ('M', 'R') : 0, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 10, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 1, ('T', 'F') : -1, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -2, ('P', 'L') : -3, ('K', 'G') : -1, ('M', 'N') : -1,
-('P', 'H') : -1, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : 0,
-('T', 'M') : 0, ('Z', 'C') : -2, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -3, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -1, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 0, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -2, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -1, ('Q', 'R') : 2,
-('N', 'N') : 7, ('W', 'M') : 1, ('Q', 'C') : -3, ('W', 'I') : -1,
-('S', 'C') : -3, ('L', 'A') : -2, ('S', 'G') : 1, ('L', 'E') : -1,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : 0,
-('M', 'Q') : -1, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : 1,
-('T', 'A') : 0, ('T', 'P') : 0, ('B', 'P') : -1, ('T', 'E') : -1,
-('V', 'N') : -2, ('P', 'G') : -2, ('M', 'A') : 0, ('K', 'H') : -2,
-('V', 'R') : -1, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : 0, ('I', 'G') : -3,
-('P', 'K') : 0, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -4,
-('Z', 'D') : 1, ('F', 'R') : -1, ('X', 'K') : 0, ('Q', 'D') : -1,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : -1,
-('B', 'L') : -2, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -1,
-('B', 'D') : 5, ('D', 'A') : -1, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -1, ('S', 'R') : -1, ('W', 'D') : -3, ('V', 'Y') : 0,
-('W', 'L') : 0, ('H', 'R') : -1, ('W', 'H') : -4, ('H', 'N') : 1,
-('W', 'T') : -2, ('T', 'T') : 5, ('S', 'F') : -1, ('W', 'P') : -4,
-('L', 'D') : -2, ('B', 'I') : -2, ('L', 'H') : -2, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 5, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -2, ('Z', 'S') : 0, ('Y', 'C') : -5, ('G', 'D') : -2,
-('B', 'V') : -2, ('E', 'A') : -1, ('Y', 'W') : 3, ('E', 'E') : 6,
-('Y', 'S') : -1, ('C', 'N') : -1, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : 0, ('V', 'K') : -2,
-('K', 'Q') : 0, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -1, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -2, ('W', 'W') : 16, ('M', 'H') : 1, ('P', 'N') : -2,
-('K', 'A') : 0, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : 0, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -1,
-('K', 'C') : -2, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -4, ('Z', 'Q') : 4, ('X', 'Z') : 0, ('F', 'G') : -3,
-('B', 'E') : 0, ('X', 'V') : 0, ('F', 'K') : -1, ('B', 'A') : -1,
-('X', 'R') : -1, ('D', 'D') : 8, ('W', 'G') : -1, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : 0, ('H', 'Q') : -1,
-('L', 'C') : -2, ('W', 'N') : -2, ('S', 'A') : 1, ('L', 'G') : -3,
-('W', 'S') : -2, ('S', 'E') : 0, ('H', 'E') : -1, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -1, ('Y', 'L') : 0, ('Y', 'H') : 0,
-('Y', 'D') : -2, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 7,
-('G', 'C') : -3, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : 0, ('A', 'A') : 5, ('P', 'Q') : 0, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -3, ('P', 'E') : 0, ('M', 'C') : -4,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -2, ('M', 'G') : -1,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : 0,
-('I', 'A') : -1, ('P', 'I') : -1, ('R', 'R') : 8, ('X', 'M') : 0,
-('L', 'I') : 2, ('X', 'I') : 0, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -3, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -3,
-('B', 'F') : -2, ('F', 'L') : 2, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -2, ('V', 'T'): 1,
+('Q', 'Q'): 7, ('N', 'A'): -1, ('Z', 'Y'): -1, ('W', 'R'): 0,
+('Q', 'A'): 0, ('S', 'D'): -1, ('H', 'H'): 12, ('S', 'H'): -1,
+('H', 'D'): 0, ('L', 'N'): -2, ('W', 'A'): -2, ('Y', 'M'): 0,
+('G', 'R'): -2, ('Y', 'I'): 0, ('Y', 'E'): -1, ('B', 'Y'): -2,
+('Y', 'A'): -1, ('V', 'D'): -2, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 1, ('E', 'C'): -1, ('Y', 'Q'): 0, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 15, ('M', 'R'): 0, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 10, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 1, ('T', 'F'): -1, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -2, ('P', 'L'): -3, ('K', 'G'): -1, ('M', 'N'): -1,
+('P', 'H'): -1, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): 0,
+('T', 'M'): 0, ('Z', 'C'): -2, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -3, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -1, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 0, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -2, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -1, ('Q', 'R'): 2,
+('N', 'N'): 7, ('W', 'M'): 1, ('Q', 'C'): -3, ('W', 'I'): -1,
+('S', 'C'): -3, ('L', 'A'): -2, ('S', 'G'): 1, ('L', 'E'): -1,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): 0,
+('M', 'Q'): -1, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): 1,
+('T', 'A'): 0, ('T', 'P'): 0, ('B', 'P'): -1, ('T', 'E'): -1,
+('V', 'N'): -2, ('P', 'G'): -2, ('M', 'A'): 0, ('K', 'H'): -2,
+('V', 'R'): -1, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): 0, ('I', 'G'): -3,
+('P', 'K'): 0, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -4,
+('Z', 'D'): 1, ('F', 'R'): -1, ('X', 'K'): 0, ('Q', 'D'): -1,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): -1,
+('B', 'L'): -2, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -1,
+('B', 'D'): 5, ('D', 'A'): -1, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -1, ('S', 'R'): -1, ('W', 'D'): -3, ('V', 'Y'): 0,
+('W', 'L'): 0, ('H', 'R'): -1, ('W', 'H'): -4, ('H', 'N'): 1,
+('W', 'T'): -2, ('T', 'T'): 5, ('S', 'F'): -1, ('W', 'P'): -4,
+('L', 'D'): -2, ('B', 'I'): -2, ('L', 'H'): -2, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 5, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -2, ('Z', 'S'): 0, ('Y', 'C'): -5, ('G', 'D'): -2,
+('B', 'V'): -2, ('E', 'A'): -1, ('Y', 'W'): 3, ('E', 'E'): 6,
+('Y', 'S'): -1, ('C', 'N'): -1, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): 0, ('V', 'K'): -2,
+('K', 'Q'): 0, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -1, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -2, ('W', 'W'): 16, ('M', 'H'): 1, ('P', 'N'): -2,
+('K', 'A'): 0, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): 0, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -1,
+('K', 'C'): -2, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -4, ('Z', 'Q'): 4, ('X', 'Z'): 0, ('F', 'G'): -3,
+('B', 'E'): 0, ('X', 'V'): 0, ('F', 'K'): -1, ('B', 'A'): -1,
+('X', 'R'): -1, ('D', 'D'): 8, ('W', 'G'): -1, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): 0, ('H', 'Q'): -1,
+('L', 'C'): -2, ('W', 'N'): -2, ('S', 'A'): 1, ('L', 'G'): -3,
+('W', 'S'): -2, ('S', 'E'): 0, ('H', 'E'): -1, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -1, ('Y', 'L'): 0, ('Y', 'H'): 0,
+('Y', 'D'): -2, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 7,
+('G', 'C'): -3, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): 0, ('A', 'A'): 5, ('P', 'Q'): 0, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -3, ('P', 'E'): 0, ('M', 'C'): -4,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -2, ('M', 'G'): -1,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): 0,
+('I', 'A'): -1, ('P', 'I'): -1, ('R', 'R'): 8, ('X', 'M'): 0,
+('L', 'I'): 2, ('X', 'I'): 0, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -3, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -3,
+('B', 'F'): -2, ('F', 'L'): 2, ('X', 'Q'): -1, ('B', 'B'): 5
 }
 blosum35 = _temp()
 del _temp
@@ -452,75 +451,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum40.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 1,
-('Q', 'Q') : 8, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -2,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 13, ('S', 'H') : -1,
-('H', 'D') : 0, ('L', 'N') : -3, ('W', 'A') : -3, ('Y', 'M') : 1,
-('G', 'R') : -3, ('Y', 'I') : 0, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : 0, ('E', 'C') : -2, ('Y', 'Q') : -1, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 16, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 11, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -1, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -2, ('I', 'H') : -3, ('I', 'D') : -4,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -2, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -2, ('W', 'E') : -2, ('Q', 'R') : 2,
-('N', 'N') : 8, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -2,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : 0, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 4, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : -3,
-('G', 'A') : 1, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : -1, ('T', 'I') : -1, ('C', 'D') : -2, ('V', 'F') : 0,
-('T', 'A') : 0, ('T', 'P') : 0, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -1, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -2, ('P', 'C') : -5, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 7, ('K', 'D') : 0, ('I', 'C') : -4,
-('Z', 'D') : 1, ('F', 'R') : -2, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -3, ('B', 'H') : 0, ('F', 'F') : 9, ('X', 'W') : -2,
-('B', 'D') : 6, ('D', 'A') : -1, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -1, ('H', 'R') : 0, ('W', 'H') : -5, ('H', 'N') : 1,
-('W', 'T') : -4, ('T', 'T') : 6, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -3, ('B', 'I') : -3, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 6, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -4, ('G', 'D') : -2,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 3, ('E', 'E') : 7,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -4, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -2, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 19, ('M', 'H') : 1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -1,
-('X', 'R') : -1, ('D', 'D') : 9, ('W', 'G') : -2, ('Z', 'F') : -4,
-('S', 'Q') : 1, ('W', 'C') : -6, ('W', 'K') : -2, ('H', 'Q') : 0,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -5, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : 0, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 8,
-('G', 'C') : -3, ('E', 'N') : -1, ('Y', 'T') : -1, ('Y', 'P') : -3,
-('T', 'K') : 0, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -3, ('P', 'E') : 0, ('M', 'C') : -3,
-('K', 'N') : 0, ('I', 'I') : 6, ('P', 'A') : -2, ('M', 'G') : -2,
-('T', 'S') : 2, ('I', 'E') : -4, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -2, ('R', 'R') : 9, ('X', 'M') : 0,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -3, ('F', 'L') : 2, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 1,
+('Q', 'Q'): 8, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -2,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 13, ('S', 'H'): -1,
+('H', 'D'): 0, ('L', 'N'): -3, ('W', 'A'): -3, ('Y', 'M'): 1,
+('G', 'R'): -3, ('Y', 'I'): 0, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): 0, ('E', 'C'): -2, ('Y', 'Q'): -1, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 16, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 11, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -1, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -2, ('I', 'H'): -3, ('I', 'D'): -4,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -2, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -2, ('W', 'E'): -2, ('Q', 'R'): 2,
+('N', 'N'): 8, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -2,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): 0, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 4, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): -3,
+('G', 'A'): 1, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): -1, ('T', 'I'): -1, ('C', 'D'): -2, ('V', 'F'): 0,
+('T', 'A'): 0, ('T', 'P'): 0, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -1, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -2, ('P', 'C'): -5, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 7, ('K', 'D'): 0, ('I', 'C'): -4,
+('Z', 'D'): 1, ('F', 'R'): -2, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -3, ('B', 'H'): 0, ('F', 'F'): 9, ('X', 'W'): -2,
+('B', 'D'): 6, ('D', 'A'): -1, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -1, ('H', 'R'): 0, ('W', 'H'): -5, ('H', 'N'): 1,
+('W', 'T'): -4, ('T', 'T'): 6, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -3, ('B', 'I'): -3, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 6, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -4, ('G', 'D'): -2,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 3, ('E', 'E'): 7,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -4, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -2, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 19, ('M', 'H'): 1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -1,
+('X', 'R'): -1, ('D', 'D'): 9, ('W', 'G'): -2, ('Z', 'F'): -4,
+('S', 'Q'): 1, ('W', 'C'): -6, ('W', 'K'): -2, ('H', 'Q'): 0,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -5, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): 0, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 8,
+('G', 'C'): -3, ('E', 'N'): -1, ('Y', 'T'): -1, ('Y', 'P'): -3,
+('T', 'K'): 0, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -3, ('P', 'E'): 0, ('M', 'C'): -3,
+('K', 'N'): 0, ('I', 'I'): 6, ('P', 'A'): -2, ('M', 'G'): -2,
+('T', 'S'): 2, ('I', 'E'): -4, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -2, ('R', 'R'): 9, ('X', 'M'): 0,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -3, ('F', 'L'): 2, ('X', 'Q'): -1, ('B', 'B'): 5
 }
 blosum40 = _temp()
 del _temp
@@ -529,75 +528,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum45.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -2,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 10, ('S', 'H') : -1,
-('H', 'D') : 0, ('L', 'N') : -3, ('W', 'A') : -2, ('Y', 'M') : 0,
-('G', 'R') : -2, ('Y', 'I') : 0, ('Y', 'E') : -2, ('B', 'Y') : -2,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 12, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 9, ('V', 'I') : 3, ('V', 'S') : -1,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -1, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -2, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -2,
-('S', 'C') : -1, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -2,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : 0,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -2, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -3,
-('Z', 'D') : 1, ('F', 'R') : -2, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -3, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -2,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -2, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -2, ('W', 'P') : -3,
-('L', 'D') : -3, ('B', 'I') : -3, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -1, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 3, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -3, ('I', 'N') : -2, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 15, ('M', 'H') : 0, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -1,
-('X', 'R') : -1, ('D', 'D') : 7, ('W', 'G') : -2, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : -2, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -3,
-('W', 'S') : -4, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : 0, ('Y', 'H') : 2,
-('Y', 'D') : -2, ('E', 'R') : 0, ('X', 'P') : -1, ('G', 'G') : 7,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -1, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -3, ('P', 'E') : 0, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -2,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -2, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -3, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -2,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 10, ('S', 'H'): -1,
+('H', 'D'): 0, ('L', 'N'): -3, ('W', 'A'): -2, ('Y', 'M'): 0,
+('G', 'R'): -2, ('Y', 'I'): 0, ('Y', 'E'): -2, ('B', 'Y'): -2,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 12, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 9, ('V', 'I'): 3, ('V', 'S'): -1,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -1, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -2, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -2,
+('S', 'C'): -1, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -2,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): 0,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -2, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -3,
+('Z', 'D'): 1, ('F', 'R'): -2, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -3, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -2,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -2, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -2, ('W', 'P'): -3,
+('L', 'D'): -3, ('B', 'I'): -3, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -1, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 3, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -3, ('I', 'N'): -2, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 15, ('M', 'H'): 0, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -1,
+('X', 'R'): -1, ('D', 'D'): 7, ('W', 'G'): -2, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): -2, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -3,
+('W', 'S'): -4, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): 0, ('Y', 'H'): 2,
+('Y', 'D'): -2, ('E', 'R'): 0, ('X', 'P'): -1, ('G', 'G'): 7,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -1, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -3, ('P', 'E'): 0, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -2,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -2, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -3, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum45 = _temp()
 del _temp
@@ -606,75 +605,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum50.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 7, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 10, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : 0,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 13, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 10, ('V', 'I') : 4, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -1, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 4, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -3,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -1,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -4, ('T', 'L') : -1, ('V', 'K') : -3,
-('K', 'Q') : 2, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 15, ('M', 'H') : -1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -2,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 8, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 8,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 2, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -4, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 7, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 10, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): 0,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 13, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 10, ('V', 'I'): 4, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -1, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 4, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -3,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -1,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -4, ('T', 'L'): -1, ('V', 'K'): -3,
+('K', 'Q'): 2, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 15, ('M', 'H'): -1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -2,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 8, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 8,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 2, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -4, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 5
 }
 blosum50 = _temp()
 del _temp
@@ -683,75 +682,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum55.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 7, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 10, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : 0,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 13, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 10, ('V', 'I') : 4, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -1, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -1, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 4, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 3, ('E', 'D') : 2, ('Y', 'R') : -1,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -3,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -1,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -4, ('T', 'L') : -1, ('V', 'K') : -3,
-('K', 'Q') : 2, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 15, ('M', 'H') : -1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 3, ('K', 'E') : 1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -2,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 8, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -5, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 8,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 2, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -4, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 5
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 7, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 10, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): 0,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 13, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 10, ('V', 'I'): 4, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -1, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -1, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 4, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 3, ('E', 'D'): 2, ('Y', 'R'): -1,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -3,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -1,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -4, ('T', 'L'): -1, ('V', 'K'): -3,
+('K', 'Q'): 2, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 15, ('M', 'H'): -1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 3, ('K', 'E'): 1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -2,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 8, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -5, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 8,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 2, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -4, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 5
 }
 blosum55 = _temp()
 del _temp
@@ -760,75 +759,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum60.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 5, ('N', 'A') : -1, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 7, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -3, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -2,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 6,
-('G', 'N') : 0, ('E', 'C') : -3, ('Y', 'Q') : -1, ('Z', 'Z') : 3,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -1, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 4, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -1, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -2, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -2,
-('S', 'C') : -1, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : 0, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -2, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -3,
-('P', 'K') : -1, ('M', 'M') : 5, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -1, ('Z', 'L') : -2, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -3, ('B', 'H') : 0, ('F', 'F') : 6, ('X', 'W') : -2,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 1,
-('W', 'T') : -2, ('T', 'T') : 4, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -3, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -2, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -2, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 10, ('M', 'H') : -1, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -2, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -2, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -1, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -1, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -2, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -1,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -2,
-('T', 'S') : 1, ('I', 'E') : -3, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 5, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 1, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -3, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -3, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 5, ('N', 'A'): -1, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 7, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -3, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -2,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 6,
+('G', 'N'): 0, ('E', 'C'): -3, ('Y', 'Q'): -1, ('Z', 'Z'): 3,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -1, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 4, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -1, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -2, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -2,
+('S', 'C'): -1, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): 0, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -2, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -3,
+('P', 'K'): -1, ('M', 'M'): 5, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -1, ('Z', 'L'): -2, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -3, ('B', 'H'): 0, ('F', 'F'): 6, ('X', 'W'): -2,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 1,
+('W', 'T'): -2, ('T', 'T'): 4, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -3, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -2, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -2, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 10, ('M', 'H'): -1, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -2, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -2, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -1, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -1, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -2, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -1,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -2,
+('T', 'S'): 1, ('I', 'E'): -3, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 5, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 1, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -3, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -3, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum60 = _temp()
 del _temp
@@ -837,75 +836,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum62.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 5, ('N', 'A') : -2, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -3, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : 0, ('E', 'C') : -4, ('Y', 'Q') : -1, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -1, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -3, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -3, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -1, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -1, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : 0, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -3, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -2,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 5, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -1, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 6, ('X', 'W') : -2,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -2, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -4, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 1,
-('W', 'T') : -2, ('T', 'T') : 5, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -2, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -1, ('X', 'F') : -1,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -2, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -2, ('W', 'K') : -3, ('H', 'Q') : 0,
-('L', 'C') : -1, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -1, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -3, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -1,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -3, ('P', 'M') : -2, ('M', 'K') : -1,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 5, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 1, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -3, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -3, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 5, ('N', 'A'): -2, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -3, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): 0, ('E', 'C'): -4, ('Y', 'Q'): -1, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -1, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -3, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -3, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -1, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -1, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): 0, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -3, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -2,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 5, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -1, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 6, ('X', 'W'): -2,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -2, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -4, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 1,
+('W', 'T'): -2, ('T', 'T'): 5, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -2, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -1, ('X', 'F'): -1,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -2, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -2, ('W', 'K'): -3, ('H', 'Q'): 0,
+('L', 'C'): -1, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -1, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -3, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -1,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -3, ('P', 'M'): -2, ('M', 'K'): -1,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 5, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 1, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -3, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -3, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum62 = _temp()
 del _temp
@@ -914,75 +913,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum65.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -2, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -2, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -4, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -3, ('I', 'D') : -3,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -4, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -3, ('F', 'E') : -3, ('D', 'N') : 1, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -3, ('Z', 'I') : -3, ('Z', 'V') : -2,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -3, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -2,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : 0, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : 0, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 2, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : 0,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : 0, ('F', 'F') : 6, ('X', 'W') : -2,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -1,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 1,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -2, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -3, ('Z', 'S') : 0, ('Y', 'C') : -2, ('G', 'D') : -1,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -3, ('T', 'L') : -1, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 10, ('M', 'H') : -2, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -3, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -3,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -3, ('Z', 'F') : -3,
-('S', 'Q') : 0, ('W', 'C') : -2, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -1, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -3, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -1, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 1, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -4, ('X', 'Y') : -1, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -3, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -2, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -2, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -4, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -3, ('I', 'D'): -3,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -4, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -3, ('F', 'E'): -3, ('D', 'N'): 1, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -3, ('Z', 'I'): -3, ('Z', 'V'): -2,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -3, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -2,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): 0, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): 0, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 2, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): 0,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): 0, ('F', 'F'): 6, ('X', 'W'): -2,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -1,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 1,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -2, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -3, ('Z', 'S'): 0, ('Y', 'C'): -2, ('G', 'D'): -1,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -3, ('T', 'L'): -1, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 10, ('M', 'H'): -2, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -3, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -3,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -3, ('Z', 'F'): -3,
+('S', 'Q'): 0, ('W', 'C'): -2, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -1, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -3, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -1, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 1, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -4, ('X', 'Y'): -1, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -3, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum65 = _temp()
 del _temp
@@ -991,75 +990,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum70.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -2, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -1,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -3, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -4, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : -2, ('F', 'Q') : -3, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -4, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -2,
-('Z', 'W') : -3, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 4, ('L', 'Q') : -2, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -3,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -3, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -4,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : -1, ('F', 'F') : 6, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -3, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 0,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -4,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -4, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -3, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -3,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -2,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -3, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -3, ('W', 'K') : -3, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -3,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -3, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -1,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -2, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -1,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -3, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -4, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): -2, ('F', 'Q'): -3, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -4, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -2,
+('Z', 'W'): -3, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 4, ('L', 'Q'): -2, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -3,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -3, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -4,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): -1, ('F', 'F'): 6, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -3, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 0,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -4,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -4, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -3, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -3,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -2,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -3, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -3, ('W', 'K'): -3, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -3,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -3, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -1,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum70 = _temp()
 del _temp
@@ -1068,75 +1067,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum75.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -3,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -3, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -5, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -2, ('F', 'Q') : -4, ('Z', 'G') : -2, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -1, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -3, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -1, ('F', 'I') : 0, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -3, ('W', 'I') : -3,
-('S', 'C') : -1, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -2, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -3, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -2,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -3, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -1,
-('Z', 'D') : 1, ('F', 'R') : -3, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -2, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : -1, ('F', 'F') : 6, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -5, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -2, ('H', 'N') : 0,
-('W', 'T') : -3, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -4, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -2, ('E', 'Q') : 2,
-('Y', 'G') : -4, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 5,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : 0, ('X', 'B') : -2, ('B', 'M') : -3,
-('F', 'C') : -2, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -3, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -3, ('W', 'K') : -4, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : 0, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -3, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 4, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -1, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 4, ('P', 'A') : -1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -1, ('B', 'N') : 3,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -3,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -3, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -5, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -2, ('F', 'Q'): -4, ('Z', 'G'): -2, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -1, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -3, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -1, ('F', 'I'): 0, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -3, ('W', 'I'): -3,
+('S', 'C'): -1, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -2, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -3, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -2,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -3, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -1,
+('Z', 'D'): 1, ('F', 'R'): -3, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -2, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): -1, ('F', 'F'): 6, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -5, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -2, ('H', 'N'): 0,
+('W', 'T'): -3, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -4, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -2, ('E', 'Q'): 2,
+('Y', 'G'): -4, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 5,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): 0, ('X', 'B'): -2, ('B', 'M'): -3,
+('F', 'C'): -2, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -3, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -3, ('W', 'K'): -4, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): 0, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -3, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 4, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -1, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 4, ('P', 'A'): -1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -1, ('B', 'N'): 3,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum75 = _temp()
 del _temp
@@ -1145,75 +1144,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum80.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -2, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -3, ('B', 'Y') : -3,
-('Y', 'A') : -2, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -5, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 1, ('T', 'F') : -2, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -4,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -4, ('X', 'H') : -2, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -1, ('F', 'I') : -1, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 6, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -3,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -3, ('G', 'Q') : -2,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 1, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -4, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -4, ('P', 'G') : -3, ('M', 'A') : -1, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -4, ('B', 'H') : -1, ('F', 'F') : 6, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -6, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -4, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -4, ('Z', 'S') : 0, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -3, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -2, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : 0, ('X', 'B') : -2, ('B', 'M') : -3,
-('F', 'C') : -3, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 1, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -1, ('D', 'D') : 6, ('W', 'G') : -4, ('Z', 'F') : -4,
-('S', 'Q') : 0, ('W', 'C') : -3, ('W', 'K') : -4, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -4, ('S', 'E') : 0, ('H', 'E') : 0, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -1,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -2, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -2, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -3, ('B', 'Y'): -3,
+('Y', 'A'): -2, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -5, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 1, ('T', 'F'): -2, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -4,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -4, ('X', 'H'): -2, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -1, ('F', 'I'): -1, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 6, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -3,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -3, ('G', 'Q'): -2,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 1, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -4, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -4, ('P', 'G'): -3, ('M', 'A'): -1, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -4, ('B', 'H'): -1, ('F', 'F'): 6, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -6, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -4, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -4, ('Z', 'S'): 0, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -3, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -2, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): 0, ('X', 'B'): -2, ('B', 'M'): -3,
+('F', 'C'): -3, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 1, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -1, ('D', 'D'): 6, ('W', 'G'): -4, ('Z', 'F'): -4,
+('S', 'Q'): 0, ('W', 'C'): -3, ('W', 'K'): -4, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -4, ('S', 'E'): 0, ('H', 'E'): 0, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -1,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -2, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum80 = _temp()
 del _temp
@@ -1222,75 +1221,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum85.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -1,
-('H', 'D') : -2, ('L', 'N') : -4, ('W', 'A') : -3, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -3, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 7,
-('G', 'N') : -1, ('E', 'C') : -5, ('Y', 'Q') : -2, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -5,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -2, ('D', 'R') : -2,
-('B', 'W') : -5, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -4, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -1, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -4, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -3,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -5, ('V', 'F') : -1,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -3, ('T', 'E') : -1,
-('V', 'N') : -4, ('P', 'G') : -3, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -2, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 1, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -2, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -6, ('V', 'Y') : -2,
-('W', 'L') : -3, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 5, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -5, ('L', 'H') : -3, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 4, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : -1, ('Y', 'C') : -3, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -2, ('C', 'N') : -4, ('V', 'C') : -1, ('T', 'H') : -2,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 4,
-('X', 'N') : -2, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -4,
-('B', 'E') : 0, ('X', 'V') : -1, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -4, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -4, ('W', 'K') : -5, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 2,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -4, ('T', 'G') : -2, ('I', 'Q') : -4, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -3, ('P', 'E') : -2, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -1,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -4, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -1,
+('H', 'D'): -2, ('L', 'N'): -4, ('W', 'A'): -3, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -3, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 7,
+('G', 'N'): -1, ('E', 'C'): -5, ('Y', 'Q'): -2, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -5,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -2, ('D', 'R'): -2,
+('B', 'W'): -5, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -4, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -1, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -4, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -3,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -5, ('V', 'F'): -1,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -3, ('T', 'E'): -1,
+('V', 'N'): -4, ('P', 'G'): -3, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -2, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 1, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -2, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -6, ('V', 'Y'): -2,
+('W', 'L'): -3, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 5, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -5, ('L', 'H'): -3, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 4, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): -1, ('Y', 'C'): -3, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -2, ('C', 'N'): -4, ('V', 'C'): -1, ('T', 'H'): -2,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 4,
+('X', 'N'): -2, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -4,
+('B', 'E'): 0, ('X', 'V'): -1, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -4, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -4, ('W', 'K'): -5, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 2,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -4, ('T', 'G'): -2, ('I', 'Q'): -4, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -3, ('P', 'E'): -2, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -1,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -4, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum85 = _temp()
 del _temp
@@ -1299,75 +1298,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum90.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -2, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -3, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -2,
-('H', 'D') : -2, ('L', 'N') : -4, ('W', 'A') : -4, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -2, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -3, ('V', 'D') : -5, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : -1, ('E', 'C') : -6, ('Y', 'Q') : -3, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -3, ('I', 'H') : -4, ('I', 'D') : -5,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -2, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -2, ('Z', 'K') : 1, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -5, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -5, ('Q', 'R') : 1,
-('N', 'N') : 7, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -4,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -3,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : 0, ('T', 'I') : -1, ('C', 'D') : -5, ('V', 'F') : -2,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -3, ('T', 'E') : -1,
-('V', 'N') : -4, ('P', 'G') : -3, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -5,
-('P', 'K') : -2, ('M', 'M') : 7, ('K', 'D') : -1, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -2, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -3,
-('B', 'D') : 4, ('D', 'A') : -3, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -6, ('V', 'Y') : -3,
-('W', 'L') : -3, ('H', 'R') : 0, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 6, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -5, ('L', 'H') : -4, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 5, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : -1, ('Y', 'C') : -4, ('G', 'D') : -2,
-('B', 'V') : -4, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -5, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -4, ('I', 'N') : -4, ('K', 'I') : -4, ('M', 'D') : -4,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 4,
-('X', 'N') : -2, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -4, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 0, ('X', 'V') : -2, ('F', 'K') : -4, ('B', 'A') : -2,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -4, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -4, ('W', 'K') : -5, ('H', 'Q') : 1,
-('L', 'C') : -2, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -3,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 1,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -2, ('G', 'G') : 6,
-('G', 'C') : -4, ('E', 'N') : -1, ('Y', 'T') : -2, ('Y', 'P') : -4,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -4, ('T', 'G') : -3, ('I', 'Q') : -4, ('Z', 'T') : -1,
-('C', 'R') : -5, ('V', 'P') : -3, ('P', 'E') : -2, ('M', 'C') : -2,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -2,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -2, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -3, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -2,
+('H', 'D'): -2, ('L', 'N'): -4, ('W', 'A'): -4, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -2, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -3, ('V', 'D'): -5, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): -1, ('E', 'C'): -6, ('Y', 'Q'): -3, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -3, ('I', 'H'): -4, ('I', 'D'): -5,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -2, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -2, ('Z', 'K'): 1, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -5, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -5, ('Q', 'R'): 1,
+('N', 'N'): 7, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -4,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -3,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): 0, ('T', 'I'): -1, ('C', 'D'): -5, ('V', 'F'): -2,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -3, ('T', 'E'): -1,
+('V', 'N'): -4, ('P', 'G'): -3, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -5,
+('P', 'K'): -2, ('M', 'M'): 7, ('K', 'D'): -1, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -2, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -3,
+('B', 'D'): 4, ('D', 'A'): -3, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -6, ('V', 'Y'): -3,
+('W', 'L'): -3, ('H', 'R'): 0, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 6, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -5, ('L', 'H'): -4, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 5, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): -1, ('Y', 'C'): -4, ('G', 'D'): -2,
+('B', 'V'): -4, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -5, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -4, ('I', 'N'): -4, ('K', 'I'): -4, ('M', 'D'): -4,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 4,
+('X', 'N'): -2, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -4, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 0, ('X', 'V'): -2, ('F', 'K'): -4, ('B', 'A'): -2,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -4, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -4, ('W', 'K'): -5, ('H', 'Q'): 1,
+('L', 'C'): -2, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -3,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 1,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -2, ('G', 'G'): 6,
+('G', 'C'): -4, ('E', 'N'): -1, ('Y', 'T'): -2, ('Y', 'P'): -4,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -4, ('T', 'G'): -3, ('I', 'Q'): -4, ('Z', 'T'): -1,
+('C', 'R'): -5, ('V', 'P'): -3, ('P', 'E'): -2, ('M', 'C'): -2,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -2,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum90 = _temp()
 del _temp
@@ -1376,75 +1375,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/blosum95.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : -2, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -4, ('W', 'R') : -4,
-('Q', 'A') : -1, ('S', 'D') : -1, ('H', 'H') : 9, ('S', 'H') : -2,
-('H', 'D') : -2, ('L', 'N') : -5, ('W', 'A') : -4, ('Y', 'M') : -3,
-('G', 'R') : -4, ('Y', 'I') : -2, ('Y', 'E') : -4, ('B', 'Y') : -4,
-('Y', 'A') : -3, ('V', 'D') : -5, ('B', 'S') : -1, ('Y', 'Y') : 8,
-('G', 'N') : -1, ('E', 'C') : -6, ('Y', 'Q') : -3, ('Z', 'Z') : 4,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : -1, ('P', 'P') : 8, ('V', 'I') : 3, ('V', 'S') : -3,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -3, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -3, ('I', 'H') : -4, ('I', 'D') : -5,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -3, ('M', 'N') : -3,
-('P', 'H') : -3, ('F', 'Q') : -4, ('Z', 'G') : -3, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -2, ('Z', 'K') : 0, ('F', 'A') : -3,
-('Z', 'W') : -4, ('F', 'E') : -5, ('D', 'N') : 1, ('B', 'K') : -1,
-('X', 'X') : -2, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -4, ('Z', 'I') : -4, ('Z', 'V') : -3,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -5, ('Q', 'R') : 0,
-('N', 'N') : 7, ('W', 'M') : -2, ('Q', 'C') : -4, ('W', 'I') : -4,
-('S', 'C') : -2, ('L', 'A') : -2, ('S', 'G') : -1, ('L', 'E') : -4,
-('W', 'Q') : -3, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -5, ('Y', 'N') : -3, ('G', 'Q') : -3,
-('Y', 'F') : 3, ('C', 'A') : -1, ('V', 'L') : 0, ('G', 'E') : -3,
-('G', 'A') : -1, ('K', 'R') : 2, ('E', 'D') : 1, ('Y', 'R') : -3,
-('M', 'Q') : -1, ('T', 'I') : -2, ('C', 'D') : -5, ('V', 'F') : -2,
-('T', 'A') : 0, ('T', 'P') : -2, ('B', 'P') : -3, ('T', 'E') : -2,
-('V', 'N') : -4, ('P', 'G') : -4, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -4, ('P', 'C') : -5, ('M', 'E') : -3, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -1, ('I', 'G') : -6,
-('P', 'K') : -2, ('M', 'M') : 7, ('K', 'D') : -2, ('I', 'C') : -2,
-('Z', 'D') : 0, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : -1,
-('X', 'G') : -3, ('Z', 'L') : -4, ('X', 'C') : -3, ('Z', 'H') : 0,
-('B', 'L') : -5, ('B', 'H') : -1, ('F', 'F') : 7, ('X', 'W') : -4,
-('B', 'D') : 4, ('D', 'A') : -3, ('S', 'L') : -3, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -2, ('W', 'D') : -6, ('V', 'Y') : -3,
-('W', 'L') : -3, ('H', 'R') : -1, ('W', 'H') : -3, ('H', 'N') : 0,
-('W', 'T') : -4, ('T', 'T') : 6, ('S', 'F') : -3, ('W', 'P') : -5,
-('L', 'D') : -5, ('B', 'I') : -5, ('L', 'H') : -4, ('S', 'N') : 0,
-('B', 'T') : -1, ('L', 'L') : 5, ('Y', 'K') : -3, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : -1, ('Y', 'C') : -4, ('G', 'D') : -2,
-('B', 'V') : -5, ('E', 'A') : -1, ('Y', 'W') : 2, ('E', 'E') : 6,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -2,
-('P', 'R') : -3, ('V', 'G') : -5, ('T', 'L') : -2, ('V', 'K') : -3,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -4, ('T', 'D') : -2,
-('P', 'F') : -5, ('I', 'N') : -4, ('K', 'I') : -4, ('M', 'D') : -5,
-('V', 'W') : -3, ('W', 'W') : 11, ('M', 'H') : -3, ('P', 'N') : -3,
-('K', 'A') : -1, ('M', 'L') : 2, ('K', 'E') : 0, ('Z', 'E') : 4,
-('X', 'N') : -2, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -5, ('B', 'Q') : -1, ('X', 'B') : -2, ('B', 'M') : -4,
-('F', 'C') : -3, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 0, ('X', 'V') : -2, ('F', 'K') : -4, ('B', 'A') : -3,
-('X', 'R') : -2, ('D', 'D') : 7, ('W', 'G') : -5, ('Z', 'F') : -4,
-('S', 'Q') : -1, ('W', 'C') : -4, ('W', 'K') : -5, ('H', 'Q') : 1,
-('L', 'C') : -3, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -4, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -3,
-('H', 'A') : -3, ('S', 'M') : -3, ('Y', 'L') : -2, ('Y', 'H') : 1,
-('Y', 'D') : -5, ('E', 'R') : -1, ('X', 'P') : -3, ('G', 'G') : 6,
-('G', 'C') : -5, ('E', 'N') : -1, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : -1, ('A', 'A') : 5, ('P', 'Q') : -2, ('T', 'C') : -2,
-('V', 'H') : -4, ('T', 'G') : -3, ('I', 'Q') : -4, ('Z', 'T') : -2,
-('C', 'R') : -5, ('V', 'P') : -4, ('P', 'E') : -2, ('M', 'C') : -3,
-('K', 'N') : 0, ('I', 'I') : 5, ('P', 'A') : -1, ('M', 'G') : -4,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -3, ('M', 'K') : -2,
-('I', 'A') : -2, ('P', 'I') : -4, ('R', 'R') : 7, ('X', 'M') : -2,
-('L', 'I') : 1, ('X', 'I') : -2, ('Z', 'B') : 0, ('X', 'E') : -2,
-('Z', 'N') : -1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 4,
-('F', 'D') : -5, ('X', 'Y') : -2, ('Z', 'R') : -1, ('F', 'H') : -2,
-('B', 'F') : -5, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): -2, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -4, ('W', 'R'): -4,
+('Q', 'A'): -1, ('S', 'D'): -1, ('H', 'H'): 9, ('S', 'H'): -2,
+('H', 'D'): -2, ('L', 'N'): -5, ('W', 'A'): -4, ('Y', 'M'): -3,
+('G', 'R'): -4, ('Y', 'I'): -2, ('Y', 'E'): -4, ('B', 'Y'): -4,
+('Y', 'A'): -3, ('V', 'D'): -5, ('B', 'S'): -1, ('Y', 'Y'): 8,
+('G', 'N'): -1, ('E', 'C'): -6, ('Y', 'Q'): -3, ('Z', 'Z'): 4,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): -1, ('P', 'P'): 8, ('V', 'I'): 3, ('V', 'S'): -3,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -3, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -3, ('I', 'H'): -4, ('I', 'D'): -5,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -3, ('M', 'N'): -3,
+('P', 'H'): -3, ('F', 'Q'): -4, ('Z', 'G'): -3, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -2, ('Z', 'K'): 0, ('F', 'A'): -3,
+('Z', 'W'): -4, ('F', 'E'): -5, ('D', 'N'): 1, ('B', 'K'): -1,
+('X', 'X'): -2, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -4, ('Z', 'I'): -4, ('Z', 'V'): -3,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -5, ('Q', 'R'): 0,
+('N', 'N'): 7, ('W', 'M'): -2, ('Q', 'C'): -4, ('W', 'I'): -4,
+('S', 'C'): -2, ('L', 'A'): -2, ('S', 'G'): -1, ('L', 'E'): -4,
+('W', 'Q'): -3, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -5, ('Y', 'N'): -3, ('G', 'Q'): -3,
+('Y', 'F'): 3, ('C', 'A'): -1, ('V', 'L'): 0, ('G', 'E'): -3,
+('G', 'A'): -1, ('K', 'R'): 2, ('E', 'D'): 1, ('Y', 'R'): -3,
+('M', 'Q'): -1, ('T', 'I'): -2, ('C', 'D'): -5, ('V', 'F'): -2,
+('T', 'A'): 0, ('T', 'P'): -2, ('B', 'P'): -3, ('T', 'E'): -2,
+('V', 'N'): -4, ('P', 'G'): -4, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -4, ('P', 'C'): -5, ('M', 'E'): -3, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -1, ('I', 'G'): -6,
+('P', 'K'): -2, ('M', 'M'): 7, ('K', 'D'): -2, ('I', 'C'): -2,
+('Z', 'D'): 0, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): -1,
+('X', 'G'): -3, ('Z', 'L'): -4, ('X', 'C'): -3, ('Z', 'H'): 0,
+('B', 'L'): -5, ('B', 'H'): -1, ('F', 'F'): 7, ('X', 'W'): -4,
+('B', 'D'): 4, ('D', 'A'): -3, ('S', 'L'): -3, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -2, ('W', 'D'): -6, ('V', 'Y'): -3,
+('W', 'L'): -3, ('H', 'R'): -1, ('W', 'H'): -3, ('H', 'N'): 0,
+('W', 'T'): -4, ('T', 'T'): 6, ('S', 'F'): -3, ('W', 'P'): -5,
+('L', 'D'): -5, ('B', 'I'): -5, ('L', 'H'): -4, ('S', 'N'): 0,
+('B', 'T'): -1, ('L', 'L'): 5, ('Y', 'K'): -3, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): -1, ('Y', 'C'): -4, ('G', 'D'): -2,
+('B', 'V'): -5, ('E', 'A'): -1, ('Y', 'W'): 2, ('E', 'E'): 6,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -2,
+('P', 'R'): -3, ('V', 'G'): -5, ('T', 'L'): -2, ('V', 'K'): -3,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -4, ('T', 'D'): -2,
+('P', 'F'): -5, ('I', 'N'): -4, ('K', 'I'): -4, ('M', 'D'): -5,
+('V', 'W'): -3, ('W', 'W'): 11, ('M', 'H'): -3, ('P', 'N'): -3,
+('K', 'A'): -1, ('M', 'L'): 2, ('K', 'E'): 0, ('Z', 'E'): 4,
+('X', 'N'): -2, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -5, ('B', 'Q'): -1, ('X', 'B'): -2, ('B', 'M'): -4,
+('F', 'C'): -3, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 0, ('X', 'V'): -2, ('F', 'K'): -4, ('B', 'A'): -3,
+('X', 'R'): -2, ('D', 'D'): 7, ('W', 'G'): -5, ('Z', 'F'): -4,
+('S', 'Q'): -1, ('W', 'C'): -4, ('W', 'K'): -5, ('H', 'Q'): 1,
+('L', 'C'): -3, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -4, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -3,
+('H', 'A'): -3, ('S', 'M'): -3, ('Y', 'L'): -2, ('Y', 'H'): 1,
+('Y', 'D'): -5, ('E', 'R'): -1, ('X', 'P'): -3, ('G', 'G'): 6,
+('G', 'C'): -5, ('E', 'N'): -1, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): -1, ('A', 'A'): 5, ('P', 'Q'): -2, ('T', 'C'): -2,
+('V', 'H'): -4, ('T', 'G'): -3, ('I', 'Q'): -4, ('Z', 'T'): -2,
+('C', 'R'): -5, ('V', 'P'): -4, ('P', 'E'): -2, ('M', 'C'): -3,
+('K', 'N'): 0, ('I', 'I'): 5, ('P', 'A'): -1, ('M', 'G'): -4,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -3, ('M', 'K'): -2,
+('I', 'A'): -2, ('P', 'I'): -4, ('R', 'R'): 7, ('X', 'M'): -2,
+('L', 'I'): 1, ('X', 'I'): -2, ('Z', 'B'): 0, ('X', 'E'): -2,
+('Z', 'N'): -1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 4,
+('F', 'D'): -5, ('X', 'Y'): -2, ('Z', 'R'): -1, ('F', 'H'): -2,
+('B', 'F'): -5, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 blosum95 = _temp()
 del _temp
@@ -1453,59 +1452,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/feng.cmp
 def _temp():
     return {
-('W', 'F') : 3, ('L', 'R') : 2, ('I', 'I') : 6, ('Q', 'Q') : 6,
-('W', 'N') : 0, ('V', 'I') : 5, ('H', 'T') : 2, ('H', 'P') : 3,
-('W', 'V') : 3, ('Q', 'E') : 4, ('W', 'R') : 2, ('Q', 'A') : 3,
-('H', 'H') : 6, ('H', 'D') : 3, ('L', 'N') : 1, ('Y', 'M') : 2,
-('Y', 'I') : 3, ('Y', 'E') : 1, ('E', 'S') : 3, ('Y', 'A') : 2,
-('Y', 'Y') : 6, ('T', 'C') : 2, ('E', 'C') : 0, ('Y', 'Q') : 2,
-('E', 'G') : 4, ('V', 'A') : 5, ('C', 'C') : 6, ('M', 'R') : 2,
-('P', 'T') : 4, ('V', 'E') : 4, ('P', 'P') : 6, ('I', 'T') : 3,
-('K', 'S') : 3, ('R', 'G') : 3, ('I', 'P') : 2, ('R', 'C') : 2,
-('A', 'T') : 5, ('K', 'K') : 6, ('A', 'P') : 5, ('V', 'M') : 4,
-('I', 'D') : 1, ('K', 'C') : 0, ('K', 'G') : 2, ('R', 'S') : 3,
-('F', 'Q') : 1, ('F', 'A') : 2, ('V', 'V') : 6, ('M', 'N') : 1,
-('F', 'E') : 0, ('D', 'N') : 5, ('F', 'I') : 4, ('F', 'M') : 2,
-('M', 'S') : 1, ('S', 'S') : 6, ('L', 'Q') : 2, ('W', 'E') : 1,
-('W', 'A') : 2, ('W', 'M') : 3, ('H', 'S') : 3, ('W', 'I') : 2,
-('S', 'C') : 4, ('L', 'A') : 2, ('L', 'E') : 1, ('W', 'Q') : 1,
-('H', 'G') : 1, ('Q', 'N') : 3, ('H', 'C') : 2, ('L', 'M') : 5,
-('W', 'Y') : 3, ('Y', 'N') : 3, ('E', 'P') : 3, ('Y', 'F') : 5,
-('E', 'T') : 3, ('A', 'A') : 6, ('I', 'N') : 2, ('G', 'A') : 5,
-('Y', 'V') : 3, ('E', 'D') : 5, ('W', 'H') : 1, ('Y', 'R') : 1,
-('M', 'Q') : 2, ('P', 'S') : 4, ('R', 'H') : 4, ('A', 'C') : 2,
-('R', 'D') : 2, ('K', 'P') : 2, ('L', 'D') : 1, ('K', 'T') : 4,
-('V', 'N') : 2, ('M', 'A') : 2, ('K', 'H') : 3, ('V', 'R') : 2,
-('P', 'C') : 2, ('M', 'E') : 1, ('A', 'S') : 5, ('T', 'T') : 6,
-('R', 'T') : 3, ('I', 'G') : 2, ('R', 'P') : 3, ('K', 'D') : 3,
-('I', 'C') : 2, ('F', 'R') : 1, ('F', 'V') : 4, ('L', 'C') : 2,
-('F', 'F') : 6, ('D', 'A') : 4, ('F', 'N') : 1, ('W', 'D') : 0,
-('L', 'P') : 3, ('Q', 'S') : 3, ('N', 'C') : 2, ('N', 'G') : 3,
-('H', 'N') : 4, ('W', 'T') : 1, ('Q', 'G') : 2, ('W', 'P') : 2,
-('Q', 'C') : 1, ('N', 'S') : 5, ('L', 'H') : 3, ('L', 'L') : 6,
-('G', 'T') : 2, ('M', 'M') : 6, ('G', 'P') : 3, ('Y', 'K') : 1,
-('Y', 'G') : 2, ('Y', 'C') : 3, ('E', 'A') : 4, ('E', 'E') : 6,
-('Y', 'S') : 3, ('M', 'P') : 2, ('V', 'C') : 2, ('M', 'T') : 3,
-('V', 'G') : 4, ('R', 'E') : 2, ('V', 'K') : 3, ('K', 'Q') : 4,
-('R', 'A') : 2, ('I', 'R') : 2, ('N', 'A') : 3, ('V', 'S') : 2,
-('M', 'D') : 0, ('M', 'H') : 1, ('K', 'A') : 3, ('R', 'Q') : 3,
-('K', 'E') : 4, ('F', 'S') : 3, ('I', 'K') : 2, ('D', 'P') : 2,
-('D', 'T') : 2, ('I', 'M') : 4, ('F', 'C') : 3, ('W', 'L') : 4,
-('F', 'G') : 1, ('F', 'K') : 0, ('F', 'T') : 1, ('D', 'D') : 6,
-('Q', 'T') : 3, ('W', 'G') : 3, ('Q', 'P') : 3, ('W', 'C') : 3,
-('W', 'K') : 1, ('H', 'Q') : 4, ('Q', 'D') : 4, ('W', 'W') : 6,
-('V', 'L') : 5, ('L', 'G') : 2, ('W', 'S') : 2, ('L', 'K') : 2,
-('N', 'P') : 2, ('H', 'E') : 2, ('N', 'T') : 4, ('H', 'A') : 2,
-('Y', 'L') : 3, ('Y', 'H') : 3, ('G', 'S') : 5, ('Y', 'D') : 2,
-('V', 'Q') : 2, ('L', 'T') : 2, ('G', 'G') : 6, ('G', 'C') : 3,
-('E', 'N') : 3, ('Y', 'T') : 2, ('Y', 'P') : 2, ('R', 'N') : 2,
-('V', 'D') : 3, ('K', 'R') : 5, ('V', 'H') : 1, ('I', 'Q') : 1,
-('V', 'P') : 3, ('M', 'C') : 2, ('K', 'N') : 4, ('V', 'T') : 3,
-('M', 'G') : 1, ('T', 'S') : 5, ('I', 'E') : 1, ('M', 'K') : 2,
-('I', 'A') : 2, ('N', 'N') : 6, ('R', 'R') : 6, ('F', 'P') : 2,
-('L', 'I') : 5, ('I', 'S') : 2, ('D', 'S') : 3, ('L', 'S') : 2,
-('I', 'H') : 1, ('F', 'D') : 1, ('D', 'C') : 1, ('F', 'H') : 2,
-('D', 'G') : 4, ('F', 'L') : 4
+('W', 'F'): 3, ('L', 'R'): 2, ('I', 'I'): 6, ('Q', 'Q'): 6,
+('W', 'N'): 0, ('V', 'I'): 5, ('H', 'T'): 2, ('H', 'P'): 3,
+('W', 'V'): 3, ('Q', 'E'): 4, ('W', 'R'): 2, ('Q', 'A'): 3,
+('H', 'H'): 6, ('H', 'D'): 3, ('L', 'N'): 1, ('Y', 'M'): 2,
+('Y', 'I'): 3, ('Y', 'E'): 1, ('E', 'S'): 3, ('Y', 'A'): 2,
+('Y', 'Y'): 6, ('T', 'C'): 2, ('E', 'C'): 0, ('Y', 'Q'): 2,
+('E', 'G'): 4, ('V', 'A'): 5, ('C', 'C'): 6, ('M', 'R'): 2,
+('P', 'T'): 4, ('V', 'E'): 4, ('P', 'P'): 6, ('I', 'T'): 3,
+('K', 'S'): 3, ('R', 'G'): 3, ('I', 'P'): 2, ('R', 'C'): 2,
+('A', 'T'): 5, ('K', 'K'): 6, ('A', 'P'): 5, ('V', 'M'): 4,
+('I', 'D'): 1, ('K', 'C'): 0, ('K', 'G'): 2, ('R', 'S'): 3,
+('F', 'Q'): 1, ('F', 'A'): 2, ('V', 'V'): 6, ('M', 'N'): 1,
+('F', 'E'): 0, ('D', 'N'): 5, ('F', 'I'): 4, ('F', 'M'): 2,
+('M', 'S'): 1, ('S', 'S'): 6, ('L', 'Q'): 2, ('W', 'E'): 1,
+('W', 'A'): 2, ('W', 'M'): 3, ('H', 'S'): 3, ('W', 'I'): 2,
+('S', 'C'): 4, ('L', 'A'): 2, ('L', 'E'): 1, ('W', 'Q'): 1,
+('H', 'G'): 1, ('Q', 'N'): 3, ('H', 'C'): 2, ('L', 'M'): 5,
+('W', 'Y'): 3, ('Y', 'N'): 3, ('E', 'P'): 3, ('Y', 'F'): 5,
+('E', 'T'): 3, ('A', 'A'): 6, ('I', 'N'): 2, ('G', 'A'): 5,
+('Y', 'V'): 3, ('E', 'D'): 5, ('W', 'H'): 1, ('Y', 'R'): 1,
+('M', 'Q'): 2, ('P', 'S'): 4, ('R', 'H'): 4, ('A', 'C'): 2,
+('R', 'D'): 2, ('K', 'P'): 2, ('L', 'D'): 1, ('K', 'T'): 4,
+('V', 'N'): 2, ('M', 'A'): 2, ('K', 'H'): 3, ('V', 'R'): 2,
+('P', 'C'): 2, ('M', 'E'): 1, ('A', 'S'): 5, ('T', 'T'): 6,
+('R', 'T'): 3, ('I', 'G'): 2, ('R', 'P'): 3, ('K', 'D'): 3,
+('I', 'C'): 2, ('F', 'R'): 1, ('F', 'V'): 4, ('L', 'C'): 2,
+('F', 'F'): 6, ('D', 'A'): 4, ('F', 'N'): 1, ('W', 'D'): 0,
+('L', 'P'): 3, ('Q', 'S'): 3, ('N', 'C'): 2, ('N', 'G'): 3,
+('H', 'N'): 4, ('W', 'T'): 1, ('Q', 'G'): 2, ('W', 'P'): 2,
+('Q', 'C'): 1, ('N', 'S'): 5, ('L', 'H'): 3, ('L', 'L'): 6,
+('G', 'T'): 2, ('M', 'M'): 6, ('G', 'P'): 3, ('Y', 'K'): 1,
+('Y', 'G'): 2, ('Y', 'C'): 3, ('E', 'A'): 4, ('E', 'E'): 6,
+('Y', 'S'): 3, ('M', 'P'): 2, ('V', 'C'): 2, ('M', 'T'): 3,
+('V', 'G'): 4, ('R', 'E'): 2, ('V', 'K'): 3, ('K', 'Q'): 4,
+('R', 'A'): 2, ('I', 'R'): 2, ('N', 'A'): 3, ('V', 'S'): 2,
+('M', 'D'): 0, ('M', 'H'): 1, ('K', 'A'): 3, ('R', 'Q'): 3,
+('K', 'E'): 4, ('F', 'S'): 3, ('I', 'K'): 2, ('D', 'P'): 2,
+('D', 'T'): 2, ('I', 'M'): 4, ('F', 'C'): 3, ('W', 'L'): 4,
+('F', 'G'): 1, ('F', 'K'): 0, ('F', 'T'): 1, ('D', 'D'): 6,
+('Q', 'T'): 3, ('W', 'G'): 3, ('Q', 'P'): 3, ('W', 'C'): 3,
+('W', 'K'): 1, ('H', 'Q'): 4, ('Q', 'D'): 4, ('W', 'W'): 6,
+('V', 'L'): 5, ('L', 'G'): 2, ('W', 'S'): 2, ('L', 'K'): 2,
+('N', 'P'): 2, ('H', 'E'): 2, ('N', 'T'): 4, ('H', 'A'): 2,
+('Y', 'L'): 3, ('Y', 'H'): 3, ('G', 'S'): 5, ('Y', 'D'): 2,
+('V', 'Q'): 2, ('L', 'T'): 2, ('G', 'G'): 6, ('G', 'C'): 3,
+('E', 'N'): 3, ('Y', 'T'): 2, ('Y', 'P'): 2, ('R', 'N'): 2,
+('V', 'D'): 3, ('K', 'R'): 5, ('V', 'H'): 1, ('I', 'Q'): 1,
+('V', 'P'): 3, ('M', 'C'): 2, ('K', 'N'): 4, ('V', 'T'): 3,
+('M', 'G'): 1, ('T', 'S'): 5, ('I', 'E'): 1, ('M', 'K'): 2,
+('I', 'A'): 2, ('N', 'N'): 6, ('R', 'R'): 6, ('F', 'P'): 2,
+('L', 'I'): 5, ('I', 'S'): 2, ('D', 'S'): 3, ('L', 'S'): 2,
+('I', 'H'): 1, ('F', 'D'): 1, ('D', 'C'): 1, ('F', 'H'): 2,
+('D', 'G'): 4, ('F', 'L'): 4
 }
 feng = _temp()
 del _temp
@@ -1514,49 +1513,49 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/fitch.cmp
 def _temp():
     return {
-('W', 'F') : 2, ('U', 'I') : 1, ('W', 'T') : 0, ('R', 'R') : 3,
-('N', 'M') : 1, ('U', 'M') : 2, ('U', 'O') : 1, ('N', 'A') : 2,
-('U', 'A') : 1, ('N', 'C') : 1, ('U', 'C') : 1, ('N', 'E') : 2,
-('U', 'E') : 1, ('W', 'H') : 1, ('W', 'R') : 1, ('W', 'V') : 2,
-('Q', 'E') : 1, ('S', 'S') : 3, ('T', 'T') : 3, ('S', 'F') : 2,
-('Q', 'A') : 1, ('Q', 'C') : 1, ('H', 'F') : 1, ('S', 'H') : 1,
-('L', 'H') : 1, ('S', 'N') : 2, ('Q', 'I') : 2, ('S', 'L') : 2,
-('L', 'L') : 3, ('Y', 'M') : 1, ('M', 'M') : 3, ('Y', 'I') : 1,
-('W', 'N') : 2, ('Y', 'R') : 2, ('Y', 'E') : 1, ('O', 'O') : 3,
-('Y', 'A') : 2, ('Y', 'C') : 2, ('U', 'S') : 2, ('Y', 'U') : 2,
-('Q', 'Q') : 3, ('E', 'A') : 1, ('N', 'L') : 1, ('E', 'C') : 1,
-('W', 'W') : 3, ('E', 'E') : 3, ('Y', 'S') : 2, ('V', 'A') : 2,
-('C', 'C') : 3, ('V', 'C') : 2, ('T', 'H') : 0, ('V', 'E') : 1,
-('T', 'N') : 0, ('O', 'I') : 1, ('R', 'E') : 2, ('O', 'E') : 1,
-('R', 'A') : 1, ('O', 'C') : 2, ('R', 'C') : 2, ('O', 'A') : 2,
-('V', 'Q') : 1, ('V', 'M') : 2, ('V', 'S') : 1, ('Q', 'O') : 2,
-('I', 'H') : 1, ('M', 'F') : 1, ('R', 'Q') : 2, ('M', 'H') : 0,
-('O', 'F') : 2, ('T', 'F') : 1, ('M', 'I') : 2, ('M', 'L') : 1,
-('V', 'T') : 1, ('Q', 'H') : 2, ('Q', 'N') : 1, ('T', 'M') : 1,
-('W', 'M') : 2, ('Y', 'Y') : 3, ('T', 'S') : 2, ('R', 'O') : 1,
-('T', 'R') : 2, ('H', 'H') : 3, ('F', 'A') : 1, ('F', 'C') : 2,
-('F', 'E') : 1, ('W', 'L') : 1, ('T', 'L') : 1, ('U', 'R') : 2,
-('U', 'H') : 2, ('N', 'H') : 2, ('U', 'Q') : 2, ('W', 'E') : 2,
-('U', 'L') : 1, ('W', 'C') : 1, ('U', 'N') : 1, ('W', 'A') : 1,
-('W', 'O') : 1, ('I', 'F') : 0, ('U', 'F') : 2, ('N', 'F') : 1,
-('L', 'C') : 1, ('S', 'C') : 2, ('L', 'A') : 2, ('S', 'A') : 1,
-('W', 'S') : 2, ('L', 'E') : 2, ('S', 'E') : 2, ('Q', 'L') : 1,
-('H', 'E') : 1, ('S', 'I') : 1, ('H', 'C') : 1, ('S', 'O') : 2,
-('H', 'A') : 2, ('S', 'M') : 1, ('Y', 'L') : 2, ('Y', 'N') : 1,
-('Y', 'H') : 1, ('O', 'M') : 0, ('Y', 'Q') : 1, ('Y', 'F') : 1,
-('W', 'I') : 1, ('C', 'A') : 1, ('R', 'I') : 2, ('Y', 'O') : 1,
-('Q', 'M') : 1, ('S', 'Q') : 2, ('U', 'T') : 2, ('Y', 'T') : 2,
-('Y', 'V') : 2, ('O', 'L') : 1, ('R', 'N') : 1, ('A', 'A') : 3,
-('N', 'I') : 2, ('R', 'L') : 1, ('T', 'I') : 1, ('L', 'F') : 1,
-('T', 'O') : 1, ('R', 'H') : 2, ('O', 'H') : 2, ('V', 'F') : 2,
-('T', 'C') : 2, ('V', 'H') : 1, ('T', 'A') : 0, ('R', 'F') : 1,
-('V', 'L') : 2, ('T', 'E') : 1, ('V', 'N') : 1, ('M', 'A') : 0,
-('Q', 'F') : 1, ('M', 'C') : 0, ('W', 'U') : 2, ('I', 'I') : 3,
-('V', 'V') : 3, ('O', 'N') : 2, ('I', 'E') : 2, ('T', 'Q') : 2,
-('I', 'A') : 1, ('N', 'N') : 3, ('I', 'C') : 0, ('S', 'R') : 2,
-('V', 'R') : 1, ('L', 'I') : 1, ('V', 'I') : 1, ('R', 'M') : 2,
-('Y', 'W') : 1, ('M', 'E') : 2, ('V', 'U') : 2, ('W', 'Q') : 0,
-('U', 'U') : 3, ('V', 'O') : 1, ('F', 'F') : 3
+('W', 'F'): 2, ('U', 'I'): 1, ('W', 'T'): 0, ('R', 'R'): 3,
+('N', 'M'): 1, ('U', 'M'): 2, ('U', 'O'): 1, ('N', 'A'): 2,
+('U', 'A'): 1, ('N', 'C'): 1, ('U', 'C'): 1, ('N', 'E'): 2,
+('U', 'E'): 1, ('W', 'H'): 1, ('W', 'R'): 1, ('W', 'V'): 2,
+('Q', 'E'): 1, ('S', 'S'): 3, ('T', 'T'): 3, ('S', 'F'): 2,
+('Q', 'A'): 1, ('Q', 'C'): 1, ('H', 'F'): 1, ('S', 'H'): 1,
+('L', 'H'): 1, ('S', 'N'): 2, ('Q', 'I'): 2, ('S', 'L'): 2,
+('L', 'L'): 3, ('Y', 'M'): 1, ('M', 'M'): 3, ('Y', 'I'): 1,
+('W', 'N'): 2, ('Y', 'R'): 2, ('Y', 'E'): 1, ('O', 'O'): 3,
+('Y', 'A'): 2, ('Y', 'C'): 2, ('U', 'S'): 2, ('Y', 'U'): 2,
+('Q', 'Q'): 3, ('E', 'A'): 1, ('N', 'L'): 1, ('E', 'C'): 1,
+('W', 'W'): 3, ('E', 'E'): 3, ('Y', 'S'): 2, ('V', 'A'): 2,
+('C', 'C'): 3, ('V', 'C'): 2, ('T', 'H'): 0, ('V', 'E'): 1,
+('T', 'N'): 0, ('O', 'I'): 1, ('R', 'E'): 2, ('O', 'E'): 1,
+('R', 'A'): 1, ('O', 'C'): 2, ('R', 'C'): 2, ('O', 'A'): 2,
+('V', 'Q'): 1, ('V', 'M'): 2, ('V', 'S'): 1, ('Q', 'O'): 2,
+('I', 'H'): 1, ('M', 'F'): 1, ('R', 'Q'): 2, ('M', 'H'): 0,
+('O', 'F'): 2, ('T', 'F'): 1, ('M', 'I'): 2, ('M', 'L'): 1,
+('V', 'T'): 1, ('Q', 'H'): 2, ('Q', 'N'): 1, ('T', 'M'): 1,
+('W', 'M'): 2, ('Y', 'Y'): 3, ('T', 'S'): 2, ('R', 'O'): 1,
+('T', 'R'): 2, ('H', 'H'): 3, ('F', 'A'): 1, ('F', 'C'): 2,
+('F', 'E'): 1, ('W', 'L'): 1, ('T', 'L'): 1, ('U', 'R'): 2,
+('U', 'H'): 2, ('N', 'H'): 2, ('U', 'Q'): 2, ('W', 'E'): 2,
+('U', 'L'): 1, ('W', 'C'): 1, ('U', 'N'): 1, ('W', 'A'): 1,
+('W', 'O'): 1, ('I', 'F'): 0, ('U', 'F'): 2, ('N', 'F'): 1,
+('L', 'C'): 1, ('S', 'C'): 2, ('L', 'A'): 2, ('S', 'A'): 1,
+('W', 'S'): 2, ('L', 'E'): 2, ('S', 'E'): 2, ('Q', 'L'): 1,
+('H', 'E'): 1, ('S', 'I'): 1, ('H', 'C'): 1, ('S', 'O'): 2,
+('H', 'A'): 2, ('S', 'M'): 1, ('Y', 'L'): 2, ('Y', 'N'): 1,
+('Y', 'H'): 1, ('O', 'M'): 0, ('Y', 'Q'): 1, ('Y', 'F'): 1,
+('W', 'I'): 1, ('C', 'A'): 1, ('R', 'I'): 2, ('Y', 'O'): 1,
+('Q', 'M'): 1, ('S', 'Q'): 2, ('U', 'T'): 2, ('Y', 'T'): 2,
+('Y', 'V'): 2, ('O', 'L'): 1, ('R', 'N'): 1, ('A', 'A'): 3,
+('N', 'I'): 2, ('R', 'L'): 1, ('T', 'I'): 1, ('L', 'F'): 1,
+('T', 'O'): 1, ('R', 'H'): 2, ('O', 'H'): 2, ('V', 'F'): 2,
+('T', 'C'): 2, ('V', 'H'): 1, ('T', 'A'): 0, ('R', 'F'): 1,
+('V', 'L'): 2, ('T', 'E'): 1, ('V', 'N'): 1, ('M', 'A'): 0,
+('Q', 'F'): 1, ('M', 'C'): 0, ('W', 'U'): 2, ('I', 'I'): 3,
+('V', 'V'): 3, ('O', 'N'): 2, ('I', 'E'): 2, ('T', 'Q'): 2,
+('I', 'A'): 1, ('N', 'N'): 3, ('I', 'C'): 0, ('S', 'R'): 2,
+('V', 'R'): 1, ('L', 'I'): 1, ('V', 'I'): 1, ('R', 'M'): 2,
+('Y', 'W'): 1, ('M', 'E'): 2, ('V', 'U'): 2, ('W', 'Q'): 0,
+('U', 'U'): 3, ('V', 'O'): 1, ('F', 'F'): 3
 }
 fitch = _temp()
 del _temp
@@ -1565,59 +1564,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/genetic.cmp
 def _temp():
     return {
-('W', 'F') : 0.0, ('L', 'R') : -0.4, ('I', 'I') : 4.1, ('Q', 'Q') : 5.5,
-('W', 'N') : -3.0, ('V', 'I') : 1.0, ('H', 'T') : -1.8, ('H', 'P') : 0.7,
-('W', 'V') : -2.1, ('Q', 'E') : 2.0, ('W', 'R') : 1.8, ('Q', 'A') : -2.1,
-('H', 'H') : 4.7, ('H', 'D') : 1.7, ('L', 'N') : -2.2, ('Y', 'M') : -2.9,
-('Y', 'I') : -1.6, ('Y', 'E') : -0.9, ('E', 'S') : -2.8, ('Y', 'A') : -2.4,
-('Y', 'Y') : 6.5, ('T', 'C') : -1.9, ('E', 'C') : -3.0, ('Y', 'Q') : -0.8,
-('E', 'G') : 1.4, ('V', 'A') : 1.0, ('C', 'C') : 5.5, ('M', 'R') : -0.4,
-('P', 'T') : 1.1, ('V', 'E') : 1.3, ('P', 'P') : 3.8, ('I', 'T') : 0.8,
-('K', 'S') : -1.5, ('R', 'G') : 0.8, ('I', 'P') : -1.6, ('R', 'C') : 0.7,
-('A', 'T') : 0.9, ('K', 'K') : 5.6, ('A', 'P') : 0.8, ('V', 'M') : 1.0,
-('I', 'D') : -2.1, ('K', 'C') : -3.2, ('K', 'G') : -2.2, ('R', 'S') : 0.3,
-('F', 'Q') : -2.1, ('F', 'A') : -2.4, ('V', 'V') : 4.1, ('M', 'N') : 0.1,
-('F', 'E') : -2.9, ('D', 'N') : 1.7, ('F', 'I') : 1.3, ('F', 'M') : 0.5,
-('M', 'S') : -1.3, ('S', 'S') : 2.6, ('L', 'Q') : 0.1, ('W', 'E') : -3.2,
-('W', 'A') : -2.2, ('W', 'M') : -2.0, ('H', 'S') : -1.6, ('W', 'I') : -2.2,
-('S', 'C') : 1.5, ('L', 'A') : -2.3, ('L', 'E') : -2.5, ('W', 'Q') : -2.3,
-('H', 'G') : -2.2, ('Q', 'N') : 0.4, ('H', 'C') : -1.6, ('L', 'M') : 1.5,
-('W', 'Y') : -0.5, ('Y', 'N') : 2.5, ('E', 'P') : -2.1, ('Y', 'F') : 2.0,
-('E', 'T') : -2.1, ('A', 'A') : 4.0, ('I', 'N') : 0.9, ('G', 'A') : 1.2,
-('Y', 'V') : -2.2, ('E', 'D') : 3.8, ('W', 'H') : -2.1, ('Y', 'R') : -1.9,
-('M', 'Q') : -1.2, ('P', 'S') : 0.4, ('R', 'H') : 3.6, ('A', 'C') : -1.9,
-('R', 'D') : -2.3, ('K', 'P') : -1.5, ('L', 'D') : -2.4, ('K', 'T') : 1.0,
-('V', 'N') : -2.2, ('M', 'A') : -2.0, ('K', 'H') : 0.6, ('V', 'R') : -2.1,
-('P', 'C') : -1.9, ('M', 'E') : -1.8, ('A', 'S') : 0.1, ('T', 'T') : 4.0,
-('R', 'T') : -0.6, ('I', 'G') : -2.5, ('R', 'P') : 0.3, ('K', 'D') : 0.3,
-('I', 'C') : -1.9, ('F', 'R') : -1.5, ('F', 'V') : 1.0, ('L', 'C') : -1.3,
-('F', 'F') : 4.5, ('D', 'A') : 1.0, ('F', 'N') : -1.3, ('W', 'D') : -2.9,
-('L', 'P') : 0.0, ('Q', 'S') : -2.3, ('N', 'C') : -1.5, ('N', 'G') : -2.6,
-('H', 'N') : 1.8, ('W', 'T') : -2.2, ('Q', 'G') : -2.1, ('W', 'P') : -1.6,
-('Q', 'C') : -3.1, ('N', 'S') : -0.3, ('L', 'H') : -0.1, ('L', 'L') : 3.4,
-('G', 'T') : -2.1, ('M', 'M') : 5.4, ('G', 'P') : -1.8, ('Y', 'K') : -0.8,
-('Y', 'G') : -1.8, ('Y', 'C') : 2.6, ('E', 'A') : 1.3, ('E', 'E') : 5.7,
-('Y', 'S') : 0.3, ('M', 'P') : -1.4, ('V', 'C') : -2.2, ('M', 'T') : 0.7,
-('V', 'G') : 1.1, ('R', 'E') : -2.0, ('V', 'K') : -2.1, ('K', 'Q') : 2.2,
-('R', 'A') : -1.6, ('I', 'R') : -1.2, ('N', 'A') : -1.7, ('V', 'S') : -2.2,
-('M', 'D') : -2.5, ('M', 'H') : -1.8, ('K', 'A') : -1.9, ('R', 'Q') : 0.3,
-('K', 'E') : 2.0, ('F', 'S') : 0.0, ('I', 'K') : 0.7, ('D', 'P') : -2.2,
-('D', 'T') : -2.1, ('I', 'M') : 3.3, ('F', 'C') : 1.8, ('W', 'L') : -0.3,
-('F', 'G') : -1.9, ('F', 'K') : -2.8, ('F', 'T') : -2.1, ('D', 'D') : 4.8,
-('Q', 'T') : -1.7, ('W', 'G') : 1.4, ('Q', 'P') : 1.0, ('W', 'C') : 4.1,
-('W', 'K') : -3.0, ('H', 'Q') : 3.6, ('Q', 'D') : 0.3, ('W', 'W') : 7.5,
-('V', 'L') : 1.1, ('L', 'G') : -2.2, ('W', 'S') : 0.8, ('L', 'K') : -2.0,
-('N', 'P') : -1.6, ('H', 'E') : 0.3, ('N', 'T') : 0.9, ('H', 'A') : -2.1,
-('Y', 'L') : -1.6, ('Y', 'H') : 2.3, ('G', 'S') : -0.6, ('Y', 'D') : 2.3,
-('V', 'Q') : -2.0, ('L', 'T') : -1.9, ('G', 'G') : 4.2, ('G', 'C') : 1.0,
-('E', 'N') : 0.3, ('Y', 'T') : -2.1, ('Y', 'P') : -2.3, ('R', 'N') : -1.5,
-('V', 'D') : 1.0, ('K', 'R') : -0.2, ('V', 'H') : -2.1, ('I', 'Q') : -1.9,
-('V', 'P') : -2.1, ('M', 'C') : -2.7, ('K', 'N') : 3.5, ('V', 'T') : -2.2,
-('M', 'G') : -2.3, ('T', 'S') : 1.0, ('I', 'E') : -2.3, ('M', 'K') : 1.6,
-('I', 'A') : -1.8, ('N', 'N') : 4.7, ('R', 'R') : 2.9, ('F', 'P') : -1.8,
-('L', 'I') : 1.2, ('I', 'S') : -0.5, ('D', 'S') : -2.1, ('L', 'S') : -1.2,
-('I', 'H') : -1.8, ('F', 'D') : -1.7, ('D', 'C') : -1.6, ('F', 'H') : -1.1,
-('D', 'G') : 1.1, ('F', 'L') : 2.2
+('W', 'F'): 0.0, ('L', 'R'): -0.4, ('I', 'I'): 4.1, ('Q', 'Q'): 5.5,
+('W', 'N'): -3.0, ('V', 'I'): 1.0, ('H', 'T'): -1.8, ('H', 'P'): 0.7,
+('W', 'V'): -2.1, ('Q', 'E'): 2.0, ('W', 'R'): 1.8, ('Q', 'A'): -2.1,
+('H', 'H'): 4.7, ('H', 'D'): 1.7, ('L', 'N'): -2.2, ('Y', 'M'): -2.9,
+('Y', 'I'): -1.6, ('Y', 'E'): -0.9, ('E', 'S'): -2.8, ('Y', 'A'): -2.4,
+('Y', 'Y'): 6.5, ('T', 'C'): -1.9, ('E', 'C'): -3.0, ('Y', 'Q'): -0.8,
+('E', 'G'): 1.4, ('V', 'A'): 1.0, ('C', 'C'): 5.5, ('M', 'R'): -0.4,
+('P', 'T'): 1.1, ('V', 'E'): 1.3, ('P', 'P'): 3.8, ('I', 'T'): 0.8,
+('K', 'S'): -1.5, ('R', 'G'): 0.8, ('I', 'P'): -1.6, ('R', 'C'): 0.7,
+('A', 'T'): 0.9, ('K', 'K'): 5.6, ('A', 'P'): 0.8, ('V', 'M'): 1.0,
+('I', 'D'): -2.1, ('K', 'C'): -3.2, ('K', 'G'): -2.2, ('R', 'S'): 0.3,
+('F', 'Q'): -2.1, ('F', 'A'): -2.4, ('V', 'V'): 4.1, ('M', 'N'): 0.1,
+('F', 'E'): -2.9, ('D', 'N'): 1.7, ('F', 'I'): 1.3, ('F', 'M'): 0.5,
+('M', 'S'): -1.3, ('S', 'S'): 2.6, ('L', 'Q'): 0.1, ('W', 'E'): -3.2,
+('W', 'A'): -2.2, ('W', 'M'): -2.0, ('H', 'S'): -1.6, ('W', 'I'): -2.2,
+('S', 'C'): 1.5, ('L', 'A'): -2.3, ('L', 'E'): -2.5, ('W', 'Q'): -2.3,
+('H', 'G'): -2.2, ('Q', 'N'): 0.4, ('H', 'C'): -1.6, ('L', 'M'): 1.5,
+('W', 'Y'): -0.5, ('Y', 'N'): 2.5, ('E', 'P'): -2.1, ('Y', 'F'): 2.0,
+('E', 'T'): -2.1, ('A', 'A'): 4.0, ('I', 'N'): 0.9, ('G', 'A'): 1.2,
+('Y', 'V'): -2.2, ('E', 'D'): 3.8, ('W', 'H'): -2.1, ('Y', 'R'): -1.9,
+('M', 'Q'): -1.2, ('P', 'S'): 0.4, ('R', 'H'): 3.6, ('A', 'C'): -1.9,
+('R', 'D'): -2.3, ('K', 'P'): -1.5, ('L', 'D'): -2.4, ('K', 'T'): 1.0,
+('V', 'N'): -2.2, ('M', 'A'): -2.0, ('K', 'H'): 0.6, ('V', 'R'): -2.1,
+('P', 'C'): -1.9, ('M', 'E'): -1.8, ('A', 'S'): 0.1, ('T', 'T'): 4.0,
+('R', 'T'): -0.6, ('I', 'G'): -2.5, ('R', 'P'): 0.3, ('K', 'D'): 0.3,
+('I', 'C'): -1.9, ('F', 'R'): -1.5, ('F', 'V'): 1.0, ('L', 'C'): -1.3,
+('F', 'F'): 4.5, ('D', 'A'): 1.0, ('F', 'N'): -1.3, ('W', 'D'): -2.9,
+('L', 'P'): 0.0, ('Q', 'S'): -2.3, ('N', 'C'): -1.5, ('N', 'G'): -2.6,
+('H', 'N'): 1.8, ('W', 'T'): -2.2, ('Q', 'G'): -2.1, ('W', 'P'): -1.6,
+('Q', 'C'): -3.1, ('N', 'S'): -0.3, ('L', 'H'): -0.1, ('L', 'L'): 3.4,
+('G', 'T'): -2.1, ('M', 'M'): 5.4, ('G', 'P'): -1.8, ('Y', 'K'): -0.8,
+('Y', 'G'): -1.8, ('Y', 'C'): 2.6, ('E', 'A'): 1.3, ('E', 'E'): 5.7,
+('Y', 'S'): 0.3, ('M', 'P'): -1.4, ('V', 'C'): -2.2, ('M', 'T'): 0.7,
+('V', 'G'): 1.1, ('R', 'E'): -2.0, ('V', 'K'): -2.1, ('K', 'Q'): 2.2,
+('R', 'A'): -1.6, ('I', 'R'): -1.2, ('N', 'A'): -1.7, ('V', 'S'): -2.2,
+('M', 'D'): -2.5, ('M', 'H'): -1.8, ('K', 'A'): -1.9, ('R', 'Q'): 0.3,
+('K', 'E'): 2.0, ('F', 'S'): 0.0, ('I', 'K'): 0.7, ('D', 'P'): -2.2,
+('D', 'T'): -2.1, ('I', 'M'): 3.3, ('F', 'C'): 1.8, ('W', 'L'): -0.3,
+('F', 'G'): -1.9, ('F', 'K'): -2.8, ('F', 'T'): -2.1, ('D', 'D'): 4.8,
+('Q', 'T'): -1.7, ('W', 'G'): 1.4, ('Q', 'P'): 1.0, ('W', 'C'): 4.1,
+('W', 'K'): -3.0, ('H', 'Q'): 3.6, ('Q', 'D'): 0.3, ('W', 'W'): 7.5,
+('V', 'L'): 1.1, ('L', 'G'): -2.2, ('W', 'S'): 0.8, ('L', 'K'): -2.0,
+('N', 'P'): -1.6, ('H', 'E'): 0.3, ('N', 'T'): 0.9, ('H', 'A'): -2.1,
+('Y', 'L'): -1.6, ('Y', 'H'): 2.3, ('G', 'S'): -0.6, ('Y', 'D'): 2.3,
+('V', 'Q'): -2.0, ('L', 'T'): -1.9, ('G', 'G'): 4.2, ('G', 'C'): 1.0,
+('E', 'N'): 0.3, ('Y', 'T'): -2.1, ('Y', 'P'): -2.3, ('R', 'N'): -1.5,
+('V', 'D'): 1.0, ('K', 'R'): -0.2, ('V', 'H'): -2.1, ('I', 'Q'): -1.9,
+('V', 'P'): -2.1, ('M', 'C'): -2.7, ('K', 'N'): 3.5, ('V', 'T'): -2.2,
+('M', 'G'): -2.3, ('T', 'S'): 1.0, ('I', 'E'): -2.3, ('M', 'K'): 1.6,
+('I', 'A'): -1.8, ('N', 'N'): 4.7, ('R', 'R'): 2.9, ('F', 'P'): -1.8,
+('L', 'I'): 1.2, ('I', 'S'): -0.5, ('D', 'S'): -2.1, ('L', 'S'): -1.2,
+('I', 'H'): -1.8, ('F', 'D'): -1.7, ('D', 'C'): -1.6, ('F', 'H'): -1.1,
+('D', 'G'): 1.1, ('F', 'L'): 2.2
 }
 genetic = _temp()
 del _temp
@@ -1626,59 +1625,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/gonnet.cmp
 def _temp():
     return {
-('W', 'F') : 3.6, ('L', 'R') : -2.2, ('I', 'I') : 4.0, ('Q', 'Q') : 2.7,
-('W', 'N') : -3.6, ('V', 'I') : 3.1, ('H', 'T') : -0.3, ('H', 'P') : -1.1,
-('W', 'V') : -2.6, ('Q', 'E') : 1.7, ('W', 'R') : -1.6, ('Q', 'A') : -0.2,
-('H', 'H') : 6.0, ('H', 'D') : 0.4, ('L', 'N') : -3.0, ('Y', 'M') : -0.2,
-('Y', 'I') : -0.7, ('Y', 'E') : -2.7, ('E', 'S') : 0.2, ('Y', 'A') : -2.2,
-('Y', 'Y') : 7.8, ('T', 'C') : -0.5, ('E', 'C') : -3.0, ('Y', 'Q') : -1.7,
-('E', 'G') : -0.8, ('V', 'A') : 0.1, ('C', 'C') : 11.5, ('M', 'R') : -1.7,
-('P', 'T') : 0.1, ('V', 'E') : -1.9, ('P', 'P') : 7.6, ('I', 'T') : -0.6,
-('K', 'S') : 0.1, ('R', 'G') : -1.0, ('I', 'P') : -2.6, ('R', 'C') : -2.2,
-('A', 'T') : 0.6, ('K', 'K') : 3.2, ('A', 'P') : 0.3, ('V', 'M') : 1.6,
-('I', 'D') : -3.8, ('K', 'C') : -2.8, ('K', 'G') : -1.1, ('R', 'S') : -0.2,
-('F', 'Q') : -2.6, ('F', 'A') : -2.3, ('V', 'V') : 3.4, ('M', 'N') : -2.2,
-('F', 'E') : -3.9, ('D', 'N') : 2.2, ('F', 'I') : 1.0, ('F', 'M') : 1.6,
-('M', 'S') : -1.4, ('S', 'S') : 2.2, ('L', 'Q') : -1.6, ('W', 'E') : -4.3,
-('W', 'A') : -3.6, ('W', 'M') : -1.0, ('H', 'S') : -0.2, ('W', 'I') : -1.8,
-('S', 'C') : 0.1, ('L', 'A') : -1.2, ('L', 'E') : -2.8, ('W', 'Q') : -2.7,
-('H', 'G') : -1.4, ('Q', 'N') : 0.7, ('H', 'C') : -1.3, ('L', 'M') : 2.8,
-('W', 'Y') : 4.1, ('Y', 'N') : -1.4, ('E', 'P') : -0.5, ('Y', 'F') : 5.1,
-('E', 'T') : -0.1, ('A', 'A') : 2.4, ('I', 'N') : -2.8, ('G', 'A') : 0.5,
-('Y', 'V') : -1.1, ('E', 'D') : 2.7, ('W', 'H') : -0.8, ('Y', 'R') : -1.8,
-('M', 'Q') : -1.0, ('P', 'S') : 0.4, ('R', 'H') : 0.6, ('A', 'C') : 0.5,
-('R', 'D') : -0.3, ('K', 'P') : -0.6, ('L', 'D') : -4.0, ('K', 'T') : 0.1,
-('V', 'N') : -2.2, ('M', 'A') : -0.7, ('K', 'H') : 0.6, ('V', 'R') : -2.0,
-('P', 'C') : -3.1, ('M', 'E') : -2.0, ('A', 'S') : 1.1, ('T', 'T') : 2.5,
-('R', 'T') : -0.2, ('I', 'G') : -4.5, ('R', 'P') : -0.9, ('K', 'D') : 0.5,
-('I', 'C') : -1.1, ('F', 'R') : -3.2, ('F', 'V') : 0.1, ('L', 'C') : -1.5,
-('F', 'F') : 7.0, ('D', 'A') : -0.3, ('F', 'N') : -3.1, ('W', 'D') : -5.2,
-('L', 'P') : -2.3, ('Q', 'S') : 0.2, ('N', 'C') : -1.8, ('N', 'G') : 0.4,
-('H', 'N') : 1.2, ('W', 'T') : -3.5, ('Q', 'G') : -1.0, ('W', 'P') : -5.0,
-('Q', 'C') : -2.4, ('N', 'S') : 0.9, ('L', 'H') : -1.9, ('L', 'L') : 4.0,
-('G', 'T') : -1.1, ('M', 'M') : 4.3, ('G', 'P') : -1.6, ('Y', 'K') : -2.1,
-('Y', 'G') : -4.0, ('Y', 'C') : -0.5, ('E', 'A') : 0.0, ('E', 'E') : 3.6,
-('Y', 'S') : -1.9, ('M', 'P') : -2.4, ('V', 'C') : 0.0, ('M', 'T') : -0.6,
-('V', 'G') : -3.3, ('R', 'E') : 0.4, ('V', 'K') : -1.7, ('K', 'Q') : 1.5,
-('R', 'A') : -0.6, ('I', 'R') : -2.4, ('N', 'A') : -0.3, ('V', 'S') : -1.0,
-('M', 'D') : -3.0, ('M', 'H') : -1.3, ('K', 'A') : -0.4, ('R', 'Q') : 1.5,
-('K', 'E') : 1.2, ('F', 'S') : -2.8, ('I', 'K') : -2.1, ('D', 'P') : -0.7,
-('D', 'T') : 0.0, ('I', 'M') : 2.5, ('F', 'C') : -0.8, ('W', 'L') : -0.7,
-('F', 'G') : -5.2, ('F', 'K') : -3.3, ('F', 'T') : -2.2, ('D', 'D') : 4.7,
-('Q', 'T') : 0.0, ('W', 'G') : -4.0, ('Q', 'P') : -0.2, ('W', 'C') : -1.0,
-('W', 'K') : -3.5, ('H', 'Q') : 1.2, ('Q', 'D') : 0.9, ('W', 'W') : 14.2,
-('V', 'L') : 1.8, ('L', 'G') : -4.4, ('W', 'S') : -3.3, ('L', 'K') : -2.1,
-('N', 'P') : -0.9, ('H', 'E') : 0.4, ('N', 'T') : 0.5, ('H', 'A') : -0.8,
-('Y', 'L') : 0.0, ('Y', 'H') : 2.2, ('G', 'S') : 0.4, ('Y', 'D') : -2.8,
-('V', 'Q') : -1.5, ('L', 'T') : -1.3, ('G', 'G') : 6.6, ('G', 'C') : -2.0,
-('E', 'N') : 0.9, ('Y', 'T') : -1.9, ('Y', 'P') : -3.1, ('R', 'N') : 0.3,
-('V', 'D') : -2.9, ('K', 'R') : 2.7, ('V', 'H') : -2.0, ('I', 'Q') : -1.9,
-('V', 'P') : -1.8, ('M', 'C') : -0.9, ('K', 'N') : 0.8, ('V', 'T') : 0.0,
-('M', 'G') : -3.5, ('T', 'S') : 1.5, ('I', 'E') : -2.7, ('M', 'K') : -1.4,
-('I', 'A') : -0.8, ('N', 'N') : 3.8, ('R', 'R') : 4.7, ('F', 'P') : -3.8,
-('L', 'I') : 2.8, ('I', 'S') : -1.8, ('D', 'S') : 0.5, ('L', 'S') : -2.1,
-('I', 'H') : -2.2, ('F', 'D') : -4.5, ('D', 'C') : -3.2, ('F', 'H') : -0.1,
-('D', 'G') : 0.1, ('F', 'L') : 2.0
+('W', 'F'): 3.6, ('L', 'R'): -2.2, ('I', 'I'): 4.0, ('Q', 'Q'): 2.7,
+('W', 'N'): -3.6, ('V', 'I'): 3.1, ('H', 'T'): -0.3, ('H', 'P'): -1.1,
+('W', 'V'): -2.6, ('Q', 'E'): 1.7, ('W', 'R'): -1.6, ('Q', 'A'): -0.2,
+('H', 'H'): 6.0, ('H', 'D'): 0.4, ('L', 'N'): -3.0, ('Y', 'M'): -0.2,
+('Y', 'I'): -0.7, ('Y', 'E'): -2.7, ('E', 'S'): 0.2, ('Y', 'A'): -2.2,
+('Y', 'Y'): 7.8, ('T', 'C'): -0.5, ('E', 'C'): -3.0, ('Y', 'Q'): -1.7,
+('E', 'G'): -0.8, ('V', 'A'): 0.1, ('C', 'C'): 11.5, ('M', 'R'): -1.7,
+('P', 'T'): 0.1, ('V', 'E'): -1.9, ('P', 'P'): 7.6, ('I', 'T'): -0.6,
+('K', 'S'): 0.1, ('R', 'G'): -1.0, ('I', 'P'): -2.6, ('R', 'C'): -2.2,
+('A', 'T'): 0.6, ('K', 'K'): 3.2, ('A', 'P'): 0.3, ('V', 'M'): 1.6,
+('I', 'D'): -3.8, ('K', 'C'): -2.8, ('K', 'G'): -1.1, ('R', 'S'): -0.2,
+('F', 'Q'): -2.6, ('F', 'A'): -2.3, ('V', 'V'): 3.4, ('M', 'N'): -2.2,
+('F', 'E'): -3.9, ('D', 'N'): 2.2, ('F', 'I'): 1.0, ('F', 'M'): 1.6,
+('M', 'S'): -1.4, ('S', 'S'): 2.2, ('L', 'Q'): -1.6, ('W', 'E'): -4.3,
+('W', 'A'): -3.6, ('W', 'M'): -1.0, ('H', 'S'): -0.2, ('W', 'I'): -1.8,
+('S', 'C'): 0.1, ('L', 'A'): -1.2, ('L', 'E'): -2.8, ('W', 'Q'): -2.7,
+('H', 'G'): -1.4, ('Q', 'N'): 0.7, ('H', 'C'): -1.3, ('L', 'M'): 2.8,
+('W', 'Y'): 4.1, ('Y', 'N'): -1.4, ('E', 'P'): -0.5, ('Y', 'F'): 5.1,
+('E', 'T'): -0.1, ('A', 'A'): 2.4, ('I', 'N'): -2.8, ('G', 'A'): 0.5,
+('Y', 'V'): -1.1, ('E', 'D'): 2.7, ('W', 'H'): -0.8, ('Y', 'R'): -1.8,
+('M', 'Q'): -1.0, ('P', 'S'): 0.4, ('R', 'H'): 0.6, ('A', 'C'): 0.5,
+('R', 'D'): -0.3, ('K', 'P'): -0.6, ('L', 'D'): -4.0, ('K', 'T'): 0.1,
+('V', 'N'): -2.2, ('M', 'A'): -0.7, ('K', 'H'): 0.6, ('V', 'R'): -2.0,
+('P', 'C'): -3.1, ('M', 'E'): -2.0, ('A', 'S'): 1.1, ('T', 'T'): 2.5,
+('R', 'T'): -0.2, ('I', 'G'): -4.5, ('R', 'P'): -0.9, ('K', 'D'): 0.5,
+('I', 'C'): -1.1, ('F', 'R'): -3.2, ('F', 'V'): 0.1, ('L', 'C'): -1.5,
+('F', 'F'): 7.0, ('D', 'A'): -0.3, ('F', 'N'): -3.1, ('W', 'D'): -5.2,
+('L', 'P'): -2.3, ('Q', 'S'): 0.2, ('N', 'C'): -1.8, ('N', 'G'): 0.4,
+('H', 'N'): 1.2, ('W', 'T'): -3.5, ('Q', 'G'): -1.0, ('W', 'P'): -5.0,
+('Q', 'C'): -2.4, ('N', 'S'): 0.9, ('L', 'H'): -1.9, ('L', 'L'): 4.0,
+('G', 'T'): -1.1, ('M', 'M'): 4.3, ('G', 'P'): -1.6, ('Y', 'K'): -2.1,
+('Y', 'G'): -4.0, ('Y', 'C'): -0.5, ('E', 'A'): 0.0, ('E', 'E'): 3.6,
+('Y', 'S'): -1.9, ('M', 'P'): -2.4, ('V', 'C'): 0.0, ('M', 'T'): -0.6,
+('V', 'G'): -3.3, ('R', 'E'): 0.4, ('V', 'K'): -1.7, ('K', 'Q'): 1.5,
+('R', 'A'): -0.6, ('I', 'R'): -2.4, ('N', 'A'): -0.3, ('V', 'S'): -1.0,
+('M', 'D'): -3.0, ('M', 'H'): -1.3, ('K', 'A'): -0.4, ('R', 'Q'): 1.5,
+('K', 'E'): 1.2, ('F', 'S'): -2.8, ('I', 'K'): -2.1, ('D', 'P'): -0.7,
+('D', 'T'): 0.0, ('I', 'M'): 2.5, ('F', 'C'): -0.8, ('W', 'L'): -0.7,
+('F', 'G'): -5.2, ('F', 'K'): -3.3, ('F', 'T'): -2.2, ('D', 'D'): 4.7,
+('Q', 'T'): 0.0, ('W', 'G'): -4.0, ('Q', 'P'): -0.2, ('W', 'C'): -1.0,
+('W', 'K'): -3.5, ('H', 'Q'): 1.2, ('Q', 'D'): 0.9, ('W', 'W'): 14.2,
+('V', 'L'): 1.8, ('L', 'G'): -4.4, ('W', 'S'): -3.3, ('L', 'K'): -2.1,
+('N', 'P'): -0.9, ('H', 'E'): 0.4, ('N', 'T'): 0.5, ('H', 'A'): -0.8,
+('Y', 'L'): 0.0, ('Y', 'H'): 2.2, ('G', 'S'): 0.4, ('Y', 'D'): -2.8,
+('V', 'Q'): -1.5, ('L', 'T'): -1.3, ('G', 'G'): 6.6, ('G', 'C'): -2.0,
+('E', 'N'): 0.9, ('Y', 'T'): -1.9, ('Y', 'P'): -3.1, ('R', 'N'): 0.3,
+('V', 'D'): -2.9, ('K', 'R'): 2.7, ('V', 'H'): -2.0, ('I', 'Q'): -1.9,
+('V', 'P'): -1.8, ('M', 'C'): -0.9, ('K', 'N'): 0.8, ('V', 'T'): 0.0,
+('M', 'G'): -3.5, ('T', 'S'): 1.5, ('I', 'E'): -2.7, ('M', 'K'): -1.4,
+('I', 'A'): -0.8, ('N', 'N'): 3.8, ('R', 'R'): 4.7, ('F', 'P'): -3.8,
+('L', 'I'): 2.8, ('I', 'S'): -1.8, ('D', 'S'): 0.5, ('L', 'S'): -2.1,
+('I', 'H'): -2.2, ('F', 'D'): -4.5, ('D', 'C'): -3.2, ('F', 'H'): -0.1,
+('D', 'G'): 0.1, ('F', 'L'): 2.0
 }
 gonnet = _temp()
 del _temp
@@ -1687,59 +1686,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/grant.cmp
 def _temp():
     return {
-('L', 'R') : 103, ('I', 'I') : 215, ('Q', 'Q') : 215, ('N', 'E') : 150,
-('Q', 'Y') : 128, ('H', 'P') : 138, ('W', 'V') : 186, ('Q', 'E') : 173,
-('H', 'L') : 129, ('W', 'R') : 118, ('H', 'H') : 215, ('N', 'Q') : 169,
-('U', 'S') : 70, ('N', 'U') : 62, ('G', 'V') : 94, ('G', 'R') : 161,
-('I', 'N') : 121, ('Y', 'E') : 156, ('E', 'S') : 157, ('G', 'F') : 75,
-('Y', 'Y') : 215, ('R', 'R') : 215, ('Y', 'U') : 77, ('C', 'C') : 215,
-('M', 'R') : 124, ('V', 'E') : 146, ('T', 'N') : 41, ('M', 'V') : 194,
-('P', 'P') : 215, ('A', 'L') : 89, ('V', 'S') : 91, ('O', 'E') : 123,
-('I', 'P') : 112, ('T', 'F') : 175, ('I', 'L') : 109, ('C', 'S') : 103,
-('A', 'P') : 107, ('C', 'W') : 17, ('M', 'F') : 187, ('O', 'Y') : 68,
-('T', 'R') : 114, ('O', 'U') : 179, ('V', 'R') : 119, ('U', 'R') : 113,
-('R', 'S') : 105, ('F', 'U') : 193, ('F', 'Y') : 62, ('M', 'N') : 73,
-('F', 'E') : 112, ('G', 'N') : 173, ('P', 'U') : 117, ('S', 'S') : 215,
-('Q', 'V') : 119, ('W', 'E') : 126, ('Q', 'R') : 172, ('N', 'N') : 215,
-('H', 'W') : 121, ('Q', 'H') : 191, ('H', 'S') : 126, ('N', 'F') : 57,
-('H', 'O') : 132, ('Q', 'F') : 99, ('W', 'U') : 210, ('L', 'E') : 157,
-('C', 'P') : 46, ('N', 'R') : 129, ('H', 'C') : 41, ('N', 'V') : 82,
-('G', 'U') : 77, ('A', 'S') : 150, ('Q', 'W') : 106, ('G', 'Q') : 186,
-('E', 'P') : 177, ('A', 'N') : 192, ('G', 'Y') : 117, ('A', 'A') : 215,
-('G', 'E') : 150, ('G', 'A') : 170, ('Y', 'V') : 106, ('Y', 'R') : 90,
-('G', 'I') : 159, ('C', 'F') : 10, ('M', 'Q') : 114, ('T', 'I') : 105,
-('P', 'S') : 141, ('M', 'U') : 200, ('T', 'M') : 148, ('A', 'C') : 61,
-('O', 'F') : 193, ('M', 'Y') : 88, ('T', 'A') : 34, ('A', 'O') : 55,
-('C', 'L') : 20, ('I', 'S') : 94, ('C', 'O') : 21, ('T', 'Y') : 31,
-('I', 'O') : 130, ('M', 'E') : 134, ('V', 'V') : 215, ('O', 'V') : 160,
-('M', 'I') : 120, ('T', 'Q') : 85, ('O', 'R') : 138, ('M', 'M') : 215,
-('T', 'U') : 154, ('I', 'C') : 13, ('F', 'R') : 118, ('A', 'W') : 47,
-('F', 'V') : 165, ('I', 'H') : 183, ('C', 'E') : 66, ('F', 'F') : 215,
-('M', 'A') : 55, ('Q', 'U') : 102, ('L', 'P') : 188, ('N', 'O') : 72,
-('Q', 'S') : 147, ('H', 'V') : 131, ('W', 'L') : 121, ('H', 'R') : 186,
-('W', 'P') : 120, ('Q', 'C') : 61, ('H', 'F') : 115, ('N', 'S') : 169,
-('Q', 'O') : 116, ('U', 'U') : 215, ('N', 'W') : 66, ('L', 'L') : 215,
-('G', 'P') : 122, ('A', 'U') : 43, ('E', 'U') : 123, ('T', 'P') : 68,
-('I', 'Q') : 162, ('G', 'L') : 108, ('E', 'E') : 215, ('G', 'H') : 175,
-('Y', 'S') : 159, ('M', 'P') : 128, ('O', 'O') : 215, ('A', 'F') : 38,
-('T', 'H') : 100, ('P', 'R') : 112, ('T', 'L') : 67, ('I', 'V') : 118,
-('I', 'R') : 189, ('A', 'V') : 63, ('A', 'R') : 119, ('C', 'U') : 17,
-('M', 'H') : 128, ('O', 'W') : 182, ('I', 'F') : 113, ('C', 'Y') : 56,
-('M', 'L') : 131, ('O', 'S') : 71, ('T', 'T') : 215, ('F', 'S') : 60,
-('F', 'W') : 194, ('L', 'S') : 116, ('N', 'H') : 147, ('A', 'H') : 134,
-('Q', 'P') : 139, ('N', 'L') : 104, ('H', 'Y') : 117, ('H', 'U') : 116,
-('A', 'I') : 114, ('W', 'W') : 215, ('W', 'S') : 73, ('Q', 'L') : 124,
-('N', 'P') : 124, ('H', 'E') : 168, ('L', 'U') : 119, ('I', 'W') : 113,
-('Y', 'L') : 155, ('G', 'W') : 81, ('G', 'S') : 135, ('E', 'R') : 144,
-('W', 'Y') : 80, ('G', 'G') : 215, ('T', 'V') : 127, ('G', 'C') : 45,
-('G', 'O') : 93, ('Y', 'P') : 173, ('A', 'E') : 130, ('O', 'L') : 103,
-('M', 'S') : 80, ('T', 'O') : 178, ('I', 'Y') : 88, ('M', 'W') : 205,
-('T', 'C') : 0, ('I', 'U') : 108, ('T', 'G') : 63, ('V', 'L') : 151,
-('C', 'R') : 35, ('V', 'P') : 147, ('M', 'C') : 19, ('C', 'V') : 23,
-('A', 'Q') : 154, ('M', 'G') : 89, ('T', 'S') : 38, ('I', 'E') : 137,
-('T', 'W') : 154, ('A', 'Y') : 121, ('O', 'P') : 105, ('M', 'O') : 179,
-('F', 'P') : 101, ('N', 'Y') : 135, ('T', 'E') : 87, ('V', 'U') : 183,
-('N', 'C') : 76, ('F', 'L') : 102
+('L', 'R'): 103, ('I', 'I'): 215, ('Q', 'Q'): 215, ('N', 'E'): 150,
+('Q', 'Y'): 128, ('H', 'P'): 138, ('W', 'V'): 186, ('Q', 'E'): 173,
+('H', 'L'): 129, ('W', 'R'): 118, ('H', 'H'): 215, ('N', 'Q'): 169,
+('U', 'S'): 70, ('N', 'U'): 62, ('G', 'V'): 94, ('G', 'R'): 161,
+('I', 'N'): 121, ('Y', 'E'): 156, ('E', 'S'): 157, ('G', 'F'): 75,
+('Y', 'Y'): 215, ('R', 'R'): 215, ('Y', 'U'): 77, ('C', 'C'): 215,
+('M', 'R'): 124, ('V', 'E'): 146, ('T', 'N'): 41, ('M', 'V'): 194,
+('P', 'P'): 215, ('A', 'L'): 89, ('V', 'S'): 91, ('O', 'E'): 123,
+('I', 'P'): 112, ('T', 'F'): 175, ('I', 'L'): 109, ('C', 'S'): 103,
+('A', 'P'): 107, ('C', 'W'): 17, ('M', 'F'): 187, ('O', 'Y'): 68,
+('T', 'R'): 114, ('O', 'U'): 179, ('V', 'R'): 119, ('U', 'R'): 113,
+('R', 'S'): 105, ('F', 'U'): 193, ('F', 'Y'): 62, ('M', 'N'): 73,
+('F', 'E'): 112, ('G', 'N'): 173, ('P', 'U'): 117, ('S', 'S'): 215,
+('Q', 'V'): 119, ('W', 'E'): 126, ('Q', 'R'): 172, ('N', 'N'): 215,
+('H', 'W'): 121, ('Q', 'H'): 191, ('H', 'S'): 126, ('N', 'F'): 57,
+('H', 'O'): 132, ('Q', 'F'): 99, ('W', 'U'): 210, ('L', 'E'): 157,
+('C', 'P'): 46, ('N', 'R'): 129, ('H', 'C'): 41, ('N', 'V'): 82,
+('G', 'U'): 77, ('A', 'S'): 150, ('Q', 'W'): 106, ('G', 'Q'): 186,
+('E', 'P'): 177, ('A', 'N'): 192, ('G', 'Y'): 117, ('A', 'A'): 215,
+('G', 'E'): 150, ('G', 'A'): 170, ('Y', 'V'): 106, ('Y', 'R'): 90,
+('G', 'I'): 159, ('C', 'F'): 10, ('M', 'Q'): 114, ('T', 'I'): 105,
+('P', 'S'): 141, ('M', 'U'): 200, ('T', 'M'): 148, ('A', 'C'): 61,
+('O', 'F'): 193, ('M', 'Y'): 88, ('T', 'A'): 34, ('A', 'O'): 55,
+('C', 'L'): 20, ('I', 'S'): 94, ('C', 'O'): 21, ('T', 'Y'): 31,
+('I', 'O'): 130, ('M', 'E'): 134, ('V', 'V'): 215, ('O', 'V'): 160,
+('M', 'I'): 120, ('T', 'Q'): 85, ('O', 'R'): 138, ('M', 'M'): 215,
+('T', 'U'): 154, ('I', 'C'): 13, ('F', 'R'): 118, ('A', 'W'): 47,
+('F', 'V'): 165, ('I', 'H'): 183, ('C', 'E'): 66, ('F', 'F'): 215,
+('M', 'A'): 55, ('Q', 'U'): 102, ('L', 'P'): 188, ('N', 'O'): 72,
+('Q', 'S'): 147, ('H', 'V'): 131, ('W', 'L'): 121, ('H', 'R'): 186,
+('W', 'P'): 120, ('Q', 'C'): 61, ('H', 'F'): 115, ('N', 'S'): 169,
+('Q', 'O'): 116, ('U', 'U'): 215, ('N', 'W'): 66, ('L', 'L'): 215,
+('G', 'P'): 122, ('A', 'U'): 43, ('E', 'U'): 123, ('T', 'P'): 68,
+('I', 'Q'): 162, ('G', 'L'): 108, ('E', 'E'): 215, ('G', 'H'): 175,
+('Y', 'S'): 159, ('M', 'P'): 128, ('O', 'O'): 215, ('A', 'F'): 38,
+('T', 'H'): 100, ('P', 'R'): 112, ('T', 'L'): 67, ('I', 'V'): 118,
+('I', 'R'): 189, ('A', 'V'): 63, ('A', 'R'): 119, ('C', 'U'): 17,
+('M', 'H'): 128, ('O', 'W'): 182, ('I', 'F'): 113, ('C', 'Y'): 56,
+('M', 'L'): 131, ('O', 'S'): 71, ('T', 'T'): 215, ('F', 'S'): 60,
+('F', 'W'): 194, ('L', 'S'): 116, ('N', 'H'): 147, ('A', 'H'): 134,
+('Q', 'P'): 139, ('N', 'L'): 104, ('H', 'Y'): 117, ('H', 'U'): 116,
+('A', 'I'): 114, ('W', 'W'): 215, ('W', 'S'): 73, ('Q', 'L'): 124,
+('N', 'P'): 124, ('H', 'E'): 168, ('L', 'U'): 119, ('I', 'W'): 113,
+('Y', 'L'): 155, ('G', 'W'): 81, ('G', 'S'): 135, ('E', 'R'): 144,
+('W', 'Y'): 80, ('G', 'G'): 215, ('T', 'V'): 127, ('G', 'C'): 45,
+('G', 'O'): 93, ('Y', 'P'): 173, ('A', 'E'): 130, ('O', 'L'): 103,
+('M', 'S'): 80, ('T', 'O'): 178, ('I', 'Y'): 88, ('M', 'W'): 205,
+('T', 'C'): 0, ('I', 'U'): 108, ('T', 'G'): 63, ('V', 'L'): 151,
+('C', 'R'): 35, ('V', 'P'): 147, ('M', 'C'): 19, ('C', 'V'): 23,
+('A', 'Q'): 154, ('M', 'G'): 89, ('T', 'S'): 38, ('I', 'E'): 137,
+('T', 'W'): 154, ('A', 'Y'): 121, ('O', 'P'): 105, ('M', 'O'): 179,
+('F', 'P'): 101, ('N', 'Y'): 135, ('T', 'E'): 87, ('V', 'U'): 183,
+('N', 'C'): 76, ('F', 'L'): 102
 }
 grant = _temp()
 del _temp
@@ -1748,59 +1747,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/ident.cmp
 def _temp():
     return {
-('W', 'F') : -1, ('L', 'R') : -1, ('I', 'I') : 6, ('Q', 'Q') : 6,
-('W', 'N') : -1, ('V', 'I') : -1, ('H', 'T') : -1, ('H', 'P') : -1,
-('W', 'V') : -1, ('Q', 'E') : -1, ('W', 'R') : -1, ('Q', 'A') : -1,
-('H', 'H') : 6, ('H', 'D') : -1, ('L', 'N') : -1, ('Y', 'M') : -1,
-('Y', 'I') : -1, ('Y', 'E') : -1, ('E', 'S') : -1, ('Y', 'A') : -1,
-('Y', 'Y') : 6, ('T', 'C') : -1, ('E', 'C') : -1, ('Y', 'Q') : -1,
-('E', 'G') : -1, ('V', 'A') : -1, ('C', 'C') : 6, ('M', 'R') : -1,
-('P', 'T') : -1, ('V', 'E') : -1, ('P', 'P') : 6, ('I', 'T') : -1,
-('K', 'S') : -1, ('R', 'G') : -1, ('I', 'P') : -1, ('R', 'C') : -1,
-('A', 'T') : -1, ('K', 'K') : 6, ('A', 'P') : -1, ('V', 'M') : -1,
-('I', 'D') : -1, ('K', 'C') : -1, ('K', 'G') : -1, ('R', 'S') : -1,
-('F', 'Q') : -1, ('F', 'A') : -1, ('V', 'V') : 6, ('M', 'N') : -1,
-('F', 'E') : -1, ('D', 'N') : -1, ('F', 'I') : -1, ('F', 'M') : -1,
-('M', 'S') : -1, ('S', 'S') : 6, ('L', 'Q') : -1, ('W', 'E') : -1,
-('W', 'A') : -1, ('W', 'M') : -1, ('H', 'S') : -1, ('W', 'I') : -1,
-('S', 'C') : -1, ('L', 'A') : -1, ('L', 'E') : -1, ('W', 'Q') : -1,
-('H', 'G') : -1, ('Q', 'N') : -1, ('H', 'C') : -1, ('L', 'M') : -1,
-('W', 'Y') : -1, ('Y', 'N') : -1, ('E', 'P') : -1, ('Y', 'F') : -1,
-('E', 'T') : -1, ('A', 'A') : 6, ('I', 'N') : -1, ('G', 'A') : -1,
-('Y', 'V') : -1, ('E', 'D') : -1, ('W', 'H') : -1, ('Y', 'R') : -1,
-('M', 'Q') : -1, ('P', 'S') : -1, ('R', 'H') : -1, ('A', 'C') : -1,
-('R', 'D') : -1, ('K', 'P') : -1, ('L', 'D') : -1, ('K', 'T') : -1,
-('V', 'N') : -1, ('M', 'A') : -1, ('K', 'H') : -1, ('V', 'R') : -1,
-('P', 'C') : -1, ('M', 'E') : -1, ('A', 'S') : -1, ('T', 'T') : 6,
-('R', 'T') : -1, ('I', 'G') : -1, ('R', 'P') : -1, ('K', 'D') : -1,
-('I', 'C') : -1, ('F', 'R') : -1, ('F', 'V') : -1, ('L', 'C') : -1,
-('F', 'F') : 6, ('D', 'A') : -1, ('F', 'N') : -1, ('W', 'D') : -1,
-('L', 'P') : -1, ('Q', 'S') : -1, ('N', 'C') : -1, ('N', 'G') : -1,
-('H', 'N') : -1, ('W', 'T') : -1, ('Q', 'G') : -1, ('W', 'P') : -1,
-('Q', 'C') : -1, ('N', 'S') : -1, ('L', 'H') : -1, ('L', 'L') : 6,
-('G', 'T') : -1, ('M', 'M') : 6, ('G', 'P') : -1, ('Y', 'K') : -1,
-('Y', 'G') : -1, ('Y', 'C') : -1, ('E', 'A') : -1, ('E', 'E') : 6,
-('Y', 'S') : -1, ('M', 'P') : -1, ('V', 'C') : -1, ('M', 'T') : -1,
-('V', 'G') : -1, ('R', 'E') : -1, ('V', 'K') : -1, ('K', 'Q') : -1,
-('R', 'A') : -1, ('I', 'R') : -1, ('N', 'A') : -1, ('V', 'S') : -1,
-('M', 'D') : -1, ('M', 'H') : -1, ('K', 'A') : -1, ('R', 'Q') : -1,
-('K', 'E') : -1, ('F', 'S') : -1, ('I', 'K') : -1, ('D', 'P') : -1,
-('D', 'T') : -1, ('I', 'M') : -1, ('F', 'C') : -1, ('W', 'L') : -1,
-('F', 'G') : -1, ('F', 'K') : -1, ('F', 'T') : -1, ('D', 'D') : 6,
-('Q', 'T') : -1, ('W', 'G') : -1, ('Q', 'P') : -1, ('W', 'C') : -1,
-('W', 'K') : -1, ('H', 'Q') : -1, ('Q', 'D') : -1, ('W', 'W') : 6,
-('V', 'L') : -1, ('L', 'G') : -1, ('W', 'S') : -1, ('L', 'K') : -1,
-('N', 'P') : -1, ('H', 'E') : -1, ('N', 'T') : -1, ('H', 'A') : -1,
-('Y', 'L') : -1, ('Y', 'H') : -1, ('G', 'S') : -1, ('Y', 'D') : -1,
-('V', 'Q') : -1, ('L', 'T') : -1, ('G', 'G') : 6, ('G', 'C') : -1,
-('E', 'N') : -1, ('Y', 'T') : -1, ('Y', 'P') : -1, ('R', 'N') : -1,
-('V', 'D') : -1, ('K', 'R') : -1, ('V', 'H') : -1, ('I', 'Q') : -1,
-('V', 'P') : -1, ('M', 'C') : -1, ('K', 'N') : -1, ('V', 'T') : -1,
-('M', 'G') : -1, ('T', 'S') : -1, ('I', 'E') : -1, ('M', 'K') : -1,
-('I', 'A') : -1, ('N', 'N') : 6, ('R', 'R') : 6, ('F', 'P') : -1,
-('L', 'I') : -1, ('I', 'S') : -1, ('D', 'S') : -1, ('L', 'S') : -1,
-('I', 'H') : -1, ('F', 'D') : -1, ('D', 'C') : -1, ('F', 'H') : -1,
-('D', 'G') : -1, ('F', 'L') : -1
+('W', 'F'): -1, ('L', 'R'): -1, ('I', 'I'): 6, ('Q', 'Q'): 6,
+('W', 'N'): -1, ('V', 'I'): -1, ('H', 'T'): -1, ('H', 'P'): -1,
+('W', 'V'): -1, ('Q', 'E'): -1, ('W', 'R'): -1, ('Q', 'A'): -1,
+('H', 'H'): 6, ('H', 'D'): -1, ('L', 'N'): -1, ('Y', 'M'): -1,
+('Y', 'I'): -1, ('Y', 'E'): -1, ('E', 'S'): -1, ('Y', 'A'): -1,
+('Y', 'Y'): 6, ('T', 'C'): -1, ('E', 'C'): -1, ('Y', 'Q'): -1,
+('E', 'G'): -1, ('V', 'A'): -1, ('C', 'C'): 6, ('M', 'R'): -1,
+('P', 'T'): -1, ('V', 'E'): -1, ('P', 'P'): 6, ('I', 'T'): -1,
+('K', 'S'): -1, ('R', 'G'): -1, ('I', 'P'): -1, ('R', 'C'): -1,
+('A', 'T'): -1, ('K', 'K'): 6, ('A', 'P'): -1, ('V', 'M'): -1,
+('I', 'D'): -1, ('K', 'C'): -1, ('K', 'G'): -1, ('R', 'S'): -1,
+('F', 'Q'): -1, ('F', 'A'): -1, ('V', 'V'): 6, ('M', 'N'): -1,
+('F', 'E'): -1, ('D', 'N'): -1, ('F', 'I'): -1, ('F', 'M'): -1,
+('M', 'S'): -1, ('S', 'S'): 6, ('L', 'Q'): -1, ('W', 'E'): -1,
+('W', 'A'): -1, ('W', 'M'): -1, ('H', 'S'): -1, ('W', 'I'): -1,
+('S', 'C'): -1, ('L', 'A'): -1, ('L', 'E'): -1, ('W', 'Q'): -1,
+('H', 'G'): -1, ('Q', 'N'): -1, ('H', 'C'): -1, ('L', 'M'): -1,
+('W', 'Y'): -1, ('Y', 'N'): -1, ('E', 'P'): -1, ('Y', 'F'): -1,
+('E', 'T'): -1, ('A', 'A'): 6, ('I', 'N'): -1, ('G', 'A'): -1,
+('Y', 'V'): -1, ('E', 'D'): -1, ('W', 'H'): -1, ('Y', 'R'): -1,
+('M', 'Q'): -1, ('P', 'S'): -1, ('R', 'H'): -1, ('A', 'C'): -1,
+('R', 'D'): -1, ('K', 'P'): -1, ('L', 'D'): -1, ('K', 'T'): -1,
+('V', 'N'): -1, ('M', 'A'): -1, ('K', 'H'): -1, ('V', 'R'): -1,
+('P', 'C'): -1, ('M', 'E'): -1, ('A', 'S'): -1, ('T', 'T'): 6,
+('R', 'T'): -1, ('I', 'G'): -1, ('R', 'P'): -1, ('K', 'D'): -1,
+('I', 'C'): -1, ('F', 'R'): -1, ('F', 'V'): -1, ('L', 'C'): -1,
+('F', 'F'): 6, ('D', 'A'): -1, ('F', 'N'): -1, ('W', 'D'): -1,
+('L', 'P'): -1, ('Q', 'S'): -1, ('N', 'C'): -1, ('N', 'G'): -1,
+('H', 'N'): -1, ('W', 'T'): -1, ('Q', 'G'): -1, ('W', 'P'): -1,
+('Q', 'C'): -1, ('N', 'S'): -1, ('L', 'H'): -1, ('L', 'L'): 6,
+('G', 'T'): -1, ('M', 'M'): 6, ('G', 'P'): -1, ('Y', 'K'): -1,
+('Y', 'G'): -1, ('Y', 'C'): -1, ('E', 'A'): -1, ('E', 'E'): 6,
+('Y', 'S'): -1, ('M', 'P'): -1, ('V', 'C'): -1, ('M', 'T'): -1,
+('V', 'G'): -1, ('R', 'E'): -1, ('V', 'K'): -1, ('K', 'Q'): -1,
+('R', 'A'): -1, ('I', 'R'): -1, ('N', 'A'): -1, ('V', 'S'): -1,
+('M', 'D'): -1, ('M', 'H'): -1, ('K', 'A'): -1, ('R', 'Q'): -1,
+('K', 'E'): -1, ('F', 'S'): -1, ('I', 'K'): -1, ('D', 'P'): -1,
+('D', 'T'): -1, ('I', 'M'): -1, ('F', 'C'): -1, ('W', 'L'): -1,
+('F', 'G'): -1, ('F', 'K'): -1, ('F', 'T'): -1, ('D', 'D'): 6,
+('Q', 'T'): -1, ('W', 'G'): -1, ('Q', 'P'): -1, ('W', 'C'): -1,
+('W', 'K'): -1, ('H', 'Q'): -1, ('Q', 'D'): -1, ('W', 'W'): 6,
+('V', 'L'): -1, ('L', 'G'): -1, ('W', 'S'): -1, ('L', 'K'): -1,
+('N', 'P'): -1, ('H', 'E'): -1, ('N', 'T'): -1, ('H', 'A'): -1,
+('Y', 'L'): -1, ('Y', 'H'): -1, ('G', 'S'): -1, ('Y', 'D'): -1,
+('V', 'Q'): -1, ('L', 'T'): -1, ('G', 'G'): 6, ('G', 'C'): -1,
+('E', 'N'): -1, ('Y', 'T'): -1, ('Y', 'P'): -1, ('R', 'N'): -1,
+('V', 'D'): -1, ('K', 'R'): -1, ('V', 'H'): -1, ('I', 'Q'): -1,
+('V', 'P'): -1, ('M', 'C'): -1, ('K', 'N'): -1, ('V', 'T'): -1,
+('M', 'G'): -1, ('T', 'S'): -1, ('I', 'E'): -1, ('M', 'K'): -1,
+('I', 'A'): -1, ('N', 'N'): 6, ('R', 'R'): 6, ('F', 'P'): -1,
+('L', 'I'): -1, ('I', 'S'): -1, ('D', 'S'): -1, ('L', 'S'): -1,
+('I', 'H'): -1, ('F', 'D'): -1, ('D', 'C'): -1, ('F', 'H'): -1,
+('D', 'G'): -1, ('F', 'L'): -1
 }
 ident = _temp()
 del _temp
@@ -1809,59 +1808,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/johnson.cmp
 def _temp():
     return {
-('W', 'F') : 3.4, ('S', 'P') : -1.0, ('N', 'M') : -3.7, ('Q', 'Q') : 9.0,
-('N', 'A') : -1.4, ('N', 'E') : -0.7, ('W', 'V') : -4.9, ('Q', 'E') : 2.4,
-('L', 'H') : -4.2, ('W', 'R') : -3.8, ('Q', 'A') : -0.6, ('S', 'D') : -0.2,
-('H', 'H') : 12.7, ('Q', 'M') : -0.6, ('S', 'H') : -2.6, ('H', 'D') : -0.7,
-('Q', 'I') : -7.0, ('S', 'L') : -5.2, ('Y', 'M') : -1.3, ('Y', 'I') : -2.5,
-('Y', 'E') : -3.7, ('Y', 'A') : -4.0, ('G', 'F') : -8.6, ('V', 'T') : -1.9,
-('Y', 'Y') : 10.5, ('V', 'H') : -3.9, ('E', 'C') : -6.9, ('Y', 'Q') : -5.1,
-('V', 'A') : -0.5, ('C', 'C') : 16.1, ('V', 'E') : -4.2, ('T', 'N') : 0.1,
-('R', 'K') : 3.2, ('P', 'P') : 10.3, ('V', 'I') : 3.9, ('R', 'G') : -2.8,
-('V', 'M') : 0.7, ('T', 'F') : -5.0, ('R', 'C') : -5.6, ('V', 'Q') : -3.6,
-('K', 'K') : 7.6, ('P', 'D') : -1.0, ('I', 'H') : -5.1, ('M', 'F') : -0.6,
-('I', 'D') : -4.8, ('K', 'C') : -8.7, ('P', 'L') : -2.8, ('K', 'G') : -3.5,
-('P', 'H') : -4.3, ('T', 'R') : -1.4, ('F', 'A') : -3.2, ('F', 'E') : -6.4,
-('S', 'S') : 5.8, ('W', 'E') : -7.6, ('N', 'N') : 8.0, ('W', 'M') : -0.9,
-('Q', 'C') : -6.9, ('N', 'F') : -3.8, ('S', 'C') : -7.7, ('L', 'A') : -3.3,
-('S', 'G') : -1.3, ('L', 'E') : -5.6, ('W', 'Q') : -8.2, ('H', 'G') : -3.2,
-('S', 'K') : -1.5, ('Q', 'N') : -0.8, ('V', 'D') : -5.2, ('H', 'C') : -8.2,
-('Y', 'N') : -1.3, ('Y', 'F') : 3.4, ('W', 'I') : -3.3, ('C', 'A') : -3.4,
-('G', 'E') : -2.5, ('G', 'A') : -0.5, ('Y', 'V') : -1.8, ('E', 'D') : 2.4,
-('W', 'H') : -4.0, ('Y', 'R') : -2.1, ('N', 'I') : -4.7, ('R', 'L') : -3.7,
-('T', 'I') : -3.2, ('Q', 'L') : -4.4, ('R', 'H') : 0.1, ('T', 'M') : -3.2,
-('V', 'F') : -1.3, ('R', 'D') : -3.4, ('T', 'A') : -0.8, ('T', 'P') : -2.0,
-('T', 'E') : -0.5, ('V', 'N') : -5.7, ('P', 'G') : -2.5, ('M', 'A') : -1.5,
-('K', 'H') : 0.1, ('V', 'R') : -4.9, ('P', 'C') : -8.9, ('M', 'E') : -2.8,
-('V', 'V') : 7.0, ('T', 'T') : 6.8, ('M', 'I') : 2.6, ('T', 'Q') : -0.4,
-('I', 'G') : -5.5, ('P', 'K') : -0.6, ('M', 'M') : 11.2, ('K', 'D') : -1.5,
-('I', 'C') : -7.7, ('L', 'C') : -8.7, ('F', 'F') : 10.4, ('D', 'A') : -1.6,
-('S', 'R') : -0.6, ('W', 'D') : -6.0, ('N', 'C') : -7.6, ('N', 'G') : -1.4,
-('W', 'T') : -9.3, ('Q', 'G') : -2.8, ('S', 'F') : -4.8, ('W', 'P') : -7.4,
-('L', 'D') : -8.0, ('H', 'F') : -1.7, ('Q', 'K') : 1.1, ('S', 'N') : 1.0,
-('L', 'L') : 7.3, ('Q', 'F') : -6.4, ('Y', 'K') : -3.7, ('Y', 'G') : -5.4,
-('Y', 'C') : -7.7, ('G', 'D') : -2.1, ('E', 'A') : -0.7, ('Y', 'W') : 2.3,
-('E', 'E') : 8.6, ('Y', 'S') : -3.4, ('R', 'M') : -4.2, ('V', 'C') : -4.8,
-('T', 'H') : -3.0, ('R', 'I') : -5.4, ('V', 'G') : -5.6, ('T', 'L') : -4.6,
-('R', 'E') : -0.2, ('V', 'K') : -3.7, ('R', 'Q') : 2.1, ('R', 'A') : -1.6,
-('T', 'D') : -1.8, ('P', 'F') : -5.0, ('V', 'S') : -4.3, ('K', 'I') : -4.7,
-('M', 'D') : -5.9, ('W', 'W') : 15.2, ('M', 'H') : -2.3, ('P', 'N') : -2.4,
-('I', 'F') : 0.5, ('K', 'A') : -0.9, ('M', 'L') : 4.4, ('K', 'E') : 1.1,
-('N', 'K') : 0.1, ('R', 'P') : -3.6, ('L', 'F') : 1.8, ('F', 'C') : -4.4,
-('W', 'G') : -6.3, ('W', 'L') : -1.0, ('D', 'D') : 8.5, ('N', 'H') : 1.7,
-('S', 'Q') : -1.2, ('Q', 'P') : -3.6, ('N', 'L') : -4.8, ('W', 'K') : -5.4,
-('Q', 'D') : -1.1, ('W', 'N') : -6.1, ('S', 'A') : 0.0, ('L', 'G') : -7.2,
-('W', 'S') : -6.2, ('S', 'E') : -2.2, ('L', 'K') : -3.4, ('H', 'E') : -2.3,
-('S', 'I') : -4.7, ('Q', 'H') : 1.4, ('H', 'A') : -3.1, ('S', 'M') : -4.8,
-('Y', 'L') : -2.4, ('Y', 'H') : -0.4, ('Y', 'D') : -3.8, ('G', 'G') : 8.0,
-('G', 'C') : -8.2, ('Y', 'T') : -2.7, ('W', 'C') : -9.1, ('Y', 'P') : -7.0,
-('T', 'K') : -0.2, ('R', 'N') : -1.5, ('A', 'A') : 6.0, ('W', 'A') : -5.8,
-('T', 'C') : -6.0, ('N', 'D') : 2.6, ('R', 'F') : -6.0, ('T', 'G') : -3.8,
-('V', 'L') : 1.8, ('V', 'P') : -5.2, ('P', 'E') : -1.5, ('M', 'C') : -4.4,
-('I', 'I') : 8.1, ('P', 'A') : -1.0, ('M', 'G') : -5.2, ('T', 'S') : 2.0,
-('I', 'E') : -4.8, ('P', 'M') : -9.8, ('M', 'K') : -1.9, ('K', 'F') : -5.6,
-('I', 'A') : -2.2, ('P', 'I') : -5.7, ('R', 'R') : 10.0, ('L', 'I') : 2.6,
-('F', 'D') : -7.0, ('D', 'C') : -9.7
+('W', 'F'): 3.4, ('S', 'P'): -1.0, ('N', 'M'): -3.7, ('Q', 'Q'): 9.0,
+('N', 'A'): -1.4, ('N', 'E'): -0.7, ('W', 'V'): -4.9, ('Q', 'E'): 2.4,
+('L', 'H'): -4.2, ('W', 'R'): -3.8, ('Q', 'A'): -0.6, ('S', 'D'): -0.2,
+('H', 'H'): 12.7, ('Q', 'M'): -0.6, ('S', 'H'): -2.6, ('H', 'D'): -0.7,
+('Q', 'I'): -7.0, ('S', 'L'): -5.2, ('Y', 'M'): -1.3, ('Y', 'I'): -2.5,
+('Y', 'E'): -3.7, ('Y', 'A'): -4.0, ('G', 'F'): -8.6, ('V', 'T'): -1.9,
+('Y', 'Y'): 10.5, ('V', 'H'): -3.9, ('E', 'C'): -6.9, ('Y', 'Q'): -5.1,
+('V', 'A'): -0.5, ('C', 'C'): 16.1, ('V', 'E'): -4.2, ('T', 'N'): 0.1,
+('R', 'K'): 3.2, ('P', 'P'): 10.3, ('V', 'I'): 3.9, ('R', 'G'): -2.8,
+('V', 'M'): 0.7, ('T', 'F'): -5.0, ('R', 'C'): -5.6, ('V', 'Q'): -3.6,
+('K', 'K'): 7.6, ('P', 'D'): -1.0, ('I', 'H'): -5.1, ('M', 'F'): -0.6,
+('I', 'D'): -4.8, ('K', 'C'): -8.7, ('P', 'L'): -2.8, ('K', 'G'): -3.5,
+('P', 'H'): -4.3, ('T', 'R'): -1.4, ('F', 'A'): -3.2, ('F', 'E'): -6.4,
+('S', 'S'): 5.8, ('W', 'E'): -7.6, ('N', 'N'): 8.0, ('W', 'M'): -0.9,
+('Q', 'C'): -6.9, ('N', 'F'): -3.8, ('S', 'C'): -7.7, ('L', 'A'): -3.3,
+('S', 'G'): -1.3, ('L', 'E'): -5.6, ('W', 'Q'): -8.2, ('H', 'G'): -3.2,
+('S', 'K'): -1.5, ('Q', 'N'): -0.8, ('V', 'D'): -5.2, ('H', 'C'): -8.2,
+('Y', 'N'): -1.3, ('Y', 'F'): 3.4, ('W', 'I'): -3.3, ('C', 'A'): -3.4,
+('G', 'E'): -2.5, ('G', 'A'): -0.5, ('Y', 'V'): -1.8, ('E', 'D'): 2.4,
+('W', 'H'): -4.0, ('Y', 'R'): -2.1, ('N', 'I'): -4.7, ('R', 'L'): -3.7,
+('T', 'I'): -3.2, ('Q', 'L'): -4.4, ('R', 'H'): 0.1, ('T', 'M'): -3.2,
+('V', 'F'): -1.3, ('R', 'D'): -3.4, ('T', 'A'): -0.8, ('T', 'P'): -2.0,
+('T', 'E'): -0.5, ('V', 'N'): -5.7, ('P', 'G'): -2.5, ('M', 'A'): -1.5,
+('K', 'H'): 0.1, ('V', 'R'): -4.9, ('P', 'C'): -8.9, ('M', 'E'): -2.8,
+('V', 'V'): 7.0, ('T', 'T'): 6.8, ('M', 'I'): 2.6, ('T', 'Q'): -0.4,
+('I', 'G'): -5.5, ('P', 'K'): -0.6, ('M', 'M'): 11.2, ('K', 'D'): -1.5,
+('I', 'C'): -7.7, ('L', 'C'): -8.7, ('F', 'F'): 10.4, ('D', 'A'): -1.6,
+('S', 'R'): -0.6, ('W', 'D'): -6.0, ('N', 'C'): -7.6, ('N', 'G'): -1.4,
+('W', 'T'): -9.3, ('Q', 'G'): -2.8, ('S', 'F'): -4.8, ('W', 'P'): -7.4,
+('L', 'D'): -8.0, ('H', 'F'): -1.7, ('Q', 'K'): 1.1, ('S', 'N'): 1.0,
+('L', 'L'): 7.3, ('Q', 'F'): -6.4, ('Y', 'K'): -3.7, ('Y', 'G'): -5.4,
+('Y', 'C'): -7.7, ('G', 'D'): -2.1, ('E', 'A'): -0.7, ('Y', 'W'): 2.3,
+('E', 'E'): 8.6, ('Y', 'S'): -3.4, ('R', 'M'): -4.2, ('V', 'C'): -4.8,
+('T', 'H'): -3.0, ('R', 'I'): -5.4, ('V', 'G'): -5.6, ('T', 'L'): -4.6,
+('R', 'E'): -0.2, ('V', 'K'): -3.7, ('R', 'Q'): 2.1, ('R', 'A'): -1.6,
+('T', 'D'): -1.8, ('P', 'F'): -5.0, ('V', 'S'): -4.3, ('K', 'I'): -4.7,
+('M', 'D'): -5.9, ('W', 'W'): 15.2, ('M', 'H'): -2.3, ('P', 'N'): -2.4,
+('I', 'F'): 0.5, ('K', 'A'): -0.9, ('M', 'L'): 4.4, ('K', 'E'): 1.1,
+('N', 'K'): 0.1, ('R', 'P'): -3.6, ('L', 'F'): 1.8, ('F', 'C'): -4.4,
+('W', 'G'): -6.3, ('W', 'L'): -1.0, ('D', 'D'): 8.5, ('N', 'H'): 1.7,
+('S', 'Q'): -1.2, ('Q', 'P'): -3.6, ('N', 'L'): -4.8, ('W', 'K'): -5.4,
+('Q', 'D'): -1.1, ('W', 'N'): -6.1, ('S', 'A'): 0.0, ('L', 'G'): -7.2,
+('W', 'S'): -6.2, ('S', 'E'): -2.2, ('L', 'K'): -3.4, ('H', 'E'): -2.3,
+('S', 'I'): -4.7, ('Q', 'H'): 1.4, ('H', 'A'): -3.1, ('S', 'M'): -4.8,
+('Y', 'L'): -2.4, ('Y', 'H'): -0.4, ('Y', 'D'): -3.8, ('G', 'G'): 8.0,
+('G', 'C'): -8.2, ('Y', 'T'): -2.7, ('W', 'C'): -9.1, ('Y', 'P'): -7.0,
+('T', 'K'): -0.2, ('R', 'N'): -1.5, ('A', 'A'): 6.0, ('W', 'A'): -5.8,
+('T', 'C'): -6.0, ('N', 'D'): 2.6, ('R', 'F'): -6.0, ('T', 'G'): -3.8,
+('V', 'L'): 1.8, ('V', 'P'): -5.2, ('P', 'E'): -1.5, ('M', 'C'): -4.4,
+('I', 'I'): 8.1, ('P', 'A'): -1.0, ('M', 'G'): -5.2, ('T', 'S'): 2.0,
+('I', 'E'): -4.8, ('P', 'M'): -9.8, ('M', 'K'): -1.9, ('K', 'F'): -5.6,
+('I', 'A'): -2.2, ('P', 'I'): -5.7, ('R', 'R'): 10.0, ('L', 'I'): 2.6,
+('F', 'D'): -7.0, ('D', 'C'): -9.7
 }
 johnson = _temp()
 del _temp
@@ -1870,59 +1869,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/levin.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -1, ('S', 'P') : 0, ('I', 'I') : 2,
-('Q', 'Q') : 2, ('N', 'A') : 0, ('H', 'T') : 0, ('N', 'E') : 0,
-('H', 'P') : 0, ('W', 'V') : 0, ('Q', 'E') : 1, ('W', 'R') : 0,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 2, ('H', 'D') : 0,
-('L', 'N') : -1, ('W', 'A') : -1, ('Y', 'M') : 0, ('Y', 'I') : 0,
-('Y', 'E') : -1, ('Y', 'A') : -1, ('Y', 'Y') : 2, ('Y', 'Q') : -1,
-('E', 'G') : 0, ('A', 'D') : 0, ('C', 'C') : 2, ('M', 'R') : -1,
-('V', 'E') : -1, ('T', 'N') : 0, ('R', 'K') : 1, ('P', 'P') : 3,
-('I', 'T') : 0, ('K', 'S') : 0, ('R', 'G') : 0, ('I', 'P') : -1,
-('C', 'G') : 0, ('C', 'S') : 0, ('A', 'P') : -1, ('I', 'D') : -1,
-('M', 'I') : 0, ('K', 'G') : 0, ('M', 'N') : -1, ('F', 'Q') : -1,
-('I', 'V') : 1, ('F', 'A') : -1, ('V', 'V') : 2, ('F', 'E') : -1,
-('C', 'M') : 0, ('F', 'I') : 1, ('F', 'M') : 0, ('S', 'S') : 2,
-('L', 'Q') : -1, ('W', 'E') : -1, ('N', 'N') : 3, ('V', 'A') : 0,
-('C', 'K') : 0, ('W', 'M') : 0, ('H', 'S') : 0, ('L', 'V') : 1,
-('L', 'A') : 0, ('H', 'K') : 0, ('S', 'G') : 0, ('L', 'E') : -1,
-('W', 'Q') : -1, ('H', 'G') : 0, ('Q', 'N') : 1, ('T', 'A') : 0,
-('L', 'M') : 2, ('W', 'Y') : 0, ('Y', 'N') : -1, ('E', 'P') : -1,
-('Y', 'F') : 1, ('W', 'I') : 0, ('R', 'S') : 0, ('Y', 'V') : 0,
-('E', 'D') : 1, ('W', 'H') : -1, ('Y', 'R') : -1, ('M', 'Q') : -1,
-('A', 'G') : 0, ('C', 'D') : 0, ('R', 'D') : 0, ('C', 'H') : 0,
-('T', 'P') : 0, ('K', 'T') : 0, ('V', 'N') : -1, ('P', 'G') : 0,
-('M', 'A') : 0, ('C', 'P') : 0, ('V', 'R') : -1, ('M', 'V') : 0,
-('M', 'E') : -1, ('C', 'T') : 0, ('I', 'K') : -1, ('R', 'T') : 0,
-('T', 'Q') : 0, ('I', 'G') : -1, ('R', 'P') : 0, ('K', 'D') : 0,
-('F', 'R') : -1, ('F', 'V') : 0, ('K', 'P') : 0, ('L', 'C') : 0,
-('F', 'F') : 2, ('F', 'N') : -1, ('V', 'D') : -1, ('L', 'P') : -1,
-('W', 'K') : -1, ('L', 'T') : 0, ('I', 'N') : -1, ('I', 'S') : -1,
-('H', 'R') : 0, ('N', 'G') : 0, ('C', 'I') : 0, ('H', 'N') : 0,
-('W', 'T') : -1, ('Q', 'G') : 0, ('W', 'P') : -1, ('L', 'D') : -1,
-('L', 'H') : -1, ('S', 'N') : 0, ('L', 'L') : 2, ('M', 'M') : 2,
-('Y', 'K') : -1, ('Y', 'G') : -1, ('Y', 'C') : -1, ('E', 'E') : 2,
-('Y', 'S') : -1, ('M', 'P') : -1, ('C', 'A') : 0, ('M', 'T') : 0,
-('V', 'G') : -1, ('C', 'E') : 0, ('R', 'E') : 0, ('V', 'K') : -1,
-('K', 'Q') : 0, ('R', 'A') : 0, ('I', 'R') : -1, ('T', 'D') : 0,
-('V', 'S') : -1, ('C', 'Q') : 0, ('M', 'D') : -1, ('W', 'W') : 2,
-('M', 'H') : -1, ('T', 'G') : 0, ('K', 'A') : 0, ('R', 'Q') : 0,
-('T', 'T') : 2, ('F', 'S') : -1, ('D', 'P') : 0, ('F', 'C') : -1,
-('W', 'L') : 0, ('F', 'G') : -1, ('F', 'K') : -1, ('D', 'D') : 2,
-('L', 'S') : -1, ('W', 'G') : -1, ('S', 'Q') : 0, ('Q', 'P') : 0,
-('W', 'C') : -1, ('N', 'D') : 1, ('H', 'Q') : 0, ('Q', 'D') : 0,
-('W', 'N') : -1, ('S', 'A') : 1, ('L', 'G') : -1, ('W', 'S') : -1,
-('S', 'E') : 0, ('L', 'K') : -1, ('N', 'P') : 0, ('H', 'E') : 0,
-('H', 'A') : 0, ('Y', 'L') : 0, ('Y', 'H') : 0, ('Y', 'D') : -1,
-('V', 'Q') : -1, ('G', 'G') : 2, ('Y', 'T') : -1, ('R', 'N') : 0,
-('Y', 'P') : -1, ('A', 'E') : 1, ('C', 'V') : 0, ('M', 'S') : -1,
-('A', 'A') : 2, ('V', 'H') : -1, ('T', 'E') : 0, ('C', 'N') : 0,
-('I', 'Q') : -1, ('C', 'R') : 0, ('V', 'P') : -1, ('K', 'E') : 0,
-('K', 'N') : 1, ('V', 'T') : 0, ('M', 'G') : -1, ('T', 'S') : 0,
-('I', 'E') : -1, ('M', 'K') : -1, ('I', 'A') : 0, ('R', 'R') : 2,
-('F', 'P') : -1, ('L', 'I') : 0, ('W', 'D') : -1, ('F', 'T') : -1,
-('K', 'K') : 2, ('I', 'H') : -1, ('F', 'D') : -1, ('F', 'H') : -1,
-('D', 'G') : 0, ('F', 'L') : 0
+('W', 'F'): 0, ('L', 'R'): -1, ('S', 'P'): 0, ('I', 'I'): 2,
+('Q', 'Q'): 2, ('N', 'A'): 0, ('H', 'T'): 0, ('N', 'E'): 0,
+('H', 'P'): 0, ('W', 'V'): 0, ('Q', 'E'): 1, ('W', 'R'): 0,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 2, ('H', 'D'): 0,
+('L', 'N'): -1, ('W', 'A'): -1, ('Y', 'M'): 0, ('Y', 'I'): 0,
+('Y', 'E'): -1, ('Y', 'A'): -1, ('Y', 'Y'): 2, ('Y', 'Q'): -1,
+('E', 'G'): 0, ('A', 'D'): 0, ('C', 'C'): 2, ('M', 'R'): -1,
+('V', 'E'): -1, ('T', 'N'): 0, ('R', 'K'): 1, ('P', 'P'): 3,
+('I', 'T'): 0, ('K', 'S'): 0, ('R', 'G'): 0, ('I', 'P'): -1,
+('C', 'G'): 0, ('C', 'S'): 0, ('A', 'P'): -1, ('I', 'D'): -1,
+('M', 'I'): 0, ('K', 'G'): 0, ('M', 'N'): -1, ('F', 'Q'): -1,
+('I', 'V'): 1, ('F', 'A'): -1, ('V', 'V'): 2, ('F', 'E'): -1,
+('C', 'M'): 0, ('F', 'I'): 1, ('F', 'M'): 0, ('S', 'S'): 2,
+('L', 'Q'): -1, ('W', 'E'): -1, ('N', 'N'): 3, ('V', 'A'): 0,
+('C', 'K'): 0, ('W', 'M'): 0, ('H', 'S'): 0, ('L', 'V'): 1,
+('L', 'A'): 0, ('H', 'K'): 0, ('S', 'G'): 0, ('L', 'E'): -1,
+('W', 'Q'): -1, ('H', 'G'): 0, ('Q', 'N'): 1, ('T', 'A'): 0,
+('L', 'M'): 2, ('W', 'Y'): 0, ('Y', 'N'): -1, ('E', 'P'): -1,
+('Y', 'F'): 1, ('W', 'I'): 0, ('R', 'S'): 0, ('Y', 'V'): 0,
+('E', 'D'): 1, ('W', 'H'): -1, ('Y', 'R'): -1, ('M', 'Q'): -1,
+('A', 'G'): 0, ('C', 'D'): 0, ('R', 'D'): 0, ('C', 'H'): 0,
+('T', 'P'): 0, ('K', 'T'): 0, ('V', 'N'): -1, ('P', 'G'): 0,
+('M', 'A'): 0, ('C', 'P'): 0, ('V', 'R'): -1, ('M', 'V'): 0,
+('M', 'E'): -1, ('C', 'T'): 0, ('I', 'K'): -1, ('R', 'T'): 0,
+('T', 'Q'): 0, ('I', 'G'): -1, ('R', 'P'): 0, ('K', 'D'): 0,
+('F', 'R'): -1, ('F', 'V'): 0, ('K', 'P'): 0, ('L', 'C'): 0,
+('F', 'F'): 2, ('F', 'N'): -1, ('V', 'D'): -1, ('L', 'P'): -1,
+('W', 'K'): -1, ('L', 'T'): 0, ('I', 'N'): -1, ('I', 'S'): -1,
+('H', 'R'): 0, ('N', 'G'): 0, ('C', 'I'): 0, ('H', 'N'): 0,
+('W', 'T'): -1, ('Q', 'G'): 0, ('W', 'P'): -1, ('L', 'D'): -1,
+('L', 'H'): -1, ('S', 'N'): 0, ('L', 'L'): 2, ('M', 'M'): 2,
+('Y', 'K'): -1, ('Y', 'G'): -1, ('Y', 'C'): -1, ('E', 'E'): 2,
+('Y', 'S'): -1, ('M', 'P'): -1, ('C', 'A'): 0, ('M', 'T'): 0,
+('V', 'G'): -1, ('C', 'E'): 0, ('R', 'E'): 0, ('V', 'K'): -1,
+('K', 'Q'): 0, ('R', 'A'): 0, ('I', 'R'): -1, ('T', 'D'): 0,
+('V', 'S'): -1, ('C', 'Q'): 0, ('M', 'D'): -1, ('W', 'W'): 2,
+('M', 'H'): -1, ('T', 'G'): 0, ('K', 'A'): 0, ('R', 'Q'): 0,
+('T', 'T'): 2, ('F', 'S'): -1, ('D', 'P'): 0, ('F', 'C'): -1,
+('W', 'L'): 0, ('F', 'G'): -1, ('F', 'K'): -1, ('D', 'D'): 2,
+('L', 'S'): -1, ('W', 'G'): -1, ('S', 'Q'): 0, ('Q', 'P'): 0,
+('W', 'C'): -1, ('N', 'D'): 1, ('H', 'Q'): 0, ('Q', 'D'): 0,
+('W', 'N'): -1, ('S', 'A'): 1, ('L', 'G'): -1, ('W', 'S'): -1,
+('S', 'E'): 0, ('L', 'K'): -1, ('N', 'P'): 0, ('H', 'E'): 0,
+('H', 'A'): 0, ('Y', 'L'): 0, ('Y', 'H'): 0, ('Y', 'D'): -1,
+('V', 'Q'): -1, ('G', 'G'): 2, ('Y', 'T'): -1, ('R', 'N'): 0,
+('Y', 'P'): -1, ('A', 'E'): 1, ('C', 'V'): 0, ('M', 'S'): -1,
+('A', 'A'): 2, ('V', 'H'): -1, ('T', 'E'): 0, ('C', 'N'): 0,
+('I', 'Q'): -1, ('C', 'R'): 0, ('V', 'P'): -1, ('K', 'E'): 0,
+('K', 'N'): 1, ('V', 'T'): 0, ('M', 'G'): -1, ('T', 'S'): 0,
+('I', 'E'): -1, ('M', 'K'): -1, ('I', 'A'): 0, ('R', 'R'): 2,
+('F', 'P'): -1, ('L', 'I'): 0, ('W', 'D'): -1, ('F', 'T'): -1,
+('K', 'K'): 2, ('I', 'H'): -1, ('F', 'D'): -1, ('F', 'H'): -1,
+('D', 'G'): 0, ('F', 'L'): 0
 }
 levin = _temp()
 del _temp
@@ -1931,59 +1930,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/mclach.cmp
 def _temp():
     return {
-('N', 'I') : 1, ('K', 'V') : 2, ('S', 'P') : 3, ('N', 'M') : 2,
-('L', 'V') : 5, ('N', 'A') : 3, ('H', 'T') : 4, ('N', 'E') : 4,
-('Q', 'Y') : 1, ('H', 'P') : 3, ('W', 'V') : 2, ('H', 'L') : 2,
-('Q', 'A') : 3, ('H', 'H') : 8, ('N', 'Q') : 4, ('Q', 'M') : 3,
-('Q', 'I') : 0, ('S', 'L') : 2, ('G', 'V') : 2, ('Y', 'M') : 2,
-('K', 'L') : 2, ('Y', 'I') : 3, ('E', 'S') : 4, ('K', 'A') : 3,
-('E', 'W') : 1, ('G', 'F') : 0, ('E', 'K') : 4, ('Y', 'Y') : 9,
-('K', 'F') : 0, ('E', 'G') : 3, ('C', 'C') : 9, ('C', 'G') : 1,
-('M', 'V') : 4, ('P', 'P') : 8, ('A', 'L') : 2, ('K', 'S') : 3,
-('R', 'G') : 3, ('K', 'W') : 1, ('R', 'C') : 1, ('I', 'L') : 5,
-('C', 'S') : 2, ('C', 'W') : 2, ('K', 'C') : 0, ('R', 'W') : 3,
-('P', 'L') : 1, ('K', 'G') : 3, ('R', 'S') : 4, ('D', 'R') : 1,
-('D', 'V') : 1, ('D', 'N') : 5, ('F', 'I') : 3, ('F', 'M') : 5,
-('D', 'F') : 1, ('S', 'S') : 8, ('Q', 'V') : 2, ('S', 'W') : 3,
-('Q', 'R') : 5, ('N', 'N') : 8, ('H', 'W') : 3, ('W', 'M') : 1,
-('H', 'S') : 3, ('W', 'I') : 3, ('T', 'F') : 1, ('Q', 'F') : 0,
-('S', 'G') : 3, ('H', 'G') : 2, ('C', 'P') : 0, ('N', 'R') : 3,
-('H', 'C') : 3, ('N', 'V') : 1, ('E', 'P') : 4, ('Y', 'F') : 6,
-('E', 'T') : 4, ('G', 'Y') : 0, ('E', 'H') : 2, ('E', 'L') : 1,
-('Y', 'V') : 3, ('G', 'M') : 1, ('G', 'I') : 1, ('P', 'W') : 0,
-('R', 'L') : 2, ('T', 'I') : 3, ('A', 'G') : 3, ('R', 'H') : 5,
-('T', 'M') : 3, ('T', 'A') : 3, ('K', 'T') : 3, ('P', 'G') : 3,
-('T', 'Y') : 1, ('A', 'W') : 1, ('C', 'T') : 2, ('V', 'V') : 8,
-('R', 'T') : 3, ('R', 'P') : 3, ('D', 'Y') : 1, ('F', 'V') : 3,
-('D', 'Q') : 4, ('K', 'P') : 3, ('D', 'I') : 1, ('D', 'M') : 2,
-('F', 'F') : 9, ('D', 'A') : 3, ('D', 'E') : 5, ('N', 'K') : 4,
-('Q', 'W') : 2, ('S', 'V') : 2, ('Q', 'S') : 4, ('H', 'V') : 2,
-('W', 'L') : 3, ('N', 'G') : 3, ('Q', 'G') : 2, ('S', 'F') : 2,
-('Q', 'C') : 0, ('H', 'F') : 4, ('N', 'S') : 5, ('Q', 'K') : 4,
-('N', 'W') : 0, ('L', 'L') : 8, ('E', 'Y') : 2, ('M', 'M') : 8,
-('E', 'Q') : 5, ('E', 'I') : 1, ('E', 'M') : 1, ('E', 'A') : 4,
-('G', 'L') : 1, ('Y', 'W') : 6, ('E', 'E') : 8, ('R', 'M') : 1,
-('P', 'V') : 2, ('A', 'F') : 1, ('C', 'A') : 1, ('R', 'I') : 1,
-('T', 'L') : 3, ('I', 'V') : 5, ('C', 'I') : 1, ('R', 'A') : 2,
-('C', 'Y') : 1, ('C', 'M') : 3, ('P', 'F') : 1, ('A', 'V') : 3,
-('K', 'I') : 1, ('R', 'Y') : 2, ('K', 'M') : 1, ('K', 'H') : 4,
-('T', 'P') : 3, ('M', 'L') : 6, ('T', 'T') : 8, ('C', 'L') : 0,
-('D', 'P') : 3, ('N', 'F') : 0, ('K', 'Y') : 1, ('D', 'T') : 3,
-('D', 'H') : 4, ('D', 'L') : 1, ('K', 'K') : 8, ('D', 'D') : 8,
-('Q', 'T') : 3, ('N', 'H') : 4, ('Q', 'P') : 3, ('N', 'L') : 1,
-('H', 'Y') : 4, ('S', 'Y') : 3, ('W', 'W') : 9, ('H', 'M') : 3,
-('S', 'A') : 4, ('H', 'I') : 2, ('Q', 'L') : 3, ('N', 'P') : 1,
-('S', 'I') : 2, ('Q', 'H') : 4, ('N', 'T') : 3, ('H', 'A') : 3,
-('S', 'M') : 2, ('Y', 'L') : 3, ('G', 'W') : 1, ('E', 'R') : 3,
-('E', 'V') : 2, ('G', 'G') : 8, ('T', 'V') : 3, ('E', 'F') : 0,
-('C', 'F') : 0, ('A', 'A') : 8, ('K', 'R') : 5, ('A', 'M') : 3,
-('Q', 'Q') : 8, ('R', 'F') : 1, ('T', 'G') : 2, ('A', 'I') : 2,
-('P', 'Y') : 0, ('C', 'V') : 1, ('I', 'I') : 8, ('P', 'A') : 4,
-('T', 'S') : 5, ('P', 'M') : 1, ('R', 'V') : 2, ('T', 'W') : 2,
-('A', 'Y') : 1, ('P', 'I') : 1, ('R', 'R') : 8, ('N', 'Y') : 2,
-('D', 'S') : 3, ('D', 'W') : 0, ('M', 'I') : 5, ('D', 'K') : 3,
-('N', 'C') : 1, ('E', 'C') : 0, ('D', 'C') : 1, ('D', 'G') : 3,
-('F', 'L') : 5, ('W', 'F') : 6
+('N', 'I'): 1, ('K', 'V'): 2, ('S', 'P'): 3, ('N', 'M'): 2,
+('L', 'V'): 5, ('N', 'A'): 3, ('H', 'T'): 4, ('N', 'E'): 4,
+('Q', 'Y'): 1, ('H', 'P'): 3, ('W', 'V'): 2, ('H', 'L'): 2,
+('Q', 'A'): 3, ('H', 'H'): 8, ('N', 'Q'): 4, ('Q', 'M'): 3,
+('Q', 'I'): 0, ('S', 'L'): 2, ('G', 'V'): 2, ('Y', 'M'): 2,
+('K', 'L'): 2, ('Y', 'I'): 3, ('E', 'S'): 4, ('K', 'A'): 3,
+('E', 'W'): 1, ('G', 'F'): 0, ('E', 'K'): 4, ('Y', 'Y'): 9,
+('K', 'F'): 0, ('E', 'G'): 3, ('C', 'C'): 9, ('C', 'G'): 1,
+('M', 'V'): 4, ('P', 'P'): 8, ('A', 'L'): 2, ('K', 'S'): 3,
+('R', 'G'): 3, ('K', 'W'): 1, ('R', 'C'): 1, ('I', 'L'): 5,
+('C', 'S'): 2, ('C', 'W'): 2, ('K', 'C'): 0, ('R', 'W'): 3,
+('P', 'L'): 1, ('K', 'G'): 3, ('R', 'S'): 4, ('D', 'R'): 1,
+('D', 'V'): 1, ('D', 'N'): 5, ('F', 'I'): 3, ('F', 'M'): 5,
+('D', 'F'): 1, ('S', 'S'): 8, ('Q', 'V'): 2, ('S', 'W'): 3,
+('Q', 'R'): 5, ('N', 'N'): 8, ('H', 'W'): 3, ('W', 'M'): 1,
+('H', 'S'): 3, ('W', 'I'): 3, ('T', 'F'): 1, ('Q', 'F'): 0,
+('S', 'G'): 3, ('H', 'G'): 2, ('C', 'P'): 0, ('N', 'R'): 3,
+('H', 'C'): 3, ('N', 'V'): 1, ('E', 'P'): 4, ('Y', 'F'): 6,
+('E', 'T'): 4, ('G', 'Y'): 0, ('E', 'H'): 2, ('E', 'L'): 1,
+('Y', 'V'): 3, ('G', 'M'): 1, ('G', 'I'): 1, ('P', 'W'): 0,
+('R', 'L'): 2, ('T', 'I'): 3, ('A', 'G'): 3, ('R', 'H'): 5,
+('T', 'M'): 3, ('T', 'A'): 3, ('K', 'T'): 3, ('P', 'G'): 3,
+('T', 'Y'): 1, ('A', 'W'): 1, ('C', 'T'): 2, ('V', 'V'): 8,
+('R', 'T'): 3, ('R', 'P'): 3, ('D', 'Y'): 1, ('F', 'V'): 3,
+('D', 'Q'): 4, ('K', 'P'): 3, ('D', 'I'): 1, ('D', 'M'): 2,
+('F', 'F'): 9, ('D', 'A'): 3, ('D', 'E'): 5, ('N', 'K'): 4,
+('Q', 'W'): 2, ('S', 'V'): 2, ('Q', 'S'): 4, ('H', 'V'): 2,
+('W', 'L'): 3, ('N', 'G'): 3, ('Q', 'G'): 2, ('S', 'F'): 2,
+('Q', 'C'): 0, ('H', 'F'): 4, ('N', 'S'): 5, ('Q', 'K'): 4,
+('N', 'W'): 0, ('L', 'L'): 8, ('E', 'Y'): 2, ('M', 'M'): 8,
+('E', 'Q'): 5, ('E', 'I'): 1, ('E', 'M'): 1, ('E', 'A'): 4,
+('G', 'L'): 1, ('Y', 'W'): 6, ('E', 'E'): 8, ('R', 'M'): 1,
+('P', 'V'): 2, ('A', 'F'): 1, ('C', 'A'): 1, ('R', 'I'): 1,
+('T', 'L'): 3, ('I', 'V'): 5, ('C', 'I'): 1, ('R', 'A'): 2,
+('C', 'Y'): 1, ('C', 'M'): 3, ('P', 'F'): 1, ('A', 'V'): 3,
+('K', 'I'): 1, ('R', 'Y'): 2, ('K', 'M'): 1, ('K', 'H'): 4,
+('T', 'P'): 3, ('M', 'L'): 6, ('T', 'T'): 8, ('C', 'L'): 0,
+('D', 'P'): 3, ('N', 'F'): 0, ('K', 'Y'): 1, ('D', 'T'): 3,
+('D', 'H'): 4, ('D', 'L'): 1, ('K', 'K'): 8, ('D', 'D'): 8,
+('Q', 'T'): 3, ('N', 'H'): 4, ('Q', 'P'): 3, ('N', 'L'): 1,
+('H', 'Y'): 4, ('S', 'Y'): 3, ('W', 'W'): 9, ('H', 'M'): 3,
+('S', 'A'): 4, ('H', 'I'): 2, ('Q', 'L'): 3, ('N', 'P'): 1,
+('S', 'I'): 2, ('Q', 'H'): 4, ('N', 'T'): 3, ('H', 'A'): 3,
+('S', 'M'): 2, ('Y', 'L'): 3, ('G', 'W'): 1, ('E', 'R'): 3,
+('E', 'V'): 2, ('G', 'G'): 8, ('T', 'V'): 3, ('E', 'F'): 0,
+('C', 'F'): 0, ('A', 'A'): 8, ('K', 'R'): 5, ('A', 'M'): 3,
+('Q', 'Q'): 8, ('R', 'F'): 1, ('T', 'G'): 2, ('A', 'I'): 2,
+('P', 'Y'): 0, ('C', 'V'): 1, ('I', 'I'): 8, ('P', 'A'): 4,
+('T', 'S'): 5, ('P', 'M'): 1, ('R', 'V'): 2, ('T', 'W'): 2,
+('A', 'Y'): 1, ('P', 'I'): 1, ('R', 'R'): 8, ('N', 'Y'): 2,
+('D', 'S'): 3, ('D', 'W'): 0, ('M', 'I'): 5, ('D', 'K'): 3,
+('N', 'C'): 1, ('E', 'C'): 0, ('D', 'C'): 1, ('D', 'G'): 3,
+('F', 'L'): 5, ('W', 'F'): 6
 }
 mclach = _temp()
 del _temp
@@ -1992,59 +1991,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/miyata.cmp
 def _temp():
     return {
-('W', 'F') : 0.14, ('L', 'R') : -1.37, ('S', 'P') : 0.69, ('I', 'I') : 1.25,
-('Q', 'Q') : 1.25, ('N', 'A') : -0.53, ('H', 'T') : -0.07, ('N', 'E') : 0.4,
-('H', 'P') : -0.9, ('W', 'V') : -1.26, ('W', 'R') : -1.47, ('Q', 'A') : -0.67,
-('H', 'H') : 1.25, ('N', 'Q') : 0.26, ('H', 'D') : -0.47, ('L', 'N') : -2.24,
-('Y', 'M') : 0.32, ('Y', 'I') : 0.39, ('Y', 'E') : -1.97, ('E', 'S') : -0.81,
-('Y', 'A') : -1.93, ('Y', 'Y') : 1.25, ('E', 'C') : -2.01, ('Y', 'Q') : -1.23,
-('E', 'G') : -1.53, ('V', 'A') : -0.6, ('C', 'C') : 1.25, ('M', 'R') : -1.04,
-('V', 'E') : -1.72, ('R', 'K') : 0.85, ('P', 'P') : 1.25, ('I', 'T') : -0.89,
-('K', 'S') : -1.46, ('R', 'G') : -2.33, ('I', 'P') : -1.37, ('R', 'C') : -1.81,
-('V', 'Q') : -0.88, ('K', 'K') : 1.25, ('A', 'P') : 1.19, ('I', 'D') : -2.73,
-('K', 'C') : -2.02, ('M', 'I') : 0.96, ('K', 'G') : -2.29, ('R', 'S') : -1.49,
-('F', 'Q') : -1.56, ('I', 'V') : 0.4, ('M', 'V') : 0.63, ('F', 'A') : -1.98,
-('V', 'V') : 1.25, ('M', 'N') : -1.83, ('F', 'E') : -2.34, ('D', 'N') : 0.6,
-('F', 'I') : 0.64, ('F', 'M') : 0.43, ('M', 'S') : -1.42, ('S', 'S') : 1.25,
-('L', 'Q') : -1.45, ('W', 'E') : -2.83, ('W', 'A') : -2.98, ('W', 'M') : -0.64,
-('H', 'S') : -0.69, ('L', 'V') : 0.34, ('S', 'C') : -0.59, ('L', 'A') : -1.51,
-('S', 'G') : 0.4, ('L', 'E') : -2.28, ('W', 'Q') : -2.17, ('H', 'G') : -1.53,
-('H', 'C') : -1.31, ('W', 'Y') : 0.19, ('Y', 'N') : -2.17, ('E', 'P') : -1.23,
-('I', 'L') : 1.11, ('E', 'T') : -0.58, ('W', 'I') : -0.47, ('A', 'A') : 1.25,
-('I', 'N') : -2.12, ('G', 'A') : 0.34, ('Y', 'V') : -0.27, ('W', 'H') : -1.91,
-('Y', 'R') : -0.77, ('M', 'Q') : -1.05, ('R', 'H') : 0.43, ('A', 'C') : -0.14,
-('R', 'D') : -1.09, ('T', 'A') : 0.35, ('T', 'P') : 0.38, ('L', 'D') : -2.85,
-('K', 'T') : -0.85, ('V', 'N') : -1.51, ('M', 'A') : -1.17, ('K', 'H') : 0.46,
-('V', 'R') : -1.18, ('P', 'C') : -0.08, ('M', 'E') : -1.88, ('I', 'K') : -1.59,
-('T', 'T') : 1.25, ('R', 'T') : -0.78, ('I', 'G') : -2.35, ('R', 'P') : -1.65,
-('K', 'D') : -0.8, ('I', 'C') : -0.38, ('F', 'R') : -1.22, ('F', 'V') : -0.18,
-('D', 'Q') : -0.22, ('K', 'P') : -1.69, ('F', 'F') : 1.25, ('D', 'A') : -1.12,
-('D', 'E') : 0.35, ('F', 'N') : -2.45, ('W', 'D') : -3.63, ('L', 'P') : -1.45,
-('Q', 'S') : -0.4, ('N', 'C') : -1.58, ('N', 'G') : -0.71, ('H', 'N') : -0.04,
-('W', 'T') : -2.25, ('Q', 'G') : -1.23, ('W', 'P') : -2.92, ('Q', 'C') : -1.23,
-('N', 'S') : -0.06, ('L', 'H') : -1.34, ('L', 'L') : 1.25, ('M', 'M') : 1.25,
-('G', 'P') : 0.28, ('Y', 'K') : -1.17, ('E', 'Q') : 0.41, ('Y', 'G') : -2.83,
-('Y', 'C') : -1.13, ('E', 'A') : -1.21, ('E', 'E') : 1.25, ('Y', 'S') : -2.08,
-('M', 'P') : -1.11, ('V', 'C') : 0.39, ('M', 'T') : -0.61, ('V', 'G') : -1.51,
-('R', 'E') : -0.2, ('V', 'K') : -1.45, ('K', 'Q') : 0.19, ('R', 'A') : -1.67,
-('I', 'R') : -1.24, ('V', 'S') : -0.9, ('M', 'L') : 0.84, ('M', 'D') : -2.44,
-('W', 'W') : 1.25, ('M', 'H') : -0.94, ('K', 'A') : -1.71, ('R', 'Q') : 0.12,
-('K', 'E') : 0.11, ('F', 'S') : -2.2, ('D', 'P') : -1.15, ('D', 'T') : -0.8,
-('F', 'C') : -0.99, ('W', 'L') : -0.48, ('F', 'G') : -2.89, ('F', 'K') : -1.6,
-('F', 'T') : -1.35, ('D', 'D') : 1.25, ('Q', 'T') : 0.13, ('W', 'G') : -3.88,
-('Q', 'P') : -0.67, ('W', 'C') : -2.09, ('W', 'K') : -1.86, ('H', 'Q') : 0.93,
-('L', 'C') : -0.4, ('W', 'N') : -3.14, ('S', 'A') : 0.74, ('L', 'G') : -2.42,
-('W', 'S') : -3.13, ('L', 'K') : -1.73, ('N', 'P') : -0.55, ('H', 'E') : 0.29,
-('N', 'T') : -0.15, ('H', 'A') : -0.92, ('Y', 'L') : 0.31, ('Y', 'H') : -1.02,
-('Y', 'D') : -2.7, ('L', 'T') : -1.0, ('G', 'G') : 1.25, ('G', 'C') : -0.97,
-('Y', 'T') : -1.2, ('Y', 'P') : -1.87, ('R', 'N') : -0.79, ('V', 'D') : -2.15,
-('T', 'C') : -0.2, ('V', 'H') : -0.86, ('T', 'G') : -0.45, ('I', 'Q') : -1.32,
-('V', 'P') : -0.54, ('M', 'C') : -0.21, ('K', 'N') : -0.59, ('V', 'T') : -0.17,
-('M', 'G') : -2.09, ('T', 'S') : 0.36, ('I', 'E') : -2.14, ('M', 'K') : -1.38,
-('I', 'A') : -1.44, ('N', 'N') : 1.25, ('R', 'R') : 1.25, ('F', 'P') : -1.92,
-('I', 'S') : -1.7, ('D', 'S') : -0.62, ('Y', 'F') : 0.77, ('L', 'S') : -1.79,
-('I', 'H') : -1.2, ('F', 'D') : -3.02, ('D', 'C') : -2.23, ('F', 'H') : -1.38,
-('D', 'G') : -1.12, ('F', 'L') : 0.62
+('W', 'F'): 0.14, ('L', 'R'): -1.37, ('S', 'P'): 0.69, ('I', 'I'): 1.25,
+('Q', 'Q'): 1.25, ('N', 'A'): -0.53, ('H', 'T'): -0.07, ('N', 'E'): 0.4,
+('H', 'P'): -0.9, ('W', 'V'): -1.26, ('W', 'R'): -1.47, ('Q', 'A'): -0.67,
+('H', 'H'): 1.25, ('N', 'Q'): 0.26, ('H', 'D'): -0.47, ('L', 'N'): -2.24,
+('Y', 'M'): 0.32, ('Y', 'I'): 0.39, ('Y', 'E'): -1.97, ('E', 'S'): -0.81,
+('Y', 'A'): -1.93, ('Y', 'Y'): 1.25, ('E', 'C'): -2.01, ('Y', 'Q'): -1.23,
+('E', 'G'): -1.53, ('V', 'A'): -0.6, ('C', 'C'): 1.25, ('M', 'R'): -1.04,
+('V', 'E'): -1.72, ('R', 'K'): 0.85, ('P', 'P'): 1.25, ('I', 'T'): -0.89,
+('K', 'S'): -1.46, ('R', 'G'): -2.33, ('I', 'P'): -1.37, ('R', 'C'): -1.81,
+('V', 'Q'): -0.88, ('K', 'K'): 1.25, ('A', 'P'): 1.19, ('I', 'D'): -2.73,
+('K', 'C'): -2.02, ('M', 'I'): 0.96, ('K', 'G'): -2.29, ('R', 'S'): -1.49,
+('F', 'Q'): -1.56, ('I', 'V'): 0.4, ('M', 'V'): 0.63, ('F', 'A'): -1.98,
+('V', 'V'): 1.25, ('M', 'N'): -1.83, ('F', 'E'): -2.34, ('D', 'N'): 0.6,
+('F', 'I'): 0.64, ('F', 'M'): 0.43, ('M', 'S'): -1.42, ('S', 'S'): 1.25,
+('L', 'Q'): -1.45, ('W', 'E'): -2.83, ('W', 'A'): -2.98, ('W', 'M'): -0.64,
+('H', 'S'): -0.69, ('L', 'V'): 0.34, ('S', 'C'): -0.59, ('L', 'A'): -1.51,
+('S', 'G'): 0.4, ('L', 'E'): -2.28, ('W', 'Q'): -2.17, ('H', 'G'): -1.53,
+('H', 'C'): -1.31, ('W', 'Y'): 0.19, ('Y', 'N'): -2.17, ('E', 'P'): -1.23,
+('I', 'L'): 1.11, ('E', 'T'): -0.58, ('W', 'I'): -0.47, ('A', 'A'): 1.25,
+('I', 'N'): -2.12, ('G', 'A'): 0.34, ('Y', 'V'): -0.27, ('W', 'H'): -1.91,
+('Y', 'R'): -0.77, ('M', 'Q'): -1.05, ('R', 'H'): 0.43, ('A', 'C'): -0.14,
+('R', 'D'): -1.09, ('T', 'A'): 0.35, ('T', 'P'): 0.38, ('L', 'D'): -2.85,
+('K', 'T'): -0.85, ('V', 'N'): -1.51, ('M', 'A'): -1.17, ('K', 'H'): 0.46,
+('V', 'R'): -1.18, ('P', 'C'): -0.08, ('M', 'E'): -1.88, ('I', 'K'): -1.59,
+('T', 'T'): 1.25, ('R', 'T'): -0.78, ('I', 'G'): -2.35, ('R', 'P'): -1.65,
+('K', 'D'): -0.8, ('I', 'C'): -0.38, ('F', 'R'): -1.22, ('F', 'V'): -0.18,
+('D', 'Q'): -0.22, ('K', 'P'): -1.69, ('F', 'F'): 1.25, ('D', 'A'): -1.12,
+('D', 'E'): 0.35, ('F', 'N'): -2.45, ('W', 'D'): -3.63, ('L', 'P'): -1.45,
+('Q', 'S'): -0.4, ('N', 'C'): -1.58, ('N', 'G'): -0.71, ('H', 'N'): -0.04,
+('W', 'T'): -2.25, ('Q', 'G'): -1.23, ('W', 'P'): -2.92, ('Q', 'C'): -1.23,
+('N', 'S'): -0.06, ('L', 'H'): -1.34, ('L', 'L'): 1.25, ('M', 'M'): 1.25,
+('G', 'P'): 0.28, ('Y', 'K'): -1.17, ('E', 'Q'): 0.41, ('Y', 'G'): -2.83,
+('Y', 'C'): -1.13, ('E', 'A'): -1.21, ('E', 'E'): 1.25, ('Y', 'S'): -2.08,
+('M', 'P'): -1.11, ('V', 'C'): 0.39, ('M', 'T'): -0.61, ('V', 'G'): -1.51,
+('R', 'E'): -0.2, ('V', 'K'): -1.45, ('K', 'Q'): 0.19, ('R', 'A'): -1.67,
+('I', 'R'): -1.24, ('V', 'S'): -0.9, ('M', 'L'): 0.84, ('M', 'D'): -2.44,
+('W', 'W'): 1.25, ('M', 'H'): -0.94, ('K', 'A'): -1.71, ('R', 'Q'): 0.12,
+('K', 'E'): 0.11, ('F', 'S'): -2.2, ('D', 'P'): -1.15, ('D', 'T'): -0.8,
+('F', 'C'): -0.99, ('W', 'L'): -0.48, ('F', 'G'): -2.89, ('F', 'K'): -1.6,
+('F', 'T'): -1.35, ('D', 'D'): 1.25, ('Q', 'T'): 0.13, ('W', 'G'): -3.88,
+('Q', 'P'): -0.67, ('W', 'C'): -2.09, ('W', 'K'): -1.86, ('H', 'Q'): 0.93,
+('L', 'C'): -0.4, ('W', 'N'): -3.14, ('S', 'A'): 0.74, ('L', 'G'): -2.42,
+('W', 'S'): -3.13, ('L', 'K'): -1.73, ('N', 'P'): -0.55, ('H', 'E'): 0.29,
+('N', 'T'): -0.15, ('H', 'A'): -0.92, ('Y', 'L'): 0.31, ('Y', 'H'): -1.02,
+('Y', 'D'): -2.7, ('L', 'T'): -1.0, ('G', 'G'): 1.25, ('G', 'C'): -0.97,
+('Y', 'T'): -1.2, ('Y', 'P'): -1.87, ('R', 'N'): -0.79, ('V', 'D'): -2.15,
+('T', 'C'): -0.2, ('V', 'H'): -0.86, ('T', 'G'): -0.45, ('I', 'Q'): -1.32,
+('V', 'P'): -0.54, ('M', 'C'): -0.21, ('K', 'N'): -0.59, ('V', 'T'): -0.17,
+('M', 'G'): -2.09, ('T', 'S'): 0.36, ('I', 'E'): -2.14, ('M', 'K'): -1.38,
+('I', 'A'): -1.44, ('N', 'N'): 1.25, ('R', 'R'): 1.25, ('F', 'P'): -1.92,
+('I', 'S'): -1.7, ('D', 'S'): -0.62, ('Y', 'F'): 0.77, ('L', 'S'): -1.79,
+('I', 'H'): -1.2, ('F', 'D'): -3.02, ('D', 'C'): -2.23, ('F', 'H'): -1.38,
+('D', 'G'): -1.12, ('F', 'L'): 0.62
 }
 miyata = _temp()
 del _temp
@@ -2053,70 +2052,70 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/nwsgappep.cmp
 def _temp():
     return {
-('W', 'F') : 1.3, ('S', 'P') : 0.4, ('W', 'B') : -0.7, ('N', 'N') : 1.5,
-('N', 'A') : 0.2, ('N', 'E') : 0.5, ('Z', 'Y') : -0.6, ('W', 'V') : -0.8,
-('L', 'B') : -0.5, ('W', 'R') : 1.4, ('Q', 'A') : 0.2, ('S', 'D') : 0.2,
-('H', 'H') : 1.5, ('Q', 'M') : 0.0, ('S', 'H') : -0.2, ('H', 'D') : 0.4,
-('Q', 'I') : -0.3, ('S', 'L') : -0.4, ('Y', 'M') : -0.1, ('Q', 'E') : 0.7,
-('Y', 'I') : 0.1, ('Y', 'E') : -0.5, ('Y', 'A') : -0.3, ('G', 'F') : -0.6,
-('V', 'T') : 0.2, ('G', 'B') : 0.6, ('Y', 'Y') : 1.5, ('N', 'L') : -0.4,
-('E', 'C') : -0.6, ('Y', 'Q') : -0.6, ('Z', 'Z') : 1.1, ('V', 'A') : 0.2,
-('C', 'C') : 1.5, ('V', 'E') : -0.2, ('T', 'N') : 0.2, ('R', 'K') : 0.8,
-('P', 'P') : 1.5, ('V', 'I') : 1.1, ('T', 'B') : 0.2, ('R', 'G') : -0.3,
-('V', 'M') : 0.6, ('T', 'F') : -0.3, ('R', 'C') : -0.3, ('V', 'Q') : -0.2,
-('K', 'K') : 1.5, ('M', 'B') : -0.3, ('P', 'D') : 0.1, ('I', 'H') : -0.3,
-('M', 'F') : 0.5, ('I', 'D') : -0.2, ('K', 'C') : -0.6, ('L', 'L') : 1.5,
-('K', 'G') : -0.1, ('P', 'H') : 0.2, ('Z', 'G') : 0.3, ('W', 'M') : -0.3,
-('Z', 'C') : -0.6, ('T', 'R') : -0.1, ('Z', 'K') : 0.4, ('F', 'A') : -0.5,
-('Z', 'W') : -0.8, ('F', 'E') : -0.7, ('Z', 'S') : 0.0, ('D', 'B') : 1.1,
-('S', 'S') : 1.5, ('W', 'E') : -1.1, ('W', 'A') : -0.8, ('N', 'B') : 1.1,
-('Q', 'C') : -0.6, ('Z', 'Q') : 1.1, ('N', 'F') : -0.5, ('S', 'C') : 0.7,
-('Q', 'F') : -0.8, ('S', 'G') : 0.6, ('Q', 'B') : 0.5, ('W', 'Q') : -0.5,
-('H', 'G') : -0.2, ('S', 'K') : 0.2, ('L', 'I') : 0.8, ('V', 'D') : -0.2,
-('H', 'C') : -0.1, ('L', 'E') : -0.3, ('Y', 'N') : -0.1, ('Y', 'F') : 1.4,
-('W', 'I') : -0.5, ('C', 'A') : 0.3, ('G', 'E') : 0.5, ('G', 'A') : 0.7,
-('Y', 'V') : -0.1, ('E', 'D') : 1.0, ('W', 'H') : -0.1, ('Y', 'R') : -0.6,
-('N', 'I') : -0.3, ('R', 'L') : -0.4, ('T', 'I') : 0.2, ('V', 'B') : -0.2,
-('R', 'H') : 0.5, ('T', 'M') : 0.0, ('V', 'F') : 0.2, ('R', 'D') : 0.0,
-('T', 'A') : 0.4, ('T', 'P') : 0.3, ('T', 'E') : 0.2, ('V', 'N') : -0.3,
-('P', 'G') : 0.3, ('M', 'A') : 0.0, ('K', 'H') : 0.1, ('V', 'R') : -0.3,
-('P', 'C') : 0.1, ('M', 'E') : -0.2, ('V', 'V') : 1.5, ('T', 'T') : 1.5,
-('M', 'I') : 0.6, ('T', 'Q') : -0.1, ('I', 'G') : -0.3, ('P', 'K') : 0.1,
-('M', 'M') : 1.5, ('K', 'D') : 0.3, ('I', 'C') : 0.2, ('Z', 'D') : 0.9,
-('Y', 'W') : 1.1, ('Z', 'L') : -0.2, ('P', 'L') : -0.3, ('Z', 'I') : -0.2,
-('Z', 'T') : 0.1, ('L', 'C') : -0.8, ('F', 'B') : -0.7, ('Z', 'P') : 0.2,
-('F', 'F') : 1.5, ('D', 'A') : 0.3, ('S', 'R') : 0.1, ('W', 'D') : -1.1,
-('R', 'R') : 1.5, ('W', 'K') : 0.1, ('N', 'M') : -0.3, ('N', 'C') : -0.3,
-('N', 'G') : 0.4, ('S', 'B') : 0.3, ('W', 'T') : -0.6, ('Q', 'G') : 0.2,
-('S', 'F') : -0.3, ('W', 'P') : -0.8, ('L', 'D') : -0.5, ('H', 'F') : -0.1,
-('L', 'H') : -0.2, ('S', 'N') : 0.3, ('H', 'B') : 0.4, ('Q', 'K') : 0.4,
-('R', 'P') : 0.3, ('Y', 'K') : -0.6, ('Y', 'B') : -0.3, ('Y', 'G') : -0.7,
-('Y', 'C') : 1.0, ('G', 'D') : 0.7, ('E', 'A') : 0.3, ('T', 'S') : 0.3,
-('E', 'E') : 1.5, ('Y', 'S') : -0.4, ('R', 'M') : 0.2, ('V', 'C') : 0.2,
-('T', 'H') : -0.1, ('R', 'I') : -0.3, ('V', 'S') : -0.1, ('V', 'G') : 0.2,
-('T', 'L') : -0.1, ('R', 'E') : 0.0, ('V', 'K') : -0.2, ('R', 'Q') : 0.4,
-('R', 'A') : -0.3, ('Z', 'H') : 0.5, ('T', 'D') : 0.2, ('P', 'F') : -0.7,
-('L', 'A') : -0.1, ('K', 'I') : -0.2, ('M', 'D') : -0.4, ('P', 'B') : 0.1,
-('W', 'W') : 1.5, ('M', 'H') : -0.3, ('P', 'N') : 0.0, ('I', 'F') : 0.7,
-('K', 'A') : 0.0, ('M', 'L') : 1.3, ('I', 'B') : -0.2, ('K', 'E') : 0.3,
-('Z', 'E') : 1.1, ('Q', 'N') : 0.4, ('Z', 'A') : 0.2, ('Z', 'M') : -0.1,
-('L', 'F') : 1.2, ('F', 'C') : -0.1, ('W', 'G') : -1.0, ('W', 'L') : 0.5,
-('C', 'B') : -0.4, ('B', 'A') : 0.2, ('D', 'D') : 1.5, ('N', 'H') : 0.5,
-('S', 'Q') : -0.1, ('Q', 'P') : 0.3, ('W', 'C') : -1.2, ('N', 'D') : 0.7,
-('Q', 'D') : 0.7, ('W', 'N') : -0.3, ('S', 'A') : 0.4, ('L', 'G') : -0.5,
-('W', 'S') : 0.3, ('S', 'E') : 0.2, ('L', 'K') : -0.3, ('H', 'E') : 0.4,
-('S', 'I') : -0.1, ('Q', 'H') : 0.7, ('H', 'A') : -0.1, ('S', 'M') : -0.3,
-('Y', 'L') : 0.3, ('Y', 'H') : 0.3, ('Y', 'D') : -0.5, ('G', 'G') : 1.5,
-('G', 'C') : 0.2, ('Y', 'T') : -0.3, ('E', 'B') : 0.7, ('Y', 'P') : -0.8,
-('T', 'K') : 0.2, ('R', 'N') : 0.1, ('A', 'A') : 1.5, ('N', 'K') : 0.4,
-('T', 'C') : 0.2, ('V', 'H') : -0.3, ('Q', 'Q') : 1.5, ('R', 'F') : -0.5,
-('T', 'G') : 0.4, ('V', 'L') : 0.8, ('R', 'B') : 0.1, ('V', 'P') : 0.1,
-('P', 'E') : 0.1, ('M', 'C') : -0.6, ('I', 'I') : 1.5, ('P', 'A') : 0.5,
-('M', 'G') : -0.3, ('K', 'B') : 0.4, ('I', 'E') : -0.2, ('P', 'M') : -0.2,
-('M', 'K') : 0.2, ('K', 'F') : -0.7, ('I', 'A') : 0.0, ('P', 'I') : -0.2,
-('Q', 'L') : -0.1, ('Z', 'F') : -0.7, ('Z', 'B') : 0.6, ('Z', 'N') : 0.4,
-('Z', 'V') : -0.2, ('F', 'D') : -1.0, ('Z', 'R') : 0.2, ('D', 'C') : -0.5,
-('B', 'B') : 1.1
+('W', 'F'): 1.3, ('S', 'P'): 0.4, ('W', 'B'): -0.7, ('N', 'N'): 1.5,
+('N', 'A'): 0.2, ('N', 'E'): 0.5, ('Z', 'Y'): -0.6, ('W', 'V'): -0.8,
+('L', 'B'): -0.5, ('W', 'R'): 1.4, ('Q', 'A'): 0.2, ('S', 'D'): 0.2,
+('H', 'H'): 1.5, ('Q', 'M'): 0.0, ('S', 'H'): -0.2, ('H', 'D'): 0.4,
+('Q', 'I'): -0.3, ('S', 'L'): -0.4, ('Y', 'M'): -0.1, ('Q', 'E'): 0.7,
+('Y', 'I'): 0.1, ('Y', 'E'): -0.5, ('Y', 'A'): -0.3, ('G', 'F'): -0.6,
+('V', 'T'): 0.2, ('G', 'B'): 0.6, ('Y', 'Y'): 1.5, ('N', 'L'): -0.4,
+('E', 'C'): -0.6, ('Y', 'Q'): -0.6, ('Z', 'Z'): 1.1, ('V', 'A'): 0.2,
+('C', 'C'): 1.5, ('V', 'E'): -0.2, ('T', 'N'): 0.2, ('R', 'K'): 0.8,
+('P', 'P'): 1.5, ('V', 'I'): 1.1, ('T', 'B'): 0.2, ('R', 'G'): -0.3,
+('V', 'M'): 0.6, ('T', 'F'): -0.3, ('R', 'C'): -0.3, ('V', 'Q'): -0.2,
+('K', 'K'): 1.5, ('M', 'B'): -0.3, ('P', 'D'): 0.1, ('I', 'H'): -0.3,
+('M', 'F'): 0.5, ('I', 'D'): -0.2, ('K', 'C'): -0.6, ('L', 'L'): 1.5,
+('K', 'G'): -0.1, ('P', 'H'): 0.2, ('Z', 'G'): 0.3, ('W', 'M'): -0.3,
+('Z', 'C'): -0.6, ('T', 'R'): -0.1, ('Z', 'K'): 0.4, ('F', 'A'): -0.5,
+('Z', 'W'): -0.8, ('F', 'E'): -0.7, ('Z', 'S'): 0.0, ('D', 'B'): 1.1,
+('S', 'S'): 1.5, ('W', 'E'): -1.1, ('W', 'A'): -0.8, ('N', 'B'): 1.1,
+('Q', 'C'): -0.6, ('Z', 'Q'): 1.1, ('N', 'F'): -0.5, ('S', 'C'): 0.7,
+('Q', 'F'): -0.8, ('S', 'G'): 0.6, ('Q', 'B'): 0.5, ('W', 'Q'): -0.5,
+('H', 'G'): -0.2, ('S', 'K'): 0.2, ('L', 'I'): 0.8, ('V', 'D'): -0.2,
+('H', 'C'): -0.1, ('L', 'E'): -0.3, ('Y', 'N'): -0.1, ('Y', 'F'): 1.4,
+('W', 'I'): -0.5, ('C', 'A'): 0.3, ('G', 'E'): 0.5, ('G', 'A'): 0.7,
+('Y', 'V'): -0.1, ('E', 'D'): 1.0, ('W', 'H'): -0.1, ('Y', 'R'): -0.6,
+('N', 'I'): -0.3, ('R', 'L'): -0.4, ('T', 'I'): 0.2, ('V', 'B'): -0.2,
+('R', 'H'): 0.5, ('T', 'M'): 0.0, ('V', 'F'): 0.2, ('R', 'D'): 0.0,
+('T', 'A'): 0.4, ('T', 'P'): 0.3, ('T', 'E'): 0.2, ('V', 'N'): -0.3,
+('P', 'G'): 0.3, ('M', 'A'): 0.0, ('K', 'H'): 0.1, ('V', 'R'): -0.3,
+('P', 'C'): 0.1, ('M', 'E'): -0.2, ('V', 'V'): 1.5, ('T', 'T'): 1.5,
+('M', 'I'): 0.6, ('T', 'Q'): -0.1, ('I', 'G'): -0.3, ('P', 'K'): 0.1,
+('M', 'M'): 1.5, ('K', 'D'): 0.3, ('I', 'C'): 0.2, ('Z', 'D'): 0.9,
+('Y', 'W'): 1.1, ('Z', 'L'): -0.2, ('P', 'L'): -0.3, ('Z', 'I'): -0.2,
+('Z', 'T'): 0.1, ('L', 'C'): -0.8, ('F', 'B'): -0.7, ('Z', 'P'): 0.2,
+('F', 'F'): 1.5, ('D', 'A'): 0.3, ('S', 'R'): 0.1, ('W', 'D'): -1.1,
+('R', 'R'): 1.5, ('W', 'K'): 0.1, ('N', 'M'): -0.3, ('N', 'C'): -0.3,
+('N', 'G'): 0.4, ('S', 'B'): 0.3, ('W', 'T'): -0.6, ('Q', 'G'): 0.2,
+('S', 'F'): -0.3, ('W', 'P'): -0.8, ('L', 'D'): -0.5, ('H', 'F'): -0.1,
+('L', 'H'): -0.2, ('S', 'N'): 0.3, ('H', 'B'): 0.4, ('Q', 'K'): 0.4,
+('R', 'P'): 0.3, ('Y', 'K'): -0.6, ('Y', 'B'): -0.3, ('Y', 'G'): -0.7,
+('Y', 'C'): 1.0, ('G', 'D'): 0.7, ('E', 'A'): 0.3, ('T', 'S'): 0.3,
+('E', 'E'): 1.5, ('Y', 'S'): -0.4, ('R', 'M'): 0.2, ('V', 'C'): 0.2,
+('T', 'H'): -0.1, ('R', 'I'): -0.3, ('V', 'S'): -0.1, ('V', 'G'): 0.2,
+('T', 'L'): -0.1, ('R', 'E'): 0.0, ('V', 'K'): -0.2, ('R', 'Q'): 0.4,
+('R', 'A'): -0.3, ('Z', 'H'): 0.5, ('T', 'D'): 0.2, ('P', 'F'): -0.7,
+('L', 'A'): -0.1, ('K', 'I'): -0.2, ('M', 'D'): -0.4, ('P', 'B'): 0.1,
+('W', 'W'): 1.5, ('M', 'H'): -0.3, ('P', 'N'): 0.0, ('I', 'F'): 0.7,
+('K', 'A'): 0.0, ('M', 'L'): 1.3, ('I', 'B'): -0.2, ('K', 'E'): 0.3,
+('Z', 'E'): 1.1, ('Q', 'N'): 0.4, ('Z', 'A'): 0.2, ('Z', 'M'): -0.1,
+('L', 'F'): 1.2, ('F', 'C'): -0.1, ('W', 'G'): -1.0, ('W', 'L'): 0.5,
+('C', 'B'): -0.4, ('B', 'A'): 0.2, ('D', 'D'): 1.5, ('N', 'H'): 0.5,
+('S', 'Q'): -0.1, ('Q', 'P'): 0.3, ('W', 'C'): -1.2, ('N', 'D'): 0.7,
+('Q', 'D'): 0.7, ('W', 'N'): -0.3, ('S', 'A'): 0.4, ('L', 'G'): -0.5,
+('W', 'S'): 0.3, ('S', 'E'): 0.2, ('L', 'K'): -0.3, ('H', 'E'): 0.4,
+('S', 'I'): -0.1, ('Q', 'H'): 0.7, ('H', 'A'): -0.1, ('S', 'M'): -0.3,
+('Y', 'L'): 0.3, ('Y', 'H'): 0.3, ('Y', 'D'): -0.5, ('G', 'G'): 1.5,
+('G', 'C'): 0.2, ('Y', 'T'): -0.3, ('E', 'B'): 0.7, ('Y', 'P'): -0.8,
+('T', 'K'): 0.2, ('R', 'N'): 0.1, ('A', 'A'): 1.5, ('N', 'K'): 0.4,
+('T', 'C'): 0.2, ('V', 'H'): -0.3, ('Q', 'Q'): 1.5, ('R', 'F'): -0.5,
+('T', 'G'): 0.4, ('V', 'L'): 0.8, ('R', 'B'): 0.1, ('V', 'P'): 0.1,
+('P', 'E'): 0.1, ('M', 'C'): -0.6, ('I', 'I'): 1.5, ('P', 'A'): 0.5,
+('M', 'G'): -0.3, ('K', 'B'): 0.4, ('I', 'E'): -0.2, ('P', 'M'): -0.2,
+('M', 'K'): 0.2, ('K', 'F'): -0.7, ('I', 'A'): 0.0, ('P', 'I'): -0.2,
+('Q', 'L'): -0.1, ('Z', 'F'): -0.7, ('Z', 'B'): 0.6, ('Z', 'N'): 0.4,
+('Z', 'V'): -0.2, ('F', 'D'): -1.0, ('Z', 'R'): 0.2, ('D', 'C'): -0.5,
+('B', 'B'): 1.1
 }
 nwsgappep = _temp()
 del _temp
@@ -2125,75 +2124,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam120.cmp
 def _temp():
     return {
-('W', 'F') : -1, ('L', 'R') : -4, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : -1, ('Z', 'Y') : -5, ('W', 'R') : 1,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 7, ('S', 'H') : -2,
-('H', 'D') : 0, ('L', 'N') : -4, ('W', 'A') : -7, ('Y', 'M') : -4,
-('G', 'R') : -4, ('Y', 'I') : -2, ('Y', 'E') : -5, ('B', 'Y') : -3,
-('Y', 'A') : -4, ('V', 'D') : -3, ('B', 'S') : 0, ('Y', 'Y') : 8,
-('G', 'N') : 0, ('E', 'C') : -7, ('Y', 'Q') : -5, ('Z', 'Z') : 4,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 6, ('V', 'I') : 3, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 1, ('T', 'F') : -4, ('V', 'Q') : -3,
-('K', 'K') : 5, ('P', 'D') : -3, ('I', 'H') : -4, ('I', 'D') : -3,
-('T', 'R') : -2, ('P', 'L') : -3, ('K', 'G') : -3, ('M', 'N') : -3,
-('P', 'H') : -1, ('F', 'Q') : -6, ('Z', 'G') : -2, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -7, ('X', 'H') : -2, ('D', 'R') : -3,
-('B', 'W') : -6, ('X', 'D') : -2, ('Z', 'K') : -1, ('F', 'A') : -4,
-('Z', 'W') : -7, ('F', 'E') : -7, ('D', 'N') : 2, ('B', 'K') : 0,
-('X', 'X') : -2, ('F', 'I') : 0, ('B', 'G') : 0, ('X', 'T') : -1,
-('F', 'M') : -1, ('B', 'C') : -6, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 3, ('L', 'Q') : -2, ('W', 'E') : -8, ('Q', 'R') : 1,
-('N', 'N') : 4, ('W', 'M') : -6, ('Q', 'C') : -7, ('W', 'I') : -6,
-('S', 'C') : 0, ('L', 'A') : -3, ('S', 'G') : 1, ('L', 'E') : -4,
-('W', 'Q') : -6, ('H', 'G') : -4, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -3,
-('Y', 'F') : 4, ('C', 'A') : -3, ('V', 'L') : 1, ('G', 'E') : -1,
-('G', 'A') : 1, ('K', 'R') : 2, ('E', 'D') : 3, ('Y', 'R') : -5,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -7, ('V', 'F') : -3,
-('T', 'A') : 1, ('T', 'P') : -1, ('B', 'P') : -2, ('T', 'E') : -2,
-('V', 'N') : -3, ('P', 'G') : -2, ('M', 'A') : -2, ('K', 'H') : -2,
-('V', 'R') : -3, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -4,
-('V', 'V') : 5, ('M', 'I') : 1, ('T', 'Q') : -2, ('I', 'G') : -4,
-('P', 'K') : -2, ('M', 'M') : 8, ('K', 'D') : -1, ('I', 'C') : -3,
-('Z', 'D') : 3, ('F', 'R') : -5, ('X', 'K') : -2, ('Q', 'D') : 1,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -4, ('Z', 'H') : 1,
-('B', 'L') : -4, ('B', 'H') : 1, ('F', 'F') : 8, ('X', 'W') : -5,
-('B', 'D') : 4, ('D', 'A') : 0, ('S', 'L') : -4, ('X', 'S') : -1,
-('F', 'N') : -4, ('S', 'R') : -1, ('W', 'D') : -8, ('V', 'Y') : -3,
-('W', 'L') : -3, ('H', 'R') : 1, ('W', 'H') : -3, ('H', 'N') : 2,
-('W', 'T') : -6, ('T', 'T') : 4, ('S', 'F') : -3, ('W', 'P') : -7,
-('L', 'D') : -5, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 5, ('Y', 'K') : -5, ('E', 'Q') : 2,
-('Y', 'G') : -6, ('Z', 'S') : -1, ('Y', 'C') : -1, ('G', 'D') : 0,
-('B', 'V') : -3, ('E', 'A') : 0, ('Y', 'W') : -2, ('E', 'E') : 5,
-('Y', 'S') : -3, ('C', 'N') : -5, ('V', 'C') : -3, ('T', 'H') : -3,
-('P', 'R') : -1, ('V', 'G') : -2, ('T', 'L') : -3, ('V', 'K') : -4,
-('K', 'Q') : 0, ('R', 'A') : -3, ('I', 'R') : -2, ('T', 'D') : -1,
-('P', 'F') : -5, ('I', 'N') : -2, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -8, ('W', 'W') : 12, ('M', 'H') : -4, ('P', 'N') : -2,
-('K', 'A') : -2, ('M', 'L') : 3, ('K', 'E') : -1, ('Z', 'E') : 4,
-('X', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -2, ('X', 'F') : -3,
-('K', 'C') : -7, ('B', 'Q') : 0, ('X', 'B') : -1, ('B', 'M') : -4,
-('F', 'C') : -6, ('Z', 'Q') : 4, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 3, ('X', 'V') : -1, ('F', 'K') : -7, ('B', 'A') : 0,
-('X', 'R') : -2, ('D', 'D') : 5, ('W', 'G') : -8, ('Z', 'F') : -6,
-('S', 'Q') : -2, ('W', 'C') : -8, ('W', 'K') : -5, ('H', 'Q') : 3,
-('L', 'C') : -7, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -5,
-('W', 'S') : -2, ('S', 'E') : -1, ('H', 'E') : -1, ('S', 'I') : -2,
-('H', 'A') : -3, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : -1,
-('Y', 'D') : -5, ('E', 'R') : -3, ('X', 'P') : -2, ('G', 'G') : 5,
-('G', 'C') : -4, ('E', 'N') : 1, ('Y', 'T') : -3, ('Y', 'P') : -6,
-('T', 'K') : -1, ('A', 'A') : 3, ('P', 'Q') : 0, ('T', 'C') : -3,
-('V', 'H') : -3, ('T', 'G') : -1, ('I', 'Q') : -3, ('Z', 'T') : -2,
-('C', 'R') : -4, ('V', 'P') : -2, ('P', 'E') : -2, ('M', 'C') : -6,
-('K', 'N') : 1, ('I', 'I') : 6, ('P', 'A') : 1, ('M', 'G') : -4,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : 0,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 6, ('X', 'M') : -2,
-('L', 'I') : 1, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 0, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 3,
-('F', 'D') : -7, ('X', 'Y') : -3, ('Z', 'R') : -1, ('F', 'H') : -3,
-('B', 'F') : -5, ('F', 'L') : 0, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): -1, ('L', 'R'): -4, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): -1, ('Z', 'Y'): -5, ('W', 'R'): 1,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 7, ('S', 'H'): -2,
+('H', 'D'): 0, ('L', 'N'): -4, ('W', 'A'): -7, ('Y', 'M'): -4,
+('G', 'R'): -4, ('Y', 'I'): -2, ('Y', 'E'): -5, ('B', 'Y'): -3,
+('Y', 'A'): -4, ('V', 'D'): -3, ('B', 'S'): 0, ('Y', 'Y'): 8,
+('G', 'N'): 0, ('E', 'C'): -7, ('Y', 'Q'): -5, ('Z', 'Z'): 4,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 6, ('V', 'I'): 3, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 1, ('T', 'F'): -4, ('V', 'Q'): -3,
+('K', 'K'): 5, ('P', 'D'): -3, ('I', 'H'): -4, ('I', 'D'): -3,
+('T', 'R'): -2, ('P', 'L'): -3, ('K', 'G'): -3, ('M', 'N'): -3,
+('P', 'H'): -1, ('F', 'Q'): -6, ('Z', 'G'): -2, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -7, ('X', 'H'): -2, ('D', 'R'): -3,
+('B', 'W'): -6, ('X', 'D'): -2, ('Z', 'K'): -1, ('F', 'A'): -4,
+('Z', 'W'): -7, ('F', 'E'): -7, ('D', 'N'): 2, ('B', 'K'): 0,
+('X', 'X'): -2, ('F', 'I'): 0, ('B', 'G'): 0, ('X', 'T'): -1,
+('F', 'M'): -1, ('B', 'C'): -6, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 3, ('L', 'Q'): -2, ('W', 'E'): -8, ('Q', 'R'): 1,
+('N', 'N'): 4, ('W', 'M'): -6, ('Q', 'C'): -7, ('W', 'I'): -6,
+('S', 'C'): 0, ('L', 'A'): -3, ('S', 'G'): 1, ('L', 'E'): -4,
+('W', 'Q'): -6, ('H', 'G'): -4, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -3,
+('Y', 'F'): 4, ('C', 'A'): -3, ('V', 'L'): 1, ('G', 'E'): -1,
+('G', 'A'): 1, ('K', 'R'): 2, ('E', 'D'): 3, ('Y', 'R'): -5,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -7, ('V', 'F'): -3,
+('T', 'A'): 1, ('T', 'P'): -1, ('B', 'P'): -2, ('T', 'E'): -2,
+('V', 'N'): -3, ('P', 'G'): -2, ('M', 'A'): -2, ('K', 'H'): -2,
+('V', 'R'): -3, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -4,
+('V', 'V'): 5, ('M', 'I'): 1, ('T', 'Q'): -2, ('I', 'G'): -4,
+('P', 'K'): -2, ('M', 'M'): 8, ('K', 'D'): -1, ('I', 'C'): -3,
+('Z', 'D'): 3, ('F', 'R'): -5, ('X', 'K'): -2, ('Q', 'D'): 1,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -4, ('Z', 'H'): 1,
+('B', 'L'): -4, ('B', 'H'): 1, ('F', 'F'): 8, ('X', 'W'): -5,
+('B', 'D'): 4, ('D', 'A'): 0, ('S', 'L'): -4, ('X', 'S'): -1,
+('F', 'N'): -4, ('S', 'R'): -1, ('W', 'D'): -8, ('V', 'Y'): -3,
+('W', 'L'): -3, ('H', 'R'): 1, ('W', 'H'): -3, ('H', 'N'): 2,
+('W', 'T'): -6, ('T', 'T'): 4, ('S', 'F'): -3, ('W', 'P'): -7,
+('L', 'D'): -5, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 5, ('Y', 'K'): -5, ('E', 'Q'): 2,
+('Y', 'G'): -6, ('Z', 'S'): -1, ('Y', 'C'): -1, ('G', 'D'): 0,
+('B', 'V'): -3, ('E', 'A'): 0, ('Y', 'W'): -2, ('E', 'E'): 5,
+('Y', 'S'): -3, ('C', 'N'): -5, ('V', 'C'): -3, ('T', 'H'): -3,
+('P', 'R'): -1, ('V', 'G'): -2, ('T', 'L'): -3, ('V', 'K'): -4,
+('K', 'Q'): 0, ('R', 'A'): -3, ('I', 'R'): -2, ('T', 'D'): -1,
+('P', 'F'): -5, ('I', 'N'): -2, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -8, ('W', 'W'): 12, ('M', 'H'): -4, ('P', 'N'): -2,
+('K', 'A'): -2, ('M', 'L'): 3, ('K', 'E'): -1, ('Z', 'E'): 4,
+('X', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -2, ('X', 'F'): -3,
+('K', 'C'): -7, ('B', 'Q'): 0, ('X', 'B'): -1, ('B', 'M'): -4,
+('F', 'C'): -6, ('Z', 'Q'): 4, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 3, ('X', 'V'): -1, ('F', 'K'): -7, ('B', 'A'): 0,
+('X', 'R'): -2, ('D', 'D'): 5, ('W', 'G'): -8, ('Z', 'F'): -6,
+('S', 'Q'): -2, ('W', 'C'): -8, ('W', 'K'): -5, ('H', 'Q'): 3,
+('L', 'C'): -7, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -5,
+('W', 'S'): -2, ('S', 'E'): -1, ('H', 'E'): -1, ('S', 'I'): -2,
+('H', 'A'): -3, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): -1,
+('Y', 'D'): -5, ('E', 'R'): -3, ('X', 'P'): -2, ('G', 'G'): 5,
+('G', 'C'): -4, ('E', 'N'): 1, ('Y', 'T'): -3, ('Y', 'P'): -6,
+('T', 'K'): -1, ('A', 'A'): 3, ('P', 'Q'): 0, ('T', 'C'): -3,
+('V', 'H'): -3, ('T', 'G'): -1, ('I', 'Q'): -3, ('Z', 'T'): -2,
+('C', 'R'): -4, ('V', 'P'): -2, ('P', 'E'): -2, ('M', 'C'): -6,
+('K', 'N'): 1, ('I', 'I'): 6, ('P', 'A'): 1, ('M', 'G'): -4,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): 0,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 6, ('X', 'M'): -2,
+('L', 'I'): 1, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 0, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 3,
+('F', 'D'): -7, ('X', 'Y'): -3, ('Z', 'R'): -1, ('F', 'H'): -3,
+('B', 'F'): -5, ('F', 'L'): 0, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 pam120 = _temp()
 del _temp
@@ -2202,75 +2201,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam180.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -4, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 6, ('N', 'A') : 0, ('Z', 'Y') : -6, ('W', 'R') : 2,
-('Q', 'A') : -1, ('S', 'D') : 0, ('H', 'H') : 8, ('S', 'H') : -2,
-('H', 'D') : 0, ('L', 'N') : -4, ('W', 'A') : -8, ('Y', 'M') : -4,
-('G', 'R') : -4, ('Y', 'I') : -2, ('Y', 'E') : -6, ('B', 'Y') : -4,
-('Y', 'A') : -5, ('V', 'D') : -3, ('B', 'S') : 1, ('Y', 'Y') : 11,
-('G', 'N') : 0, ('E', 'C') : -7, ('Y', 'Q') : -6, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 13, ('M', 'R') : -1, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 8, ('V', 'I') : 5, ('V', 'S') : -2,
-('Z', 'P') : -1, ('V', 'M') : 2, ('T', 'F') : -4, ('V', 'Q') : -3,
-('K', 'K') : 6, ('P', 'D') : -2, ('I', 'H') : -4, ('I', 'D') : -3,
-('T', 'R') : -2, ('P', 'L') : -4, ('K', 'G') : -3, ('M', 'N') : -3,
-('P', 'H') : -1, ('F', 'Q') : -6, ('Z', 'G') : -1, ('X', 'L') : -2,
-('T', 'M') : -1, ('Z', 'C') : -7, ('X', 'H') : -1, ('D', 'R') : -3,
-('B', 'W') : -7, ('X', 'D') : -1, ('Z', 'K') : 0, ('F', 'A') : -5,
-('Z', 'W') : -8, ('F', 'E') : -7, ('D', 'N') : 3, ('B', 'K') : 0,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 0, ('X', 'T') : -1,
-('F', 'M') : 0, ('B', 'C') : -6, ('Z', 'I') : -3, ('Z', 'V') : -3,
-('S', 'S') : 3, ('L', 'Q') : -2, ('W', 'E') : -9, ('Q', 'R') : 1,
-('N', 'N') : 4, ('W', 'M') : -6, ('Q', 'C') : -7, ('W', 'I') : -7,
-('S', 'C') : 0, ('L', 'A') : -3, ('S', 'G') : 1, ('L', 'E') : -5,
-('W', 'Q') : -6, ('H', 'G') : -3, ('S', 'K') : -1, ('Q', 'N') : 0,
-('N', 'R') : -1, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -2,
-('Y', 'F') : 7, ('C', 'A') : -3, ('V', 'L') : 2, ('G', 'E') : 0,
-('G', 'A') : 1, ('K', 'R') : 4, ('E', 'D') : 4, ('Y', 'R') : -6,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -7, ('V', 'F') : -2,
-('T', 'A') : 2, ('T', 'P') : 0, ('B', 'P') : -2, ('T', 'E') : -1,
-('V', 'N') : -3, ('P', 'G') : -1, ('M', 'A') : -2, ('K', 'H') : -1,
-('V', 'R') : -4, ('P', 'C') : -4, ('M', 'E') : -3, ('K', 'L') : -4,
-('V', 'V') : 6, ('M', 'I') : 2, ('T', 'Q') : -2, ('I', 'G') : -4,
-('P', 'K') : -2, ('M', 'M') : 9, ('K', 'D') : 0, ('I', 'C') : -3,
-('Z', 'D') : 3, ('F', 'R') : -6, ('X', 'K') : -1, ('Q', 'D') : 2,
-('X', 'G') : -2, ('Z', 'L') : -3, ('X', 'C') : -4, ('Z', 'H') : 2,
-('B', 'L') : -5, ('B', 'H') : 1, ('F', 'F') : 10, ('X', 'W') : -6,
-('B', 'D') : 4, ('D', 'A') : 0, ('S', 'L') : -4, ('X', 'S') : 0,
-('F', 'N') : -5, ('S', 'R') : -1, ('W', 'D') : -9, ('V', 'Y') : -4,
-('W', 'L') : -3, ('H', 'R') : 2, ('W', 'H') : -4, ('H', 'N') : 2,
-('W', 'T') : -7, ('T', 'T') : 4, ('S', 'F') : -4, ('W', 'P') : -7,
-('L', 'D') : -6, ('B', 'I') : -3, ('L', 'H') : -3, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 7, ('Y', 'K') : -6, ('E', 'Q') : 3,
-('Y', 'G') : -7, ('Z', 'S') : -1, ('Y', 'C') : 0, ('G', 'D') : 0,
-('B', 'V') : -3, ('E', 'A') : 0, ('Y', 'W') : -1, ('E', 'E') : 5,
-('Y', 'S') : -4, ('C', 'N') : -5, ('V', 'C') : -3, ('T', 'H') : -2,
-('P', 'R') : -1, ('V', 'G') : -2, ('T', 'L') : -3, ('V', 'K') : -4,
-('K', 'Q') : 0, ('R', 'A') : -3, ('I', 'R') : -3, ('T', 'D') : -1,
-('P', 'F') : -6, ('I', 'N') : -3, ('K', 'I') : -3, ('M', 'D') : -4,
-('V', 'W') : -8, ('W', 'W') : 18, ('M', 'H') : -3, ('P', 'N') : -1,
-('K', 'A') : -2, ('M', 'L') : 4, ('K', 'E') : -1, ('Z', 'E') : 5,
-('X', 'N') : -1, ('Z', 'A') : 0, ('Z', 'M') : -2, ('X', 'F') : -3,
-('K', 'C') : -7, ('B', 'Q') : 1, ('X', 'B') : -1, ('B', 'M') : -3,
-('F', 'C') : -6, ('Z', 'Q') : 5, ('X', 'Z') : -1, ('F', 'G') : -6,
-('B', 'E') : 3, ('X', 'V') : -1, ('F', 'K') : -7, ('B', 'A') : 0,
-('X', 'R') : -2, ('D', 'D') : 5, ('W', 'G') : -9, ('Z', 'F') : -7,
-('S', 'Q') : -1, ('W', 'C') : -10, ('W', 'K') : -5, ('H', 'Q') : 4,
-('L', 'C') : -8, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -6,
-('W', 'S') : -3, ('S', 'E') : -1, ('H', 'E') : 0, ('S', 'I') : -2,
-('H', 'A') : -2, ('S', 'M') : -2, ('Y', 'L') : -2, ('Y', 'H') : 0,
-('Y', 'D') : -6, ('E', 'R') : -2, ('X', 'P') : -1, ('G', 'G') : 6,
-('G', 'C') : -5, ('E', 'N') : 2, ('Y', 'T') : -4, ('Y', 'P') : -7,
-('T', 'K') : 0, ('A', 'A') : 3, ('P', 'Q') : 0, ('T', 'C') : -3,
-('V', 'H') : -3, ('T', 'G') : -1, ('I', 'Q') : -3, ('Z', 'T') : -1,
-('C', 'R') : -5, ('V', 'P') : -2, ('P', 'E') : -1, ('M', 'C') : -7,
-('K', 'N') : 1, ('I', 'I') : 6, ('P', 'A') : 1, ('M', 'G') : -4,
-('T', 'S') : 2, ('I', 'E') : -3, ('P', 'M') : -3, ('M', 'K') : 1,
-('I', 'A') : -1, ('P', 'I') : -3, ('R', 'R') : 8, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 3, ('X', 'E') : -1,
-('Z', 'N') : 1, ('X', 'A') : -1, ('B', 'R') : -2, ('B', 'N') : 3,
-('F', 'D') : -8, ('X', 'Y') : -3, ('Z', 'R') : 0, ('F', 'H') : -3,
-('B', 'F') : -6, ('F', 'L') : 1, ('X', 'Q') : -1, ('B', 'B') : 4
+('W', 'F'): 0, ('L', 'R'): -4, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 6, ('N', 'A'): 0, ('Z', 'Y'): -6, ('W', 'R'): 2,
+('Q', 'A'): -1, ('S', 'D'): 0, ('H', 'H'): 8, ('S', 'H'): -2,
+('H', 'D'): 0, ('L', 'N'): -4, ('W', 'A'): -8, ('Y', 'M'): -4,
+('G', 'R'): -4, ('Y', 'I'): -2, ('Y', 'E'): -6, ('B', 'Y'): -4,
+('Y', 'A'): -5, ('V', 'D'): -3, ('B', 'S'): 1, ('Y', 'Y'): 11,
+('G', 'N'): 0, ('E', 'C'): -7, ('Y', 'Q'): -6, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 13, ('M', 'R'): -1, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 8, ('V', 'I'): 5, ('V', 'S'): -2,
+('Z', 'P'): -1, ('V', 'M'): 2, ('T', 'F'): -4, ('V', 'Q'): -3,
+('K', 'K'): 6, ('P', 'D'): -2, ('I', 'H'): -4, ('I', 'D'): -3,
+('T', 'R'): -2, ('P', 'L'): -4, ('K', 'G'): -3, ('M', 'N'): -3,
+('P', 'H'): -1, ('F', 'Q'): -6, ('Z', 'G'): -1, ('X', 'L'): -2,
+('T', 'M'): -1, ('Z', 'C'): -7, ('X', 'H'): -1, ('D', 'R'): -3,
+('B', 'W'): -7, ('X', 'D'): -1, ('Z', 'K'): 0, ('F', 'A'): -5,
+('Z', 'W'): -8, ('F', 'E'): -7, ('D', 'N'): 3, ('B', 'K'): 0,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 0, ('X', 'T'): -1,
+('F', 'M'): 0, ('B', 'C'): -6, ('Z', 'I'): -3, ('Z', 'V'): -3,
+('S', 'S'): 3, ('L', 'Q'): -2, ('W', 'E'): -9, ('Q', 'R'): 1,
+('N', 'N'): 4, ('W', 'M'): -6, ('Q', 'C'): -7, ('W', 'I'): -7,
+('S', 'C'): 0, ('L', 'A'): -3, ('S', 'G'): 1, ('L', 'E'): -5,
+('W', 'Q'): -6, ('H', 'G'): -3, ('S', 'K'): -1, ('Q', 'N'): 0,
+('N', 'R'): -1, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -2,
+('Y', 'F'): 7, ('C', 'A'): -3, ('V', 'L'): 2, ('G', 'E'): 0,
+('G', 'A'): 1, ('K', 'R'): 4, ('E', 'D'): 4, ('Y', 'R'): -6,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -7, ('V', 'F'): -2,
+('T', 'A'): 2, ('T', 'P'): 0, ('B', 'P'): -2, ('T', 'E'): -1,
+('V', 'N'): -3, ('P', 'G'): -1, ('M', 'A'): -2, ('K', 'H'): -1,
+('V', 'R'): -4, ('P', 'C'): -4, ('M', 'E'): -3, ('K', 'L'): -4,
+('V', 'V'): 6, ('M', 'I'): 2, ('T', 'Q'): -2, ('I', 'G'): -4,
+('P', 'K'): -2, ('M', 'M'): 9, ('K', 'D'): 0, ('I', 'C'): -3,
+('Z', 'D'): 3, ('F', 'R'): -6, ('X', 'K'): -1, ('Q', 'D'): 2,
+('X', 'G'): -2, ('Z', 'L'): -3, ('X', 'C'): -4, ('Z', 'H'): 2,
+('B', 'L'): -5, ('B', 'H'): 1, ('F', 'F'): 10, ('X', 'W'): -6,
+('B', 'D'): 4, ('D', 'A'): 0, ('S', 'L'): -4, ('X', 'S'): 0,
+('F', 'N'): -5, ('S', 'R'): -1, ('W', 'D'): -9, ('V', 'Y'): -4,
+('W', 'L'): -3, ('H', 'R'): 2, ('W', 'H'): -4, ('H', 'N'): 2,
+('W', 'T'): -7, ('T', 'T'): 4, ('S', 'F'): -4, ('W', 'P'): -7,
+('L', 'D'): -6, ('B', 'I'): -3, ('L', 'H'): -3, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 7, ('Y', 'K'): -6, ('E', 'Q'): 3,
+('Y', 'G'): -7, ('Z', 'S'): -1, ('Y', 'C'): 0, ('G', 'D'): 0,
+('B', 'V'): -3, ('E', 'A'): 0, ('Y', 'W'): -1, ('E', 'E'): 5,
+('Y', 'S'): -4, ('C', 'N'): -5, ('V', 'C'): -3, ('T', 'H'): -2,
+('P', 'R'): -1, ('V', 'G'): -2, ('T', 'L'): -3, ('V', 'K'): -4,
+('K', 'Q'): 0, ('R', 'A'): -3, ('I', 'R'): -3, ('T', 'D'): -1,
+('P', 'F'): -6, ('I', 'N'): -3, ('K', 'I'): -3, ('M', 'D'): -4,
+('V', 'W'): -8, ('W', 'W'): 18, ('M', 'H'): -3, ('P', 'N'): -1,
+('K', 'A'): -2, ('M', 'L'): 4, ('K', 'E'): -1, ('Z', 'E'): 5,
+('X', 'N'): -1, ('Z', 'A'): 0, ('Z', 'M'): -2, ('X', 'F'): -3,
+('K', 'C'): -7, ('B', 'Q'): 1, ('X', 'B'): -1, ('B', 'M'): -3,
+('F', 'C'): -6, ('Z', 'Q'): 5, ('X', 'Z'): -1, ('F', 'G'): -6,
+('B', 'E'): 3, ('X', 'V'): -1, ('F', 'K'): -7, ('B', 'A'): 0,
+('X', 'R'): -2, ('D', 'D'): 5, ('W', 'G'): -9, ('Z', 'F'): -7,
+('S', 'Q'): -1, ('W', 'C'): -10, ('W', 'K'): -5, ('H', 'Q'): 4,
+('L', 'C'): -8, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -6,
+('W', 'S'): -3, ('S', 'E'): -1, ('H', 'E'): 0, ('S', 'I'): -2,
+('H', 'A'): -2, ('S', 'M'): -2, ('Y', 'L'): -2, ('Y', 'H'): 0,
+('Y', 'D'): -6, ('E', 'R'): -2, ('X', 'P'): -1, ('G', 'G'): 6,
+('G', 'C'): -5, ('E', 'N'): 2, ('Y', 'T'): -4, ('Y', 'P'): -7,
+('T', 'K'): 0, ('A', 'A'): 3, ('P', 'Q'): 0, ('T', 'C'): -3,
+('V', 'H'): -3, ('T', 'G'): -1, ('I', 'Q'): -3, ('Z', 'T'): -1,
+('C', 'R'): -5, ('V', 'P'): -2, ('P', 'E'): -1, ('M', 'C'): -7,
+('K', 'N'): 1, ('I', 'I'): 6, ('P', 'A'): 1, ('M', 'G'): -4,
+('T', 'S'): 2, ('I', 'E'): -3, ('P', 'M'): -3, ('M', 'K'): 1,
+('I', 'A'): -1, ('P', 'I'): -3, ('R', 'R'): 8, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 3, ('X', 'E'): -1,
+('Z', 'N'): 1, ('X', 'A'): -1, ('B', 'R'): -2, ('B', 'N'): 3,
+('F', 'D'): -8, ('X', 'Y'): -3, ('Z', 'R'): 0, ('F', 'H'): -3,
+('B', 'F'): -6, ('F', 'L'): 1, ('X', 'Q'): -1, ('B', 'B'): 4
 }
 pam180 = _temp()
 del _temp
@@ -2279,75 +2278,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam250.cmp
 def _temp():
     return {
-('W', 'F') : 0, ('L', 'R') : -3, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 4, ('N', 'A') : 0, ('Z', 'Y') : -4, ('W', 'R') : 2,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 6, ('S', 'H') : -1,
-('H', 'D') : 1, ('L', 'N') : -3, ('W', 'A') : -6, ('Y', 'M') : -2,
-('G', 'R') : -3, ('Y', 'I') : -1, ('Y', 'E') : -4, ('B', 'Y') : -3,
-('Y', 'A') : -3, ('V', 'D') : -2, ('B', 'S') : 0, ('Y', 'Y') : 10,
-('G', 'N') : 0, ('E', 'C') : -5, ('Y', 'Q') : -4, ('Z', 'Z') : 3,
-('V', 'A') : 0, ('C', 'C') : 12, ('M', 'R') : 0, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 6, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 2, ('T', 'F') : -3, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -2, ('I', 'D') : -2,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : 0, ('F', 'Q') : -5, ('Z', 'G') : 0, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -5, ('X', 'H') : -1, ('D', 'R') : -1,
-('B', 'W') : -5, ('X', 'D') : -1, ('Z', 'K') : 0, ('F', 'A') : -3,
-('Z', 'W') : -6, ('F', 'E') : -5, ('D', 'N') : 2, ('B', 'K') : 1,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 0, ('X', 'T') : 0,
-('F', 'M') : 0, ('B', 'C') : -4, ('Z', 'I') : -2, ('Z', 'V') : -2,
-('S', 'S') : 2, ('L', 'Q') : -2, ('W', 'E') : -7, ('Q', 'R') : 1,
-('N', 'N') : 2, ('W', 'M') : -4, ('Q', 'C') : -5, ('W', 'I') : -5,
-('S', 'C') : 0, ('L', 'A') : -2, ('S', 'G') : 1, ('L', 'E') : -3,
-('W', 'Q') : -5, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : 0, ('H', 'C') : -3, ('Y', 'N') : -2, ('G', 'Q') : -1,
-('Y', 'F') : 7, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : 0,
-('G', 'A') : 1, ('K', 'R') : 3, ('E', 'D') : 3, ('Y', 'R') : -4,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -5, ('V', 'F') : -1,
-('T', 'A') : 1, ('T', 'P') : 0, ('B', 'P') : -1, ('T', 'E') : 0,
-('V', 'N') : -2, ('P', 'G') : 0, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -2, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 4, ('M', 'I') : 2, ('T', 'Q') : -1, ('I', 'G') : -3,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -2,
-('Z', 'D') : 3, ('F', 'R') : -4, ('X', 'K') : -1, ('Q', 'D') : 2,
-('X', 'G') : -1, ('Z', 'L') : -3, ('X', 'C') : -3, ('Z', 'H') : 2,
-('B', 'L') : -3, ('B', 'H') : 1, ('F', 'F') : 9, ('X', 'W') : -4,
-('B', 'D') : 3, ('D', 'A') : 0, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -3, ('S', 'R') : 0, ('W', 'D') : -7, ('V', 'Y') : -2,
-('W', 'L') : -2, ('H', 'R') : 2, ('W', 'H') : -3, ('H', 'N') : 2,
-('W', 'T') : -5, ('T', 'T') : 3, ('S', 'F') : -3, ('W', 'P') : -6,
-('L', 'D') : -4, ('B', 'I') : -2, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 6, ('Y', 'K') : -4, ('E', 'Q') : 2,
-('Y', 'G') : -5, ('Z', 'S') : 0, ('Y', 'C') : 0, ('G', 'D') : 1,
-('B', 'V') : -2, ('E', 'A') : 0, ('Y', 'W') : 0, ('E', 'E') : 4,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -1,
-('P', 'R') : 0, ('V', 'G') : -1, ('T', 'L') : -2, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -2, ('I', 'R') : -2, ('T', 'D') : 0,
-('P', 'F') : -5, ('I', 'N') : -2, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -6, ('W', 'W') : 17, ('M', 'H') : -2, ('P', 'N') : 0,
-('K', 'A') : -1, ('M', 'L') : 4, ('K', 'E') : 0, ('Z', 'E') : 3,
-('X', 'N') : 0, ('Z', 'A') : 0, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -5, ('B', 'Q') : 1, ('X', 'B') : -1, ('B', 'M') : -2,
-('F', 'C') : -4, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 3, ('X', 'V') : -1, ('F', 'K') : -5, ('B', 'A') : 0,
-('X', 'R') : -1, ('D', 'D') : 4, ('W', 'G') : -7, ('Z', 'F') : -5,
-('S', 'Q') : -1, ('W', 'C') : -8, ('W', 'K') : -3, ('H', 'Q') : 3,
-('L', 'C') : -6, ('W', 'N') : -4, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -2, ('S', 'E') : 0, ('H', 'E') : 1, ('S', 'I') : -1,
-('H', 'A') : -1, ('S', 'M') : -2, ('Y', 'L') : -1, ('Y', 'H') : 0,
-('Y', 'D') : -4, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 5,
-('G', 'C') : -3, ('E', 'N') : 1, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : 0, ('A', 'A') : 2, ('P', 'Q') : 0, ('T', 'C') : -2,
-('V', 'H') : -2, ('T', 'G') : 0, ('I', 'Q') : -2, ('Z', 'T') : -1,
-('C', 'R') : -4, ('V', 'P') : -1, ('P', 'E') : -1, ('M', 'C') : -5,
-('K', 'N') : 1, ('I', 'I') : 5, ('P', 'A') : 1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -2, ('P', 'M') : -2, ('M', 'K') : 0,
-('I', 'A') : -1, ('P', 'I') : -2, ('R', 'R') : 6, ('X', 'M') : -1,
-('L', 'I') : 2, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 1, ('X', 'A') : 0, ('B', 'R') : -1, ('B', 'N') : 2,
-('F', 'D') : -6, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -4, ('F', 'L') : 2, ('X', 'Q') : -1, ('B', 'B') : 3
+('W', 'F'): 0, ('L', 'R'): -3, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 4, ('N', 'A'): 0, ('Z', 'Y'): -4, ('W', 'R'): 2,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 6, ('S', 'H'): -1,
+('H', 'D'): 1, ('L', 'N'): -3, ('W', 'A'): -6, ('Y', 'M'): -2,
+('G', 'R'): -3, ('Y', 'I'): -1, ('Y', 'E'): -4, ('B', 'Y'): -3,
+('Y', 'A'): -3, ('V', 'D'): -2, ('B', 'S'): 0, ('Y', 'Y'): 10,
+('G', 'N'): 0, ('E', 'C'): -5, ('Y', 'Q'): -4, ('Z', 'Z'): 3,
+('V', 'A'): 0, ('C', 'C'): 12, ('M', 'R'): 0, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 6, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 2, ('T', 'F'): -3, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -2, ('I', 'D'): -2,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): 0, ('F', 'Q'): -5, ('Z', 'G'): 0, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -5, ('X', 'H'): -1, ('D', 'R'): -1,
+('B', 'W'): -5, ('X', 'D'): -1, ('Z', 'K'): 0, ('F', 'A'): -3,
+('Z', 'W'): -6, ('F', 'E'): -5, ('D', 'N'): 2, ('B', 'K'): 1,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 0, ('X', 'T'): 0,
+('F', 'M'): 0, ('B', 'C'): -4, ('Z', 'I'): -2, ('Z', 'V'): -2,
+('S', 'S'): 2, ('L', 'Q'): -2, ('W', 'E'): -7, ('Q', 'R'): 1,
+('N', 'N'): 2, ('W', 'M'): -4, ('Q', 'C'): -5, ('W', 'I'): -5,
+('S', 'C'): 0, ('L', 'A'): -2, ('S', 'G'): 1, ('L', 'E'): -3,
+('W', 'Q'): -5, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): 0, ('H', 'C'): -3, ('Y', 'N'): -2, ('G', 'Q'): -1,
+('Y', 'F'): 7, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): 0,
+('G', 'A'): 1, ('K', 'R'): 3, ('E', 'D'): 3, ('Y', 'R'): -4,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -5, ('V', 'F'): -1,
+('T', 'A'): 1, ('T', 'P'): 0, ('B', 'P'): -1, ('T', 'E'): 0,
+('V', 'N'): -2, ('P', 'G'): 0, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -2, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 4, ('M', 'I'): 2, ('T', 'Q'): -1, ('I', 'G'): -3,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -2,
+('Z', 'D'): 3, ('F', 'R'): -4, ('X', 'K'): -1, ('Q', 'D'): 2,
+('X', 'G'): -1, ('Z', 'L'): -3, ('X', 'C'): -3, ('Z', 'H'): 2,
+('B', 'L'): -3, ('B', 'H'): 1, ('F', 'F'): 9, ('X', 'W'): -4,
+('B', 'D'): 3, ('D', 'A'): 0, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -3, ('S', 'R'): 0, ('W', 'D'): -7, ('V', 'Y'): -2,
+('W', 'L'): -2, ('H', 'R'): 2, ('W', 'H'): -3, ('H', 'N'): 2,
+('W', 'T'): -5, ('T', 'T'): 3, ('S', 'F'): -3, ('W', 'P'): -6,
+('L', 'D'): -4, ('B', 'I'): -2, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 6, ('Y', 'K'): -4, ('E', 'Q'): 2,
+('Y', 'G'): -5, ('Z', 'S'): 0, ('Y', 'C'): 0, ('G', 'D'): 1,
+('B', 'V'): -2, ('E', 'A'): 0, ('Y', 'W'): 0, ('E', 'E'): 4,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -1,
+('P', 'R'): 0, ('V', 'G'): -1, ('T', 'L'): -2, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -2, ('I', 'R'): -2, ('T', 'D'): 0,
+('P', 'F'): -5, ('I', 'N'): -2, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -6, ('W', 'W'): 17, ('M', 'H'): -2, ('P', 'N'): 0,
+('K', 'A'): -1, ('M', 'L'): 4, ('K', 'E'): 0, ('Z', 'E'): 3,
+('X', 'N'): 0, ('Z', 'A'): 0, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -5, ('B', 'Q'): 1, ('X', 'B'): -1, ('B', 'M'): -2,
+('F', 'C'): -4, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 3, ('X', 'V'): -1, ('F', 'K'): -5, ('B', 'A'): 0,
+('X', 'R'): -1, ('D', 'D'): 4, ('W', 'G'): -7, ('Z', 'F'): -5,
+('S', 'Q'): -1, ('W', 'C'): -8, ('W', 'K'): -3, ('H', 'Q'): 3,
+('L', 'C'): -6, ('W', 'N'): -4, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -2, ('S', 'E'): 0, ('H', 'E'): 1, ('S', 'I'): -1,
+('H', 'A'): -1, ('S', 'M'): -2, ('Y', 'L'): -1, ('Y', 'H'): 0,
+('Y', 'D'): -4, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 5,
+('G', 'C'): -3, ('E', 'N'): 1, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): 0, ('A', 'A'): 2, ('P', 'Q'): 0, ('T', 'C'): -2,
+('V', 'H'): -2, ('T', 'G'): 0, ('I', 'Q'): -2, ('Z', 'T'): -1,
+('C', 'R'): -4, ('V', 'P'): -1, ('P', 'E'): -1, ('M', 'C'): -5,
+('K', 'N'): 1, ('I', 'I'): 5, ('P', 'A'): 1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -2, ('P', 'M'): -2, ('M', 'K'): 0,
+('I', 'A'): -1, ('P', 'I'): -2, ('R', 'R'): 6, ('X', 'M'): -1,
+('L', 'I'): 2, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 1, ('X', 'A'): 0, ('B', 'R'): -1, ('B', 'N'): 2,
+('F', 'D'): -6, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -4, ('F', 'L'): 2, ('X', 'Q'): -1, ('B', 'B'): 3
 }
 pam250 = _temp()
 del _temp
@@ -2356,75 +2355,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam30.cmp
 def _temp():
     return {
-('W', 'F') : -4, ('L', 'R') : -8, ('S', 'P') : -2, ('V', 'T') : -3,
-('Q', 'Q') : 8, ('N', 'A') : -4, ('Z', 'Y') : -9, ('W', 'R') : -2,
-('Q', 'A') : -4, ('S', 'D') : -4, ('H', 'H') : 9, ('S', 'H') : -6,
-('H', 'D') : -4, ('L', 'N') : -7, ('W', 'A') : -13, ('Y', 'M') : -11,
-('G', 'R') : -9, ('Y', 'I') : -6, ('Y', 'E') : -8, ('B', 'Y') : -6,
-('Y', 'A') : -8, ('V', 'D') : -8, ('B', 'S') : -1, ('Y', 'Y') : 10,
-('G', 'N') : -3, ('E', 'C') : -14, ('Y', 'Q') : -12, ('Z', 'Z') : 6,
-('V', 'A') : -2, ('C', 'C') : 10, ('M', 'R') : -4, ('V', 'E') : -6,
-('T', 'N') : -2, ('P', 'P') : 8, ('V', 'I') : 2, ('V', 'S') : -6,
-('Z', 'P') : -4, ('V', 'M') : -1, ('T', 'F') : -9, ('V', 'Q') : -7,
-('K', 'K') : 7, ('P', 'D') : -8, ('I', 'H') : -9, ('I', 'D') : -7,
-('T', 'R') : -6, ('P', 'L') : -7, ('K', 'G') : -7, ('M', 'N') : -9,
-('P', 'H') : -4, ('F', 'Q') : -13, ('Z', 'G') : -5, ('X', 'L') : -6,
-('T', 'M') : -4, ('Z', 'C') : -14, ('X', 'H') : -5, ('D', 'R') : -10,
-('B', 'W') : -10, ('X', 'D') : -5, ('Z', 'K') : -4, ('F', 'A') : -8,
-('Z', 'W') : -14, ('F', 'E') : -14, ('D', 'N') : 2, ('B', 'K') : -2,
-('X', 'X') : -5, ('F', 'I') : -2, ('B', 'G') : -3, ('X', 'T') : -4,
-('F', 'M') : -4, ('B', 'C') : -12, ('Z', 'I') : -6, ('Z', 'V') : -6,
-('S', 'S') : 6, ('L', 'Q') : -5, ('W', 'E') : -17, ('Q', 'R') : -2,
-('N', 'N') : 8, ('W', 'M') : -13, ('Q', 'C') : -14, ('W', 'I') : -14,
-('S', 'C') : -3, ('L', 'A') : -6, ('S', 'G') : -2, ('L', 'E') : -9,
-('W', 'Q') : -13, ('H', 'G') : -9, ('S', 'K') : -4, ('Q', 'N') : -3,
-('N', 'R') : -6, ('H', 'C') : -7, ('Y', 'N') : -4, ('G', 'Q') : -7,
-('Y', 'F') : 2, ('C', 'A') : -6, ('V', 'L') : -2, ('G', 'E') : -4,
-('G', 'A') : -2, ('K', 'R') : 0, ('E', 'D') : 2, ('Y', 'R') : -10,
-('M', 'Q') : -4, ('T', 'I') : -2, ('C', 'D') : -14, ('V', 'F') : -8,
-('T', 'A') : -1, ('T', 'P') : -4, ('B', 'P') : -7, ('T', 'E') : -6,
-('V', 'N') : -8, ('P', 'G') : -6, ('M', 'A') : -5, ('K', 'H') : -6,
-('V', 'R') : -8, ('P', 'C') : -8, ('M', 'E') : -7, ('K', 'L') : -8,
-('V', 'V') : 7, ('M', 'I') : -1, ('T', 'Q') : -5, ('I', 'G') : -11,
-('P', 'K') : -6, ('M', 'M') : 11, ('K', 'D') : -4, ('I', 'C') : -6,
-('Z', 'D') : 1, ('F', 'R') : -9, ('X', 'K') : -5, ('Q', 'D') : -2,
-('X', 'G') : -5, ('Z', 'L') : -7, ('X', 'C') : -9, ('Z', 'H') : -1,
-('B', 'L') : -9, ('B', 'H') : -1, ('F', 'F') : 9, ('X', 'W') : -11,
-('B', 'D') : 6, ('D', 'A') : -3, ('S', 'L') : -8, ('X', 'S') : -3,
-('F', 'N') : -9, ('S', 'R') : -3, ('W', 'D') : -15, ('V', 'Y') : -7,
-('W', 'L') : -6, ('H', 'R') : -2, ('W', 'H') : -7, ('H', 'N') : 0,
-('W', 'T') : -13, ('T', 'T') : 7, ('S', 'F') : -6, ('W', 'P') : -14,
-('L', 'D') : -12, ('B', 'I') : -6, ('L', 'H') : -6, ('S', 'N') : 0,
-('B', 'T') : -3, ('L', 'L') : 7, ('Y', 'K') : -9, ('E', 'Q') : 1,
-('Y', 'G') : -14, ('Z', 'S') : -5, ('Y', 'C') : -4, ('G', 'D') : -3,
-('B', 'V') : -8, ('E', 'A') : -2, ('Y', 'W') : -5, ('E', 'E') : 8,
-('Y', 'S') : -7, ('C', 'N') : -11, ('V', 'C') : -6, ('T', 'H') : -7,
-('P', 'R') : -4, ('V', 'G') : -5, ('T', 'L') : -7, ('V', 'K') : -9,
-('K', 'Q') : -3, ('R', 'A') : -7, ('I', 'R') : -5, ('T', 'D') : -5,
-('P', 'F') : -10, ('I', 'N') : -5, ('K', 'I') : -6, ('M', 'D') : -11,
-('V', 'W') : -15, ('W', 'W') : 13, ('M', 'H') : -10, ('P', 'N') : -6,
-('K', 'A') : -7, ('M', 'L') : 1, ('K', 'E') : -4, ('Z', 'E') : 6,
-('X', 'N') : -3, ('Z', 'A') : -3, ('Z', 'M') : -5, ('X', 'F') : -8,
-('K', 'C') : -14, ('B', 'Q') : -3, ('X', 'B') : -5, ('B', 'M') : -10,
-('F', 'C') : -13, ('Z', 'Q') : 6, ('X', 'Z') : -5, ('F', 'G') : -9,
-('B', 'E') : 1, ('X', 'V') : -5, ('F', 'K') : -14, ('B', 'A') : -3,
-('X', 'R') : -6, ('D', 'D') : 8, ('W', 'G') : -15, ('Z', 'F') : -13,
-('S', 'Q') : -5, ('W', 'C') : -15, ('W', 'K') : -12, ('H', 'Q') : 1,
-('L', 'C') : -15, ('W', 'N') : -8, ('S', 'A') : 0, ('L', 'G') : -10,
-('W', 'S') : -5, ('S', 'E') : -4, ('H', 'E') : -5, ('S', 'I') : -7,
-('H', 'A') : -7, ('S', 'M') : -5, ('Y', 'L') : -7, ('Y', 'H') : -3,
-('Y', 'D') : -11, ('E', 'R') : -9, ('X', 'P') : -5, ('G', 'G') : 6,
-('G', 'C') : -9, ('E', 'N') : -2, ('Y', 'T') : -6, ('Y', 'P') : -13,
-('T', 'K') : -3, ('A', 'A') : 6, ('P', 'Q') : -3, ('T', 'C') : -8,
-('V', 'H') : -6, ('T', 'G') : -6, ('I', 'Q') : -8, ('Z', 'T') : -6,
-('C', 'R') : -8, ('V', 'P') : -6, ('P', 'E') : -5, ('M', 'C') : -13,
-('K', 'N') : -1, ('I', 'I') : 8, ('P', 'A') : -2, ('M', 'G') : -8,
-('T', 'S') : 0, ('I', 'E') : -5, ('P', 'M') : -8, ('M', 'K') : -2,
-('I', 'A') : -5, ('P', 'I') : -8, ('R', 'R') : 8, ('X', 'M') : -5,
-('L', 'I') : -1, ('X', 'I') : -5, ('Z', 'B') : 0, ('X', 'E') : -5,
-('Z', 'N') : -3, ('X', 'A') : -3, ('B', 'R') : -7, ('B', 'N') : 6,
-('F', 'D') : -15, ('X', 'Y') : -7, ('Z', 'R') : -4, ('F', 'H') : -6,
-('B', 'F') : -10, ('F', 'L') : -3, ('X', 'Q') : -5, ('B', 'B') : 6
+('W', 'F'): -4, ('L', 'R'): -8, ('S', 'P'): -2, ('V', 'T'): -3,
+('Q', 'Q'): 8, ('N', 'A'): -4, ('Z', 'Y'): -9, ('W', 'R'): -2,
+('Q', 'A'): -4, ('S', 'D'): -4, ('H', 'H'): 9, ('S', 'H'): -6,
+('H', 'D'): -4, ('L', 'N'): -7, ('W', 'A'): -13, ('Y', 'M'): -11,
+('G', 'R'): -9, ('Y', 'I'): -6, ('Y', 'E'): -8, ('B', 'Y'): -6,
+('Y', 'A'): -8, ('V', 'D'): -8, ('B', 'S'): -1, ('Y', 'Y'): 10,
+('G', 'N'): -3, ('E', 'C'): -14, ('Y', 'Q'): -12, ('Z', 'Z'): 6,
+('V', 'A'): -2, ('C', 'C'): 10, ('M', 'R'): -4, ('V', 'E'): -6,
+('T', 'N'): -2, ('P', 'P'): 8, ('V', 'I'): 2, ('V', 'S'): -6,
+('Z', 'P'): -4, ('V', 'M'): -1, ('T', 'F'): -9, ('V', 'Q'): -7,
+('K', 'K'): 7, ('P', 'D'): -8, ('I', 'H'): -9, ('I', 'D'): -7,
+('T', 'R'): -6, ('P', 'L'): -7, ('K', 'G'): -7, ('M', 'N'): -9,
+('P', 'H'): -4, ('F', 'Q'): -13, ('Z', 'G'): -5, ('X', 'L'): -6,
+('T', 'M'): -4, ('Z', 'C'): -14, ('X', 'H'): -5, ('D', 'R'): -10,
+('B', 'W'): -10, ('X', 'D'): -5, ('Z', 'K'): -4, ('F', 'A'): -8,
+('Z', 'W'): -14, ('F', 'E'): -14, ('D', 'N'): 2, ('B', 'K'): -2,
+('X', 'X'): -5, ('F', 'I'): -2, ('B', 'G'): -3, ('X', 'T'): -4,
+('F', 'M'): -4, ('B', 'C'): -12, ('Z', 'I'): -6, ('Z', 'V'): -6,
+('S', 'S'): 6, ('L', 'Q'): -5, ('W', 'E'): -17, ('Q', 'R'): -2,
+('N', 'N'): 8, ('W', 'M'): -13, ('Q', 'C'): -14, ('W', 'I'): -14,
+('S', 'C'): -3, ('L', 'A'): -6, ('S', 'G'): -2, ('L', 'E'): -9,
+('W', 'Q'): -13, ('H', 'G'): -9, ('S', 'K'): -4, ('Q', 'N'): -3,
+('N', 'R'): -6, ('H', 'C'): -7, ('Y', 'N'): -4, ('G', 'Q'): -7,
+('Y', 'F'): 2, ('C', 'A'): -6, ('V', 'L'): -2, ('G', 'E'): -4,
+('G', 'A'): -2, ('K', 'R'): 0, ('E', 'D'): 2, ('Y', 'R'): -10,
+('M', 'Q'): -4, ('T', 'I'): -2, ('C', 'D'): -14, ('V', 'F'): -8,
+('T', 'A'): -1, ('T', 'P'): -4, ('B', 'P'): -7, ('T', 'E'): -6,
+('V', 'N'): -8, ('P', 'G'): -6, ('M', 'A'): -5, ('K', 'H'): -6,
+('V', 'R'): -8, ('P', 'C'): -8, ('M', 'E'): -7, ('K', 'L'): -8,
+('V', 'V'): 7, ('M', 'I'): -1, ('T', 'Q'): -5, ('I', 'G'): -11,
+('P', 'K'): -6, ('M', 'M'): 11, ('K', 'D'): -4, ('I', 'C'): -6,
+('Z', 'D'): 1, ('F', 'R'): -9, ('X', 'K'): -5, ('Q', 'D'): -2,
+('X', 'G'): -5, ('Z', 'L'): -7, ('X', 'C'): -9, ('Z', 'H'): -1,
+('B', 'L'): -9, ('B', 'H'): -1, ('F', 'F'): 9, ('X', 'W'): -11,
+('B', 'D'): 6, ('D', 'A'): -3, ('S', 'L'): -8, ('X', 'S'): -3,
+('F', 'N'): -9, ('S', 'R'): -3, ('W', 'D'): -15, ('V', 'Y'): -7,
+('W', 'L'): -6, ('H', 'R'): -2, ('W', 'H'): -7, ('H', 'N'): 0,
+('W', 'T'): -13, ('T', 'T'): 7, ('S', 'F'): -6, ('W', 'P'): -14,
+('L', 'D'): -12, ('B', 'I'): -6, ('L', 'H'): -6, ('S', 'N'): 0,
+('B', 'T'): -3, ('L', 'L'): 7, ('Y', 'K'): -9, ('E', 'Q'): 1,
+('Y', 'G'): -14, ('Z', 'S'): -5, ('Y', 'C'): -4, ('G', 'D'): -3,
+('B', 'V'): -8, ('E', 'A'): -2, ('Y', 'W'): -5, ('E', 'E'): 8,
+('Y', 'S'): -7, ('C', 'N'): -11, ('V', 'C'): -6, ('T', 'H'): -7,
+('P', 'R'): -4, ('V', 'G'): -5, ('T', 'L'): -7, ('V', 'K'): -9,
+('K', 'Q'): -3, ('R', 'A'): -7, ('I', 'R'): -5, ('T', 'D'): -5,
+('P', 'F'): -10, ('I', 'N'): -5, ('K', 'I'): -6, ('M', 'D'): -11,
+('V', 'W'): -15, ('W', 'W'): 13, ('M', 'H'): -10, ('P', 'N'): -6,
+('K', 'A'): -7, ('M', 'L'): 1, ('K', 'E'): -4, ('Z', 'E'): 6,
+('X', 'N'): -3, ('Z', 'A'): -3, ('Z', 'M'): -5, ('X', 'F'): -8,
+('K', 'C'): -14, ('B', 'Q'): -3, ('X', 'B'): -5, ('B', 'M'): -10,
+('F', 'C'): -13, ('Z', 'Q'): 6, ('X', 'Z'): -5, ('F', 'G'): -9,
+('B', 'E'): 1, ('X', 'V'): -5, ('F', 'K'): -14, ('B', 'A'): -3,
+('X', 'R'): -6, ('D', 'D'): 8, ('W', 'G'): -15, ('Z', 'F'): -13,
+('S', 'Q'): -5, ('W', 'C'): -15, ('W', 'K'): -12, ('H', 'Q'): 1,
+('L', 'C'): -15, ('W', 'N'): -8, ('S', 'A'): 0, ('L', 'G'): -10,
+('W', 'S'): -5, ('S', 'E'): -4, ('H', 'E'): -5, ('S', 'I'): -7,
+('H', 'A'): -7, ('S', 'M'): -5, ('Y', 'L'): -7, ('Y', 'H'): -3,
+('Y', 'D'): -11, ('E', 'R'): -9, ('X', 'P'): -5, ('G', 'G'): 6,
+('G', 'C'): -9, ('E', 'N'): -2, ('Y', 'T'): -6, ('Y', 'P'): -13,
+('T', 'K'): -3, ('A', 'A'): 6, ('P', 'Q'): -3, ('T', 'C'): -8,
+('V', 'H'): -6, ('T', 'G'): -6, ('I', 'Q'): -8, ('Z', 'T'): -6,
+('C', 'R'): -8, ('V', 'P'): -6, ('P', 'E'): -5, ('M', 'C'): -13,
+('K', 'N'): -1, ('I', 'I'): 8, ('P', 'A'): -2, ('M', 'G'): -8,
+('T', 'S'): 0, ('I', 'E'): -5, ('P', 'M'): -8, ('M', 'K'): -2,
+('I', 'A'): -5, ('P', 'I'): -8, ('R', 'R'): 8, ('X', 'M'): -5,
+('L', 'I'): -1, ('X', 'I'): -5, ('Z', 'B'): 0, ('X', 'E'): -5,
+('Z', 'N'): -3, ('X', 'A'): -3, ('B', 'R'): -7, ('B', 'N'): 6,
+('F', 'D'): -15, ('X', 'Y'): -7, ('Z', 'R'): -4, ('F', 'H'): -6,
+('B', 'F'): -10, ('F', 'L'): -3, ('X', 'Q'): -5, ('B', 'B'): 6
 }
 pam30 = _temp()
 del _temp
@@ -2433,75 +2432,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam300.cmp
 def _temp():
     return {
-('W', 'F') : 1, ('L', 'R') : -3, ('S', 'P') : 1, ('V', 'T') : 0,
-('Q', 'Q') : 4, ('N', 'A') : 0, ('Z', 'Y') : -5, ('W', 'R') : 3,
-('Q', 'A') : 0, ('S', 'D') : 0, ('H', 'H') : 7, ('S', 'H') : -1,
-('H', 'D') : 1, ('L', 'N') : -3, ('W', 'A') : -6, ('Y', 'M') : -2,
-('G', 'R') : -2, ('Y', 'I') : -1, ('Y', 'E') : -5, ('B', 'Y') : -4,
-('Y', 'A') : -4, ('V', 'D') : -2, ('B', 'S') : 1, ('Y', 'Y') : 12,
-('G', 'N') : 1, ('E', 'C') : -6, ('Y', 'Q') : -4, ('Z', 'Z') : 3,
-('V', 'A') : 0, ('C', 'C') : 15, ('M', 'R') : 0, ('V', 'E') : -2,
-('T', 'N') : 0, ('P', 'P') : 6, ('V', 'I') : 4, ('V', 'S') : -1,
-('Z', 'P') : 0, ('V', 'M') : 2, ('T', 'F') : -3, ('V', 'Q') : -2,
-('K', 'K') : 5, ('P', 'D') : -1, ('I', 'H') : -2, ('I', 'D') : -2,
-('T', 'R') : -1, ('P', 'L') : -3, ('K', 'G') : -2, ('M', 'N') : -2,
-('P', 'H') : 0, ('F', 'Q') : -5, ('Z', 'G') : 0, ('X', 'L') : -1,
-('T', 'M') : -1, ('Z', 'C') : -6, ('X', 'H') : 0, ('D', 'R') : -1,
-('B', 'W') : -6, ('X', 'D') : -1, ('Z', 'K') : 1, ('F', 'A') : -4,
-('Z', 'W') : -6, ('F', 'E') : -6, ('D', 'N') : 2, ('B', 'K') : 1,
-('X', 'X') : -1, ('F', 'I') : 1, ('B', 'G') : 1, ('X', 'T') : 0,
-('F', 'M') : 1, ('B', 'C') : -5, ('Z', 'I') : -2, ('Z', 'V') : -2,
-('S', 'S') : 1, ('L', 'Q') : -2, ('W', 'E') : -8, ('Q', 'R') : 2,
-('N', 'N') : 2, ('W', 'M') : -5, ('Q', 'C') : -6, ('W', 'I') : -6,
-('S', 'C') : 0, ('L', 'A') : -2, ('S', 'G') : 1, ('L', 'E') : -4,
-('W', 'Q') : -5, ('H', 'G') : -2, ('S', 'K') : 0, ('Q', 'N') : 1,
-('N', 'R') : 0, ('H', 'C') : -4, ('Y', 'N') : -2, ('G', 'Q') : -1,
-('Y', 'F') : 9, ('C', 'A') : -2, ('V', 'L') : 2, ('G', 'E') : 0,
-('G', 'A') : 2, ('K', 'R') : 4, ('E', 'D') : 4, ('Y', 'R') : -5,
-('M', 'Q') : -1, ('T', 'I') : 0, ('C', 'D') : -6, ('V', 'F') : -1,
-('T', 'A') : 1, ('T', 'P') : 1, ('B', 'P') : 0, ('T', 'E') : 0,
-('V', 'N') : -2, ('P', 'G') : 0, ('M', 'A') : -1, ('K', 'H') : 0,
-('V', 'R') : -3, ('P', 'C') : -3, ('M', 'E') : -2, ('K', 'L') : -3,
-('V', 'V') : 5, ('M', 'I') : 3, ('T', 'Q') : -1, ('I', 'G') : -3,
-('P', 'K') : -1, ('M', 'M') : 6, ('K', 'D') : 0, ('I', 'C') : -3,
-('Z', 'D') : 3, ('F', 'R') : -5, ('X', 'K') : -1, ('Q', 'D') : 2,
-('X', 'G') : -1, ('Z', 'L') : -3, ('X', 'C') : -3, ('Z', 'H') : 2,
-('B', 'L') : -4, ('B', 'H') : 1, ('F', 'F') : 11, ('X', 'W') : -4,
-('B', 'D') : 3, ('D', 'A') : 0, ('S', 'L') : -3, ('X', 'S') : 0,
-('F', 'N') : -4, ('S', 'R') : 0, ('W', 'D') : -7, ('V', 'Y') : -3,
-('W', 'L') : -2, ('H', 'R') : 2, ('W', 'H') : -3, ('H', 'N') : 2,
-('W', 'T') : -6, ('T', 'T') : 2, ('S', 'F') : -4, ('W', 'P') : -6,
-('L', 'D') : -4, ('B', 'I') : -2, ('L', 'H') : -2, ('S', 'N') : 1,
-('B', 'T') : 0, ('L', 'L') : 7, ('Y', 'K') : -5, ('E', 'Q') : 3,
-('Y', 'G') : -6, ('Z', 'S') : 0, ('Y', 'C') : 1, ('G', 'D') : 1,
-('B', 'V') : -2, ('E', 'A') : 0, ('Y', 'W') : 0, ('E', 'E') : 4,
-('Y', 'S') : -3, ('C', 'N') : -4, ('V', 'C') : -2, ('T', 'H') : -1,
-('P', 'R') : 0, ('V', 'G') : -1, ('T', 'L') : -2, ('V', 'K') : -2,
-('K', 'Q') : 1, ('R', 'A') : -1, ('I', 'R') : -2, ('T', 'D') : 0,
-('P', 'F') : -5, ('I', 'N') : -2, ('K', 'I') : -2, ('M', 'D') : -3,
-('V', 'W') : -7, ('W', 'W') : 22, ('M', 'H') : -2, ('P', 'N') : 0,
-('K', 'A') : -1, ('M', 'L') : 4, ('K', 'E') : 0, ('Z', 'E') : 3,
-('X', 'N') : 0, ('Z', 'A') : 0, ('Z', 'M') : -2, ('X', 'F') : -2,
-('K', 'C') : -6, ('B', 'Q') : 2, ('X', 'B') : 0, ('B', 'M') : -2,
-('F', 'C') : -5, ('Z', 'Q') : 3, ('X', 'Z') : -1, ('F', 'G') : -5,
-('B', 'E') : 3, ('X', 'V') : 0, ('F', 'K') : -6, ('B', 'A') : 0,
-('X', 'R') : -1, ('D', 'D') : 4, ('W', 'G') : -8, ('Z', 'F') : -5,
-('S', 'Q') : 0, ('W', 'C') : -9, ('W', 'K') : -4, ('H', 'Q') : 3,
-('L', 'C') : -7, ('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -4,
-('W', 'S') : -3, ('S', 'E') : 0, ('H', 'E') : 1, ('S', 'I') : -1,
-('H', 'A') : -1, ('S', 'M') : -2, ('Y', 'L') : 0, ('Y', 'H') : 0,
-('Y', 'D') : -5, ('E', 'R') : -1, ('X', 'P') : -1, ('G', 'G') : 5,
-('G', 'C') : -4, ('E', 'N') : 2, ('Y', 'T') : -3, ('Y', 'P') : -5,
-('T', 'K') : 0, ('A', 'A') : 2, ('P', 'Q') : 0, ('T', 'C') : -2,
-('V', 'H') : -2, ('T', 'G') : 0, ('I', 'Q') : -2, ('Z', 'T') : 0,
-('C', 'R') : -4, ('V', 'P') : -1, ('P', 'E') : 0, ('M', 'C') : -6,
-('K', 'N') : 1, ('I', 'I') : 5, ('P', 'A') : 1, ('M', 'G') : -3,
-('T', 'S') : 1, ('I', 'E') : -2, ('P', 'M') : -2, ('M', 'K') : 0,
-('I', 'A') : 0, ('P', 'I') : -2, ('R', 'R') : 7, ('X', 'M') : -1,
-('L', 'I') : 3, ('X', 'I') : -1, ('Z', 'B') : 2, ('X', 'E') : -1,
-('Z', 'N') : 1, ('X', 'A') : 0, ('B', 'R') : 0, ('B', 'N') : 2,
-('F', 'D') : -6, ('X', 'Y') : -2, ('Z', 'R') : 0, ('F', 'H') : -2,
-('B', 'F') : -5, ('F', 'L') : 3, ('X', 'Q') : 0, ('B', 'B') : 3
+('W', 'F'): 1, ('L', 'R'): -3, ('S', 'P'): 1, ('V', 'T'): 0,
+('Q', 'Q'): 4, ('N', 'A'): 0, ('Z', 'Y'): -5, ('W', 'R'): 3,
+('Q', 'A'): 0, ('S', 'D'): 0, ('H', 'H'): 7, ('S', 'H'): -1,
+('H', 'D'): 1, ('L', 'N'): -3, ('W', 'A'): -6, ('Y', 'M'): -2,
+('G', 'R'): -2, ('Y', 'I'): -1, ('Y', 'E'): -5, ('B', 'Y'): -4,
+('Y', 'A'): -4, ('V', 'D'): -2, ('B', 'S'): 1, ('Y', 'Y'): 12,
+('G', 'N'): 1, ('E', 'C'): -6, ('Y', 'Q'): -4, ('Z', 'Z'): 3,
+('V', 'A'): 0, ('C', 'C'): 15, ('M', 'R'): 0, ('V', 'E'): -2,
+('T', 'N'): 0, ('P', 'P'): 6, ('V', 'I'): 4, ('V', 'S'): -1,
+('Z', 'P'): 0, ('V', 'M'): 2, ('T', 'F'): -3, ('V', 'Q'): -2,
+('K', 'K'): 5, ('P', 'D'): -1, ('I', 'H'): -2, ('I', 'D'): -2,
+('T', 'R'): -1, ('P', 'L'): -3, ('K', 'G'): -2, ('M', 'N'): -2,
+('P', 'H'): 0, ('F', 'Q'): -5, ('Z', 'G'): 0, ('X', 'L'): -1,
+('T', 'M'): -1, ('Z', 'C'): -6, ('X', 'H'): 0, ('D', 'R'): -1,
+('B', 'W'): -6, ('X', 'D'): -1, ('Z', 'K'): 1, ('F', 'A'): -4,
+('Z', 'W'): -6, ('F', 'E'): -6, ('D', 'N'): 2, ('B', 'K'): 1,
+('X', 'X'): -1, ('F', 'I'): 1, ('B', 'G'): 1, ('X', 'T'): 0,
+('F', 'M'): 1, ('B', 'C'): -5, ('Z', 'I'): -2, ('Z', 'V'): -2,
+('S', 'S'): 1, ('L', 'Q'): -2, ('W', 'E'): -8, ('Q', 'R'): 2,
+('N', 'N'): 2, ('W', 'M'): -5, ('Q', 'C'): -6, ('W', 'I'): -6,
+('S', 'C'): 0, ('L', 'A'): -2, ('S', 'G'): 1, ('L', 'E'): -4,
+('W', 'Q'): -5, ('H', 'G'): -2, ('S', 'K'): 0, ('Q', 'N'): 1,
+('N', 'R'): 0, ('H', 'C'): -4, ('Y', 'N'): -2, ('G', 'Q'): -1,
+('Y', 'F'): 9, ('C', 'A'): -2, ('V', 'L'): 2, ('G', 'E'): 0,
+('G', 'A'): 2, ('K', 'R'): 4, ('E', 'D'): 4, ('Y', 'R'): -5,
+('M', 'Q'): -1, ('T', 'I'): 0, ('C', 'D'): -6, ('V', 'F'): -1,
+('T', 'A'): 1, ('T', 'P'): 1, ('B', 'P'): 0, ('T', 'E'): 0,
+('V', 'N'): -2, ('P', 'G'): 0, ('M', 'A'): -1, ('K', 'H'): 0,
+('V', 'R'): -3, ('P', 'C'): -3, ('M', 'E'): -2, ('K', 'L'): -3,
+('V', 'V'): 5, ('M', 'I'): 3, ('T', 'Q'): -1, ('I', 'G'): -3,
+('P', 'K'): -1, ('M', 'M'): 6, ('K', 'D'): 0, ('I', 'C'): -3,
+('Z', 'D'): 3, ('F', 'R'): -5, ('X', 'K'): -1, ('Q', 'D'): 2,
+('X', 'G'): -1, ('Z', 'L'): -3, ('X', 'C'): -3, ('Z', 'H'): 2,
+('B', 'L'): -4, ('B', 'H'): 1, ('F', 'F'): 11, ('X', 'W'): -4,
+('B', 'D'): 3, ('D', 'A'): 0, ('S', 'L'): -3, ('X', 'S'): 0,
+('F', 'N'): -4, ('S', 'R'): 0, ('W', 'D'): -7, ('V', 'Y'): -3,
+('W', 'L'): -2, ('H', 'R'): 2, ('W', 'H'): -3, ('H', 'N'): 2,
+('W', 'T'): -6, ('T', 'T'): 2, ('S', 'F'): -4, ('W', 'P'): -6,
+('L', 'D'): -4, ('B', 'I'): -2, ('L', 'H'): -2, ('S', 'N'): 1,
+('B', 'T'): 0, ('L', 'L'): 7, ('Y', 'K'): -5, ('E', 'Q'): 3,
+('Y', 'G'): -6, ('Z', 'S'): 0, ('Y', 'C'): 1, ('G', 'D'): 1,
+('B', 'V'): -2, ('E', 'A'): 0, ('Y', 'W'): 0, ('E', 'E'): 4,
+('Y', 'S'): -3, ('C', 'N'): -4, ('V', 'C'): -2, ('T', 'H'): -1,
+('P', 'R'): 0, ('V', 'G'): -1, ('T', 'L'): -2, ('V', 'K'): -2,
+('K', 'Q'): 1, ('R', 'A'): -1, ('I', 'R'): -2, ('T', 'D'): 0,
+('P', 'F'): -5, ('I', 'N'): -2, ('K', 'I'): -2, ('M', 'D'): -3,
+('V', 'W'): -7, ('W', 'W'): 22, ('M', 'H'): -2, ('P', 'N'): 0,
+('K', 'A'): -1, ('M', 'L'): 4, ('K', 'E'): 0, ('Z', 'E'): 3,
+('X', 'N'): 0, ('Z', 'A'): 0, ('Z', 'M'): -2, ('X', 'F'): -2,
+('K', 'C'): -6, ('B', 'Q'): 2, ('X', 'B'): 0, ('B', 'M'): -2,
+('F', 'C'): -5, ('Z', 'Q'): 3, ('X', 'Z'): -1, ('F', 'G'): -5,
+('B', 'E'): 3, ('X', 'V'): 0, ('F', 'K'): -6, ('B', 'A'): 0,
+('X', 'R'): -1, ('D', 'D'): 4, ('W', 'G'): -8, ('Z', 'F'): -5,
+('S', 'Q'): 0, ('W', 'C'): -9, ('W', 'K'): -4, ('H', 'Q'): 3,
+('L', 'C'): -7, ('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -4,
+('W', 'S'): -3, ('S', 'E'): 0, ('H', 'E'): 1, ('S', 'I'): -1,
+('H', 'A'): -1, ('S', 'M'): -2, ('Y', 'L'): 0, ('Y', 'H'): 0,
+('Y', 'D'): -5, ('E', 'R'): -1, ('X', 'P'): -1, ('G', 'G'): 5,
+('G', 'C'): -4, ('E', 'N'): 2, ('Y', 'T'): -3, ('Y', 'P'): -5,
+('T', 'K'): 0, ('A', 'A'): 2, ('P', 'Q'): 0, ('T', 'C'): -2,
+('V', 'H'): -2, ('T', 'G'): 0, ('I', 'Q'): -2, ('Z', 'T'): 0,
+('C', 'R'): -4, ('V', 'P'): -1, ('P', 'E'): 0, ('M', 'C'): -6,
+('K', 'N'): 1, ('I', 'I'): 5, ('P', 'A'): 1, ('M', 'G'): -3,
+('T', 'S'): 1, ('I', 'E'): -2, ('P', 'M'): -2, ('M', 'K'): 0,
+('I', 'A'): 0, ('P', 'I'): -2, ('R', 'R'): 7, ('X', 'M'): -1,
+('L', 'I'): 3, ('X', 'I'): -1, ('Z', 'B'): 2, ('X', 'E'): -1,
+('Z', 'N'): 1, ('X', 'A'): 0, ('B', 'R'): 0, ('B', 'N'): 2,
+('F', 'D'): -6, ('X', 'Y'): -2, ('Z', 'R'): 0, ('F', 'H'): -2,
+('B', 'F'): -5, ('F', 'L'): 3, ('X', 'Q'): 0, ('B', 'B'): 3
 }
 pam300 = _temp()
 del _temp
@@ -2510,75 +2509,75 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam60.cmp
 def _temp():
     return {
-('W', 'F') : -3, ('L', 'R') : -6, ('S', 'P') : 0, ('V', 'T') : -1,
-('Q', 'Q') : 7, ('N', 'A') : -2, ('Z', 'Y') : -7, ('W', 'R') : 0,
-('Q', 'A') : -3, ('S', 'D') : -2, ('H', 'H') : 8, ('S', 'H') : -4,
-('H', 'D') : -2, ('L', 'N') : -5, ('W', 'A') : -10, ('Y', 'M') : -7,
-('G', 'R') : -7, ('Y', 'I') : -4, ('Y', 'E') : -7, ('B', 'Y') : -5,
-('Y', 'A') : -6, ('V', 'D') : -6, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : -1, ('E', 'C') : -10, ('Y', 'Q') : -8, ('Z', 'Z') : 5,
-('V', 'A') : -1, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -4,
-('T', 'N') : -1, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -4,
-('Z', 'P') : -2, ('V', 'M') : 0, ('T', 'F') : -6, ('V', 'Q') : -5,
-('K', 'K') : 6, ('P', 'D') : -5, ('I', 'H') : -6, ('I', 'D') : -5,
-('T', 'R') : -4, ('P', 'L') : -5, ('K', 'G') : -5, ('M', 'N') : -6,
-('P', 'H') : -2, ('F', 'Q') : -9, ('Z', 'G') : -3, ('X', 'L') : -4,
-('T', 'M') : -2, ('Z', 'C') : -10, ('X', 'H') : -3, ('D', 'R') : -6,
-('B', 'W') : -8, ('X', 'D') : -3, ('Z', 'K') : -2, ('F', 'A') : -6,
-('Z', 'W') : -11, ('F', 'E') : -10, ('D', 'N') : 2, ('B', 'K') : -1,
-('X', 'X') : -3, ('F', 'I') : -1, ('B', 'G') : -2, ('X', 'T') : -2,
-('F', 'M') : -2, ('B', 'C') : -9, ('Z', 'I') : -4, ('Z', 'V') : -5,
-('S', 'S') : 5, ('L', 'Q') : -3, ('W', 'E') : -12, ('Q', 'R') : 0,
-('N', 'N') : 6, ('W', 'M') : -9, ('Q', 'C') : -10, ('W', 'I') : -10,
-('S', 'C') : -1, ('L', 'A') : -4, ('S', 'G') : 0, ('L', 'E') : -7,
-('W', 'Q') : -9, ('H', 'G') : -6, ('S', 'K') : -2, ('Q', 'N') : -2,
-('N', 'R') : -3, ('H', 'C') : -6, ('Y', 'N') : -3, ('G', 'Q') : -5,
-('Y', 'F') : 3, ('C', 'A') : -5, ('V', 'L') : -1, ('G', 'E') : -2,
-('G', 'A') : 0, ('K', 'R') : 2, ('E', 'D') : 3, ('Y', 'R') : -8,
-('M', 'Q') : -2, ('T', 'I') : -1, ('C', 'D') : -10, ('V', 'F') : -5,
-('T', 'A') : 1, ('T', 'P') : -2, ('B', 'P') : -4, ('T', 'E') : -4,
-('V', 'N') : -5, ('P', 'G') : -4, ('M', 'A') : -3, ('K', 'H') : -4,
-('V', 'R') : -5, ('P', 'C') : -6, ('M', 'E') : -5, ('K', 'L') : -6,
-('V', 'V') : 6, ('M', 'I') : 1, ('T', 'Q') : -4, ('I', 'G') : -7,
-('P', 'K') : -4, ('M', 'M') : 10, ('K', 'D') : -2, ('I', 'C') : -4,
-('Z', 'D') : 2, ('F', 'R') : -7, ('X', 'K') : -3, ('Q', 'D') : -1,
-('X', 'G') : -3, ('Z', 'L') : -5, ('X', 'C') : -6, ('Z', 'H') : 0,
-('B', 'L') : -7, ('B', 'H') : 0, ('F', 'F') : 8, ('X', 'W') : -8,
-('B', 'D') : 5, ('D', 'A') : -2, ('S', 'L') : -6, ('X', 'S') : -2,
-('F', 'N') : -6, ('S', 'R') : -2, ('W', 'D') : -11, ('V', 'Y') : -5,
-('W', 'L') : -4, ('H', 'R') : 0, ('W', 'H') : -5, ('H', 'N') : 1,
-('W', 'T') : -9, ('T', 'T') : 6, ('S', 'F') : -5, ('W', 'P') : -10,
-('L', 'D') : -9, ('B', 'I') : -4, ('L', 'H') : -4, ('S', 'N') : 1,
-('B', 'T') : -2, ('L', 'L') : 6, ('Y', 'K') : -7, ('E', 'Q') : 2,
-('Y', 'G') : -10, ('Z', 'S') : -3, ('Y', 'C') : -2, ('G', 'D') : -2,
-('B', 'V') : -5, ('E', 'A') : -1, ('Y', 'W') : -3, ('E', 'E') : 7,
-('Y', 'S') : -5, ('C', 'N') : -7, ('V', 'C') : -4, ('T', 'H') : -5,
-('P', 'R') : -2, ('V', 'G') : -4, ('T', 'L') : -5, ('V', 'K') : -6,
-('K', 'Q') : -1, ('R', 'A') : -5, ('I', 'R') : -4, ('T', 'D') : -3,
-('P', 'F') : -7, ('I', 'N') : -4, ('K', 'I') : -4, ('M', 'D') : -7,
-('V', 'W') : -11, ('W', 'W') : 13, ('M', 'H') : -7, ('P', 'N') : -4,
-('K', 'A') : -5, ('M', 'L') : 2, ('K', 'E') : -3, ('Z', 'E') : 5,
-('X', 'N') : -2, ('Z', 'A') : -2, ('Z', 'M') : -4, ('X', 'F') : -5,
-('K', 'C') : -10, ('B', 'Q') : -1, ('X', 'B') : -3, ('B', 'M') : -6,
-('F', 'C') : -9, ('Z', 'Q') : 6, ('X', 'Z') : -3, ('F', 'G') : -7,
-('B', 'E') : 2, ('X', 'V') : -3, ('F', 'K') : -10, ('B', 'A') : -2,
-('X', 'R') : -4, ('D', 'D') : 7, ('W', 'G') : -11, ('Z', 'F') : -10,
-('S', 'Q') : -3, ('W', 'C') : -12, ('W', 'K') : -8, ('H', 'Q') : 2,
-('L', 'C') : -11, ('W', 'N') : -6, ('S', 'A') : 1, ('L', 'G') : -8,
-('W', 'S') : -4, ('S', 'E') : -2, ('H', 'E') : -3, ('S', 'I') : -4,
-('H', 'A') : -5, ('S', 'M') : -4, ('Y', 'L') : -5, ('Y', 'H') : -2,
-('Y', 'D') : -8, ('E', 'R') : -6, ('X', 'P') : -3, ('G', 'G') : 6,
-('G', 'C') : -7, ('E', 'N') : 0, ('Y', 'T') : -5, ('Y', 'P') : -10,
-('T', 'K') : -2, ('A', 'A') : 5, ('P', 'Q') : -1, ('T', 'C') : -5,
-('V', 'H') : -5, ('T', 'G') : -3, ('I', 'Q') : -5, ('Z', 'T') : -4,
-('C', 'R') : -6, ('V', 'P') : -4, ('P', 'E') : -3, ('M', 'C') : -10,
-('K', 'N') : 0, ('I', 'I') : 7, ('P', 'A') : 0, ('M', 'G') : -6,
-('T', 'S') : 1, ('I', 'E') : -4, ('P', 'M') : -6, ('M', 'K') : 0,
-('I', 'A') : -3, ('P', 'I') : -6, ('R', 'R') : 8, ('X', 'M') : -3,
-('L', 'I') : 0, ('X', 'I') : -3, ('Z', 'B') : 1, ('X', 'E') : -3,
-('Z', 'N') : -1, ('X', 'A') : -2, ('B', 'R') : -5, ('B', 'N') : 5,
-('F', 'D') : -11, ('X', 'Y') : -5, ('Z', 'R') : -2, ('F', 'H') : -4,
-('B', 'F') : -8, ('F', 'L') : -1, ('X', 'Q') : -3, ('B', 'B') : 5
+('W', 'F'): -3, ('L', 'R'): -6, ('S', 'P'): 0, ('V', 'T'): -1,
+('Q', 'Q'): 7, ('N', 'A'): -2, ('Z', 'Y'): -7, ('W', 'R'): 0,
+('Q', 'A'): -3, ('S', 'D'): -2, ('H', 'H'): 8, ('S', 'H'): -4,
+('H', 'D'): -2, ('L', 'N'): -5, ('W', 'A'): -10, ('Y', 'M'): -7,
+('G', 'R'): -7, ('Y', 'I'): -4, ('Y', 'E'): -7, ('B', 'Y'): -5,
+('Y', 'A'): -6, ('V', 'D'): -6, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): -1, ('E', 'C'): -10, ('Y', 'Q'): -8, ('Z', 'Z'): 5,
+('V', 'A'): -1, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -4,
+('T', 'N'): -1, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -4,
+('Z', 'P'): -2, ('V', 'M'): 0, ('T', 'F'): -6, ('V', 'Q'): -5,
+('K', 'K'): 6, ('P', 'D'): -5, ('I', 'H'): -6, ('I', 'D'): -5,
+('T', 'R'): -4, ('P', 'L'): -5, ('K', 'G'): -5, ('M', 'N'): -6,
+('P', 'H'): -2, ('F', 'Q'): -9, ('Z', 'G'): -3, ('X', 'L'): -4,
+('T', 'M'): -2, ('Z', 'C'): -10, ('X', 'H'): -3, ('D', 'R'): -6,
+('B', 'W'): -8, ('X', 'D'): -3, ('Z', 'K'): -2, ('F', 'A'): -6,
+('Z', 'W'): -11, ('F', 'E'): -10, ('D', 'N'): 2, ('B', 'K'): -1,
+('X', 'X'): -3, ('F', 'I'): -1, ('B', 'G'): -2, ('X', 'T'): -2,
+('F', 'M'): -2, ('B', 'C'): -9, ('Z', 'I'): -4, ('Z', 'V'): -5,
+('S', 'S'): 5, ('L', 'Q'): -3, ('W', 'E'): -12, ('Q', 'R'): 0,
+('N', 'N'): 6, ('W', 'M'): -9, ('Q', 'C'): -10, ('W', 'I'): -10,
+('S', 'C'): -1, ('L', 'A'): -4, ('S', 'G'): 0, ('L', 'E'): -7,
+('W', 'Q'): -9, ('H', 'G'): -6, ('S', 'K'): -2, ('Q', 'N'): -2,
+('N', 'R'): -3, ('H', 'C'): -6, ('Y', 'N'): -3, ('G', 'Q'): -5,
+('Y', 'F'): 3, ('C', 'A'): -5, ('V', 'L'): -1, ('G', 'E'): -2,
+('G', 'A'): 0, ('K', 'R'): 2, ('E', 'D'): 3, ('Y', 'R'): -8,
+('M', 'Q'): -2, ('T', 'I'): -1, ('C', 'D'): -10, ('V', 'F'): -5,
+('T', 'A'): 1, ('T', 'P'): -2, ('B', 'P'): -4, ('T', 'E'): -4,
+('V', 'N'): -5, ('P', 'G'): -4, ('M', 'A'): -3, ('K', 'H'): -4,
+('V', 'R'): -5, ('P', 'C'): -6, ('M', 'E'): -5, ('K', 'L'): -6,
+('V', 'V'): 6, ('M', 'I'): 1, ('T', 'Q'): -4, ('I', 'G'): -7,
+('P', 'K'): -4, ('M', 'M'): 10, ('K', 'D'): -2, ('I', 'C'): -4,
+('Z', 'D'): 2, ('F', 'R'): -7, ('X', 'K'): -3, ('Q', 'D'): -1,
+('X', 'G'): -3, ('Z', 'L'): -5, ('X', 'C'): -6, ('Z', 'H'): 0,
+('B', 'L'): -7, ('B', 'H'): 0, ('F', 'F'): 8, ('X', 'W'): -8,
+('B', 'D'): 5, ('D', 'A'): -2, ('S', 'L'): -6, ('X', 'S'): -2,
+('F', 'N'): -6, ('S', 'R'): -2, ('W', 'D'): -11, ('V', 'Y'): -5,
+('W', 'L'): -4, ('H', 'R'): 0, ('W', 'H'): -5, ('H', 'N'): 1,
+('W', 'T'): -9, ('T', 'T'): 6, ('S', 'F'): -5, ('W', 'P'): -10,
+('L', 'D'): -9, ('B', 'I'): -4, ('L', 'H'): -4, ('S', 'N'): 1,
+('B', 'T'): -2, ('L', 'L'): 6, ('Y', 'K'): -7, ('E', 'Q'): 2,
+('Y', 'G'): -10, ('Z', 'S'): -3, ('Y', 'C'): -2, ('G', 'D'): -2,
+('B', 'V'): -5, ('E', 'A'): -1, ('Y', 'W'): -3, ('E', 'E'): 7,
+('Y', 'S'): -5, ('C', 'N'): -7, ('V', 'C'): -4, ('T', 'H'): -5,
+('P', 'R'): -2, ('V', 'G'): -4, ('T', 'L'): -5, ('V', 'K'): -6,
+('K', 'Q'): -1, ('R', 'A'): -5, ('I', 'R'): -4, ('T', 'D'): -3,
+('P', 'F'): -7, ('I', 'N'): -4, ('K', 'I'): -4, ('M', 'D'): -7,
+('V', 'W'): -11, ('W', 'W'): 13, ('M', 'H'): -7, ('P', 'N'): -4,
+('K', 'A'): -5, ('M', 'L'): 2, ('K', 'E'): -3, ('Z', 'E'): 5,
+('X', 'N'): -2, ('Z', 'A'): -2, ('Z', 'M'): -4, ('X', 'F'): -5,
+('K', 'C'): -10, ('B', 'Q'): -1, ('X', 'B'): -3, ('B', 'M'): -6,
+('F', 'C'): -9, ('Z', 'Q'): 6, ('X', 'Z'): -3, ('F', 'G'): -7,
+('B', 'E'): 2, ('X', 'V'): -3, ('F', 'K'): -10, ('B', 'A'): -2,
+('X', 'R'): -4, ('D', 'D'): 7, ('W', 'G'): -11, ('Z', 'F'): -10,
+('S', 'Q'): -3, ('W', 'C'): -12, ('W', 'K'): -8, ('H', 'Q'): 2,
+('L', 'C'): -11, ('W', 'N'): -6, ('S', 'A'): 1, ('L', 'G'): -8,
+('W', 'S'): -4, ('S', 'E'): -2, ('H', 'E'): -3, ('S', 'I'): -4,
+('H', 'A'): -5, ('S', 'M'): -4, ('Y', 'L'): -5, ('Y', 'H'): -2,
+('Y', 'D'): -8, ('E', 'R'): -6, ('X', 'P'): -3, ('G', 'G'): 6,
+('G', 'C'): -7, ('E', 'N'): 0, ('Y', 'T'): -5, ('Y', 'P'): -10,
+('T', 'K'): -2, ('A', 'A'): 5, ('P', 'Q'): -1, ('T', 'C'): -5,
+('V', 'H'): -5, ('T', 'G'): -3, ('I', 'Q'): -5, ('Z', 'T'): -4,
+('C', 'R'): -6, ('V', 'P'): -4, ('P', 'E'): -3, ('M', 'C'): -10,
+('K', 'N'): 0, ('I', 'I'): 7, ('P', 'A'): 0, ('M', 'G'): -6,
+('T', 'S'): 1, ('I', 'E'): -4, ('P', 'M'): -6, ('M', 'K'): 0,
+('I', 'A'): -3, ('P', 'I'): -6, ('R', 'R'): 8, ('X', 'M'): -3,
+('L', 'I'): 0, ('X', 'I'): -3, ('Z', 'B'): 1, ('X', 'E'): -3,
+('Z', 'N'): -1, ('X', 'A'): -2, ('B', 'R'): -5, ('B', 'N'): 5,
+('F', 'D'): -11, ('X', 'Y'): -5, ('Z', 'R'): -2, ('F', 'H'): -4,
+('B', 'F'): -8, ('F', 'L'): -1, ('X', 'Q'): -3, ('B', 'B'): 5
 }
 pam60 = _temp()
 del _temp
@@ -2587,70 +2586,70 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/pam90.cmp
 def _temp():
     return {
-('W', 'F') : -2, ('L', 'R') : -5, ('S', 'P') : 0, ('V', 'T') : -1,
-('Q', 'Q') : 6, ('N', 'A') : -1, ('Z', 'Y') : -6, ('W', 'R') : 0,
-('Q', 'A') : -2, ('S', 'D') : -1, ('H', 'H') : 8, ('S', 'H') : -3,
-('H', 'D') : -1, ('L', 'N') : -4, ('W', 'A') : -8, ('Y', 'M') : -6,
-('G', 'R') : -5, ('Y', 'I') : -3, ('Y', 'E') : -6, ('B', 'Y') : -4,
-('Y', 'A') : -5, ('V', 'D') : -4, ('B', 'S') : 0, ('Y', 'Y') : 9,
-('G', 'N') : -1, ('E', 'C') : -8, ('Y', 'Q') : -6, ('Z', 'Z') : 5,
-('V', 'A') : 0, ('C', 'C') : 9, ('M', 'R') : -2, ('V', 'E') : -3,
-('T', 'N') : 0, ('P', 'P') : 7, ('V', 'I') : 3, ('V', 'S') : -3,
-('Z', 'P') : -2, ('V', 'M') : 1, ('T', 'F') : -5, ('V', 'Q') : -4,
-('K', 'K') : 5, ('P', 'D') : -4, ('I', 'H') : -5, ('I', 'D') : -4,
-('T', 'R') : -3, ('P', 'L') : -4, ('K', 'G') : -4, ('M', 'N') : -4,
-('P', 'H') : -2, ('F', 'Q') : -7, ('Z', 'G') : -2, ('T', 'M') : -2,
-('Z', 'C') : -8, ('D', 'R') : -5, ('B', 'W') : -7, ('Z', 'K') : -1,
-('F', 'A') : -5, ('Z', 'W') : -8, ('F', 'E') : -8, ('D', 'N') : 3,
-('B', 'K') : 0, ('F', 'I') : 0, ('B', 'G') : -1, ('F', 'M') : -1,
-('B', 'C') : -7, ('Z', 'I') : -3, ('Z', 'V') : -3, ('S', 'S') : 4,
-('L', 'Q') : -3, ('W', 'E') : -10, ('Q', 'R') : 0, ('N', 'N') : 5,
-('W', 'M') : -7, ('Q', 'C') : -8, ('W', 'I') : -8, ('S', 'C') : -1,
-('L', 'A') : -3, ('S', 'G') : 0, ('L', 'E') : -5, ('W', 'Q') : -7,
-('H', 'G') : -5, ('S', 'K') : -1, ('L', 'I') : 1, ('N', 'R') : -2,
-('H', 'C') : -5, ('Y', 'N') : -2, ('G', 'Q') : -3, ('Y', 'F') : 4,
-('C', 'A') : -3, ('V', 'L') : 0, ('G', 'E') : -1, ('G', 'A') : 0,
-('K', 'R') : 2, ('E', 'D') : 4, ('Y', 'R') : -6, ('M', 'Q') : -2,
-('T', 'I') : 0, ('C', 'D') : -8, ('V', 'F') : -4, ('T', 'A') : 1,
-('T', 'P') : -1, ('B', 'P') : -3, ('T', 'E') : -2, ('V', 'N') : -4,
-('P', 'G') : -3, ('M', 'A') : -2, ('K', 'H') : -2, ('V', 'R') : -4,
-('P', 'C') : -5, ('M', 'E') : -4, ('K', 'L') : -5, ('V', 'V') : 6,
-('M', 'I') : 1, ('T', 'Q') : -3, ('I', 'G') : -5, ('P', 'K') : -3,
-('M', 'M') : 9, ('K', 'D') : -2, ('I', 'C') : -3, ('Z', 'D') : 3,
-('F', 'R') : -6, ('Q', 'D') : 0, ('Z', 'L') : -4, ('Z', 'H') : 1,
-('B', 'L') : -5, ('B', 'H') : 1, ('F', 'F') : 8, ('B', 'D') : 5,
-('D', 'A') : -1, ('S', 'L') : -5, ('F', 'N') : -5, ('S', 'R') : -1,
-('W', 'D') : -9, ('W', 'L') : -3, ('H', 'R') : 1, ('W', 'H') : -4,
-('H', 'N') : 2, ('W', 'T') : -7, ('T', 'T') : 5, ('S', 'F') : -4,
-('W', 'P') : -8, ('L', 'D') : -7, ('B', 'I') : -3, ('L', 'H') : -3,
-('S', 'N') : 1, ('B', 'T') : -1, ('L', 'L') : 6, ('Y', 'K') : -6,
-('E', 'Q') : 2, ('Y', 'G') : -8, ('Z', 'S') : -2, ('Y', 'C') : -1,
-('G', 'D') : -1, ('B', 'V') : -4, ('E', 'A') : 0, ('Y', 'W') : -2,
-('E', 'E') : 6, ('Y', 'S') : -4, ('C', 'N') : -6, ('V', 'C') : -3,
-('T', 'H') : -3, ('P', 'R') : -1, ('V', 'G') : -3, ('T', 'L') : -3,
-('V', 'K') : -5, ('K', 'Q') : -1, ('R', 'A') : -4, ('I', 'R') : -3,
-('T', 'D') : -2, ('P', 'F') : -6, ('I', 'N') : -3, ('K', 'I') : -3,
-('M', 'D') : -5, ('V', 'W') : -9, ('W', 'W') : 13, ('M', 'H') : -5,
-('P', 'N') : -2, ('K', 'A') : -3, ('M', 'L') : 2, ('K', 'E') : -2,
-('Z', 'E') : 5, ('Q', 'N') : -1, ('Z', 'A') : -1, ('Z', 'M') : -3,
-('K', 'C') : -8, ('B', 'Q') : 0, ('B', 'M') : -5, ('F', 'C') : -7,
-('Z', 'Q') : 5, ('F', 'G') : -6, ('B', 'E') : 2, ('F', 'K') : -8,
-('B', 'A') : -1, ('D', 'D') : 6, ('W', 'G') : -9, ('S', 'Q') : -2,
-('W', 'C') : -10, ('W', 'K') : -6, ('H', 'Q') : 2, ('L', 'C') : -9,
-('W', 'N') : -5, ('S', 'A') : 1, ('L', 'G') : -6, ('W', 'S') : -3,
-('S', 'E') : -2, ('H', 'E') : -1, ('S', 'I') : -3, ('H', 'A') : -4,
-('S', 'M') : -3, ('Y', 'L') : -3, ('Y', 'H') : -1, ('Y', 'D') : -6,
-('E', 'R') : -4, ('G', 'G') : 5, ('G', 'C') : -5, ('E', 'N') : 0,
-('Y', 'T') : -4, ('Y', 'P') : -8, ('T', 'K') : -1, ('A', 'A') : 4,
-('P', 'Q') : -1, ('T', 'C') : -4, ('V', 'H') : -4, ('T', 'G') : -2,
-('I', 'Q') : -4, ('Z', 'T') : -2, ('C', 'R') : -5, ('V', 'P') : -3,
-('P', 'E') : -2, ('M', 'C') : -8, ('K', 'N') : 1, ('I', 'I') : 6,
-('P', 'A') : 0, ('M', 'G') : -5, ('T', 'S') : 2, ('I', 'E') : -3,
-('P', 'M') : -4, ('M', 'K') : 0, ('I', 'A') : -2, ('P', 'I') : -4,
-('R', 'R') : 7, ('Z', 'F') : -8, ('Z', 'B') : 2, ('Z', 'N') : 0,
-('B', 'R') : -3, ('B', 'N') : 4, ('F', 'D') : -8, ('Z', 'R') : -1,
-('F', 'H') : -3, ('B', 'F') : -6, ('F', 'L') : 0, ('V', 'Y') : -4,
-('B', 'B') : 4
+('W', 'F'): -2, ('L', 'R'): -5, ('S', 'P'): 0, ('V', 'T'): -1,
+('Q', 'Q'): 6, ('N', 'A'): -1, ('Z', 'Y'): -6, ('W', 'R'): 0,
+('Q', 'A'): -2, ('S', 'D'): -1, ('H', 'H'): 8, ('S', 'H'): -3,
+('H', 'D'): -1, ('L', 'N'): -4, ('W', 'A'): -8, ('Y', 'M'): -6,
+('G', 'R'): -5, ('Y', 'I'): -3, ('Y', 'E'): -6, ('B', 'Y'): -4,
+('Y', 'A'): -5, ('V', 'D'): -4, ('B', 'S'): 0, ('Y', 'Y'): 9,
+('G', 'N'): -1, ('E', 'C'): -8, ('Y', 'Q'): -6, ('Z', 'Z'): 5,
+('V', 'A'): 0, ('C', 'C'): 9, ('M', 'R'): -2, ('V', 'E'): -3,
+('T', 'N'): 0, ('P', 'P'): 7, ('V', 'I'): 3, ('V', 'S'): -3,
+('Z', 'P'): -2, ('V', 'M'): 1, ('T', 'F'): -5, ('V', 'Q'): -4,
+('K', 'K'): 5, ('P', 'D'): -4, ('I', 'H'): -5, ('I', 'D'): -4,
+('T', 'R'): -3, ('P', 'L'): -4, ('K', 'G'): -4, ('M', 'N'): -4,
+('P', 'H'): -2, ('F', 'Q'): -7, ('Z', 'G'): -2, ('T', 'M'): -2,
+('Z', 'C'): -8, ('D', 'R'): -5, ('B', 'W'): -7, ('Z', 'K'): -1,
+('F', 'A'): -5, ('Z', 'W'): -8, ('F', 'E'): -8, ('D', 'N'): 3,
+('B', 'K'): 0, ('F', 'I'): 0, ('B', 'G'): -1, ('F', 'M'): -1,
+('B', 'C'): -7, ('Z', 'I'): -3, ('Z', 'V'): -3, ('S', 'S'): 4,
+('L', 'Q'): -3, ('W', 'E'): -10, ('Q', 'R'): 0, ('N', 'N'): 5,
+('W', 'M'): -7, ('Q', 'C'): -8, ('W', 'I'): -8, ('S', 'C'): -1,
+('L', 'A'): -3, ('S', 'G'): 0, ('L', 'E'): -5, ('W', 'Q'): -7,
+('H', 'G'): -5, ('S', 'K'): -1, ('L', 'I'): 1, ('N', 'R'): -2,
+('H', 'C'): -5, ('Y', 'N'): -2, ('G', 'Q'): -3, ('Y', 'F'): 4,
+('C', 'A'): -3, ('V', 'L'): 0, ('G', 'E'): -1, ('G', 'A'): 0,
+('K', 'R'): 2, ('E', 'D'): 4, ('Y', 'R'): -6, ('M', 'Q'): -2,
+('T', 'I'): 0, ('C', 'D'): -8, ('V', 'F'): -4, ('T', 'A'): 1,
+('T', 'P'): -1, ('B', 'P'): -3, ('T', 'E'): -2, ('V', 'N'): -4,
+('P', 'G'): -3, ('M', 'A'): -2, ('K', 'H'): -2, ('V', 'R'): -4,
+('P', 'C'): -5, ('M', 'E'): -4, ('K', 'L'): -5, ('V', 'V'): 6,
+('M', 'I'): 1, ('T', 'Q'): -3, ('I', 'G'): -5, ('P', 'K'): -3,
+('M', 'M'): 9, ('K', 'D'): -2, ('I', 'C'): -3, ('Z', 'D'): 3,
+('F', 'R'): -6, ('Q', 'D'): 0, ('Z', 'L'): -4, ('Z', 'H'): 1,
+('B', 'L'): -5, ('B', 'H'): 1, ('F', 'F'): 8, ('B', 'D'): 5,
+('D', 'A'): -1, ('S', 'L'): -5, ('F', 'N'): -5, ('S', 'R'): -1,
+('W', 'D'): -9, ('W', 'L'): -3, ('H', 'R'): 1, ('W', 'H'): -4,
+('H', 'N'): 2, ('W', 'T'): -7, ('T', 'T'): 5, ('S', 'F'): -4,
+('W', 'P'): -8, ('L', 'D'): -7, ('B', 'I'): -3, ('L', 'H'): -3,
+('S', 'N'): 1, ('B', 'T'): -1, ('L', 'L'): 6, ('Y', 'K'): -6,
+('E', 'Q'): 2, ('Y', 'G'): -8, ('Z', 'S'): -2, ('Y', 'C'): -1,
+('G', 'D'): -1, ('B', 'V'): -4, ('E', 'A'): 0, ('Y', 'W'): -2,
+('E', 'E'): 6, ('Y', 'S'): -4, ('C', 'N'): -6, ('V', 'C'): -3,
+('T', 'H'): -3, ('P', 'R'): -1, ('V', 'G'): -3, ('T', 'L'): -3,
+('V', 'K'): -5, ('K', 'Q'): -1, ('R', 'A'): -4, ('I', 'R'): -3,
+('T', 'D'): -2, ('P', 'F'): -6, ('I', 'N'): -3, ('K', 'I'): -3,
+('M', 'D'): -5, ('V', 'W'): -9, ('W', 'W'): 13, ('M', 'H'): -5,
+('P', 'N'): -2, ('K', 'A'): -3, ('M', 'L'): 2, ('K', 'E'): -2,
+('Z', 'E'): 5, ('Q', 'N'): -1, ('Z', 'A'): -1, ('Z', 'M'): -3,
+('K', 'C'): -8, ('B', 'Q'): 0, ('B', 'M'): -5, ('F', 'C'): -7,
+('Z', 'Q'): 5, ('F', 'G'): -6, ('B', 'E'): 2, ('F', 'K'): -8,
+('B', 'A'): -1, ('D', 'D'): 6, ('W', 'G'): -9, ('S', 'Q'): -2,
+('W', 'C'): -10, ('W', 'K'): -6, ('H', 'Q'): 2, ('L', 'C'): -9,
+('W', 'N'): -5, ('S', 'A'): 1, ('L', 'G'): -6, ('W', 'S'): -3,
+('S', 'E'): -2, ('H', 'E'): -1, ('S', 'I'): -3, ('H', 'A'): -4,
+('S', 'M'): -3, ('Y', 'L'): -3, ('Y', 'H'): -1, ('Y', 'D'): -6,
+('E', 'R'): -4, ('G', 'G'): 5, ('G', 'C'): -5, ('E', 'N'): 0,
+('Y', 'T'): -4, ('Y', 'P'): -8, ('T', 'K'): -1, ('A', 'A'): 4,
+('P', 'Q'): -1, ('T', 'C'): -4, ('V', 'H'): -4, ('T', 'G'): -2,
+('I', 'Q'): -4, ('Z', 'T'): -2, ('C', 'R'): -5, ('V', 'P'): -3,
+('P', 'E'): -2, ('M', 'C'): -8, ('K', 'N'): 1, ('I', 'I'): 6,
+('P', 'A'): 0, ('M', 'G'): -5, ('T', 'S'): 2, ('I', 'E'): -3,
+('P', 'M'): -4, ('M', 'K'): 0, ('I', 'A'): -2, ('P', 'I'): -4,
+('R', 'R'): 7, ('Z', 'F'): -8, ('Z', 'B'): 2, ('Z', 'N'): 0,
+('B', 'R'): -3, ('B', 'N'): 4, ('F', 'D'): -8, ('Z', 'R'): -1,
+('F', 'H'): -3, ('B', 'F'): -6, ('F', 'L'): 0, ('V', 'Y'): -4,
+('B', 'B'): 4
 }
 pam90 = _temp()
 del _temp
@@ -2659,120 +2658,120 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/rao.cmp
 def _temp():
     return {
-('W', 'F') : 11, ('S', 'P') : 10, ('N', 'M') : 6, ('Q', 'Q') : 16,
-('N', 'A') : 9, ('N', 'E') : 10, ('W', 'V') : 11, ('Q', 'E') : 11,
-('L', 'H') : 10, ('W', 'R') : 7, ('Q', 'A') : 11, ('S', 'D') : 10,
-('H', 'H') : 16, ('Q', 'M') : 9, ('S', 'H') : 10, ('H', 'D') : 9,
-('Q', 'I') : 6, ('S', 'L') : 8, ('Y', 'M') : 8, ('Y', 'I') : 10,
-('Y', 'E') : 6, ('Y', 'A') : 9, ('G', 'F') : 7, ('V', 'T') : 10,
-('Y', 'Y') : 16, ('V', 'H') : 9, ('E', 'C') : 9, ('Y', 'Q') : 8,
-('V', 'A') : 9, ('C', 'C') : 16, ('V', 'E') : 4, ('T', 'N') : 10,
-('R', 'K') : 11, ('P', 'P') : 16, ('V', 'I') : 12, ('R', 'G') : 7,
-('V', 'M') : 9, ('T', 'F') : 10, ('R', 'C') : 8, ('V', 'Q') : 6,
-('K', 'K') : 16, ('P', 'D') : 8, ('I', 'H') : 8, ('M', 'F') : 10,
-('I', 'D') : 3, ('K', 'C') : 9, ('P', 'L') : 4, ('K', 'G') : 7,
-('P', 'H') : 5, ('T', 'R') : 9, ('F', 'A') : 10, ('F', 'E') : 6,
-('S', 'S') : 16, ('W', 'E') : 7, ('N', 'N') : 16, ('W', 'M') : 10,
-('Q', 'C') : 10, ('N', 'F') : 6, ('S', 'C') : 10, ('L', 'A') : 11,
-('S', 'G') : 11, ('L', 'E') : 7, ('W', 'Q') : 9, ('H', 'G') : 7,
-('S', 'K') : 10, ('Q', 'N') : 11, ('V', 'D') : 3, ('H', 'C') : 10,
-('Y', 'N') : 8, ('Y', 'F') : 10, ('W', 'I') : 11, ('C', 'A') : 11,
-('G', 'E') : 6, ('G', 'A') : 8, ('Y', 'V') : 10, ('E', 'D') : 11,
-('W', 'H') : 10, ('Y', 'R') : 7, ('N', 'I') : 5, ('R', 'L') : 6,
-('T', 'I') : 10, ('Q', 'L') : 9, ('R', 'H') : 10, ('T', 'M') : 8,
-('V', 'F') : 11, ('R', 'D') : 10, ('T', 'A') : 10, ('T', 'P') : 8,
-('T', 'E') : 8, ('V', 'N') : 5, ('P', 'G') : 11, ('M', 'A') : 11,
-('K', 'H') : 11, ('V', 'R') : 5, ('P', 'C') : 7, ('M', 'E') : 8,
-('V', 'V') : 16, ('T', 'T') : 16, ('M', 'I') : 9, ('T', 'Q') : 10,
-('I', 'G') : 6, ('P', 'K') : 6, ('M', 'M') : 16, ('K', 'D') : 11,
-('I', 'C') : 8, ('L', 'C') : 11, ('F', 'F') : 16, ('D', 'A') : 9,
-('S', 'R') : 9, ('W', 'D') : 6, ('N', 'C') : 9, ('N', 'G') : 10,
-('W', 'T') : 11, ('Q', 'G') : 8, ('S', 'F') : 8, ('W', 'P') : 6,
-('L', 'D') : 6, ('H', 'F') : 9, ('Q', 'K') : 12, ('S', 'N') : 11,
-('L', 'L') : 16, ('Q', 'F') : 7, ('Y', 'K') : 7, ('Y', 'G') : 10,
-('Y', 'C') : 10, ('G', 'D') : 9, ('E', 'A') : 10, ('Y', 'W') : 11,
-('E', 'E') : 16, ('Y', 'S') : 11, ('R', 'M') : 6, ('V', 'C') : 8,
-('T', 'H') : 10, ('R', 'I') : 4, ('V', 'G') : 6, ('T', 'L') : 9,
-('R', 'E') : 9, ('V', 'K') : 5, ('R', 'Q') : 10, ('R', 'A') : 8,
-('T', 'D') : 9, ('P', 'F') : 4, ('V', 'S') : 8, ('K', 'I') : 4,
-('M', 'D') : 5, ('W', 'W') : 16, ('M', 'H') : 10, ('P', 'N') : 9,
-('I', 'F') : 12, ('K', 'A') : 10, ('M', 'L') : 11, ('K', 'E') : 11,
-('N', 'K') : 11, ('R', 'P') : 6, ('L', 'F') : 11, ('F', 'C') : 10,
-('W', 'G') : 8, ('W', 'L') : 11, ('D', 'D') : 16, ('N', 'H') : 10,
-('S', 'Q') : 10, ('Q', 'P') : 7, ('N', 'L') : 7, ('W', 'K') : 7,
-('Q', 'D') : 11, ('W', 'N') : 8, ('S', 'A') : 10, ('L', 'G') : 6,
-('W', 'S') : 10, ('S', 'E') : 9, ('L', 'K') : 7, ('H', 'E') : 11,
-('S', 'I') : 8, ('Q', 'H') : 11, ('H', 'A') : 11, ('S', 'M') : 7,
-('Y', 'L') : 9, ('Y', 'H') : 9, ('Y', 'D') : 7, ('G', 'G') : 16,
-('G', 'C') : 8, ('Y', 'T') : 11, ('W', 'C') : 11, ('Y', 'P') : 8,
-('T', 'K') : 9, ('R', 'N') : 10, ('A', 'A') : 16, ('W', 'A') : 11,
-('T', 'C') : 10, ('N', 'D') : 11, ('R', 'F') : 5, ('T', 'G') : 10,
-('V', 'L') : 10, ('V', 'P') : 3, ('P', 'E') : 5, ('M', 'C') : 10,
-('I', 'I') : 16, ('P', 'A') : 6, ('M', 'G') : 4, ('T', 'S') : 11,
-('I', 'E') : 4, ('P', 'M') : 2, ('M', 'K') : 8, ('K', 'F') : 6,
-('I', 'A') : 9, ('P', 'I') : 3, ('R', 'R') : 16, ('L', 'I') : 10,
-('F', 'D') : 4, ('D', 'C') : 8
+('W', 'F'): 11, ('S', 'P'): 10, ('N', 'M'): 6, ('Q', 'Q'): 16,
+('N', 'A'): 9, ('N', 'E'): 10, ('W', 'V'): 11, ('Q', 'E'): 11,
+('L', 'H'): 10, ('W', 'R'): 7, ('Q', 'A'): 11, ('S', 'D'): 10,
+('H', 'H'): 16, ('Q', 'M'): 9, ('S', 'H'): 10, ('H', 'D'): 9,
+('Q', 'I'): 6, ('S', 'L'): 8, ('Y', 'M'): 8, ('Y', 'I'): 10,
+('Y', 'E'): 6, ('Y', 'A'): 9, ('G', 'F'): 7, ('V', 'T'): 10,
+('Y', 'Y'): 16, ('V', 'H'): 9, ('E', 'C'): 9, ('Y', 'Q'): 8,
+('V', 'A'): 9, ('C', 'C'): 16, ('V', 'E'): 4, ('T', 'N'): 10,
+('R', 'K'): 11, ('P', 'P'): 16, ('V', 'I'): 12, ('R', 'G'): 7,
+('V', 'M'): 9, ('T', 'F'): 10, ('R', 'C'): 8, ('V', 'Q'): 6,
+('K', 'K'): 16, ('P', 'D'): 8, ('I', 'H'): 8, ('M', 'F'): 10,
+('I', 'D'): 3, ('K', 'C'): 9, ('P', 'L'): 4, ('K', 'G'): 7,
+('P', 'H'): 5, ('T', 'R'): 9, ('F', 'A'): 10, ('F', 'E'): 6,
+('S', 'S'): 16, ('W', 'E'): 7, ('N', 'N'): 16, ('W', 'M'): 10,
+('Q', 'C'): 10, ('N', 'F'): 6, ('S', 'C'): 10, ('L', 'A'): 11,
+('S', 'G'): 11, ('L', 'E'): 7, ('W', 'Q'): 9, ('H', 'G'): 7,
+('S', 'K'): 10, ('Q', 'N'): 11, ('V', 'D'): 3, ('H', 'C'): 10,
+('Y', 'N'): 8, ('Y', 'F'): 10, ('W', 'I'): 11, ('C', 'A'): 11,
+('G', 'E'): 6, ('G', 'A'): 8, ('Y', 'V'): 10, ('E', 'D'): 11,
+('W', 'H'): 10, ('Y', 'R'): 7, ('N', 'I'): 5, ('R', 'L'): 6,
+('T', 'I'): 10, ('Q', 'L'): 9, ('R', 'H'): 10, ('T', 'M'): 8,
+('V', 'F'): 11, ('R', 'D'): 10, ('T', 'A'): 10, ('T', 'P'): 8,
+('T', 'E'): 8, ('V', 'N'): 5, ('P', 'G'): 11, ('M', 'A'): 11,
+('K', 'H'): 11, ('V', 'R'): 5, ('P', 'C'): 7, ('M', 'E'): 8,
+('V', 'V'): 16, ('T', 'T'): 16, ('M', 'I'): 9, ('T', 'Q'): 10,
+('I', 'G'): 6, ('P', 'K'): 6, ('M', 'M'): 16, ('K', 'D'): 11,
+('I', 'C'): 8, ('L', 'C'): 11, ('F', 'F'): 16, ('D', 'A'): 9,
+('S', 'R'): 9, ('W', 'D'): 6, ('N', 'C'): 9, ('N', 'G'): 10,
+('W', 'T'): 11, ('Q', 'G'): 8, ('S', 'F'): 8, ('W', 'P'): 6,
+('L', 'D'): 6, ('H', 'F'): 9, ('Q', 'K'): 12, ('S', 'N'): 11,
+('L', 'L'): 16, ('Q', 'F'): 7, ('Y', 'K'): 7, ('Y', 'G'): 10,
+('Y', 'C'): 10, ('G', 'D'): 9, ('E', 'A'): 10, ('Y', 'W'): 11,
+('E', 'E'): 16, ('Y', 'S'): 11, ('R', 'M'): 6, ('V', 'C'): 8,
+('T', 'H'): 10, ('R', 'I'): 4, ('V', 'G'): 6, ('T', 'L'): 9,
+('R', 'E'): 9, ('V', 'K'): 5, ('R', 'Q'): 10, ('R', 'A'): 8,
+('T', 'D'): 9, ('P', 'F'): 4, ('V', 'S'): 8, ('K', 'I'): 4,
+('M', 'D'): 5, ('W', 'W'): 16, ('M', 'H'): 10, ('P', 'N'): 9,
+('I', 'F'): 12, ('K', 'A'): 10, ('M', 'L'): 11, ('K', 'E'): 11,
+('N', 'K'): 11, ('R', 'P'): 6, ('L', 'F'): 11, ('F', 'C'): 10,
+('W', 'G'): 8, ('W', 'L'): 11, ('D', 'D'): 16, ('N', 'H'): 10,
+('S', 'Q'): 10, ('Q', 'P'): 7, ('N', 'L'): 7, ('W', 'K'): 7,
+('Q', 'D'): 11, ('W', 'N'): 8, ('S', 'A'): 10, ('L', 'G'): 6,
+('W', 'S'): 10, ('S', 'E'): 9, ('L', 'K'): 7, ('H', 'E'): 11,
+('S', 'I'): 8, ('Q', 'H'): 11, ('H', 'A'): 11, ('S', 'M'): 7,
+('Y', 'L'): 9, ('Y', 'H'): 9, ('Y', 'D'): 7, ('G', 'G'): 16,
+('G', 'C'): 8, ('Y', 'T'): 11, ('W', 'C'): 11, ('Y', 'P'): 8,
+('T', 'K'): 9, ('R', 'N'): 10, ('A', 'A'): 16, ('W', 'A'): 11,
+('T', 'C'): 10, ('N', 'D'): 11, ('R', 'F'): 5, ('T', 'G'): 10,
+('V', 'L'): 10, ('V', 'P'): 3, ('P', 'E'): 5, ('M', 'C'): 10,
+('I', 'I'): 16, ('P', 'A'): 6, ('M', 'G'): 4, ('T', 'S'): 11,
+('I', 'E'): 4, ('P', 'M'): 2, ('M', 'K'): 8, ('K', 'F'): 6,
+('I', 'A'): 9, ('P', 'I'): 3, ('R', 'R'): 16, ('L', 'I'): 10,
+('F', 'D'): 4, ('D', 'C'): 8
 }
 rao = _temp()
 del _temp
 
 
 # http://www.embl-heidelberg.de/~vogt/matrices/risler.cmp
-def _temp() :
+def _temp():
     return {
-('W', 'F') : -0.9, ('S', 'P') : -0.3, ('N', 'M') : 0.0, ('Q', 'Q') : 2.2,
-('N', 'A') : 1.3, ('N', 'E') : 1.4, ('W', 'V') : -0.7, ('Q', 'E') : 2.1,
-('L', 'H') : -0.9, ('W', 'R') : -0.8, ('Q', 'A') : 1.8, ('S', 'D') : 0.7,
-('H', 'H') : 2.2, ('Q', 'M') : 1.2, ('S', 'H') : -0.4, ('H', 'D') : -1.3,
-('Q', 'I') : 1.4, ('S', 'L') : 1.3, ('Y', 'M') : -0.2, ('Y', 'I') : 0.4,
-('Y', 'E') : 0.2, ('Y', 'A') : 0.2, ('G', 'F') : -0.4, ('V', 'T') : 1.6,
-('Y', 'Y') : 2.2, ('V', 'H') : -0.7, ('E', 'C') : -1.5, ('Y', 'Q') : 0.5,
-('V', 'A') : 2.0, ('C', 'C') : 2.2, ('V', 'E') : 1.6, ('T', 'N') : 1.1,
-('R', 'K') : 2.1, ('P', 'P') : 2.2, ('V', 'I') : 2.2, ('R', 'G') : 0.1,
-('V', 'M') : 0.8, ('T', 'F') : 0.3, ('R', 'C') : -1.5, ('V', 'Q') : 1.5,
-('K', 'K') : 2.2, ('P', 'D') : -1.2, ('I', 'H') : -0.8, ('M', 'F') : -0.2,
-('I', 'D') : 0.0, ('K', 'C') : -1.6, ('P', 'L') : -0.8, ('K', 'G') : -0.1,
-('P', 'H') : -1.6, ('T', 'R') : 1.9, ('F', 'A') : 0.6, ('F', 'E') : 0.6,
-('S', 'S') : 2.2, ('W', 'E') : -1.0, ('N', 'N') : 2.2, ('W', 'M') : -1.3,
-('Q', 'C') : -1.4, ('N', 'F') : 0.4, ('S', 'C') : -1.3, ('L', 'A') : 1.3,
-('S', 'G') : 0.7, ('L', 'E') : 0.9, ('W', 'Q') : -1.0, ('H', 'G') : -1.2,
-('S', 'K') : 1.4, ('Q', 'N') : 1.6, ('V', 'D') : 0.0, ('H', 'C') : -1.8,
-('Y', 'N') : -0.1, ('Y', 'F') : 2.0, ('W', 'I') : -0.7, ('C', 'A') : -1.5,
-('G', 'E') : 0.3, ('G', 'A') : 0.6, ('Y', 'V') : 0.3, ('E', 'D') : 1.0,
-('W', 'H') : -1.7, ('Y', 'R') : 0.8, ('N', 'I') : 0.9, ('R', 'L') : 1.2,
-('T', 'I') : 1.6, ('Q', 'L') : 1.1, ('R', 'H') : -0.4, ('T', 'M') : 0.8,
-('V', 'F') : 0.8, ('R', 'D') : -0.1, ('T', 'A') : 1.9, ('T', 'P') : -0.5,
-('T', 'E') : 1.6, ('V', 'N') : 1.1, ('P', 'G') : -1.2, ('M', 'A') : 1.0,
-('K', 'H') : -1.0, ('V', 'R') : 1.5, ('P', 'C') : -1.8, ('M', 'E') : 0.6,
-('V', 'V') : 2.2, ('T', 'T') : 2.2, ('M', 'I') : 0.9, ('T', 'Q') : 1.7,
-('I', 'G') : 0.0, ('P', 'K') : -0.7, ('M', 'M') : 2.2, ('K', 'D') : 0.1,
-('I', 'C') : -1.6, ('L', 'C') : -1.5, ('F', 'F') : 2.2, ('D', 'A') : 0.2,
-('S', 'R') : 2.0, ('W', 'D') : -1.4, ('N', 'C') : -1.6, ('N', 'G') : 0.2,
-('W', 'T') : -1.0, ('Q', 'G') : 0.2, ('S', 'F') : 0.5, ('W', 'P') : -1.6,
-('L', 'D') : -0.2, ('H', 'F') : -1.1, ('Q', 'K') : 1.7, ('S', 'N') : 1.9,
-('L', 'L') : 2.2, ('Q', 'F') : 0.7, ('Y', 'K') : 0.5, ('Y', 'G') : -0.2,
-('Y', 'C') : -1.1, ('G', 'D') : -0.4, ('E', 'A') : 1.7, ('Y', 'W') : -0.6,
-('E', 'E') : 2.2, ('Y', 'S') : 0.4, ('R', 'M') : 1.1, ('V', 'C') : -1.4,
-('T', 'H') : -0.9, ('R', 'I') : 1.4, ('V', 'G') : 0.1, ('T', 'L') : 1.2,
-('R', 'E') : 1.9, ('V', 'K') : 1.2, ('R', 'Q') : 2.0, ('R', 'A') : 1.5,
-('T', 'D') : 0.0, ('P', 'F') : -1.1, ('V', 'S') : 1.8, ('K', 'I') : 1.0,
-('M', 'D') : -0.5, ('W', 'W') : 2.2, ('M', 'H') : -1.2, ('P', 'N') : -1.0,
-('I', 'F') : 1.0, ('K', 'A') : 1.4, ('M', 'L') : 1.8, ('K', 'E') : 1.4,
-('N', 'K') : 1.0, ('R', 'P') : -0.3, ('L', 'F') : 1.0, ('F', 'C') : -1.6,
-('W', 'G') : -1.3, ('W', 'L') : -0.8, ('D', 'D') : 2.2, ('N', 'H') : -0.3,
-('S', 'Q') : 1.8, ('Q', 'P') : -0.6, ('N', 'L') : 0.8, ('W', 'K') : -1.1,
-('Q', 'D') : 0.6, ('W', 'N') : -1.1, ('S', 'A') : 2.0, ('L', 'G') : -0.2,
-('W', 'S') : -0.8, ('S', 'E') : 1.8, ('L', 'K') : 0.7, ('H', 'E') : -0.6,
-('S', 'I') : 1.6, ('Q', 'H') : -0.5, ('H', 'A') : -0.6, ('S', 'M') : 0.6,
-('Y', 'L') : 0.5, ('Y', 'H') : -0.8, ('Y', 'D') : -0.4, ('G', 'G') : 2.2,
-('G', 'C') : -1.7, ('Y', 'T') : 0.3, ('W', 'C') : -1.8, ('Y', 'P') : -1.2,
-('T', 'K') : 1.2, ('R', 'N') : 1.2, ('A', 'A') : 2.2, ('W', 'A') : -0.9,
-('T', 'C') : -1.4, ('N', 'D') : 0.8, ('R', 'F') : 0.4, ('T', 'G') : 0.2,
-('V', 'L') : 2.0, ('V', 'P') : -0.6, ('P', 'E') : -0.1, ('M', 'C') : -1.6,
-('I', 'I') : 2.2, ('P', 'A') : -0.2, ('M', 'G') : -0.4, ('T', 'S') : 2.1,
-('I', 'E') : 1.5, ('P', 'M') : -1.2, ('M', 'K') : 0.4, ('K', 'F') : 0.1,
-('I', 'A') : 1.7, ('P', 'I') : -0.6, ('R', 'R') : 2.2, ('L', 'I') : 2.1,
-('F', 'D') : -0.3, ('D', 'C') : -1.7
+('W', 'F'): -0.9, ('S', 'P'): -0.3, ('N', 'M'): 0.0, ('Q', 'Q'): 2.2,
+('N', 'A'): 1.3, ('N', 'E'): 1.4, ('W', 'V'): -0.7, ('Q', 'E'): 2.1,
+('L', 'H'): -0.9, ('W', 'R'): -0.8, ('Q', 'A'): 1.8, ('S', 'D'): 0.7,
+('H', 'H'): 2.2, ('Q', 'M'): 1.2, ('S', 'H'): -0.4, ('H', 'D'): -1.3,
+('Q', 'I'): 1.4, ('S', 'L'): 1.3, ('Y', 'M'): -0.2, ('Y', 'I'): 0.4,
+('Y', 'E'): 0.2, ('Y', 'A'): 0.2, ('G', 'F'): -0.4, ('V', 'T'): 1.6,
+('Y', 'Y'): 2.2, ('V', 'H'): -0.7, ('E', 'C'): -1.5, ('Y', 'Q'): 0.5,
+('V', 'A'): 2.0, ('C', 'C'): 2.2, ('V', 'E'): 1.6, ('T', 'N'): 1.1,
+('R', 'K'): 2.1, ('P', 'P'): 2.2, ('V', 'I'): 2.2, ('R', 'G'): 0.1,
+('V', 'M'): 0.8, ('T', 'F'): 0.3, ('R', 'C'): -1.5, ('V', 'Q'): 1.5,
+('K', 'K'): 2.2, ('P', 'D'): -1.2, ('I', 'H'): -0.8, ('M', 'F'): -0.2,
+('I', 'D'): 0.0, ('K', 'C'): -1.6, ('P', 'L'): -0.8, ('K', 'G'): -0.1,
+('P', 'H'): -1.6, ('T', 'R'): 1.9, ('F', 'A'): 0.6, ('F', 'E'): 0.6,
+('S', 'S'): 2.2, ('W', 'E'): -1.0, ('N', 'N'): 2.2, ('W', 'M'): -1.3,
+('Q', 'C'): -1.4, ('N', 'F'): 0.4, ('S', 'C'): -1.3, ('L', 'A'): 1.3,
+('S', 'G'): 0.7, ('L', 'E'): 0.9, ('W', 'Q'): -1.0, ('H', 'G'): -1.2,
+('S', 'K'): 1.4, ('Q', 'N'): 1.6, ('V', 'D'): 0.0, ('H', 'C'): -1.8,
+('Y', 'N'): -0.1, ('Y', 'F'): 2.0, ('W', 'I'): -0.7, ('C', 'A'): -1.5,
+('G', 'E'): 0.3, ('G', 'A'): 0.6, ('Y', 'V'): 0.3, ('E', 'D'): 1.0,
+('W', 'H'): -1.7, ('Y', 'R'): 0.8, ('N', 'I'): 0.9, ('R', 'L'): 1.2,
+('T', 'I'): 1.6, ('Q', 'L'): 1.1, ('R', 'H'): -0.4, ('T', 'M'): 0.8,
+('V', 'F'): 0.8, ('R', 'D'): -0.1, ('T', 'A'): 1.9, ('T', 'P'): -0.5,
+('T', 'E'): 1.6, ('V', 'N'): 1.1, ('P', 'G'): -1.2, ('M', 'A'): 1.0,
+('K', 'H'): -1.0, ('V', 'R'): 1.5, ('P', 'C'): -1.8, ('M', 'E'): 0.6,
+('V', 'V'): 2.2, ('T', 'T'): 2.2, ('M', 'I'): 0.9, ('T', 'Q'): 1.7,
+('I', 'G'): 0.0, ('P', 'K'): -0.7, ('M', 'M'): 2.2, ('K', 'D'): 0.1,
+('I', 'C'): -1.6, ('L', 'C'): -1.5, ('F', 'F'): 2.2, ('D', 'A'): 0.2,
+('S', 'R'): 2.0, ('W', 'D'): -1.4, ('N', 'C'): -1.6, ('N', 'G'): 0.2,
+('W', 'T'): -1.0, ('Q', 'G'): 0.2, ('S', 'F'): 0.5, ('W', 'P'): -1.6,
+('L', 'D'): -0.2, ('H', 'F'): -1.1, ('Q', 'K'): 1.7, ('S', 'N'): 1.9,
+('L', 'L'): 2.2, ('Q', 'F'): 0.7, ('Y', 'K'): 0.5, ('Y', 'G'): -0.2,
+('Y', 'C'): -1.1, ('G', 'D'): -0.4, ('E', 'A'): 1.7, ('Y', 'W'): -0.6,
+('E', 'E'): 2.2, ('Y', 'S'): 0.4, ('R', 'M'): 1.1, ('V', 'C'): -1.4,
+('T', 'H'): -0.9, ('R', 'I'): 1.4, ('V', 'G'): 0.1, ('T', 'L'): 1.2,
+('R', 'E'): 1.9, ('V', 'K'): 1.2, ('R', 'Q'): 2.0, ('R', 'A'): 1.5,
+('T', 'D'): 0.0, ('P', 'F'): -1.1, ('V', 'S'): 1.8, ('K', 'I'): 1.0,
+('M', 'D'): -0.5, ('W', 'W'): 2.2, ('M', 'H'): -1.2, ('P', 'N'): -1.0,
+('I', 'F'): 1.0, ('K', 'A'): 1.4, ('M', 'L'): 1.8, ('K', 'E'): 1.4,
+('N', 'K'): 1.0, ('R', 'P'): -0.3, ('L', 'F'): 1.0, ('F', 'C'): -1.6,
+('W', 'G'): -1.3, ('W', 'L'): -0.8, ('D', 'D'): 2.2, ('N', 'H'): -0.3,
+('S', 'Q'): 1.8, ('Q', 'P'): -0.6, ('N', 'L'): 0.8, ('W', 'K'): -1.1,
+('Q', 'D'): 0.6, ('W', 'N'): -1.1, ('S', 'A'): 2.0, ('L', 'G'): -0.2,
+('W', 'S'): -0.8, ('S', 'E'): 1.8, ('L', 'K'): 0.7, ('H', 'E'): -0.6,
+('S', 'I'): 1.6, ('Q', 'H'): -0.5, ('H', 'A'): -0.6, ('S', 'M'): 0.6,
+('Y', 'L'): 0.5, ('Y', 'H'): -0.8, ('Y', 'D'): -0.4, ('G', 'G'): 2.2,
+('G', 'C'): -1.7, ('Y', 'T'): 0.3, ('W', 'C'): -1.8, ('Y', 'P'): -1.2,
+('T', 'K'): 1.2, ('R', 'N'): 1.2, ('A', 'A'): 2.2, ('W', 'A'): -0.9,
+('T', 'C'): -1.4, ('N', 'D'): 0.8, ('R', 'F'): 0.4, ('T', 'G'): 0.2,
+('V', 'L'): 2.0, ('V', 'P'): -0.6, ('P', 'E'): -0.1, ('M', 'C'): -1.6,
+('I', 'I'): 2.2, ('P', 'A'): -0.2, ('M', 'G'): -0.4, ('T', 'S'): 2.1,
+('I', 'E'): 1.5, ('P', 'M'): -1.2, ('M', 'K'): 0.4, ('K', 'F'): 0.1,
+('I', 'A'): 1.7, ('P', 'I'): -0.6, ('R', 'R'): 2.2, ('L', 'I'): 2.1,
+('F', 'D'): -0.3, ('D', 'C'): -1.7
 }
 risler = _temp()
 del _temp
@@ -2781,59 +2780,59 @@ del _temp
 # http://www.embl-heidelberg.de/~vogt/matrices/str.cmp
 def _temp():
     return {
-('W', 'F') : 2, ('L', 'R') : -3, ('I', 'I') : 6, ('Q', 'Q') : 6,
-('W', 'N') : -5, ('V', 'I') : 2, ('H', 'T') : -2, ('H', 'P') : -3,
-('W', 'V') : -4, ('Q', 'E') : 2, ('W', 'R') : -2, ('Q', 'A') : 0,
-('H', 'H') : 8, ('H', 'D') : 0, ('L', 'N') : -3, ('Y', 'M') : -1,
-('Y', 'I') : -1, ('Y', 'E') : -2, ('E', 'S') : -1, ('Y', 'A') : -3,
-('Y', 'Y') : 7, ('T', 'C') : -5, ('E', 'C') : -3, ('Y', 'Q') : -3,
-('E', 'G') : -2, ('V', 'A') : 0, ('C', 'C') : 11, ('M', 'R') : -4,
-('P', 'T') : -1, ('V', 'E') : -2, ('P', 'P') : 7, ('I', 'T') : -2,
-('K', 'S') : -1, ('R', 'G') : -2, ('I', 'P') : -4, ('R', 'C') : -2,
-('A', 'T') : -1, ('K', 'K') : 5, ('A', 'P') : -1, ('V', 'M') : 0,
-('I', 'D') : -3, ('K', 'C') : -4, ('K', 'G') : -3, ('R', 'S') : 0,
-('F', 'Q') : -4, ('F', 'A') : -3, ('V', 'V') : 5, ('M', 'N') : -2,
-('F', 'E') : -4, ('D', 'N') : 2, ('F', 'I') : 1, ('F', 'M') : 0,
-('M', 'S') : -4, ('S', 'S') : 4, ('L', 'Q') : -3, ('W', 'E') : -6,
-('W', 'A') : -3, ('W', 'M') : -2, ('H', 'S') : -2, ('W', 'I') : -2,
-('S', 'C') : -4, ('L', 'A') : -2, ('L', 'E') : -4, ('W', 'Q') : -5,
-('H', 'G') : -3, ('Q', 'N') : 0, ('H', 'C') : -6, ('L', 'M') : 3,
-('W', 'Y') : 2, ('Y', 'N') : -1, ('E', 'P') : -1, ('Y', 'F') : 3,
-('E', 'T') : 0, ('A', 'A') : 4, ('I', 'N') : -3, ('G', 'A') : 0,
-('Y', 'V') : -1, ('E', 'D') : 2, ('W', 'H') : -3, ('Y', 'R') : -1,
-('M', 'Q') : 1, ('P', 'S') : -1, ('R', 'H') : 0, ('A', 'C') : -2,
-('R', 'D') : -2, ('K', 'P') : -1, ('L', 'D') : -6, ('K', 'T') : 0,
-('V', 'N') : -4, ('M', 'A') : 0, ('K', 'H') : 0, ('V', 'R') : -3,
-('P', 'C') : -8, ('M', 'E') : -2, ('A', 'S') : 0, ('T', 'T') : 5,
-('R', 'T') : -1, ('I', 'G') : -5, ('R', 'P') : -2, ('K', 'D') : -1,
-('I', 'C') : -4, ('F', 'R') : -4, ('F', 'V') : -1, ('L', 'C') : -6,
-('F', 'F') : 7, ('D', 'A') : -1, ('F', 'N') : -3, ('W', 'D') : -6,
-('L', 'P') : -3, ('Q', 'S') : -1, ('N', 'C') : -6, ('N', 'G') : -1,
-('H', 'N') : 2, ('W', 'T') : -5, ('Q', 'G') : -2, ('W', 'P') : -4,
-('Q', 'C') : -3, ('N', 'S') : 0, ('L', 'H') : -3, ('L', 'L') : 5,
-('G', 'T') : -3, ('M', 'M') : 8, ('G', 'P') : -2, ('Y', 'K') : -2,
-('Y', 'G') : -3, ('Y', 'C') : -6, ('E', 'A') : 0, ('E', 'E') : 5,
-('Y', 'S') : -2, ('M', 'P') : -6, ('V', 'C') : -4, ('M', 'T') : -2,
-('V', 'G') : -4, ('R', 'E') : 0, ('V', 'K') : -3, ('K', 'Q') : 1,
-('R', 'A') : -1, ('I', 'R') : -3, ('N', 'A') : -1, ('V', 'S') : -3,
-('M', 'D') : -4, ('M', 'H') : -2, ('K', 'A') : -1, ('R', 'Q') : 1,
-('K', 'E') : 1, ('F', 'S') : -3, ('I', 'K') : -3, ('D', 'P') : -1,
-('D', 'T') : -1, ('I', 'M') : 1, ('F', 'C') : -2, ('W', 'L') : -1,
-('F', 'G') : -6, ('F', 'K') : -3, ('F', 'T') : -3, ('D', 'D') : 6,
-('Q', 'T') : 0, ('W', 'G') : -4, ('Q', 'P') : -2, ('W', 'C') : -6,
-('W', 'K') : -3, ('H', 'Q') : 0, ('Q', 'D') : 0, ('W', 'W') : 10,
-('V', 'L') : 1, ('L', 'G') : -5, ('W', 'S') : -5, ('L', 'K') : -2,
-('N', 'P') : -2, ('H', 'E') : -2, ('N', 'T') : 0, ('H', 'A') : -2,
-('Y', 'L') : -2, ('Y', 'H') : 0, ('G', 'S') : -1, ('Y', 'D') : -3,
-('V', 'Q') : -2, ('L', 'T') : -3, ('G', 'G') : 5, ('G', 'C') : -6,
-('E', 'N') : 0, ('Y', 'T') : -2, ('Y', 'P') : -6, ('R', 'N') : -1,
-('V', 'D') : -4, ('K', 'R') : 2, ('V', 'H') : -2, ('I', 'Q') : -5,
-('V', 'P') : -4, ('M', 'C') : -5, ('K', 'N') : 0, ('V', 'T') : -1,
-('M', 'G') : -4, ('T', 'S') : 1, ('I', 'E') : -3, ('M', 'K') : -1,
-('I', 'A') : -2, ('N', 'N') : 5, ('R', 'R') : 7, ('F', 'P') : -5,
-('L', 'I') : 2, ('I', 'S') : -3, ('D', 'S') : 0, ('L', 'S') : -4,
-('I', 'H') : -5, ('F', 'D') : -5, ('D', 'C') : -7, ('F', 'H') : -2,
-('D', 'G') : -1, ('F', 'L') : 2
+('W', 'F'): 2, ('L', 'R'): -3, ('I', 'I'): 6, ('Q', 'Q'): 6,
+('W', 'N'): -5, ('V', 'I'): 2, ('H', 'T'): -2, ('H', 'P'): -3,
+('W', 'V'): -4, ('Q', 'E'): 2, ('W', 'R'): -2, ('Q', 'A'): 0,
+('H', 'H'): 8, ('H', 'D'): 0, ('L', 'N'): -3, ('Y', 'M'): -1,
+('Y', 'I'): -1, ('Y', 'E'): -2, ('E', 'S'): -1, ('Y', 'A'): -3,
+('Y', 'Y'): 7, ('T', 'C'): -5, ('E', 'C'): -3, ('Y', 'Q'): -3,
+('E', 'G'): -2, ('V', 'A'): 0, ('C', 'C'): 11, ('M', 'R'): -4,
+('P', 'T'): -1, ('V', 'E'): -2, ('P', 'P'): 7, ('I', 'T'): -2,
+('K', 'S'): -1, ('R', 'G'): -2, ('I', 'P'): -4, ('R', 'C'): -2,
+('A', 'T'): -1, ('K', 'K'): 5, ('A', 'P'): -1, ('V', 'M'): 0,
+('I', 'D'): -3, ('K', 'C'): -4, ('K', 'G'): -3, ('R', 'S'): 0,
+('F', 'Q'): -4, ('F', 'A'): -3, ('V', 'V'): 5, ('M', 'N'): -2,
+('F', 'E'): -4, ('D', 'N'): 2, ('F', 'I'): 1, ('F', 'M'): 0,
+('M', 'S'): -4, ('S', 'S'): 4, ('L', 'Q'): -3, ('W', 'E'): -6,
+('W', 'A'): -3, ('W', 'M'): -2, ('H', 'S'): -2, ('W', 'I'): -2,
+('S', 'C'): -4, ('L', 'A'): -2, ('L', 'E'): -4, ('W', 'Q'): -5,
+('H', 'G'): -3, ('Q', 'N'): 0, ('H', 'C'): -6, ('L', 'M'): 3,
+('W', 'Y'): 2, ('Y', 'N'): -1, ('E', 'P'): -1, ('Y', 'F'): 3,
+('E', 'T'): 0, ('A', 'A'): 4, ('I', 'N'): -3, ('G', 'A'): 0,
+('Y', 'V'): -1, ('E', 'D'): 2, ('W', 'H'): -3, ('Y', 'R'): -1,
+('M', 'Q'): 1, ('P', 'S'): -1, ('R', 'H'): 0, ('A', 'C'): -2,
+('R', 'D'): -2, ('K', 'P'): -1, ('L', 'D'): -6, ('K', 'T'): 0,
+('V', 'N'): -4, ('M', 'A'): 0, ('K', 'H'): 0, ('V', 'R'): -3,
+('P', 'C'): -8, ('M', 'E'): -2, ('A', 'S'): 0, ('T', 'T'): 5,
+('R', 'T'): -1, ('I', 'G'): -5, ('R', 'P'): -2, ('K', 'D'): -1,
+('I', 'C'): -4, ('F', 'R'): -4, ('F', 'V'): -1, ('L', 'C'): -6,
+('F', 'F'): 7, ('D', 'A'): -1, ('F', 'N'): -3, ('W', 'D'): -6,
+('L', 'P'): -3, ('Q', 'S'): -1, ('N', 'C'): -6, ('N', 'G'): -1,
+('H', 'N'): 2, ('W', 'T'): -5, ('Q', 'G'): -2, ('W', 'P'): -4,
+('Q', 'C'): -3, ('N', 'S'): 0, ('L', 'H'): -3, ('L', 'L'): 5,
+('G', 'T'): -3, ('M', 'M'): 8, ('G', 'P'): -2, ('Y', 'K'): -2,
+('Y', 'G'): -3, ('Y', 'C'): -6, ('E', 'A'): 0, ('E', 'E'): 5,
+('Y', 'S'): -2, ('M', 'P'): -6, ('V', 'C'): -4, ('M', 'T'): -2,
+('V', 'G'): -4, ('R', 'E'): 0, ('V', 'K'): -3, ('K', 'Q'): 1,
+('R', 'A'): -1, ('I', 'R'): -3, ('N', 'A'): -1, ('V', 'S'): -3,
+('M', 'D'): -4, ('M', 'H'): -2, ('K', 'A'): -1, ('R', 'Q'): 1,
+('K', 'E'): 1, ('F', 'S'): -3, ('I', 'K'): -3, ('D', 'P'): -1,
+('D', 'T'): -1, ('I', 'M'): 1, ('F', 'C'): -2, ('W', 'L'): -1,
+('F', 'G'): -6, ('F', 'K'): -3, ('F', 'T'): -3, ('D', 'D'): 6,
+('Q', 'T'): 0, ('W', 'G'): -4, ('Q', 'P'): -2, ('W', 'C'): -6,
+('W', 'K'): -3, ('H', 'Q'): 0, ('Q', 'D'): 0, ('W', 'W'): 10,
+('V', 'L'): 1, ('L', 'G'): -5, ('W', 'S'): -5, ('L', 'K'): -2,
+('N', 'P'): -2, ('H', 'E'): -2, ('N', 'T'): 0, ('H', 'A'): -2,
+('Y', 'L'): -2, ('Y', 'H'): 0, ('G', 'S'): -1, ('Y', 'D'): -3,
+('V', 'Q'): -2, ('L', 'T'): -3, ('G', 'G'): 5, ('G', 'C'): -6,
+('E', 'N'): 0, ('Y', 'T'): -2, ('Y', 'P'): -6, ('R', 'N'): -1,
+('V', 'D'): -4, ('K', 'R'): 2, ('V', 'H'): -2, ('I', 'Q'): -5,
+('V', 'P'): -4, ('M', 'C'): -5, ('K', 'N'): 0, ('V', 'T'): -1,
+('M', 'G'): -4, ('T', 'S'): 1, ('I', 'E'): -3, ('M', 'K'): -1,
+('I', 'A'): -2, ('N', 'N'): 5, ('R', 'R'): 7, ('F', 'P'): -5,
+('L', 'I'): 2, ('I', 'S'): -3, ('D', 'S'): 0, ('L', 'S'): -4,
+('I', 'H'): -5, ('F', 'D'): -5, ('D', 'C'): -7, ('F', 'H'): -2,
+('D', 'G'): -1, ('F', 'L'): 2
 }
 structure = _temp()
 del _temp
diff --git a/Bio/SubsMat/__init__.py b/Bio/SubsMat/__init__.py
index e4c668e..287c150 100644
--- a/Bio/SubsMat/__init__.py
+++ b/Bio/SubsMat/__init__.py
@@ -122,7 +122,6 @@ import Bio
 from Bio import Alphabet
 from Bio.SubsMat import FreqTable
 
-__docformat__ = "restructuredtext en"
 
 log = math.log
 # Matrix types
diff --git a/Bio/SwissProt/KeyWList.py b/Bio/SwissProt/KeyWList.py
index cff8770..4736f25 100644
--- a/Bio/SwissProt/KeyWList.py
+++ b/Bio/SwissProt/KeyWList.py
@@ -22,8 +22,6 @@ Functions:
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"
-
 
 class Record(dict):
     """
diff --git a/Bio/SwissProt/__init__.py b/Bio/SwissProt/__init__.py
index d43633b..93cffbd 100644
--- a/Bio/SwissProt/__init__.py
+++ b/Bio/SwissProt/__init__.py
@@ -26,8 +26,6 @@ from __future__ import print_function
 
 from Bio._py3k import _as_string
 
-__docformat__ = "restructuredtext en"
-
 
 class Record(object):
     """Holds information from a SwissProt record.
@@ -131,7 +129,10 @@ def read(handle):
     if not record:
         raise ValueError("No SwissProt record found")
     # We should have reached the end of the record by now
-    remainder = handle.read()
+    # Used to check with handle.read() but that breaks on Python 3.5
+    # due to http://bugs.python.org/issue26499 and could download
+    # lot of data needlessly if there were more records.
+    remainder = handle.readline()
     if remainder:
         raise ValueError("More than one SwissProt record found")
     return record
@@ -307,7 +308,7 @@ def _read_dt(record, line):
 
         # find where the version information will be located
         # This is needed for when you have cases like IPI where
-        # the release verison is in a different spot:
+        # the release version is in a different spot:
         # DT   08-JAN-2002 (IPI Human rel. 2.3, Created)
         uprcols = uprline.split()
         rel_index = -1
@@ -429,6 +430,7 @@ def _read_rn(reference, rn):
         assert evidence.startswith('{') and evidence.endswith('}'), "Missing braces %s" % evidence
         reference.evidence = evidence[1:-1].split('|')
 
+
 def _read_rc(reference, value):
     cols = value.split(';')
     if value[-1] == ';':
diff --git a/Bio/TogoWS/__init__.py b/Bio/TogoWS/__init__.py
index 1b4f7a4..3c393f1 100644
--- a/Bio/TogoWS/__init__.py
+++ b/Bio/TogoWS/__init__.py
@@ -39,7 +39,6 @@ from Bio._py3k import _binary_to_string_handle, _as_bytes
 from Bio._py3k import urlopen as _urlopen
 from Bio._py3k import quote as _quote
 
-__docformat__ = "restructuredtext en"
 
 # Constant
 _BASE_URL = "http://togows.dbcls.jp"
@@ -317,7 +316,7 @@ def _open(url, post=None):
 
     Open a handle to TogoWS, will raise an IOError if it encounters an error.
 
-    In the absense of clear guidelines, this function enforces a limit of
+    In the absence of clear guidelines, this function enforces a limit of
     "up to three queries per second" to avoid abusing the TogoWS servers.
     """
     delay = 0.333333333  # one third of a second
diff --git a/Bio/UniGene/__init__.py b/Bio/UniGene/__init__.py
index 3c4f19b..d881bac 100644
--- a/Bio/UniGene/__init__.py
+++ b/Bio/UniGene/__init__.py
@@ -78,8 +78,6 @@ Here is an overview of the flat file format that this parser deals with:
                          NCBI Trace Archive
 """
 
-__docformat__ = "restructuredtext en"
-
 
 class SequenceLine(object):
     """Store the information for one SEQUENCE line from a Unigene file
@@ -87,7 +85,7 @@ class SequenceLine(object):
     Initialize with the text part of the SEQUENCE line, or nothing.
 
     Attributes and descriptions (access as LOWER CASE):
-    
+
         - ACC=         GenBank/EMBL/DDBJ accession number of sequence
         - NID=         Unique nucleotide sequence identifier (gi)
         - PID=         Unique protein sequence identifier (used for non-ESTs)
diff --git a/Bio/UniProt/GOA.py b/Bio/UniProt/GOA.py
index b53a591..75c9b72 100644
--- a/Bio/UniProt/GOA.py
+++ b/Bio/UniProt/GOA.py
@@ -23,6 +23,7 @@ import sys
 
 from Bio._py3k import zip
 
+
 # GAF: GO Annotation Format
 #
 # GAF version 2.0
diff --git a/Bio/Wise/__init__.py b/Bio/Wise/__init__.py
index 1008ed6..bed9637 100755
--- a/Bio/Wise/__init__.py
+++ b/Bio/Wise/__init__.py
@@ -3,13 +3,15 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 #
-# Bio.Wise contains modules for running and processing the output of
-# some of the models in the Wise2 package by Ewan Birney available from:
-# ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
-# http://www.ebi.ac.uk/Wise2/
-#
-# Bio.Wise.psw is for protein Smith-Waterman alignments
-# Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
+Bio.Wise contains modules for running and processing the output of
+some of the models in the Wise2 package by Ewan Birney available from:
+ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
+http://www.ebi.ac.uk/Wise2/
+
+Bio.Wise.psw is for protein Smith-Waterman alignments
+Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
 
 from __future__ import print_function
 
diff --git a/Bio/Wise/dnal.py b/Bio/Wise/dnal.py
index 5b7d284..e34f831 100755
--- a/Bio/Wise/dnal.py
+++ b/Bio/Wise/dnal.py
@@ -3,13 +3,15 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 #
-# Bio.Wise contains modules for running and processing the output of
-# some of the models in the Wise2 package by Ewan Birney available from:
-# ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
-# http://www.ebi.ac.uk/Wise2/
-#
-# Bio.Wise.psw is for protein Smith-Waterman alignments
-# Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
+Bio.Wise contains modules for running and processing the output of
+some of the models in the Wise2 package by Ewan Birney available from:
+ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
+http://www.ebi.ac.uk/Wise2/
+
+Bio.Wise.psw is for protein Smith-Waterman alignments
+Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
 
 from __future__ import print_function
 
@@ -22,6 +24,7 @@ from Bio._py3k import map
 
 from Bio import Wise
 
+
 _SCORE_MATCH = 4
 _SCORE_MISMATCH = -1
 _SCORE_GAP_START = -5
diff --git a/Bio/Wise/psw.py b/Bio/Wise/psw.py
index b381f02..c2d6612 100755
--- a/Bio/Wise/psw.py
+++ b/Bio/Wise/psw.py
@@ -3,13 +3,15 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 #
-# Bio.Wise contains modules for running and processing the output of
-# some of the models in the Wise2 package by Ewan Birney available from:
-# ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
-# http://www.ebi.ac.uk/Wise2/
-#
-# Bio.Wise.psw is for protein Smith-Waterman alignments
-# Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
+Bio.Wise contains modules for running and processing the output of
+some of the models in the Wise2 package by Ewan Birney available from:
+ftp://ftp.ebi.ac.uk/pub/software/unix/wise2/
+http://www.ebi.ac.uk/Wise2/
+
+Bio.Wise.psw is for protein Smith-Waterman alignments
+Bio.Wise.dnal is for Smith-Waterman DNA alignments
+"""
 
 from __future__ import print_function
 
@@ -19,6 +21,7 @@ import sys
 
 from Bio import Wise
 
+
 _CMDLINE_PSW = ["psw", "-l", "-F"]
 _OPTION_GAP_START = "-g"
 _OPTION_GAP_EXTENSION = "-e"
diff --git a/Bio/__init__.py b/Bio/__init__.py
index bcb4744..ed35488 100644
--- a/Bio/__init__.py
+++ b/Bio/__init__.py
@@ -10,9 +10,7 @@ of freely available Python tools for computational molecular biology.
 http://biopython.org
 """
 
-__docformat__ = "restructuredtext en"  # not just plaintext
-
-__version__ = "1.66"
+__version__ = "1.67"
 
 
 class MissingExternalDependencyError(Exception):
@@ -90,7 +88,7 @@ class BiopythonExperimentalWarning(BiopythonWarning):
 
     Biopython uses this warning for experimental code ('alpha' or 'beta'
     level code) which is released as part of the standard releases to mark
-    sub-modules or functions for early adopters to test & give feedback..
+    sub-modules or functions for early adopters to test & give feedback.
 
     Code issuing this warning is likely to change (or even be removed) in
     a subsequent release of Biopython. Such code should NOT be used for
@@ -99,9 +97,9 @@ class BiopythonExperimentalWarning(BiopythonWarning):
         - You are running the latest release of Biopython, or ideally the
           latest code from our repository.
         - You are subscribed to the biopython-dev mailing list to provide
-          feedback on this code, and to be alterted to changes to it.
+          feedback on this code, and to be alerted of changes to it.
 
     If all goes well, experimental code would be promoted to stable in
-    a subsequence release, and this warning removed from it.
+    a subsequent release, and this warning removed from it.
     """
     pass
diff --git a/Bio/_py3k/__init__.py b/Bio/_py3k/__init__.py
index 04e464f..4d7f4a4 100644
--- a/Bio/_py3k/__init__.py
+++ b/Bio/_py3k/__init__.py
@@ -15,7 +15,7 @@ There is no similar option for range yet, other than::
     range = xrange
     input = raw_input
 
-or::
+or:
 
     from __builtin__ import xrange as range
     from __builtin__ import raw_input as input
@@ -25,11 +25,11 @@ importing of built in functions like open changes from Python 2::
 
     from __builtin__ import open
 
-to this under Python 3::
+To do this under Python 3:
 
     from builtins import open
 
-Instead, we can do this under either Python 2 or 3::
+Instead, we can do this under either Python 2 or 3:
 
     from Bio._py3k import open
     from Bio._py3k import zip
@@ -39,6 +39,7 @@ go away.
 """
 import sys
 
+
 if sys.version_info[0] >= 3:
     # Code for Python 3
     from builtins import open, zip, map, filter, range, input
@@ -89,59 +90,70 @@ if sys.version_info[0] >= 3:
 
     import io
 
-    def _binary_to_string_handle(handle):
-        """Treat a binary (bytes) handle like a text (unicode) handle."""
-        # TODO, once drop all of Python 3.0 - 3.3, replace this with just:
-        #
-        # return io.TextIOWrapper(io.BufferedReader(handle))
-        #
-        # See also http://bugs.python.org/issue5628
-        # and http://bugs.python.org/issue13541
-        # and http://bugs.python.org/issue13464 which should be fixed in Python 3.3
-        #
-        # However, still have problems under Python 3.3.0, e.g.
-        #
-        # $ python3.3 test_SeqIO_online.py
-        # test_nuccore_X52960 (__main__.EntrezTests)
-        # Bio.Entrez.efetch('nuccore', id='X52960', ...) ... ERROR
-        # test_nucleotide_6273291 (__main__.EntrezTests)
-        # Bio.Entrez.efetch('nucleotide', id='6273291', ...) ... ERROR
-        # test_protein_16130152 (__main__.EntrezTests)
-        # Bio.Entrez.efetch('protein', id='16130152', ...) ... ERROR
-        # test_get_sprot_raw (__main__.ExPASyTests)
-        # Bio.ExPASy.get_sprot_raw("O23729") ... ok
-        # ..
-        # ValueError: I/O operation on closed file.
-        #
-        class EvilHandleHack(object):
-            def __init__(self, handle):
-                self._handle = handle
-                try:
-                    # If wrapping an online handle, this this is nice to have:
-                    self.url = handle.url
-                except AttributeError:
-                    pass
-
-            def read(self, length=None):
-                return _as_string(self._handle.read(length))
-
-            def readline(self):
-                return _as_string(self._handle.readline())
-
-            def __iter__(self):
-                for line in self._handle:
-                    yield _as_string(line)
-
-            def close(self):
-                return self._handle.close()
-
-            def seek(self, pos):
-                return self._handle.seek(pos)
-
-            def tell(self):
-                return self._handle.tell()
-
-        return EvilHandleHack(handle)
+    if sys.version_info[:2] <= (3, 3):
+        def _binary_to_string_handle(handle):
+            """Treat a binary (bytes) handle like a text (unicode) handle."""
+            # TODO, once drop all of Python 3.0 - 3.3, remove this!
+            #
+            # See also http://bugs.python.org/issue5628
+            # and http://bugs.python.org/issue13541
+            # and http://bugs.python.org/issue13464 which should be fixed in Python 3.3
+            #
+            # However, still have problems under Python 3.3.0, e.g.
+            #
+            # $ python3.3 test_SeqIO_online.py
+            # test_nuccore_X52960 (__main__.EntrezTests)
+            # Bio.Entrez.efetch('nuccore', id='X52960', ...) ... ERROR
+            # test_nucleotide_6273291 (__main__.EntrezTests)
+            # Bio.Entrez.efetch('nucleotide', id='6273291', ...) ... ERROR
+            # test_protein_16130152 (__main__.EntrezTests)
+            # Bio.Entrez.efetch('protein', id='16130152', ...) ... ERROR
+            # test_get_sprot_raw (__main__.ExPASyTests)
+            # Bio.ExPASy.get_sprot_raw("O23729") ... ok
+            # ..
+            # ValueError: I/O operation on closed file.
+            #
+            class EvilHandleHack(object):
+                """Biopython internal class to work around bugs in early versions of Python 3."""
+                def __init__(self, handle):
+                    self._handle = handle
+                    try:
+                        # If wrapping an online handle, this this is nice to have:
+                        self.url = handle.url
+                    except AttributeError:
+                        pass
+
+                def read(self, length=None):
+                    return _as_string(self._handle.read(length))
+
+                def readline(self):
+                    return _as_string(self._handle.readline())
+
+                def __iter__(self):
+                    for line in self._handle:
+                        yield _as_string(line)
+
+                def close(self):
+                    return self._handle.close()
+
+                def seek(self, pos):
+                    return self._handle.seek(pos)
+
+                def tell(self):
+                    return self._handle.tell()
+
+            return EvilHandleHack(handle)
+    else:
+        # Python 3.4 onwards, the standard library wrappers should work:
+        def _binary_to_string_handle(handle):
+            """Treat a binary (bytes) handle like a text (unicode) handle."""
+            wrapped = io.TextIOWrapper(io.BufferedReader(handle))
+            try:
+                # If wrapping an online handle, this this is nice to have:
+                wrapped.url = handle.url
+            except AttributeError:
+                pass
+            return wrapped
 
     # This is to avoid the deprecation warning from open(filename, "rU")
     _universal_read_mode = "r"  # text mode does universal new lines
diff --git a/Bio/_py3k/_ordereddict.py b/Bio/_py3k/_ordereddict.py
index 2d1d813..f5e02f6 100644
--- a/Bio/_py3k/_ordereddict.py
+++ b/Bio/_py3k/_ordereddict.py
@@ -19,6 +19,7 @@
 #     WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 #     FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
 #     OTHER DEALINGS IN THE SOFTWARE.
+"""Python 3 compatibility tools (PRIVATE)."""
 
 from UserDict import DictMixin
 
diff --git a/Bio/_utils.py b/Bio/_utils.py
index f0270f3..ef798e0 100644
--- a/Bio/_utils.py
+++ b/Bio/_utils.py
@@ -106,7 +106,7 @@ def run_doctest(target_dir=None, *args, **kwargs):
 
     cur_dir = os.path.abspath(os.curdir)
 
-    print("Runing doctests...")
+    print("Running doctests...")
     try:
         os.chdir(find_test_dir(target_dir))
         doctest.testmod(*args, **kwargs)
diff --git a/Bio/bgzf.py b/Bio/bgzf.py
index 168d30e..eb5d3d0 100755
--- a/Bio/bgzf.py
+++ b/Bio/bgzf.py
@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-# Copyright 2010-2013 by Peter Cock.
+# Copyright 2010-2015 by Peter Cock.
 # All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -227,14 +227,13 @@ NC_000932.1
 
 from __future__ import print_function
 
-import sys  # to detect when under Python 2
+import sys
 import zlib
 import struct
 
 from Bio._py3k import _as_bytes, _as_string
 from Bio._py3k import open as _open
 
-__docformat__ = "restructuredtext en"
 
 # For Python 2 can just use: _bgzf_magic = '\x1f\x8b\x08\x04'
 # but need to use bytes on Python 3
@@ -588,7 +587,7 @@ class BgzfReader(object):
 
     def tell(self):
         """Returns a 64-bit unsigned BGZF virtual offset."""
-        if 0 < self._within_block_offset == len(self._buffer):
+        if 0 < self._within_block_offset and self._within_block_offset == len(self._buffer):
             # Special case where we're right at the end of a (non empty) block.
             # For non-maximal blocks could give two possible virtual offsets,
             # but for a maximal block can't use 65536 as the within block
@@ -824,7 +823,6 @@ class BgzfWriter(object):
 
 
 if __name__ == "__main__":
-    import sys
     if len(sys.argv) > 1:
         print("Call this with no arguments and pipe uncompressed data in on stdin")
         print("and it will produce BGZF compressed data on stdout. e.g.")
diff --git a/Bio/codonalign/__init__.py b/Bio/codonalign/__init__.py
index cf9ce0a..baedcba 100644
--- a/Bio/codonalign/__init__.py
+++ b/Bio/codonalign/__init__.py
@@ -7,15 +7,10 @@
 """Code for dealing with Codon Alignment.
 """
 from __future__ import print_function
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
 
+from Bio import BiopythonWarning
 from Bio import BiopythonExperimentalWarning
 
-import warnings
-warnings.warn('Bio.codonalign is an experimental module which may undergo '
-              'significant changes prior to its future official release.',
-              BiopythonExperimentalWarning)
-
 try:
     from itertools import izip
 except ImportError:
@@ -30,6 +25,11 @@ from Bio.codonalign.codonalphabet import CodonAlphabet
 from Bio.codonalign.codonalphabet import default_codon_table, default_codon_alphabet
 from Bio.codonalign.codonalphabet import get_codon_alphabet as _get_codon_alphabet
 
+import warnings
+warnings.warn('Bio.codonalign is an experimental module which may undergo '
+              'significant changes prior to its future official release.',
+              BiopythonExperimentalWarning)
+
 
 def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
           codon_table=default_codon_table, alphabet=None,
@@ -40,12 +40,12 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
     Arguments:
         - pro_align  - a protein MultipleSeqAlignment object
         - nucl_align - an object returned by SeqIO.parse or SeqIO.index
-          or a colloction of SeqRecord.
+          or a collection of SeqRecord.
         - alphabet   - alphabet for the returned codon alignment
         - corr_dict  - a dict that maps protein id to nucleotide id
         - complete_protein - whether the sequence begins with a start
           codon
-        - frameshift - whether to appply frameshift detection
+        - frameshift - whether to apply frameshift detection
 
     Return a CodonAlignment object
 
@@ -93,12 +93,12 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
             nucl_seqs = tuple(nucl_seqs)
         nucl_num = len(nucl_seqs)
         if pro_num > nucl_num:
-            raise ValueError("More Number of SeqRecords in Protein Alignment "
+            raise ValueError("Higher Number of SeqRecords in Protein Alignment "
                              "({0}) than the Number of Nucleotide SeqRecords "
                              "({1}) are found!".format(pro_num, nucl_num))
 
         # Determine the protein sequences and nucl sequences
-        # correspondance. If nucl_seqs is a list, tuple or read by
+        # correspondence. If nucl_seqs is a list, tuple or read by
         # SeqIO.parse(), we assume the order of sequences in pro_align
         # and nucl_seqs are the same. If nucl_seqs is a dict or read by
         # SeqIO.index(), we match seqs in pro_align and those in
@@ -109,7 +109,7 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
             corr_method = 0
         else:
             raise TypeError("Nucl Sequences Error, Unknown type to assign "
-                            "correspondance method")
+                            "correspondence method")
     else:
         if not isinstance(corr_dict, dict):
             raise TypeError("corr_dict should be a dict that corresponds "
@@ -134,7 +134,7 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
                                "than number of protein records "
                                "({1})".format(len(corr_dict), pro_num))
 
-    # set up pro-nucl correspondance based on corr_method
+    # set up pro-nucl correspondence based on corr_method
     # corr_method = 0, consecutive pairing
     if corr_method == 0:
         pro_nucl_pair = izip(pro_align, nucl_seqs)
@@ -164,9 +164,9 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
             pro_nucl_pair.append((pro_rec, nucl_seqs[nucl_id]))
 
     codon_aln = []
-    shift = None
+    shift = False
     for pair in pro_nucl_pair:
-        # Beaware that the following span corresponds to an ungapped
+        # Beware that the following span corresponds to an ungapped
         # nucleotide sequence.
         corr_span = _check_corr(pair[0], pair[1], gap_char=gap_char,
                                 codon_table=codon_table,
@@ -184,7 +184,7 @@ def build(pro_align, nucl_seqs, corr_dict=None, gap_char='-', unknown='X',
             codon_aln.append(codon_rec)
             if corr_span[1] == 2:
                 shift = True
-    if shift is True:
+    if shift:
         return CodonAlignment(_align_shift_recs(codon_aln), alphabet=alphabet)
     else:
         return CodonAlignment(codon_aln, alphabet=alphabet)
@@ -204,7 +204,7 @@ def _codons2re(codons):
 
 def _get_aa_regex(codon_table, stop='*', unknown='X'):
     """Set up the regular expression of a given CodonTable for
-    futher use.
+    further use.
 
     >>> from Bio.Data.CodonTable import generic_by_id
     >>> p = generic_by_id[1]
@@ -245,8 +245,8 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
     import re
     from Bio.Alphabet import NucleotideAlphabet
 
-    if not all([isinstance(pro, SeqRecord), isinstance(nucl, SeqRecord)]):
-        raise TypeError("_check_corr accept two SeqRecord object. Please "
+    if not isinstance(pro, SeqRecord) or not isinstance(nucl, SeqRecord):
+        raise TypeError("_check_corr accepts two SeqRecord object. Please "
                         "check your input.")
 
     def get_alpha(alpha):
@@ -299,7 +299,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
             # pro_re
             if this_anchor_len == anchor_len:
                 for aa in anchor:
-                    if complete_protein is True and i == 0:
+                    if complete_protein and i == 0:
                         qcodon += _codons2re(codon_table.start_codons)
                         fncodon += aa2re['X']
                         continue
@@ -336,7 +336,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
             first_anchor = True
             shift_id_pos = 0
             # check the first anchor
-            if first_anchor is True and anchor_pos[0][2] != 0:
+            if first_anchor and anchor_pos[0][2] != 0:
                 shift_val_lst = [1, 2, 3 * anchor_len - 2, 3 * anchor_len - 1, 0]
                 sh_anc = anchors[0]
                 for shift_val in shift_val_lst:
@@ -364,7 +364,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
                         break
                 if qcodon == -1:
                     warnings.warn("first frameshift detection failed for "
-                                  "{0}".format(nucl.id))
+                                  "{0}".format(nucl.id), BiopythonWarning)
             # check anchors in the middle
             for i in range(len(anchor_pos) - 1):
                 shift_val = (anchor_pos[i + 1][0] - anchor_pos[i][0]) % \
@@ -383,7 +383,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
                     qcodon = None
                 elif qcodon == -1:
                     warnings.warn("middle frameshift detection failed for "
-                                  "{0}".format(nucl.id))
+                                  "{0}".format(nucl.id), BiopythonWarning)
             # check the last anchor
             if anchor_pos[-1][2] + 1 == len(anchors) - 1:
                 sh_anc = anchors[-1]
@@ -414,7 +414,7 @@ def _check_corr(pro, nucl, gap_char='-', codon_table=default_codon_table,
                         break
                 if qcodon == -1:
                     warnings.warn("last frameshift detection failed for "
-                                  "{0}".format(nucl.id))
+                                  "{0}".format(nucl.id), BiopythonWarning)
             # try global match
             full_pro_re = "".join(pro_re)
             match = re.search(full_pro_re, nucl_seq)
@@ -561,7 +561,7 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
         for aa in pro.seq:
             if aa == "-":
                 codon_seq += "---"
-            elif complete_protein is True and aa_num == 0:
+            elif complete_protein and aa_num == 0:
                 this_codon = nucl_seq._data[span[0]:span[0] + 3]
                 if not re.search(_codons2re[codon_table.start_codons],
                                  this_codon.upper()):
@@ -569,8 +569,8 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
                     warnings.warn("start codon of {0} ({1} {2}) does not "
                                   "correspond to {3} "
                                   "({4})".format(pro.id, aa, aa_num,
-                                                 nucl.id, this_codon)
-                                  )
+                                                 nucl.id, this_codon),
+                                  BiopythonWarning)
                 if max_score == 0:
                     raise RuntimeError("max_score reached for {0}! Please "
                                        "raise up the tolerance to get an "
@@ -583,7 +583,8 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
                 if not str(Seq(this_codon.upper()).translate(table=codon_table)) == aa:
                     max_score -= 1
                     warnings.warn("%s(%s %d) does not correspond to %s(%s)"
-                                  % (pro.id, aa, aa_num, nucl.id, this_codon))
+                                  % (pro.id, aa, aa_num, nucl.id, this_codon),
+                                  BiopythonWarning)
                 if max_score == 0:
                     raise RuntimeError("max_score reached for {0}! Please "
                                        "raise up the tolerance to get an "
@@ -623,15 +624,15 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
         for aa in pro.seq:
             if aa == "-":
                 codon_seq += "---"
-            elif complete_protein is True and aa_num == 0:
+            elif complete_protein and aa_num == 0:
                 this_codon = nucl_seq._data[rf_table[0]:rf_table[0] + 3]
                 if not re.search(_codons2re[codon_table.start_codons],
                                  this_codon.upper()):
                     max_score -= 1
                     warnings.warn("start codon of {0}({1} {2}) does not "
                                   "correspond to {3}({4})".format(
-                                      pro.id, aa, aa_num, nucl.id, this_codon)
-                                  )
+                                      pro.id, aa, aa_num, nucl.id, this_codon),
+                                  BiopythonWarning)
                     codon_seq += this_codon
                     aa_num += 1
             else:
@@ -658,8 +659,8 @@ def _get_codon_rec(pro, nucl, span_mode, alphabet, gap_char="-",
                         warnings.warn("Codon of {0}({1} {2}) does not "
                                       "correspond to {3}({4})".format(
                                           pro.id, aa, aa_num, nucl.id,
-                                          this_codon)
-                                      )
+                                          this_codon),
+                                      BiopythonWarning)
                 if max_score == 0:
                     raise RuntimeError("max_score reached for {0}! Please "
                                        "raise up the tolerance to get an "
diff --git a/Bio/codonalign/chisq.py b/Bio/codonalign/chisq.py
index 077dd7a..1a4f130 100644
--- a/Bio/codonalign/chisq.py
+++ b/Bio/codonalign/chisq.py
@@ -4,9 +4,257 @@ Adapted from SciPy: scipy/special/cephes/{chdtr,igam}.
 """
 
 import math
-import sys
 
-__docformat__ = "restructuredtext en"
+try:
+    from math import lgamma as _lgamma
+except ImportError:
+    # Missing in Python 2.6, using Python Python recipe from
+    # http://code.activestate.com/recipes/576393/
+    # with PEP8 whitespace and minor import changes
+    #
+    # * ====================================================
+    # * Copyright (C) 1993 by Sun Microsystems, Inc. All rights reserved.
+    # *
+    # * Developed at SunPro, a Sun Microsystems, Inc. business.
+    # * Permission to use, copy, modify, and distribute this
+    # * software is freely granted, provided that this notice
+    # * is preserved.
+    # * ====================================================
+    two52 = 4.50359962737049600000e+15
+    half = 5.00000000000000000000e-01
+    one = 1.00000000000000000000e+00
+    pi = 3.14159265358979311600e+00
+    a0 = 7.72156649015328655494e-02
+    a1 = 3.22467033424113591611e-01
+    a2 = 6.73523010531292681824e-02
+    a3 = 2.05808084325167332806e-02
+    a4 = 7.38555086081402883957e-03
+    a5 = 2.89051383673415629091e-03
+    a6 = 1.19270763183362067845e-03
+    a7 = 5.10069792153511336608e-04
+    a8 = 2.20862790713908385557e-04
+    a9 = 1.08011567247583939954e-04
+    a10 = 2.52144565451257326939e-05
+    a11 = 4.48640949618915160150e-05
+    tc = 1.46163214496836224576e+00
+    tf = -1.21486290535849611461e-01
+    # /* tt = -(tail of tf) */
+    tt = -3.63867699703950536541e-18
+    t0 = 4.83836122723810047042e-01
+    t1 = -1.47587722994593911752e-01
+    t2 = 6.46249402391333854778e-02
+    t3 = -3.27885410759859649565e-02
+    t4 = 1.79706750811820387126e-02
+    t5 = -1.03142241298341437450e-02
+    t6 = 6.10053870246291332635e-03
+    t7 = -3.68452016781138256760e-03
+    t8 = 2.25964780900612472250e-03
+    t9 = -1.40346469989232843813e-03
+    t10 = 8.81081882437654011382e-04
+    t11 = -5.38595305356740546715e-04
+    t12 = 3.15632070903625950361e-04
+    t13 = -3.12754168375120860518e-04
+    t14 = 3.35529192635519073543e-04
+    u0 = -7.72156649015328655494e-02
+    u1 = 6.32827064025093366517e-01
+    u2 = 1.45492250137234768737e+00
+    u3 = 9.77717527963372745603e-01
+    u4 = 2.28963728064692451092e-01
+    u5 = 1.33810918536787660377e-02
+    v1 = 2.45597793713041134822e+00
+    v2 = 2.12848976379893395361e+00
+    v3 = 7.69285150456672783825e-01
+    v4 = 1.04222645593369134254e-01
+    v5 = 3.21709242282423911810e-03
+    s0 = -7.72156649015328655494e-02
+    s1 = 2.14982415960608852501e-01
+    s2 = 3.25778796408930981787e-01
+    s3 = 1.46350472652464452805e-01
+    s4 = 2.66422703033638609560e-02
+    s5 = 1.84028451407337715652e-03
+    s6 = 3.19475326584100867617e-05
+    r1 = 1.39200533467621045958e+00
+    r2 = 7.21935547567138069525e-01
+    r3 = 1.71933865632803078993e-01
+    r4 = 1.86459191715652901344e-02
+    r5 = 7.77942496381893596434e-04
+    r6 = 7.32668430744625636189e-06
+    w0 = 4.18938533204672725052e-01
+    w1 = 8.33333333333329678849e-02
+    w2 = -2.77777777728775536470e-03
+    w3 = 7.93650558643019558500e-04
+    w4 = -5.95187557450339963135e-04
+    w5 = 8.36339918996282139126e-04
+    w6 = -1.63092934096575273989e-03
+    zero = 0.00000000000000000000e+00
+
+    # inf = float('inf')
+    # nan = float('nan')
+    inf = float(9e999)
+
+    def _sin_pi(x):
+        x = float(x)
+        e, ix = math.frexp(x)
+        if(abs(x) < 0.25):
+            return -math.sin(pi * x)
+        y = -x  # /* x is assume negative */
+
+        # * argument reduction, make sure inexact flag not raised if input
+        # * is an integer
+        z = floor(y)
+        if(z != y):
+            y *= 0.5
+            y = 2.0 * (y - floor(y))  # /* y = |x| mod 2.0 */
+            n = int(y * 4.0)
+        else:
+            if(abs(ix) >= 53):
+                y = zero
+                n = 0  # /* y must be even */
+            else:
+                if(abs(ix) < 52):
+                    z = y + two52  # /* exact */
+                e, n = math.frexp(z)
+                n &= 1
+                y = n
+                n <<= 2
+
+        if n == 0:
+            y = sin(pi * y)
+        elif (n == 1 or n == 2):
+            y = cos(pi * (0.5 - y))
+        elif (n == 3 or n == 4):
+            y = sin(pi * (one - y))
+        elif (n == 5 or n == 6):
+            y = -cos(pi * (y - 1.5))
+        else:
+            y = sin(pi * (y - 2.0))
+
+        z = cos(pi * (z + 1.0))
+        return -y * z
+
+    def _lgamma(x):
+        """Natural logarithm of gamma function of x
+
+        raise ValueError if x is negative integer."""
+        x = float(x)
+
+        # /* purge off +-inf, NaN, +-0, and negative arguments */
+        if ((x == inf) or (x == -inf)):
+            return inf
+        # if (x is nan):
+        # return nan
+
+        e, ix = math.frexp(x)
+        nadj = 0
+        signgamp = 1
+
+        if ((e == 0.0) and (ix == 0)):
+            return inf
+
+        if (ix > 1020):
+            return inf
+
+        if ((e != 0.0) and (ix < -71)):
+            if (x < 0):
+                return -math.log(-x)
+            else:
+                return -math.log(x)
+
+        if e < 0:
+            if ix > 52:
+                return inf  # one/zero
+            t = sin_pi(x)
+            if t == zero:
+                # return inf
+                raise ValueError('gamma not defined for negative integer')
+            nadj = math.log(pi / fabs(t * x))
+            if t < zero:
+                signgamp = -1
+            x = -x
+
+        # /* purge off 1 and 2 */
+        if x == 2.0 or x == 1.0:
+            r = 0.0
+
+        # /* for x < 2.0 */
+        elif ix < 2:
+            if x <= 0.9:  # /* lgamma(x) = lgamma(x+1)-log(x) */
+                r = -math.log(x)
+                if x >= 0.7316:
+                    y = one - x
+                    z = y * y
+                    p1 = a0 + z * (a2 + z * (a4 + z * (a6 + z * (a8 + z * a10))))
+                    p2 = z * (a1 + z * (a3 + z * (a5 + z * (a7 + z * (a9 + z * a11)))))
+                    p = y * p1 + p2
+                    r += (p - 0.5 * y)
+                elif (x >= 0.23164):
+                    y = x - (tc - one)
+                    z = y * y
+                    w = z * y
+                    p1 = t0 + w * (t3 + w * (t6 + w * (t9 + w * t12)))  # /* parallel comp */
+                    p2 = t1 + w * (t4 + w * (t7 + w * (t10 + w * t13)))
+                    p3 = t2 + w * (t5 + w * (t8 + w * (t11 + w * t14)))
+                    p = z * p1 - (tt - w * (p2 + y * p3))
+                    r += (tf + p)
+                else:
+                    y = x
+                    p1 = y * (u0 + y * (u1 + y * (u2 + y * (u3 + y * (u4 + y * u5)))))
+                    p2 = one + y * (v1 + y * (v2 + y * (v3 + y * (v4 + y * v5))))
+                    r += (-0.5 * y + p1 / p2)
+            else:
+                r = zero
+                if(x >= 1.7316):
+                    y = 2.0 - x  # /* [1.7316,2] */
+                    z = y * y
+                    p1 = a0 + z * (a2 + z * (a4 + z * (a6 + z * (a8 + z * a10))))
+                    p2 = z * (a1 + z * (a3 + z * (a5 + z * (a7 + z * (a9 + z * a11)))))
+                    p = y * p1 + p2
+                    r += (p - 0.5 * y)
+                elif(x >= 1.23164):
+                    y = x - tc  # /* [1.23,1.73] */
+                    z = y * y
+                    w = z * y
+                    p1 = t0 + w * (t3 + w * (t6 + w * (t9 + w * t12)))  # /* parallel comp */
+                    p2 = t1 + w * (t4 + w * (t7 + w * (t10 + w * t13)))
+                    p3 = t2 + w * (t5 + w * (t8 + w * (t11 + w * t14)))
+                    p = z * p1 - (tt - w * (p2 + y * p3))
+                    r += (tf + p)
+                else:
+                    y = x - one
+                    p1 = y * (u0 + y * (u1 + y * (u2 + y * (u3 + y * (u4 + y * u5)))))
+                    p2 = one + y * (v1 + y * (v2 + y * (v3 + y * (v4 + y * v5))))
+                    r += (-0.5 * y + p1 / p2)
+
+        # /* x < 8.0 */
+        elif(ix < 4):
+            i = int(x)
+            t = zero
+            y = x - i
+            p = y * (s0 + y * (s1 + y * (s2 + y * (s3 + y * (s4 + y * (s5 + y * s6))))))
+            q = one + y * (r1 + y * (r2 + y * (r3 + y * (r4 + y * (r5 + y * r6)))))
+            r = half * y + p / q
+            z = one  # /* lgamma(1+s) = log(s) + lgamma(s) */
+            while (i > 2):
+                i -= 1
+                z *= (y + i)
+                r += log(z)
+
+        # /* 8.0 <= x < 2**58 */
+        elif (ix < 58):
+            t = log(x)
+            z = one / x
+            y = z * z
+            w = w0 + z * (w1 + y * (w2 + y * (w3 + y * (w4 + y * (w5 + y * w6)))))
+            r = (x - half) * (t - one) + w
+
+        # /* 2**58 <= x <= inf */
+        else:
+            r = x * (log(x) - one)
+
+        if (e < 0):
+            r = nadj - r
+        return signgamp * r
+
 
 # Cephes Math Library Release 2.0:  April, 1987
 # Copyright 1985, 1987 by Stephen L. Moshier
@@ -74,7 +322,8 @@ def _igamc(a, x):
     values of a and x.
     """
     # Compute  x**a * exp(-x) / Gamma(a)
-    ax = math.exp(a * math.log(x) - x - math.lgamma(a))
+    # TODO: Return this to math.lgamma once drop Python 2.6
+    ax = math.exp(a * math.log(x) - x - _lgamma(a))
 
     # Continued fraction
     y = 1.0 - a
diff --git a/Bio/codonalign/codonalignment.py b/Bio/codonalign/codonalignment.py
index 5796883..f75ae77 100644
--- a/Bio/codonalign/codonalignment.py
+++ b/Bio/codonalign/codonalignment.py
@@ -5,13 +5,11 @@
 # as part of this package.
 """Code for dealing with Codon Alignment.
 
-CodonAlignment class is interited from MultipleSeqAlignment class. This is
+CodonAlignment class is inherited from MultipleSeqAlignment class. This is
 the core class to deal with codon alignment in biopython.
 """
 from __future__ import division, print_function
 
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 from Bio.Align import MultipleSeqAlignment
 from Bio.SeqRecord import SeqRecord
 
@@ -43,7 +41,7 @@ class CodonAlignment(MultipleSeqAlignment):
         # check the type of the alignment to be nucleotide
         for rec in self:
             if not isinstance(rec.seq, CodonSeq):
-                raise TypeError("CodonSeq object are expected in each "
+                raise TypeError("CodonSeq objects are expected in each "
                                 "SeqRecord in CodonAlignment")
 
         assert self.get_alignment_length() % 3 == 0, \
@@ -155,7 +153,7 @@ class CodonAlignment(MultipleSeqAlignment):
             dn_tree = dn_constructor.nj(dn_dm)
             ds_tree = ds_constructor.nj(ds_dm)
         else:
-            raise RuntimeError("Unkown tree method ({0}). Only NJ and UPGMA "
+            raise RuntimeError("Unknown tree method ({0}). Only NJ and UPGMA "
                                "are accepted.".format(tree_method))
         return dn_tree, ds_tree
 
@@ -182,8 +180,8 @@ def mktest(codon_alns, codon_table=default_codon_table, alpha=0.05):
     Return the p-value of test result
     """
     import copy
-    if not all([isinstance(i, CodonAlignment) for i in codon_alns]):
-        raise TypeError("mktest accept CodonAlignment list.")
+    if not all(isinstance(i, CodonAlignment) for i in codon_alns):
+        raise TypeError("mktest accepts CodonAlignment list.")
     codon_aln_len = [i.get_alignment_length() for i in codon_alns]
     if len(set(codon_aln_len)) != 1:
         raise RuntimeError("CodonAlignment object for mktest should be of"
@@ -212,7 +210,7 @@ def mktest(codon_alns, codon_table=default_codon_table, alpha=0.05):
         all_codon = i[0].union(*i[1:])
         if '-' in all_codon or len(all_codon) == 1:
             continue
-        fix_or_not = all([len(k) == 1 for k in i])
+        fix_or_not = all(len(k) == 1 for k in i)
         if fix_or_not:
             # fixed
             nonsyn_subgraph = _get_subgraph(all_codon, nonsyn_G)
@@ -233,7 +231,7 @@ def mktest(codon_alns, codon_table=default_codon_table, alpha=0.05):
 
 
 def _get_codon2codon_matrix(codon_table=default_codon_table):
-    """Function to get codon codon subsitution matrix. Elements
+    """Function to get codon codon substitution matrix. Elements
     in the matrix are number of synonymous and nonsynonymous
     substitutions required for the substitution (PRIVATE).
     """
@@ -287,7 +285,7 @@ def _dijkstra(graph, start, end):
     Dijkstra's algorithm Python implementation.
     Algorithm adapted from
     http://thomas.pelletier.im/2010/02/dijkstras-algorithm-python-implementation/.
-    However, an abvious bug in::
+    However, an obvious bug in::
 
         if D[child_node] >(<) D[node] + child_value:
 
@@ -296,12 +294,12 @@ def _dijkstra(graph, start, end):
 
     Arguments:
 
-        - graph: Dictionnary of dictionnary (keys are vertices).
+        - graph: Dictionary of dictionary (keys are vertices).
         - start: Start vertex.
         - end: End vertex.
 
     Output:
-        List of vertices from the beggining to the end.
+        List of vertices from the beginning to the end.
     """
     D = {}  # Final distances dict
     P = {}  # Predecessor dict
@@ -419,7 +417,7 @@ def _G_test(site_counts):
 
         - site_counts - [syn_fix, nonsyn_fix, syn_poly, nonsyn_poly]
 
-    >>> round(_G_test([17, 7, 42, 2]), 7)
+    >>> print("%0.6f" % _G_test([17, 7, 42, 2]))
     0.004924
     """
     # TODO:
diff --git a/Bio/codonalign/codonalphabet.py b/Bio/codonalign/codonalphabet.py
index e45033d..4426284 100644
--- a/Bio/codonalign/codonalphabet.py
+++ b/Bio/codonalign/codonalphabet.py
@@ -5,11 +5,9 @@
 # as part of this package.
 """Code for Codon Alphabet.
 
-CodonAlphabet class is interited from Alphabet class. It is an
+CodonAlphabet class is inherited from Alphabet class. It is an
 alphabet for CodonSeq class.
 """
-__docformat__ = "restructuredtext en"  # Don't just use plain text in epydoc API pages!
-
 
 import copy
 try:
@@ -17,9 +15,7 @@ try:
 except ImportError:
     izip = zip
 
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import IUPAC, Gapped, HasStopCodon, Alphabet, generic_dna
+from Bio.Alphabet import IUPAC, Gapped, HasStopCodon, Alphabet
 from Bio.Data.CodonTable import generic_by_id
 
 default_codon_table = copy.deepcopy(generic_by_id[1])
@@ -27,8 +23,8 @@ default_codon_table = copy.deepcopy(generic_by_id[1])
 
 def get_codon_alphabet(alphabet, gap="-", stop="*"):
     """Gets alignment alphabet for codon alignment.
-    
-    Only nucleotide alphabet is accepted. Raise an error when the type of 
+
+    Only nucleotide alphabet is accepted. Raise an error when the type of
     alphabet is incompatible.
     """
     from Bio.Alphabet import NucleotideAlphabet
diff --git a/Bio/codonalign/codonseq.py b/Bio/codonalign/codonseq.py
index f01435e..0afe6e3 100644
--- a/Bio/codonalign/codonseq.py
+++ b/Bio/codonalign/codonseq.py
@@ -1,11 +1,11 @@
-# Copyright 2013 by Zheng Ruan (zruan1991 at gmai.com).
+# Copyright 2013 by Zheng Ruan (zruan1991 at gmail.com).
 # All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 """Code for dealing with coding sequence.
 
-CodonSeq class is interited from Seq class. This is the core class to
+CodonSeq class is inherited from Seq class. This is the core class to
 deal with sequences in CodonAlignment in biopython.
 
 """
@@ -15,14 +15,10 @@ from math import log
 
 from Bio.Seq import Seq
 from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import IUPAC, Gapped, HasStopCodon, Alphabet
 from Bio.Alphabet import generic_dna, _ungap
-from Bio.Data.CodonTable import generic_by_id
 
 from Bio.codonalign.codonalphabet import default_codon_alphabet, default_codon_table
 
-__docformat__ = "restructuredtext en"
-
 
 class CodonSeq(Seq):
     """CodonSeq is designed to be within the SeqRecords of a CodonAlignment class.
@@ -116,7 +112,7 @@ class CodonSeq(Seq):
             raise RuntimeError("frameshift detected. "
                                "CodonSeq object is not able to deal "
                                "with codon sequence with frameshift. "
-                               "Plase use normal slice option.")
+                               "Please use normal slice option.")
         if isinstance(index, int):
             if index != -1:
                 return self._data[index * 3:(index + 1) * 3]
@@ -156,7 +152,7 @@ class CodonSeq(Seq):
         purpose.
         """
         amino_acids = []
-        if ungap_seq is True:
+        if ungap_seq:
             tr_seq = self._data.replace(self.gap_char, "")
         else:
             tr_seq = self._data
@@ -188,8 +184,8 @@ class CodonSeq(Seq):
             try:
                 amino_acids.append(codon_table.forward_table[codon])
             except KeyError:
-                raise RuntimeError("Unknown codon detected ({0}). Do you "
-                                   "forget to speficy ungap_seq "
+                raise RuntimeError("Unknown codon detected ({0}). Did you "
+                                   "forget to specify the ungap_seq "
                                    "argument?".format(codon))
         return "".join(amino_acids)
 
@@ -231,7 +227,7 @@ class CodonSeq(Seq):
             try:
                 this_len = len(self._data[i:i + 3].replace("-", ""))
                 relative_pos[codon_num] -= this_len
-            except:
+            except Exception:  # TODO: IndexError?
                 # we probably reached the last codon
                 pass
         return full_rf_table
@@ -290,7 +286,7 @@ def _get_codon_list(codonseq):
         elif str(codonseq[int(k):int(k) + 3]) == "---":
             codon_lst.append("---")
         else:
-            # this may be problematic, as normally no codon shoud
+            # this may be problematic, as normally no codon should
             # fall into this condition
             codon_lst.append(codonseq[int(k):int(k) + 3])
     return codon_lst
@@ -314,16 +310,14 @@ def cal_dn_ds(codon_seq1, codon_seq2, method="NG86",
     Arguments:
         - codon_seq1 - CodonSeq or or SeqRecord that contains a CodonSeq
         - codon_seq2 - CodonSeq or or SeqRecord that contains a CodonSeq
-        - w  - transition/transvertion ratio
+        - w  - transition/transversion ratio
         - cfreq - Current codon frequency vector can only be specified
           when you are using ML method. Possible ways of
           getting cfreq are: F1x4, F3x4 and F61.
     """
-    if all([isinstance(codon_seq1, CodonSeq),
-           isinstance(codon_seq2, CodonSeq)]):
+    if isinstance(codon_seq1, CodonSeq) and isinstance(codon_seq2, CodonSeq):
         pass
-    elif all([isinstance(codon_seq1, SeqRecord),
-             isinstance(codon_seq2, SeqRecord)]):
+    elif isinstance(codon_seq1, SeqRecord) and isinstance(codon_seq2, SeqRecord):
         codon_seq1 = codon_seq1.seq
         codon_seq2 = codon_seq2.seq
     else:
@@ -459,12 +453,10 @@ def _count_diff_NG86(codon1, codon2, codon_table=default_codon_table):
     The function will take multiple pathways from codon1 to codon2
     into account.
     """
-    if not all([isinstance(codon1, str), isinstance(codon2, str)]):
-        raise TypeError("_count_diff_NG86 accept string object to represent "
-                        "codon ({0}, {1} detected)".format(
-                                                        type(codon1),
-                                                        type(codon2))
-                       )
+    if not isinstance(codon1, str) or not isinstance(codon2, str):
+        raise TypeError("_count_diff_NG86 accepts string object "
+                        "to represent codon ({0}, {1} "
+                        "detected)".format(type(codon1), type(codon2)))
     if len(codon1) != 3 or len(codon2) != 3:
         raise RuntimeError("codon should be three letter string ({0}, {1} "
                            "detected)".format(len(codon1), len(codon2)))
@@ -472,13 +464,13 @@ def _count_diff_NG86(codon1, codon2, codon_table=default_codon_table):
     if codon1 == '---' or codon2 == '---':
         return SN
     base_tuple = ('A', 'C', 'G', 'T')
-    if not all([i in base_tuple for i in codon1]):
+    if not all(i in base_tuple for i in codon1):
         raise RuntimeError("Unrecognized character detected in codon1 {0} "
-                           "(Codon is consist of "
+                           "(Codons consist of "
                            "A, T, C or G)".format(codon1))
-    if not all([i in base_tuple for i in codon2]):
+    if not all(i in base_tuple for i in codon2):
         raise RuntimeError("Unrecognized character detected in codon2 {0} "
-                           "(Codon is consist of "
+                           "(Codons consist of "
                            "A, T, C or G)".format(codon2))
     if codon1 == codon2:
         return SN
@@ -650,8 +642,8 @@ def _diff_codon(codon1, codon2, fold_dict):
                     P4 += 1
                 else:
                     raise RuntimeError("Unexpected fold_num %d" % fold_num[n])
-            if i != j and ((i in purine and j in pyrimidine)
-                    or (i in pyrimidine and j in purine)):
+            if i != j and ((i in purine and j in pyrimidine) or
+                           (i in pyrimidine and j in purine)):
                 if fold_num[n] == '0':
                     Q0 += 1
                 elif fold_num[n] == '2':
@@ -750,7 +742,7 @@ def _yn00(seq1, seq2, k, codon_table):
                                 codon_table.stop_codons if 'U' not in i]
         Q = _get_Q(pi, kappa, w, codon_lst, codon_table)
         P = expm(Q * t)
-        TV = [0, 0, 0, 0]  # synonymous/nonsynonymous transition/transvertion
+        TV = [0, 0, 0, 0]  # synonymous/nonsynonymous transition/transversion
         sites = [0, 0]
         codon_npath = {}
         for i, j in zip(seq1, seq2):
@@ -896,30 +888,28 @@ def _count_diff_YN00(codon1, codon2, P, codon_lst,
 
     The function will weighted multiple pathways from codon1 to codon2
     according to P matrix of codon substitution. The proportion
-    of transition and transvertion (TV) will also be calculated in
+    of transition and transversion (TV) will also be calculated in
     the function.
     """
-    if not all([isinstance(codon1, str), isinstance(codon2, str)]):
-        raise TypeError("_count_diff_YN00 accept string object to represent "
-                        "codon ({0}, {1} detected)".format(
-                                                        type(codon1),
-                                                        type(codon2))
-                       )
+    if not isinstance(codon1, str) or not isinstance(codon2, str):
+        raise TypeError("_count_diff_YN00 accepts string object "
+                        "to represent codon ({0}, {1} "
+                        "detected)".format(type(codon1), type(codon2)))
     if len(codon1) != 3 or len(codon2) != 3:
         raise RuntimeError("codon should be three letter string ({0}, {1} "
                            "detected)".format(len(codon1), len(codon2)))
-    TV = [0, 0, 0, 0]  # transition and transvertion counts (synonymous and nonsynonymous)
+    TV = [0, 0, 0, 0]  # transition and transversion counts (synonymous and nonsynonymous)
     site = 0
     if codon1 == '---' or codon2 == '---':
         return TV
     base_tuple = ('A', 'C', 'G', 'T')
-    if not all([i in base_tuple for i in codon1]):
+    if not all(i in base_tuple for i in codon1):
         raise RuntimeError("Unrecognized character detected in codon1 {0} "
-                           "(Codon is consist of "
+                           "(Codons consist of "
                            "A, T, C or G)".format(codon1))
-    if not all([i in base_tuple for i in codon2]):
+    if not all(i in base_tuple for i in codon2):
         raise RuntimeError("Unrecognized character detected in codon2 {0} "
-                           "(Codon is consist of "
+                           "(Codons consist of "
                            "A, T, C or G)".format(codon2))
     if codon1 == codon2:
         return TV
diff --git a/Bio/kNN.py b/Bio/kNN.py
index fc000a6..e5d0f65 100644
--- a/Bio/kNN.py
+++ b/Bio/kNN.py
@@ -33,14 +33,12 @@ Weighting Functions:
 
 import numpy
 
-__docformat__ = "restructuredtext en"
-
 
 class kNN(object):
     """Holds information necessary to do nearest neighbors classification.
 
     Members:
-    
+
         - classes  Set of the possible classes.
         - xs       List of the neighbors.
         - ys       List of the classes that the neighbors belong to.
diff --git a/Bio/motifs/__init__.py b/Bio/motifs/__init__.py
index 2ade664..4118fe2 100644
--- a/Bio/motifs/__init__.py
+++ b/Bio/motifs/__init__.py
@@ -19,8 +19,6 @@ from Bio._py3k import range
 
 import math
 
-__docformat__ = "restructuredtext en"
-
 
 def create(instances, alphabet=None):
     instances = Instances(instances, alphabet)
@@ -440,6 +438,16 @@ class Motif(object):
 
         """
         from Bio._py3k import urlopen, urlencode, Request
+        from Bio import Alphabet
+
+        if isinstance(self.alphabet, Alphabet.ProteinAlphabet):
+            alpha = "alphabet_protein"
+        elif isinstance(self.alphabet, Alphabet.RNAAlphabet):
+            alpha = "alphabet_rna"
+        elif isinstance(self.alphabet, Alphabet.DNAAlphabet):
+            alpha = "alphabet_dna"
+        else:
+            alpha = "auto"
 
         frequencies = self.format('transfac')
         url = 'http://weblogo.threeplusone.com/create.cgi'
@@ -447,7 +455,7 @@ class Motif(object):
                   'format': format.lower(),
                   'stack_width': 'medium',
                   'stack_per_line': '40',
-                  'alphabet': 'alphabet_dna',
+                  'alphabet': alpha,
                   'ignore_lower_case': True,
                   'unit_name': "bits",
                   'first_index': '1',
diff --git a/Bio/motifs/applications/__init__.py b/Bio/motifs/applications/__init__.py
index 243d576..c2743e0 100644
--- a/Bio/motifs/applications/__init__.py
+++ b/Bio/motifs/applications/__init__.py
@@ -4,6 +4,4 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 """Motif command line tool wrappers."""
-from ._alignace import AlignAceCommandline
-from ._alignace import CompareAceCommandline
 from ._xxmotif import XXmotifCommandline
diff --git a/Bio/motifs/applications/_alignace.py b/Bio/motifs/applications/_alignace.py
deleted file mode 100644
index 37ea10d..0000000
--- a/Bio/motifs/applications/_alignace.py
+++ /dev/null
@@ -1,146 +0,0 @@
-# Copyright 2003-2009 by Bartek Wilczynski.  All rights reserved.
-# Revisions copyright 2009 by Peter Cock.
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-"""This module provides code to work with the standalone version of AlignACE,
-for motif search in DNA sequences.
-
-AlignACE homepage:
-
-http://arep.med.harvard.edu/mrnadata/mrnasoft.html
-
-AlignACE Citations:
-
-Computational identification of cis-regulatory elements associated with
-groups of functionally related genes in Saccharomyces cerevisiae,
-Hughes, JD, Estep, PW, Tavazoie S, & GM Church, Journal of Molecular
-Biology 2000 Mar 10;296(5):1205-14.
-
-Finding DNA Regulatory Motifs within Unaligned Non-Coding Sequences
-Clustered by Whole-Genome mRNA Quantitation,
-Roth, FR, Hughes, JD, Estep, PE & GM Church, Nature Biotechnology
-1998 Oct;16(10):939-45.
-
-"""
-from __future__ import print_function
-
-from Bio.Application import AbstractCommandline, _Option, _Argument
-
-import warnings
-from Bio import BiopythonDeprecationWarning
-
-
-class AlignAceCommandline(AbstractCommandline):
-    """Create a commandline for the AlignAce program (DEPRECATED).
-
-    Example:
-
-    >>> from Bio.motifs.applications import AlignAceCommandline
-    >>> in_file = "sequences.fasta"
-    >>> alignace_cline = AlignAceCommandline(infile=in_file, gcback=0.55)
-    >>> print(alignace_cline)
-    AlignACE -i sequences.fasta -gcback 0.55
-
-    You would typically run the command line with alignace_cline() or via
-    the Python subprocess module, as described in the Biopython tutorial.
-    """
-    def __init__(self, cmd="AlignACE", **kwargs):
-        warnings.warn("""The AlignACE application wrapper is deprecated and
-                      is likely to be removed in a future release of Biopython,
-                      since an up to date version of the AlignACE software
-                      cannot be obtained anymore. If you have a copy of
-                      AlignACE 4, please consider contacting the Biopython
-                      developers.""", BiopythonDeprecationWarning)
-        self.parameters = \
-          [
-            _Option(["-i", "infile"],
-                    "Input Sequence file in FASTA format.",
-                    checker_function=lambda x: isinstance(x, str),
-                    equate=False,
-                    filename=True),
-
-            _Option(["-numcols", "numcols"],
-                    "Number of columns to align",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, int)),
-
-            _Option(["-expect", "expect"],
-                    "number of sites expected in model",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, int)),
-
-            _Option(["-gcback", "gcback"],
-                    "background fractional GC content of input sequence",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, float)),
-
-            _Option(["-minpass", "minpass"],
-                    "minimum number of non-improved passes in phase 1",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, int)),
-
-            _Option(["-seed", "seed"],
-                    "set seed for random number generator (time)",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, int)),
-
-            _Option(["-undersample", "undersample"],
-                    "possible sites / (expect * numcols * seedings)",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, int)),
-
-            _Option(["-oversample", "oversample"],
-                    "1/undersample",
-                    equate=False,
-                    checker_function=lambda x: isinstance(x, int)),
-          ]
-        AbstractCommandline.__init__(self, cmd, **kwargs)
-
-
-class CompareAceCommandline(AbstractCommandline):
-    """Create a commandline for the CompareAce program.
-
-    Example:
-
-    >>> from Bio.motifs.applications import CompareAceCommandline
-    >>> m1_file = "sequences1.fasta"
-    >>> m2_file = "sequences2.fasta"
-    >>> compareace_cline = CompareAceCommandline(motif1=m1_file, motif2=m2_file)
-    >>> print(compareace_cline)
-    CompareACE sequences1.fasta sequences2.fasta
-
-    You would typically run the command line with compareace_cline() or via
-    the Python subprocess module, as described in the Biopython tutorial.
-    """
-    def __init__(self, cmd="CompareACE", **kwargs):
-        warnings.warn("""The CompareACE application wrapper is deprecated and
-                      is likely to be removed in a future release of Biopython,
-                      since an up to date version of the AlignACE software
-                      cannot be obtained anymore. If you have a copy of
-                      AlignACE 4, please consider contacting the Biopython
-                      developers.""", BiopythonDeprecationWarning)
-        self.parameters = \
-          [
-            _Argument(["motif1"],
-                        "name of file containing motif 1",
-                        checker_function=lambda x: isinstance(x, str),
-                        filename=True),
-            _Argument(["motif2"],
-                        "name of file containing motif 2",
-                        checker_function=lambda x: isinstance(x, str),
-                        filename=True),
-          ]
-        AbstractCommandline.__init__(self, cmd, **kwargs)
-
-
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running alignace doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
-
-if __name__ == "__main__":
-    _test()
diff --git a/Bio/motifs/applications/_xxmotif.py b/Bio/motifs/applications/_xxmotif.py
index 2da96bc..f6c0ea7 100644
--- a/Bio/motifs/applications/_xxmotif.py
+++ b/Bio/motifs/applications/_xxmotif.py
@@ -177,13 +177,6 @@ class XXmotifCommandline(AbstractCommandline):
         AbstractCommandline.__init__(self, cmd, **kwargs)
 
 
-def _test():
-    """Run the module's doctests (PRIVATE)."""
-    print("Running XXmotif doctests...")
-    import doctest
-    doctest.testmod()
-    print("Done")
-
-
 if __name__ == "__main__":
-    _test()
+    from Bio._utils import run_doctest
+    run_doctest()
diff --git a/Bio/motifs/jaspar/__init__.py b/Bio/motifs/jaspar/__init__.py
index 1233e25..ff4f549 100644
--- a/Bio/motifs/jaspar/__init__.py
+++ b/Bio/motifs/jaspar/__init__.py
@@ -127,7 +127,7 @@ class Record(list):
         return "\n".join(str(the_motif) for the_motif in self)
 
     def to_dict(self):
-        """Return the list of matrices as a dictionnary of matrices."""
+        """Return the list of matrices as a dictionary of matrices."""
         dic = {}
         for motif in self:
             dic[motif.matrix_id] = motif
diff --git a/Bio/motifs/jaspar/db.py b/Bio/motifs/jaspar/db.py
index 90b47f3..e50085c 100644
--- a/Bio/motifs/jaspar/db.py
+++ b/Bio/motifs/jaspar/db.py
@@ -76,7 +76,6 @@ except:
 from Bio.Alphabet.IUPAC import unambiguous_dna as dna
 from Bio.motifs import jaspar, matrix
 
-__docformat__ = "restructuredtext en"
 
 JASPAR_DFLT_COLLECTION = 'CORE'
 
@@ -235,7 +234,7 @@ class JASPAR5(object):
                           ('ChIP-seq', 'PBM', 'SELEX' etc.) are returned.
                           NOTE - must match exactly as stored in the database.
             pazar_id    - Only motifs with the given PAZAR TF ID are returned.
-            medline     - Only motifs with the given medline (PubmMed IDs) are 
+            medline     - Only motifs with the given medline (PubmMed IDs) are
                           returned.
             min_ic      - Only motifs whose profile matrices have at least this
                           information content (specificty) are returned.
@@ -388,7 +387,7 @@ class JASPAR5(object):
 
         # Many JASPAR motifs (especially those not in the CORE collection)
         # do not have taxonomy IDs. So this warning would get annoying.
-        #if not tax_ids:
+        # if not tax_ids:
         #    warnings.warn("Could not fetch any taxonomy IDs for JASPAR motif {0}".format(motif.matrix_id), BiopythonWarning)
 
         motif.species = tax_ids
diff --git a/Bio/motifs/matrix.py b/Bio/motifs/matrix.py
index 8df895a..1308627 100644
--- a/Bio/motifs/matrix.py
+++ b/Bio/motifs/matrix.py
@@ -7,34 +7,13 @@ and position-specific scoring matrices.
 """
 
 import math
+import platform
 
 from Bio._py3k import range
 
 from Bio.Seq import Seq
 from Bio.Alphabet import IUPAC
-
-# Hack for Python 2.5, isnan and isinf were new in Python 2.6
-try:
-    from math import isnan as _isnan
-except ImportError:
-    def _isnan(value):
-        # This is tricky due to cross platform float differences
-        if str(value).lower() == "nan":
-            return True
-        return value != value
-try:
-    from math import isinf as _isinf
-except ImportError:
-    def _isinf(value):
-        # This is tricky due to cross platform float differences
-        if str(value).lower().endswith("inf"):
-            return True
-        return False
-# Hack for Python 2.5 on Windows:
-try:
-    _nan = float("nan")
-except ValueError:
-    _nan = 1e1000 / 1e1000
+from Bio import Alphabet
 
 
 class GenericPositionMatrix(dict):
@@ -221,9 +200,17 @@ class GenericPositionMatrix(dict):
                 key = "".join(sorted(nucleotides[:3]))
             else:
                 key = "ACGT"
-            nucleotide = degenerate_nucleotide[key]
+            nucleotide = degenerate_nucleotide.get(key, key)
             sequence += nucleotide
-        return Seq(sequence, alphabet=IUPAC.ambiguous_dna)
+        if isinstance(self.alphabet, Alphabet.DNAAlphabet):
+            alpha = IUPAC.ambiguous_dna
+        elif isinstance(self.alphabet, Alphabet.RNAAlphabet):
+            alpha = IUPAC.ambiguous_rna
+        elif isinstance(self.alphabet, Alphabet.ProteinAlphabet):
+            alpha = IUPAC.protein
+        else:
+            raise Exception("Unknown alphabet")
+        return Seq(sequence, alphabet=alpha)
 
     @property
     def gc_content(self):
@@ -328,7 +315,7 @@ class PositionWeightMatrix(GenericPositionMatrix):
                     if p > 0:
                         logodds = float("inf")
                     else:
-                        logodds = _nan
+                        logodds = float("nan")
                 values[letter].append(logodds)
         pssm = PositionSpecificScoringMatrix(alphabet, values)
         return pssm
@@ -336,6 +323,34 @@ class PositionWeightMatrix(GenericPositionMatrix):
 
 class PositionSpecificScoringMatrix(GenericPositionMatrix):
 
+    # Make sure that we use C-accelerated PWM calculations if running under CPython.
+    # Fall back to the slower Python implementation if Jython or IronPython.
+    try:
+        from . import _pwm
+
+        def _calculate(self, sequence, m, n):
+            logodds = [[self[letter][i] for letter in "ACGT"] for i in range(m)]
+            return self._pwm.calculate(sequence, logodds)
+    except ImportError:
+        if platform.python_implementation() == 'CPython':
+            raise
+        else:
+            def _calculate(self, sequence, m, n):
+                # The C code handles mixed case so Python version must too:
+                sequence = sequence.upper()
+                scores = []
+                for i in range(n - m + 1):
+                    score = 0.0
+                    for position in range(m):
+                        letter = sequence[i + position]
+                        try:
+                            score += self[letter][position]
+                        except KeyError:
+                            score = float("nan")
+                            break
+                    scores.append(score)
+                return scores
+
     def calculate(self, sequence):
         """Returns the PWM score for a given sequence for all positions.
 
@@ -361,29 +376,8 @@ class PositionSpecificScoringMatrix(GenericPositionMatrix):
         m = self.length
         n = len(sequence)
 
-        scores = []
-        # check if the fast C code can be used
-        try:
-            import _pwm
-        except ImportError:
-            # use the slower Python code otherwise
-            # The C code handles mixed case so Python version must too:
-            sequence = sequence.upper()
-            for i in range(n - m + 1):
-                score = 0.0
-                for position in range(m):
-                    letter = sequence[i + position]
-                    try:
-                        score += self[letter][position]
-                    except KeyError:
-                        score = _nan
-                        break
-                scores.append(score)
-        else:
-            # get the log-odds matrix into a proper shape
-            # (each row contains sorted (ACGT) log-odds values)
-            logodds = [[self[letter][i] for letter in "ACGT"] for i in range(m)]
-            scores = _pwm.calculate(sequence, logodds)
+        scores = self._calculate(sequence, m, n)
+
         if len(scores) == 1:
             return scores[0]
         else:
@@ -451,9 +445,9 @@ class PositionSpecificScoringMatrix(GenericPositionMatrix):
         for i in range(self.length):
             for letter in self._letters:
                 logodds = self[letter, i]
-                if _isnan(logodds):
+                if math.isnan(logodds):
                     continue
-                if _isinf(logodds) and logodds < 0:
+                if math.isinf(logodds) and logodds < 0:
                     continue
                 b = background[letter]
                 p = b * math.pow(2, logodds)
@@ -475,9 +469,9 @@ class PositionSpecificScoringMatrix(GenericPositionMatrix):
             sxx = 0.0
             for letter in self._letters:
                 logodds = self[letter, i]
-                if _isnan(logodds):
+                if math.isnan(logodds):
                     continue
-                if _isinf(logodds) and logodds < 0:
+                if math.isinf(logodds) and logodds < 0:
                     continue
                 b = background[letter]
                 p = b * math.pow(2, logodds)
diff --git a/Bio/motifs/meme.py b/Bio/motifs/meme.py
index 0f8e2c2..9f23cbc 100644
--- a/Bio/motifs/meme.py
+++ b/Bio/motifs/meme.py
@@ -47,7 +47,7 @@ def read(handle):
         motif.evalue = evalue
         motif.name = name
         record.append(motif)
-        assert len(record)==motif_number
+        assert len(record) == motif_number
         __skip_unused_lines(handle)
         try:
             line = next(handle)
@@ -220,18 +220,18 @@ def __read_motif_statistics(line):
     # or like
     #    MOTIF  1 MEME    width =  19  sites =   3  llr = 43  E-value = 6.9e-002
     words = line.split()
-    assert words[0]=='MOTIF'
+    assert words[0] == 'MOTIF'
     motif_number = int(words[1])
-    if words[2]=='MEME':
+    if words[2] == 'MEME':
         key_values = words[3:]
     else:
         key_values = words[2:]
     keys = key_values[::3]
     equal_signs = key_values[1::3]
     values = key_values[2::3]
-    assert keys==['width', 'sites', 'llr', 'E-value']
+    assert keys == ['width', 'sites', 'llr', 'E-value']
     for equal_sign in equal_signs:
-        assert equal_sign=='='
+        assert equal_sign == '='
     length = int(values[0])
     num_occurrences = int(values[1])
     evalue = float(values[3])
diff --git a/Bio/motifs/thresholds.py b/Bio/motifs/thresholds.py
index c16713b..1d6de80 100644
--- a/Bio/motifs/thresholds.py
+++ b/Bio/motifs/thresholds.py
@@ -10,9 +10,9 @@
 class ScoreDistribution(object):
     """ Class representing approximate score distribution for a given motif.
 
-    Utilizes a dynamic programming approch to calculate the distribution of
+    Utilizes a dynamic programming approach to calculate the distribution of
     scores with a predefined precision. Provides a number of methods for calculating
-    thresholds for motif occurences.
+    thresholds for motif occurrences.
     """
     def __init__(self, motif=None, precision=10 ** 3, pssm=None, background=None):
         if pssm is None:
diff --git a/Bio/motifs/transfac.py b/Bio/motifs/transfac.py
index 6b8e1ca..164805d 100644
--- a/Bio/motifs/transfac.py
+++ b/Bio/motifs/transfac.py
@@ -9,8 +9,6 @@
 from Bio import motifs
 from Bio.Alphabet import IUPAC
 
-__docformat__ = "restructuredtext en"
-
 
 class Motif(motifs.Motif, dict):
     """A Bio.motifs.transfac.Motif stores the information in one TRANSFAC
@@ -185,17 +183,20 @@ XX
                     if length == 0:
                         continue
                     sequence = motif.degenerate_consensus
-                    line = "P0      A      C      G      T"
+                    letters = sorted(motif.alphabet.letters)
+                    line = "      ".join(["P0"] + letters)
+
                     lines.append(line)
                     for i in range(length):
-                        line = "%02.d %6.20g %6.20g %6.20g %6.20g      %s" % (
-                                             i + 1,
-                                             motif.counts['A'][i],
-                                             motif.counts['C'][i],
-                                             motif.counts['G'][i],
-                                             motif.counts['T'][i],
-                                             sequence[i],
-                                            )
+                        line = " ".join(
+                            ["%02.d"] +
+                            ["%6.20g" for l in letters]) + \
+                            "      %s"
+                        line = line % tuple(
+                            [i + 1] +
+                            [motif.counts[l][i] for l in letters] +
+                            [sequence[i]]
+                        )
                         lines.append(line)
                     blank = True
                 else:
diff --git a/Bio/pairwise2.py b/Bio/pairwise2.py
index 103a575..73d1bd0 100644
--- a/Bio/pairwise2.py
+++ b/Bio/pairwise2.py
@@ -155,7 +155,6 @@ type help(pairwise2.align.localds) at the Python prompt.
 
 from __future__ import print_function
 
-__docformat__ = "restructuredtext en"
 
 MAX_ALIGNMENTS = 1000   # maximum alignments recovered in traceback
 
@@ -163,7 +162,7 @@ MAX_ALIGNMENTS = 1000   # maximum alignments recovered in traceback
 class align(object):
     """This class provides functions that do alignments."""
 
-    class alignment_function:
+    class alignment_function(object):
         """This class is callable impersonates an alignment function.
         The constructor takes the name of the function.  This class
         will decode the name of the function to figure out how to
diff --git a/Bio/phenotype/__init__.py b/Bio/phenotype/__init__.py
new file mode 100644
index 0000000..f3ed9de
--- /dev/null
+++ b/Bio/phenotype/__init__.py
@@ -0,0 +1,244 @@
+# Copyright 2014-2016 by Marco Galardini.  All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+#
+
+r"""phenotype data input/output.
+
+Input
+=====
+The main function is Bio.phenotype.parse(...) which takes an input file,
+and format string.  This returns an iterator giving PlateRecord objects:
+
+    >>> from Bio import phenotype
+    >>> for record in phenotype.parse("plates.csv", "pm-csv"):
+    ...     print("%s %i" % (record.id, len(record)))
+    ...
+    PM01 96
+    PM01 96
+    PM09 96
+    PM09 96
+
+Note that the parse() function will invoke the relevant parser for the
+format with its default settings.  You may want more control, in which case
+you need to create a format specific sequence iterator directly.
+
+Input - Single Records
+======================
+If you expect your file to contain one-and-only-one record, then we provide
+the following 'helper' function which will return a single PlateRecord, or
+raise an exception if there are no records or more than one record:
+
+    >>> from Bio import phenotype
+    >>> record = phenotype.read("plate.json", "pm-json")
+    >>> print("%s %i" % (record.id, len(record)))
+    PM01 96
+
+This style is useful when you expect a single record only (and would
+consider multiple records an error).  For example, when dealing with PM
+JSON files saved by the opm library.
+
+However, if you just want the first record from a file containing multiple
+record, use the next() function on the iterator (or under Python 2, the
+iterator's next() method):
+
+    >>> from Bio import phenotype
+    >>> record = next(phenotype.parse("plates.csv", "pm-csv"))
+    >>> print("%s %i" % (record.id, len(record)))
+    PM01 96
+
+The above code will work as long as the file contains at least one record.
+Note that if there is more than one record, the remaining records will be
+silently ignored.
+
+Output
+======
+Use the function Bio.phenotype.write(...), which takes a complete set of
+PlateRecord objects (either as a list, or an iterator), an output file handle
+(or in recent versions of Biopython an output filename as a string) and of
+course the file format:
+
+    >>> from Bio import phenotype
+    >>> records = ...
+    >>> phenotype.write(records, "example.json", "pm-json")
+
+Or, using a handle::
+
+    >>> from Bio import phenotype
+    >>> records = ...
+    >>> with open("example.json", "w") as handle:
+    >>>    phenotype.write(records, handle, "pm-json")
+
+You are expected to call this function once (with all your records) and if
+using a handle, make sure you close it to flush the data to the hard disk.
+
+
+File Formats
+============
+When specifying the file format, use lowercase strings.
+
+ - pm-json - Phenotype Microarray plates in JSON format.
+ - pm-csv  - Phenotype Microarray plates in CSV format, which is the
+             machine vendor format
+
+Note that while Bio.phenotype can read the above file formats, it can only
+write in JSON format.
+"""
+
+from __future__ import print_function
+from Bio._py3k import basestring
+
+from Bio import BiopythonExperimentalWarning
+from Bio.File import as_handle
+from . import phen_micro
+
+import warnings
+
+__docformat__ = "epytext en"  # not just plaintext
+
+warnings.warn('Bio.phenotype is an experimental submodule which may undergo '
+        'significant changes prior to its future official release.',
+        BiopythonExperimentalWarning)
+
+# Convention for format names is "mainname-format" in lower case.
+
+_FormatToIterator = {"pm-csv": phen_micro.CsvIterator,
+                     "pm-json": phen_micro.JsonIterator,
+                     }
+
+_FormatToWriter = {"pm-json": phen_micro.JsonWriter,
+                   }
+
+
+def write(plates, handle, format):
+    """Write complete set of PlateRecords to a file.
+
+     - plates    - A list (or iterator) of PlateRecord objects.
+     - handle    - File handle object to write to, or filename as string
+                   (note older versions of Biopython only took a handle).
+     - format    - lower case string describing the file format to write.
+
+    You should close the handle after calling this function.
+
+    Returns the number of records written (as an integer).
+    """
+    # Try and give helpful error messages:
+    if not isinstance(format, basestring):
+        raise TypeError("Need a string for the file format (lower case)")
+    if not format:
+        raise ValueError("Format required (lower case string)")
+    if format != format.lower():
+        raise ValueError("Format string '%s' should be lower case" % format)
+
+    if isinstance(plates, phen_micro.PlateRecord):
+        plates = [plates]
+
+    with as_handle(handle, 'w') as fp:
+        # Map the file format to a writer class
+        if format in _FormatToWriter:
+            writer_class = _FormatToWriter[format]
+            count = writer_class(plates).write(fp)
+        else:
+            raise ValueError("Unknown format '%s'" % format)
+
+        assert isinstance(count, int), "Internal error - the underlying %s " \
+            "writer should have returned the record count, not %s" \
+            % (format, repr(count))
+
+    return count
+
+
+def parse(handle, format):
+    """Turns a phenotype file into an iterator returning PlateRecords.
+
+     - handle   - handle to the file, or the filename as a string
+                  (note older versions of Biopython only took a handle).
+     - format   - lower case string describing the file format.
+
+    Typical usage, opening a file to read in, and looping over the record(s):
+
+    >>> from Bio import phenotype
+    >>> filename = "plates.csv"
+    >>> for record in phenotype.parse(filename, "pm-csv"):
+    ...    print("ID %s" % record.id)
+    ...    print("Number of wells %i" % len(record))
+    ...
+    ID PM01
+    Number of wells 96
+
+    Use the Bio.phenotype.read(...) function when you expect a single record
+    only.
+    """
+    # Try and give helpful error messages:
+    if not isinstance(format, basestring):
+        raise TypeError("Need a string for the file format (lower case)")
+    if not format:
+        raise ValueError("Format required (lower case string)")
+    if format != format.lower():
+        raise ValueError("Format string '%s' should be lower case" % format)
+
+    with as_handle(handle, 'rU') as fp:
+        # Map the file format to a sequence iterator:
+        if format in _FormatToIterator:
+            iterator_generator = _FormatToIterator[format]
+            i = iterator_generator(fp)
+        else:
+            raise ValueError("Unknown format '%s'" % format)
+        # This imposes some overhead... wait until we drop Python 2.4 to fix it
+        for r in i:
+            yield r
+
+
+def read(handle, format):
+    """Turns a phenotype file into a single PlateRecord.
+
+     - handle   - handle to the file, or the filename as a string
+                  (note older versions of Biopython only took a handle).
+     - format   - string describing the file format.
+
+    This function is for use parsing phenotype files containing
+    exactly one record.  For example, reading a PM JSON file:
+
+    >>> from Bio import phenotype
+    >>> record = phenotype.read("plate.json", "pm-json")
+    >>> print("ID %s" % record.id)
+    PM09
+    >>> print("Number of wells %i" % len(record))
+    Number of wells 96
+
+    If the handle contains no records, or more than one record,
+    an exception is raised.  For example:
+
+    >>> from Bio import phenotype
+    >>> record = phenotype.read("plates.csv", "pm-csv")
+    Traceback (most recent call last):
+        ...
+    ValueError: More than one record found in handle
+
+    If however you want the first record from a file containing
+    multiple records this function would raise an exception (as
+    shown in the example above).  Instead use:
+
+    >>> from Bio import phenotype
+    >>> record = next(phenotype.parse("plates.csv", "pm-csv"))
+    >>> print("First record's ID %s" % record.id)
+    First record's ID PM09
+
+    Use the Bio.phenotype.parse(handle, format) function if you want
+    to read multiple records from the handle.
+    """
+    iterator = parse(handle, format)
+    try:
+        first = next(iterator)
+    except StopIteration:
+        first = None
+    if first is None:
+        raise ValueError("No records found in handle")
+    try:
+        second = next(iterator)
+    except StopIteration:
+        second = None
+    if second is not None:
+        raise ValueError("More than one record found in handle")
+    return first
diff --git a/Bio/phenotype/phen_micro.py b/Bio/phenotype/phen_micro.py
new file mode 100644
index 0000000..864a615
--- /dev/null
+++ b/Bio/phenotype/phen_micro.py
@@ -0,0 +1,1131 @@
+# Copyright 2014-2016 by Marco Galardini.  All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+"""This module provides classes to work with Phenotype Microarray data.
+
+More information on the single plates can be found here: http://www.biolog.com/
+
+Classes:
+
+ - PlateRecord - Object that contain time course data on each well of the
+   plate, as well as metadata (if any).
+ - WellRecord - Object that contains the time course data of a single well
+ - JsonWriter - Writer of PlateRecord objects in JSON format.
+
+Functions:
+
+ - JsonIterator -  Incremental PM JSON parser, this is an iterator that returns
+   PlateRecord objects.
+ - CsvIterator - Incremental PM CSV parser, this is an iterator that returns
+   PlateRecord objects.
+ - _toOPM - Used internally by JsonWriter, converts PlateRecord objects in
+   dictionaries ready to be serialized in JSON format.
+
+"""
+
+import warnings
+import json
+import csv
+import numpy as np
+
+from Bio._py3k import basestring
+from Bio._py3k import _is_int_or_long
+from Bio import BiopythonParserWarning
+
+# Private csv headers - hardcoded because this are supposedly never changed
+_datafile = 'Data File'
+_plate = 'Plate Type'
+_strainType = 'Strain Type'
+_sample = 'Sample Number'
+_strainName = 'Strain Name'
+_strainNumber = 'Strain Number'
+_other = 'Other'
+_hour = 'Hour'
+_file = 'File'
+_position = 'Position'
+_setupTime = 'Setup Time'
+
+_platesPrefix = 'PM'
+_platesPrefixMammalian = 'PM-M'
+#
+
+# Json identifiers - hardcoded as they are set by the creators of opm
+_csvData = 'csv_data'
+_measurements = 'measurements'
+#
+
+
+class PlateRecord(object):
+    """PlateRecord object for storing Phenotype Microarray plates data.
+
+    A PlateRecord stores all the wells of a particular phenotype
+    Microarray plate, along with metadata (if any). The single wells can be
+    accessed calling their id as an index or iterating on the PlateRecord:
+
+    >>> from Bio import phenotype
+    >>> plate = phenotype.read("plate.csv", "pm-csv")
+    >>> well = plate['A05']
+    >>> for well in plate:
+    ...    print("%s" % well.id)
+    ...
+    A01
+    A02
+    ...
+
+    The plate rows and columns can be queried with an indexing system similar
+    to NumPy and other matrices:
+
+    >>> print(plate[1])
+    PlateRecord('WellRecord['B01'], WellRecord['B02'], WellRecord['B03'], ..., WellRecord['B12']')
+
+    >>> print(plate[:,1])
+    PlateRecord('WellRecord['A02'], WellRecord['B02'], WellRecord['C02'], ..., WellRecord['H02']')
+
+    Single WellRecord objects can be accessed using this indexing system:
+
+    >>> print(plate[1,2])
+    WellRecord('(0.0, 11.0), (0.25, 11.0), (0.5, 11.0), (0.75, 11.0), (1.0, 11.0), ..., (95.75, 11.0)')
+
+    The presence of a particular well can be inspected with the "in" keyword:
+    >>> 'A01' in plate
+    True
+
+    All the wells belonging to a "row" (identified by the first charachter of
+    the well id) in the plate can be obtained:
+
+    >>> for well in plate.get_row('H'):
+    ...     print("%s" % well.id)
+    ...
+    H01
+    H02
+    H03
+    ...
+
+    All the wells belonging to a "column" (identified by the number of the well)
+    in the plate can be obtained:
+
+    >>> for well in plate.get_column(12):
+    ...     print("%s" % well.id)
+    ...
+    A01
+    B12
+    C12
+    ...
+
+    Two PlateRecord objects can be compared: if all their wells are equal the
+    two plates are considered equal:
+
+    >>> plate2 = phenotype.read("plate.json", "pm-json")
+    >>> plate == plate2
+    True
+
+    Two PlateRecord object can be summed up or subracted from each other: the
+    the signals of each well will be summed up or subtracted. The id of the
+    left operand will be kept:
+
+    >>> plate3 = plate + plate2
+    >>> print(plate3.id)
+    PM01
+
+    Many Phenotype Microarray plate have a "negative control" well, which can
+    be subtracted to all wells:
+
+    >>> subplate = plate.subtract_control()
+    """
+
+    def __init__(self, plateid, wells=None):
+        self.id = plateid
+
+        if wells is None:
+            wells = []
+
+        # Similar behaviour as GenBank
+        # Contains all the attributes
+        self.qualifiers = {}
+
+        # Well_id --> WellRecord objects
+        self._wells = {}
+        try:
+            for w in wells:
+                self._is_well(w)
+                self[w.id] = w
+        except TypeError:
+            raise TypeError('You must provide an iterator-like object ' +
+                            'containing the single wells')
+
+        self._update()
+
+    def _update(self):
+        """Update the rows and columns string identifiers."""
+        self._rows = sorted(set(x[0] for x in self._wells))
+        self._columns = sorted(set(x[1:] for x in self._wells))
+
+    def _is_well(self, obj):
+        """Check if the given object is a WellRecord object
+
+        Used both for the class constructor and the __setitem__ method
+        """
+        # Value should be of WellRecord type
+        if not isinstance(obj, WellRecord):
+            raise ValueError('A WellRecord type object is needed as value' +
+                             ' (got %s)' % type(obj))
+
+    def __getitem__(self, index):
+        """Access part of the plate.
+
+        Depending on the indices, you can get a WellRecord object
+        (representing a single well of the plate),
+        or another plate
+        (representing some part or all of the original plate).
+
+        plate[wid] gives a WellRecord (if wid is a WellRecord id)
+        plate[r,c] gives a WellRecord
+        plate[r] gives a row as a PlateRecord
+        plate[r,:] gives a row as a PlateRecord
+        plate[:,c] gives a column as a PlateRecord
+
+        plate[:] and plate[:,:] give a copy of the plate
+
+        Anything else gives a subset of the original plate, e.g.
+        plate[0:2] or plate[0:2,:] uses only row 0 and 1
+        plate[:,1:3] uses only columns 1 and 2
+        plate[0:2,1:3] uses only rows 0 & 1 and only cols 1 & 2
+
+        >>> from Bio import phenotype
+        >>> plate = phenotype.read("plate.csv", "pm-csv")
+
+        You can access a well of the plate, using its id.
+
+        >>> w = plate['A01']
+
+        You can access a row of the plate as a PlateRecord using an integer
+        index:
+
+        >>> first_row = plate[0]
+        >>> print(first_row)
+        PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], ..., WellRecord['A12']')
+        >>> last_row = plate[-1]
+        >>> print(last_row)
+        PlateRecord('WellRecord['H01'], WellRecord['H02'], WellRecord['H03'], ..., WellRecord['H12']')
+
+        You can also access use python's slice notation to sub-plates
+        containing only some of the plate rows:
+
+        >>> sub_plate = plate[2:5]
+        >>> print(sub_plate)
+        PlateRecord('WellRecord['C01'], WellRecord['C02'], WellRecord['C03'], ..., WellRecord['E12']')
+
+        This includes support for a step, i.e. plate[start:end:step], which
+        can be used to select every second row:
+
+        >>> sub_plate = plate[::2]
+
+        You can also use two indices to specify both rows and columns.
+        Using simple integers gives you the single wells. e.g.
+
+        >>> w = plate[3, 4]
+        >>> print(w.id)
+        'D05'
+
+        To get a single column use this syntax:
+
+        >>> sub_plate = plate[:, 4]
+        >>> print(sub_plate)
+        PlateRecord('WellRecord['A05'], WellRecord['B05'], WellRecord['C05'], ..., WellRecord['H05']')
+
+        Or, to get part of a column,
+
+        >>> sub_plate = plate[1:3, 4]
+        >>> print(sub_plate)
+        PlateRecord(WellRecord['B05'], WellRecord['C05'])
+
+        However, in general you get a sub-plate,
+
+        >>> print(align[1:5, 3:6])
+        PlateRecord('WellRecord['B04'], WellRecord['B05'], WellRecord['B06'], ..., WellRecord['E06']')
+
+        This should all seem familiar to anyone who has used the NumPy
+        array or matrix objects.
+        """
+
+        # Well identifier access
+        if isinstance(index, basestring):
+            try:
+                return self._wells[index]
+            except KeyError:
+                raise KeyError('Well %s not found!' % index)
+
+        # Integer index
+        elif isinstance(index, int):
+            try:
+                row = self._rows[index]
+            except IndexError:
+                raise IndexError('Row %d not found!' % index)
+            return PlateRecord(self.id,
+                               filter(lambda x: x.id.startswith(row),
+                                      self._wells.values()))
+
+        # Slice
+        elif isinstance(index, slice):
+            rows = self._rows[index]
+            return PlateRecord(self.id,
+                               filter(lambda x: x.id[0] in rows,
+                                      self._wells.values()))
+
+        # Other access
+        elif len(index) != 2:
+            raise TypeError('Invalid index type.')
+
+        row_index, col_index = index
+        if isinstance(row_index, int) and isinstance(col_index, int):
+            # Return a single WellRecord
+            try:
+                row = self._rows[row_index]
+            except IndexError:
+                raise IndexError('Row %d not found!' % row_index)
+            try:
+                col = self._columns[col_index]
+            except IndexError:
+                raise IndexError('Column %d not found!' % col_index)
+
+            return self._wells[row + col]
+
+        elif isinstance(row_index, int):
+            try:
+                row = self._rows[row_index]
+            except IndexError:
+                raise IndexError('Row %d not found!' % row_index)
+            cols = self._columns[col_index]
+
+            return PlateRecord(self.id,
+                               filter(lambda x: x.id.startswith(row) and
+                                      x.id[1:] in cols,
+                                      self._wells.values()))
+
+        elif isinstance(col_index, int):
+            try:
+                col = self._columns[col_index]
+            except IndexError:
+                raise IndexError('Columns %d not found!' % col_index)
+            rows = self._rows[row_index]
+
+            return PlateRecord(self.id,
+                               filter(lambda x: x.id.endswith(col) and
+                                      x.id[0] in rows,
+                                      self._wells.values()))
+
+        else:
+            rows = self._rows[row_index]
+            cols = self._columns[col_index]
+
+            return PlateRecord(self.id,
+                               filter(lambda x: x.id[0] in rows and
+                                      x.id[1:] in cols,
+                                      self._wells.values()))
+
+    def __setitem__(self, key, value):
+        if not isinstance(key, basestring):
+            raise ValueError('Well identifier should be string-like')
+        self._is_well(value)
+        # Provided key and well ID should be the same
+        if value.id != key:
+            raise ValueError('WellRecord ID and provided key are different' +
+                             ' (got "%s" and "%s")' % (type(value.id), type(key)))
+        self._wells[key] = value
+
+        self._update()
+
+    def __delitem__(self, key):
+        if not isinstance(key, basestring):
+            raise ValueError('Well identifier should be string-like')
+        del self._wells[key]
+
+        self._update()
+
+    def __iter__(self):
+        for well in sorted(self._wells):
+            yield self._wells[well]
+
+    def __contains__(self, wellid):
+        if wellid in self._wells:
+            return True
+        return False
+
+    def __len__(self):
+        """Returns the number of wells in this plate"""
+        return len(self._wells)
+
+    def __eq__(self, other):
+        if isinstance(other, self.__class__):
+            return self._wells == other._wells
+        else:
+            return False
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def __add__(self, plate):
+        """Add another PlateRecord object
+
+        The wells in both plates must be the same
+
+        A new PlateRecord object is returned, having the same id as the
+        left operand
+        """
+        if not isinstance(plate, PlateRecord):
+            raise TypeError('Expecting a PlateRecord object')
+
+        if set(x.id for x in self) != set(x.id for x in plate):
+            raise ValueError('The two plates have different wells')
+
+        wells = []
+
+        for w in self:
+            wells.append(w + plate[w.id])
+
+        newp = PlateRecord(self.id, wells=wells)
+
+        return newp
+
+    def __sub__(self, plate):
+        """Subtract another PlateRecord object
+
+        The wells in both plates must be the same
+
+        A new PlateRecord object is returned, having the same id as the
+        left operand
+        """
+        if not isinstance(plate, PlateRecord):
+            raise TypeError('Expecting a PlateRecord object')
+
+        if set(x.id for x in self) != set(x.id for x in plate):
+            raise ValueError('The two plates have different wells')
+
+        wells = []
+
+        for w in self:
+            wells.append(w - plate[w.id])
+
+        newp = PlateRecord(self.id, wells=wells)
+
+        return newp
+
+    def get_row(self, row):
+        """Get all the wells of a given row
+
+        A row is identified with a letter (e.g. 'A')
+        """
+        # Key is casted to str implicitly
+        try:
+            row = str(row)
+        except Exception:
+            # Is it even possible to get an exception here?
+            raise ValueError('Row identifier should be string-like')
+        if len(row) > 1:
+            raise ValueError('Row identifier must be of maximum one letter')
+
+        for w in sorted(filter(lambda x: x.startswith(row), self._wells)):
+            yield self._wells[w]
+
+    def get_column(self, column):
+        """Get all the wells of a given column
+
+        A column is identified with a number (e.g. '6')
+        """
+        # Column is casted to int implicitly
+        try:
+            column = int(column)
+        except Exception:
+            raise ValueError('Column identifier should be a number')
+
+        # A 96-well plate has well numbers in two digits
+        for w in sorted(filter(lambda x: x.endswith('%02d' % column),
+                               self._wells)):
+            yield self._wells[w]
+
+    def subtract_control(self, control='A01', wells=None):
+        """Subtract a 'control' well from the other plates wells
+
+        By default the control is subtracted to all wells, unless
+        a list of well ID is provided
+
+        The control well should belong to the plate
+        A new PlateRecord object is returned
+        """
+        if control not in self:
+            raise ValueError('Control well not present in plate')
+        wcontrol = self[control]
+
+        if wells is None:
+            wells = self._wells.keys()
+
+        missing = set(w for w in wells if w not in self)
+        if missing:
+            raise ValueError('Some wells to be subtracted are not present')
+
+        nwells = []
+
+        for w in self:
+            if w.id in wells:
+                nwells.append(w - wcontrol)
+            else:
+                nwells.append(w)
+
+        newp = PlateRecord(self.id, wells=nwells)
+
+        return newp
+
+    def __repr__(self):
+        """Returns a (truncated) representation of the plate for debugging."""
+        if len(self._wells) > 4:
+            # Show the last well and the first three
+            return "%s('%s, ..., %s')" % (self.__class__.__name__,
+                                      ', '.join(
+                                          ["%s['%s']" % (str(self[x].__class__.__name__), self[x].id)
+                                           for x in sorted(self._wells.keys())[:3]]),
+                                      "%s['%s']" % (
+                                          self[
+                                              sorted(self._wells.keys())[-1]].__class__.__name__,
+                                          self[sorted(self._wells.keys())[-1]].id))
+        else:
+            return "%s(%s)" % (self.__class__.__name__,
+                               ', '.join(
+                                   ["%s['%s']" % (str(self[x].__class__.__name__), self[x].id)
+                                    for x in sorted(self._wells.keys())]
+                               ))
+
+    def __str__(self):
+        """A human readable summary of the record (string).
+
+        The python built in function str works by calling the object's ___str__
+        method.  e.g.
+
+        >>> from Bio import phenotype
+        >>> record = phenotype.read("plates.csv", "pm-csv")
+        >>> print(record)
+        Plate ID: PM09
+        Well: 96
+        Rows: 8
+        Columns: 12
+        PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], WellRecord['A04']...WellRecord['H12']')
+
+        Note that long well lists are shown truncated.
+        """
+        lines = []
+        if self.id:
+            lines.append("Plate ID: %s" % self.id)
+        lines.append("Well: %i" % len(self))
+        # Here we assume that all well ID start with a char
+        lines.append("Rows: %d" %
+                     len(set(x.id[0] for x in self)))
+        # Here we assume that well number is a two-digit number
+        lines.append("Columns: %d" %
+                     len(set(x.id[1:3] for x in self)))
+        lines.append(repr(self))
+        return "\n".join(lines)
+
+
+class WellRecord(object):
+    """WellRecord object stores all the time course signals of a phenotype
+    Microarray well.
+
+    The single time points and signals can be
+    accessed iterating on the WellRecord or using lists indeces or slices:
+
+    >>> from Bio import phenotype
+    >>> plate = phenotype.read("plate.csv", "pm-csv")
+    >>> well = plate['A05']
+    >>> for time, signal in well:
+    ...    print("Time: %f, Signal: %f" % (time, signal))
+    ...
+    Time: 0.0, Signal: 10.0
+    Time: 0.25, Signal: 14.0
+    Time: 0.5, Signal: 19.0
+    Time: 24.25, Signal: 32.0
+    ...
+    >>> well[1]
+    23.0
+    >>> well[1:5]
+    [23.0, 23.0, 26.0, 26.0]
+    >>> well[1:5:0.5]
+    [23.0, 23.0, 23.0, 26.0, 26.0, 26.0, 26.0, 26.0]
+
+    If a time point was not present in the input file but it's between the
+    minimum and maximum time point, the interpolated signal is returned,
+    otherwise a nan value:
+
+    >>> well[1.3]
+    23.0
+    >>> well[1250]
+    nan
+
+    Two WellRecord objects can be compared: if their input time/signal pairs
+    are exactly the same, the two records are considered equal:
+
+    >>> well2 = plate['H12']
+    >>> well == well2
+    False
+
+    Two WellRecord objects can be summed up or subtracted from each other: a new
+    WellRecord object is returned, having the left operand id.
+
+    >>> well2 = well + well1
+    >>> print(well2.id)
+    A05
+
+    If SciPy is installed, a sigmoid function can be fitted to the PM curve,
+    in order to extract some parameters; three sigmoid functions are available:
+    * gompertz
+    * logistic
+    * richards
+    The functions are described in Zwietering et al., 1990 (PMID: 16348228)
+
+    >>> well.fit()
+    >>> print(well.slope, well.model)
+    (61.853516785566917, 'logistic')
+
+    If not sigmoid function is specified, the first one that is succesfully
+    fitted is used. The user can also specify a specific function.
+
+    >>> well.fit('gompertz')
+    >>> print(well.slope, well.model)
+    (127.94630059171354, 'gompertz')
+
+    If no function can be fitted, the parameters are left as None, except for
+    the max, min, average_height and area.
+    """
+
+    def __init__(self, wellid, plate=None, signals=None):
+        if plate is None:
+            self.plate = PlateRecord(None)
+        else:
+            self.plate = plate
+
+        self.id = wellid
+
+        # Curve parameters (to be calculated with the "fit" function)
+        # Parameters that don't need scipy
+        self.max = None
+        self.min = None
+        self.average_height = None
+
+        # Parameters that need scipy
+        self.area = None
+        self.plateau = None
+        self.slope = None
+        self.lag = None
+        self.v = None
+        self.y0 = None
+        self.model = None
+
+        # Original signals (private)
+        if signals is None:
+            self._signals = {}
+        else:
+            self._signals = signals
+
+    def _interpolate(self, time):
+        """Private method to get a linear interpolation of the signals
+        at certain time points.
+        """
+        times = sorted(self._signals.keys())
+
+        return np.interp(time,
+                         times,
+                         [self._signals[x] for x in times],
+                         left=np.nan, right=np.nan)
+
+    def __setitem__(self, time, signal):
+        """Assign a signal at a certain time point.
+        """
+        try:
+            time = float(time)
+        except ValueError:
+            raise ValueError('Time point should be a number')
+        try:
+            signal = float(signal)
+        except ValueError:
+            raise ValueError('Signal should be a number')
+
+        self._signals[time] = signal
+
+    def __getitem__(self, time):
+        """Returns a subset of signals or a single signal.
+        """
+        if isinstance(time, slice):
+            # Fix the missing values in the slice
+            if time.start is None:
+                start = 0
+            else:
+                start = time.start
+
+            if time.stop is None:
+                stop = max(self.get_times())
+            else:
+                stop = time.stop
+
+            time = np.arange(start, stop, time.step)
+            return list(self._interpolate(time))
+
+        elif _is_int_or_long(time) or isinstance(time, float):
+            return self._interpolate(time)
+
+        raise ValueError('Invalid index')
+
+    def __iter__(self):
+        for time in sorted(self._signals.keys()):
+            yield time, self._signals[time]
+
+    def __eq__(self, other):
+        if isinstance(other, self.__class__):
+            if list(self._signals.keys()) != list(other._signals.keys()):
+                return False
+            # Account for the presence of NaNs
+            for k in self._signals:
+                if np.isnan(self[k]) and np.isnan(other[k]):
+                    continue
+                elif self[k] != other[k]:
+                    return False
+            return True
+        else:
+            return False
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def __add__(self, well):
+        """Add another WellRecord object
+
+        A new WellRecord object is returned, having the same id as the
+        left operand
+        """
+        if not isinstance(well, WellRecord):
+            raise TypeError('Expecting a WellRecord object')
+
+        signals = {}
+
+        times = set(self._signals.keys()).union(set(well._signals.keys()))
+        for t in sorted(times):
+            signals[t] = self[t] + well[t]
+
+        neww = WellRecord(self.id, signals=signals)
+
+        return neww
+
+    def __sub__(self, well):
+        """Subtract another WellRecord object
+
+        A new WellRecord object is returned, having the same id as the
+        left operand
+        """
+        if not isinstance(well, WellRecord):
+            raise TypeError('Expecting a WellRecord object')
+
+        signals = {}
+
+        times = set(self._signals.keys()).union(set(well._signals.keys()))
+        for t in sorted(times):
+            signals[t] = self[t] - well[t]
+
+        neww = WellRecord(self.id, signals=signals)
+
+        return neww
+
+    def __len__(self):
+        """Returns the number of time points sampled"""
+        return len(self._signals)
+
+    def __repr__(self):
+        """Returns a (truncated) representation of the signals for debugging."""
+        if len(self) > 7:
+            # Shows the last time point and the first five
+            return "%s('%s, ..., %s')" % (self.__class__.__name__,
+                                      ', '.join([str(x)
+                                                 for x in self.get_raw()[:5]]),
+                                      str(self.get_raw()[-1]))
+        else:
+            return "%s(%s)" % (self.__class__.__name__,
+                               ', '.join([str(x) for x in self.get_raw()]))
+
+    def __str__(self):
+        """A human readable summary of the record (string).
+
+        The python built in function str works by calling the object's ___str__
+        method.  e.g.
+
+        >>> from Bio import phenotype
+        >>> plate = phenotype.read("plates.csv", "pm-csv")
+        >>> record = plate['A05']
+        >>> print(record)
+        Plate ID: PM09
+        Well ID: A05
+        Time points: 288
+        Minum signal 0.00 at time 10.00
+        Maximum signal 7.50 at time 32.00
+        WellRecord('(0.0, 10.0), (0.25, 14.0), (0.5, 19.0), (0.75, 19.0), (1.0, 23.0)...(71.75, 32.0)')
+
+        Note that long time spans are shown truncated.
+        """
+        lines = []
+        if self.plate and self.plate.id:
+            lines.append("Plate ID: %s" % self.plate.id)
+        if self.id:
+            lines.append("Well ID: %s" % self.id)
+        lines.append("Time points: %i" % len(self))
+        lines.append("Minum signal %.2f at time %.2f" %
+                     min(self, key=lambda x: x[1]))
+        lines.append("Maximum signal %.2f at time %.2f" %
+                     max(self, key=lambda x: x[1]))
+        lines.append(repr(self))
+        return "\n".join(lines)
+
+    def get_raw(self):
+        """Get a list of time/signal pairs"""
+        return [(t, self._signals[t]) for t in sorted(self._signals.keys())]
+
+    def get_times(self):
+        """Get a list of the recorded time points"""
+        return sorted(self._signals.keys())
+
+    def get_signals(self):
+        """Get a list of the recorded signals (ordered by collection time)"""
+        return [self._signals[t] for t in sorted(self._signals.keys())]
+
+    def fit(self, function=("gompertz", "logistic", "richards")):
+        """Fit a sigmoid function to this well and extract curve parameters.
+
+        If function is None or an empty tuple/list, then no fitting is done.
+        Only the object's ``.min``, ``.max`` and ``.average_height`` are
+        calculated.
+
+        By default the following fitting functions will be used in order:
+        * gompertz
+        * logistic
+        * richards
+
+        The first function that is succesfuly fitted to the signals will
+        be used to extract the curve parameters and update ``.area`` and
+        ``.model``. If no function can be fitted an exception is raised.
+
+        The function argument should be a tuple or list of any of these three
+        function names as strings.
+
+        There is no return value.
+        """
+        avail_func = ('gompertz', 'logistic', 'richards', )
+
+        # Parameters not dependent on curve fitting
+        self.max = max(self, key=lambda x: x[1])[1]
+        self.min = min(self, key=lambda x: x[1])[1]
+
+        self.average_height = np.array(self.get_signals()).mean()
+
+        if not function:
+            self.area = None
+            self.model = None
+            return
+        for sigmoid_func in function:
+            if sigmoid_func not in avail_func:
+                raise ValueError("Fitting function %r not supported" % sigmoid_func)
+
+        # Parameters that depend on scipy curve_fit
+        from .pm_fitting import fit, get_area
+        from .pm_fitting import logistic, gompertz, richards
+        function_map = {'logistic': logistic,
+                        'gompertz': gompertz,
+                        'richards': richards}
+
+        self.area = get_area(self.get_signals(), self.get_times())
+
+        self.model = None
+        for sigmoid_func in function:
+            func = function_map[sigmoid_func]
+            try:
+                (self.plateau, self.slope,
+                 self.lag, self.v, self.y0), pcov = fit(func,
+                                                        self.get_times(),
+                                                        self.get_signals())
+
+                self.model = sigmoid_func
+                return
+            except RuntimeError:
+                continue
+        raise RuntimeError("Could not fit any sigmoid function")
+
+
+def JsonIterator(handle):
+    """Generator function to iterate over PM json records (as PlateRecord
+    objects).
+
+    handle - input file
+    """
+    try:
+        data = json.load(handle)
+    except ValueError:
+        raise ValueError('Could not parse JSON file')
+
+    # We can have one single plate or several
+    # we need to discriminate
+    if hasattr(data, 'keys'):
+        data = [data]
+
+    for pobj in data:
+        try:
+            plateID = pobj[_csvData][_plate]
+        except TypeError:
+            raise TypeError('Malformed JSON input')
+        except KeyError:
+            raise KeyError('Could not retrieve plate id')
+
+        # Parse also non-standard plate IDs
+        if not plateID.startswith(_platesPrefix) and not plateID.startswith(_platesPrefixMammalian):
+            warnings.warn('Non-standard plate ID found (%s)' % plateID,
+                          BiopythonParserWarning)
+        else:
+            # Simplify the plates IDs, removing letters, as opm does
+            if plateID.startswith(_platesPrefixMammalian):
+                pID = plateID[len(_platesPrefixMammalian):]
+            else:
+                pID = plateID[len(_platesPrefix):]
+            while len(pID) > 0:
+                try:
+                    int(pID)
+                    break
+                except ValueError:
+                    pID = pID[:-1]
+
+            # No luck
+            if len(pID) == 0:
+                warnings.warn('Non-standard plate ID found (%s)' % plateID,
+                             BiopythonParserWarning)
+            elif int(pID) < 0:
+                warnings.warn('Non-standard plate ID found (%s), using %s' %
+                             (plateID, _platesPrefix + abs(int(pID))))
+                plateID = _platesPrefix + abs(int(pID))
+            else:
+                if plateID.startswith(_platesPrefixMammalian):
+                    plateID = _platesPrefixMammalian + '%02d' % int(pID)
+                else:
+                    plateID = _platesPrefix + '%02d' % int(pID)
+
+        try:
+            times = pobj[_measurements][_hour]
+        except KeyError:
+            raise KeyError('Could not retrieve the time points')
+
+        plate = PlateRecord(plateID)
+
+        for k in pobj[_measurements]:
+            # Skip the time points
+            if k == _hour:
+                continue
+
+            plate[k] = WellRecord(k, plate=plate,
+                                  signals=dict([(times[i], pobj[_measurements][k][i])
+                                                for i in range(len(times))]))
+
+        # Remove the measurements and assign the other qualifiers
+        del pobj['measurements']
+        plate.qualifiers = pobj
+
+        yield plate
+
+
+def CsvIterator(handle):
+    """Generator function to iterate over PM csv records (as PlateRecord
+    objects).
+
+    handle - input file
+    """
+    plate = None
+    data = False
+    qualifiers = {}
+    idx = {}
+    wells = {}
+
+    tblreader = csv.reader(handle, delimiter=',',
+                           quotechar='"')
+    for line in tblreader:
+        if len(line) < 2:
+            continue
+
+        elif _datafile in line[0].strip():
+            # Do we have a previous plate?
+            if plate is not None:
+                qualifiers[_csvData][_datafile] = line[1].strip()
+                plate = PlateRecord(plate.id)
+                for k, v in wells.items():
+                    plate[k] = WellRecord(k, plate, v)
+                plate.qualifiers = qualifiers
+                yield plate
+            plate = PlateRecord(None)
+            data = False
+            qualifiers[_csvData] = {}
+            idx = {}
+            wells = {}
+
+        elif _plate in line[0].strip():
+            plateID = line[1].strip()
+
+            qualifiers[_csvData][_plate] = plateID
+
+            # Parse also non-standard plate IDs
+            if not plateID.startswith(_platesPrefix) and not plateID.startswith(_platesPrefixMammalian):
+                warnings.warn('Non-standard plate ID found (%s)' % plateID,
+                              BiopythonParserWarning)
+            else:
+                # Simplify the plates IDs, removing letters, as opm does
+                if plateID.startswith(_platesPrefixMammalian):
+                    pID = plateID[len(_platesPrefixMammalian):]
+                else:
+                    pID = plateID[len(_platesPrefix):]
+                while len(pID) > 0:
+                    try:
+                        int(pID)
+                        break
+                    except ValueError:
+                        pID = pID[:-1]
+
+                # No luck
+                if len(pID) == 0:
+                    warnings.warn('Non-standard plate ID found (%s)' % plateID,
+                                 BiopythonParserWarning)
+                elif int(pID) < 0:
+                    warnings.warn('Non-standard plate ID found (%s), using %s' %
+                                 (plateID, _platesPrefix + abs(int(pID))))
+                    plateID = _platesPrefix + abs(int(pID))
+                else:
+                    if plateID.startswith(_platesPrefixMammalian):
+                        plateID = _platesPrefixMammalian + '%02d' % int(pID)
+                    else:
+                        plateID = _platesPrefix + '%02d' % int(pID)
+
+            plate.id = plateID
+
+        elif _strainType in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_strainType] = line[1].strip()
+
+        elif _sample in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_sample] = line[1].strip()
+
+        elif _strainNumber in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_strainNumber] = line[1].strip()
+
+        elif _strainName in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_strainName] = line[1].strip()
+
+        elif _other in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_other] = line[1].strip()
+
+        elif _file in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_file] = line[1].strip()
+
+        elif _position in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_position] = line[1].strip()
+
+        elif _other in line[0].strip():
+            if plate is None:
+                continue
+            qualifiers[_csvData][_setupTime] = line[1].strip()
+
+        elif _hour in line[0].strip():
+            if plate is None:
+                continue
+            data = True
+            for i in range(1, len(line)):
+                x = line[i]
+                if x == '':
+                    continue
+                wells[x.strip()] = {}
+                idx[i] = x.strip()
+
+        elif data:
+            if plate is None:
+                continue
+
+            # Workaround for bad-formatted files
+            try:
+                float(line[0])
+            except ValueError:
+                continue
+
+            time = float(line[0])
+
+            for i in range(1, len(line)):
+                x = line[i]
+
+                try:
+                    signal = float(x)
+                except ValueError:
+                    continue
+
+                well = idx[i]
+                wells[well][time] = signal
+
+    if plate is not None and plate.id is not None:
+        plate = PlateRecord(plate.id)
+        for k, v in wells.items():
+            plate[k] = WellRecord(k, plate, v)
+        plate.qualifiers = qualifiers
+        yield plate
+
+
+def _toOPM(plate):
+    """Transform a PlateRecord object into a dictionary (PRIVATE)."""
+    d = dict(plate.qualifiers.items())
+
+    d[_csvData] = {}
+    d[_csvData][_plate] = plate.id
+    d[_measurements] = {}
+    d[_measurements][_hour] = []
+    times = set()
+    for wid, w in plate._wells.items():
+        d[_measurements][wid] = []
+        for hour in w._signals:
+            times.add(hour)
+
+    for hour in sorted(times):
+        d[_measurements][_hour].append(hour)
+        for wid, w in plate._wells.items():
+            if hour in w._signals:
+                d[_measurements][wid].append(w[hour])
+            # This shouldn't happen
+            else:
+                d[_measurements][wid].append(float('nan'))
+
+    return d
+
+
+class JsonWriter(object):
+    """Class to write PM Json format files."""
+
+    def __init__(self, plates):
+        self.plates = plates
+
+    def write(self, handle):
+        """Write this instance's plates to a file handle."""
+        out = []
+        for plate in self.plates:
+            try:
+                out.append(_toOPM(plate))
+            except ValueError:
+                raise ValueError('Could not export plate(s) in JSON format')
+
+        handle.write(json.dumps(out) + '\n')
+
+        return len(out)
diff --git a/Bio/phenotype/pm_fitting.py b/Bio/phenotype/pm_fitting.py
new file mode 100644
index 0000000..3a2aa1e
--- /dev/null
+++ b/Bio/phenotype/pm_fitting.py
@@ -0,0 +1,133 @@
+# Copyright 2014-2016 by Marco Galardini.  All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+"""Growth curves fitting and parameters extraction for phenotype data
+
+This module provides functions to perform sigmoid functions fitting to
+Phenotype Microarray data. This module depends on scipy curve_fit function.
+If not available, a warning is raised.
+
+Functions:
+logistic           Logistic growth model.
+gompertz           Gompertz growth model.
+richards           Richards growth model.
+guess_plateau      Guess the plateau point to improve sigmoid fitting.
+guess_lag          Guess the lag point to improve sigmoid fitting.
+fit                Sigmoid functions fit.
+get_area           Calculate the area under the PM curve."""
+
+import numpy as np
+
+try:
+    from scipy.optimize.minpack import curve_fit
+    from scipy.integrate import trapz
+except ImportError:
+    from Bio import MissingPythonDependencyError
+    raise MissingPythonDependencyError(
+        'Install scipy to extract curve parameters.')
+
+
+def logistic(x, A, u, d, v, y0):
+    """Logistic growth model
+
+    Proposed in Zwietering et al., 1990 (PMID: 16348228)
+    """
+    y = (A / (1 + np.exp((((4 * u) / A) * (d - x)) + 2))) + y0
+    return y
+
+
+def gompertz(x, A, u, d, v, y0):
+    """Gompertz growth model
+
+    Proposed in Zwietering et al., 1990 (PMID: 16348228)
+    """
+    y = (A * np.exp(-np.exp((((u * np.e) / A) * (d - x)) + 1))) + y0
+    return y
+
+
+def richards(x, A, u, d, v, y0):
+    """Gompertz growth model (equivalent to Stannard)
+
+    Proposed in Zwietering et al., 1990 (PMID: 16348228)
+    """
+    y = (A * pow(1 + (v + (np.exp(1 + v) * np.exp((u / A) *
+                                                  (1 + v) * (1 + (1 / v)) * (d - x)))), -(1 / v))) + y0
+    return y
+
+
+def guess_lag(x, y):
+    """Given two axes returns a guess of the lag point.
+
+    The lag point is defined as the x point where the difference in y
+    with the next point is higher then the mean differences between
+    the points plus one standard deviation. If such point is not found
+    or x and y have different lengths the function returns zero.
+    """
+    if len(x) != len(y):
+        return 0
+
+    diffs = []
+    indexes = range(len(x))
+
+    for i in indexes:
+        if i + 1 not in indexes:
+            continue
+        diffs.append(y[i + 1] - y[i])
+    diffs = np.array(diffs)
+
+    flex = x[-1]
+    for i in indexes:
+        if i + 1 not in indexes:
+            continue
+        if (y[i + 1] - y[i]) > (diffs.mean() + (diffs.std())):
+            flex = x[i]
+            break
+
+    return flex
+
+
+def guess_plateau(x, y):
+    """Given two axes returns a guess of the plateau point.
+
+    The plateau point is defined as the x point where the y point
+    is near one standard deviation of the differences between the y points to
+    the maximum y value. If such point is not found or x and y have
+    different lengths the function returns zero.
+    """
+    if len(x) != len(y):
+        return 0
+
+    diffs = []
+    indexes = range(len(y))
+
+    for i in indexes:
+        if i + 1 not in indexes:
+            continue
+        diffs.append(y[i + 1] - y[i])
+    diffs = np.array(diffs)
+
+    ymax = y[-1]
+    for i in indexes:
+        if y[i] > (ymax - diffs.std()) and y[i] < (ymax + diffs.std()):
+            ymax = y[i]
+            break
+
+    return ymax
+
+
+def fit(function, x, y):
+    """Fit the provided functrion to the x and y values.
+
+    The function parameters and the parameters covariance."""
+    # Compute guesses for the parameters
+    # This is necessary to get significant fits
+    p0 = [guess_plateau(x, y), 4.0, guess_lag(x, y), 0.1, min(y)]
+
+    params, pcov = curve_fit(function, x, y, p0=p0)
+    return params, pcov
+
+
+def get_area(y, x):
+    """Get the area under the curve"""
+    return trapz(y=y, x=x)
diff --git a/Bio/triefind.py b/Bio/triefind.py
index fc1e766..e0c1019 100644
--- a/Bio/triefind.py
+++ b/Bio/triefind.py
@@ -16,6 +16,7 @@ find          Find keys in a trie matching anywhere in a string.
 find_words    Find keys in a trie matching whole words in a string.
 
 """
+
 import string
 import re
 
diff --git a/BioSQL/BioSeq.py b/BioSQL/BioSeq.py
index 0d8c175..d430c17 100644
--- a/BioSQL/BioSeq.py
+++ b/BioSQL/BioSeq.py
@@ -137,6 +137,17 @@ class DBSeq(Seq):
         return other + self.toseq()
 
 
+def _retrieve_seq_len(adaptor, primary_id):
+    # The database schema ensures there will be only one matching row
+    seqs = adaptor.execute_and_fetchall(
+        "SELECT length FROM biosequence WHERE bioentry_id = %s", (primary_id,))
+    if not seqs:
+        return None
+    assert len(seqs) == 1
+    given_length, = seqs[0]
+    return int(given_length)
+
+
 def _retrieve_seq(adaptor, primary_id):
     # The database schema ensures there will be only one matching
     # row in the table.
@@ -510,11 +521,8 @@ class DBSeqRecord(SeqRecord):
         # We don't yet record any per-letter-annotations in the
         # BioSQL database, but we should set this property up
         # for completeness (and the __str__ method).
-        try:
-            length = len(self.seq)
-        except:
-            # Could be no sequence in the database!
-            length = 0
+        # We do NOT want to load the sequence from the DB here!
+        length = _retrieve_seq_len(adaptor, primary_id)
         self._per_letter_annotations = _RestrictedDict(length=length)
 
     def __get_seq(self):
@@ -523,6 +531,7 @@ class DBSeqRecord(SeqRecord):
         return self._seq
 
     def __set_seq(self, seq):
+        # TODO - Check consistent with self._per_letter_annotations
         self._seq = seq
 
     def __del_seq(self):
diff --git a/BioSQL/BioSeqDatabase.py b/BioSQL/BioSeqDatabase.py
index 35d8f58..72cfc3f 100644
--- a/BioSQL/BioSeqDatabase.py
+++ b/BioSQL/BioSeqDatabase.py
@@ -1,5 +1,5 @@
 # Copyright 2002 by Andrew Dalke.  All rights reserved.
-# Revisions 2007-2014 copyright by Peter Cock.  All rights reserved.
+# Revisions 2007-2016 copyright by Peter Cock.  All rights reserved.
 # Revisions 2009 copyright by Cymon J. Cox.  All rights reserved.
 # Revisions 2013-2014 copyright by Tiago Antao.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
@@ -24,6 +24,7 @@ from . import BioSeq
 from . import Loader
 from . import DBUtils
 
+
 _POSTGRES_RULES_PRESENT = False  # Hack for BioSQL Bug 2839
 
 
@@ -112,17 +113,23 @@ def open_database(driver="MySQLdb", **kwargs):
         if server.adaptor.execute_and_fetchall(sql):
             import warnings
             from Bio import BiopythonWarning
-            warnings.warn("Your BioSQL PostgreSQL schema includes some "
-                          "rules currently required for bioperl-db but "
-                          "which may cause problems loading data using "
-                          "Biopython (see BioSQL Bug 2839). If you do not "
-                          "use BioPerl, please remove these rules. "
-                          "Biopython should cope with the rules present, "
-                          "but with a performance penalty when loading "
-                          "new records.", BiopythonWarning)
+            warnings.warn("Your BioSQL PostgreSQL schema includes some rules "
+                          "currently required for bioperl-db but which may"
+                          "cause problems loading data using Biopython (see "
+                          "BioSQL's RedMine Bug 2839 aka GitHub Issue 4 "
+                          "https://github.com/biosql/biosql/issues/4). "
+                          "If you do not use BioPerl, please remove these "
+                          "rules. Biopython should cope with the rules "
+                          "present, but with a performance penalty when "
+                          "loading new records.", BiopythonWarning)
             global _POSTGRES_RULES_PRESENT
             _POSTGRES_RULES_PRESENT = True
 
+    elif driver == 'sqlite3':
+        # Tell SQLite that we want to use foreign keys
+        # https://www.sqlite.org/foreignkeys.html#fk_enable
+        server.adaptor.execute('PRAGMA foreign_keys = ON')
+
     return server
 
 
@@ -304,9 +311,18 @@ class _CursorWrapper(object):
         self.real_cursor = real_cursor
 
     def execute(self, operation, params=None, multi=False):
+        """Execute a sql statement
+        """
         self.real_cursor.execute(operation, params, multi)
 
+    def executemany(self, operation, params):
+        """Execute many sql statements
+        """
+        self.real_cursor.executemany(operation, params)
+
     def _convert_tuple(self, tuple_):
+        """Decode any bytestrings present in the row
+        """
         tuple_list = list(tuple_)
         for i, elem in enumerate(tuple_list):
             if type(elem) is bytes:
@@ -482,6 +498,13 @@ class Adaptor(object):
             sql = sql.replace("%s", "?")
         self.dbutils.execute(self.cursor, sql, args)
 
+    def executemany(self, sql, args):
+        """Execute many sql commands.
+        """
+        if os.name == "java":
+            sql = sql.replace("%s", "?")
+        self.dbutils.executemany(self.cursor, sql, args)
+
     def get_subseq_as_string(self, seqid, start, end):
         length = end - start
         # XXX Check this on MySQL and PostgreSQL. substr should be general,
@@ -499,10 +522,14 @@ class Adaptor(object):
             (start + 1, length, seqid))[0])
 
     def execute_and_fetch_col0(self, sql, args=None):
+        """Return a list of values from the first column in the row
+        """
         self.execute(sql, args or ())
         return [field[0] for field in self.cursor.fetchall()]
 
     def execute_and_fetchall(self, sql, args=None):
+        """Return a list of tuples of all rows
+        """
         self.execute(sql, args or ())
         return self.cursor.fetchall()
 
@@ -622,12 +649,15 @@ class BioSeqDatabase(object):
         return list(self.keys())
 
     def __getitem__(self, key):
-        return BioSeq.DBSeqRecord(self.adaptor, key)
+        record = BioSeq.DBSeqRecord(self.adaptor, key)
+        if record._biodatabase_id != self.dbid:
+            raise KeyError("Entry %r does exist, but not in current name space" % key)
+        return record
 
     def __delitem__(self, key):
         """Remove an entry and all its annotation."""
         if key not in self:
-            raise KeyError(key)
+            raise KeyError("Entry %r cannot be deleted. It was not found or is invalid" % key)
         # Assuming this will automatically cascade to the other tables...
         sql = "DELETE FROM bioentry " + \
               "WHERE biodatabase_id=%s AND bioentry_id=%s;"
@@ -765,7 +795,7 @@ class BioSeqDatabase(object):
                 else:
                     accession = cur_record.id
                     version = 0
-                gi = cur_record.annotations.get("gi", None)
+                gi = cur_record.annotations.get("gi")
                 sql = "SELECT bioentry_id FROM bioentry WHERE (identifier " + \
                       "= '%s' AND biodatabase_id = '%s') OR (accession = " + \
                       "'%s' AND version = '%s' AND biodatabase_id = '%s')"
diff --git a/BioSQL/DBUtils.py b/BioSQL/DBUtils.py
index 8c64457..5e16a5d 100644
--- a/BioSQL/DBUtils.py
+++ b/BioSQL/DBUtils.py
@@ -10,6 +10,7 @@
 # available and licensed separately.  Please consult www.biosql.org
 import os
 
+
 _dbutils = {}
 
 
@@ -38,6 +39,11 @@ class Generic_dbutils(object):
         """
         cursor.execute(sql, args or ())
 
+    def executemany(self, cursor, sql, seq):
+        """Execute many sql commands
+        """
+        cursor.executemany(sql, seq)
+
     def autocommit(self, conn, y=1):
         # Let's hope it was not really needed
         pass
@@ -46,10 +52,21 @@ class Generic_dbutils(object):
 class Sqlite_dbutils(Generic_dbutils):
     """Custom database utilities for SQLite."""
 
+    def _sub_placeholder(self, sql):
+        """Format the argument placeholders for sqlite
+        """
+        return sql.replace("%s", "?")
+
     def execute(self, cursor, sql, args=None):
         """Execute SQL command, replacing %s with ? for variable substitution in sqlite3.
         """
-        cursor.execute(sql.replace("%s", "?"), args or ())
+        sql = self._sub_placeholder(sql)
+        cursor.execute(sql, args or ())
+
+    def executemany(self, cursor, sql, seq):
+        sql = self._sub_placeholder(sql)
+        cursor.executemany(sql, seq)
+
 
 _dbutils["sqlite3"] = Sqlite_dbutils
 
diff --git a/BioSQL/Loader.py b/BioSQL/Loader.py
index 7b0952c..0f08f37 100644
--- a/BioSQL/Loader.py
+++ b/BioSQL/Loader.py
@@ -1,5 +1,5 @@
 # Copyright 2002 by Andrew Dalke.  All rights reserved.
-# Revisions 2007-2009 copyright by Peter Cock.  All rights reserved.
+# Revisions 2007-2016 copyright by Peter Cock.  All rights reserved.
 # Revisions 2008 copyright by Cymon J. Cox.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
@@ -30,8 +30,6 @@ from Bio._py3k import _is_int_or_long
 from Bio._py3k import range
 from Bio._py3k import basestring
 
-__docformat__ = "restructuredtext en"
-
 
 class DatabaseLoader(object):
     """Object used to load SeqRecord objects into a BioSQL database."""
@@ -329,6 +327,45 @@ class DatabaseLoader(object):
         assert answer == answer.lower()
         return answer
 
+    def _update_left_right_taxon_values(self, left_value):
+        """update the left and right values in the table
+        """
+        if not left_value:
+            return
+        # Due to the UNIQUE constraint on the left and right values in the taxon
+        # table we cannot simply update them through an SQL statement as we risk
+        # colliding values. Instead we must select all of the rows that we want to
+        # update, modify the values in python and then update the rows
+        # self.adaptor.execute("UPDATE taxon SET right_value = right_value + 2 WHERE right_value >= %s", (left_value,))
+        # self.adaptor.execute("UPDATE taxon SET left_value = left_value + 2 WHERE left_value > %s", (left_value,))
+
+        rows = self.adaptor.execute_and_fetchall(
+                "SELECT left_value, right_value, taxon_id FROM taxon WHERE right_value >= %s or left_value > %s",
+                # "SELECT left_value, right_value, taxon_id FROM taxon",
+                (left_value, left_value)
+                )
+
+        right_rows = []
+        left_rows = []
+        for row in rows:
+            new_right = row[1]
+            new_left = row[0]
+            if new_right >= left_value:
+                new_right += 2
+
+            if new_left > left_value:
+                new_left += 2
+            right_rows.append((new_right, row[2]))
+            left_rows.append((new_left, row[2]))
+
+        # sort the rows based on the value from largest to smallest
+        # should ensure no overlaps
+        right_rows = sorted(right_rows, key=lambda x: x[0], reverse=True)
+        left_rows = sorted(left_rows, key=lambda x: x[0], reverse=True)
+
+        self.adaptor.executemany("UPDATE taxon SET left_value = %s WHERE taxon_id = %s", left_rows)
+        self.adaptor.executemany("UPDATE taxon SET right_value = %s WHERE taxon_id = %s", right_rows)
+
     def _get_taxon_id_from_ncbi_taxon_id(self, ncbi_taxon_id,
                                          scientific_name=None,
                                          common_name=None):
@@ -372,6 +409,10 @@ class DatabaseLoader(object):
         rank = "species"
         genetic_code = None
         mito_genetic_code = None
+        parent_left_value = None
+        parent_right_value = None
+        left_value = None
+        right_value = None
         species_names = []
         if scientific_name:
             species_names.append(("scientific name", scientific_name))
@@ -387,14 +428,22 @@ class DatabaseLoader(object):
                 assert taxonomic_record[0]["TaxId"] == str(ncbi_taxon_id), \
                     "%s versus %s" % (taxonomic_record[0]["TaxId"],
                                       ncbi_taxon_id)
-                parent_taxon_id = self._get_taxon_id_from_ncbi_lineage(
+
+                parent_taxon_id, parent_left_value, parent_right_value = self._get_taxon_id_from_ncbi_lineage(
                     taxonomic_record[0]["LineageEx"])
-                rank = taxonomic_record[0]["Rank"]
-                genetic_code = taxonomic_record[0]["GeneticCode"]["GCId"]
-                mito_genetic_code = taxonomic_record[
-                    0]["MitoGeneticCode"]["MGCId"]
+
+                left_value = parent_right_value
+                right_value = parent_right_value + 1
+
+                rank = str(taxonomic_record[0]["Rank"])
+
+                genetic_code = int(taxonomic_record[0]["GeneticCode"]["GCId"])
+
+                mito_genetic_code = int(taxonomic_record[
+                    0]["MitoGeneticCode"]["MGCId"])
+
                 species_names = [("scientific name",
-                                  taxonomic_record[0]["ScientificName"])]
+                                  str(taxonomic_record[0]["ScientificName"]))]
                 try:
                     for name_class, names in taxonomic_record[0]["OtherNames"].items():
                         name_class = self._fix_name_class(name_class)
@@ -422,6 +471,8 @@ class DatabaseLoader(object):
             # is in the record annotation as a list of names, as we won't
             # know the NCBI taxon IDs for these parent nodes.
 
+        self._update_left_right_taxon_values(left_value)
+
         self.adaptor.execute(
             "INSERT INTO taxon(parent_taxon_id, ncbi_taxon_id, node_rank,"
             " genetic_code, mito_genetic_code, left_value, right_value)"
@@ -430,8 +481,9 @@ class DatabaseLoader(object):
                                                      rank,
                                                      genetic_code,
                                                      mito_genetic_code,
-                                                     None,
-                                                     None))
+                                                     left_value,
+                                                     right_value))
+
         taxon_id = self.adaptor.last_id("taxon")
 
         # Record the scientific name, common name, etc
@@ -458,46 +510,59 @@ class DatabaseLoader(object):
         This method will record all the lineage given, returning the taxon id
         (database key, not NCBI taxon id) of the final entry (the species).
         """
-        ncbi_taxon_id = taxonomic_lineage[-1]["TaxId"]
-
+        ncbi_taxon_id = int(taxonomic_lineage[-1]["TaxId"])
+        left_value = None
+        right_value = None
+        parent_left_value = None
+        parent_right_value = None
         # Is this in the database already?  Check the taxon table...
-        taxon_id = self.adaptor.execute_and_fetch_col0(
-            "SELECT taxon_id FROM taxon"
+        rows = self.adaptor.execute_and_fetchall(
+            "SELECT taxon_id, left_value, right_value FROM taxon"
             " WHERE ncbi_taxon_id=%s" % ncbi_taxon_id)
-        if taxon_id:
+        if rows:
             # we could verify that the Scientific Name etc in the database
             # is the same and update it or print a warning if not...
-            if isinstance(taxon_id, list):
-                assert len(taxon_id) == 1
-                return taxon_id[0]
-            else:
-                return taxon_id
+            assert len(rows) == 1
+            return rows[0]
 
         # We have to record this.
         if len(taxonomic_lineage) > 1:
             # Use recursion to find out the taxon id (database key) of the
             # parent.
-            parent_taxon_id = self._get_taxon_id_from_ncbi_lineage(
+            parent_taxon_id, parent_left_value, parent_right_value = self._get_taxon_id_from_ncbi_lineage(
                 taxonomic_lineage[:-1])
+            left_value = parent_right_value
+            right_value = parent_right_value + 1
             assert _is_int_or_long(parent_taxon_id), repr(parent_taxon_id)
         else:
+            # we have reached the top of the lineage but no current taxonomy
+            # id has been found
             parent_taxon_id = None
+            left_value = self.adaptor.execute_one(
+                "SELECT MAX(left_value) FROM taxon")[0]
+            if not left_value:
+                left_value = 0
+
+            right_value = left_value + 1
+
+        self._update_left_right_taxon_values(left_value)
 
         # INSERT new taxon
-        rank = taxonomic_lineage[-1].get("Rank", None)
+        rank = str(taxonomic_lineage[-1].get("Rank"))
         self.adaptor.execute(
-            "INSERT INTO taxon(ncbi_taxon_id, parent_taxon_id, node_rank)"
-            " VALUES (%s, %s, %s)", (ncbi_taxon_id, parent_taxon_id, rank))
+            "INSERT INTO taxon(ncbi_taxon_id, parent_taxon_id, node_rank, left_value, right_value)"
+            " VALUES (%s, %s, %s, %s, %s)", (ncbi_taxon_id, parent_taxon_id, rank, left_value, right_value))
+
         taxon_id = self.adaptor.last_id("taxon")
-        assert isinstance(taxon_id, (int, long)), repr(taxon_id)
+        # assert isinstance(taxon_id, int), repr(taxon_id)
         # ... and its name in taxon_name
-        scientific_name = taxonomic_lineage[-1].get("ScientificName", None)
+        scientific_name = taxonomic_lineage[-1].get("ScientificName")
         if scientific_name:
             self.adaptor.execute(
                 "INSERT INTO taxon_name(taxon_id, name, name_class)"
                 " VALUES (%s, %s, 'scientific name')", (taxon_id,
                                                         scientific_name[:255]))
-        return taxon_id
+        return taxon_id, left_value, right_value
 
     def _load_bioentry_table(self, record):
         """Fill the bioentry table with sequence information (PRIVATE).
@@ -536,7 +601,7 @@ class DatabaseLoader(object):
 
         # Allow description and division to default to NULL as in BioPerl.
         description = getattr(record, 'description', None)
-        division = record.annotations.get("data_file_division", None)
+        division = record.annotations.get("data_file_division")
 
         sql = """
         INSERT INTO bioentry (
@@ -668,10 +733,10 @@ class DatabaseLoader(object):
                 # Handled separately
                 continue
             term_id = self._get_term_id(key, ontology_id=tag_ontology_id)
-            if isinstance(value, list) or isinstance(value, tuple):
+            if isinstance(value, (list, tuple)):
                 rank = 0
                 for entry in value:
-                    if isinstance(entry, str) or isinstance(entry, int):
+                    if isinstance(entry, (str, int)):
                         # Easy case
                         rank += 1
                         self.adaptor.execute(many_sql,
@@ -680,7 +745,7 @@ class DatabaseLoader(object):
                         pass
                         # print "Ignoring annotation '%s' sub-entry of type '%s'" \
                         #      % (key, str(type(entry)))
-            elif isinstance(value, str) or isinstance(value, int):
+            elif isinstance(value, (str, int)):
                 # Have a simple single entry, leave rank as the DB default
                 self.adaptor.execute(mono_sql,
                                      (bioentry_id, term_id, str(value)))
diff --git a/CONTRIB b/CONTRIB
index 4cef19e..d45870a 100644
--- a/CONTRIB
+++ b/CONTRIB
@@ -16,11 +16,14 @@ Paul T. Bathen
 Yair Benita <Y.Benita at domain pharm.uu.nl>
 Peter Bienstman <Peter.Bienstman at domain rug.ac.be>
 Jose Blanca
+Kai Blin < https://github.com/kblin >
+Steve Bond < https://github.com/biologyguy >
 Bob Bussell <rgb2003 at domain med.cornell.edu>
 John Bradley < https://github.com/johnbradley >
 Diego Brouard <diego at domain conysis.com>
 Christian Brueffer <christian at domain brueffer.de>
 David Cain <gmail, david joseph cain>
+Christiam Camacho < https://github.com/christiam >
 Franco Caramia < https://github.com/fcaramia >
 James Casbon <j.a.casbon at domain qmul.ac.uk>
 Hye-Shik Chang <perky at domain fallin.lv>
@@ -30,6 +33,7 @@ Saket Choudhary < https://github.com/saketkc >
 Peter Cock <p.j.a.cock at googlemail dot com / https://github.com/peterjc >
 Marc Colosimo <mcolosimo at domain mitre.org>
 Andres Colubri <andres dot colubri at gmail dot com>
+Joe Cora < https://github.com/JoeCora >
 Cymon J Cox <cymon at domain duke.edu>
 Gavin E Crooks <gec at domain compbio.berkeley.edu>
 Andrew Dalke <dalke at domain acm.org>
@@ -42,14 +46,17 @@ Nathan J. Edwards <nje5 at edu domain georgetown>
 Kyle Ellrott
 Gokcen Eraslan < https://github.com/gokceneraslan >
 Tarcisio Fedrizzi < https://github.com/hcraT >
+João D Ferreira < https://github.com/jdferreira >
 Jeffrey Finkelstein <jeffrey.finkelstein at domain gmail.com>
 Konrad Förstner < https://github.com/konrad >
 Iddo Friedberg <idoerg at domain burnham.org>
 Bertrand Frottier <bertrand.frottier at domain free.fr>
 Ben Fulton < https://github.com/benfulton >
+Marco Galardini < https://github.com/mgalardini >
 Aaron Gallagher <habnabit at gmail>
 Phillip Garland <pgarland at gmail>
 Walter Gillett < https://github.com/wgillett >
+Chaitanya Gupta < https://github.com/iCHAIT >
 Melissa Gymrek < https://github.com/mgymrek >
 Frederik Gwinner
 Jason A. Hackney <jhackney at domain stanford.edu>
@@ -63,9 +70,11 @@ Jeff Hussmann <first dot last at gmail dot com>
 Kevin Jacobs <jacobs at bioinformed dot com>
 Diana Jaunzeikare
 Sunhwan Jo < https://github.com/sunhwan >
+Terry Jones < https://github.com/terrycojones >
 Joanna & Dominik Kasprzak
 Frank Kauff <fkauff at domain duke.edu>
 Siong Kong
+David Koppstein < https://github.com/dkoppstein >
 Andreas Kuntzagk <andreas.kuntzagk at domain mdc-berlin.de>
 Michal Kurowski <michal at domain genesilico.pl>
 Gleb Kuznetsov < https://github.com/glebkuznetsov >
@@ -85,12 +94,15 @@ Alan Medlar < https://github.com/ajm >
 Tarjei Mikkelsen <tarjei at domain genome.wi.mit.edu>
 Chris Mitchell < https://github.com/chrismit >
 Ben Morris
+Nader Morshed < https://github.com/naderm >
+Barbara Mühlemann < https://github.com/bamueh >
 Bertrand Néron < https://github.com/bneron >
 David Nicholson < https://github.com/danich1 >
 Kozo Nishida < https://github.com/kozo2 >
 Emmanuel Noutahi < https://github.com/maclandrol >
 Konstantin Okonechnikov <k.okonechnikov at domain gmail.com>
 Cheng Soon Ong <chengsoon.ong at tuebingen.mpg.de>
+Brian Osborne < https://github.com/bosborne >
 Anne Pajon <ap one two at sanger ac uk>
 Alessio Papini <first dot last at unifi dot it>
 Claude Paroz <claude at two (as digit) xlibre dot net>
@@ -98,36 +110,44 @@ Carlos Pena < https://github.com/carlosp420 >
 Lenna Peterson <ark first-name at gmail dot com>
 Andrea Pierleoni <andrea at the Italian domain biocomp dot unibo>
 Markus Piotrowski < https://github.com/MarkusPiotrowski >
+Anders Pitman < https://github.com/anderspitman >
 Mike Poidinger <Michael.Poidinger at domain eBioinformatics.com>
 Leighton Pritchard <lpritc at domain scri.sari.ac.uk>
 Leszek Pryszcz < https://github.com/lpryszcz >
 Eric Rasche < https://github.com/erasche >
 Andrea Rizzi < https://github.com/andrrizzi >
 Joao Rodrigues <anaryin at the domain gmail dot com>
+Aaron Rosenfeld < https://github.com/arosenfeld >
 Thomas Rosleff Soerensen <rosleff at domain mpiz-koeln.mpg.de>
+Matt Ruffalo < https://github.com/mruffalo >
 Thomas Schmitt <Thomas dot Schmitt at Swedish domain sbc.su>
 Wolfgang Schueler <wolfgang at domain proceryon.at>
 Anuj Sharma < https://github.com/xulesc >
 Matt Shirley < https://github.com/mdshw5 >
+Thomas Sicheritz-Ponten <thomas at domain cbs.dtu.dk>
 Seth Sims <seth.sims at gmail>
 Lucas Sinclair < https://github.com/xapple >
+Connor T. Skennerton < https://github.com/ctSkennerton >
 Peter Slickers <piet at domain clondiag.com>
 Kamil Slowikowski <k no dot slowikowski at gmail dot com>
-Thomas Sicheritz-Ponten <thomas at domain cbs.dtu.dk>
-Connor T. Skennerton < https://github.com/ctSkennerton >
+Jacek Śmietański < https://github.com/dadoskawina >
 Frederic Sohm <fsms at domain users.sourceforge.net>
+Owen Solberg < https://github.com/odoublewen >
+Matteo Sticco < https://github.com/sticken88/ >
 Nate Sutton
 Anuj Sharma
-Eric Talevich <eric.talevich at domain gmail.com>
+Eric Talevich <etal at domain uga.edu / https://github.com/etal>
 Bartosz Telenczuk <bartosz.telenczuk at domain gmail.com>
 Bogdan T. (bogdan at pearlgen dot com)
 Tyghe Vallard < https://github.com/necrolyte2 >
 Carlos Rios Vera <crosvera at domain gmail.com>
 Johann Visagie <wjv at domain cityip.co.za>
 Dan Vogel <dmv at domain andrew.cmu.edu>
+Chris Warth < https://github.com/cswarth >
 David Weisman <david.weisman at domain acm.org>
 Bartek Wilczynski <bartek at domain rezolwenta.eu.org>
 David Winter <david dot winter at gmail dot com>
+Ben Woodcroft < https://github.com/wwood >
 Chunlei Wu < https://github.com/newgene/ >
 Kevin Wu < https://github.com/kevinwuhoo >
 Yanbo Ye < https://github.com/lijax >
diff --git a/DEPRECATED b/DEPRECATED
index 2b4598c..2721a1a 100644
--- a/DEPRECATED
+++ b/DEPRECATED
@@ -28,11 +28,29 @@ Never officially supported, these triggered a warning in Release 1.62
 recommending Python 3.3 or later. As of Biopython Release 1.63 onwards,
 installation simply aborts with a error message.
 
+Python 3.3
+==========
+Still suported but deprecated as of Release 1.67, triggering a warning
+at installation time.
+
 Bio.CodonAlign
 ==============
 This new experimental module included in Biopython 1.64 was renamed to
 Bio.codonalign in Biopython 1.65 to follow PEP8 module naming rules.
 
+Bio.SeqRecord equality
+======================
+As of Release 1.67, the SeqRecord objects (and their subclasses) no longer use
+the default Python object comparison. Instead they will raise an exception if
+you try to compare them.
+
+For backward compatibility and/or to explicitly use object comparison, please
+use id(record1) == id(record2) instead.
+
+Otherwise please test whichever specific attributes you are interested in
+explicitly, for example record1.id == record2.id or record1.seq == record.seq
+(see also the note below about sequence equality).
+
 Bio.Seq sequence equality
 =========================
 As of Release 1.65, the Seq and MutableSeq objects (and their subclasses)
@@ -88,12 +106,14 @@ attribute sub_features was deprecated.
 Bio.Motif
 =========
 Declared obsolete with a PendingDeprecationWarning in Release 1.61, formally
-deprecated in Release 1.62. Please use the newer Bio.motifs module instead.
+deprecated in Release 1.62, removed in Release 1.67. Please use the newer
+Bio.motifs module instead.
 
 AlignAceCommandline and CompareAceCommandline
 =============================================
-Deprecated in release 1.62. An up to date version of the software cannot
-be obtained anymore (affects Bio.Motif and its replacement Bio.motifs).
+Deprecated in release 1.62, removed in Release 1.67. An up to date version of
+the software cannot be obtained anymore (affects Bio.Motif and its replacement
+Bio.motifs).
 
 Bio.SeqIO.Interfaces
 ====================
@@ -529,10 +549,10 @@ Functions GC_Frame, fasta_uniqids, apply_on_multi_fasta, and
 quicker_apply_on_multi_fasta were deprecated in Release 1.55, and removed
 in Release 1.58.
 
-Function quick_FASTA_reader was declared obsolete in Release 1.61, and
-deprecated in Release 1.64. Use function list(SimpleFastaParser(handle))
-from Bio.SeqIO.FastaIO instead (but ideally convert your code to using
-an iterator approach).
+Function quick_FASTA_reader was declared obsolete in Release 1.61,
+deprecated in Release 1.64, and removed in Release 1.67. Use function
+list(SimpleFastaParser(handle)) from Bio.SeqIO.FastaIO instead (but
+ideally convert your code to using an iterator approach).
 
 Bio.GFF (for accessing a MySQL database created with BioPerl, etc)
 =======
@@ -650,7 +670,7 @@ method get_seq_by_num were deprecated in Release 1.57.
 Bio.ParserSupport
 =================
 Bio.ParserSupport was declared obsolete in Release 1.59, and deprecated in
-Release 1.63.
+Release 1.63. The Martel specific EventGenerator was removed in Release 1.67..
 
 Bio.ParserSupport.SGMLStrippingConsumer was deprecated in Release 1.59, and
 removed in Release 1.61.
diff --git a/Doc/Tutorial.html b/Doc/Tutorial.html
deleted file mode 100644
index 8612f99..0000000
--- a/Doc/Tutorial.html
+++ /dev/null
@@ -1,10952 +0,0 @@
-<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"
-            "http://www.w3.org/TR/REC-html40/loose.dtd">
-<HTML>
-<HEAD>
-<TITLE>Biopython Tutorial and Cookbook
-</TITLE>
-
-<META http-equiv="Content-Type" content="text/html; charset=US-ASCII">
-<META name="GENERATOR" content="hevea 1.10+9 of 2008-12-17">
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-.li-itemize{margin:1ex 0ex;}
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-.thefootnotes{text-align:left;margin:0ex;}
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-BLOCKQUOTE{margin-left:4ex;margin-right:4ex;text-align:left;}
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-.boxed{border:1px solid black}
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-.theorem{text-align:left;margin:1ex auto 1ex 0ex;}
-.part{margin:2ex auto;text-align:center}
-</STYLE>
-</HEAD>
-<BODY >
-<!--HEVEA command line is: hevea -fix Tutorial.tex -->
-<!--CUT DEF chapter 1 --><P>
-<P ALIGN="center">
-<IMG ALIGN="center" SRC="images/biopython.jpg" TITLE="Biopython Logo" ALT="[Biopython Logo]" width="1024" height="288" />
-</p>
-</P><TABLE CLASS="title"><TR><TD><H1 CLASS="titlemain">Biopython Tutorial and Cookbook</H1><H3 CLASS="titlerest">Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck,<BR>
-Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy&#X144;ski</H3><H3 CLASS="titlerest">Last Update &#X2013; 21 October 2015 (Biopython 1.66)</H3></TD></TR>
-</TABLE><!--TOC chapter Contents-->
-<H1 CLASS="chapter"><!--SEC ANCHOR -->Contents</H1><!--SEC END --><UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc1">Chapter&#XA0;1&#XA0;&#XA0;Introduction</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc2">1.1&#XA0;&#XA0;What is Biopython?</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc3">1.2&#XA0;&#XA0;What can I find in the Biopython package</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc4">1.3&#XA0;&#XA0;Installing Biopython</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc5">1.4&#XA0;&#XA0;Frequently Asked Questions (FAQ)</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc6">Chapter&#XA0;2&#XA0;&#XA0;Quick Start &#X2013; What can you do with Biopython?</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc7">2.1&#XA0;&#XA0;General overview of what Biopython provides</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc8">2.2&#XA0;&#XA0;Working with sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc9">2.3&#XA0;&#XA0;A usage example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc10">2.4&#XA0;&#XA0;Parsing sequence file formats</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc11">2.4.1&#XA0;&#XA0;Simple FASTA parsing example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc12">2.4.2&#XA0;&#XA0;Simple GenBank parsing example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc13">2.4.3&#XA0;&#XA0;I love parsing &#X2013; please don&#X2019;t stop talking about it!</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc14">2.5&#XA0;&#XA0;Connecting with biological databases</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc15">2.6&#XA0;&#XA0;What to do next</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc16">Chapter&#XA0;3&#XA0;&#XA0;Sequence objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc17">3.1&#XA0;&#XA0;Sequences and Alphabets</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc18">3.2&#XA0;&#XA0;Sequences act like strings</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc19">3.3&#XA0;&#XA0;Slicing a sequence</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc20">3.4&#XA0;&#XA0;Turning Seq objects into strings</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc21">3.5&#XA0;&#XA0;Concatenating or adding sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc22">3.6&#XA0;&#XA0;Changing case</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc23">3.7&#XA0;&#XA0;Nucleotide sequences and (reverse) complements</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc24">3.8&#XA0;&#XA0;Transcription</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc25">3.9&#XA0;&#XA0;Translation</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc26">3.10&#XA0;&#XA0;Translation Tables</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc27">3.11&#XA0;&#XA0;Comparing Seq objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc28">3.12&#XA0;&#XA0;MutableSeq objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc29">3.13&#XA0;&#XA0;UnknownSeq objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc30">3.14&#XA0;&#XA0;Working with strings directly</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc31">Chapter&#XA0;4&#XA0;&#XA0;Sequence annotation objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc32">4.1&#XA0;&#XA0;The SeqRecord object</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc33">4.2&#XA0;&#XA0;Creating a SeqRecord</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc34">4.2.1&#XA0;&#XA0;SeqRecord objects from scratch</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc35">4.2.2&#XA0;&#XA0;SeqRecord objects from FASTA files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc36">4.2.3&#XA0;&#XA0;SeqRecord objects from GenBank files</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc37">4.3&#XA0;&#XA0;Feature, location and position objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc38">4.3.1&#XA0;&#XA0;SeqFeature objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc39">4.3.2&#XA0;&#XA0;Positions and locations</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc40">4.3.3&#XA0;&#XA0;Sequence described by a feature or location</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc41">4.4&#XA0;&#XA0;References</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc42">4.5&#XA0;&#XA0;The format method</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc43">4.6&#XA0;&#XA0;Slicing a SeqRecord</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc44">4.7&#XA0;&#XA0;Adding SeqRecord objects</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc45">4.8&#XA0;&#XA0;Reverse-complementing SeqRecord objects</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc46">Chapter&#XA0;5&#XA0;&#XA0;Sequence Input/Output</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc47">5.1&#XA0;&#XA0;Parsing or Reading Sequences</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc48">5.1.1&#XA0;&#XA0;Reading Sequence Files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc49">5.1.2&#XA0;&#XA0;Iterating over the records in a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc50">5.1.3&#XA0;&#XA0;Getting a list of the records in a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc51">5.1.4&#XA0;&#XA0;Extracting data</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc52">5.2&#XA0;&#XA0;Parsing sequences from compressed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc53">5.3&#XA0;&#XA0;Parsing sequences from the net</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc54">5.3.1&#XA0;&#XA0;Parsing GenBank records from the net</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc55">5.3.2&#XA0;&#XA0;Parsing SwissProt sequences from the net</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc56">5.4&#XA0;&#XA0;Sequence files as Dictionaries</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc57">5.4.1&#XA0;&#XA0;Sequence files as Dictionaries &#X2013; In memory</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc58">5.4.2&#XA0;&#XA0;Sequence files as Dictionaries &#X2013; Indexed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc59">5.4.3&#XA0;&#XA0;Sequence files as Dictionaries &#X2013; Database indexed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc60">5.4.4&#XA0;&#XA0;Indexing compressed files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc61">5.4.5&#XA0;&#XA0;Discussion</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc62">5.5&#XA0;&#XA0;Writing Sequence Files</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc63">5.5.1&#XA0;&#XA0;Round trips</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc64">5.5.2&#XA0;&#XA0;Converting between sequence file formats</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc65">5.5.3&#XA0;&#XA0;Converting a file of sequences to their reverse complements</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc66">5.5.4&#XA0;&#XA0;Getting your SeqRecord objects as formatted strings</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc67">Chapter&#XA0;6&#XA0;&#XA0;Multiple Sequence Alignment objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc68">6.1&#XA0;&#XA0;Parsing or Reading Sequence Alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc69">6.1.1&#XA0;&#XA0;Single Alignments</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc70">6.1.2&#XA0;&#XA0;Multiple Alignments</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc71">6.1.3&#XA0;&#XA0;Ambiguous Alignments</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc72">6.2&#XA0;&#XA0;Writing Alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc73">6.2.1&#XA0;&#XA0;Converting between sequence alignment file formats</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc74">6.2.2&#XA0;&#XA0;Getting your alignment objects as formatted strings</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc75">6.3&#XA0;&#XA0;Manipulating Alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc76">6.3.1&#XA0;&#XA0;Slicing alignments</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc77">6.3.2&#XA0;&#XA0;Alignments as arrays</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc78">6.4&#XA0;&#XA0;Alignment Tools</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc79">6.4.1&#XA0;&#XA0;ClustalW</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc80">6.4.2&#XA0;&#XA0;MUSCLE</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc81">6.4.3&#XA0;&#XA0;MUSCLE using stdout</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc82">6.4.4&#XA0;&#XA0;MUSCLE using stdin and stdout</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc83">6.4.5&#XA0;&#XA0;EMBOSS needle and water</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc84">Chapter&#XA0;7&#XA0;&#XA0;BLAST</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc85">7.1&#XA0;&#XA0;Running BLAST over the Internet</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc86">7.2&#XA0;&#XA0;Running BLAST locally</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc87">7.2.1&#XA0;&#XA0;Introduction</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc88">7.2.2&#XA0;&#XA0;Standalone NCBI BLAST+</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc89">7.2.3&#XA0;&#XA0;Other versions of BLAST</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc90">7.3&#XA0;&#XA0;Parsing BLAST output</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc91">7.4&#XA0;&#XA0;The BLAST record class</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc92">7.5&#XA0;&#XA0;Deprecated BLAST parsers</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc93">7.5.1&#XA0;&#XA0;Parsing plain-text BLAST output</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc94">7.5.2&#XA0;&#XA0;Parsing a plain-text BLAST file full of BLAST runs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc95">7.5.3&#XA0;&#XA0;Finding a bad record somewhere in a huge plain-text BLAST file</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc96">7.6&#XA0;&#XA0;Dealing with PSI-BLAST</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc97">7.7&#XA0;&#XA0;Dealing with RPS-BLAST</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc98">Chapter&#XA0;8&#XA0;&#XA0;BLAST and other sequence search tools (<I>experimental code</I>)</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc99">8.1&#XA0;&#XA0;The SearchIO object model</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc100">8.1.1&#XA0;&#XA0;QueryResult</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc101">8.1.2&#XA0;&#XA0;Hit</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc102">8.1.3&#XA0;&#XA0;HSP</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc103">8.1.4&#XA0;&#XA0;HSPFragment</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc104">8.2&#XA0;&#XA0;A note about standards and conventions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc105">8.3&#XA0;&#XA0;Reading search output files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc106">8.4&#XA0;&#XA0;Dealing with large search output files with indexing</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc107">8.5&#XA0;&#XA0;Writing and converting search output files</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc108">Chapter&#XA0;9&#XA0;&#XA0;Accessing NCBI&#X2019;s Entrez databases</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc109">9.1&#XA0;&#XA0;Entrez Guidelines</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc110">9.2&#XA0;&#XA0;EInfo: Obtaining information about the Entrez databases</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc111">9.3&#XA0;&#XA0;ESearch: Searching the Entrez databases</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc112">9.4&#XA0;&#XA0;EPost: Uploading a list of identifiers</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc113">9.5&#XA0;&#XA0;ESummary: Retrieving summaries from primary IDs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc114">9.6&#XA0;&#XA0;EFetch: Downloading full records from Entrez</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc115">9.7&#XA0;&#XA0;ELink: Searching for related items in NCBI Entrez</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc116">9.8&#XA0;&#XA0;EGQuery: Global Query - counts for search terms</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc117">9.9&#XA0;&#XA0;ESpell: Obtaining spelling suggestions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc118">9.10&#XA0;&#XA0;Parsing huge Entrez XML files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc119">9.11&#XA0;&#XA0;Handling errors</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc120">9.12&#XA0;&#XA0;Specialized parsers</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc121">9.12.1&#XA0;&#XA0;Parsing Medline records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc122">9.12.2&#XA0;&#XA0;Parsing GEO records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc123">9.12.3&#XA0;&#XA0;Parsing UniGene records</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc124">9.13&#XA0;&#XA0;Using a proxy</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc125">9.14&#XA0;&#XA0;Examples</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc126">9.14.1&#XA0;&#XA0;PubMed and Medline</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc127">9.14.2&#XA0;&#XA0;Searching, downloading, and parsing Entrez Nucleotide records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc128">9.14.3&#XA0;&#XA0;Searching, downloading, and parsing GenBank records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc129">9.14.4&#XA0;&#XA0;Finding the lineage of an organism</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc130">9.15&#XA0;&#XA0;Using the history and WebEnv</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc131">9.15.1&#XA0;&#XA0;Searching for and downloading sequences using the history</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc132">9.15.2&#XA0;&#XA0;Searching for and downloading abstracts using the history</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc133">9.15.3&#XA0;&#XA0;Searching for citations</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc134">Chapter&#XA0;10&#XA0;&#XA0;Swiss-Prot and ExPASy</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc135">10.1&#XA0;&#XA0;Parsing Swiss-Prot files</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc136">10.1.1&#XA0;&#XA0;Parsing Swiss-Prot records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc137">10.1.2&#XA0;&#XA0;Parsing the Swiss-Prot keyword and category list</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc138">10.2&#XA0;&#XA0;Parsing Prosite records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc139">10.3&#XA0;&#XA0;Parsing Prosite documentation records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc140">10.4&#XA0;&#XA0;Parsing Enzyme records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc141">10.5&#XA0;&#XA0;Accessing the ExPASy server</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc142">10.5.1&#XA0;&#XA0;Retrieving a Swiss-Prot record</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc143">10.5.2&#XA0;&#XA0;Searching Swiss-Prot</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc144">10.5.3&#XA0;&#XA0;Retrieving Prosite and Prosite documentation records</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc145">10.6&#XA0;&#XA0;Scanning the Prosite database</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc146">Chapter&#XA0;11&#XA0;&#XA0;Bio.PopGen: Population genetics</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc147">11.1&#XA0;&#XA0;GenePop</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc148">11.2&#XA0;&#XA0;Coalescent simulation</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc149">11.2.1&#XA0;&#XA0;Creating scenarios</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc150">11.2.2&#XA0;&#XA0;Running Fastsimcoal2</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc151">11.3&#XA0;&#XA0;Other applications</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc152">11.3.1&#XA0;&#XA0;FDist: Detecting selection and molecular adaptation</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc153">11.4&#XA0;&#XA0;Future Developments</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc154">Chapter&#XA0;12&#XA0;&#XA0;Phylogenetics with Bio.Phylo</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc155">12.1&#XA0;&#XA0;Demo: What&#X2019;s in a Tree?</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc156">12.1.1&#XA0;&#XA0;Coloring branches within a tree</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc157">12.2&#XA0;&#XA0;I/O functions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc158">12.3&#XA0;&#XA0;View and export trees</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc159">12.4&#XA0;&#XA0;Using Tree and Clade objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc160">12.4.1&#XA0;&#XA0;Search and traversal methods</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc161">12.4.2&#XA0;&#XA0;Information methods</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc162">12.4.3&#XA0;&#XA0;Modification methods</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc163">12.4.4&#XA0;&#XA0;Features of PhyloXML trees</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc164">12.5&#XA0;&#XA0;Running external applications</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc165">12.6&#XA0;&#XA0;PAML integration</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc166">12.7&#XA0;&#XA0;Future plans</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc167">Chapter&#XA0;13&#XA0;&#XA0;Sequence motif analysis using Bio.motifs</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc168">13.1&#XA0;&#XA0;Motif objects</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc169">13.1.1&#XA0;&#XA0;Creating a motif from instances</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc170">13.1.2&#XA0;&#XA0;Creating a sequence logo</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc171">13.2&#XA0;&#XA0;Reading motifs</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc172">13.2.1&#XA0;&#XA0;JASPAR</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc173">13.2.2&#XA0;&#XA0;MEME</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc174">13.2.3&#XA0;&#XA0;TRANSFAC</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc175">13.3&#XA0;&#XA0;Writing motifs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc176">13.4&#XA0;&#XA0;Position-Weight Matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc177">13.5&#XA0;&#XA0;Position-Specific Scoring Matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc178">13.6&#XA0;&#XA0;Searching for instances</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc179">13.6.1&#XA0;&#XA0;Searching for exact matches</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc180">13.6.2&#XA0;&#XA0;Searching for matches using the PSSM score</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc181">13.6.3&#XA0;&#XA0;Selecting a score threshold</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc182">13.7&#XA0;&#XA0;Each motif object has an associated Position-Specific Scoring Matrix</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc183">13.8&#XA0;&#XA0;Comparing motifs</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc184">13.9&#XA0;&#XA0;<EM>De novo</EM> motif finding</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc185">13.9.1&#XA0;&#XA0;MEME</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc186">13.9.2&#XA0;&#XA0;AlignAce</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc187">13.10&#XA0;&#XA0;Useful links </A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc188">Chapter&#XA0;14&#XA0;&#XA0;Cluster analysis</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc189">14.1&#XA0;&#XA0;Distance functions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc190">14.2&#XA0;&#XA0;Calculating cluster properties</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc191">14.3&#XA0;&#XA0;Partitioning algorithms</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc192">14.4&#XA0;&#XA0;Hierarchical clustering</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc193">14.5&#XA0;&#XA0;Self-Organizing Maps</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc194">14.6&#XA0;&#XA0;Principal Component Analysis</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc195">14.7&#XA0;&#XA0;Handling Cluster/TreeView-type files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc196">14.8&#XA0;&#XA0;Example calculation</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc197">14.9&#XA0;&#XA0;Auxiliary functions</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc198">Chapter&#XA0;15&#XA0;&#XA0;Supervised learning methods</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc199">15.1&#XA0;&#XA0;The Logistic Regression Model</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc200">15.1.1&#XA0;&#XA0;Background and Purpose</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc201">15.1.2&#XA0;&#XA0;Training the logistic regression model</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc202">15.1.3&#XA0;&#XA0;Using the logistic regression model for classification</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc203">15.1.4&#XA0;&#XA0;Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc204">15.2&#XA0;&#XA0;<I>k</I>-Nearest Neighbors</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc205">15.2.1&#XA0;&#XA0;Background and purpose</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc206">15.2.2&#XA0;&#XA0;Initializing a <I>k</I>-nearest neighbors model</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc207">15.2.3&#XA0;&#XA0;Using a <I>k</I>-nearest neighbors model for classification</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc208">15.3&#XA0;&#XA0;Na&#XEF;ve Bayes</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc209">15.4&#XA0;&#XA0;Maximum Entropy</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc210">15.5&#XA0;&#XA0;Markov Models</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc211">Chapter&#XA0;16&#XA0;&#XA0;Graphics including GenomeDiagram</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc212">16.1&#XA0;&#XA0;GenomeDiagram</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc213">16.1.1&#XA0;&#XA0;Introduction</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc214">16.1.2&#XA0;&#XA0;Diagrams, tracks, feature-sets and features</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc215">16.1.3&#XA0;&#XA0;A top down example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc216">16.1.4&#XA0;&#XA0;A bottom up example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc217">16.1.5&#XA0;&#XA0;Features without a SeqFeature</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc218">16.1.6&#XA0;&#XA0;Feature captions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc219">16.1.7&#XA0;&#XA0;Feature sigils</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc220">16.1.8&#XA0;&#XA0;Arrow sigils</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc221">16.1.9&#XA0;&#XA0;A nice example</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc222">16.1.10&#XA0;&#XA0;Multiple tracks</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc223">16.1.11&#XA0;&#XA0;Cross-Links between tracks</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc224">16.1.12&#XA0;&#XA0;Further options</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc225">16.1.13&#XA0;&#XA0;Converting old code</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc226">16.2&#XA0;&#XA0;Chromosomes</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc227">16.2.1&#XA0;&#XA0;Simple Chromosomes</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc228">16.2.2&#XA0;&#XA0;Annotated Chromosomes</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc229">Chapter&#XA0;17&#XA0;&#XA0;KEGG</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc230">17.1&#XA0;&#XA0;Parsing KEGG records</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc231">17.2&#XA0;&#XA0;Querying the KEGG API</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc232">Chapter&#XA0;18&#XA0;&#XA0;Cookbook &#X2013; Cool things to do with it</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc233">18.1&#XA0;&#XA0;Working with sequence files</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc234">18.1.1&#XA0;&#XA0;Filtering a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc235">18.1.2&#XA0;&#XA0;Producing randomised genomes</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc236">18.1.3&#XA0;&#XA0;Translating a FASTA file of CDS entries</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc237">18.1.4&#XA0;&#XA0;Making the sequences in a FASTA file upper case</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc238">18.1.5&#XA0;&#XA0;Sorting a sequence file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc239">18.1.6&#XA0;&#XA0;Simple quality filtering for FASTQ files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc240">18.1.7&#XA0;&#XA0;Trimming off primer sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc241">18.1.8&#XA0;&#XA0;Trimming off adaptor sequences</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc242">18.1.9&#XA0;&#XA0;Converting FASTQ files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc243">18.1.10&#XA0;&#XA0;Converting FASTA and QUAL files into FASTQ files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc244">18.1.11&#XA0;&#XA0;Indexing a FASTQ file</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc245">18.1.12&#XA0;&#XA0;Converting SFF files</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc246">18.1.13&#XA0;&#XA0;Identifying open reading frames</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc247">18.2&#XA0;&#XA0;Sequence parsing plus simple plots</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc248">18.2.1&#XA0;&#XA0;Histogram of sequence lengths</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc249">18.2.2&#XA0;&#XA0;Plot of sequence GC%</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc250">18.2.3&#XA0;&#XA0;Nucleotide dot plots</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc251">18.2.4&#XA0;&#XA0;Plotting the quality scores of sequencing read data</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc252">18.3&#XA0;&#XA0;Dealing with alignments</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc253">18.3.1&#XA0;&#XA0;Calculating summary information</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc254">18.3.2&#XA0;&#XA0;Calculating a quick consensus sequence</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc255">18.3.3&#XA0;&#XA0;Position Specific Score Matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc256">18.3.4&#XA0;&#XA0;Information Content</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc257">18.4&#XA0;&#XA0;Substitution Matrices</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc258">18.4.1&#XA0;&#XA0;Using common substitution matrices</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc259">18.4.2&#XA0;&#XA0;Creating your own substitution matrix from an alignment</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc260">18.5&#XA0;&#XA0;BioSQL &#X2013; storing sequences in a relational database</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc261">Chapter&#XA0;19&#XA0;&#XA0;The Biopython testing framework</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc262">19.1&#XA0;&#XA0;Running the tests</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc263">19.1.1&#XA0;&#XA0;Running the tests using Tox</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc264">19.2&#XA0;&#XA0;Writing tests</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc265">19.2.1&#XA0;&#XA0;Writing a print-and-compare test</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc266">19.2.2&#XA0;&#XA0;Writing a unittest-based test</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc267">19.3&#XA0;&#XA0;Writing doctests</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc268">Chapter&#XA0;20&#XA0;&#XA0;Advanced</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc269">20.1&#XA0;&#XA0;Parser Design</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc270">20.2&#XA0;&#XA0;Substitution Matrices</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc271">20.2.1&#XA0;&#XA0;SubsMat</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc272">20.2.2&#XA0;&#XA0;FreqTable</A>
-</LI></UL>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc273">Chapter&#XA0;21&#XA0;&#XA0;Where to go from here &#X2013; contributing to Biopython</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc274">21.1&#XA0;&#XA0;Bug Reports + Feature Requests</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc275">21.2&#XA0;&#XA0;Mailing lists and helping newcomers</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc276">21.3&#XA0;&#XA0;Contributing Documentation</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc277">21.4&#XA0;&#XA0;Contributing cookbook examples</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc278">21.5&#XA0;&#XA0;Maintaining a distribution for a platform</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc279">21.6&#XA0;&#XA0;Contributing Unit Tests</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc280">21.7&#XA0;&#XA0;Contributing Code</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc281">Chapter&#XA0;22&#XA0;&#XA0;Appendix: Useful stuff about Python</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc282">22.1&#XA0;&#XA0;What the heck is a handle?</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc283">22.1.1&#XA0;&#XA0;Creating a handle from a string</A>
-</LI></UL>
-</LI></UL>
-</LI></UL><!--TOC chapter Introduction-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc1">Chapter&#XA0;1</A>&#XA0;&#XA0;Introduction</H1><!--SEC END --><P>
-<A NAME="chapter:introduction"></A></P><!--TOC section What is Biopython?-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc2">1.1</A>&#XA0;&#XA0;What is Biopython?</H2><!--SEC END --><P>The Biopython Project is an international association of developers of freely available Python (<A HREF="http://www.python.org"><TT>http://www.python.org</TT></A>) tools for computational molecular biology. Python is an object oriented, interpreted, flexible language that is becoming increasingly popular for scientific computing. Python is easy to learn, has a very clear synt [...]
-an online resource for modules, scripts, and web links for developers
-of Python-based software for bioinformatics use and research. Basically,
-the goal of Biopython is to make it as easy as possible to use Python
-for bioinformatics by creating high-quality, reusable modules and
-classes. Biopython features include parsers for various Bioinformatics
-file formats (BLAST, Clustalw, FASTA, Genbank,...), access to online
-services (NCBI, Expasy,...), interfaces to common and not-so-common
-programs (Clustalw, DSSP, MSMS...), a standard sequence class, various
-clustering modules, a KD tree data structure etc. and even documentation.</P><P>Basically, we just like to program in Python and want to make it as easy as possible to use Python for bioinformatics by creating high-quality, reusable modules and scripts.</P><!--TOC section What can I find in the Biopython package-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc3">1.2</A>&#XA0;&#XA0;What can I find in the Biopython package</H2><!--SEC END --><P>The main Biopython releases have lots of functionality, including:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The ability to parse bioinformatics files into Python utilizable data structures, including support for the following formats:<UL CLASS="itemize"><LI CLASS="li-itemize">
-Blast output &#X2013; both from standalone and WWW Blast
-</LI><LI CLASS="li-itemize">Clustalw
-</LI><LI CLASS="li-itemize">FASTA
-</LI><LI CLASS="li-itemize">GenBank
-</LI><LI CLASS="li-itemize">PubMed and Medline
-</LI><LI CLASS="li-itemize">ExPASy files, like Enzyme and Prosite
-</LI><LI CLASS="li-itemize">SCOP, including &#X2018;dom&#X2019; and &#X2018;lin&#X2019; files
-</LI><LI CLASS="li-itemize">UniGene
-</LI><LI CLASS="li-itemize">SwissProt
-</LI></UL></LI><LI CLASS="li-itemize">Files in the supported formats can be iterated over record by record or indexed and accessed via a Dictionary interface.</LI><LI CLASS="li-itemize">Code to deal with popular on-line bioinformatics destinations such as:<UL CLASS="itemize"><LI CLASS="li-itemize">
-NCBI &#X2013; Blast, Entrez and PubMed services
-</LI><LI CLASS="li-itemize">ExPASy &#X2013; Swiss-Prot and Prosite entries, as well as Prosite searches
-</LI></UL></LI><LI CLASS="li-itemize">Interfaces to common bioinformatics programs such as:<UL CLASS="itemize"><LI CLASS="li-itemize">
-Standalone Blast from NCBI
-</LI><LI CLASS="li-itemize">Clustalw alignment program
-</LI><LI CLASS="li-itemize">EMBOSS command line tools
-</LI></UL></LI><LI CLASS="li-itemize">A standard sequence class that deals with sequences, ids on sequences, and sequence features.</LI><LI CLASS="li-itemize">Tools for performing common operations on sequences, such as translation, transcription and weight calculations.</LI><LI CLASS="li-itemize">Code to perform classification of data using k Nearest Neighbors, Naive Bayes or Support Vector Machines.</LI><LI CLASS="li-itemize">Code for dealing with alignments, including a standard way t [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc4">1.3</A>&#XA0;&#XA0;Installing Biopython</H2><!--SEC END --><P>All of the installation information for Biopython was separated from
-this document to make it easier to keep updated.</P><P>The short version is go to our downloads page (<A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A>),
-download and install the listed dependencies, then download and install Biopython.
-Biopython runs on many platforms (Windows, Mac, and on the various flavors of Linux and Unix).
-For Windows we provide pre-compiled click-and-run installers, while for Unix and other
-operating systems you must install from source as described in the included README file.
-This is usually as simple as the standard commands:</P><PRE CLASS="verbatim">python setup.py build
-python setup.py test
-sudo python setup.py install
-</PRE><P>(You can in fact skip the build and test, and go straight to the install &#X2013;
-but its better to make sure everything seems to be working.)</P><P>The longer version of our installation instructions covers
-installation of Python, Biopython dependencies and Biopython itself.
-It is available in PDF
-(<A HREF="http://biopython.org/DIST/docs/install/Installation.pdf"><TT>http://biopython.org/DIST/docs/install/Installation.pdf</TT></A>)
-and HTML formats
-(<A HREF="http://biopython.org/DIST/docs/install/Installation.html"><TT>http://biopython.org/DIST/docs/install/Installation.html</TT></A>).</P><!--TOC section Frequently Asked Questions (FAQ)-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc5">1.4</A>&#XA0;&#XA0;Frequently Asked Questions (FAQ)</H2><!--SEC END --><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate"><EM>How do I cite Biopython in a scientific publication?</EM><BR>
- Please cite our application note [<A HREF="#cock2009">1</A>, Cock <I>et al.</I>, 2009]
-as the main Biopython reference.
-In addition, please cite any publications from the following list if appropriate, in particular as a reference for specific modules within Biopython (more information can be found on our website):
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-For the official project announcement: [<A HREF="#chapman2000">13</A>, Chapman and Chang, 2000];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.PDB</CODE>: [<A HREF="#hamelryck2003a">18</A>, Hamelryck and Manderick, 2003];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.Cluster</CODE>: [<A HREF="#dehoon2004">14</A>, De Hoon <I>et al.</I>, 2004];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.Graphics.GenomeDiagram</CODE>: [<A HREF="#pritchard2006">2</A>, Pritchard <I>et al.</I>, 2006];
-</LI><LI CLASS="li-itemize">For <CODE>Bio.Phylo</CODE> and <CODE>Bio.Phylo.PAML</CODE>: [<A HREF="#talevich2012">9</A>, Talevich <I>et al.</I>, 2012];
-</LI><LI CLASS="li-itemize">For the FASTQ file format as supported in Biopython, BioPerl, BioRuby, BioJava, and EMBOSS: [<A HREF="#cock2010">7</A>, Cock <I>et al.</I>, 2010].
-</LI></UL></LI><LI CLASS="li-enumerate"><EM>How should I capitalize &#X201C;Biopython&#X201D;? Is &#X201C;BioPython&#X201D; OK?</EM><BR>
- The correct capitalization is &#X201C;Biopython&#X201D;, not &#X201C;BioPython&#X201D; (even though
-that would have matched BioPerl, BioJava and BioRuby).</LI><LI CLASS="li-enumerate"><EM>What is going wrong with my print commands?</EM><BR>
- This tutorial now uses the Python 3 style print <EM>function</EM>.
-As of Biopython 1.62, we support both Python 2 and Python 3.
-The most obvious language difference is the print <EM>statement</EM>
-in Python 2 became a print <EM>function</EM> in Python 3.<P>For example, this will only work under Python 2:</P><PRE CLASS="verbatim">>>> print "Hello World!"
-Hello World!
-</PRE><P>If you try that on Python 3 you&#X2019;ll get a <CODE>SyntaxError</CODE>.
-Under Python 3 you must write:</P><PRE CLASS="verbatim">>>> print("Hello World!")
-Hello World!
-</PRE><P>Surprisingly that will also work on Python 2 &#X2013; but only for simple
-examples printing one thing. In general you need to add this magic
-line to the start of your Python scripts to use the print function
-under Python 2.6 and 2.7:</P><PRE CLASS="verbatim">from __future__ import print_function
-</PRE><P>If you forget to add this magic import, under Python 2 you&#X2019;ll see
-extra brackets produced by trying to use the print function when
-Python 2 is interpreting it as a print statement and a tuple.</P></LI><LI CLASS="li-enumerate"><EM>How do I find out what version of Biopython I have installed?</EM><BR>
- Use this:
-<PRE CLASS="verbatim">  >>> import Bio
-  >>> print(Bio.__version__)
-  ...
-  </PRE>If the &#X201C;<CODE>import Bio</CODE>&#X201D; line fails, Biopython is not installed.
-Note that those are double underscores before and after version.
-If the second line fails, your version is <EM>very</EM> out of date.
-If the version string ends with a plus, you don&#X2019;t have an official
-release, but a snapshot of the in development code.</LI><LI CLASS="li-enumerate"><EM>Where is the latest version of this document?</EM><BR>
- If you download a Biopython source code archive, it will include the
-relevant version in both HTML and PDF formats. The latest published
-version of this document (updated at each release) is online:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial.html"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial.html</TT></A>
-</LI><LI CLASS="li-itemize"><A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial.pdf"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial.pdf</TT></A>
-</LI></UL>
-If you are using the very latest unreleased code from our repository
-you can find copies of the in-progress tutorial here:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial-dev.html"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial-dev.html</TT></A>
-</LI><LI CLASS="li-itemize"><A HREF="http://biopython.org/DIST/docs/tutorial/Tutorial-dev.pdf"><TT>http://biopython.org/DIST/docs/tutorial/Tutorial-dev.pdf</TT></A>
-</LI></UL></LI><LI CLASS="li-enumerate"><EM>What is wrong with my sequence comparisons?</EM><BR>
- There was a major change in Biopython 1.65 making the <CODE>Seq</CODE> and
-<CODE>MutableSeq</CODE> classes (and subclasses) use simple string-based
-comparison (ignoring the alphabet other than if giving a warning),
-which you can do explicitly with <CODE>str(seq1) == str(seq2)</CODE>.<P>Older versions of Biopython would use instance-based comparison
-for <CODE>Seq</CODE> objects which you can do explicitly with
-<CODE>id(seq1) == id(seq2)</CODE>.</P><P>If you still need to support old versions of Biopython, use these
-explicit forms to avoid problems. See Section&#XA0;<A HREF="#sec:seq-comparison">3.11</A>.</P></LI><LI CLASS="li-enumerate"><EM>Why is the</EM> <CODE>Seq</CODE> <EM>object missing the upper & lower methods described in this Tutorial?</EM><BR>
- You need Biopython 1.53 or later. Alternatively, use <CODE>str(my_seq).upper()</CODE> to get an upper case string.
-If you need a Seq object, try <CODE>Seq(str(my_seq).upper())</CODE> but be careful about blindly re-using the same alphabet.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t the</EM> <CODE>Seq</CODE> <EM>object translation method support the</EM> <CODE>cds</CODE> <EM>option described in this Tutorial?</EM><BR>
- You need Biopython 1.51 or later.</LI><LI CLASS="li-enumerate"><EM>What file formats do</EM> <CODE>Bio.SeqIO</CODE> <EM>and</EM> <CODE>Bio.AlignIO</CODE> <EM>read and write?</EM><BR>
- Check the built in docstrings (<TT>from Bio import SeqIO</TT>, then <TT>help(SeqIO)</TT>), or see <A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A> and <A HREF="http://biopython.org/wiki/AlignIO"><TT>http://biopython.org/wiki/AlignIO</TT></A> on the wiki for the latest listing.</LI><LI CLASS="li-enumerate"><EM>Why won&#X2019;t the </EM> <CODE>Bio.SeqIO</CODE> <EM>and</EM> <CODE>Bio.AlignIO</CODE> <EM>functions</EM> <CODE>parse</CODE><EM>,</EM> <CODE> [...]
- You need Biopython 1.54 or later, or just use handles explicitly (see Section&#XA0;<A HREF="#sec:appendix-handles">22.1</A>).
-It is especially important to remember to close output handles explicitly after writing your data.</LI><LI CLASS="li-enumerate"><EM>Why won&#X2019;t the </EM> <CODE>Bio.SeqIO.write()</CODE> <EM>and</EM> <CODE>Bio.AlignIO.write()</CODE> <EM>functions accept a single record or alignment? They insist on a list or iterator!</EM><BR>
- You need Biopython 1.54 or later, or just wrap the item with <CODE>[...]</CODE> to create a list of one element.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>str(...)</CODE> <EM>give me the full sequence of a</EM> <CODE>Seq</CODE> <EM>object?</EM><BR>
- You need Biopython 1.45 or later.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.Blast</CODE> <EM>work with the latest plain text NCBI blast output?</EM><BR>
- The NCBI keep tweaking the plain text output from the BLAST tools, and keeping our parser up to date is/was an ongoing struggle.
-If you aren&#X2019;t using the latest version of Biopython, you could try upgrading.
-However, we (and the NCBI) recommend you use the XML output instead, which is designed to be read by a computer program.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.Entrez.parse()</CODE> <EM>work? The module imports fine but there is no parse function!</EM><BR>
- You need Biopython 1.52 or later.</LI><LI CLASS="li-enumerate"><EM>Why has my script using</EM> <CODE>Bio.Entrez.efetch()</CODE> <EM>stopped working?</EM><BR>
- This could be due to NCBI changes in February 2012 introducing EFetch 2.0.
-First, they changed the default return modes - you probably want to add <CODE>retmode="text"</CODE> to
-your call.
-Second, they are now stricter about how to provide a list of IDs &#X2013; Biopython 1.59 onwards
-turns a list into a comma separated string automatically.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.Blast.NCBIWWW.qblast()</CODE> <EM>give the same results as the NCBI BLAST website?</EM><BR>
- You need to specify the same options &#X2013; the NCBI often adjust the default settings on the website,
-and they do not match the QBLAST defaults anymore. Check things like the gap penalties and expectation threshold.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.Blast.NCBIXML.read()</CODE> <EM>work? The module imports but there is no read function!</EM><BR>
- You need Biopython 1.50 or later. Or, use <TT>next(Bio.Blast.NCBIXML.parse(...))</TT> instead.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t my</EM> <CODE>SeqRecord</CODE> <EM>object have a</EM> <CODE>letter_annotations</CODE> <EM>attribute?</EM><BR>
- Per-letter-annotation support was added in Biopython 1.50.</LI><LI CLASS="li-enumerate"><EM>Why can&#X2019;t I slice my</EM> <CODE>SeqRecord</CODE> <EM>to get a sub-record?</EM><BR>
- You need Biopython 1.50 or later.</LI><LI CLASS="li-enumerate"><EM>Why can&#X2019;t I add</EM> <CODE>SeqRecord</CODE> <EM>objects together?</EM><BR>
- You need Biopython 1.53 or later.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.SeqIO.convert()</CODE> <EM>or</EM> <CODE>Bio.AlignIO.convert()</CODE> <EM>work? The modules import fine but there is no convert function!</EM><BR>
- You need Biopython 1.52 or later. Alternatively, combine the <CODE>parse</CODE> and <CODE>write</CODE>
-functions as described in this tutorial (see Sections&#XA0;<A HREF="#sec:SeqIO-conversion">5.5.2</A> and&#XA0;<A HREF="#sec:converting-alignments">6.2.1</A>).</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.SeqIO.index()</CODE> <EM>work? The module imports fine but there is no index function!</EM><BR>
- You need Biopython 1.52 or later.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.SeqIO.index_db()</CODE> <EM>work? The module imports fine but there is no </EM><EM><TT>index_db</TT></EM><EM> function!</EM><BR>
- You need Biopython 1.57 or later (and a Python with SQLite3 support).</LI><LI CLASS="li-enumerate"><EM>Where is the</EM> <CODE>MultipleSeqAlignment</CODE> <EM>object? The</EM> <CODE>Bio.Align</CODE> <EM>module imports fine but this class isn&#X2019;t there!</EM><BR>
- You need Biopython 1.54 or later. Alternatively, the older <CODE>Bio.Align.Generic.Alignment</CODE> class supports some of its functionality, but using this is now discouraged.</LI><LI CLASS="li-enumerate"><EM>Why can&#X2019;t I run command line tools directly from the application wrappers?</EM><BR>
- You need Biopython 1.55 or later. Alternatively, use the Python <CODE>subprocess</CODE> module directly.</LI><LI CLASS="li-enumerate"><EM>I looked in a directory for code, but I couldn&#X2019;t find the code that does something. Where&#X2019;s it hidden?</EM><BR>
- One thing to know is that we put code in <CODE>__init__.py</CODE> files. If you are not used to looking for code in this file this can be confusing. The reason we do this is to make the imports easier for users. For instance, instead of having to do a &#X201C;repetitive&#X201D; import like <CODE>from Bio.GenBank import GenBank</CODE>, you can just use <CODE>from Bio import GenBank</CODE>.</LI><LI CLASS="li-enumerate"><EM>Why does the code from CVS seem out of date?</EM><BR>
- In late September 2009, just after the release of Biopython 1.52, we switched from using CVS to git, a distributed version control system. The old CVS server will remain available as a static and read only backup, but if you want to grab the latest code, you&#X2019;ll need to use git instead. See our website for more details.</LI><LI CLASS="li-enumerate"><EM>Why doesn&#X2019;t</EM> <CODE>Bio.Fasta</CODE> <EM>work?</EM><BR>
- We deprecated the <CODE>Bio.Fasta</CODE> module in Biopython 1.51 (August 2009) and removed it in Biopython 1.55 (August 2010). There is a brief example showing how to convert old code to use <CODE>Bio.SeqIO</CODE> instead in the <A HREF="http://biopython.org/SRC/biopython/DEPRECATED">DEPRECATED</A> file.</LI></OL><P>For more general questions, the Python FAQ pages <A HREF="http://www.python.org/doc/faq/"><TT>http://www.python.org/doc/faq/</TT></A> may be useful.</P><!--TOC chapter Quic [...]
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc6">Chapter&#XA0;2</A>&#XA0;&#XA0;Quick Start &#X2013; What can you do with Biopython?</H1><!--SEC END --><P>
-<A NAME="chapter:quick-start"></A></P><P>This section is designed to get you started quickly with Biopython, and to give a general overview of what is available and how to use it. All of the examples in this section assume that you have some general working knowledge of Python, and that you have successfully installed Biopython on your system. If you think you need to brush up on your Python, the main Python web site provides quite a bit of free documentation to get started with (<A HREF [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc7">2.1</A>&#XA0;&#XA0;General overview of what Biopython provides</H2><!--SEC END --><P>As mentioned in the introduction, Biopython is a set of libraries to provide the ability to deal with &#X201C;things&#X201D; of interest to biologists working on the computer. In general this means that you will need to have at least some programming experience (in Python, of course!) or at least an interest in learning to program. Biopython&#X2019;s  [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc8">2.2</A>&#XA0;&#XA0;Working with sequences</H2><!--SEC END --><P>
-<A NAME="sec:sequences"></A></P><P>Disputably (of course!), the central object in bioinformatics is the sequence. Thus, we&#X2019;ll start with a quick introduction to the Biopython mechanisms for dealing with sequences, the <CODE>Seq</CODE> object, which we&#X2019;ll discuss in more detail in Chapter&#XA0;<A HREF="#chapter:Bio.Seq">3</A>.</P><P>Most of the time when we think about sequences we have in my mind a string of letters like &#X2018;<CODE>AGTACACTGGT</CODE>&#X2019;. You can cre [...]
->>> my_seq = Seq("AGTACACTGGT")
->>> my_seq
-Seq('AGTACACTGGT', Alphabet())
->>> print(my_seq)
-AGTACACTGGT
->>> my_seq.alphabet
-Alphabet()
-</PRE><P>What we have here is a sequence object with a <EM>generic</EM> alphabet - reflecting the fact we have <EM>not</EM> specified if this is a DNA or protein sequence (okay, a protein with a lot of Alanines, Glycines, Cysteines and Threonines!). We&#X2019;ll talk more about alphabets in Chapter&#XA0;<A HREF="#chapter:Bio.Seq">3</A>.</P><P>In addition to having an alphabet, the <CODE>Seq</CODE> object differs from the Python string in the methods it supports. You can&#X2019;t do this  [...]
-Seq('AGTACACTGGT', Alphabet())
->>> my_seq.complement()
-Seq('TCATGTGACCA', Alphabet())
->>> my_seq.reverse_complement()
-Seq('ACCAGTGTACT', Alphabet())
-</PRE><P>The next most important class is the <CODE>SeqRecord</CODE> or Sequence Record. This holds a sequence (as a <CODE>Seq</CODE> object) with additional annotation including an identifier, name and description. The <CODE>Bio.SeqIO</CODE> module for reading and writing sequence file formats works with <CODE>SeqRecord</CODE> objects, which will be introduced below and covered in more detail by Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>.</P><P>This covers the basic features and us [...]
-Now that you&#X2019;ve got some idea of what it is like to interact with the Biopython libraries, it&#X2019;s time to delve into the fun, fun world of dealing with biological file formats!</P><!--TOC section A usage example-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc9">2.3</A>&#XA0;&#XA0;A usage example</H2><!--SEC END --><P>
-<A NAME="sec:orchids"></A></P><P>Before we jump right into parsers and everything else to do with Biopython, let&#X2019;s set up an example to motivate everything we do and make life more interesting. After all, if there wasn&#X2019;t any biology in this tutorial, why would you want you read it?</P><P>Since I love plants, I think we&#X2019;re just going to have to have a plant based example (sorry to all the fans of other organisms out there!). Having just completed a recent trip to our  [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc10">2.4</A>&#XA0;&#XA0;Parsing sequence file formats</H2><!--SEC END --><P>
-<A NAME="sec:sequence-parsing"></A></P><P>A large part of much bioinformatics work involves dealing with the many types of file formats designed to hold biological data. These files are loaded with interesting biological data, and a special challenge is parsing these files into a format so that you can manipulate them with some kind of programming language. However the task of parsing these files can be frustrated by the fact that the formats can change quite regularly, and that formats  [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc11">2.4.1</A>&#XA0;&#XA0;Simple FASTA parsing example</H3><!--SEC END --><P>
-<A NAME="sec:fasta-parsing"></A></P><P>If you open the lady slipper orchids FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A> in your favourite text editor, you&#X2019;ll see that the file starts like this:</P><PRE CLASS="verbatim">>gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
-CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
-AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
-...
-</PRE><P>It contains 94 records, each has a line starting with &#X201C;<CODE>></CODE>&#X201D; (greater-than symbol) followed by the sequence on one or more lines. Now try this in Python:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
-    print(seq_record.id)
-    print(repr(seq_record.seq))
-    print(len(seq_record))
-</PRE><P>You should get something like this on your screen:</P><PRE CLASS="verbatim">gi|2765658|emb|Z78533.1|CIZ78533
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet())
-740
-...
-gi|2765564|emb|Z78439.1|PBZ78439
-Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet())
-592
-</PRE><P>Notice that the FASTA format does not specify the alphabet, so <CODE>Bio.SeqIO</CODE> has defaulted to the rather generic <CODE>SingleLetterAlphabet()</CODE> rather than something DNA specific.</P><!--TOC subsection Simple GenBank parsing example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc12">2.4.2</A>&#XA0;&#XA0;Simple GenBank parsing example</H3><!--SEC END --><P>Now let&#X2019;s load the GenBank file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk"><TT>ls_orchid.gbk</TT></A> instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-    print(seq_record.id)
-    print(repr(seq_record.seq))
-    print(len(seq_record))
-</PRE><P>This should give:</P><PRE CLASS="verbatim">Z78533.1
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
-740
-...
-Z78439.1
-Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA())
-592
-</PRE><P>This time <CODE>Bio.SeqIO</CODE> has been able to choose a sensible alphabet, IUPAC Ambiguous DNA. You&#X2019;ll also notice that a shorter string has been used as the <CODE>seq_record.id</CODE> in this case.</P><!--TOC subsection I love parsing &#X2013; please don&#X2019;t stop talking about it!-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc13">2.4.3</A>&#XA0;&#XA0;I love parsing &#X2013; please don&#X2019;t stop talking about it!</H3><!--SEC END --><P>Biopython has a lot of parsers, and each has its own little special niches based on the sequence format it is parsing and all of that. Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> covers <CODE>Bio.SeqIO</CODE> in more detail, while Chapter&#XA0;<A HREF="#chapter:Bio.AlignIO">6</A> introduces <CODE>Bio.AlignIO</CODE> for [...]
-Please also check the wiki pages <A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A> and <A HREF="http://biopython.org/wiki/AlignIO"><TT>http://biopython.org/wiki/AlignIO</TT></A> for the latest information, or ask on the mailing list. The wiki pages should include an up to date list of supported file types, and some additional examples.</P><P>The next place to look for information about specific parsers and how to do cool things with them is in the Coo [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc14">2.5</A>&#XA0;&#XA0;Connecting with biological databases</H2><!--SEC END --><P>
-<A NAME="sec:connecting-with-biological-databases"></A></P><P>One of the very common things that you need to do in bioinformatics is extract information from biological databases. It can be quite tedious to access these databases manually, especially if you have a lot of repetitive work to do. Biopython attempts to save you time and energy by making some on-line databases available from Python scripts. Currently, Biopython has code to extract information from the following databases:</P> [...]
-<A HREF="http://www.ncbi.nlm.nih.gov/Entrez/">Entrez</A> (and <A HREF="http://www.ncbi.nlm.nih.gov/PubMed/">PubMed</A>) from the NCBI &#X2013; See Chapter&#XA0;<A HREF="#chapter:entrez">9</A>.
-</LI><LI CLASS="li-itemize"><A HREF="http://www.expasy.org/">ExPASy</A> &#X2013; See Chapter&#XA0;<A HREF="#chapter:swiss_prot">10</A>.
-</LI><LI CLASS="li-itemize"><A HREF="http://scop.mrc-lmb.cam.ac.uk/scop/">SCOP</A> &#X2013; See the <CODE>Bio.SCOP.search()</CODE> function.
-</LI></UL><P>The code in these modules basically makes it easy to write Python code that interact with the CGI scripts on these pages, so that you can get results in an easy to deal with format. In some cases, the results can be tightly integrated with the Biopython parsers to make it even easier to extract information.</P><!--TOC section What to do next-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc15">2.6</A>&#XA0;&#XA0;What to do next</H2><!--SEC END --><P>Now that you&#X2019;ve made it this far, you hopefully have a good understanding of the basics of Biopython and are ready to start using it for doing useful work. The best thing to do now is finish reading this tutorial, and then if you want start snooping around in the source code, and looking at the automatically generated documentation.</P><P>Once you get a picture of what y [...]
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc16">Chapter&#XA0;3</A>&#XA0;&#XA0;Sequence objects</H1><!--SEC END --><P>
-<A NAME="chapter:Bio.Seq"></A></P><P>Biological sequences are arguably the central object in Bioinformatics, and in this chapter we&#X2019;ll introduce the Biopython mechanism for dealing with sequences, the <CODE>Seq</CODE> object.
-Chapter&#XA0;<A HREF="#chapter:SeqRecord">4</A> will introduce the related <CODE>SeqRecord</CODE> object, which combines the sequence information with any annotation, used again in Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> for Sequence Input/Output.</P><P>Sequences are essentially strings of letters like <CODE>AGTACACTGGT</CODE>, which seems very natural since this is the most common way that sequences are seen in biological file formats.</P><P>There are two important differences b [...]
-First of all, they have different methods. Although the <CODE>Seq</CODE> object supports many of the same methods as a plain string, its <CODE>translate()</CODE> method differs by doing biological translation, and there are also additional biologically relevant methods like <CODE>reverse_complement()</CODE>.
-Secondly, the <CODE>Seq</CODE> object has an important attribute, <CODE>alphabet</CODE>, which is an object describing what the individual characters making up the sequence string &#X201C;mean&#X201D;, and how they should be interpreted. For example, is <CODE>AGTACACTGGT</CODE> a DNA sequence, or just a protein sequence that happens to be rich in Alanines, Glycines, Cysteines
-and Threonines?</P><!--TOC section Sequences and Alphabets-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc17">3.1</A>&#XA0;&#XA0;Sequences and Alphabets</H2><!--SEC END --><P>The alphabet object is perhaps the important thing that makes the <CODE>Seq</CODE> object more than just a string. The currently available alphabets for Biopython are defined in the <CODE>Bio.Alphabet</CODE> module. We&#X2019;ll use the IUPAC alphabets (<A HREF="http://www.chem.qmw.ac.uk/iupac/"><TT>http://www.chem.qmw.ac.uk/iupac/</TT></A>) here to deal with some of ou [...]
-You can create an ambiguous sequence with the default generic alphabet like this:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> my_seq = Seq("AGTACACTGGT")
->>> my_seq
-Seq('AGTACACTGGT', Alphabet())
->>> my_seq.alphabet
-Alphabet()
-</PRE><P>However, where possible you should specify the alphabet explicitly when creating your sequence objects - in this case an unambiguous DNA alphabet object:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("AGTACACTGGT", IUPAC.unambiguous_dna)
->>> my_seq
-Seq('AGTACACTGGT', IUPACUnambiguousDNA())
->>> my_seq.alphabet
-IUPACUnambiguousDNA()
-</PRE><P>Unless of course, this really is an amino acid sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_prot = Seq("AGTACACTGGT", IUPAC.protein)
->>> my_prot
-Seq('AGTACACTGGT', IUPACProtein())
->>> my_prot.alphabet
-IUPACProtein()
-</PRE><!--TOC section Sequences act like strings-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc18">3.2</A>&#XA0;&#XA0;Sequences act like strings</H2><!--SEC END --><P>In many ways, we can deal with Seq objects as if they were normal Python strings, for example getting the length, or iterating over the elements:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GATCG", IUPAC.unambiguous_dna)
->>> for index, letter in enumerate(my_seq):
-...     print("%i %s" % (index, letter))
-0 G
-1 A
-2 T
-3 C
-4 G
->>> print(len(my_seq))
-5
-</PRE><P>You can access elements of the sequence in the same way as for strings (but remember, Python counts from zero!):</P><PRE CLASS="verbatim">>>> print(my_seq[0]) #first letter
-G
->>> print(my_seq[2]) #third letter
-T
->>> print(my_seq[-1]) #last letter
-G
-</PRE><P>The <CODE>Seq</CODE> object has a <CODE>.count()</CODE> method, just like a string.
-Note that this means that like a Python string, this gives a
-<EM>non-overlapping</EM> count:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> "AAAA".count("AA")
-2
->>> Seq("AAAA").count("AA")
-2
-</PRE><P>For some biological uses, you may actually want an overlapping count
-(i.e. 3 in this trivial example). When searching for single letters, this
-makes no difference:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
->>> len(my_seq)
-32
->>> my_seq.count("G")
-9
->>> 100 * float(my_seq.count("G") + my_seq.count("C")) / len(my_seq)
-46.875
-</PRE><P>While you could use the above snippet of code to calculate a GC%, note that the <CODE>Bio.SeqUtils</CODE> module has several GC functions already built. For example:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> from Bio.SeqUtils import GC
->>> my_seq = Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPAC.unambiguous_dna)
->>> GC(my_seq)
-46.875
-</PRE><P>Note that using the <CODE>Bio.SeqUtils.GC()</CODE> function should automatically cope with mixed case sequences and the ambiguous nucleotide S which means G or C.</P><P>Also note that just like a normal Python string, the <CODE>Seq</CODE> object is in some ways &#X201C;read-only&#X201D;. If you need to edit your sequence, for example simulating a point mutation, look at the Section&#XA0;<A HREF="#sec:mutable-seq">3.12</A> below which talks about the <CODE>MutableSeq</CODE> objec [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc19">3.3</A>&#XA0;&#XA0;Slicing a sequence</H2><!--SEC END --><P>A more complicated example, let&#X2019;s get a slice of the sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
->>> my_seq[4:12]
-Seq('GATGGGCC', IUPACUnambiguousDNA())
-</PRE><P>Two things are interesting to note. First, this follows the normal conventions for Python strings. So the first element of the sequence is 0 (which is normal for computer science, but not so normal for biology). When you do a slice the first item is included (i.e.&#XA0;4 in this case) and the last is excluded (12 in this case), which is the way things work in Python, but of course not necessarily the way everyone in the world would expect. The main goal is to stay consistent wit [...]
-Seq('GCTGTAGTAAG', IUPACUnambiguousDNA())
->>> my_seq[1::3]
-Seq('AGGCATGCATC', IUPACUnambiguousDNA())
->>> my_seq[2::3]
-Seq('TAGCTAAGAC', IUPACUnambiguousDNA())
-</PRE><P>Another stride trick you might have seen with a Python string is the use of a -1 stride to reverse the string. You can do this with a <CODE>Seq</CODE> object too:</P><PRE CLASS="verbatim">>>> my_seq[::-1]
-Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())
-</PRE><!--TOC section Turning Seq objects into strings-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc20">3.4</A>&#XA0;&#XA0;Turning Seq objects into strings</H2><!--SEC END --><P>
-<A NAME="sec:seq-to-string"></A></P><P>If you really do just need a plain string, for example to write to a file, or insert into a database, then this is very easy to get:
-</P><PRE CLASS="verbatim">>>> str(my_seq)
-'GATCGATGGGCCTATATAGGATCGAAAATCGC'
-</PRE><P>Since calling <CODE>str()</CODE> on a <CODE>Seq</CODE> object returns the full sequence as a string,
-you often don&#X2019;t actually have to do this conversion explicitly.
-Python does this automatically in the print function
-(and the print statement under Python 2):
-</P><PRE CLASS="verbatim">>>> print(my_seq)
-GATCGATGGGCCTATATAGGATCGAAAATCGC
-</PRE><P>You can also use the <CODE>Seq</CODE> object directly with a <CODE>%s</CODE> placeholder when using the Python string formatting or interpolation operator (<CODE>%</CODE>):
-</P><PRE CLASS="verbatim">>>> fasta_format_string = ">Name\n%s\n" % my_seq
->>> print(fasta_format_string)
->Name
-GATCGATGGGCCTATATAGGATCGAAAATCGC
-<BLANKLINE>
-</PRE><P>This line of code constructs a simple FASTA format record (without worrying about line wrapping).
-Section&#XA0;<A HREF="#sec:SeqRecord-format">4.5</A> describes a neat way to get a FASTA formatted
-string from a <CODE>SeqRecord</CODE> object, while the more general topic of reading and
-writing FASTA format sequence files is covered in Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>.</P><PRE CLASS="verbatim">>>> str(my_seq)
-'GATCGATGGGCCTATATAGGATCGAAAATCGC'
-</PRE><!--TOC section Concatenating or adding sequences-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc21">3.5</A>&#XA0;&#XA0;Concatenating or adding sequences</H2><!--SEC END --><P>Naturally, you can in principle add any two Seq objects together - just like you can with Python strings to concatenate them. However, you can&#X2019;t add sequences with incompatible alphabets, such as a protein sequence and a DNA sequence:</P><PRE CLASS="verbatim">>>> from Bio.Alphabet import IUPAC
->>> from Bio.Seq import Seq
->>> protein_seq = Seq("EVRNAK", IUPAC.protein)
->>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
->>> protein_seq + dna_seq
-Traceback (most recent call last):
-...
-TypeError: Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA()
-</PRE><P>If you <EM>really</EM> wanted to do this, you&#X2019;d have to first give both sequences generic alphabets:</P><PRE CLASS="verbatim">>>> from Bio.Alphabet import generic_alphabet
->>> protein_seq.alphabet = generic_alphabet
->>> dna_seq.alphabet = generic_alphabet
->>> protein_seq + dna_seq
-Seq('EVRNAKACGT', Alphabet())
-</PRE><P>Here is an example of adding a generic nucleotide sequence to an unambiguous IUPAC DNA sequence, resulting in an ambiguous nucleotide sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_nucleotide
->>> from Bio.Alphabet import IUPAC
->>> nuc_seq = Seq("GATCGATGC", generic_nucleotide)
->>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
->>> nuc_seq
-Seq('GATCGATGC', NucleotideAlphabet())
->>> dna_seq
-Seq('ACGT', IUPACUnambiguousDNA())
->>> nuc_seq + dna_seq
-Seq('GATCGATGCACGT', NucleotideAlphabet())
-</PRE><P>You may often have many sequences to add together, which can be done with a for loop like this:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
->>> concatenated = Seq("", generic_dna)
->>> for s in list_of_seqs:
-...      concatenated += s
-...
->>> concatenated
-Seq('ACGTAACCGGTT', DNAAlphabet())
-</PRE><P>Or, a more elegant approach is to the use built in <CODE>sum</CODE> function with its optional start value argument (which otherwise defaults to zero):</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> list_of_seqs = [Seq("ACGT", generic_dna), Seq("AACC", generic_dna), Seq("GGTT", generic_dna)]
->>> sum(list_of_seqs, Seq("", generic_dna))
-Seq('ACGTAACCGGTT', DNAAlphabet())
-</PRE><P>Unlike the Python string, the Biopython <CODE>Seq</CODE> does not (currently) have a <CODE>.join</CODE> method.</P><!--TOC section Changing case-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc22">3.6</A>&#XA0;&#XA0;Changing case</H2><!--SEC END --><P>Python strings have very useful <CODE>upper</CODE> and <CODE>lower</CODE> methods for changing the case.
-As of Biopython 1.53, the <CODE>Seq</CODE> object gained similar methods which are alphabet aware.
-For example,</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> dna_seq = Seq("acgtACGT", generic_dna)
->>> dna_seq
-Seq('acgtACGT', DNAAlphabet())
->>> dna_seq.upper()
-Seq('ACGTACGT', DNAAlphabet())
->>> dna_seq.lower()
-Seq('acgtacgt', DNAAlphabet())
-</PRE><P>These are useful for doing case insensitive matching:</P><PRE CLASS="verbatim">>>> "GTAC" in dna_seq
-False
->>> "GTAC" in dna_seq.upper()
-True
-</PRE><P>Note that strictly speaking the IUPAC alphabets are for upper case
-sequences only, thus:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> dna_seq = Seq("ACGT", IUPAC.unambiguous_dna)
->>> dna_seq
-Seq('ACGT', IUPACUnambiguousDNA())
->>> dna_seq.lower()
-Seq('acgt', DNAAlphabet())
-</PRE><!--TOC section Nucleotide sequences and (reverse) complements-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc23">3.7</A>&#XA0;&#XA0;Nucleotide sequences and (reverse) complements</H2><!--SEC END --><P>
-<A NAME="sec:seq-reverse-complement"></A></P><P>For nucleotide sequences, you can easily obtain the complement or reverse
-complement of a <CODE>Seq</CODE> object using its built-in methods:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC", IUPAC.unambiguous_dna)
->>> my_seq
-Seq('GATCGATGGGCCTATATAGGATCGAAAATCGC', IUPACUnambiguousDNA())
->>> my_seq.complement()
-Seq('CTAGCTACCCGGATATATCCTAGCTTTTAGCG', IUPACUnambiguousDNA())
->>> my_seq.reverse_complement()
-Seq('GCGATTTTCGATCCTATATAGGCCCATCGATC', IUPACUnambiguousDNA())
-</PRE><P>As mentioned earlier, an easy way to just reverse a <CODE>Seq</CODE> object (or a
-Python string) is slice it with -1 step:</P><PRE CLASS="verbatim">>>> my_seq[::-1]
-Seq('CGCTAAAAGCTAGGATATATCCGGGTAGCTAG', IUPACUnambiguousDNA())
-</PRE><P>In all of these operations, the alphabet property is maintained. This is very
-useful in case you accidentally end up trying to do something weird like take
-the (reverse)complement of a protein sequence:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> protein_seq = Seq("EVRNAK", IUPAC.protein)
->>> protein_seq.complement()
-Traceback (most recent call last):
-...
-ValueError: Proteins do not have complements!
-</PRE><P>The example in Section&#XA0;<A HREF="#sec:SeqIO-reverse-complement">5.5.3</A> combines the <CODE>Seq</CODE>
-object&#X2019;s reverse complement method with <CODE>Bio.SeqIO</CODE> for sequence input/output.</P><!--TOC section Transcription-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc24">3.8</A>&#XA0;&#XA0;Transcription</H2><!--SEC END --><P>
-Before talking about transcription, I want to try to clarify the strand issue.
-Consider the following (made up) stretch of double stranded DNA which
-encodes a short peptide:</P><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>DNA coding strand (aka Crick strand, strand +1)</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>5&#X2019;</TD><TD ALIGN=center NOWRAP><TT>ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG</TT></TD><TD ALIGN=left NOWRAP>3&#X2019;</TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP><TT>|||||||||||||||||||||||||||||||||||||||</TT></TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>3&#X2019;</TD><TD ALIGN=center NOWRAP><TT>TACCGGTAACATTACCCGGCGACTTTCCCACGGGCTATC</TT></TD><TD ALIGN=left NOWRAP>5&#X2019;</TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>DNA template strand (aka Watson strand, strand &#X2212;1)</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP><FONT SIZE=5>|</FONT></TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>Transcription</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP><FONT SIZE=5>&#X2193;</FONT></TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>5&#X2019;</TD><TD ALIGN=center NOWRAP><TT>AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG</TT></TD><TD ALIGN=left NOWRAP>3&#X2019;</TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD><TD ALIGN=center NOWRAP>Single stranded messenger RNA</TD><TD ALIGN=left NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP> </TD></TR>
-</TABLE><P>The actual biological transcription process works from the template strand, doing a reverse complement (TCAG &#X2192; CUGA) to give the mRNA. However, in Biopython and bioinformatics in general, we typically work directly with the coding strand because this means we can get the mRNA sequence just by switching T &#X2192; U.</P><P>Now let&#X2019;s actually get down to doing a transcription in Biopython. First, let&#X2019;s create <CODE>Seq</CODE> objects for the coding and templ [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
->>> coding_dna
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
->>> template_dna = coding_dna.reverse_complement()
->>> template_dna
-Seq('CTATCGGGCACCCTTTCAGCGGCCCATTACAATGGCCAT', IUPACUnambiguousDNA())
-</PRE><P>These should match the figure above - remember by convention nucleotide sequences are normally read from the 5&#X2019; to 3&#X2019; direction, while in the figure the template strand is shown reversed.</P><P>Now let&#X2019;s transcribe the coding strand into the corresponding mRNA, using the <CODE>Seq</CODE> object&#X2019;s built in <CODE>transcribe</CODE> method:
-</P><PRE CLASS="verbatim">>>> coding_dna
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
->>> messenger_rna = coding_dna.transcribe()
->>> messenger_rna
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
-</PRE><P>As you can see, all this does is switch T &#X2192; U, and adjust the alphabet.</P><P>If you do want to do a true biological transcription starting with the template strand, then this becomes a two-step process:
-</P><PRE CLASS="verbatim">>>> template_dna.reverse_complement().transcribe()
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
-</PRE><P>The <CODE>Seq</CODE> object also includes a back-transcription method for going from the mRNA to the coding strand of the DNA. Again, this is a simple U &#X2192; T substitution and associated change of alphabet:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna)
->>> messenger_rna
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
->>> messenger_rna.back_transcribe()
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
-</PRE><P><EM>Note:</EM> The <CODE>Seq</CODE> object&#X2019;s <CODE>transcribe</CODE> and <CODE>back_transcribe</CODE> methods
-were added in Biopython 1.49. For older releases you would have to use the <CODE>Bio.Seq</CODE>
-module&#X2019;s functions instead, see Section&#XA0;<A HREF="#sec:seq-module-functions">3.14</A>.</P><!--TOC section Translation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc25">3.9</A>&#XA0;&#XA0;Translation</H2><!--SEC END --><P>
-<A NAME="sec:translation"></A>
-Sticking with the same example discussed in the transcription section above,
-now let&#X2019;s translate this mRNA into the corresponding protein sequence - again taking
-advantage of one of the <CODE>Seq</CODE> object&#X2019;s biological methods:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> messenger_rna = Seq("AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG", IUPAC.unambiguous_rna)
->>> messenger_rna
-Seq('AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG', IUPACUnambiguousRNA())
->>> messenger_rna.translate()
-Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>You can also translate directly from the coding strand DNA sequence:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna)
->>> coding_dna
-Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())
->>> coding_dna.translate()
-Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>You should notice in the above protein sequences that in addition to the end stop character, there is an internal stop as well. This was a deliberate choice of example, as it gives an excuse to talk about some optional arguments, including different translation tables (Genetic Codes).</P><P>The translation tables available in Biopython are based on those <A HREF="http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi">from the NCBI</A> (see the next section of this tutorial). B [...]
-Suppose we are dealing with a mitochondrial sequence. We need to tell the translation function to use the relevant genetic code instead:
-</P><PRE CLASS="verbatim">>>> coding_dna.translate(table="Vertebrate Mitochondrial")
-Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>You can also specify the table using the NCBI table number which is shorter, and often included in the feature annotation of GenBank files:
-</P><PRE CLASS="verbatim">>>> coding_dna.translate(table=2)
-Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
-</PRE><P>Now, you may want to translate the nucleotides up to the first in frame stop codon,
-and then stop (as happens in nature):
-</P><PRE CLASS="verbatim">>>> coding_dna.translate()
-Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))
->>> coding_dna.translate(to_stop=True)
-Seq('MAIVMGR', IUPACProtein())
->>> coding_dna.translate(table=2)
-Seq('MAIVMGRWKGAR*', HasStopCodon(IUPACProtein(), '*'))
->>> coding_dna.translate(table=2, to_stop=True)
-Seq('MAIVMGRWKGAR', IUPACProtein())
-</PRE><P>Notice that when you use the <CODE>to_stop</CODE> argument, the stop codon itself
-is not translated - and the stop symbol is not included at the end of your protein
-sequence.</P><P>You can even specify the stop symbol if you don&#X2019;t like the default asterisk:
-</P><PRE CLASS="verbatim">>>> coding_dna.translate(table=2, stop_symbol="@")
-Seq('MAIVMGRWKGAR@', HasStopCodon(IUPACProtein(), '@'))
-</PRE><P>Now, suppose you have a complete coding sequence CDS, which is to say a
-nucleotide sequence (e.g. mRNA &#X2013; after any splicing) which is a whole number
-of codons (i.e. the length is a multiple of three), commences with a start
-codon, ends with a stop codon, and has no internal in-frame stop codons.
-In general, given a complete CDS, the default translate method will do what
-you want (perhaps with the <CODE>to_stop</CODE> option). However, what if your
-sequence uses a non-standard start codon? This happens a lot in bacteria &#X2013;
-for example the gene yaaX in <TT>E. coli</TT> K12:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna
->>> gene = Seq("GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA" + \
-...            "GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT" + \
-...            "AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT" + \
-...            "TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT" + \
-...            "AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA",
-...            generic_dna)
->>> gene.translate(table="Bacterial")
-Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*',
-HasStopCodon(ExtendedIUPACProtein(), '*')
->>> gene.translate(table="Bacterial", to_stop=True)
-Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR',
-ExtendedIUPACProtein())
-</PRE><P>In the bacterial genetic code <TT>GTG</TT> is a valid start codon,
-and while it does <EM>normally</EM> encode Valine, if used as a start codon it
-should be translated as methionine. This happens if you tell Biopython your
-sequence is a complete CDS:</P><PRE CLASS="verbatim">>>> gene.translate(table="Bacterial", cds=True)
-Seq('MKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR',
-ExtendedIUPACProtein())
-</PRE><P>In addition to telling Biopython to translate an alternative start codon as
-methionine, using this option also makes sure your sequence really is a valid
-CDS (you&#X2019;ll get an exception if not).</P><P>The example in Section&#XA0;<A HREF="#sec:SeqIO-translate">18.1.3</A> combines the <CODE>Seq</CODE> object&#X2019;s
-translate method with <CODE>Bio.SeqIO</CODE> for sequence input/output.</P><!--TOC section Translation Tables-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc26">3.10</A>&#XA0;&#XA0;Translation Tables</H2><!--SEC END --><P>In the previous sections we talked about the <CODE>Seq</CODE> object translation method (and mentioned the equivalent function in the <CODE>Bio.Seq</CODE> module &#X2013; see
-Section&#XA0;<A HREF="#sec:seq-module-functions">3.14</A>).
-Internally these use codon table objects derived from the NCBI information at
-<A HREF="ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt"><TT>ftp://ftp.ncbi.nlm.nih.gov/entrez/misc/data/gc.prt</TT></A>, also shown on
-<A HREF="http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi"><TT>http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi</TT></A> in a much more readable layout.</P><P>As before, let&#X2019;s just focus on two choices: the Standard translation table, and the
-translation table for Vertebrate Mitochondrial DNA.</P><PRE CLASS="verbatim">>>> from Bio.Data import CodonTable
->>> standard_table = CodonTable.unambiguous_dna_by_name["Standard"]
->>> mito_table = CodonTable.unambiguous_dna_by_name["Vertebrate Mitochondrial"]
-</PRE><P>Alternatively, these tables are labeled with ID numbers 1 and 2, respectively:
-</P><PRE CLASS="verbatim">>>> from Bio.Data import CodonTable
->>> standard_table = CodonTable.unambiguous_dna_by_id[1]
->>> mito_table = CodonTable.unambiguous_dna_by_id[2]
-</PRE><P>You can compare the actual tables visually by printing them:
-</P><PRE CLASS="verbatim">>>> print(standard_table)
-Table 1 Standard, SGC0
-
-  |  T      |  C      |  A      |  G      |
---+---------+---------+---------+---------+--
-T | TTT F   | TCT S   | TAT Y   | TGT C   | T
-T | TTC F   | TCC S   | TAC Y   | TGC C   | C
-T | TTA L   | TCA S   | TAA Stop| TGA Stop| A
-T | TTG L(s)| TCG S   | TAG Stop| TGG W   | G
---+---------+---------+---------+---------+--
-C | CTT L   | CCT P   | CAT H   | CGT R   | T
-C | CTC L   | CCC P   | CAC H   | CGC R   | C
-C | CTA L   | CCA P   | CAA Q   | CGA R   | A
-C | CTG L(s)| CCG P   | CAG Q   | CGG R   | G
---+---------+---------+---------+---------+--
-A | ATT I   | ACT T   | AAT N   | AGT S   | T
-A | ATC I   | ACC T   | AAC N   | AGC S   | C
-A | ATA I   | ACA T   | AAA K   | AGA R   | A
-A | ATG M(s)| ACG T   | AAG K   | AGG R   | G
---+---------+---------+---------+---------+--
-G | GTT V   | GCT A   | GAT D   | GGT G   | T
-G | GTC V   | GCC A   | GAC D   | GGC G   | C
-G | GTA V   | GCA A   | GAA E   | GGA G   | A
-G | GTG V   | GCG A   | GAG E   | GGG G   | G
---+---------+---------+---------+---------+--
-</PRE><P>and:
-</P><PRE CLASS="verbatim">>>> print(mito_table)
-Table 2 Vertebrate Mitochondrial, SGC1
-
-  |  T      |  C      |  A      |  G      |
---+---------+---------+---------+---------+--
-T | TTT F   | TCT S   | TAT Y   | TGT C   | T
-T | TTC F   | TCC S   | TAC Y   | TGC C   | C
-T | TTA L   | TCA S   | TAA Stop| TGA W   | A
-T | TTG L   | TCG S   | TAG Stop| TGG W   | G
---+---------+---------+---------+---------+--
-C | CTT L   | CCT P   | CAT H   | CGT R   | T
-C | CTC L   | CCC P   | CAC H   | CGC R   | C
-C | CTA L   | CCA P   | CAA Q   | CGA R   | A
-C | CTG L   | CCG P   | CAG Q   | CGG R   | G
---+---------+---------+---------+---------+--
-A | ATT I(s)| ACT T   | AAT N   | AGT S   | T
-A | ATC I(s)| ACC T   | AAC N   | AGC S   | C
-A | ATA M(s)| ACA T   | AAA K   | AGA Stop| A
-A | ATG M(s)| ACG T   | AAG K   | AGG Stop| G
---+---------+---------+---------+---------+--
-G | GTT V   | GCT A   | GAT D   | GGT G   | T
-G | GTC V   | GCC A   | GAC D   | GGC G   | C
-G | GTA V   | GCA A   | GAA E   | GGA G   | A
-G | GTG V(s)| GCG A   | GAG E   | GGG G   | G
---+---------+---------+---------+---------+--
-</PRE><P>You may find these following properties useful &#X2013; for example if you are trying
-to do your own gene finding:
-</P><PRE CLASS="verbatim">>>> mito_table.stop_codons
-['TAA', 'TAG', 'AGA', 'AGG']
->>> mito_table.start_codons
-['ATT', 'ATC', 'ATA', 'ATG', 'GTG']
->>> mito_table.forward_table["ACG"]
-'T'
-</PRE><!--TOC section Comparing Seq objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc27">3.11</A>&#XA0;&#XA0;Comparing Seq objects</H2><!--SEC END --><P>
-<A NAME="sec:seq-comparison"></A></P><P>Sequence comparison is actually a very complicated topic, and there is no easy
-way to decide if two sequences are equal. The basic problem is the meaning of
-the letters in a sequence are context dependent - the letter &#X201C;A&#X201D; could be part
-of a DNA, RNA or protein sequence. Biopython uses alphabet objects as part of
-each <CODE>Seq</CODE> object to try to capture this information - so comparing two
-<CODE>Seq</CODE> objects could mean considering both the sequence strings <EM>and</EM>
-the alphabets.</P><P>For example, you might argue that the two DNA <CODE>Seq</CODE> objects
-<TT>Seq("ACGT", IUPAC.unambiguous_dna)</TT> and
-<TT>Seq("ACGT", IUPAC.ambiguous_dna)</TT> should be equal, even though
-they do have different alphabets. Depending on the context this could be
-important.</P><P>This gets worse &#X2013; suppose you think <TT>Seq("ACGT",
-IUPAC.unambiguous_dna)</TT> and <TT>Seq("ACGT")</TT> (i.e. the default generic
-alphabet) should be equal. Then, logically, <TT>Seq("ACGT", IUPAC.protein)</TT>
-and <TT>Seq("ACGT")</TT> should also be equal. Now, in logic if <I>A</I>=<I>B</I> and
-<I>B</I>=<I>C</I>, by transitivity we expect <I>A</I>=<I>C</I>. So for logical consistency we&#X2019;d
-require <TT>Seq("ACGT", IUPAC.unambiguous_dna)</TT> and <TT>Seq("ACGT",
-IUPAC.protein)</TT> to be equal &#X2013; which most people would agree is just not right.
-This transitivity also has implications for using <CODE>Seq</CODE> objects as
-Python dictionary keys.</P><P>Now, in everyday use, your sequences will probably all have the same
-alphabet, or at least all be the same type of sequence (all DNA, all RNA, or
-all protein). What you probably want is to just compare the sequences as
-strings &#X2013; which you can do explicitly:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> seq1 = Seq("ACGT", IUPAC.unambiguous_dna)
->>> seq2 = Seq("ACGT", IUPAC.ambiguous_dna)
->>> str(seq1) == str(seq2)
-True
->>> str(seq1) == str(seq1)
-True
-</PRE><P>So, what does Biopython do? Well, as of Biopython 1.65, sequence comparison
-only looks at the sequence, essentially ignoring the alphabet:</P><PRE CLASS="verbatim">>>> seq1 == seq2
-True
->>> seq1 == "ACGT"
-True
-</PRE><P>As an extension to this, using sequence objects as keys in a Python dictionary
-is now equivalent to using the sequence as a plain string for the key.
-See also Section&#XA0;<A HREF="#sec:seq-to-string">3.4</A>.</P><P>Note if you compare sequences with incompatible alphabets (e.g. DNA vs RNA,
-or nucleotide versus protein), then you will get a warning but for the
-comparison itself only the string of letters in the sequence is used:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import generic_dna, generic_protein
->>> dna_seq = Seq("ACGT", generic_dna)
->>> prot_seq = Seq(``ACGT'', generic_protein)
->>> dna_seq == prot_seq
-BiopythonWarning: Incompatible alphabets DNAAlphabet() and ProteinAlphabet()
-True
-</PRE><P><EM>WARNING:</EM> Older versions of Biopython instead used to check if the
-<CODE>Seq</CODE> objects were the same object in memory.
-This is important if you need to support scripts on both old and new
-versions of Biopython. Here make the comparison explicit by wrapping
-your sequence objects with either <CODE>str(...)</CODE> for string based
-comparison or <CODE>id(...)</CODE> for object instance based comparison.</P><!--TOC section MutableSeq objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc28">3.12</A>&#XA0;&#XA0;MutableSeq objects</H2><!--SEC END --><P>
-<A NAME="sec:mutable-seq"></A></P><P>Just like the normal Python string, the <CODE>Seq</CODE> object is &#X201C;read only&#X201D;, or in Python terminology, immutable. Apart from wanting the <CODE>Seq</CODE> object to act like a string, this is also a useful default since in many biological applications you want to ensure you are not changing your sequence data:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.Alphabet import IUPAC
->>> my_seq = Seq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)
-</PRE><P>Observe what happens if you try to edit the sequence:
-</P><PRE CLASS="verbatim">>>> my_seq[5] = "G"
-Traceback (most recent call last):
-...
-TypeError: 'Seq' object does not support item assignment
-</PRE><P>However, you can convert it into a mutable sequence (a <CODE>MutableSeq</CODE> object) and do pretty much anything you want with it:</P><PRE CLASS="verbatim">>>> mutable_seq = my_seq.tomutable()
->>> mutable_seq
-MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
-</PRE><P>Alternatively, you can create a <CODE>MutableSeq</CODE> object directly from a string:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import MutableSeq
->>> from Bio.Alphabet import IUPAC
->>> mutable_seq = MutableSeq("GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA", IUPAC.unambiguous_dna)
-</PRE><P>Either way will give you a sequence object which can be changed:
-</P><PRE CLASS="verbatim">>>> mutable_seq
-MutableSeq('GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
->>> mutable_seq[5] = "C"
->>> mutable_seq
-MutableSeq('GCCATCGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
->>> mutable_seq.remove("T")
->>> mutable_seq
-MutableSeq('GCCACGTAATGGGCCGCTGAAAGGGTGCCCGA', IUPACUnambiguousDNA())
->>> mutable_seq.reverse()
->>> mutable_seq
-MutableSeq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA())
-</PRE><P>Do note that unlike the <CODE>Seq</CODE> object, the <CODE>MutableSeq</CODE> object&#X2019;s methods like <CODE>reverse_complement()</CODE> and <CODE>reverse()</CODE> act in-situ!</P><P>An important technical difference between mutable and immutable objects in Python means that you can&#X2019;t use a <CODE>MutableSeq</CODE> object as a dictionary key, but you can use a Python string or a <CODE>Seq</CODE> object in this way.</P><P>Once you have finished editing your a <CODE>Mutab [...]
->>> new_seq
-Seq('AGCCCGTGGGAAAGTCGCCGGGTAATGCACCG', IUPACUnambiguousDNA())
-</PRE><P>You can also get a string from a <CODE>MutableSeq</CODE> object just like from a <CODE>Seq</CODE> object (Section&#XA0;<A HREF="#sec:seq-to-string">3.4</A>).</P><!--TOC section UnknownSeq objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc29">3.13</A>&#XA0;&#XA0;UnknownSeq objects</H2><!--SEC END --><P>
-The <CODE>UnknownSeq</CODE> object is a subclass of the basic <CODE>Seq</CODE> object
-and its purpose is to represent a
-sequence where we know the length, but not the actual letters making it up.
-You could of course use a normal <CODE>Seq</CODE> object in this situation, but it wastes
-rather a lot of memory to hold a string of a million &#X201C;N&#X201D; characters when you could
-just store a single letter &#X201C;N&#X201D; and the desired length as an integer.</P><PRE CLASS="verbatim">>>> from Bio.Seq import UnknownSeq
->>> unk = UnknownSeq(20)
->>> unk
-UnknownSeq(20, alphabet = Alphabet(), character = '?')
->>> print(unk)
-????????????????????
->>> len(unk)
-20
-</PRE><P>You can of course specify an alphabet, meaning for nucleotide sequences
-the letter defaults to &#X201C;N&#X201D; and for proteins &#X201C;X&#X201D;, rather than just &#X201C;?&#X201D;.</P><PRE CLASS="verbatim">>>> from Bio.Seq import UnknownSeq
->>> from Bio.Alphabet import IUPAC
->>> unk_dna = UnknownSeq(20, alphabet=IUPAC.ambiguous_dna)
->>> unk_dna
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> print(unk_dna)
-NNNNNNNNNNNNNNNNNNNN
-</PRE><P>You can use all the usual <CODE>Seq</CODE> object methods too, note these give back
-memory saving <CODE>UnknownSeq</CODE> objects where appropriate as you might expect:</P><PRE CLASS="verbatim">>>> unk_dna
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> unk_dna.complement()
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> unk_dna.reverse_complement()
-UnknownSeq(20, alphabet = IUPACAmbiguousDNA(), character = 'N')
->>> unk_dna.transcribe()
-UnknownSeq(20, alphabet = IUPACAmbiguousRNA(), character = 'N')
->>> unk_protein = unk_dna.translate()
->>> unk_protein
-UnknownSeq(6, alphabet = ProteinAlphabet(), character = 'X')
->>> print(unk_protein)
-XXXXXX
->>> len(unk_protein)
-6
-</PRE><P>You may be able to find a use for the <CODE>UnknownSeq</CODE> object in your own
-code, but it is more likely that you will first come across them in a
-<CODE>SeqRecord</CODE> object created by <CODE>Bio.SeqIO</CODE>
-(see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).
-Some sequence file formats don&#X2019;t always include the actual sequence, for
-example GenBank and EMBL files may include a list of features but for the
-sequence just present the contig information. Alternatively, the QUAL files
-used in sequencing work hold quality scores but they <EM>never</EM> contain a
-sequence &#X2013; instead there is a partner FASTA file which <EM>does</EM> have the
-sequence.</P><!--TOC section Working with strings directly-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc30">3.14</A>&#XA0;&#XA0;Working with strings directly</H2><!--SEC END --><P>
-<A NAME="sec:seq-module-functions"></A>
-To close this chapter, for those you who <EM>really</EM> don&#X2019;t want to use the sequence
-objects (or who prefer a functional programming style to an object orientated one),
-there are module level functions in <CODE>Bio.Seq</CODE> will accept plain Python strings,
-<CODE>Seq</CODE> objects (including <CODE>UnknownSeq</CODE> objects) or <CODE>MutableSeq</CODE> objects:</P><PRE CLASS="verbatim">>>> from Bio.Seq import reverse_complement, transcribe, back_transcribe, translate
->>> my_string = "GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG"
->>> reverse_complement(my_string)
-'CTAACCAGCAGCACGACCACCCTTCCAACGACCCATAACAGC'
->>> transcribe(my_string)
-'GCUGUUAUGGGUCGUUGGAAGGGUGGUCGUGCUGCUGGUUAG'
->>> back_transcribe(my_string)
-'GCTGTTATGGGTCGTTGGAAGGGTGGTCGTGCTGCTGGTTAG'
->>> translate(my_string)
-'AVMGRWKGGRAAG*'
-</PRE><P>You are, however, encouraged to work with <CODE>Seq</CODE> objects by default.</P><!--TOC chapter Sequence annotation objects-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc31">Chapter&#XA0;4</A>&#XA0;&#XA0;Sequence annotation objects</H1><!--SEC END --><P>
-<A NAME="chapter:SeqRecord"></A></P><P>Chapter&#XA0;<A HREF="#chapter:Bio.Seq">3</A> introduced the sequence classes. Immediately &#X201C;above&#X201D; the <CODE>Seq</CODE> class is the Sequence Record or <CODE>SeqRecord</CODE> class, defined in the <CODE>Bio.SeqRecord</CODE> module. This class allows higher level features such as identifiers and features (as <CODE>SeqFeature</CODE> objects) to be associated with the sequence, and is used throughout the sequence input/output interface <C [...]
-for now. If on the other hand you are going to be using richly annotated sequence data, say from GenBank
-or EMBL files, this information is quite important.</P><P>While this chapter should cover most things to do with the <CODE>SeqRecord</CODE> and <CODE>SeqFeature</CODE> objects in this chapter, you may also want to read the <CODE>SeqRecord</CODE> wiki page (<A HREF="http://biopython.org/wiki/SeqRecord"><TT>http://biopython.org/wiki/SeqRecord</TT></A>), and the built in documentation (also online &#X2013; <A HREF="http://biopython.org/DIST/docs/api/Bio.SeqRecord.SeqRecord-class.html">SeqRe [...]
->>> help(SeqRecord)
-...
-</PRE><!--TOC section The SeqRecord object-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc32">4.1</A>&#XA0;&#XA0;The SeqRecord object</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord"></A></P><P>The <CODE>SeqRecord</CODE> (Sequence Record) class is defined in the <CODE>Bio.SeqRecord</CODE> module. This class allows higher level features such as identifiers and features to be associated with a sequence (see Chapter&#XA0;<A HREF="#chapter:Bio.Seq">3</A>), and is the basic data type for the <CODE>Bio.SeqIO</CODE> sequence input/output interface (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>The <CODE>SeqRecord</CODE> class itself is  [...]
-<B>.seq</B></DT><DD CLASS="dd-description"> &#X2013; The sequence itself, typically a <CODE>Seq</CODE> object.</DD><DT CLASS="dt-description"><B>.id</B></DT><DD CLASS="dd-description"> &#X2013; The primary ID used to identify the sequence &#X2013; a string. In most cases this is something like an accession number.</DD><DT CLASS="dt-description"><B>.name</B></DT><DD CLASS="dd-description"> &#X2013; A &#X201C;common&#X201D; name/id for the sequence &#X2013; a string. In some cases this wil [...]
-</DD></DL><!--TOC section Creating a SeqRecord-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc33">4.2</A>&#XA0;&#XA0;Creating a SeqRecord</H2><!--SEC END --><P>Using a <CODE>SeqRecord</CODE> object is not very complicated, since all of the
-information is presented as attributes of the class. Usually you won&#X2019;t create
-a <CODE>SeqRecord</CODE> &#X201C;by hand&#X201D;, but instead use <CODE>Bio.SeqIO</CODE> to read in a
-sequence file for you (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> and the examples
-below). However, creating <CODE>SeqRecord</CODE> can be quite simple.</P><!--TOC subsection SeqRecord objects from scratch-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc34">4.2.1</A>&#XA0;&#XA0;SeqRecord objects from scratch</H3><!--SEC END --><P>To create a <CODE>SeqRecord</CODE> at a minimum you just need a <CODE>Seq</CODE> object:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> simple_seq = Seq("GATC")
->>> from Bio.SeqRecord import SeqRecord
->>> simple_seq_r = SeqRecord(simple_seq)
-</PRE><P>Additionally, you can also pass the id, name and description to the initialization function, but if not they will be set as strings indicating they are unknown, and can be modified subsequently:</P><PRE CLASS="verbatim">>>> simple_seq_r.id
-'<unknown id>'
->>> simple_seq_r.id = "AC12345"
->>> simple_seq_r.description = "Made up sequence I wish I could write a paper about"
->>> print(simple_seq_r.description)
-Made up sequence I wish I could write a paper about
->>> simple_seq_r.seq
-Seq('GATC', Alphabet())
-</PRE><P>Including an identifier is very important if you want to output your <CODE>SeqRecord</CODE> to a file. You would normally include this when creating the object:</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> simple_seq = Seq("GATC")
->>> from Bio.SeqRecord import SeqRecord
->>> simple_seq_r = SeqRecord(simple_seq, id="AC12345")
-</PRE><P>As mentioned above, the <CODE>SeqRecord</CODE> has an dictionary attribute <CODE>annotations</CODE>. This is used
-for any miscellaneous annotations that doesn&#X2019;t fit under one of the other more specific attributes.
-Adding annotations is easy, and just involves dealing directly with the annotation dictionary:</P><PRE CLASS="verbatim">>>> simple_seq_r.annotations["evidence"] = "None. I just made it up."
->>> print(simple_seq_r.annotations)
-{'evidence': 'None. I just made it up.'}
->>> print(simple_seq_r.annotations["evidence"])
-None. I just made it up.
-</PRE><P>Working with per-letter-annotations is similar, <CODE>letter_annotations</CODE> is a
-dictionary like attribute which will let you assign any Python sequence (i.e.
-a string, list or tuple) which has the same length as the sequence:</P><PRE CLASS="verbatim">>>> simple_seq_r.letter_annotations["phred_quality"] = [40, 40, 38, 30]
->>> print(simple_seq_r.letter_annotations)
-{'phred_quality': [40, 40, 38, 30]}
->>> print(simple_seq_r.letter_annotations["phred_quality"])
-[40, 40, 38, 30]
-</PRE><P>The <CODE>dbxrefs</CODE> and <CODE>features</CODE> attributes are just Python lists, and
-should be used to store strings and <CODE>SeqFeature</CODE> objects (discussed later
-in this chapter) respectively.</P><!--TOC subsection SeqRecord objects from FASTA files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc35">4.2.2</A>&#XA0;&#XA0;SeqRecord objects from FASTA files</H3><!--SEC END --><P>This example uses a fairly large FASTA file containing the whole sequence for <I>Yersinia pestis biovar Microtus</I> str. 91001 plasmid pPCP1, originally downloaded from the NCBI. This file is included with the Biopython unit tests under the GenBank folder, or online <A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.fna"><TT>NC_005816.f [...]
-TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
-...
-</PRE><P>Back in Chapter&#XA0;<A HREF="#chapter:quick-start">2</A> you will have seen the function <CODE>Bio.SeqIO.parse(...)</CODE>
-used to loop over all the records in a file as <CODE>SeqRecord</CODE> objects. The <CODE>Bio.SeqIO</CODE> module
-has a sister function for use on files which contain just one record which we&#X2019;ll use here (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> for details):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.fna", "fasta")
->>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-SingleLetterAlphabet()), id='gi|45478711|ref|NC_005816.1|', name='gi|45478711|ref|NC_005816.1|',
-description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... sequence',
-dbxrefs=[])
-</PRE><P>Now, let&#X2019;s have a look at the key attributes of this <CODE>SeqRecord</CODE>
-individually &#X2013; starting with the <CODE>seq</CODE> attribute which gives you a
-<CODE>Seq</CODE> object:</P><PRE CLASS="verbatim">>>> record.seq
-Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet())
-</PRE><P>Here <CODE>Bio.SeqIO</CODE> has defaulted to a generic alphabet, rather
-than guessing that this is DNA. If you know in advance what kind of sequence
-your FASTA file contains, you can tell <CODE>Bio.SeqIO</CODE> which alphabet to use
-(see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>Next, the identifiers and description:</P><PRE CLASS="verbatim">>>> record.id
-'gi|45478711|ref|NC_005816.1|'
->>> record.name
-'gi|45478711|ref|NC_005816.1|'
->>> record.description
-'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus ... pPCP1, complete sequence'
-</PRE><P>As you can see above, the first word of the FASTA record&#X2019;s title line (after
-removing the greater than symbol) is used for both the <CODE>id</CODE> and
-<CODE>name</CODE> attributes. The whole title line (after removing the greater than
-symbol) is used for the record description. This is deliberate, partly for
-backwards compatibility reasons, but it also makes sense if you have a FASTA
-file like this:</P><PRE CLASS="verbatim">>Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1
-TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGGGGGTAATCTGCTCTCC
-...
-</PRE><P>Note that none of the other annotation attributes get populated when reading a
-FASTA file:</P><PRE CLASS="verbatim">>>> record.dbxrefs
-[]
->>> record.annotations
-{}
->>> record.letter_annotations
-{}
->>> record.features
-[]
-</PRE><P>In this case our example FASTA file was from the NCBI, and they have a fairly well defined set of conventions for formatting their FASTA lines. This means it would be possible to parse this information and extract the GI number and accession for example. However, FASTA files from other sources vary, so this isn&#X2019;t possible in general.</P><!--TOC subsection SeqRecord objects from GenBank files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc36">4.2.3</A>&#XA0;&#XA0;SeqRecord objects from GenBank files</H3><!--SEC END --><P>As in the previous example, we&#X2019;re going to look at the whole sequence for <I>Yersinia pestis biovar Microtus</I> str. 91001 plasmid pPCP1, originally downloaded from the NCBI, but this time as a GenBank file.
-Again, this file is included with the Biopython unit tests under the GenBank folder, or online <A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb"><TT>NC_005816.gb</TT></A> from our website.</P><P>This file contains a single record (i.e. only one LOCUS line) and starts:
-</P><PRE CLASS="verbatim">LOCUS       NC_005816               9609 bp    DNA     circular BCT 21-JUL-2008
-DEFINITION  Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete
-            sequence.
-ACCESSION   NC_005816
-VERSION     NC_005816.1  GI:45478711
-PROJECT     GenomeProject:10638
-...
-</PRE><P>Again, we&#X2019;ll use <CODE>Bio.SeqIO</CODE> to read this file in, and the code is almost identical to that for used above for the FASTA file (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> for details):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
->>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=['Project:10638'])
-</PRE><P>You should be able to spot some differences already! But taking the attributes individually,
-the sequence string is the same as before, but this time <CODE>Bio.SeqIO</CODE> has been able to automatically assign a more specific alphabet (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> for details):</P><PRE CLASS="verbatim">>>> record.seq
-Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA())
-</PRE><P>The <CODE>name</CODE> comes from the LOCUS line, while the <CODE>id</CODE> includes the version suffix.
-The description comes from the DEFINITION line:</P><PRE CLASS="verbatim">>>> record.id
-'NC_005816.1'
->>> record.name
-'NC_005816'
->>> record.description
-'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.'
-</PRE><P>GenBank files don&#X2019;t have any per-letter annotations:</P><PRE CLASS="verbatim">>>> record.letter_annotations
-{}
-</PRE><P>Most of the annotations information gets recorded in the <CODE>annotations</CODE> dictionary, for example:</P><PRE CLASS="verbatim">>>> len(record.annotations)
-11
->>> record.annotations["source"]
-'Yersinia pestis biovar Microtus str. 91001'
-</PRE><P>The <CODE>dbxrefs</CODE> list gets populated from any PROJECT or DBLINK lines:</P><PRE CLASS="verbatim">>>> record.dbxrefs
-['Project:10638']
-</PRE><P>Finally, and perhaps most interestingly, all the entries in the features table (e.g. the genes or CDS features) get recorded as <CODE>SeqFeature</CODE> objects in the <CODE>features</CODE> list.</P><PRE CLASS="verbatim">>>> len(record.features)
-29
-</PRE><P>We&#X2019;ll talk about <CODE>SeqFeature</CODE> objects next, in
-Section&#XA0;<A HREF="#sec:seq_features">4.3</A>.</P><!--TOC section Feature, location and position objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc37">4.3</A>&#XA0;&#XA0;Feature, location and position objects</H2><!--SEC END --><P>
-<A NAME="sec:seq_features"></A></P><!--TOC subsection SeqFeature objects-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc38">4.3.1</A>&#XA0;&#XA0;SeqFeature objects</H3><!--SEC END --><P>Sequence features are an essential part of describing a sequence. Once you get beyond the sequence itself, you need some way to organize and easily get at the more &#X201C;abstract&#X201D; information that is known about the sequence. While it is probably impossible to develop a general sequence feature class that will cover everything, the Biopython <CODE>SeqFeature</C [...]
-<B>.type</B></DT><DD CLASS="dd-description"> &#X2013; This is a textual description of the type of feature (for instance, this will be something like &#X2018;CDS&#X2019; or &#X2018;gene&#X2019;).</DD><DT CLASS="dt-description"><B>.location</B></DT><DD CLASS="dd-description"> &#X2013; The location of the <CODE>SeqFeature</CODE> on the sequence
-that you are dealing with, see Section&#XA0;<A HREF="#sec:locations">4.3.2</A> below. The
-<CODE>SeqFeature</CODE> delegates much of its functionality to the location
-object, and includes a number of shortcut attributes for properties
-of the location:<DL CLASS="description"><DT CLASS="dt-description">
-<B>.ref</B></DT><DD CLASS="dd-description"> &#X2013; shorthand for <CODE>.location.ref</CODE> &#X2013; any (different)
-reference sequence the location is referring to. Usually just None.</DD><DT CLASS="dt-description"><B>.ref_db</B></DT><DD CLASS="dd-description"> &#X2013; shorthand for <CODE>.location.ref_db</CODE> &#X2013; specifies
-the database any identifier in <CODE>.ref</CODE> refers to. Usually just None.</DD><DT CLASS="dt-description"><B>.strand</B></DT><DD CLASS="dd-description"> &#X2013; shorthand for <CODE>.location.strand</CODE> &#X2013; the strand on
-the sequence that the feature is located on. For double stranded nucleotide
-sequence this may either be 1 for the top strand, &#X2212;1 for the bottom
-strand, 0 if the strand is important but is unknown, or <TT>None</TT>
-if it doesn&#X2019;t matter. This is None for proteins, or single stranded sequences.
-</DD></DL></DD><DT CLASS="dt-description"><B>.qualifiers</B></DT><DD CLASS="dd-description"> &#X2013; This is a Python dictionary of additional information about the feature. The key is some kind of terse one-word description of what the information contained in the value is about, and the value is the actual information. For example, a common key for a qualifier might be &#X201C;evidence&#X201D; and the value might be &#X201C;computational (non-experimental).&#X201D; This is just a way  [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc39">4.3.2</A>&#XA0;&#XA0;Positions and locations</H3><!--SEC END --><P>
-<A NAME="sec:locations"></A></P><P>The key idea about each <CODE>SeqFeature</CODE> object is to describe a
-region on a parent sequence, for which we use a location object,
-typically describing a range between two positions. Two try to
-clarify the terminology we&#X2019;re using:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>position</B></DT><DD CLASS="dd-description"> &#X2013; This refers to a single position on a sequence,
-which may be fuzzy or not. For instance, 5, 20, <CODE><100</CODE> and
-<CODE>>200</CODE> are all positions.</DD><DT CLASS="dt-description"><B>location</B></DT><DD CLASS="dd-description"> &#X2013; A location is region of sequence bounded by
-some positions. For instance 5..20 (i.&#XA0;e.&#XA0;5 to 20) is a location.
-</DD></DL><P>I just mention this because sometimes I get confused between the two.</P><!--TOC subsubsection FeatureLocation object-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.1&#XA0;&#XA0;FeatureLocation object</H4><!--SEC END --><P>Unless you work with eukaryotic genes, most <CODE>SeqFeature</CODE> locations are
-extremely simple - you just need start and end coordinates and a strand.
-That&#X2019;s essentially all the basic <CODE>FeatureLocation</CODE> object does.</P><P>In practise of course, things can be more complicated. First of all
-we have to handle compound locations made up of several regions.
-Secondly, the positions themselves may be fuzzy (inexact).</P><!--TOC subsubsection CompoundLocation object-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.2&#XA0;&#XA0;CompoundLocation object</H4><!--SEC END --><P>Biopython 1.62 introduced the <CODE>CompoundLocation</CODE> as part of
-a restructuring of how complex locations made up of multiple regions
-are represented.
-The main usage is for handling &#X2018;join&#X2019; locations in EMBL/GenBank files.</P><!--TOC subsubsection Fuzzy Positions-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.3&#XA0;&#XA0;Fuzzy Positions</H4><!--SEC END --><P>So far we&#X2019;ve only used simple positions. One complication in dealing
-with feature locations comes in the positions themselves.
-In biology many times things aren&#X2019;t entirely certain
-(as much as us wet lab biologists try to make them certain!). For
-instance, you might do a dinucleotide priming experiment and discover
-that the start of mRNA transcript starts at one of two sites. This
-is very useful information, but the complication comes in how to
-represent this as a position. To help us deal with this, we have
-the concept of fuzzy positions. Basically there are several types
-of fuzzy positions, so we have five classes do deal with them:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>ExactPosition</B></DT><DD CLASS="dd-description"> &#X2013; As its name suggests, this class represents a position which is specified as exact along the sequence. This is represented as just a number, and you can get the position by looking at the <CODE>position</CODE> attribute of the object.</DD><DT CLASS="dt-description"><B>BeforePosition</B></DT><DD CLASS="dd-description"> &#X2013; This class represents a fuzzy position
-that occurs prior to some specified site. In GenBank/EMBL notation,
-this is represented as something like <CODE>`<13'</CODE>, signifying that
-the real position is located somewhere less than 13. To get
-the specified upper boundary, look at the <CODE>position</CODE>
-attribute of the object.</DD><DT CLASS="dt-description"><B>AfterPosition</B></DT><DD CLASS="dd-description"> &#X2013; Contrary to <CODE>BeforePosition</CODE>, this
-class represents a position that occurs after some specified site.
-This is represented in GenBank as <CODE>`>13'</CODE>, and like
-<CODE>BeforePosition</CODE>, you get the boundary number by looking
-at the <CODE>position</CODE> attribute of the object.</DD><DT CLASS="dt-description"><B>WithinPosition</B></DT><DD CLASS="dd-description"> &#X2013; Occasionally used for GenBank/EMBL locations,
-this class models a position which occurs somewhere between two
-specified nucleotides. In GenBank/EMBL notation, this would be
-represented as &#X2018;(1.5)&#X2019;, to represent that the position is somewhere
-within the range 1 to 5. To get the information in this class you
-have to look at two attributes. The <CODE>position</CODE> attribute
-specifies the lower boundary of the range we are looking at, so in
-our example case this would be one. The <CODE>extension</CODE> attribute
-specifies the range to the higher boundary, so in this case it
-would be 4. So <CODE>object.position</CODE> is the lower boundary and
-<CODE>object.position + object.extension</CODE> is the upper boundary.</DD><DT CLASS="dt-description"><B>OneOfPosition</B></DT><DD CLASS="dd-description"> &#X2013; Occasionally used for GenBank/EMBL locations,
-this class deals with a position where several possible values exist,
-for instance you could use this if the start codon was unclear and
-there where two candidates for the start of the gene. Alternatively,
-that might be handled explicitly as two related gene features.</DD><DT CLASS="dt-description"><B>UnknownPosition</B></DT><DD CLASS="dd-description"> &#X2013; This class deals with a position of unknown
-location. This is not used in GenBank/EMBL, but corresponds to the &#X2018;?&#X2019;
-feature coordinate used in UniProt.</DD></DL><P>Here&#X2019;s an example where we create a location with fuzzy end points:</P><PRE CLASS="verbatim">>>> from Bio import SeqFeature
->>> start_pos = SeqFeature.AfterPosition(5)
->>> end_pos = SeqFeature.BetweenPosition(9, left=8, right=9)
->>> my_location = SeqFeature.FeatureLocation(start_pos, end_pos)
-</PRE><P>Note that the details of some of the fuzzy-locations changed in Biopython 1.59,
-in particular for BetweenPosition and WithinPosition you must now make it explicit
-which integer position should be used for slicing etc. For a start position this
-is generally the lower (left) value, while for an end position this would generally
-be the higher (right) value.</P><P>If you print out a <CODE>FeatureLocation</CODE> object, you can get a nice representation of the information:</P><PRE CLASS="verbatim">>>> print(my_location)
-[>5:(8^9)]
-</PRE><P>We can access the fuzzy start and end positions using the start and end attributes of the location:</P><PRE CLASS="verbatim">>>> my_location.start
-AfterPosition(5)
->>> print(my_location.start)
->5
->>> my_location.end
-BetweenPosition(9, left=8, right=9)
->>> print(my_location.end)
-(8^9)
-</PRE><P>If you don&#X2019;t want to deal with fuzzy positions and just want numbers,
-they are actually subclasses of integers so should work like integers:</P><PRE CLASS="verbatim">>>> int(my_location.start)
-5
->>> int(my_location.end)
-9
-</PRE><P>For compatibility with older versions of Biopython you can ask for the
-<CODE>nofuzzy_start</CODE> and <CODE>nofuzzy_end</CODE> attributes of the location
-which are plain integers:</P><PRE CLASS="verbatim">>>> my_location.nofuzzy_start
-5
->>> my_location.nofuzzy_end
-9
-</PRE><P>Notice that this just gives you back the position attributes of the fuzzy locations.</P><P>Similarly, to make it easy to create a position without worrying about fuzzy positions, you can just pass in numbers to the <CODE>FeaturePosition</CODE> constructors, and you&#X2019;ll get back out <CODE>ExactPosition</CODE> objects:</P><PRE CLASS="verbatim">>>> exact_location = SeqFeature.FeatureLocation(5, 9)
->>> print(exact_location)
-[5:9]
->>> exact_location.start
-ExactPosition(5)
->>> int(exact_location.start)
-5
->>> exact_location.nofuzzy_start
-5
-</PRE><P>That is most of the nitty gritty about dealing with fuzzy positions in Biopython.
-It has been designed so that dealing with fuzziness is not that much more
-complicated than dealing with exact positions, and hopefully you find that true!</P><!--TOC subsubsection Location testing-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->4.3.2.4&#XA0;&#XA0;Location testing</H4><!--SEC END --><P>You can use the Python keyword <CODE>in</CODE> with a <CODE>SeqFeature</CODE> or location
-object to see if the base/residue for a parent coordinate is within the
-feature/location or not.</P><P>For example, suppose you have a SNP of interest and you want to know which
-features this SNP is within, and lets suppose this SNP is at index 4350
-(Python counting!). Here is a simple brute force solution where we just
-check all the features one by one in a loop:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> my_snp = 4350
->>> record = SeqIO.read("NC_005816.gb", "genbank")
->>> for feature in record.features:
-...     if my_snp in feature:
-...         print("%s %s" % (feature.type, feature.qualifiers.get('db_xref')))
-...
-source ['taxon:229193']
-gene ['GeneID:2767712']
-CDS ['GI:45478716', 'GeneID:2767712']
-</PRE><P>Note that gene and CDS features from GenBank or EMBL files defined with joins
-are the union of the exons &#X2013; they do not cover any introns.</P><!--TOC subsection Sequence described by a feature or location-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc40">4.3.3</A>&#XA0;&#XA0;Sequence described by a feature or location</H3><!--SEC END --><P>A <CODE>SeqFeature</CODE> or location object doesn&#X2019;t directly contain a sequence, instead the location (see Section&#XA0;<A HREF="#sec:locations">4.3.2</A>) describes how to get this from the parent sequence. For example consider a (short) gene sequence with location 5:18 on the reverse strand, which in GenBank/EMBL notation using 1-based [...]
->>> from Bio.SeqFeature import SeqFeature, FeatureLocation
->>> example_parent = Seq("ACCGAGACGGCAAAGGCTAGCATAGGTATGAGACTTCCTTCCTGCCAGTGCTGAGGAACTGGGAGCCTAC")
->>> example_feature = SeqFeature(FeatureLocation(5, 18), type="gene", strand=-1)
-</PRE><P>You could take the parent sequence, slice it to extract 5:18, and then take the reverse complement.
-If you are using Biopython 1.59 or later, the feature location&#X2019;s start and end are integer like so this works:</P><PRE CLASS="verbatim">>>> feature_seq = example_parent[example_feature.location.start:example_feature.location.end].reverse_complement()
->>> print(feature_seq)
-AGCCTTTGCCGTC
-</PRE><P>This is a simple example so this isn&#X2019;t too bad &#X2013; however once you have to deal with compound features (joins) this is rather messy. Instead, the <CODE>SeqFeature</CODE> object has an <CODE>extract</CODE> method to take care of all this:</P><PRE CLASS="verbatim">>>> feature_seq = example_feature.extract(example_parent)
->>> print(feature_seq)
-AGCCTTTGCCGTC
-</PRE><P>The length of a <CODE>SeqFeature</CODE> or location matches
-that of the region of sequence it describes.</P><PRE CLASS="verbatim">>>> print(example_feature.extract(example_parent))
-AGCCTTTGCCGTC
->>> print(len(example_feature.extract(example_parent)))
-13
->>> print(len(example_feature))
-13
->>> print(len(example_feature.location))
-13
-</PRE><P>For simple <CODE>FeatureLocation</CODE> objects the length is just
-the difference between the start and end positions. However,
-for a <CODE>CompoundLocation</CODE> the length is the sum of the
-constituent regions.</P><!--TOC section References-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc41">4.4</A>&#XA0;&#XA0;References</H2><!--SEC END --><P>Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython &#X2013; we have a <CODE>Bio.SeqFeature.Reference</CODE> class that stores the relevant information about a reference as attributes of an object.</P><P>The attributes include things that yo [...]
-That&#X2019;s all there is too it. References are meant to be easy to deal with, and hopefully general enough to cover lots of usage cases.</P><!--TOC section The format method-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc42">4.5</A>&#XA0;&#XA0;The format method</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-format"></A></P><P>The <CODE>format()</CODE> method of the <CODE>SeqRecord</CODE> class gives a string
-containing your record formatted using one of the output file formats
-supported by <CODE>Bio.SeqIO</CODE>, such as FASTA:</P><PRE CLASS="verbatim">from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import generic_protein
-
-record = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
-                      +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
-                      +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
-                      +"SSAC", generic_protein),
-                   id="gi|14150838|gb|AAK54648.1|AF376133_1",
-                   description="chalcone synthase [Cucumis sativus]")
-
-print(record.format("fasta"))
-</PRE><P>which should give:
-</P><PRE CLASS="verbatim">>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus]
-MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
-GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
-NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
-SSAC
-</PRE><P>This <CODE>format</CODE> method takes a single mandatory argument, a lower case string which is
-supported by <CODE>Bio.SeqIO</CODE> as an output format (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).
-However, some of the file formats <CODE>Bio.SeqIO</CODE> can write to <EM>require</EM> more than
-one record (typically the case for multiple sequence alignment formats), and thus won&#X2019;t
-work via this <CODE>format()</CODE> method. See also Section&#XA0;<A HREF="#sec:Bio.SeqIO-and-StringIO">5.5.4</A>.</P><!--TOC section Slicing a SeqRecord-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc43">4.6</A>&#XA0;&#XA0;Slicing a SeqRecord</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-slicing"></A></P><P>You can slice a <CODE>SeqRecord</CODE>, to give you a new <CODE>SeqRecord</CODE> covering just
-part of the sequence. What is important
-here is that any per-letter annotations are also sliced, and any features which fall
-completely within the new sequence are preserved (with their locations adjusted).</P><P>For example, taking the same GenBank file used earlier:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><PRE CLASS="verbatim">>>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=['Project:10638'])
-</PRE><PRE CLASS="verbatim">>>> len(record)
-9609
->>> len(record.features)
-41
-</PRE><P>For this example we&#X2019;re going to focus in on the <CODE>pim</CODE> gene, <CODE>YP_pPCP05</CODE>.
-If you have a look at the GenBank file directly you&#X2019;ll find this gene/CDS has
-location string <TT>4343..4780</TT>, or in Python counting <TT>4342:4780</TT>.
-From looking at the file you can work out that these are the twelfth and
-thirteenth entries in the file, so in Python zero-based counting they are
-entries 11 and 12 in the <TT>features</TT> list:</P><PRE CLASS="verbatim">>>> print(record.features[20])
-type: gene
-location: [4342:4780](+)
-qualifiers:
-    Key: db_xref, Value: ['GeneID:2767712']
-    Key: gene, Value: ['pim']
-    Key: locus_tag, Value: ['YP_pPCP05']
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(record.features[21])
-type: CDS
-location: [4342:4780](+)
-qualifiers:
-    Key: codon_start, Value: ['1']
-    Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
-    Key: gene, Value: ['pim']
-    Key: locus_tag, Value: ['YP_pPCP05']
-    Key: note, Value: ['similar to many previously sequenced pesticin immunity ...']
-    Key: product, Value: ['pesticin immunity protein']
-    Key: protein_id, Value: ['NP_995571.1']
-    Key: transl_table, Value: ['11']
-    Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...']
-</PRE><P>Let&#X2019;s slice this parent record from 4300 to 4800 (enough to include the <CODE>pim</CODE>
-gene/CDS), and see how many features we get:</P><PRE CLASS="verbatim">>>> sub_record = record[4300:4800]
-</PRE><PRE CLASS="verbatim">>>> sub_record
-SeqRecord(seq=Seq('ATAAATAGATTATTCCAAATAATTTATTTATGTAAGAACAGGATGGGAGGGGGA...TTA',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=[])
-</PRE><PRE CLASS="verbatim">>>> len(sub_record)
-500
->>> len(sub_record.features)
-2
-</PRE><P>Our sub-record just has two features, the gene and CDS entries for <CODE>YP_pPCP05</CODE>:</P><PRE CLASS="verbatim">>>> print(sub_record.features[0])
-type: gene
-location: [42:480](+)
-qualifiers:
-    Key: db_xref, Value: ['GeneID:2767712']
-    Key: gene, Value: ['pim']
-    Key: locus_tag, Value: ['YP_pPCP05']
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(sub_record.features[20])
-type: CDS
-location: [42:480](+)
-qualifiers:
-    Key: codon_start, Value: ['1']
-    Key: db_xref, Value: ['GI:45478716', 'GeneID:2767712']
-    Key: gene, Value: ['pim']
-    Key: locus_tag, Value: ['YP_pPCP05']
-    Key: note, Value: ['similar to many previously sequenced pesticin immunity ...']
-    Key: product, Value: ['pesticin immunity protein']
-    Key: protein_id, Value: ['NP_995571.1']
-    Key: transl_table, Value: ['11']
-    Key: translation, Value: ['MGGGMISKLFCLALIFLSSSGLAEKNTYTAKDILQNLELNTFGNSLSH...']
-</PRE><P>Notice that their locations have been adjusted to reflect the new parent sequence!</P><P>While Biopython has done something sensible and hopefully intuitive with the features
-(and any per-letter annotation), for the other annotation it is impossible to know if
-this still applies to the sub-sequence or not. To avoid guessing, the <TT>annotations</TT>
-and <TT>dbxrefs</TT> are omitted from the sub-record, and it is up to you to transfer
-any relevant information as appropriate.</P><PRE CLASS="verbatim">>>> sub_record.annotations
-{}
->>> sub_record.dbxrefs
-[]
-</PRE><P>The same point could be made about the record <TT>id</TT>, <TT>name</TT>
-and <TT>description</TT>, but for practicality these are preserved:</P><PRE CLASS="verbatim">>>> sub_record.id
-'NC_005816.1'
->>> sub_record.name
-'NC_005816'
->>> sub_record.description
-'Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.'
-</PRE><P>This illustrates the problem nicely though, our new sub-record is
-<EM>not</EM> the complete sequence of the plasmid, so the description is wrong!
-Let&#X2019;s fix this and then view the sub-record as a reduced GenBank file using
-the <TT>format</TT> method described above in Section&#XA0;<A HREF="#sec:SeqRecord-format">4.5</A>:</P><PRE CLASS="verbatim">>>> sub_record.description = "Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, partial."
->>> print(sub_record.format("genbank"))
-...
-</PRE><P>See Sections&#XA0;<A HREF="#sec:FASTQ-slicing-off-primer">18.1.7</A>
-and&#XA0;<A HREF="#sec:FASTQ-slicing-off-adaptor">18.1.8</A> for some FASTQ examples where the
-per-letter annotations (the read quality scores) are also sliced.</P><!--TOC section Adding SeqRecord objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc44">4.7</A>&#XA0;&#XA0;Adding SeqRecord objects</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-addition"></A></P><P>You can add <CODE>SeqRecord</CODE> objects together, giving a new <CODE>SeqRecord</CODE>.
-What is important here is that any common
-per-letter annotations are also added, all the features are preserved (with their
-locations adjusted), and any other common annotation is also kept (like the id, name
-and description).</P><P>For an example with per-letter annotation, we&#X2019;ll use the first record in a
-FASTQ file. Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> will explain the <CODE>SeqIO</CODE> functions:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = next(SeqIO.parse("example.fastq", "fastq"))
->>> len(record)
-25
->>> print(record.seq)
-CCCTTCTTGTCTTCAGCGTTTCTCC
-</PRE><PRE CLASS="verbatim">>>> print(record.letter_annotations["phred_quality"])
-[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26,
-26, 26, 26, 23, 23]
-</PRE><P>Let&#X2019;s suppose this was Roche 454 data, and that from other information
-you think the <TT>TTT</TT> should be only <TT>TT</TT>. We can make a new edited
-record by first slicing the <CODE>SeqRecord</CODE> before and after the &#X201C;extra&#X201D;
-third <TT>T</TT>:</P><PRE CLASS="verbatim">>>> left = record[:20]
->>> print(left.seq)
-CCCTTCTTGTCTTCAGCGTT
->>> print(left.letter_annotations["phred_quality"])
-[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26]
->>> right = record[21:]
->>> print(right.seq)
-CTCC
->>> print(right.letter_annotations["phred_quality"])
-[26, 26, 23, 23]
-</PRE><P>Now add the two parts together:</P><PRE CLASS="verbatim">>>> edited = left + right
->>> len(edited)
-24
->>> print(edited.seq)
-CCCTTCTTGTCTTCAGCGTTCTCC
-</PRE><PRE CLASS="verbatim">>>> print(edited.letter_annotations["phred_quality"])
-[26, 26, 18, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 22, 26, 26, 26, 26,
-26, 26, 23, 23]
-</PRE><P>Easy and intuitive? We hope so! You can make this shorter with just:</P><PRE CLASS="verbatim">>>> edited = record[:20] + record[21:]
-</PRE><P>Now, for an example with features, we&#X2019;ll use a GenBank file.
-Suppose you have a circular genome:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><PRE CLASS="verbatim">>>> record
-SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=['Project:10638'])
-</PRE><PRE CLASS="verbatim">>>> len(record)
-9609
->>> len(record.features)
-41
->>> record.dbxrefs
-['Project:58037']
-</PRE><PRE CLASS="verbatim">>>> record.annotations.keys()
-['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references',
-'accessions', 'data_file_division', 'date', 'organism', 'gi']
-</PRE><P>You can shift the origin like this:</P><PRE CLASS="verbatim">>>> shifted = record[2000:] + record[:2000]
-</PRE><PRE CLASS="verbatim">>>> shifted
-SeqRecord(seq=Seq('GATACGCAGTCATATTTTTTACACAATTCTCTAATCCCGACAAGGTCGTAGGTC...GGA',
-IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816',
-description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence.',
-dbxrefs=[])
-</PRE><PRE CLASS="verbatim">>>> len(shifted)
-9609
-</PRE><P>Note that this isn&#X2019;t perfect in that some annotation like the database cross references
-and one of the features (the source feature) have been lost:</P><PRE CLASS="verbatim">>>> len(shifted.features)
-40
->>> shifted.dbxrefs
-[]
->>> shifted.annotations.keys()
-[]
-</PRE><P>This is because the <CODE>SeqRecord</CODE> slicing step is cautious in what annotation
-it preserves (erroneously propagating annotation can cause major problems). If
-you want to keep the database cross references or the annotations dictionary,
-this must be done explicitly:</P><PRE CLASS="verbatim">>>> shifted.dbxrefs = record.dbxrefs[:]
->>> shifted.annotations = record.annotations.copy()
->>> shifted.dbxrefs
-['Project:10638']
->>> shifted.annotations.keys()
-['comment', 'sequence_version', 'source', 'taxonomy', 'keywords', 'references',
-'accessions', 'data_file_division', 'date', 'organism', 'gi']
-</PRE><P>Also note that in an example like this, you should probably change the record
-identifiers since the NCBI references refer to the <EM>original</EM> unmodified
-sequence.</P><!--TOC section Reverse-complementing SeqRecord objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc45">4.8</A>&#XA0;&#XA0;Reverse-complementing SeqRecord objects</H2><!--SEC END --><P>
-<A NAME="sec:SeqRecord-reverse-complement"></A></P><P>One of the new features in Biopython 1.57 was the <CODE>SeqRecord</CODE> object&#X2019;s
-<CODE>reverse_complement</CODE> method. This tries to balance easy of use with worries
-about what to do with the annotation in the reverse complemented record.</P><P>For the sequence, this uses the Seq object&#X2019;s reverse complement method. Any
-features are transferred with the location and strand recalculated. Likewise
-any per-letter-annotation is also copied but reversed (which makes sense for
-typical examples like quality scores). However, transfer of most annotation
-is problematical.</P><P>For instance, if the record ID was an accession, that accession should not really
-apply to the reverse complemented sequence, and transferring the identifier by
-default could easily cause subtle data corruption in downstream analysis.
-Therefore by default, the <CODE>SeqRecord</CODE>&#X2019;s id, name, description, annotations
-and database cross references are all <EM>not</EM> transferred by default.</P><P>The <CODE>SeqRecord</CODE> object&#X2019;s <CODE>reverse_complement</CODE> method takes a number
-of optional arguments corresponding to properties of the record. Setting these
-arguments to <CODE>True</CODE> means copy the old values, while <CODE>False</CODE> means
-drop the old values and use the default value. You can alternatively provide
-the new desired value instead.</P><P>Consider this example record:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.gb", "genbank")
->>> print("%s %i %i %i %i" % (record.id, len(record), len(record.features), len(record.dbxrefs), len(record.annotations)))
-NC_005816.1 9609 41 1 11
-</PRE><P>Here we take the reverse complement and specify a new identifier &#X2013; but notice
-how most of the annotation is dropped (but not the features):</P><PRE CLASS="verbatim">>>> rc = record.reverse_complement(id="TESTING")
->>> print("%s %i %i %i %i" % (rc.id, len(rc), len(rc.features), len(rc.dbxrefs), len(rc.annotations)))
-TESTING 9609 41 0 0
-</PRE><!--TOC chapter Sequence Input/Output-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc46">Chapter&#XA0;5</A>&#XA0;&#XA0;Sequence Input/Output</H1><!--SEC END --><P>
-<A NAME="chapter:Bio.SeqIO"></A></P><P>In this chapter we&#X2019;ll discuss in more detail the <CODE>Bio.SeqIO</CODE> module, which was briefly introduced in Chapter&#XA0;<A HREF="#chapter:quick-start">2</A> and also used in Chapter&#XA0;<A HREF="#chapter:SeqRecord">4</A>. This aims to provide a simple interface for working with assorted sequence file formats in a uniform way.
-See also the <CODE>Bio.SeqIO</CODE> wiki page (<A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A>), and the built in documentation (also <A HREF="http://biopython.org/DIST/docs/api/Bio.SeqIO-module.html">online</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> help(SeqIO)
-...
-</PRE><P>The &#X201C;catch&#X201D; is that you have to work with <CODE>SeqRecord</CODE> objects (see Chapter&#XA0;<A HREF="#chapter:SeqRecord">4</A>), which contain a <CODE>Seq</CODE> object (see Chapter&#XA0;<A HREF="#chapter:Bio.Seq">3</A>) plus annotation like an identifier and description.</P><!--TOC section Parsing or Reading Sequences-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc47">5.1</A>&#XA0;&#XA0;Parsing or Reading Sequences</H2><!--SEC END --><P>
-<A NAME="sec:Bio.SeqIO-input"></A></P><P>The workhorse function <CODE>Bio.SeqIO.parse()</CODE> is used to read in sequence data as SeqRecord objects. This function expects two arguments:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-The first argument is a <I>handle</I> to read the data from, or a filename. A handle is typically a file opened for reading, but could be the output from a command line program, or data downloaded from the internet (see Section&#XA0;<A HREF="#sec:SeqIO_Online">5.3</A>). See Section&#XA0;<A HREF="#sec:appendix-handles">22.1</A> for more about handles.
-</LI><LI CLASS="li-enumerate">The second argument is a lower case string specifying sequence format &#X2013; we don&#X2019;t try and guess the file format for you! See <A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A> for a full listing of supported formats.
-</LI></OL><P>There is an optional argument <CODE>alphabet</CODE> to specify the alphabet to be used. This is useful for file formats like FASTA where otherwise <CODE>Bio.SeqIO</CODE> will default to a generic alphabet.</P><P>The <CODE>Bio.SeqIO.parse()</CODE> function returns an <I>iterator</I> which gives <CODE>SeqRecord</CODE> objects. Iterators are typically used in a for loop as shown below.</P><P>Sometimes you&#X2019;ll find yourself dealing with files which contain only a single re [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc48">5.1.1</A>&#XA0;&#XA0;Reading Sequence Files</H3><!--SEC END --><P>In general <CODE>Bio.SeqIO.parse()</CODE> is used to read in sequence files as <CODE>SeqRecord</CODE> objects, and is typically used with a for loop like this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
-    print(seq_record.id)
-    print(repr(seq_record.seq))
-    print(len(seq_record))
-</PRE><P>The above example is repeated from the introduction in Section&#XA0;<A HREF="#sec:sequence-parsing">2.4</A>, and will load the orchid DNA sequences in the FASTA format file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta">ls_orchid.fasta</A>. If instead you wanted to load a GenBank format file like <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls_orchid.gbk</A> then all you need to do is change the filename and the format stri [...]
-for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-    print(seq_record.id)
-    print(seq_record.seq)
-    print(len(seq_record))
-</PRE><P>Similarly, if you wanted to read in a file in another file format, then assuming <CODE>Bio.SeqIO.parse()</CODE> supports it you would just need to change the format string as appropriate, for example &#X201C;swiss&#X201D; for SwissProt files or &#X201C;embl&#X201D; for EMBL text files. There is a full listing on the wiki page (<A HREF="http://biopython.org/wiki/SeqIO"><TT>http://biopython.org/wiki/SeqIO</TT></A>) and in the built in documentation (also <A HREF="http://biopython. [...]
-a generator expression). For example, if all you wanted to extract from the file was
-a list of the record identifiers we can easily do this with the following list comprehension:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> identifiers = [seq_record.id for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank")]
->>> identifiers
-['Z78533.1', 'Z78532.1', 'Z78531.1', 'Z78530.1', 'Z78529.1', 'Z78527.1', ..., 'Z78439.1']
-</PRE><P>There are more examples using <CODE>SeqIO.parse()</CODE> in a list
-comprehension like this in Section&#XA0;<A HREF="#seq:sequence-parsing-plus-pylab">18.2</A>
-(e.g. for plotting sequence lengths or GC%).</P><!--TOC subsection Iterating over the records in a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc49">5.1.2</A>&#XA0;&#XA0;Iterating over the records in a sequence file</H3><!--SEC END --><P>In the above examples, we have usually used a for loop to iterate over all the records one by one. You can use the for loop with all sorts of Python objects (including lists, tuples and strings) which support the iteration interface.</P><P>The object returned by <CODE>Bio.SeqIO</CODE> is actually an iterator which returns <CODE>SeqRecord</CODE [...]
-record_iterator = SeqIO.parse("ls_orchid.fasta", "fasta")
-
-first_record = next(record_iterator)
-print(first_record.id)
-print(first_record.description)
-
-second_record = next(record_iterator)
-print(second_record.id)
-print(second_record.description)
-</PRE><P>Note that if you try to use <CODE>next()</CODE> and there are no more results, you&#X2019;ll get the special <CODE>StopIteration</CODE> exception.</P><P>One special case to consider is when your sequence files have multiple records, but you only want the first one. In this situation the following code is very concise:</P><PRE CLASS="verbatim">from Bio import SeqIO
-first_record = next(SeqIO.parse("ls_orchid.gbk", "genbank"))
-</PRE><P>A word of warning here &#X2013; using the <CODE>next()</CODE> function like this will silently ignore any additional records in the file.
-If your files have <I>one and only one</I> record, like some of the online examples later in this chapter, or a GenBank file for a single chromosome, then use the new <CODE>Bio.SeqIO.read()</CODE> function instead.
-This will check there are no extra unexpected records present.</P><!--TOC subsection Getting a list of the records in a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc50">5.1.3</A>&#XA0;&#XA0;Getting a list of the records in a sequence file</H3><!--SEC END --><P>In the previous section we talked about the fact that <CODE>Bio.SeqIO.parse()</CODE> gives you a <CODE>SeqRecord</CODE> iterator, and that you get the records one by one. Very often you need to be able to access the records in any order. The Python <CODE>list</CODE> data type is perfect for this, and we can turn the record iterator into a l [...]
-records = list(SeqIO.parse("ls_orchid.gbk", "genbank"))
-
-print("Found %i records" % len(records))
-
-print("The last record")
-last_record = records[-1] #using Python's list tricks
-print(last_record.id)
-print(repr(last_record.seq))
-print(len(last_record))
-
-print("The first record")
-first_record = records[0] #remember, Python counts from zero
-print(first_record.id)
-print(repr(first_record.seq))
-print(len(first_record))
-</PRE><P>Giving:</P><PRE CLASS="verbatim">Found 94 records
-The last record
-Z78439.1
-Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', IUPACAmbiguousDNA())
-592
-The first record
-Z78533.1
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
-740
-</PRE><P>You can of course still use a for loop with a list of <CODE>SeqRecord</CODE> objects. Using a list is much more flexible than an iterator (for example, you can determine the number of records from the length of the list), but does need more memory because it will hold all the records in memory at once.</P><!--TOC subsection Extracting data-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc51">5.1.4</A>&#XA0;&#XA0;Extracting data</H3><!--SEC END --><P>The <CODE>SeqRecord</CODE> object and its annotation structures are described more fully in
-Chapter&#XA0;<A HREF="#chapter:SeqRecord">4</A>. As an example of how annotations are stored, we&#X2019;ll look at the output from parsing the first record in the GenBank file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls_orchid.gbk</A>.</P><PRE CLASS="verbatim">from Bio import SeqIO
-record_iterator = SeqIO.parse("ls_orchid.gbk", "genbank")
-first_record = next(record_iterator)
-print(first_record)
-</PRE><P>That should give something like this:</P><PRE CLASS="verbatim">ID: Z78533.1
-Name: Z78533
-Description: C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA.
-Number of features: 5
-/sequence_version=1
-/source=Cypripedium irapeanum
-/taxonomy=['Eukaryota', 'Viridiplantae', 'Streptophyta', ..., 'Cypripedium']
-/keywords=['5.8S ribosomal RNA', '5.8S rRNA gene', ..., 'ITS1', 'ITS2']
-/references=[...]
-/accessions=['Z78533']
-/data_file_division=PLN
-/date=30-NOV-2006
-/organism=Cypripedium irapeanum
-/gi=2765658
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', IUPACAmbiguousDNA())
-</PRE><P>This gives a human readable summary of most of the annotation data for the <CODE>SeqRecord</CODE>.
-For this example we&#X2019;re going to use the <CODE>.annotations</CODE> attribute which is just a Python dictionary.
-The contents of this annotations dictionary were shown when we printed the record above.
-You can also print them out directly:
-</P><PRE CLASS="verbatim">print(first_record.annotations)
-</PRE><P>Like any Python dictionary, you can easily get a list of the keys:
-</P><PRE CLASS="verbatim">print(first_record.annotations.keys())
-</PRE><P>or values:
-</P><PRE CLASS="verbatim">print(first_record.annotations.values())
-</PRE><P>In general, the annotation values are strings, or lists of strings. One special case is any references in the file get stored as reference objects.</P><P>Suppose you wanted to extract a list of the species from the <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls_orchid.gbk</A> GenBank file. The information we want, <EM>Cypripedium irapeanum</EM>, is held in the annotations dictionary under &#X2018;source&#X2019; and &#X2018;organism&#X2019;, which we  [...]
-Cypripedium irapeanum
-</PRE><P>or:</P><PRE CLASS="verbatim">>>> print(first_record.annotations["organism"])
-Cypripedium irapeanum
-</PRE><P>In general, &#X2018;organism&#X2019; is used for the scientific name (in Latin, e.g. <I>Arabidopsis thaliana</I>),
-while &#X2018;source&#X2019; will often be the common name (e.g. thale cress). In this example, as is often the case,
-the two fields are identical.</P><P>Now let&#X2019;s go through all the records, building up a list of the species each orchid sequence is from:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species = []
-for seq_record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-    all_species.append(seq_record.annotations["organism"])
-print(all_species)
-</PRE><P>Another way of writing this code is to use a list comprehension:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species = [seq_record.annotations["organism"] for seq_record in \
-               SeqIO.parse("ls_orchid.gbk", "genbank")]
-print(all_species)
-</PRE><P>In either case, the result is:</P><PRE CLASS="verbatim">['Cypripedium irapeanum', 'Cypripedium californicum', ..., 'Paphiopedilum barbatum']
-</PRE><P>Great. That was pretty easy because GenBank files are annotated in a standardised way.</P><P>Now, let&#X2019;s suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file. The bad news is you will have to write some code to extract the data you want from the record&#X2019;s description line - if the information is in the file in the first place! Our example FASTA format file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orch [...]
-CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGGAATAAACGATCGAGTG
-AATCCGGAGGACCGGTGTACTCAGCTCACCGGGGGCATTGCTCCCGTGGTGACCCTGATTTGTTGTTGGG
-...
-</PRE><P>You can check by hand, but for every record the species name is in the description line as the second word. This means if we break up each record&#X2019;s <CODE>.description</CODE> at the spaces, then the species is there as field number one (field zero is the record identifier). That means we can do this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species = []
-for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
-    all_species.append(seq_record.description.split()[1])
-print(all_species)
-</PRE><P>This gives:</P><PRE CLASS="verbatim">['C.irapeanum', 'C.californicum', 'C.fasciculatum', 'C.margaritaceum', ..., 'P.barbatum']
-</PRE><P>The concise alternative using list comprehensions would be:</P><PRE CLASS="verbatim">from Bio import SeqIO
-all_species == [seq_record.description.split()[1] for seq_record in \
-                SeqIO.parse("ls_orchid.fasta", "fasta")]
-print(all_species)
-</PRE><P>In general, extracting information from the FASTA description line is not very nice.
-If you can get your sequences in a well annotated file format like GenBank or EMBL,
-then this sort of annotation information is much easier to deal with.</P><!--TOC section Parsing sequences from compressed files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc52">5.2</A>&#XA0;&#XA0;Parsing sequences from compressed files</H2><!--SEC END --><P>
-<A NAME="sec:SeqIO_compressed"></A>
-In the previous section, we looked at parsing sequence data from a file.
-Instead of using a filename, you can give <CODE>Bio.SeqIO</CODE> a handle
-(see Section&#XA0;<A HREF="#sec:appendix-handles">22.1</A>), and in this section
-we&#X2019;ll use handles to parse sequence from compressed files.</P><P>As you&#X2019;ll have seen above, we can use <CODE>Bio.SeqIO.read()</CODE> or
-<CODE>Bio.SeqIO.parse()</CODE> with a filename - for instance this quick
-example calculates the total length of the sequences in a multiple
-record GenBank file using a generator expression:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> print(sum(len(r) for r in SeqIO.parse("ls_orchid.gbk", "gb")))
-67518
-</PRE><P>Here we use a file handle instead, using the <CODE>with</CODE> statement
-to close the handle automatically:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> with open("ls_orchid.gbk") as handle:
-...     print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
-</PRE><P>Or, the old fashioned way where you manually close the handle:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> handle = open("ls_orchid.gbk")
->>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
->>> handle.close()
-</PRE><P>Now, suppose we have a gzip compressed file instead? These are very
-commonly used on Linux. We can use Python&#X2019;s <CODE>gzip</CODE> module to open
-the compressed file for reading - which gives us a handle object:</P><PRE CLASS="verbatim">>>> import gzip
->>> from Bio import SeqIO
->>> handle = gzip.open("ls_orchid.gbk.gz", "r")
->>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
->>> handle.close()
-</PRE><P>Similarly if we had a bzip2 compressed file (sadly the function name isn&#X2019;t
-quite as consistent):</P><PRE CLASS="verbatim">>>> import bz2
->>> from Bio import SeqIO
->>> handle = bz2.BZ2File("ls_orchid.gbk.bz2", "r")
->>> print(sum(len(r) for r in SeqIO.parse(handle, "gb")))
-67518
->>> handle.close()
-</PRE><P>If you are using Python 2.7 or later, the <CODE>with</CODE>-version works for
-gzip and bz2 as well. Unfortunately this is broken on older versions of
-Python (<A HREF="http://bugs.python.org/issue3860">Issue 3860</A>) and you&#X2019;d
-get an <CODE>AttributeError</CODE> about <CODE>__exit__</CODE> being missing.</P><P>There is a gzip (GNU Zip) variant called BGZF (Blocked GNU Zip Format),
-which can be treated like an ordinary gzip file for reading, but has
-advantages for random access later which we&#X2019;ll talk about later in
-Section&#XA0;<A HREF="#sec:SeqIO-index-bgzf">5.4.4</A>.</P><!--TOC section Parsing sequences from the net-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc53">5.3</A>&#XA0;&#XA0;Parsing sequences from the net</H2><!--SEC END --><P>
-<A NAME="sec:SeqIO_Online"></A>
-In the previous sections, we looked at parsing sequence data from a file
-(using a filename or handle), and from compressed files (using a handle).
-Here we&#X2019;ll use <CODE>Bio.SeqIO</CODE> with another type of handle, a network
-connection, to download and parse sequences from the internet.</P><P>Note that just because you <EM>can</EM> download sequence data and parse it into
-a <CODE>SeqRecord</CODE> object in one go doesn&#X2019;t mean this is a good idea.
-In general, you should probably download sequences <EM>once</EM> and save them to
-a file for reuse.</P><!--TOC subsection Parsing GenBank records from the net-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc54">5.3.1</A>&#XA0;&#XA0;Parsing GenBank records from the net</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO_GenBank_Online"></A>
-Section&#XA0;<A HREF="#sec:efetch">9.6</A> talks about the Entrez EFetch interface in more detail,
-but for now let&#X2019;s just connect to the NCBI and get a few <I>Opuntia</I> (prickly-pear)
-sequences from GenBank using their GI numbers.</P><P>First of all, let&#X2019;s fetch just one record. If you don&#X2019;t care about the
-annotations and features downloading a FASTA file is a good choice as these
-are compact. Now remember, when you expect the handle to contain one and
-only one record, use the <CODE>Bio.SeqIO.read()</CODE> function:</P><PRE CLASS="verbatim">from Bio import Entrez
-from Bio import SeqIO
-Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text", id="6273291")
-seq_record = SeqIO.read(handle, "fasta")
-handle.close()
-print("%s with %i features" % (seq_record.id, len(seq_record.features)))
-</PRE><P>Expected output:</P><PRE CLASS="verbatim">gi|6273291|gb|AF191665.1|AF191665 with 0 features
-</PRE><P>The NCBI will also let you ask for the file in other formats, in particular as
-a GenBank file. Until Easter 2009, the Entrez EFetch API let you use &#X201C;genbank&#X201D;
-as the return type, however the NCBI now insist on using the official
-return types of &#X201C;gb&#X201D; (or &#X201C;gp&#X201D; for proteins) as described on
-<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/efetchseq_help.html">EFetch for Sequence and other Molecular Biology Databases</A>.
-As a result, in Biopython 1.50 onwards, we support &#X201C;gb&#X201D; as an
-alias for &#X201C;genbank&#X201D; in <CODE>Bio.SeqIO</CODE>.</P><PRE CLASS="verbatim">from Bio import Entrez
-from Bio import SeqIO
-Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", id="6273291")
-seq_record = SeqIO.read(handle, "gb") #using "gb" as an alias for "genbank"
-handle.close()
-print("%s with %i features" % (seq_record.id, len(seq_record.features)))
-</PRE><P>The expected output of this example is:</P><PRE CLASS="verbatim">AF191665.1 with 3 features
-</PRE><P>Notice this time we have three features.</P><P>Now let&#X2019;s fetch several records. This time the handle contains multiple records,
-so we must use the <CODE>Bio.SeqIO.parse()</CODE> function:</P><PRE CLASS="verbatim">from Bio import Entrez
-from Bio import SeqIO
-Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", \
-                       id="6273291,6273290,6273289")
-for seq_record in SeqIO.parse(handle, "gb"):
-    print seq_record.id, seq_record.description[:50] + "..."
-    print "Sequence length %i," % len(seq_record),
-    print "%i features," % len(seq_record.features),
-    print "from: %s" % seq_record.annotations["source"]
-handle.close()
-</PRE><P>That should give the following output:</P><PRE CLASS="verbatim">AF191665.1 Opuntia marenae rpl16 gene; chloroplast gene for c...
-Sequence length 902, 3 features, from: chloroplast Opuntia marenae
-AF191664.1 Opuntia clavata rpl16 gene; chloroplast gene for c...
-Sequence length 899, 3 features, from: chloroplast Grusonia clavata
-AF191663.1 Opuntia bradtiana rpl16 gene; chloroplast gene for...
-Sequence length 899, 3 features, from: chloroplast Opuntia bradtianaa
-</PRE><P>See Chapter&#XA0;<A HREF="#chapter:entrez">9</A> for more about the <CODE>Bio.Entrez</CODE> module, and make sure to read about the NCBI guidelines for using Entrez (Section&#XA0;<A HREF="#sec:entrez-guidelines">9.1</A>).</P><!--TOC subsection Parsing SwissProt sequences from the net-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc55">5.3.2</A>&#XA0;&#XA0;Parsing SwissProt sequences from the net</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO_ExPASy_and_SwissProt"></A>
-Now let&#X2019;s use a handle to download a SwissProt file from ExPASy,
-something covered in more depth in Chapter&#XA0;<A HREF="#chapter:swiss_prot">10</A>.
-As mentioned above, when you expect the handle to contain one and only one record,
-use the <CODE>Bio.SeqIO.read()</CODE> function:</P><PRE CLASS="verbatim">from Bio import ExPASy
-from Bio import SeqIO
-handle = ExPASy.get_sprot_raw("O23729")
-seq_record = SeqIO.read(handle, "swiss")
-handle.close()
-print(seq_record.id)
-print(seq_record.name)
-print(seq_record.description)
-print(repr(seq_record.seq))
-print("Length %i" % len(seq_record))
-print(seq_record.annotations["keywords"])
-</PRE><P>Assuming your network connection is OK, you should get back:</P><PRE CLASS="verbatim">O23729
-CHS3_BROFI
-RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;
-Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE', ProteinAlphabet())
-Length 394
-['Acyltransferase', 'Flavonoid biosynthesis', 'Transferase']
-</PRE><!--TOC section Sequence files as Dictionaries-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc56">5.4</A>&#XA0;&#XA0;Sequence files as Dictionaries</H2><!--SEC END --><P>We&#X2019;re now going to introduce three related functions in the <CODE>Bio.SeqIO</CODE>
-module which allow dictionary like random access to a multi-sequence file.
-There is a trade off here between flexibility and memory usage. In summary:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>Bio.SeqIO.to_dict()</CODE> is the most flexible but also the most
-memory demanding option (see Section&#XA0;<A HREF="#SeqIO:to_dict">5.4.1</A>). This is basically
-a helper function to build a normal Python <CODE>dictionary</CODE> with each entry
-held as a <CODE>SeqRecord</CODE> object in memory, allowing you to modify the
-records.
-</LI><LI CLASS="li-itemize"><CODE>Bio.SeqIO.index()</CODE> is a useful middle ground, acting like a
-read only dictionary and parsing sequences into <CODE>SeqRecord</CODE> objects
-on demand (see Section&#XA0;<A HREF="#sec:SeqIO-index">5.4.2</A>).
-</LI><LI CLASS="li-itemize"><CODE>Bio.SeqIO.index_db()</CODE> also acts like a read only dictionary
-but stores the identifiers and file offsets in a file on disk (as an
-SQLite3 database), meaning it has very low memory requirements (see
-Section&#XA0;<A HREF="#sec:SeqIO-index-db">5.4.3</A>), but will be a little bit slower.
-</LI></UL><P>
-See the discussion for an broad overview
-(Section&#XA0;<A HREF="#sec:SeqIO-indexing-discussion">5.4.5</A>).</P><!--TOC subsection Sequence files as Dictionaries &#X2013; In memory-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc57">5.4.1</A>&#XA0;&#XA0;Sequence files as Dictionaries &#X2013; In memory</H3><!--SEC END --><P>
-<A NAME="SeqIO:to_dict"></A></P><P>The next thing that we&#X2019;ll do with our ubiquitous orchid files is to show how
-to index them and access them like a database using the Python <CODE>dictionary</CODE>
-data type (like a hash in Perl). This is very useful for moderately large files
-where you only need to access certain elements of the file, and makes for a nice
-quick &#X2019;n dirty database. For dealing with larger files where memory becomes a
-problem, see Section&#XA0;<A HREF="#sec:SeqIO-index">5.4.2</A> below.</P><P>You can use the function <CODE>Bio.SeqIO.to_dict()</CODE> to make a SeqRecord dictionary
-(in memory). By default this will use each record&#X2019;s identifier (i.e. the <CODE>.id</CODE>
-attribute) as the key. Let&#X2019;s try this using our GenBank file:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"))
-</PRE><P>There is just one required argument for <CODE>Bio.SeqIO.to_dict()</CODE>, a list or
-generator giving <CODE>SeqRecord</CODE> objects. Here we have just used the output
-from the <CODE>SeqIO.parse</CODE> function. As the name suggests, this returns a
-Python dictionary.</P><P>Since this variable <CODE>orchid_dict</CODE> is an ordinary Python dictionary,
-we can look at all of the keys we have available:</P><PRE CLASS="verbatim">>>> len(orchid_dict)
-94
-</PRE><PRE CLASS="verbatim">>>> list(orchid_dict.keys())
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><P>You can leave out the &#X201C;list(...)&#X201C; bit if you are still using Python 2.
-Under Python 3 the dictionary methods like &#X201C;.keys()&#X201C; and &#X201C;.values()&#X201C;
-are iterators rather than lists.</P><P>If you really want to, you can even look at all the records at once:
-</P><PRE CLASS="verbatim">>>> list(orchid_dict.values()) #lots of output!
-...
-</PRE><P>We can access a single <CODE>SeqRecord</CODE> object via the keys and manipulate the object as normal:</P><PRE CLASS="verbatim">>>> seq_record = orchid_dict["Z78475.1"]
->>> print(seq_record.description)
-P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA.
->>> print(repr(seq_record.seq))
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA())
-</PRE><P>So, it is very easy to create an in memory &#X201C;database&#X201D; of our GenBank records. Next we&#X2019;ll try this for the FASTA file instead.</P><P>Note that those of you with prior Python experience should all be able to construct a dictionary like this &#X201C;by hand&#X201D;. However, typical dictionary construction methods will not deal with the case of repeated keys very nicely. Using the <CODE>Bio.SeqIO.to_dict()</CODE> will explicitly check for duplicate keys, and ra [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.1.1&#XA0;&#XA0;Specifying the dictionary keys</H4><!--SEC END --><P>
-<A NAME="seq:seqio-todict-functionkey"></A></P><P>Using the same code as above, but for the FASTA file instead:</P><PRE CLASS="verbatim">from Bio import SeqIO
-orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"))
-print(orchid_dict.keys())
-</PRE><P>This time the keys are:</P><PRE CLASS="verbatim">['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ...
- ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']
-</PRE><P>You should recognise these strings from when we parsed the FASTA file earlier in Section&#XA0;<A HREF="#sec:fasta-parsing">2.4.1</A>. Suppose you would rather have something else as the keys - like the accession numbers. This brings us nicely to <CODE>SeqIO.to_dict()</CODE>&#X2019;s optional argument <CODE>key_function</CODE>, which lets you define what to use as the dictionary key for your records.</P><P>First you must write your own function to return the key you want (as a st [...]
-    """"Given a SeqRecord, return the accession number as a string.
-
-    e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
-    """
-    parts = record.id.split("|")
-    assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
-    return parts[3]
-</PRE><P>Then we can give this function to the <CODE>SeqIO.to_dict()</CODE> function to use in building the dictionary:</P><PRE CLASS="verbatim">from Bio import SeqIO
-orchid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.fasta", "fasta"), key_function=get_accession)
-print(orchid_dict.keys())
-</PRE><P>Finally, as desired, the new dictionary keys:</P><PRE CLASS="verbatim">>>> print(orchid_dict.keys())
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><P>Not too complicated, I hope!</P><!--TOC subsubsection Indexing a dictionary using the SEGUID checksum-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.1.2&#XA0;&#XA0;Indexing a dictionary using the SEGUID checksum</H4><!--SEC END --><P>To give another example of working with dictionaries of <CODE>SeqRecord</CODE> objects, we&#X2019;ll use the SEGUID checksum function. This is a relatively recent checksum, and collisions should be very rare (i.e. two different sequences with the same checksum), an improvement on the CRC64 checksum.</P><P>Once again, working with the orchids GenBank file:</ [...]
-from Bio.SeqUtils.CheckSum import seguid
-for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-    print(record.id, seguid(record.seq))
-</PRE><P>This should give:</P><PRE CLASS="verbatim">Z78533.1 JUEoWn6DPhgZ9nAyowsgtoD9TTo
-Z78532.1 MN/s0q9zDoCVEEc+k/IFwCNF2pY
-...
-Z78439.1 H+JfaShya/4yyAj7IbMqgNkxdxQ
-</PRE><P>Now, recall the <CODE>Bio.SeqIO.to_dict()</CODE> function&#X2019;s <CODE>key_function</CODE> argument expects a function which turns a <CODE>SeqRecord</CODE> into a string. We can&#X2019;t use the <CODE>seguid()</CODE> function directly because it expects to be given a <CODE>Seq</CODE> object (or a string). However, we can use Python&#X2019;s <CODE>lambda</CODE> feature to create a &#X201C;one off&#X201D; function to give to <CODE>Bio.SeqIO.to_dict()</CODE> instead:</P><PRE CLAS [...]
->>> from Bio.SeqUtils.CheckSum import seguid
->>> seguid_dict = SeqIO.to_dict(SeqIO.parse("ls_orchid.gbk", "genbank"),
-...                             lambda rec : seguid(rec.seq))
->>> record = seguid_dict["MN/s0q9zDoCVEEc+k/IFwCNF2pY"]
->>> print(record.id)
-Z78532.1
->>> print(record.description)
-C.californicum 5.8S rRNA gene and ITS1 and ITS2 DNA.
-</PRE><P>That should have retrieved the record <TT>Z78532.1</TT>, the second entry in the file.</P><!--TOC subsection Sequence files as Dictionaries &#X2013; Indexed files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc58">5.4.2</A>&#XA0;&#XA0;Sequence files as Dictionaries &#X2013; Indexed files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-index"></A></P><P>As the previous couple of examples tried to illustrate, using
-<CODE>Bio.SeqIO.to_dict()</CODE> is very flexible. However, because it holds
-everything in memory, the size of file you can work with is limited by your
-computer&#X2019;s RAM. In general, this will only work on small to medium files.</P><P>For larger files you should consider
-<CODE>Bio.SeqIO.index()</CODE>, which works a little differently. Although
-it still returns a dictionary like object, this does <EM>not</EM> keep
-<EM>everything</EM> in memory. Instead, it just records where each record
-is within the file &#X2013; when you ask for a particular record, it then parses
-it on demand.</P><P>As an example, let&#X2019;s use the same GenBank file as before:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
->>> len(orchid_dict)
-94
-</PRE><PRE CLASS="verbatim">>>> orchid_dict.keys()
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><PRE CLASS="verbatim">>>> seq_record = orchid_dict["Z78475.1"]
->>> print(seq_record.description)
-P.supardii 5.8S rRNA gene and ITS1 and ITS2 DNA.
->>> seq_record.seq
-Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', IUPACAmbiguousDNA())
->>> orchid_dict.close()
-</PRE><P>Note that <CODE>Bio.SeqIO.index()</CODE> won&#X2019;t take a handle,
-but only a filename. There are good reasons for this, but it is a little
-technical. The second argument is the file format (a lower case string as
-used in the other <CODE>Bio.SeqIO</CODE> functions). You can use many other
-simple file formats, including FASTA and FASTQ files (see the example in
-Section&#XA0;<A HREF="#sec:fastq-indexing">18.1.11</A>). However, alignment
-formats like PHYLIP or Clustal are not supported. Finally as an optional
-argument you can supply an alphabet, or a key function.</P><P>Here is the same example using the FASTA file - all we change is the
-filename and the format name:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta")
->>> len(orchid_dict)
-94
->>> orchid_dict.keys()
-['gi|2765596|emb|Z78471.1|PDZ78471', 'gi|2765646|emb|Z78521.1|CCZ78521', ...
- ..., 'gi|2765613|emb|Z78488.1|PTZ78488', 'gi|2765583|emb|Z78458.1|PHZ78458']
-</PRE><!--TOC subsubsection Specifying the dictionary keys-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.2.1&#XA0;&#XA0;Specifying the dictionary keys</H4><!--SEC END --><P>
-<A NAME="seq:seqio-index-functionkey"></A></P><P>Suppose you want to use the same keys as before? Much like with the
-<CODE>Bio.SeqIO.to_dict()</CODE> example in Section&#XA0;<A HREF="#seq:seqio-todict-functionkey">5.4.1.1</A>,
-you&#X2019;ll need to write a tiny function to map from the FASTA identifier
-(as a string) to the key you want:</P><PRE CLASS="verbatim">def get_acc(identifier):
-    """"Given a SeqRecord identifier string, return the accession number as a string.
-
-    e.g. "gi|2765613|emb|Z78488.1|PTZ78488" -> "Z78488.1"
-    """
-    parts = identifier.split("|")
-    assert len(parts) == 5 and parts[0] == "gi" and parts[2] == "emb"
-    return parts[3]
-</PRE><P>Then we can give this function to the <CODE>Bio.SeqIO.index()</CODE>
-function to use in building the dictionary:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.fasta", "fasta", key_function=get_acc)
->>> print(orchid_dict.keys())
-['Z78484.1', 'Z78464.1', 'Z78455.1', 'Z78442.1', 'Z78532.1', 'Z78453.1', ..., 'Z78471.1']
-</PRE><P>Easy when you know how?</P><!--TOC subsubsection Getting the raw data for a record-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.2.2&#XA0;&#XA0;Getting the raw data for a record</H4><!--SEC END --><P>
-<A NAME="sec:seqio-index-getraw"></A></P><P>The dictionary-like object from <CODE>Bio.SeqIO.index()</CODE> gives you each
-entry as a <CODE>SeqRecord</CODE> object. However, it is sometimes useful to
-be able to get the original raw data straight from the file. For this
-use the <CODE>get_raw()</CODE> method which takes a
-single argument (the record identifier) and returns a string (extracted
-from the file without modification).</P><P>A motivating example is extracting a subset of a records from a large
-file where either <CODE>Bio.SeqIO.write()</CODE> does not (yet) support the
-output file format (e.g. the plain text SwissProt file format) or
-where you need to preserve the text exactly (e.g. GenBank or EMBL
-output from Biopython does not yet preserve every last bit of
-annotation).</P><P>Let&#X2019;s suppose you have download the whole of UniProt in the plain
-text SwissPort file format from their FTP site
-(<A HREF="ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz"><TT>ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz</TT></A>)
-and uncompressed it as the file <CODE>uniprot_sprot.dat</CODE>, and you
-want to extract just a few records from it:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss")
->>> handle = open("selected.dat", "w")
->>> for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]:
-...     handle.write(uniprot.get_raw(acc))
->>> handle.close()
-</PRE><P>There is a longer example in Section&#XA0;<A HREF="#sec:SeqIO-sort">18.1.5</A> using the
-<CODE>SeqIO.index()</CODE> function to sort a large sequence file (without
-loading everything into memory at once).</P><!--TOC subsection Sequence files as Dictionaries &#X2013; Database indexed files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc59">5.4.3</A>&#XA0;&#XA0;Sequence files as Dictionaries &#X2013; Database indexed files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-index-db"></A></P><P>Biopython 1.57 introduced an alternative, <CODE>Bio.SeqIO.index_db()</CODE>, which
-can work on even extremely large files since it stores the record information
-as a file on disk (using an SQLite3 database) rather than in memory. Also,
-you can index multiple files together (providing all the record identifiers
-are unique).</P><P>The <CODE>Bio.SeqIO.index()</CODE> function takes three required arguments:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Index filename, we suggest using something ending <TT>.idx</TT>.
-This index file is actually an SQLite3 database.
-</LI><LI CLASS="li-itemize">List of sequence filenames to index (or a single filename)
-</LI><LI CLASS="li-itemize">File format (lower case string as used in the rest of the
-<CODE>SeqIO</CODE> module).
-</LI></UL><P>As an example, consider the GenBank flat file releases from the NCBI FTP site,
-<A HREF="ftp://ftp.ncbi.nih.gov/genbank/"><TT>ftp://ftp.ncbi.nih.gov/genbank/</TT></A>, which are gzip compressed GenBank files.
-As of GenBank release 182, there are 16 files making up the viral sequences,
-<TT>gbvrl1.seq</TT>, &#X2026;, <TT>gbvrl16.seq</TT>, containing in total almost
-one million records. You can index them like this:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> files = ["gbvrl%i.seq" % (i+1) for i in range(16)]
->>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank")
->>> print("%i sequences indexed" % len(gb_vrl))
-958086 sequences indexed
-</PRE><P>That takes about two minutes to run on my machine. If you rerun it then the
-index file (here <TT>gbvrl.idx</TT>) is reloaded in under a second. You can
-use the index as a read only Python dictionary - without having to worry
-about which file the sequence comes from, e.g.</P><PRE CLASS="verbatim">>>> print(gb_vrl["GQ333173.1"].description)
-HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds.
-</PRE><!--TOC subsubsection Getting the raw data for a record-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->5.4.3.1&#XA0;&#XA0;Getting the raw data for a record</H4><!--SEC END --><P>Just as with the <CODE>Bio.SeqIO.index()</CODE> function discussed above in
-Section&#XA0;<A HREF="#sec:seqio-index-getraw">5.4.2.2</A>, the dictionary like object also lets you
-get at the raw text of each record:</P><PRE CLASS="verbatim">>>> print(gb_vrl.get_raw("GQ333173.1"))
-LOCUS       GQ333173                 459 bp    DNA     linear   VRL 21-OCT-2009
-DEFINITION  HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial
-            cds.
-ACCESSION   GQ333173
-...
-//
-</PRE><!--TOC subsection Indexing compressed files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc60">5.4.4</A>&#XA0;&#XA0;Indexing compressed files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-index-bgzf"></A></P><P>Very often when you are indexing a sequence file it can be quite large &#X2013; so
-you may want to compress it on disk. Unfortunately efficient random access
-is difficult with the more common file formats like gzip and bzip2. In this
-setting, BGZF (Blocked GNU Zip Format) can be very helpful. This is a variant
-of gzip (and can be decompressed using standard gzip tools) popularised by
-the BAM file format, <A HREF="http://samtools.sourceforge.net/">samtools</A>, and
-<A HREF="http://samtools.sourceforge.net/tabix.shtml">tabix</A>.</P><P>To create a BGZF compressed file you can use the command line tool <CODE>bgzip</CODE>
-which comes with samtools. In our examples we use a filename extension
-<CODE>*.bgz</CODE>, so they can be distinguished from normal gzipped files (named
-<CODE>*.gz</CODE>). You can also use the <CODE>Bio.bgzf</CODE> module to read and write
-BGZF files from within Python.</P><P>The <CODE>Bio.SeqIO.index()</CODE> and <CODE>Bio.SeqIO.index_db()</CODE> can both be
-used with BGZF compressed files. For example, if you started with an
-uncompressed GenBank file:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.gbk", "genbank")
->>> len(orchid_dict)
-94
->>> orchid_dict.close()
-</PRE><P>You could compress this (while keeping the original file) at the command
-line using the following command &#X2013; but don&#X2019;t worry, the compressed file
-is already included with the other example files:</P><PRE CLASS="verbatim">$ bgzip -c ls_orchid.gbk > ls_orchid.gbk.bgz
-</PRE><P>You can use the compressed file in exactly the same way:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index("ls_orchid.gbk.bgz", "genbank")
->>> len(orchid_dict)
-94
->>> orchid_dict.close()
-</PRE><P>or:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> orchid_dict = SeqIO.index_db("ls_orchid.gbk.bgz.idx", "ls_orchid.gbk.bgz", "genbank")
->>> len(orchid_dict)
-94
->>> orchid_dict.close()
-</PRE><P>The <CODE>SeqIO</CODE> indexing automatically detects the BGZF compression. Note
-that you can&#X2019;t use the same index file for the uncompressed and compressed files.</P><!--TOC subsection Discussion-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc61">5.4.5</A>&#XA0;&#XA0;Discussion</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-indexing-discussion"></A></P><P>So, which of these methods should you use and why? It depends on what you are
-trying to do (and how much data you are dealing with). However, in general
-picking <CODE>Bio.SeqIO.index()</CODE> is a good starting point. If you are dealing
-with millions of records, multiple files, or repeated analyses, then look at
-<CODE>Bio.SeqIO.index_db()</CODE>.</P><P>Reasons to choose <CODE>Bio.SeqIO.to_dict()</CODE> over either
-<CODE>Bio.SeqIO.index()</CODE> or <CODE>Bio.SeqIO.index_db()</CODE> boil down to a need
-for flexibility despite its high memory needs. The advantage of storing the
-<CODE>SeqRecord</CODE> objects in memory is they can be changed, added to, or
-removed at will. In addition to the downside of high memory consumption,
-indexing can also take longer because all the records must be fully parsed.</P><P>Both <CODE>Bio.SeqIO.index()</CODE> and <CODE>Bio.SeqIO.index_db()</CODE> only parse
-records on demand. When indexing, they scan the file once looking for the
-start of each record and do as little work as possible to extract the
-identifier.</P><P>Reasons to choose <CODE>Bio.SeqIO.index()</CODE> over <CODE>Bio.SeqIO.index_db()</CODE>
-include:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Faster to build the index (more noticeable in simple file formats)
-</LI><LI CLASS="li-itemize">Slightly faster access as SeqRecord objects (but the difference is only
-really noticeable for simple to parse file formats).
-</LI><LI CLASS="li-itemize">Can use any immutable Python object as the dictionary keys (e.g. a
-tuple of strings, or a frozen set) not just strings.
-</LI><LI CLASS="li-itemize">Don&#X2019;t need to worry about the index database being out of date if the
-sequence file being indexed has changed.
-</LI></UL><P>Reasons to choose <CODE>Bio.SeqIO.index_db()</CODE> over <CODE>Bio.SeqIO.index()</CODE>
-include:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Not memory limited &#X2013; this is already important with files from second
-generation sequencing where 10s of millions of sequences are common, and
-using <CODE>Bio.SeqIO.index()</CODE> can require more than 4GB of RAM and therefore
-a 64bit version of Python.
-</LI><LI CLASS="li-itemize">Because the index is kept on disk, it can be reused. Although building
-the index database file takes longer, if you have a script which will be
-rerun on the same datafiles in future, this could save time in the long run.
-</LI><LI CLASS="li-itemize">Indexing multiple files together
-</LI><LI CLASS="li-itemize">The <CODE>get_raw()</CODE> method can be much faster, since for most file
-formats the length of each record is stored as well as its offset.
-</LI></UL><!--TOC section Writing Sequence Files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc62">5.5</A>&#XA0;&#XA0;Writing Sequence Files</H2><!--SEC END --><P>We&#X2019;ve talked about using <CODE>Bio.SeqIO.parse()</CODE> for sequence input (reading files), and now we&#X2019;ll look at <CODE>Bio.SeqIO.write()</CODE> which is for sequence output (writing files). This is a function taking three arguments: some <CODE>SeqRecord</CODE> objects, a handle or filename to write to, and a sequence format.</P><P>Here is an example, where [...]
-from Bio.SeqRecord import SeqRecord
-from Bio.Alphabet import generic_protein
-
-rec1 = SeqRecord(Seq("MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD" \
-                    +"GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK" \
-                    +"NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM" \
-                    +"SSAC", generic_protein),
-                 id="gi|14150838|gb|AAK54648.1|AF376133_1",
-                 description="chalcone synthase [Cucumis sativus]")
-
-rec2 = SeqRecord(Seq("YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ" \
-                    +"DMVVVEIPKLGKEAAVKAIKEWGQ", generic_protein),
-                 id="gi|13919613|gb|AAK33142.1|",
-                 description="chalcone synthase [Fragaria vesca subsp. bracteata]")
-
-rec3 = SeqRecord(Seq("MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC" \
-                    +"EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP" \
-                    +"KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN" \
-                    +"NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV" \
-                    +"SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW" \
-                    +"IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT" \
-                    +"TGEGLEWGVLFGFGPGLTVETVVLHSVAT", generic_protein),
-                 id="gi|13925890|gb|AAK49457.1|",
-                 description="chalcone synthase [Nicotiana tabacum]")
-
-my_records = [rec1, rec2, rec3]
-</PRE><P>Now we have a list of <CODE>SeqRecord</CODE> objects, we&#X2019;ll write them to a FASTA format file:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.write(my_records, "my_example.faa", "fasta")
-</PRE><P>And if you open this file in your favourite text editor it should look like this:</P><PRE CLASS="verbatim">>gi|14150838|gb|AAK54648.1|AF376133_1 chalcone synthase [Cucumis sativus]
-MMYQQGCFAGGTVLRLAKDLAENNRGARVLVVCSEITAVTFRGPSETHLDSMVGQALFGD
-GAGAVIVGSDPDLSVERPLYELVWTGATLLPDSEGAIDGHLREVGLTFHLLKDVPGLISK
-NIEKSLKEAFTPLGISDWNSTFWIAHPGGPAILDQVEAKLGLKEEKMRATREVLSEYGNM
-SSAC
->gi|13919613|gb|AAK33142.1| chalcone synthase [Fragaria vesca subsp. bracteata]
-YPDYYFRITNREHKAELKEKFQRMCDKSMIKKRYMYLTEEILKENPSMCEYMAPSLDARQ
-DMVVVEIPKLGKEAAVKAIKEWGQ
->gi|13925890|gb|AAK49457.1| chalcone synthase [Nicotiana tabacum]
-MVTVEEFRRAQCAEGPATVMAIGTATPSNCVDQSTYPDYYFRITNSEHKVELKEKFKRMC
-EKSMIKKRYMHLTEEILKENPNICAYMAPSLDARQDIVVVEVPKLGKEAAQKAIKEWGQP
-KSKITHLVFCTTSGVDMPGCDYQLTKLLGLRPSVKRFMMYQQGCFAGGTVLRMAKDLAEN
-NKGARVLVVCSEITAVTFRGPNDTHLDSLVGQALFGDGAAAVIIGSDPIPEVERPLFELV
-SAAQTLLPDSEGAIDGHLREVGLTFHLLKDVPGLISKNIEKSLVEAFQPLGISDWNSLFW
-IAHPGGPAILDQVELKLGLKQEKLKATRKVLSNYGNMSSACVLFILDEMRKASAKEGLGT
-TGEGLEWGVLFGFGPGLTVETVVLHSVAT
-</PRE><P>Suppose you wanted to know how many records the <CODE>Bio.SeqIO.write()</CODE> function wrote to the handle?
-If your records were in a list you could just use <CODE>len(my_records)</CODE>, however you can&#X2019;t do that when your records come from a generator/iterator. The <CODE>Bio.SeqIO.write()</CODE> function returns the number of <CODE>SeqRecord</CODE> objects written to the file.</P><P><EM>Note</EM> - If you tell the <CODE>Bio.SeqIO.write()</CODE> function to write to a file that already exists, the old file will be overwritten without any warning.</P><!--TOC subsection Round trips-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc63">5.5.1</A>&#XA0;&#XA0;Round trips</H3><!--SEC END --><P>Some people like their parsers to be &#X201C;round-tripable&#X201D;, meaning if you read in
-a file and write it back out again it is unchanged. This requires that the parser
-must extract enough information to reproduce the original file <EM>exactly</EM>.
-<CODE>Bio.SeqIO</CODE> does <EM>not</EM> aim to do this.</P><P>As a trivial example, any line wrapping of the sequence data in FASTA files is
-allowed. An identical <CODE>SeqRecord</CODE> would be given from parsing the following
-two examples which differ only in their line breaks:</P><PRE CLASS="verbatim">>YAL068C-7235.2170 Putative promoter sequence
-TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCACAGTTTTCGTTAAGA
-GAACTTAACATTTTCTTATGACGTAAATGAAGTTTATATATAAATTTCCTTTTTATTGGA
-
->YAL068C-7235.2170 Putative promoter sequence
-TACGAGAATAATTTCTCATCATCCAGCTTTAACACAAAATTCGCA
-CAGTTTTCGTTAAGAGAACTTAACATTTTCTTATGACGTAAATGA
-AGTTTATATATAAATTTCCTTTTTATTGGA
-</PRE><P>To make a round-tripable FASTA parser you would need to keep track of where the
-sequence line breaks occurred, and this extra information is usually pointless.
-Instead Biopython uses a default line wrapping of 60 characters on output.
-The same problem with white space applies in many other file formats too.
-Another issue in some cases is that Biopython does not (yet) preserve every
-last bit of annotation (e.g. GenBank and EMBL).</P><P>Occasionally preserving the original layout (with any quirks it may have) is
-important. See Section&#XA0;<A HREF="#sec:seqio-index-getraw">5.4.2.2</A> about the <CODE>get_raw()</CODE>
-method of the <CODE>Bio.SeqIO.index()</CODE> dictionary-like object for one potential
-solution.</P><!--TOC subsection Converting between sequence file formats-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc64">5.5.2</A>&#XA0;&#XA0;Converting between sequence file formats</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-conversion"></A></P><P>In previous example we used a list of <CODE>SeqRecord</CODE> objects as input to the <CODE>Bio.SeqIO.write()</CODE> function, but it will also accept a <CODE>SeqRecord</CODE> iterator like we get from <CODE>Bio.SeqIO.parse()</CODE> &#X2013; this lets us do file conversion by combining these two functions.</P><P>For this example we&#X2019;ll read in the GenBank format file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk">ls [...]
-records = SeqIO.parse("ls_orchid.gbk", "genbank")
-count = SeqIO.write(records, "my_example.fasta", "fasta")
-print("Converted %i records" % count)
-</PRE><P>Still, that is a little bit complicated. So, because file conversion is such a
-common task, there is a helper function letting you replace that with just:</P><PRE CLASS="verbatim">from Bio import SeqIO
-count = SeqIO.convert("ls_orchid.gbk", "genbank", "my_example.fasta", "fasta")
-print("Converted %i records" % count)
-</PRE><P>The <CODE>Bio.SeqIO.convert()</CODE> function will take handles <EM>or</EM> filenames.
-Watch out though &#X2013; if the output file already exists, it will overwrite it!
-To find out more, see the built in help:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> help(SeqIO.convert)
-...
-</PRE><P>In principle, just by changing the filenames and the format names, this code
-could be used to convert between any file formats available in Biopython.
-However, writing some formats requires information (e.g. quality scores) which
-other files formats don&#X2019;t contain. For example, while you can turn a FASTQ
-file into a FASTA file, you can&#X2019;t do the reverse. See also
-Sections&#XA0;<A HREF="#sec:SeqIO-fastq-conversion">18.1.9</A> and&#XA0;<A HREF="#sec:SeqIO-fasta-qual-conversion">18.1.10</A>
-in the cookbook chapter which looks at inter-converting between different FASTQ formats.</P><P>Finally, as an added incentive for using the <CODE>Bio.SeqIO.convert()</CODE> function
-(on top of the fact your code will be shorter), doing it this way may also be
-faster! The reason for this is the convert function can take advantage of
-several file format specific optimisations and tricks.</P><!--TOC subsection Converting a file of sequences to their reverse complements-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc65">5.5.3</A>&#XA0;&#XA0;Converting a file of sequences to their reverse complements</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-reverse-complement"></A></P><P>Suppose you had a file of nucleotide sequences, and you wanted to turn it into a file containing their reverse complement sequences. This time a little bit of work is required to transform the <CODE>SeqRecord</CODE> objects we get from our input file into something suitable for saving to our output file.</P><P>To start with, we&#X2019;ll use <CODE>Bio.SeqIO.parse()</CODE> to load some nucleotide
-sequences from a file, then print out their reverse complements using
-the <CODE>Seq</CODE> object&#X2019;s built in <CODE>.reverse_complement()</CODE> method (see Section&#XA0;<A HREF="#sec:seq-reverse-complement">3.7</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-...     print(record.id)
-...     print(record.seq.reverse_complement())
-</PRE><P>Now, if we want to save these reverse complements to a file, we&#X2019;ll need to make <CODE>SeqRecord</CODE> objects.
-We can use the <CODE>SeqRecord</CODE> object&#X2019;s built in <CODE>.reverse_complement()</CODE> method (see Section&#XA0;<A HREF="#sec:SeqRecord-reverse-complement">4.8</A>) but we must decide how to name our new records.</P><P>This is an excellent place to demonstrate the power of list comprehensions which make a list in memory:
-</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
->>> len(records)
-94
-</PRE><P>Now list comprehensions have a nice trick up their sleeves, you can add a conditional statement:</P><PRE CLASS="verbatim">>>> records = [rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700]
->>> len(records)
-18
-</PRE><P>That would create an in memory list of reverse complement records where the sequence length was under 700 base pairs. However, we can do exactly the same with a generator expression - but with the advantage that this does not create a list of all the records in memory at once:</P><PRE CLASS="verbatim">>>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-...           for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700)
-</PRE><P>As a complete example:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = (rec.reverse_complement(id="rc_"+rec.id, description = "reverse complement") \
-...            for rec in SeqIO.parse("ls_orchid.fasta", "fasta") if len(rec)<700)
->>> SeqIO.write(records, "rev_comp.fasta", "fasta")
-18
-</PRE><P>There is a related example in Section&#XA0;<A HREF="#sec:SeqIO-translate">18.1.3</A>, translating each
-record in a FASTA file from nucleotides to amino acids.</P><!--TOC subsection Getting your SeqRecord objects as formatted strings-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc66">5.5.4</A>&#XA0;&#XA0;Getting your SeqRecord objects as formatted strings</H3><!--SEC END --><P>
-<A NAME="sec:Bio.SeqIO-and-StringIO"></A>
-Suppose that you don&#X2019;t really want to write your records to a file or handle &#X2013; instead you want a string containing the records in a particular file format. The <CODE>Bio.SeqIO</CODE> interface is based on handles, but Python has a useful built in module which provides a string based handle.</P><P>For an example of how you might use this, let&#X2019;s load in a bunch of <CODE>SeqRecord</CODE> objects from our orchids GenBank file, and create a string containing the records  [...]
-from StringIO import StringIO
-records = SeqIO.parse("ls_orchid.gbk", "genbank")
-out_handle = StringIO()
-SeqIO.write(records, out_handle, "fasta")
-fasta_data = out_handle.getvalue()
-print(fasta_data)
-</PRE><P>This isn&#X2019;t entirely straightforward the first time you see it! On the bright side, for the special case where you would like a string containing a <EM>single</EM> record in a particular file format, use the the <CODE>SeqRecord</CODE> class&#X2019; <CODE>format()</CODE> method (see Section&#XA0;<A HREF="#sec:SeqRecord-format">4.5</A>).</P><P>Note that although we don&#X2019;t encourage it, you <EM>can</EM> use the <CODE>format()</CODE> method to write to a file, for exampl [...]
-</P><PRE CLASS="verbatim">from Bio import SeqIO
-out_handle = open("ls_orchid_long.tab", "w")
-for record in SeqIO.parse("ls_orchid.gbk", "genbank"):
-    if len(record) > 100:
-        out_handle.write(record.format("tab"))
-out_handle.close()
-</PRE><P>While this style of code will work for a simple sequential file format like FASTA or the simple tab separated format used here, it will <EM>not</EM> work for more complex or interlaced file formats. This is why we still recommend using <CODE>Bio.SeqIO.write()</CODE>, as in the following example:
-</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = (rec for rec in SeqIO.parse("ls_orchid.gbk", "genbank") if len(rec) > 100)
-SeqIO.write(records, "ls_orchid.tab", "tab")
-</PRE><P>Making a single call to <CODE>SeqIO.write(...)</CODE> is also much quicker than
-multiple calls to the <CODE>SeqRecord.format(...)</CODE> method.</P><!--TOC chapter Multiple Sequence Alignment objects-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc67">Chapter&#XA0;6</A>&#XA0;&#XA0;Multiple Sequence Alignment objects</H1><!--SEC END --><P>
-<A NAME="chapter:Bio.AlignIO"></A></P><P>This chapter is about Multiple Sequence Alignments, by which we mean a collection of
-multiple sequences which have been aligned together &#X2013; usually with the insertion of gap
-characters, and addition of leading or trailing gaps &#X2013; such that all the sequence
-strings are the same length. Such an alignment can be regarded as a matrix of letters,
-where each row is held as a <CODE>SeqRecord</CODE> object internally.</P><P>We will introduce the <CODE>MultipleSeqAlignment</CODE> object which holds this kind of data,
-and the <CODE>Bio.AlignIO</CODE> module for reading and writing them as various file formats
-(following the design of the <CODE>Bio.SeqIO</CODE> module from the previous chapter).
-Note that both <CODE>Bio.SeqIO</CODE> and <CODE>Bio.AlignIO</CODE> can read and write sequence
-alignment files. The appropriate choice will depend largely on what you want to do
-with the data.</P><P>The final part of this chapter is about our command line wrappers for common multiple
-sequence alignment tools like ClustalW and MUSCLE.</P><!--TOC section Parsing or Reading Sequence Alignments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc68">6.1</A>&#XA0;&#XA0;Parsing or Reading Sequence Alignments</H2><!--SEC END --><P>We have two functions for reading in sequence alignments, <CODE>Bio.AlignIO.read()</CODE> and <CODE>Bio.AlignIO.parse()</CODE> which following the convention introduced in <CODE>Bio.SeqIO</CODE> are for files containing one or multiple alignments respectively.</P><P>Using <CODE>Bio.AlignIO.parse()</CODE> will return an <I>iterator</I> which gives <CODE>Mu [...]
-The first argument is a <I>handle</I> to read the data from, typically an open file (see Section&#XA0;<A HREF="#sec:appendix-handles">22.1</A>), or a filename.
-</LI><LI CLASS="li-enumerate">The second argument is a lower case string specifying the alignment format. As in <CODE>Bio.SeqIO</CODE> we don&#X2019;t try and guess the file format for you! See <A HREF="http://biopython.org/wiki/AlignIO"><TT>http://biopython.org/wiki/AlignIO</TT></A> for a full listing of supported formats.
-</LI></OL><P>There is also an optional <CODE>seq_count</CODE> argument which is discussed in Section&#XA0;<A HREF="#sec:AlignIO-count-argument">6.1.3</A> below for dealing with ambiguous file formats which may contain more than one alignment.</P><P>A further optional <CODE>alphabet</CODE> argument allowing you to specify the expected alphabet. This can be useful as many alignment file formats do not explicitly label the sequences as RNA, DNA or protein &#X2013; which means <CODE>Bio.Alig [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc69">6.1.1</A>&#XA0;&#XA0;Single Alignments</H3><!--SEC END --><P>
-As an example, consider the following annotation rich protein alignment in the PFAM or Stockholm file format:</P><PRE CLASS="verbatim"># STOCKHOLM 1.0
-#=GS COATB_BPIKE/30-81  AC P03620.1
-#=GS COATB_BPIKE/30-81  DR PDB; 1ifl ; 1-52;
-#=GS Q9T0Q8_BPIKE/1-52  AC Q9T0Q8.1
-#=GS COATB_BPI22/32-83  AC P15416.1
-#=GS COATB_BPM13/24-72  AC P69541.1
-#=GS COATB_BPM13/24-72  DR PDB; 2cpb ; 1-49;
-#=GS COATB_BPM13/24-72  DR PDB; 2cps ; 1-49;
-#=GS COATB_BPZJ2/1-49   AC P03618.1
-#=GS Q9T0Q9_BPFD/1-49   AC Q9T0Q9.1
-#=GS Q9T0Q9_BPFD/1-49   DR PDB; 1nh4 A; 1-49;
-#=GS COATB_BPIF1/22-73  AC P03619.2
-#=GS COATB_BPIF1/22-73  DR PDB; 1ifk ; 1-50;
-COATB_BPIKE/30-81             AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
-#=GR COATB_BPIKE/30-81  SS    -HHHHHHHHHHHHHH--HHHHHHHH--HHHHHHHHHHHHHHHHHHHHH----
-Q9T0Q8_BPIKE/1-52             AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
-COATB_BPI22/32-83             DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
-COATB_BPM13/24-72             AEGDDP...AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
-#=GR COATB_BPM13/24-72  SS    ---S-T...CHCHHHHCCCCTCCCTTCHHHHHHHHHHHHHHHHHHHHCTT--
-COATB_BPZJ2/1-49              AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
-Q9T0Q9_BPFD/1-49              AEGDDP...AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
-#=GR Q9T0Q9_BPFD/1-49   SS    ------...-HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH--
-COATB_BPIF1/22-73             FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
-#=GR COATB_BPIF1/22-73  SS    XX-HHHH--HHHHHH--HHHHHHH--HHHHHHHHHHHHHHHHHHHHHHH---
-#=GC SS_cons                  XHHHHHHHHHHHHHHHCHHHHHHHHCHHHHHHHHHHHHHHHHHHHHHHHC--
-#=GC seq_cons                 AEssss...AptAhDSLpspAT-hIu.sWshVsslVsAsluIKLFKKFsSKA
-//
-</PRE><P>This is the seed alignment for the Phage_Coat_Gp8 (PF05371) PFAM entry, downloaded from a now out of date release of PFAM from <A HREF="http://pfam.sanger.ac.uk/"><TT>http://pfam.sanger.ac.uk/</TT></A>. We can load this file as follows (assuming it has been saved to disk as &#X201C;PF05371_seed.sth&#X201D; in the current working directory):</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-</PRE><P>This code will print out a summary of the alignment:</P><PRE CLASS="verbatim">>>> print(alignment)
-SingleLetterAlphabet() alignment with 7 rows and 52 columns
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
-</PRE><P>You&#X2019;ll notice in the above output the sequences have been truncated. We could instead write our own code to format this as we please by iterating over the rows as <CODE>SeqRecord</CODE> objects:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
->>> print("Alignment length %i" % alignment.get_alignment_length())
-Alignment length 52
->>> for record in alignment:
-...     print("%s - %s" % (record.seq, record.id))
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73
-</PRE><P>You could also use the alignment object&#X2019;s <CODE>format</CODE> method to show it in a particular file format &#X2013; see Section&#XA0;<A HREF="#sec:alignment-format-method">6.2.2</A> for details.</P><P>Did you notice in the raw file above that several of the sequences include database cross-references to the PDB and the associated known secondary structure? Try this:</P><PRE CLASS="verbatim">>>> for record in alignment:
-...     if record.dbxrefs:
-...         print("%s %s" % (record.id, record.dbxrefs))
-COATB_BPIKE/30-81 ['PDB; 1ifl ; 1-52;']
-COATB_BPM13/24-72 ['PDB; 2cpb ; 1-49;', 'PDB; 2cps ; 1-49;']
-Q9T0Q9_BPFD/1-49 ['PDB; 1nh4 A; 1-49;']
-COATB_BPIF1/22-73 ['PDB; 1ifk ; 1-50;']
-</PRE><P>To have a look at all the sequence annotation, try this:</P><PRE CLASS="verbatim">>>> for record in alignment:
-...     print(record)
-</PRE><P>Sanger provide a nice web interface at <A HREF="http://pfam.sanger.ac.uk/family?acc=PF05371"><TT>http://pfam.sanger.ac.uk/family?acc=PF05371</TT></A> which will actually let you download this alignment in several other formats. This is what the file looks like in the FASTA file format:</P><PRE CLASS="verbatim">>COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA
->Q9T0Q8_BPIKE/1-52
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA
->COATB_BPI22/32-83
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA
->COATB_BPM13/24-72
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
->COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA
->Q9T0Q9_BPFD/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA
->COATB_BPIF1/22-73
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA
-</PRE><P>Note the website should have an option about showing gaps as periods (dots) or dashes, we&#X2019;ve shown dashes above. Assuming you download and save this as file &#X201C;PF05371_seed.faa&#X201D; then you can load it with almost exactly the same code:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.faa", "fasta")
-print(alignment)
-</PRE><P>All that has changed in this code is the filename and the format string. You&#X2019;ll get the same output as before, the sequences and record identifiers are the same.
-However, as you should expect, if you check each <CODE>SeqRecord</CODE> there is no annotation nor database cross-references because these are not included in the FASTA file format.</P><P>Note that rather than using the Sanger website, you could have used <CODE>Bio.AlignIO</CODE> to convert the original Stockholm format file into a FASTA file yourself (see below).</P><P>With any supported file format, you can load an alignment in exactly the same way just by changing the format string. F [...]
->>> help(AlignIO)
-...
-</PRE><!--TOC subsection Multiple Alignments-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc70">6.1.2</A>&#XA0;&#XA0;Multiple Alignments</H3><!--SEC END --><P>The previous section focused on reading files containing a single alignment. In general however, files can contain more than one alignment, and to read these files we must use the <CODE>Bio.AlignIO.parse()</CODE> function.</P><P>Suppose you have a small alignment in PHYLIP format:</P><PRE CLASS="verbatim">    5    6
-Alpha     AACAAC
-Beta      AACCCC
-Gamma     ACCAAC
-Delta     CCACCA
-Epsilon   CCAAAC
-</PRE><P>If you wanted to bootstrap a phylogenetic tree using the PHYLIP tools, one of the steps would be to create a set of many resampled alignments using the tool <CODE>bootseq</CODE>. This would give output something like this, which has been abbreviated for conciseness:</P><PRE CLASS="verbatim">    5     6
-Alpha     AAACCA
-Beta      AAACCC
-Gamma     ACCCCA
-Delta     CCCAAC
-Epsilon   CCCAAA
-    5     6
-Alpha     AAACAA
-Beta      AAACCC
-Gamma     ACCCAA
-Delta     CCCACC
-Epsilon   CCCAAA
-    5     6
-Alpha     AAAAAC
-Beta      AAACCC
-Gamma     AACAAC
-Delta     CCCCCA
-Epsilon   CCCAAC
-...
-    5     6
-Alpha     AAAACC
-Beta      ACCCCC
-Gamma     AAAACC
-Delta     CCCCAA
-Epsilon   CAAACC
-</PRE><P>If you wanted to read this in using <CODE>Bio.AlignIO</CODE> you could use:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignments = AlignIO.parse("resampled.phy", "phylip")
-for alignment in alignments:
-    print(alignment)
-    print("")
-</PRE><P>This would give the following output, again abbreviated for display:</P><PRE CLASS="verbatim">SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAACCA Alpha
-AAACCC Beta
-ACCCCA Gamma
-CCCAAC Delta
-CCCAAA Epsilon
-
-SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAACAA Alpha
-AAACCC Beta
-ACCCAA Gamma
-CCCACC Delta
-CCCAAA Epsilon
-
-SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAAAAC Alpha
-AAACCC Beta
-AACAAC Gamma
-CCCCCA Delta
-CCCAAC Epsilon
-
-...
-
-SingleLetterAlphabet() alignment with 5 rows and 6 columns
-AAAACC Alpha
-ACCCCC Beta
-AAAACC Gamma
-CCCCAA Delta
-CAAACC Epsilon
-</PRE><P>As with the function <CODE>Bio.SeqIO.parse()</CODE>, using <CODE>Bio.AlignIO.parse()</CODE> returns an iterator.
-If you want to keep all the alignments in memory at once, which will allow you to access them in any order, then turn the iterator into a list:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignments = list(AlignIO.parse("resampled.phy", "phylip"))
-last_align = alignments[-1]
-first_align = alignments[0]
-</PRE><!--TOC subsection Ambiguous Alignments-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc71">6.1.3</A>&#XA0;&#XA0;Ambiguous Alignments</H3><!--SEC END --><P>
-<A NAME="sec:AlignIO-count-argument"></A>
-Many alignment file formats can explicitly store more than one alignment, and the division between each alignment is clear. However, when a general sequence file format has been used there is no such block structure. The most common such situation is when alignments have been saved in the FASTA file format. For example consider the following:</P><PRE CLASS="verbatim">>Alpha
-ACTACGACTAGCTCAG--G
->Beta
-ACTACCGCTAGCTCAGAAG
->Gamma
-ACTACGGCTAGCACAGAAG
->Alpha
-ACTACGACTAGCTCAGG--
->Beta
-ACTACCGCTAGCTCAGAAG
->Gamma
-ACTACGGCTAGCACAGAAG
-</PRE><P>This could be a single alignment containing six sequences (with repeated identifiers). Or, judging from the identifiers, this is probably two different alignments each with three sequences, which happen to all have the same length.</P><P>What about this next example?</P><PRE CLASS="verbatim">>Alpha
-ACTACGACTAGCTCAG--G
->Beta
-ACTACCGCTAGCTCAGAAG
->Alpha
-ACTACGACTAGCTCAGG--
->Gamma
-ACTACGGCTAGCACAGAAG
->Alpha
-ACTACGACTAGCTCAGG--
->Delta
-ACTACGGCTAGCACAGAAG
-</PRE><P>Again, this could be a single alignment with six sequences. However this time based on the identifiers we might guess this is three pairwise alignments which by chance have all got the same lengths.</P><P>This final example is similar:</P><PRE CLASS="verbatim">>Alpha
-ACTACGACTAGCTCAG--G
->XXX
-ACTACCGCTAGCTCAGAAG
->Alpha
-ACTACGACTAGCTCAGG
->YYY
-ACTACGGCAAGCACAGG
->Alpha
---ACTACGAC--TAGCTCAGG
->ZZZ
-GGACTACGACAATAGCTCAGG
-</PRE><P>In this third example, because of the differing lengths, this cannot be treated as a single alignment containing all six records. However, it could be three pairwise alignments.</P><P>Clearly trying to store more than one alignment in a FASTA file is not ideal. However, if you are forced to deal with these as input files <CODE>Bio.AlignIO</CODE> can cope with the most common situation where all the alignments have the same number of records.
-One example of this is a collection of pairwise alignments, which can be produced by the EMBOSS tools <CODE>needle</CODE> and <CODE>water</CODE> &#X2013; although in this situation, <CODE>Bio.AlignIO</CODE> should be able to understand their native output using &#X201C;emboss&#X201D; as the format string.</P><P>To interpret these FASTA examples as several separate alignments, we can use <CODE>Bio.AlignIO.parse()</CODE> with the optional <CODE>seq_count</CODE> argument which specifies how [...]
-For example, using the third example as the input data:</P><PRE CLASS="verbatim">for alignment in AlignIO.parse(handle, "fasta", seq_count=2):
-    print("Alignment length %i" % alignment.get_alignment_length())
-    for record in alignment:
-        print("%s - %s" % (record.seq, record.id))
-    print("")
-</PRE><P>giving:</P><PRE CLASS="verbatim">Alignment length 19
-ACTACGACTAGCTCAG--G - Alpha
-ACTACCGCTAGCTCAGAAG - XXX
-
-Alignment length 17
-ACTACGACTAGCTCAGG - Alpha
-ACTACGGCAAGCACAGG - YYY
-
-Alignment length 21
---ACTACGAC--TAGCTCAGG - Alpha
-GGACTACGACAATAGCTCAGG - ZZZ
-</PRE><P>Using <CODE>Bio.AlignIO.read()</CODE> or <CODE>Bio.AlignIO.parse()</CODE> without the <CODE>seq_count</CODE> argument would give a single alignment containing all six records for the first two examples. For the third example, an exception would be raised because the lengths differ preventing them being turned into a single alignment.</P><P>If the file format itself has a block structure allowing <CODE>Bio.AlignIO</CODE> to determine the number of sequences in each alignment dire [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc72">6.2</A>&#XA0;&#XA0;Writing Alignments</H2><!--SEC END --><P>We&#X2019;ve talked about using <CODE>Bio.AlignIO.read()</CODE> and <CODE>Bio.AlignIO.parse()</CODE> for alignment input (reading files), and now we&#X2019;ll look at <CODE>Bio.AlignIO.write()</CODE> which is for alignment output (writing files). This is a function taking three arguments: some <CODE>MultipleSeqAlignment</CODE> objects (or for backwards compatibility the obso [...]
-Note we create some <CODE>SeqRecord</CODE> objects to construct the alignment from.</P><PRE CLASS="verbatim">from Bio.Alphabet import generic_dna
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio.Align import MultipleSeqAlignment
-
-align1 = MultipleSeqAlignment([
-             SeqRecord(Seq("ACTGCTAGCTAG", generic_dna), id="Alpha"),
-             SeqRecord(Seq("ACT-CTAGCTAG", generic_dna), id="Beta"),
-             SeqRecord(Seq("ACTGCTAGDTAG", generic_dna), id="Gamma"),
-         ])
-
-align2 = MultipleSeqAlignment([
-             SeqRecord(Seq("GTCAGC-AG", generic_dna), id="Delta"),
-             SeqRecord(Seq("GACAGCTAG", generic_dna), id="Epsilon"),
-             SeqRecord(Seq("GTCAGCTAG", generic_dna), id="Zeta"),
-         ])
-
-align3 = MultipleSeqAlignment([
-             SeqRecord(Seq("ACTAGTACAGCTG", generic_dna), id="Eta"),
-             SeqRecord(Seq("ACTAGTACAGCT-", generic_dna), id="Theta"),
-             SeqRecord(Seq("-CTACTACAGGTG", generic_dna), id="Iota"),
-         ])
-
-my_alignments = [align1, align2, align3]
-</PRE><P>Now we have a list of <CODE>Alignment</CODE> objects, we&#X2019;ll write them to a PHYLIP format file:</P><PRE CLASS="verbatim">from Bio import AlignIO
-AlignIO.write(my_alignments, "my_example.phy", "phylip")
-</PRE><P>And if you open this file in your favourite text editor it should look like this:</P><PRE CLASS="verbatim"> 3 12
-Alpha      ACTGCTAGCT AG
-Beta       ACT-CTAGCT AG
-Gamma      ACTGCTAGDT AG
- 3 9
-Delta      GTCAGC-AG
-Epislon    GACAGCTAG
-Zeta       GTCAGCTAG
- 3 13
-Eta        ACTAGTACAG CTG
-Theta      ACTAGTACAG CT-
-Iota       -CTACTACAG GTG
-</PRE><P>Its more common to want to load an existing alignment, and save that, perhaps after some simple manipulation like removing certain rows or columns.</P><P>Suppose you wanted to know how many alignments the <CODE>Bio.AlignIO.write()</CODE> function wrote to the handle? If your alignments were in a list like the example above, you could just use <CODE>len(my_alignments)</CODE>, however you can&#X2019;t do that when your records come from a generator/iterator. Therefore the <CODE>Bi [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc73">6.2.1</A>&#XA0;&#XA0;Converting between sequence alignment file formats</H3><!--SEC END --><P>
-<A NAME="sec:converting-alignments"></A></P><P>Converting between sequence alignment file formats with <CODE>Bio.AlignIO</CODE> works
-in the same way as converting between sequence file formats with <CODE>Bio.SeqIO</CODE>
-(Section&#XA0;<A HREF="#sec:SeqIO-conversion">5.5.2</A>). We load generally the alignment(s) using
-<CODE>Bio.AlignIO.parse()</CODE> and then save them using the <CODE>Bio.AlignIO.write()</CODE>
-&#X2013; or just use the <CODE>Bio.AlignIO.convert()</CODE> helper function.</P><P>For this example, we&#X2019;ll load the PFAM/Stockholm format file used earlier and save it as a Clustal W format file:</P><PRE CLASS="verbatim">from Bio import AlignIO
-count = AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.aln", "clustal")
-print("Converted %i alignments" % count)
-</PRE><P>Or, using <CODE>Bio.AlignIO.parse()</CODE> and <CODE>Bio.AlignIO.write()</CODE>:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
-count = AlignIO.write(alignments, "PF05371_seed.aln", "clustal")
-print("Converted %i alignments" % count)
-</PRE><P>The <CODE>Bio.AlignIO.write()</CODE> function expects to be given multiple alignment objects. In the example above we gave it the alignment iterator returned by <CODE>Bio.AlignIO.parse()</CODE>.</P><P>In this case, we know there is only one alignment in the file so we could have used <CODE>Bio.AlignIO.read()</CODE> instead, but notice we have to pass this alignment to <CODE>Bio.AlignIO.write()</CODE> as a single element list:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-AlignIO.write([alignment], "PF05371_seed.aln", "clustal")
-</PRE><P>Either way, you should end up with the same new Clustal W format file &#X201C;PF05371_seed.aln&#X201D; with the following content:</P><PRE CLASS="verbatim">CLUSTAL X (1.81) multiple sequence alignment
-
-
-COATB_BPIKE/30-81                   AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSS
-Q9T0Q8_BPIKE/1-52                   AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVS
-COATB_BPI22/32-83                   DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSS
-COATB_BPM13/24-72                   AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
-COATB_BPZJ2/1-49                    AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFAS
-Q9T0Q9_BPFD/1-49                    AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTS
-COATB_BPIF1/22-73                   FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVS
-
-COATB_BPIKE/30-81                   KA
-Q9T0Q8_BPIKE/1-52                   RA
-COATB_BPI22/32-83                   KA
-COATB_BPM13/24-72                   KA
-COATB_BPZJ2/1-49                    KA
-Q9T0Q9_BPFD/1-49                    KA
-COATB_BPIF1/22-73                   RA
-</PRE><P>Alternatively, you could make a PHYLIP format file which we&#X2019;ll name &#X201C;PF05371_seed.phy&#X201D;:</P><PRE CLASS="verbatim">from Bio import AlignIO
-AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip")
-</PRE><P>This time the output looks like this:</P><PRE CLASS="verbatim"> 7 52
-COATB_BPIK AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
-Q9T0Q8_BPI AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
-COATB_BPI2 DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
-COATB_BPM1 AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPZJ AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
-Q9T0Q9_BPF AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPIF FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
-
-           KA
-           RA
-           KA
-           KA
-           KA
-           KA
-           RA
-</PRE><P>One of the big handicaps of the original PHYLIP alignment file format is
-that the sequence identifiers are strictly truncated at ten characters.
-In this example, as you can see the resulting names are still unique -
-but they are not very readable. As a result, a more relaxed variant of
-the original PHYLIP format is now quite widely used:</P><PRE CLASS="verbatim">from Bio import AlignIO
-AlignIO.convert("PF05371_seed.sth", "stockholm", "PF05371_seed.phy", "phylip-relaxed")
-</PRE><P>This time the output looks like this, using a longer indentation to
-allow all the identifers to be given in full::</P><PRE CLASS="verbatim"> 7 52
-COATB_BPIKE/30-81  AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
-Q9T0Q8_BPIKE/1-52  AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
-COATB_BPI22/32-83  DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
-COATB_BPM13/24-72  AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPZJ2/1-49   AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
-Q9T0Q9_BPFD/1-49   AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-COATB_BPIF1/22-73  FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
-
-                   KA
-                   RA
-                   KA
-                   KA
-                   KA
-                   KA
-                   RA
-</PRE><P>If you have to work with the original strict PHYLIP format, then you may need to
-compress the identifers somehow &#X2013; or assign your own names or numbering system.
-This following bit of code manipulates the record identifiers before saving the output:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-name_mapping = {}
-for i, record in enumerate(alignment):
-    name_mapping[i] = record.id
-    record.id = "seq%i" % i
-print(name_mapping)
-
-AlignIO.write([alignment], "PF05371_seed.phy", "phylip")
-</PRE><P>This code used a Python dictionary to record a simple mapping from the new sequence system to the original identifier:
-</P><PRE CLASS="verbatim">{0: 'COATB_BPIKE/30-81', 1: 'Q9T0Q8_BPIKE/1-52', 2: 'COATB_BPI22/32-83', ...}
-</PRE><P>Here is the new (strict) PHYLIP format output:
-</P><PRE CLASS="verbatim"> 7 52
-seq0       AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIRLFKKFSS
-seq1       AEPNAATNYA TEAMDSLKTQ AIDLISQTWP VVTTVVVAGL VIKLFKKFVS
-seq2       DGTSTATSYA TEAMNSLKTQ ATDLIDQTWP VVTSVAVAGL AIRLFKKFSS
-seq3       AEGDDP---A KAAFNSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-seq4       AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFAS
-seq5       AEGDDP---A KAAFDSLQAS ATEYIGYAWA MVVVIVGATI GIKLFKKFTS
-seq6       FAADDATSQA KAAFDSLTAQ ATEMSGYAWA LVVLVVGATV GIKLFKKFVS
-
-           KA
-           RA
-           KA
-           KA
-           KA
-           KA
-           RA
-</PRE><P>In general, because of the identifier limitation, working with
-<I>strict</I> PHYLIP file formats shouldn&#X2019;t be your first choice.
-Using the PFAM/Stockholm format on the other hand allows you to record a lot of additional annotation too.</P><!--TOC subsection Getting your alignment objects as formatted strings-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc74">6.2.2</A>&#XA0;&#XA0;Getting your alignment objects as formatted strings</H3><!--SEC END --><P>
-<A NAME="sec:alignment-format-method"></A>
-The <CODE>Bio.AlignIO</CODE> interface is based on handles, which means if you want to get your alignment(s) into a string in a particular file format you need to do a little bit more work (see below).
-However, you will probably prefer to take advantage of the alignment object&#X2019;s <CODE>format()</CODE> method.
-This takes a single mandatory argument, a lower case string which is supported by <CODE>Bio.AlignIO</CODE> as an output format. For example:</P><PRE CLASS="verbatim">from Bio import AlignIO
-alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
-print(alignment.format("clustal"))
-</PRE><P>As described in Section&#XA0;<A HREF="#sec:SeqRecord-format">4.5</A>, the <CODE>SeqRecord</CODE> object has a similar method using output formats supported by <CODE>Bio.SeqIO</CODE>.</P><P>Internally the <CODE>format()</CODE> method is using the <CODE>StringIO</CODE> string based handle and calling
-<CODE>Bio.AlignIO.write()</CODE>. You can do this in your own code if for example you are using an
-older version of Biopython:</P><PRE CLASS="verbatim">from Bio import AlignIO
-from StringIO import StringIO
-
-alignments = AlignIO.parse("PF05371_seed.sth", "stockholm")
-
-out_handle = StringIO()
-AlignIO.write(alignments, out_handle, "clustal")
-clustal_data = out_handle.getvalue()
-
-print(clustal_data)
-</PRE><!--TOC section Manipulating Alignments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc75">6.3</A>&#XA0;&#XA0;Manipulating Alignments</H2><!--SEC END --><P>
-<A NAME="sec:manipulating-alignments"></A></P><P>Now that we&#X2019;ve covered loading and saving alignments, we&#X2019;ll look at what else you can do
-with them.</P><!--TOC subsection Slicing alignments-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc76">6.3.1</A>&#XA0;&#XA0;Slicing alignments</H3><!--SEC END --><P>
-First of all, in some senses the alignment objects act like a Python <CODE>list</CODE> of
-<CODE>SeqRecord</CODE> objects (the rows). With this model in mind hopefully the actions
-of <CODE>len()</CODE> (the number of rows) and iteration (each row as a <CODE>SeqRecord</CODE>)
-make sense:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
->>> print("Number of rows: %i" % len(alignment))
-Number of rows: 7
->>> for record in alignment:
-...     print("%s - %s" % (record.seq, record.id))
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA - COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA - Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA - COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA - COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA - Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA - COATB_BPIF1/22-73
-</PRE><P>You can also use the list-like <CODE>append</CODE> and <CODE>extend</CODE> methods to add
-more rows to the alignment (as <CODE>SeqRecord</CODE> objects). Keeping the list
-metaphor in mind, simple slicing of the alignment should also make sense -
-it selects some of the rows giving back another alignment object:</P><PRE CLASS="verbatim">>>> print(alignment)
-SingleLetterAlphabet() alignment with 7 rows and 52 columns
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRL...SKA COATB_BPIKE/30-81
-AEPNAATNYATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKL...SRA Q9T0Q8_BPIKE/1-52
-DGTSTATSYATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRL...SKA COATB_BPI22/32-83
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
->>> print(alignment[3:7])
-SingleLetterAlphabet() alignment with 4 rows and 52 columns
-AEGDDP---AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPM13/24-72
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA COATB_BPZJ2/1-49
-AEGDDP---AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKL...SKA Q9T0Q9_BPFD/1-49
-FAADDATSQAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKL...SRA COATB_BPIF1/22-73
-</PRE><P>What if you wanted to select by column? Those of you who have used the NumPy
-matrix or array objects won&#X2019;t be surprised at this - you use a double index.</P><PRE CLASS="verbatim">>>> print(alignment[2, 6])
-T
-</PRE><P>Using two integer indices pulls out a single letter, short hand for this:</P><PRE CLASS="verbatim">>>> print(alignment[2].seq[6])
-T
-</PRE><P>You can pull out a single column as a string like this:</P><PRE CLASS="verbatim">>>> print(alignment[:, 6])
-TTT---T
-</PRE><P>You can also select a range of columns. For example, to pick out those same
-three rows we extracted earlier, but take just their first six columns:</P><PRE CLASS="verbatim">>>> print(alignment[3:6, :6])
-SingleLetterAlphabet() alignment with 3 rows and 6 columns
-AEGDDP COATB_BPM13/24-72
-AEGDDP COATB_BPZJ2/1-49
-AEGDDP Q9T0Q9_BPFD/1-49
-</PRE><P>Leaving the first index as <CODE>:</CODE> means take all the rows:</P><PRE CLASS="verbatim">>>> print(alignment[:, :6])
-SingleLetterAlphabet() alignment with 7 rows and 6 columns
-AEPNAA COATB_BPIKE/30-81
-AEPNAA Q9T0Q8_BPIKE/1-52
-DGTSTA COATB_BPI22/32-83
-AEGDDP COATB_BPM13/24-72
-AEGDDP COATB_BPZJ2/1-49
-AEGDDP Q9T0Q9_BPFD/1-49
-FAADDA COATB_BPIF1/22-73
-</PRE><P>This brings us to a neat way to remove a section. Notice columns
-7, 8 and 9 which are gaps in three of the seven sequences:</P><PRE CLASS="verbatim">>>> print(alignment[:, 6:9])
-SingleLetterAlphabet() alignment with 7 rows and 3 columns
-TNY COATB_BPIKE/30-81
-TNY Q9T0Q8_BPIKE/1-52
-TSY COATB_BPI22/32-83
---- COATB_BPM13/24-72
---- COATB_BPZJ2/1-49
---- Q9T0Q9_BPFD/1-49
-TSQ COATB_BPIF1/22-73
-</PRE><P>Again, you can slice to get everything after the ninth column:</P><PRE CLASS="verbatim">>>> print(alignment[:, 9:])
-SingleLetterAlphabet() alignment with 7 rows and 43 columns
-ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
-ATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
-ATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
-AKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
-AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
-AKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
-AKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
-</PRE><P>Now, the interesting thing is that addition of alignment objects works
-by column. This lets you do this as a way to remove a block of columns:</P><PRE CLASS="verbatim">>>> edited = alignment[:, :6] + alignment[:, 9:]
->>> print(edited)
-SingleLetterAlphabet() alignment with 7 rows and 49 columns
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
-DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
-AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
-FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
-</PRE><P>Another common use of alignment addition would be to combine alignments for
-several different genes into a meta-alignment. Watch out though - the identifiers
-need to match up (see Section&#XA0;<A HREF="#sec:SeqRecord-addition">4.7</A> for how adding
-<CODE>SeqRecord</CODE> objects works). You may find it helpful to first sort the
-alignment rows alphabetically by id:</P><PRE CLASS="verbatim">>>> edited.sort()
->>> print(edited)
-SingleLetterAlphabet() alignment with 7 rows and 49 columns
-DGTSTAATEAMNSLKTQATDLIDQTWPVVTSVAVAGLAIRLFKKFSSKA COATB_BPI22/32-83
-FAADDAAKAAFDSLTAQATEMSGYAWALVVLVVGATVGIKLFKKFVSRA COATB_BPIF1/22-73
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIRLFKKFSSKA COATB_BPIKE/30-81
-AEGDDPAKAAFNSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA COATB_BPM13/24-72
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFASKA COATB_BPZJ2/1-49
-AEPNAAATEAMDSLKTQAIDLISQTWPVVTTVVVAGLVIKLFKKFVSRA Q9T0Q8_BPIKE/1-52
-AEGDDPAKAAFDSLQASATEYIGYAWAMVVVIVGATIGIKLFKKFTSKA Q9T0Q9_BPFD/1-49
-</PRE><P>Note that you can only add two alignments together if they
-have the same number of rows.</P><!--TOC subsection Alignments as arrays-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc77">6.3.2</A>&#XA0;&#XA0;Alignments as arrays</H3><!--SEC END --><P>
-Depending on what you are doing, it can be more useful to turn the alignment
-object into an array of letters &#X2013; and you can do this with NumPy:</P><PRE CLASS="verbatim">>>> import numpy as np
->>> from Bio import AlignIO
->>> alignment = AlignIO.read("PF05371_seed.sth", "stockholm")
->>> align_array = np.array([list(rec) for rec in alignment], np.character)
->>> print("Array shape %i by %i" % align_array.shape)
-Array shape 7 by 52
-</PRE><P>If you will be working heavily with the columns, you can tell NumPy to store
-the array by column (as in Fortran) rather then its default of by row (as in C):</P><PRE CLASS="verbatim">>>> align_array = np.array([list(rec) for rec in alignment], np.character, order="F")
-</PRE><P>Note that this leaves the original Biopython alignment object and the NumPy array
-in memory as separate objects - editing one will not update the other!</P><!--TOC section Alignment Tools-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc78">6.4</A>&#XA0;&#XA0;Alignment Tools</H2><!--SEC END --><P>
-<A NAME="sec:alignment-tools"></A></P><P>There are <EM>lots</EM> of algorithms out there for aligning sequences, both pairwise alignments
-and multiple sequence alignments. These calculations are relatively slow, and you generally
-wouldn&#X2019;t want to write such an algorithm in Python. Instead, you can use Biopython to invoke
-a command line tool on your behalf. Normally you would:
-</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Prepare an input file of your unaligned sequences, typically this will be a FASTA file
-which you might create using <CODE>Bio.SeqIO</CODE> (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).
-</LI><LI CLASS="li-enumerate">Call the command line tool to process this input file, typically via one of Biopython&#X2019;s
-command line wrappers (which we&#X2019;ll discuss here).
-</LI><LI CLASS="li-enumerate">Read the output from the tool, i.e. your aligned sequences, typically using
-<CODE>Bio.AlignIO</CODE> (see earlier in this chapter).
-</LI></OL><P>All the command line wrappers we&#X2019;re going to talk about in this chapter follow the same style.
-You create a command line object specifying the options (e.g. the input filename and the
-output filename), then invoke this command line via a Python operating system call (e.g.
-using the <TT>subprocess</TT> module).</P><P>Most of these wrappers are defined in the <CODE>Bio.Align.Applications</CODE> module:</P><PRE CLASS="verbatim">>>> import Bio.Align.Applications
->>> dir(Bio.Align.Applications)
-...
-['ClustalwCommandline', 'DialignCommandline', 'MafftCommandline', 'MuscleCommandline',
-'PrankCommandline', 'ProbconsCommandline', 'TCoffeeCommandline' ...]
-</PRE><P>(Ignore the entries starting with an underscore &#X2013; these have
-special meaning in Python.)
-The module <CODE>Bio.Emboss.Applications</CODE> has wrappers for some of the
-<A HREF="http://emboss.sourceforge.net/">EMBOSS suite</A>, including
-<TT>needle</TT> and <TT>water</TT>, which are described below in
-Section&#XA0;<A HREF="#seq:emboss-needle-water">6.4.5</A>, and wrappers for the EMBOSS
-packaged versions of the PHYLIP tools (which EMBOSS refer to as one
-of their EMBASSY packages - third party tools with an EMBOSS style
-interface).
-We won&#X2019;t explore all these alignment tools here in the section, just a
-sample, but the same principles apply.</P><!--TOC subsection ClustalW-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc79">6.4.1</A>&#XA0;&#XA0;ClustalW</H3><!--SEC END --><P>
-<A NAME="sec:align_clustal"></A>
-ClustalW is a popular command line tool for multiple sequence alignment
-(there is also a graphical interface called ClustalX). Biopython&#X2019;s
-<CODE>Bio.Align.Applications</CODE> module has a wrapper for this alignment tool
-(and several others).</P><P>Before trying to use ClustalW from within Python, you should first try running
-the ClustalW tool yourself by hand at the command line, to familiarise
-yourself the other options. You&#X2019;ll find the Biopython wrapper is very
-faithful to the actual command line API:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import ClustalwCommandline
->>> help(ClustalwCommandline)
-...
-</PRE><P>For the most basic usage, all you need is to have a FASTA input file, such as
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta">opuntia.fasta</A>
-(available online or in the Doc/examples subdirectory of the Biopython source
-code). This is a small FASTA file containing seven prickly-pear DNA sequences
-(from the cactus family <I>Opuntia</I>).</P><P>By default ClustalW will generate an alignment and guide tree file with names
-based on the input FASTA file, in this case <TT>opuntia.aln</TT> and
-<TT>opuntia.dnd</TT>, but you can override this or make it explicit:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import ClustalwCommandline
->>> cline = ClustalwCommandline("clustalw2", infile="opuntia.fasta")
->>> print(cline)
-clustalw2 -infile=opuntia.fasta
-</PRE><P>Notice here we have given the executable name as <TT>clustalw2</TT>,
-indicating we have version two installed, which has a different filename to
-version one (<TT>clustalw</TT>, the default). Fortunately both versions
-support the same set of arguments at the command line (and indeed, should be
-functionally identical).</P><P>You may find that even though you have ClustalW installed, the above command
-doesn&#X2019;t work &#X2013; you may get a message about &#X201C;command not found&#X201D; (especially
-on Windows). This indicated that the ClustalW executable is not on your PATH
-(an environment variable, a list of directories to be searched). You can
-either update your PATH setting to include the location of your copy of
-ClustalW tools (how you do this will depend on your OS), or simply type in
-the full path of the tool. For example:</P><PRE CLASS="verbatim">>>> import os
->>> from Bio.Align.Applications import ClustalwCommandline
->>> clustalw_exe = r"C:\Program Files\new clustal\clustalw2.exe"
->>> clustalw_cline = ClustalwCommandline(clustalw_exe, infile="opuntia.fasta")
-</PRE><PRE CLASS="verbatim">>>> assert os.path.isfile(clustalw_exe), "Clustal W executable missing"
->>> stdout, stderr = clustalw_cline()
-</PRE><P>Remember, in Python strings <CODE>\n</CODE> and <CODE>\t</CODE> are by default
-interpreted as a new line and a tab &#X2013; which is why we&#X2019;re put a letter
-&#X201C;r&#X201D; at the start for a raw string that isn&#X2019;t translated in this way.
-This is generally good practice when specifying a Windows style file name.</P><P>Internally this uses the
-<CODE>subprocess</CODE> module which is now the recommended way to run another
-program in Python. This replaces older options like the <CODE>os.system()</CODE>
-and the <CODE>os.popen*</CODE> functions.</P><P>Now, at this point it helps to know about how command line tools &#X201C;work&#X201D;.
-When you run a tool at the command line, it will often print text output
-directly to screen. This text can be captured or redirected, via
-two &#X201C;pipes&#X201D;, called standard output (the normal results) and standard
-error (for error messages and debug messages). There is also standard
-input, which is any text fed into the tool. These names get shortened
-to stdin, stdout and stderr. When the tool finishes, it has a return
-code (an integer), which by convention is zero for success.</P><P>When you run the command line tool like this via the Biopython wrapper,
-it will wait for it to finish, and check the return code. If this is
-non zero (indicating an error), an exception is raised. The wrapper
-then returns two strings, stdout and stderr.</P><P>In the case of ClustalW, when run at the command line all the important
-output is written directly to the output files. Everything normally printed to
-screen while you wait (via stdout or stderr) is boring and can be
-ignored (assuming it worked).</P><P>What we care about are the two output files, the alignment and the guide
-tree. We didn&#X2019;t tell ClustalW what filenames to use, but it defaults to
-picking names based on the input file. In this case the output should be
-in the file <CODE>opuntia.aln</CODE>.
-You should be able to work out how to read in the alignment using
-<CODE>Bio.AlignIO</CODE> by now:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> align = AlignIO.read("opuntia.aln", "clustal")
->>> print(align)
-SingleLetterAlphabet() alignment with 7 rows and 906 columns
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191
-</PRE><P>In case you are interested (and this is an aside from the main thrust of this
-chapter), the <TT>opuntia.dnd</TT> file ClustalW creates is just a standard
-Newick tree file, and <CODE>Bio.Phylo</CODE> can parse these:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("opuntia.dnd", "newick")
->>> Phylo.draw_ascii(tree)
-                             _______________ gi|6273291|gb|AF191665.1|AF191665
-  __________________________|
- |                          |   ______ gi|6273290|gb|AF191664.1|AF191664
- |                          |__|
- |                             |_____ gi|6273289|gb|AF191663.1|AF191663
- |
-_|_________________ gi|6273287|gb|AF191661.1|AF191661
- |
- |__________ gi|6273286|gb|AF191660.1|AF191660
- |
- |    __ gi|6273285|gb|AF191659.1|AF191659
- |___|
-     | gi|6273284|gb|AF191658.1|AF191658
-<BLANKLINE>
-</PRE><P>Chapter <A HREF="#sec:Phylo">12</A> covers Biopython&#X2019;s support for phylogenetic trees in more
-depth.</P><!--TOC subsection MUSCLE-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc80">6.4.2</A>&#XA0;&#XA0;MUSCLE</H3><!--SEC END --><P>
-MUSCLE is a more recent multiple sequence alignment tool than ClustalW, and
-Biopython also has a wrapper for it under the <CODE>Bio.Align.Applications</CODE>
-module. As before, we recommend you try using MUSCLE from the command line before
-trying it from within Python, as the Biopython wrapper is very faithful to the
-actual command line API:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> help(MuscleCommandline)
-...
-</PRE><P>For the most basic usage, all you need is to have a FASTA input file, such as
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta">opuntia.fasta</A>
-(available online or in the Doc/examples subdirectory of the Biopython source
-code). You can then tell MUSCLE to read in this FASTA file, and write the
-alignment to an output file:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.txt")
->>> print(cline)
-muscle -in opuntia.fasta -out opuntia.txt
-</PRE><P>Note that MUSCLE uses &#X201C;-in&#X201D; and &#X201C;-out&#X201D; but in Biopython we have to use
-&#X201C;input&#X201D; and &#X201C;out&#X201D; as the keyword arguments or property names. This is
-because &#X201C;in&#X201D; is a reserved word in Python.</P><P>By default MUSCLE will output the alignment as a FASTA file (using gapped
-sequences). The <CODE>Bio.AlignIO</CODE> module should be able to read this
-alignment using <TT>format="fasta"</TT>.
-You can also ask for ClustalW-like output:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clw=True)
->>> print(cline)
-muscle -in opuntia.fasta -out opuntia.aln -clw
-</PRE><P>Or, strict ClustalW output where the original ClustalW header line is
-used for maximum compatibility:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> cline = MuscleCommandline(input="opuntia.fasta", out="opuntia.aln", clwstrict=True)
->>> print(cline)
-muscle -in opuntia.fasta -out opuntia.aln -clwstrict
-</PRE><P>The <CODE>Bio.AlignIO</CODE> module should be able to read these alignments
-using <TT>format="clustal"</TT>.</P><P>MUSCLE can also output in GCG MSF format (using the <TT>msf</TT> argument), but
-Biopython can&#X2019;t currently parse that, or using HTML which would give a human
-readable web page (not suitable for parsing).</P><P>You can also set the other optional parameters, for example the maximum number
-of iterations. See the built in help for details.</P><P>You would then run MUSCLE command line string as described above for
-ClustalW, and parse the output using <CODE>Bio.AlignIO</CODE> to get an
-alignment object.</P><!--TOC subsection MUSCLE using stdout-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc81">6.4.3</A>&#XA0;&#XA0;MUSCLE using stdout</H3><!--SEC END --><P>Using a MUSCLE command line as in the examples above will write the alignment
-to a file. This means there will be no important information written to the
-standard out (stdout) or standard error (stderr) handles. However, by default
-MUSCLE will write the alignment to standard output (stdout). We can take
-advantage of this to avoid having a temporary output file! For example:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
->>> print(muscle_cline)
-muscle -in opuntia.fasta
-</PRE><P>If we run this via the wrapper, we get back the output as a string. In order
-to parse this we can use <CODE>StringIO</CODE> to turn it into a handle.
-Remember that MUSCLE defaults to using FASTA as the output format:</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
->>> stdout, stderr = muscle_cline()
->>> from StringIO import StringIO
->>> from Bio import AlignIO
->>> align = AlignIO.read(StringIO(stdout), "fasta")
->>> print(align)
-SingleLetterAlphabet() alignment with 7 rows and 906 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658
-</PRE><P>The above approach is fairly simple, but if you are dealing with very large output
-text the fact that all of stdout and stderr is loaded into memory as a string can
-be a potential drawback. Using the <CODE>subprocess</CODE> module we can work directly
-with handles instead:</P><PRE CLASS="verbatim">>>> import subprocess
->>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(input="opuntia.fasta")
->>> child = subprocess.Popen(str(muscle_cline),
-...                          stdout=subprocess.PIPE,
-...                          stderr=subprocess.PIPE,
-...                          shell=(sys.platform!="win32"))
->>> from Bio import AlignIO
->>> align = AlignIO.read(child.stdout, "fasta")
->>> print(align)
-SingleLetterAlphabet() alignment with 7 rows and 906 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF191663
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273291|gb|AF191665.1|AF191665
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF191664
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF191661
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF191660
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF191659
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF191658
-</PRE><!--TOC subsection MUSCLE using stdin and stdout-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc82">6.4.4</A>&#XA0;&#XA0;MUSCLE using stdin and stdout</H3><!--SEC END --><P>We don&#X2019;t actually <EM>need</EM> to have our FASTA input sequences prepared in a file,
-because by default MUSCLE will read in the input sequence from standard input!
-Note this is a bit more advanced and fiddly, so don&#X2019;t bother with this technique
-unless you need to.</P><P>First, we&#X2019;ll need some unaligned sequences in memory as <CODE>SeqRecord</CODE> objects.
-For this demonstration I&#X2019;m going to use a filtered version of the original FASTA
-file (using a generator expression), taking just six of the seven sequences:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900)
-</PRE><P>Then we create the MUSCLE command line, leaving the input and output to their
-defaults (stdin and stdout). I&#X2019;m also going to ask for strict ClustalW format
-as for the output.</P><PRE CLASS="verbatim">>>> from Bio.Align.Applications import MuscleCommandline
->>> muscle_cline = MuscleCommandline(clwstrict=True)
->>> print(muscle_cline)
-muscle -clwstrict
-</PRE><P>Now for the fiddly bits using the <CODE>subprocess</CODE> module, stdin and stdout:</P><PRE CLASS="verbatim">>>> import subprocess
->>> import sys
->>> child = subprocess.Popen(str(cline),
-...                          stdin=subprocess.PIPE,
-...                          stdout=subprocess.PIPE,
-...                          stderr=subprocess.PIPE,
-...                          universal_newlines=True,
-...                          shell=(sys.platform!="win32"))
-</PRE><P>That should start MUSCLE, but it will be sitting waiting for its FASTA input
-sequences, which we must supply via its stdin handle:</P><PRE CLASS="verbatim">>>> SeqIO.write(records, child.stdin, "fasta")
-6
->>> child.stdin.close()
-</PRE><P>After writing the six sequences to the handle, MUSCLE will still be waiting
-to see if that is all the FASTA sequences or not &#X2013; so we must signal that
-this is all the input data by closing the handle. At that point MUSCLE should
-start to run, and we can ask for the output:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> align = AlignIO.read(child.stdout, "clustal")
->>> print(align)
-SingleLetterAlphabet() alignment with 6 rows and 900 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165
-</PRE><P>Wow! There we are with a new alignment of just the six records, without having created
-a temporary FASTA input file, or a temporary alignment output file. However, a word of
-caution: Dealing with errors with this style of calling external programs is much more
-complicated.
-It also becomes far harder to diagnose problems, because you can&#X2019;t try running MUSCLE
-manually outside of Biopython (because you don&#X2019;t have the input file to supply).
-There can also be subtle cross platform issues (e.g. Windows versus Linux,
-Python 2 versus Python 3), and how
-you run your script can have an impact (e.g. at the command line, from IDLE or an
-IDE, or as a GUI script). These are all generic Python issues though, and not
-specific to Biopython.</P><P>If you find working directly with <TT>subprocess</TT> like this scary, there is an
-alternative. If you execute the tool with <TT>muscle_cline()</TT> you can supply
-any standard input as a big string, <TT>muscle_cline(stdin=...)</TT>. So,
-provided your data isn&#X2019;t very big, you can prepare the FASTA input in memory as
-a string using <TT>StringIO</TT> (see Section&#XA0;<A HREF="#sec:appendix-handles">22.1</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> records = (r for r in SeqIO.parse("opuntia.fasta", "fasta") if len(r) < 900)
->>> from StringIO import StringIO
->>> handle = StringIO()
->>> SeqIO.write(records, handle, "fasta")
-6
->>> data = handle.getvalue()
-</PRE><P>You can then run the tool and parse the alignment as follows:</P><PRE CLASS="verbatim">>>> stdout, stderr = muscle_cline(stdin=data)
->>> from Bio import AlignIO
->>> align = AlignIO.read(StringIO(stdout), "clustal")
->>> print(align)
-SingleLetterAlphabet() alignment with 6 rows and 900 columns
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273290|gb|AF191664.1|AF19166
-TATACATTAAAGGAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273289|gb|AF191663.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273287|gb|AF191661.1|AF19166
-TATACATAAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273286|gb|AF191660.1|AF19166
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273285|gb|AF191659.1|AF19165
-TATACATTAAAGAAGGGGGATGCGGATAAATGGAAAGGCGAAAG...AGA gi|6273284|gb|AF191658.1|AF19165
-</PRE><P>You might find this easier, but it does require more memory (RAM) for the strings
-used for the input FASTA and output Clustal formatted data.</P><!--TOC subsection EMBOSS needle and water-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc83">6.4.5</A>&#XA0;&#XA0;EMBOSS needle and water</H3><!--SEC END --><P>
-<A NAME="seq:emboss-needle-water"></A>
-The <A HREF="http://emboss.sourceforge.net/">EMBOSS</A> suite includes the <TT>water</TT> and
-<TT>needle</TT> tools for Smith-Waterman algorithm local alignment, and Needleman-Wunsch
-global alignment. The tools share the same style interface, so switching between the two
-is trivial &#X2013; we&#X2019;ll just use <TT>needle</TT> here.</P><P>Suppose you want to do a global pairwise alignment between two sequences, prepared in
-FASTA format as follows:</P><PRE CLASS="verbatim">>HBA_HUMAN
-MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG
-KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP
-AVHASLDKFLASVSTVLTSKYR
-</PRE><P>in a file <TT>alpha.fasta</TT>, and secondly in a file <TT>beta.fasta</TT>:</P><PRE CLASS="verbatim">>HBB_HUMAN
-MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPK
-VKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFG
-KEFTPPVQAAYQKVVAGVANALAHKYH
-</PRE><P>Let&#X2019;s start by creating a complete <TT>needle</TT> command line object in one go:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> needle_cline = NeedleCommandline(asequence="alpha.faa", bsequence="beta.faa",
-...                                  gapopen=10, gapextend=0.5, outfile="needle.txt")
->>> print(needle_cline)
-needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5
-</PRE><P>Why not try running this by hand at the command prompt? You should see it does a
-pairwise comparison and records the output in the file <TT>needle.txt</TT> (in the
-default EMBOSS alignment file format).</P><P>Even if you have EMBOSS installed, running this command may not work &#X2013; you
-might get a message about &#X201C;command not found&#X201D; (especially on Windows). This
-probably means that the EMBOSS tools are not on your PATH environment
-variable. You can either update your PATH setting, or simply tell Biopython
-the full path to the tool, for example:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> needle_cline = NeedleCommandline(r"C:\EMBOSS\needle.exe",
-...                                  asequence="alpha.faa", bsequence="beta.faa",
-...                                  gapopen=10, gapextend=0.5, outfile="needle.txt")
-</PRE><P>Remember in Python that for a default string <CODE>\n</CODE> or <CODE>\t</CODE> means a
-new line or a tab &#X2013; which is why we&#X2019;re put a letter &#X201C;r&#X201D; at the start for a raw string.</P><P>At this point it might help to try running the EMBOSS tools yourself by hand at the
-command line, to familiarise yourself the other options and compare them to the
-Biopython help text:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> help(NeedleCommandline)
-...
-</PRE><P>Note that you can also specify (or change or look at) the settings like this:</P><PRE CLASS="verbatim">>>> from Bio.Emboss.Applications import NeedleCommandline
->>> needle_cline = NeedleCommandline()
->>> needle_cline.asequence="alpha.faa"
->>> needle_cline.bsequence="beta.faa"
->>> needle_cline.gapopen=10
->>> needle_cline.gapextend=0.5
->>> needle_cline.outfile="needle.txt"
->>> print(needle_cline)
-needle -outfile=needle.txt -asequence=alpha.faa -bsequence=beta.faa -gapopen=10 -gapextend=0.5
->>> print(needle_cline.outfile)
-needle.txt
-</PRE><P>Next we want to use Python to run this command for us. As explained above,
-for full control, we recommend you use the built in Python <TT>subprocess</TT>
-module, but for simple usage the wrapper object usually suffices:</P><PRE CLASS="verbatim">>>> stdout, stderr = needle_cline()
->>> print(stdout + stderr)
-Needleman-Wunsch global alignment of two sequences
-</PRE><P>Next we can load the output file with <CODE>Bio.AlignIO</CODE> as
-discussed earlier in this chapter, as the <TT>emboss</TT> format:</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> align = AlignIO.read("needle.txt", "emboss")
->>> print(align)
-SingleLetterAlphabet() alignment with 2 rows and 149 columns
-MV-LSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTY...KYR HBA_HUMAN
-MVHLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRF...KYH HBB_HUMAN
-</PRE><P>In this example, we told EMBOSS to write the output to a file, but you
-<EM>can</EM> tell it to write the output to stdout instead (useful if you
-don&#X2019;t want a temporary output file to get rid of &#X2013; use
-<TT>stdout=True</TT> rather than the <TT>outfile</TT> argument), and
-also to read <EM>one</EM> of the one of the inputs from stdin (e.g.
-<TT>asequence="stdin"</TT>, much like in the MUSCLE example in the
-section above).</P><P>This has only scratched the surface of what you can do with <TT>needle</TT>
-and <TT>water</TT>. One useful trick is that the second file can contain
-multiple sequences (say five), and then EMBOSS will do five pairwise
-alignments.</P><P>Note - Biopython includes its own pairwise alignment code in the <CODE>Bio.pairwise2</CODE>
-module (written in C for speed, but with a pure Python fallback available too). This
-doesn&#X2019;t work with alignment objects, so we have not covered it within this chapter.
-See the module&#X2019;s docstring (built in help) for details.</P><!--TOC chapter BLAST-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc84">Chapter&#XA0;7</A>&#XA0;&#XA0;BLAST</H1><!--SEC END --><P>
-<A NAME="chapter:blast"></A>
-Hey, everybody loves BLAST right? I mean, geez, how can it get any easier to do comparisons between one of your sequences and every other sequence in the known world? But, of course, this section isn&#X2019;t about how cool BLAST is, since we already know that. It is about the problem with BLAST &#X2013; it can be really difficult to deal with the volume of data generated by large runs, and to automate BLAST runs in general.</P><P>Fortunately, the Biopython folks know this only too well, [...]
-Firstly, running BLAST for your query sequence(s), and getting some output.
-Secondly, parsing the BLAST output in Python for further analysis.</P><P>Your first introduction to running BLAST was probably via the NCBI web-service.
-In fact, there are lots of ways you can run BLAST, which can be categorised in several ways.
-The most important distinction is running BLAST locally (on your own machine),
-and running BLAST remotely (on another machine, typically the NCBI servers).
-We&#X2019;re going to start this chapter by invoking the NCBI online BLAST service
-from within a Python script.</P><P><EM>NOTE</EM>: The following Chapter&#XA0;<A HREF="#chapter:searchio">8</A> describes
-<CODE>Bio.SearchIO</CODE>, an <EM>experimental</EM> module in Biopython. We
-intend this to ultimately replace the older <CODE>Bio.Blast</CODE> module, as it
-provides a more general framework handling other related sequence
-searching tools as well. However, until that is declared stable, for
-production code please continue to use the <CODE>Bio.Blast</CODE> module
-for dealing with NCBI BLAST.</P><!--TOC section Running BLAST over the Internet-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc85">7.1</A>&#XA0;&#XA0;Running BLAST over the Internet</H2><!--SEC END --><P>
-<A NAME="sec:running-www-blast"></A></P><P>We use the function <CODE>qblast()</CODE> in the <CODE>Bio.Blast.NCBIWWW</CODE> module
-to call the online version of BLAST. This has three non-optional arguments:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The first argument is the blast program to use for the search, as a
-lower case string. The options and descriptions of the programs are
-available at <A HREF="http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml"><TT>http://www.ncbi.nlm.nih.gov/BLAST/blast_program.shtml</TT></A>.
-Currently <CODE>qblast</CODE> only works with blastn, blastp, blastx, tblast
-and tblastx.
-</LI><LI CLASS="li-itemize">The second argument specifies the databases to search against. Again,
-the options for this are available on the NCBI web pages at
-<A HREF="http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml"><TT>http://www.ncbi.nlm.nih.gov/BLAST/blast_databases.shtml</TT></A>.
-</LI><LI CLASS="li-itemize">The third argument is a string containing your query sequence. This
-can either be the sequence itself, the sequence in fasta format,
-or an identifier like a GI number.
-</LI></UL><P>The <CODE>qblast</CODE> function also take a number of other option arguments
-which are basically analogous to the different parameters you can set
-on the BLAST web page. We&#X2019;ll just highlight a few of them here:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The <CODE>qblast</CODE> function can return the BLAST results in various
-formats, which you can choose with the optional <CODE>format_type</CODE> keyword:
-<CODE>"HTML"</CODE>, <CODE>"Text"</CODE>, <CODE>"ASN.1"</CODE>, or <CODE>"XML"</CODE>.
-The default is <CODE>"XML"</CODE>, as that is the format expected by the parser,
-described in section&#XA0;<A HREF="#sec:parsing-blast">7.3</A> below.
-</LI><LI CLASS="li-itemize">The argument <CODE>expect</CODE> sets the expectation or e-value threshold.
-</LI></UL><P>For more about the optional BLAST arguments, we refer you to the NCBI&#X2019;s own
-documentation, or that built into Biopython:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> help(NCBIWWW.qblast)
-...
-</PRE><P>Note that the default settings on the NCBI BLAST website are not quite
-the same as the defaults on QBLAST. If you get different results, you&#X2019;ll
-need to check the parameters (e.g., the expectation value threshold and
-the gap values).</P><P>For example, if you have a nucleotide sequence you want to search against
-the nucleotide database (nt) using BLASTN, and you know the GI number of your
-query sequence, you can use:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
-</PRE><P>Alternatively, if we have our query sequence already in a FASTA formatted
-file, we just need to open the file and read in this record as a string,
-and use that as the query argument:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> fasta_string = open("m_cold.fasta").read()
->>> result_handle = NCBIWWW.qblast("blastn", "nt", fasta_string)
-</PRE><P>We could also have read in the FASTA file as a <CODE>SeqRecord</CODE> and then
-supplied just the sequence itself:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> from Bio import SeqIO
->>> record = SeqIO.read("m_cold.fasta", format="fasta")
->>> result_handle = NCBIWWW.qblast("blastn", "nt", record.seq)
-</PRE><P>Supplying just the sequence means that BLAST will assign an identifier
-for your sequence automatically. You might prefer to use the
-<CODE>SeqRecord</CODE> object&#X2019;s format method to make a FASTA string
-(which will include the existing identifier):</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> from Bio import SeqIO
->>> record = SeqIO.read("m_cold.fasta", format="fasta")
->>> result_handle = NCBIWWW.qblast("blastn", "nt", record.format("fasta"))
-</PRE><P>This approach makes more sense if you have your sequence(s) in a
-non-FASTA file format which you can extract using <CODE>Bio.SeqIO</CODE>
-(see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>Whatever arguments you give the <CODE>qblast()</CODE> function, you should
-get back your results in a handle object (by default in XML format).
-The next step would be to parse the XML output into Python objects
-representing the search results (Section&#XA0;<A HREF="#sec:parsing-blast">7.3</A>),
-but you might want to save a local copy of the output file first.
-I find this especially useful when debugging my code that extracts
-info from the BLAST results (because re-running the online search
-is slow and wastes the NCBI computer time).</P><P><A NAME="sec:saving-blast-output"></A></P><P>We need to be a bit careful since we can use <CODE>result_handle.read()</CODE> to
-read the BLAST output only once &#X2013; calling <CODE>result_handle.read()</CODE> again
-returns an empty string.</P><PRE CLASS="verbatim">>>> save_file = open("my_blast.xml", "w")
->>> save_file.write(result_handle.read())
->>> save_file.close()
->>> result_handle.close()
-</PRE><P>After doing this, the results are in the file <CODE>my_blast.xml</CODE> and the
-original handle has had all its data extracted (so we closed it). However,
-the <CODE>parse</CODE> function of the BLAST parser (described
-in&#XA0;<A HREF="#sec:parsing-blast">7.3</A>) takes a file-handle-like object, so
-we can just open the saved file for input:</P><PRE CLASS="verbatim">>>> result_handle = open("my_blast.xml")
-</PRE><P>Now that we&#X2019;ve got the BLAST results back into a handle again, we are ready
-to do something with them, so this leads us right into the parsing section
-(see Section&#XA0;<A HREF="#sec:parsing-blast">7.3</A> below). You may want to jump ahead to
-that now &#X2026;.</P><!--TOC section Running BLAST locally-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc86">7.2</A>&#XA0;&#XA0;Running BLAST locally</H2><!--SEC END --><P>
-<A NAME="sec:running-local-blast"></A></P><!--TOC subsection Introduction-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc87">7.2.1</A>&#XA0;&#XA0;Introduction</H3><!--SEC END --><P>Running BLAST locally (as opposed to over the internet, see
-Section&#XA0;<A HREF="#sec:running-www-blast">7.1</A>) has at least major two advantages:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Local BLAST may be faster than BLAST over the internet;
-</LI><LI CLASS="li-itemize">Local BLAST allows you to make your own database to search for sequences against.
-</LI></UL><P>
-Dealing with proprietary or unpublished sequence data can be another reason to run BLAST
-locally. You may not be allowed to redistribute the sequences, so submitting them to the
-NCBI as a BLAST query would not be an option.</P><P>Unfortunately, there are some major drawbacks too &#X2013; installing all the bits and getting
-it setup right takes some effort:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Local BLAST requires command line tools to be installed.
-</LI><LI CLASS="li-itemize">Local BLAST requires (large) BLAST databases to be setup (and potentially kept up to date).
-</LI></UL><P>To further confuse matters there are several different BLAST packages available,
-and there are also other tools which can produce imitation BLAST output files, such as BLAT.</P><!--TOC subsection Standalone NCBI BLAST+-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc88">7.2.2</A>&#XA0;&#XA0;Standalone NCBI BLAST+</H3><!--SEC END --><P>The &#X201C;new&#X201D;
-<A HREF="http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download">NCBI BLAST+</A> suite was released in 2009. This replaces the old NCBI &#X201C;legacy&#X201D; BLAST
-package (see below).</P><P>This section will show briefly how to use these tools from within Python. If you have
-already read or tried the alignment tool examples in Section&#XA0;<A HREF="#sec:alignment-tools">6.4</A>
-this should all seem quite straightforward. First, we construct a command line string
-(as you would type in at the command line prompt if running standalone BLAST by hand).
-Then we can execute this command from within Python.</P><P>For example, taking a FASTA file of gene nucleotide sequences, you might want to
-run a BLASTX (translation) search against the non-redundant (NR) protein database.
-Assuming you (or your systems administrator) has downloaded and installed the NR
-database, you might run:</P><PRE CLASS="verbatim">blastx -query opuntia.fasta -db nr -out opuntia.xml -evalue 0.001 -outfmt 5
-</PRE><P>This should run BLASTX against the NR database, using an expectation cut-off value
-of 0.001 and produce XML output to the specified file (which we can then parse).
-On my computer this takes about six minutes - a good reason to save the output
-to a file so you can repeat any analysis as needed.</P><P>From within Biopython we can use the NCBI BLASTX wrapper from the
-<CODE>Bio.Blast.Applications</CODE> module to build the command line string,
-and run it:</P><PRE CLASS="verbatim">>>> from Bio.Blast.Applications import NcbiblastxCommandline
->>> help(NcbiblastxCommandline)
-...
->>> blastx_cline = NcbiblastxCommandline(query="opuntia.fasta", db="nr", evalue=0.001,
-...                                      outfmt=5, out="opuntia.xml")
->>> blastx_cline
-NcbiblastxCommandline(cmd='blastx', out='opuntia.xml', outfmt=5, query='opuntia.fasta',
-db='nr', evalue=0.001)
->>> print(blastx_cline)
-blastx -out opuntia.xml -outfmt 5 -query opuntia.fasta -db nr -evalue 0.001
->>> stdout, stderr = blastx_cline()
-</PRE><P>In this example there shouldn&#X2019;t be any output from BLASTX to the terminal,
-so stdout and stderr should be empty. You may want to check the output file
-<CODE>opuntia.xml</CODE> has been created.</P><P>As you may recall from earlier examples in the tutorial, the <CODE>opuntia.fasta</CODE>
-contains seven sequences, so the BLAST XML output should contain multiple results.
-Therefore use <CODE>Bio.Blast.NCBIXML.parse()</CODE> to parse it as described below in
-Section&#XA0;<A HREF="#sec:parsing-blast">7.3</A>.</P><!--TOC subsection Other versions of BLAST-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc89">7.2.3</A>&#XA0;&#XA0;Other versions of BLAST</H3><!--SEC END --><P>NCBI BLAST+ (written in C++) was first released in 2009 as a replacement for
-the original NCBI &#X201C;legacy&#X201D; BLAST (written in C) which is no longer being updated.
-There were a lot of changes &#X2013; the old version had a single core command line
-tool <CODE>blastall</CODE> which covered multiple different BLAST search types (which
-are now separate commands in BLAST+), and all the command line options
-were renamed.
-Biopython&#X2019;s wrappers for the NCBI &#X201C;legacy&#X201D; BLAST tools have been deprecated
-and will be removed in a future release.
-To try to avoid confusion, we do not cover calling these old tools from Biopython
-in this tutorial.</P><P>You may also come across <A HREF="http://blast.wustl.edu/">Washington University BLAST</A>
-(WU-BLAST), and its successor, <A HREF="http://blast.advbiocomp.com">Advanced Biocomputing
-BLAST</A> (AB-BLAST, released in 2009, not free/open source). These packages include
-the command line tools <CODE>wu-blastall</CODE> and <CODE>ab-blastall</CODE>, which mimicked
-<CODE>blastall</CODE> from the NCBI &#X201C;legacy&#X201D; BLAST suite.
-Biopython does not currently provide wrappers for calling these tools, but should be able
-to parse any NCBI compatible output from them.</P><!--TOC section Parsing BLAST output-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc90">7.3</A>&#XA0;&#XA0;Parsing BLAST output</H2><!--SEC END --><P>
-<A NAME="sec:parsing-blast"></A></P><P>As mentioned above, BLAST can generate output in various formats, such as
-XML, HTML, and plain text. Originally, Biopython had parsers for BLAST
-plain text and HTML output, as these were the only output formats offered
-at the time. Unfortunately, the BLAST output in these formats kept changing,
-each time breaking the Biopython parsers. Our HTML BLAST parser has been
-removed, but the plain text BLAST parser is still available (see
-Section&#XA0;<A HREF="#sec:parsing-blast-deprecated">7.5</A>). Use it at your own risk,
-it may or may not work, depending on which BLAST version you&#X2019;re using.</P><P>As keeping up with changes in BLAST
-became a hopeless endeavor, especially with users running different BLAST
-versions, we now recommend to parse the output in XML format, which can be
-generated by recent versions of BLAST. Not only is the XML output more stable
-than the plain text and HTML output, it is also much easier to parse
-automatically, making Biopython a whole lot more stable.</P><P>You can get BLAST output in XML format in various ways. For the parser, it
-doesn&#X2019;t matter how the output was generated, as long as it is in the XML format.
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-You can use Biopython to run BLAST over the internet, as described in
-section&#XA0;<A HREF="#sec:running-www-blast">7.1</A>.
-</LI><LI CLASS="li-itemize">You can use Biopython to run BLAST locally, as described in
-section&#XA0;<A HREF="#sec:running-local-blast">7.2</A>.
-</LI><LI CLASS="li-itemize">You can do the BLAST search yourself on the NCBI site through your
-web browser, and then save the results. You need to choose XML as the format
-in which to receive the results, and save the final BLAST page you get
-(you know, the one with all of the interesting results!) to a file.
-</LI><LI CLASS="li-itemize">You can also run BLAST locally without using Biopython, and save
-the output in a file. Again, you need to choose XML as the format in which
-to receive the results.
-</LI></UL><P>
-The important point is that you do not have to use Biopython
-scripts to fetch the data in order to be able to parse it.
-Doing things in one of these ways, you then need to get a handle
-to the results. In Python, a handle is just a nice general way of
-describing input to any info source so that the info can be retrieved
-using <CODE>read()</CODE> and <CODE>readline()</CODE> functions
-(see Section&#XA0;sec:appendix-handles).</P><P>If you followed the code above for interacting with BLAST through a
-script, then you already have <CODE>result_handle</CODE>, the handle to the
-BLAST results. For example, using a GI number to do an online search:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIWWW
->>> result_handle = NCBIWWW.qblast("blastn", "nt", "8332116")
-</PRE><P>If instead you ran BLAST some other way, and have the
-BLAST output (in XML format) in the file <CODE>my_blast.xml</CODE>, all you
-need to do is to open the file for reading:</P><PRE CLASS="verbatim">>>> result_handle = open("my_blast.xml")
-</PRE><P>Now that we&#X2019;ve got a handle, we are ready to parse the output. The
-code to parse it is really quite small. If you expect a single
-BLAST result (i.e., you used a single query):</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_record = NCBIXML.read(result_handle)
-</PRE><P>or, if you have lots of results (i.e., multiple query sequences):</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_records = NCBIXML.parse(result_handle)
-</PRE><P>Just like <CODE>Bio.SeqIO</CODE> and <CODE>Bio.AlignIO</CODE>
-(see Chapters&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A> and&#XA0;<A HREF="#chapter:Bio.AlignIO">6</A>),
-we have a pair of input functions, <CODE>read</CODE> and <CODE>parse</CODE>, where
-<CODE>read</CODE> is for when you have exactly one object, and <CODE>parse</CODE>
-is an iterator for when you can have lots of objects &#X2013; but instead of
-getting <CODE>SeqRecord</CODE> or <CODE>MultipleSeqAlignment</CODE> objects, we
-get BLAST record objects.</P><P>To be able to handle the situation where the BLAST file may be huge,
-containing thousands of results, <CODE>NCBIXML.parse()</CODE> returns an
-iterator. In plain English, an iterator allows you to step through
-the BLAST output, retrieving BLAST records one by one for each BLAST
-search result:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_records = NCBIXML.parse(result_handle)
->>> blast_record = next(blast_records)
-# ... do something with blast_record
->>> blast_record = next(blast_records)
-# ... do something with blast_record
->>> blast_record = next(blast_records)
-# ... do something with blast_record
->>> blast_record = next(blast_records)
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-StopIteration
-# No further records
-</PRE><P>Or, you can use a <CODE>for</CODE>-loop:
-</P><PRE CLASS="verbatim">>>> for blast_record in blast_records:
-...     # Do something with blast_record
-</PRE><P>Note though that you can step through the BLAST records only once.
-Usually, from each BLAST record you would save the information that
-you are interested in. If you want to save all returned BLAST records,
-you can convert the iterator into a list:
-</P><PRE CLASS="verbatim">>>> blast_records = list(blast_records)
-</PRE><P>Now you can access each BLAST record in the list with an index as usual.
-If your BLAST file is huge though, you may run into memory problems trying to
-save them all in a list.</P><P>Usually, you&#X2019;ll be running one BLAST search at a time. Then, all you need
-to do is to pick up the first (and only) BLAST record in <CODE>blast_records</CODE>:
-</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_records = NCBIXML.parse(result_handle)
->>> blast_record = next(blast_records)
-</PRE><P>or more elegantly:
-</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIXML
->>> blast_record = NCBIXML.read(result_handle)
-</PRE><P>I guess by now you&#X2019;re wondering what is in a BLAST record.</P><!--TOC section The BLAST record class-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc91">7.4</A>&#XA0;&#XA0;The BLAST record class</H2><!--SEC END --><P>A BLAST Record contains everything you might ever want to extract from the
-BLAST output. Right now we&#X2019;ll just show
-an example of how to get some info out of the BLAST report, but if you
-want something in particular that is not described here, look at the
-info on the record class in detail, and take a gander into the code or
-automatically generated documentation &#X2013; the docstrings have lots of
-good info about what is stored in each piece of information.</P><P>To continue with our example, let&#X2019;s just print out some summary info
-about all hits in our blast report greater than a particular
-threshold. The following code does this:</P><PRE CLASS="verbatim">>>> E_VALUE_THRESH = 0.04
-
->>> for alignment in blast_record.alignments:
-...     for hsp in alignment.hsps:
-...         if hsp.expect < E_VALUE_THRESH:
-...             print('****Alignment****')
-...             print('sequence:', alignment.title)
-...             print('length:', alignment.length)
-...             print('e value:', hsp.expect)
-...             print(hsp.query[0:75] + '...')
-...             print(hsp.match[0:75] + '...')
-...             print(hsp.sbjct[0:75] + '...')
-</PRE><P>This will print out summary reports like the following:</P><PRE CLASS="verbatim">****Alignment****
-sequence: >gb|AF283004.1|AF283004 Arabidopsis thaliana cold acclimation protein WCOR413-like protein
-alpha form mRNA, complete cds
-length: 783
-e value: 0.034
-tacttgttgatattggatcgaacaaactggagaaccaacatgctcacgtcacttttagtcccttacatattcctc...
-||||||||| | ||||||||||| || ||||  || || |||||||| |||||| |  | |||||||| ||| ||...
-tacttgttggtgttggatcgaaccaattggaagacgaatatgctcacatcacttctcattccttacatcttcttc...
-</PRE><P>Basically, you can do anything you want to with the info in the BLAST
-report once you have parsed it. This will, of course, depend on what
-you want to use it for, but hopefully this helps you get started on
-doing what you need to do!</P><P>An important consideration for extracting information from a BLAST report is the type of objects that the information is stored in. In Biopython, the parsers return <CODE>Record</CODE> objects, either <CODE>Blast</CODE> or <CODE>PSIBlast</CODE> depending on what you are parsing. These objects are defined in <CODE>Bio.Blast.Record</CODE> and are quite complete.</P><P>Here are my attempts at UML class diagrams for the <CODE>Blast</CODE> and <CODE>PSIBlast</ [...]
-<A NAME="fig:blastrecord"></A>
-<IMG SRC="images/BlastRecord.png" width=650, height=750>
-</P><P>The PSIBlast record object is similar, but has support for the rounds that are used in the iteration steps of PSIBlast. The class diagram for PSIBlast is shown in Figure&#XA0;<A HREF="#fig:psiblastrecord">7.4</A>.</P><P>
-<A NAME="fig:psiblastrecord"></A>
-<IMG SRC="images/PSIBlastRecord.png" width=650, height=750>
-</P><!--TOC section Deprecated BLAST parsers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc92">7.5</A>&#XA0;&#XA0;Deprecated BLAST parsers</H2><!--SEC END --><P>
-<A NAME="sec:parsing-blast-deprecated"></A></P><P>Older versions of Biopython had parsers for BLAST output in plain text or HTML
-format. Over the years, we discovered that it is very hard to maintain these
-parsers in working order. Basically, any small change to the BLAST output in
-newly released BLAST versions tends to cause the plain text and HTML parsers
-to break. We therefore recommend parsing BLAST output in XML format, as
-described in section&#XA0;<A HREF="#sec:parsing-blast">7.3</A>.</P><P>Depending on which BLAST versions or programs you&#X2019;re using, our plain text BLAST parser may or may not work. Use it at your own risk!</P><!--TOC subsection Parsing plain-text BLAST output-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc93">7.5.1</A>&#XA0;&#XA0;Parsing plain-text BLAST output</H3><!--SEC END --><P>The plain text BLAST parser is located in <CODE>Bio.Blast.NCBIStandalone</CODE>.</P><P>As with the XML parser, we need to have a handle object that we can pass to the parser. The handle must implement the <CODE>readline()</CODE> method and do this properly. The common ways to get such a handle are to either use the provided <CODE>blastall</CODE> or <CODE>bl [...]
-</PRE><P>Well, now that we&#X2019;ve got a handle (which we&#X2019;ll call <CODE>result_handle</CODE>),
-we are ready to parse it. This can be done with the following code:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIStandalone
->>> blast_parser = NCBIStandalone.BlastParser()
->>> blast_record = blast_parser.parse(result_handle)
-</PRE><P>This will parse the BLAST report into a Blast Record class (either a Blast or a PSIBlast record, depending on what you are parsing) so that you can extract the information from it. In our case, let&#X2019;s just print out a quick summary of all of the alignments greater than some threshold value.</P><PRE CLASS="verbatim">>>> E_VALUE_THRESH = 0.04
->>> for alignment in blast_record.alignments:
-...     for hsp in alignment.hsps:
-...         if hsp.expect < E_VALUE_THRESH:
-...             print('****Alignment****')
-...             print('sequence:', alignment.title)
-...             print('length:', alignment.length)
-...             print('e value:', hsp.expect)
-...             print(hsp.query[0:75] + '...')
-...             print(hsp.match[0:75] + '...')
-...             print(hsp.sbjct[0:75] + '...')
-</PRE><P>If you also read the section&#XA0;<A HREF="#sec:parsing-blast">7.3</A> on parsing BLAST XML output, you&#X2019;ll notice that the above code is identical to what is found in that section. Once you parse something into a record class you can deal with it independent of the format of the original BLAST info you were parsing. Pretty snazzy!</P><P>Sure, parsing one record is great, but I&#X2019;ve got a BLAST file with tons of records &#X2013; how can I parse them all? Well, fear no [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc94">7.5.2</A>&#XA0;&#XA0;Parsing a plain-text BLAST file full of BLAST runs</H3><!--SEC END --><P>We can do this using the blast iterator. To set up an iterator, we first set up a parser, to parse our blast reports in Blast Record objects:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIStandalone
->>> blast_parser = NCBIStandalone.BlastParser()
-</PRE><P>Then we will assume we have a handle to a bunch of blast records, which we&#X2019;ll call <CODE>result_handle</CODE>. Getting a handle is described in full detail above in the blast parsing sections.</P><P>Now that we&#X2019;ve got a parser and a handle, we are ready to set up the iterator with the following command:</P><PRE CLASS="verbatim">>>> blast_iterator = NCBIStandalone.Iterator(result_handle, blast_parser)
-</PRE><P>The second option, the parser, is optional. If we don&#X2019;t supply a parser, then the iterator will just return the raw BLAST reports one at a time.</P><P>Now that we&#X2019;ve got an iterator, we start retrieving blast records (generated by our parser) using <CODE>next()</CODE>:</P><PRE CLASS="verbatim">>>> blast_record = next(blast_iterator)
-</PRE><P>Each call to next will return a new record that we can deal with. Now we can iterate through these records and generate our old favorite, a nice little blast report:</P><PRE CLASS="verbatim">>>> for blast_record in blast_iterator:
-...     E_VALUE_THRESH = 0.04
-...     for alignment in blast_record.alignments:
-...         for hsp in alignment.hsps:
-...             if hsp.expect < E_VALUE_THRESH:
-...                 print('****Alignment****')
-...                 print('sequence:', alignment.title)
-...                 print('length:', alignment.length)
-...                 print('e value:', hsp.expect)
-...                 if len(hsp.query) > 75:
-...                     dots = '...'
-...                 else:
-...                     dots = ''
-...                 print(hsp.query[0:75] + dots)
-...                 print(hsp.match[0:75] + dots)
-...                 print(hsp.sbjct[0:75] + dots)
-</PRE><P>The iterator allows you to deal with huge blast records without any memory problems, since things are read in one at a time. I have parsed tremendously huge files without any problems using this.</P><!--TOC subsection Finding a bad record somewhere in a huge plain-text BLAST file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc95">7.5.3</A>&#XA0;&#XA0;Finding a bad record somewhere in a huge plain-text BLAST file</H3><!--SEC END --><P>One really ugly problem that happens to me is that I&#X2019;ll be parsing a huge blast file for a while, and the parser will bomb out with a ValueError. This is a serious problem, since you can&#X2019;t tell if the ValueError is due to a parser problem, or a problem with the BLAST. To make it even worse, you have no idea where [...]
->>> blast_file = os.path.join(os.getcwd(), "blast_out", "big_blast.out")
->>> error_file = os.path.join(os.getcwd(), "blast_out", "big_blast.problems")
-</PRE><P>Now we want to get a <CODE>BlastErrorParser</CODE>:</P><PRE CLASS="verbatim">>>> from Bio.Blast import NCBIStandalone
->>> error_handle = open(error_file, "w")
->>> blast_error_parser = NCBIStandalone.BlastErrorParser(error_handle)
-</PRE><P>Notice that the parser take an optional argument of a handle. If a handle is passed, then the parser will write any blast records which generate a ValueError to this handle. Otherwise, these records will not be recorded.</P><P>Now we can use the <CODE>BlastErrorParser</CODE> just like a regular blast parser. Specifically, we might want to make an iterator that goes through our blast records one at a time and parses them with the error parser:</P><PRE CLASS="verbatim">>>&gt [...]
->>> iterator = NCBIStandalone.Iterator(result_handle, blast_error_parser)
-</PRE><P>We can read these records one a time, but now we can catch and deal with errors that are due to problems with Blast (and not with the parser itself):</P><PRE CLASS="verbatim">>>> try:
-...     next_record = next(iterator)
-... except NCBIStandalone.LowQualityBlastError as info:
-...     print("LowQualityBlastError detected in id %s" % info[1])
-</PRE><P>The <CODE>next()</CODE> functionality is normally called indirectly via a <CODE>for</CODE>-loop.
-Right now the <CODE>BlastErrorParser</CODE> can generate the following errors:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>ValueError</CODE> &#X2013; This is the same error generated by the regular BlastParser, and is due to the parser not being able to parse a specific file. This is normally either due to a bug in the parser, or some kind of discrepancy between the version of BLAST you are using and the versions the parser is able to handle.</LI><LI CLASS="li-itemize"><CODE>LowQualityBlastError</CODE> &#X2013; When BLASTing a sequence that is of really bad quality (for example, a short sequence that i [...]
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>item[0]</CODE> &#X2013; The error message
-</LI><LI CLASS="li-itemize"><CODE>item[1]</CODE> &#X2013; The id of the input record that caused the error. This is really useful if you want to record all of the records that are causing problems.
-</LI></UL>
-</LI></UL><P>As mentioned, with each error generated, the BlastErrorParser will write the offending record to the specified <CODE>error_handle</CODE>. You can then go ahead and look and these and deal with them as you see fit. Either you will be able to debug the parser with a single blast report, or will find out problems in your blast runs. Either way, it will definitely be a useful experience!</P><P>Hopefully the <CODE>BlastErrorParser</CODE> will make it much easier to debug and deal [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc96">7.6</A>&#XA0;&#XA0;Dealing with PSI-BLAST</H2><!--SEC END --><P>You can run the standalone version of PSI-BLAST (the legacy NCBI command line
-tool <CODE>blastpgp</CODE>, or its replacement <CODE>psiblast</CODE>) using the wrappers
-in <CODE>Bio.Blast.Applications</CODE> module.</P><P>At the time of writing, the NCBI do not appear to support tools running a
-PSI-BLAST search via the internet.</P><P>Note that the <CODE>Bio.Blast.NCBIXML</CODE> parser can read the XML output from
-current versions of PSI-BLAST, but information like which sequences in each
-iteration is new or reused isn&#X2019;t present in the XML file.
-If you care about this information you may have more joy with the plain text
-output and the <CODE>PSIBlastParser</CODE> in <CODE>Bio.Blast.NCBIStandalone</CODE>.</P><!--TOC section Dealing with RPS-BLAST-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc97">7.7</A>&#XA0;&#XA0;Dealing with RPS-BLAST</H2><!--SEC END --><P>You can run the standalone version of RPS-BLAST (either the legacy NCBI
-command line tool <CODE>rpsblast</CODE>, or its replacement with the same name)
-using the wrappers in <CODE>Bio.Blast.Applications</CODE> module.</P><P>At the time of writing, the NCBI do not appear to support tools running an
-RPS-BLAST search via the internet.</P><P>You can use the <CODE>Bio.Blast.NCBIXML</CODE> parser to read the XML output from
-current versions of RPS-BLAST.</P><!--TOC chapter BLAST and other sequence search tools (<I>experimental code</I>)-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc98">Chapter&#XA0;8</A>&#XA0;&#XA0;BLAST and other sequence search tools (<I>experimental code</I>)</H1><!--SEC END --><P>
-<A NAME="chapter:searchio"></A></P><P><EM>WARNING</EM>: This chapter of the Tutorial describes an <EM>experimental</EM>
-module in Biopython. It is being included in Biopython and documented
-here in the tutorial in a pre-final state to allow a period of feedback
-and refinement before we declare it stable. Until then the details will
-probably change, and any scripts using the current <CODE>Bio.SearchIO</CODE>
-would need to be updated. Please keep this in mind! For stable code
-working with NCBI BLAST, please continue to use Bio.Blast described
-in the preceding Chapter&#XA0;<A HREF="#chapter:blast">7</A>.</P><P>Biological sequence identification is an integral part of bioinformatics.
-Several tools are available for this, each with their own algorithms and
-approaches, such as BLAST (arguably the most popular), FASTA, HMMER, and many
-more. In general, these tools usually use your sequence to search a database of
-potential matches. With the growing number of known sequences (hence the
-growing number of potential matches), interpreting the results becomes
-increasingly hard as there could be hundreds or even thousands of potential
-matches. Naturally, manual interpretation of these searches&#X2019; results is out of
-the question. Moreover, you often need to work with several sequence search
-tools, each with its own statistics, conventions, and output format. Imagine how
-daunting it would be when you need to work with multiple sequences using
-multiple search tools.</P><P>We know this too well ourselves, which is why we created the <CODE>Bio.SearchIO</CODE>
-submodule in Biopython. <CODE>Bio.SearchIO</CODE> allows you to extract information
-from your search results in a convenient way, while also dealing with the
-different standards and conventions used by different search tools.
-The name <CODE>SearchIO</CODE> is a homage to BioPerl&#X2019;s module of the same name.</P><P>In this chapter, we&#X2019;ll go through the main features of <CODE>Bio.SearchIO</CODE> to
-show what it can do for you. We&#X2019;ll use two popular search tools along the way:
-BLAST and BLAT. They are used merely for illustrative purposes, and you should
-be able to adapt the workflow to any other search tools supported by
-<CODE>Bio.SearchIO</CODE> in a breeze. You&#X2019;re very welcome to follow along with the
-search output files we&#X2019;ll be using. The BLAST output file can be downloaded
-<A HREF="http://biopython.org/SRC/Tests/Tutorial/my_blast.xml">here</A>,
-and the BLAT output file
-<A HREF="http://biopython.org/SRC/Tests/Tutorial/my_blat.psl">here</A>.
-Both output files were generated using this sequence:</P><PRE CLASS="verbatim">>mystery_seq
-CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-</PRE><P>The BLAST result is an XML file generated using <CODE>blastn</CODE> against the NCBI
-<CODE>refseq_rna</CODE> database. For BLAT, the sequence database was the February 2009
-<CODE>hg19</CODE> human genome draft and the output format is PSL.</P><P>We&#X2019;ll start from an introduction to the <CODE>Bio.SearchIO</CODE> object model. The
-model is the representation of your search results, thus it is core to
-<CODE>Bio.SearchIO</CODE> itself. After that, we&#X2019;ll check out the main functions in
-<CODE>Bio.SearchIO</CODE> that you may often use.</P><P>Now that we&#X2019;re all set, let&#X2019;s go to the first step: introducing the core
-object model.</P><!--TOC section The SearchIO object model-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc99">8.1</A>&#XA0;&#XA0;The SearchIO object model</H2><!--SEC END --><P>
-<A NAME="sec:searchio-model"></A></P><P>Despite the wildly differing output styles among many sequence search tools,
-it turns out that their underlying concept is similar:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The output file may contain results from one or more search queries.
-</LI><LI CLASS="li-itemize">In each search query, you will see one or more hits from the given
-search database.
-</LI><LI CLASS="li-itemize">In each database hit, you will see one or more regions containing the
-actual sequence alignment between your query sequence and the database
-sequence.
-</LI><LI CLASS="li-itemize">Some programs like BLAT or Exonerate may further split these regions into
-several alignment fragments (or blocks in BLAT and possibly exons in
-exonerate). This is not something you always see, as programs like BLAST and
-HMMER do not do this.
-</LI></UL><P>Realizing this generality, we decided use it as base for creating the
-<CODE>Bio.SearchIO</CODE> object model. The object model consists of a nested
-hierarchy of Python objects, each one representing one concept outlined above.
-These objects are:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>QueryResult</CODE>, to represent a single search query.
-</LI><LI CLASS="li-itemize"><CODE>Hit</CODE>, to represent a single database hit. <CODE>Hit</CODE> objects are
-contained within <CODE>QueryResult</CODE> and in each <CODE>QueryResult</CODE> there is
-zero or more <CODE>Hit</CODE> objects.
-</LI><LI CLASS="li-itemize"><CODE>HSP</CODE> (short for high-scoring pair), to represent region(s) of
-significant alignments between query and hit sequences. <CODE>HSP</CODE> objects
-are contained within <CODE>Hit</CODE> objects and each <CODE>Hit</CODE> has one or more
-<CODE>HSP</CODE> objects.
-</LI><LI CLASS="li-itemize"><CODE>HSPFragment</CODE>, to represent a single contiguous alignment between
-query and hit sequences. <CODE>HSPFragment</CODE> objects are contained within
-<CODE>HSP</CODE> objects. Most sequence search tools like BLAST and HMMER unify
-<CODE>HSP</CODE> and <CODE>HSPFragment</CODE> objects as each <CODE>HSP</CODE> will only have
-a single <CODE>HSPFragment</CODE>. However there are tools like BLAT and Exonerate
-that produce <CODE>HSP</CODE> containing multiple <CODE>HSPFragment</CODE>. Don&#X2019;t worry
-if this seems a tad confusing now, we&#X2019;ll elaborate more on these two objects
-later on.
-</LI></UL><P>These four objects are the ones you will interact with when you use
-<CODE>Bio.SearchIO</CODE>. They are created using one of the main <CODE>Bio.SearchIO</CODE>
-methods: <CODE>read</CODE>, <CODE>parse</CODE>, <CODE>index</CODE>, or <CODE>index_db</CODE>. The
-details of these methods are provided in later sections. For this section, we&#X2019;ll
-only be using read and parse. These functions behave similarly to their
-<CODE>Bio.SeqIO</CODE> and <CODE>Bio.AlignIO</CODE> counterparts:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>read</CODE> is used for search output files with a single query and
-returns a <CODE>QueryResult</CODE> object
-</LI><LI CLASS="li-itemize"><CODE>parse</CODE> is used for search output files with multiple queries and
-returns a generator that yields <CODE>QueryResult</CODE> objects
-</LI></UL><P>With that settled, let&#X2019;s start probing each <CODE>Bio.SearchIO</CODE> object,
-beginning with <CODE>QueryResult</CODE>.</P><!--TOC subsection QueryResult-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc100">8.1.1</A>&#XA0;&#XA0;QueryResult</H3><!--SEC END --><P>
-<A NAME="sec:searchio-qresult"></A></P><P>The QueryResult object represents a single search query and contains zero or
-more Hit objects. Let&#X2019;s see what it looks like using the BLAST file we have:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> print(blast_qresult)
-Program: blastn (2.2.27+)
-  Query: 42291 (61)
-         mystery_seq
- Target: refseq_rna
-   Hits: ----  -----  ----------------------------------------------------------
-            #  # HSP  ID + description
-         ----  -----  ----------------------------------------------------------
-            0      1  gi|262205317|ref|NR_030195.1|  Homo sapiens microRNA 52...
-            1      1  gi|301171311|ref|NR_035856.1|  Pan troglodytes microRNA...
-            2      1  gi|270133242|ref|NR_032573.1|  Macaca mulatta microRNA ...
-            3      2  gi|301171322|ref|NR_035857.1|  Pan troglodytes microRNA...
-            4      1  gi|301171267|ref|NR_035851.1|  Pan troglodytes microRNA...
-            5      2  gi|262205330|ref|NR_030198.1|  Homo sapiens microRNA 52...
-            6      1  gi|262205302|ref|NR_030191.1|  Homo sapiens microRNA 51...
-            7      1  gi|301171259|ref|NR_035850.1|  Pan troglodytes microRNA...
-            8      1  gi|262205451|ref|NR_030222.1|  Homo sapiens microRNA 51...
-            9      2  gi|301171447|ref|NR_035871.1|  Pan troglodytes microRNA...
-           10      1  gi|301171276|ref|NR_035852.1|  Pan troglodytes microRNA...
-           11      1  gi|262205290|ref|NR_030188.1|  Homo sapiens microRNA 51...
-           12      1  gi|301171354|ref|NR_035860.1|  Pan troglodytes microRNA...
-           13      1  gi|262205281|ref|NR_030186.1|  Homo sapiens microRNA 52...
-           14      2  gi|262205298|ref|NR_030190.1|  Homo sapiens microRNA 52...
-           15      1  gi|301171394|ref|NR_035865.1|  Pan troglodytes microRNA...
-           16      1  gi|262205429|ref|NR_030218.1|  Homo sapiens microRNA 51...
-           17      1  gi|262205423|ref|NR_030217.1|  Homo sapiens microRNA 52...
-           18      1  gi|301171401|ref|NR_035866.1|  Pan troglodytes microRNA...
-           19      1  gi|270133247|ref|NR_032574.1|  Macaca mulatta microRNA ...
-           20      1  gi|262205309|ref|NR_030193.1|  Homo sapiens microRNA 52...
-           21      2  gi|270132717|ref|NR_032716.1|  Macaca mulatta microRNA ...
-           22      2  gi|301171437|ref|NR_035870.1|  Pan troglodytes microRNA...
-           23      2  gi|270133306|ref|NR_032587.1|  Macaca mulatta microRNA ...
-           24      2  gi|301171428|ref|NR_035869.1|  Pan troglodytes microRNA...
-           25      1  gi|301171211|ref|NR_035845.1|  Pan troglodytes microRNA...
-           26      2  gi|301171153|ref|NR_035838.1|  Pan troglodytes microRNA...
-           27      2  gi|301171146|ref|NR_035837.1|  Pan troglodytes microRNA...
-           28      2  gi|270133254|ref|NR_032575.1|  Macaca mulatta microRNA ...
-           29      2  gi|262205445|ref|NR_030221.1|  Homo sapiens microRNA 51...
-           ~~~
-           97      1  gi|356517317|ref|XM_003527287.1|  PREDICTED: Glycine ma...
-           98      1  gi|297814701|ref|XM_002875188.1|  Arabidopsis lyrata su...
-           99      1  gi|397513516|ref|XM_003827011.1|  PREDICTED: Pan panisc...
-</PRE><P>We&#X2019;ve just begun to scratch the surface of the object model, but you can see that
-there&#X2019;s already some useful information. By invoking <CODE>print</CODE> on the
-<CODE>QueryResult</CODE> object, you can see:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The program name and version (blastn version 2.2.27+)
-</LI><LI CLASS="li-itemize">The query ID, description, and its sequence length (ID is 42291,
-description is &#X2018;mystery_seq&#X2019;, and it is 61 nucleotides long)
-</LI><LI CLASS="li-itemize">The target database to search against (refseq_rna)
-</LI><LI CLASS="li-itemize">A quick overview of the resulting hits. For our query sequence, there are
-100 potential hits (numbered 0&#X2013;99 in the table). For each hit, we can also see
-how many HSPs it contains, its ID, and a snippet of its description. Notice
-here that <CODE>Bio.SearchIO</CODE> truncates the hit table overview, by showing
-only hits numbered 0&#X2013;29, and then 97&#X2013;99.
-</LI></UL><P>Now let&#X2019;s check our BLAT results using the same procedure as above:</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> print(blat_qresult)
-Program: blat (<unknown version>)
-  Query: mystery_seq (61)
-         <unknown description>
- Target: <unknown target>
-   Hits: ----  -----  ----------------------------------------------------------
-            #  # HSP  ID + description
-         ----  -----  ----------------------------------------------------------
-            0     17  chr19  <unknown description>
-</PRE><P>You&#X2019;ll immediately notice that there are some differences. Some of these are
-caused by the way PSL format stores its details, as you&#X2019;ll see. The rest are
-caused by the genuine program and target database differences between our BLAST
-and BLAT searches:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The program name and version. <CODE>Bio.SearchIO</CODE> knows that the program
-is BLAT, but in the output file there is no information regarding the
-program version so it defaults to &#X2018;<unknown version>&#X2019;.
-</LI><LI CLASS="li-itemize">The query ID, description, and its sequence length. Notice here that these
-details are slightly different from the ones we saw in BLAST. The ID is
-&#X2018;mystery_seq&#X2019; instead of 42991, there is no known description, but the query
-length is still 61. This is actually a difference introduced by the file
-formats themselves. BLAST sometimes creates its own query IDs and uses your
-original ID as the sequence description.
-</LI><LI CLASS="li-itemize">The target database is not known, as it is not stated in the BLAT output
-file.
-</LI><LI CLASS="li-itemize">And finally, the list of hits we have is completely different. Here, we
-see that our query sequence only hits the &#X2018;chr19&#X2019; database entry, but in it
-we see 17 HSP regions. This should not be surprising however, given that we
-are using a different program, each with its own target database.
-</LI></UL><P>All the details you saw when invoking the <CODE>print</CODE> method can be accessed
-individually using Python&#X2019;s object attribute access notation (a.k.a. the dot
-notation). There are also other format-specific attributes that you can access
-using the same method.</P><PRE CLASS="verbatim">>>> print("%s %s" % (blast_qresult.program, blast_qresult.version))
-blastn 2.2.27+
->>> print("%s %s" % (blat_qresult.program, blat_qresult.version))
-blat <unknown version>
->>> blast_qresult.param_evalue_threshold    # blast-xml specific
-10.0
-</PRE><P>For a complete list of accessible attributes, you can check each format-specific
-documentation. Here are the ones
-<A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO.BlastIO-module.html">for BLAST</A>
-and for
-<A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO.BlatIO-module.html">BLAT</A>.</P><P>Having looked at using <CODE>print</CODE> on <CODE>QueryResult</CODE> objects, let&#X2019;s drill
-down deeper. What exactly is a <CODE>QueryResult</CODE>? In terms of Python objects,
-<CODE>QueryResult</CODE> is a hybrid between a list and a dictionary. In other words,
-it is a container object with all the convenient features of lists and
-dictionaries.</P><P>Like Python lists and dictionaries, <CODE>QueryResult</CODE> objects are iterable.
-Each iteration returns a <CODE>Hit</CODE> object:</P><PRE CLASS="verbatim">>>> for hit in blast_qresult:
-...     hit
-Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
-Hit(id='gi|301171311|ref|NR_035856.1|', query_id='42291', 1 hsps)
-Hit(id='gi|270133242|ref|NR_032573.1|', query_id='42291', 1 hsps)
-Hit(id='gi|301171322|ref|NR_035857.1|', query_id='42291', 2 hsps)
-Hit(id='gi|301171267|ref|NR_035851.1|', query_id='42291', 1 hsps)
-...
-</PRE><P>To check how many items (hits) a <CODE>QueryResult</CODE> has, you can simply invoke
-Python&#X2019;s <CODE>len</CODE> method:</P><PRE CLASS="verbatim">>>> len(blast_qresult)
-100
->>> len(blat_qresult)
-1
-</PRE><P>Like Python lists, you can retrieve items (hits) from a <CODE>QueryResult</CODE> using
-the slice notation:</P><PRE CLASS="verbatim">>>> blast_qresult[0]        # retrieves the top hit
-Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
->>> blast_qresult[-1]       # retrieves the last hit
-Hit(id='gi|397513516|ref|XM_003827011.1|', query_id='42291', 1 hsps)
-</PRE><P>To retrieve multiple hits, you can slice <CODE>QueryResult</CODE> objects using the
-slice notation as well. In this case, the slice will return a new
-<CODE>QueryResult</CODE> object containing only the sliced hits:</P><PRE CLASS="verbatim">>>> blast_slice = blast_qresult[:3]     # slices the first three hits
->>> print(blast_slice)
-Program: blastn (2.2.27+)
-  Query: 42291 (61)
-         mystery_seq
- Target: refseq_rna
-   Hits: ----  -----  ----------------------------------------------------------
-            #  # HSP  ID + description
-         ----  -----  ----------------------------------------------------------
-            0      1  gi|262205317|ref|NR_030195.1|  Homo sapiens microRNA 52...
-            1      1  gi|301171311|ref|NR_035856.1|  Pan troglodytes microRNA...
-            2      1  gi|270133242|ref|NR_032573.1|  Macaca mulatta microRNA ...
-</PRE><P>Like Python dictionaries, you can also retrieve hits using the hit&#X2019;s ID. This is
-particularly useful if you know a given hit ID exists within a search query
-results:</P><PRE CLASS="verbatim">>>> blast_qresult['gi|262205317|ref|NR_030195.1|']
-Hit(id='gi|262205317|ref|NR_030195.1|', query_id='42291', 1 hsps)
-</PRE><P>You can also get a full list of <CODE>Hit</CODE> objects using <CODE>hits</CODE> and a full
-list of <CODE>Hit</CODE> IDs using <CODE>hit_keys</CODE>:</P><PRE CLASS="verbatim">>>> blast_qresult.hits
-[...]       # list of all hits
->>> blast_qresult.hit_keys
-[...]       # list of all hit IDs
-</PRE><P>What if you just want to check whether a particular hit is present in the query
-results? You can do a simple Python membership test using the <CODE>in</CODE> keyword:</P><PRE CLASS="verbatim">>>> 'gi|262205317|ref|NR_030195.1|' in blast_qresult
-True
->>> 'gi|262205317|ref|NR_030194.1|' in blast_qresult
-False
-</PRE><P>Sometimes, knowing whether a hit is present is not enough; you also want to know
-the rank of the hit. Here, the <CODE>index</CODE> method comes to the rescue:</P><PRE CLASS="verbatim">>>> blast_qresult.index('gi|301171437|ref|NR_035870.1|')
-22
-</PRE><P>Remember that we&#X2019;re using Python&#X2019;s indexing style here, which is zero-based.
-This means our hit above is ranked at no. 23, not 22.</P><P>Also, note that the hit rank you see here is based on the native hit ordering
-present in the original search output file. Different search tools may order
-these hits based on different criteria.</P><P>If the native hit ordering doesn&#X2019;t suit your taste, you can use the <CODE>sort</CODE>
-method of the <CODE>QueryResult</CODE> object. It is very similar to Python&#X2019;s
-<CODE>list.sort</CODE> method, with the addition of an option to create a new sorted
-<CODE>QueryResult</CODE> object or not.</P><P>Here is an example of using <CODE>QueryResult.sort</CODE> to sort the hits based on
-each hit&#X2019;s full sequence length. For this particular sort, we&#X2019;ll set the
-<CODE>in_place</CODE> flag to <CODE>False</CODE> so that sorting will return a new
-<CODE>QueryResult</CODE> object and leave our initial object unsorted. We&#X2019;ll also set
-the <CODE>reverse</CODE> flag to <CODE>True</CODE> so that we sort in descending order.</P><PRE CLASS="verbatim">>>> for hit in blast_qresult[:5]:   # id and sequence length of the first five hits
-...     print("%s %i" % (hit.id, hit.seq_len))
-...
-gi|262205317|ref|NR_030195.1| 61
-gi|301171311|ref|NR_035856.1| 60
-gi|270133242|ref|NR_032573.1| 85
-gi|301171322|ref|NR_035857.1| 86
-gi|301171267|ref|NR_035851.1| 80
-
->>> sort_key = lambda hit: hit.seq_len
->>> sorted_qresult = blast_qresult.sort(key=sort_key, reverse=True, in_place=False)
->>> for hit in sorted_qresult[:5]:
-...     print("%s %i" % (hit.id, hit.seq_len))
-...
-gi|397513516|ref|XM_003827011.1| 6002
-gi|390332045|ref|XM_776818.2| 4082
-gi|390332043|ref|XM_003723358.1| 4079
-gi|356517317|ref|XM_003527287.1| 3251
-gi|356543101|ref|XM_003539954.1| 2936
-</PRE><P>The advantage of having the <CODE>in_place</CODE> flag here is that we&#X2019;re preserving
-the native ordering, so we may use it again later. You should note that this is
-not the default behavior of <CODE>QueryResult.sort</CODE>, however, which is why we
-needed to set the <CODE>in_place</CODE> flag to <CODE>True</CODE> explicitly.</P><P>At this point, you&#X2019;ve known enough about <CODE>QueryResult</CODE> objects to make it
-work for you. But before we go on to the next object in the <CODE>Bio.SearchIO</CODE>
-model, let&#X2019;s take a look at two more sets of methods that could make it even
-easier to work with <CODE>QueryResult</CODE> objects: the <CODE>filter</CODE> and <CODE>map</CODE>
-methods.</P><P>If you&#X2019;re familiar with Python&#X2019;s list comprehensions, generator expressions
-or the built in <CODE>filter</CODE> and <CODE>map</CODE> functions,
-you&#X2019;ll know how useful they are for working with list-like objects (if you&#X2019;re
-not, check them out!). You can use these built in methods to manipulate
-<CODE>QueryResult</CODE> objects, but you&#X2019;ll end up with regular Python lists and lose
-the ability to do more interesting manipulations.</P><P>That&#X2019;s why, <CODE>QueryResult</CODE> objects provide its own flavor of
-<CODE>filter</CODE> and <CODE>map</CODE> methods. Analogous to <CODE>filter</CODE>, there are
-<CODE>hit_filter</CODE> and <CODE>hsp_filter</CODE> methods. As their name implies, these
-methods filter its <CODE>QueryResult</CODE> object either on its <CODE>Hit</CODE> objects
-or <CODE>HSP</CODE> objects. Similarly, analogous to <CODE>map</CODE>, <CODE>QueryResult</CODE>
-objects also provide the <CODE>hit_map</CODE> and <CODE>hsp_map</CODE> methods. These
-methods apply a given function to all hits or HSPs in a <CODE>QueryResult</CODE>
-object, respectively.</P><P>Let&#X2019;s see these methods in action, beginning with <CODE>hit_filter</CODE>. This method
-accepts a callback function that checks whether a given <CODE>Hit</CODE> object passes
-the condition you set or not. In other words, the function must accept as its
-argument a single <CODE>Hit</CODE> object and returns <CODE>True</CODE> or <CODE>False</CODE>.</P><P>Here is an example of using <CODE>hit_filter</CODE> to filter out <CODE>Hit</CODE> objects
-that only have one HSP:</P><PRE CLASS="verbatim">>>> filter_func = lambda hit: len(hit.hsps) > 1     # the callback function
->>> len(blast_qresult)      # no. of hits before filtering
-100
->>> filtered_qresult = blast_qresult.hit_filter(filter_func)
->>> len(filtered_qresult)   # no. of hits after filtering
-37
->>> for hit in filtered_qresult[:5]:    # quick check for the hit lengths
-...     print("%s %i" % (hit.id, len(hit.hsps)))
-gi|301171322|ref|NR_035857.1| 2
-gi|262205330|ref|NR_030198.1| 2
-gi|301171447|ref|NR_035871.1| 2
-gi|262205298|ref|NR_030190.1| 2
-gi|270132717|ref|NR_032716.1| 2
-</PRE><P><CODE>hsp_filter</CODE> works the same as <CODE>hit_filter</CODE>, only instead of looking
-at the <CODE>Hit</CODE> objects, it performs filtering on the <CODE>HSP</CODE> objects in
-each hits.</P><P>As for the <CODE>map</CODE> methods, they too accept a callback function as their
-arguments. However, instead of returning <CODE>True</CODE> or <CODE>False</CODE>, the
-callback function must return the modified <CODE>Hit</CODE> or <CODE>HSP</CODE> object
-(depending on whether you&#X2019;re using <CODE>hit_map</CODE> or <CODE>hsp_map</CODE>).</P><P>Let&#X2019;s see an example where we&#X2019;re using <CODE>hit_map</CODE> to rename the hit IDs:</P><PRE CLASS="verbatim">>>> def map_func(hit):
-...     hit.id = hit.id.split('|')[3]   # renames 'gi|301171322|ref|NR_035857.1|' to 'NR_035857.1'
-...     return hit
-...
->>> mapped_qresult = blast_qresult.hit_map(map_func)
->>> for hit in mapped_qresult[:5]:
-...     print(hit.id)
-NR_030195.1
-NR_035856.1
-NR_032573.1
-NR_035857.1
-NR_035851.1
-</PRE><P>Again, <CODE>hsp_map</CODE> works the same as <CODE>hit_map</CODE>, but on <CODE>HSP</CODE>
-objects instead of <CODE>Hit</CODE> objects.</P><!--TOC subsection Hit-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc101">8.1.2</A>&#XA0;&#XA0;Hit</H3><!--SEC END --><P>
-<A NAME="sec:searchio-hit"></A></P><P><CODE>Hit</CODE> objects represent all query results from a single database entry.
-They are the second-level container in the <CODE>Bio.SearchIO</CODE> object hierarchy.
-You&#X2019;ve seen that they are contained by <CODE>QueryResult</CODE> objects, but they
-themselves contain <CODE>HSP</CODE> objects.</P><P>Let&#X2019;s see what they look like, beginning with our BLAST search:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> blast_hit = blast_qresult[3]    # fourth hit from the query result
->>> print(blast_hit)
-Query: 42291
-       mystery_seq
-  Hit: gi|301171322|ref|NR_035857.1| (86)
-       Pan troglodytes microRNA mir-520c (MIR520C), microRNA
- HSPs: ----  --------  ---------  ------  ---------------  ---------------------
-          #   E-value  Bit score    Span      Query range              Hit range
-       ----  --------  ---------  ------  ---------------  ---------------------
-          0   8.9e-20     100.47      60           [1:61]                [13:73]
-          1   3.3e-06      55.39      60           [0:60]                [13:73]
-</PRE><P>You see that we&#X2019;ve got the essentials covered here:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The query ID and description is present. A hit is always tied to a query,
-so we want to keep track of the originating query as well. These values can
-be accessed from a hit using the <CODE>query_id</CODE> and
-<CODE>query_description</CODE> attributes.
-</LI><LI CLASS="li-itemize">We also have the unique hit ID, description, and full sequence lengths.
-They can be accessed using <CODE>id</CODE>, <CODE>description</CODE>, and
-<CODE>seq_len</CODE>, respectively.
-</LI><LI CLASS="li-itemize">Finally, there&#X2019;s a table containing quick information about the HSPs this
-hit contains. In each row, we&#X2019;ve got the important HSP details listed: the
-HSP index, its e-value, its bit score, its span (the alignment length
-including gaps), its query coordinates, and its hit coordinates.
-</LI></UL><P>Now let&#X2019;s contrast this with the BLAT search. Remember that in the BLAT search we
-had one hit with 17 HSPs.</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> blat_hit = blat_qresult[0]      # the only hit
->>> print(blat_hit)
-Query: mystery_seq
-       <unknown description>
-  Hit: chr19 (59128983)
-       <unknown description>
- HSPs: ----  --------  ---------  ------  ---------------  ---------------------
-          #   E-value  Bit score    Span      Query range              Hit range
-       ----  --------  ---------  ------  ---------------  ---------------------
-          0         ?          ?       ?           [0:61]    [54204480:54204541]
-          1         ?          ?       ?           [0:61]    [54233104:54264463]
-          2         ?          ?       ?           [0:61]    [54254477:54260071]
-          3         ?          ?       ?           [1:61]    [54210720:54210780]
-          4         ?          ?       ?           [0:60]    [54198476:54198536]
-          5         ?          ?       ?           [0:61]    [54265610:54265671]
-          6         ?          ?       ?           [0:61]    [54238143:54240175]
-          7         ?          ?       ?           [0:60]    [54189735:54189795]
-          8         ?          ?       ?           [0:61]    [54185425:54185486]
-          9         ?          ?       ?           [0:60]    [54197657:54197717]
-         10         ?          ?       ?           [0:61]    [54255662:54255723]
-         11         ?          ?       ?           [0:61]    [54201651:54201712]
-         12         ?          ?       ?           [8:60]    [54206009:54206061]
-         13         ?          ?       ?          [10:61]    [54178987:54179038]
-         14         ?          ?       ?           [8:61]    [54212018:54212071]
-         15         ?          ?       ?           [8:51]    [54234278:54234321]
-         16         ?          ?       ?           [8:61]    [54238143:54238196]
-</PRE><P>Here, we&#X2019;ve got a similar level of detail as with the BLAST hit we saw earlier.
-There are some differences worth explaining, though:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The e-value and bit score column values. As BLAT HSPs do not have e-values
-and bit scores, the display defaults to &#X2018;?&#X2019;.
-</LI><LI CLASS="li-itemize">What about the span column? The span values is meant to display the
-complete alignment length, which consists of all residues and any gaps that
-may be present. The PSL format do not have this information readily available
-and <CODE>Bio.SearchIO</CODE> does not attempt to try guess what it is, so we get a
-&#X2018;?&#X2019; similar to the e-value and bit score columns.
-</LI></UL><P>In terms of Python objects, <CODE>Hit</CODE> behaves almost the same as Python lists,
-but contain <CODE>HSP</CODE> objects exclusively. If you&#X2019;re familiar with lists, you
-should encounter no difficulties working with the <CODE>Hit</CODE> object.</P><P>Just like Python lists, <CODE>Hit</CODE> objects are iterable, and each iteration
-returns one <CODE>HSP</CODE> object it contains:</P><PRE CLASS="verbatim">>>> for hsp in blast_hit:
-...     hsp
-HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments)
-HSP(hit_id='gi|301171322|ref|NR_035857.1|', query_id='42291', 1 fragments)
-</PRE><P>You can invoke <CODE>len</CODE> on a <CODE>Hit</CODE> to see how many <CODE>HSP</CODE> objects it
-has:</P><PRE CLASS="verbatim">>>> len(blast_hit)
-2
->>> len(blat_hit)
-17
-</PRE><P>You can use the slice notation on <CODE>Hit</CODE> objects, whether to retrieve single
-<CODE>HSP</CODE> or multiple <CODE>HSP</CODE> objects. Like <CODE>QueryResult</CODE>, if you slice
-for multiple <CODE>HSP</CODE>, a new <CODE>Hit</CODE> object will be returned containing
-only the sliced <CODE>HSP</CODE> objects:</P><PRE CLASS="verbatim">>>> blat_hit[0]                 # retrieve single items
-HSP(hit_id='chr19', query_id='mystery_seq', 1 fragments)
->>> sliced_hit = blat_hit[4:9]  # retrieve multiple items
->>> len(sliced_hit)
-5
->>> print(sliced_hit)
-Query: mystery_seq
-       <unknown description>
-  Hit: chr19 (59128983)
-       <unknown description>
- HSPs: ----  --------  ---------  ------  ---------------  ---------------------
-          #   E-value  Bit score    Span      Query range              Hit range
-       ----  --------  ---------  ------  ---------------  ---------------------
-          0         ?          ?       ?           [0:60]    [54198476:54198536]
-          1         ?          ?       ?           [0:61]    [54265610:54265671]
-          2         ?          ?       ?           [0:61]    [54238143:54240175]
-          3         ?          ?       ?           [0:60]    [54189735:54189795]
-          4         ?          ?       ?           [0:61]    [54185425:54185486]
-</PRE><P>You can also sort the <CODE>HSP</CODE> inside a <CODE>Hit</CODE>, using the exact same
-arguments like the sort method you saw in the <CODE>QueryResult</CODE> object.</P><P>Finally, there are also the <CODE>filter</CODE> and <CODE>map</CODE> methods you can use
-on <CODE>Hit</CODE> objects. Unlike in the <CODE>QueryResult</CODE> object, <CODE>Hit</CODE>
-objects only have one variant of <CODE>filter</CODE> (<CODE>Hit.filter</CODE>) and one
-variant of <CODE>map</CODE> (<CODE>Hit.map</CODE>). Both of <CODE>Hit.filter</CODE> and
-<CODE>Hit.map</CODE> work on the <CODE>HSP</CODE> objects a <CODE>Hit</CODE> has.</P><!--TOC subsection HSP-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc102">8.1.3</A>&#XA0;&#XA0;HSP</H3><!--SEC END --><P>
-<A NAME="sec:searchio-hsp"></A></P><P><CODE>HSP</CODE> (high-scoring pair) represents region(s) in the hit sequence that
-contains significant alignment(s) to the query sequence. It contains the actual
-match between your query sequence and a database entry. As this match is
-determined by the sequence search tool&#X2019;s algorithms, the <CODE>HSP</CODE> object
-contains the bulk of the statistics computed by the search tool. This also makes
-the distinction between <CODE>HSP</CODE> objects from different search tools more
-apparent compared to the differences you&#X2019;ve seen in <CODE>QueryResult</CODE> or
-<CODE>Hit</CODE> objects.</P><P>Let&#X2019;s see some examples from our BLAST and BLAT searches. We&#X2019;ll look at the
-BLAST HSP first:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> blast_hsp = blast_qresult[0][0]    # first hit, first hsp
->>> print(blast_hsp)
-      Query: 42291 mystery_seq
-        Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
-Query range: [0:61] (1)
-  Hit range: [0:61] (1)
-Quick stats: evalue 4.9e-23; bitscore 111.29
-  Fragments: 1 (61 columns)
-     Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-             |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
-       Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-</PRE><P>Just like <CODE>QueryResult</CODE> and <CODE>Hit</CODE>, invoking <CODE>print</CODE> on an
-<CODE>HSP</CODE> shows its general details:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-There are the query and hit IDs and descriptions. We need these to
-identify our <CODE>HSP</CODE>.
-</LI><LI CLASS="li-itemize">We&#X2019;ve also got the matching range of the query and hit sequences. The
-slice notation we&#X2019;re using here is an indication that the range is displayed
-using Python&#X2019;s indexing style (zero-based, half open). The number inside the
-parenthesis denotes the strand. In this case, both sequences have the plus
-strand.
-</LI><LI CLASS="li-itemize">Some quick statistics are available: the e-value and bitscore.
-</LI><LI CLASS="li-itemize">There is information about the HSP fragments. Ignore this for now; it will
-be explained later on.
-</LI><LI CLASS="li-itemize">And finally, we have the query and hit sequence alignment itself.
-</LI></UL><P>These details can be accessed on their own using the dot notation, just like in
-<CODE>QueryResult</CODE> and <CODE>Hit</CODE>:</P><PRE CLASS="verbatim">>>> blast_hsp.query_range
-(0, 61)
-</PRE><PRE CLASS="verbatim">>>> blast_hsp.evalue
-4.91307e-23
-</PRE><P>They&#X2019;re not the only attributes available, though. <CODE>HSP</CODE> objects come with
-a default set of properties that makes it easy to probe their various
-details. Here are some examples:</P><PRE CLASS="verbatim">>>> blast_hsp.hit_start         # start coordinate of the hit sequence
-0
->>> blast_hsp.query_span        # how many residues in the query sequence
-61
->>> blast_hsp.aln_span          # how long the alignment is
-61
-</PRE><P>Check out the <CODE>HSP</CODE>
-<A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO._model.hsp-module.html">documentation</A>
-for a full list of these predefined properties.</P><P>Furthermore, each sequence search tool usually computes its own statistics /
-details for its <CODE>HSP</CODE> objects. For example, an XML BLAST search also
-outputs the number of gaps and identical residues. These attributes can be
-accessed like so:</P><PRE CLASS="verbatim">>>> blast_hsp.gap_num       # number of gaps
-0
->>> blast_hsp.ident_num     # number of identical residues
-61
-</PRE><P>These details are format-specific; they may not be present in other formats.
-To see which details are available for a given sequence search tool, you
-should check the format&#X2019;s documentation in <CODE>Bio.SearchIO</CODE>. Alternatively,
-you may also use <CODE>.__dict__.keys()</CODE> for a quick list of what&#X2019;s available:</P><PRE CLASS="verbatim">>>> blast_hsp.__dict__.keys()
-['bitscore', 'evalue', 'ident_num', 'gap_num', 'bitscore_raw', 'pos_num', '_items']
-</PRE><P>Finally, you may have noticed that the <CODE>query</CODE> and <CODE>hit</CODE> attributes
-of our HSP are not just regular strings:</P><PRE CLASS="verbatim">>>> blast_hsp.query
-SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='42291', name='aligned query sequence', description='mystery_seq', dbxrefs=[])
->>> blast_hsp.hit
-SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])
-</PRE><P>They are <CODE>SeqRecord</CODE> objects you saw earlier in
-Section&#XA0;<A HREF="#chapter:SeqRecord">4</A>! This means that you can do all sorts of
-interesting things you can do with <CODE>SeqRecord</CODE> objects on <CODE>HSP.query</CODE>
-and/or <CODE>HSP.hit</CODE>.</P><P>It should not surprise you now that the <CODE>HSP</CODE> object has an
-<CODE>alignment</CODE> property which is a <CODE>MultipleSeqAlignment</CODE> object:</P><PRE CLASS="verbatim">>>> print(blast_hsp.aln)
-DNAAlphabet() alignment with 2 rows and 61 columns
-CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG 42291
-CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG gi|262205317|ref|NR_030195.1|
-</PRE><P>Having probed the BLAST HSP, let&#X2019;s now take a look at HSPs from our BLAT
-results for a different kind of HSP. As usual, we&#X2019;ll begin by invoking
-<CODE>print</CODE> on it:</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> blat_hsp = blat_qresult[0][0]       # first hit, first hsp
->>> print(blat_hsp)
-      Query: mystery_seq <unknown description>
-        Hit: chr19 <unknown description>
-Query range: [0:61] (1)
-  Hit range: [54204480:54204541] (1)
-Quick stats: evalue ?; bitscore ?
-  Fragments: 1 (? columns)
-</PRE><P>Some of the outputs you may have already guessed. We have the query and hit IDs
-and descriptions and the sequence coordinates. Values for evalue and bitscore is
-&#X2018;?&#X2019; as BLAT HSPs do not have these attributes. But The biggest difference here
-is that you don&#X2019;t see any sequence alignments displayed. If you look closer, PSL
-formats themselves do not have any hit or query sequences, so
-<CODE>Bio.SearchIO</CODE> won&#X2019;t create any sequence or alignment objects. What happens
-if you try to access <CODE>HSP.query</CODE>, <CODE>HSP.hit</CODE>, or <CODE>HSP.aln</CODE>?
-You&#X2019;ll get the default values for these attributes, which is <CODE>None</CODE>:</P><PRE CLASS="verbatim">>>> blat_hsp.hit is None
-True
->>> blat_hsp.query is None
-True
->>> blat_hsp.aln is None
-True
-</PRE><P>This does not affect other attributes, though. For example, you can still
-access the length of the query or hit alignment. Despite not displaying any
-attributes, the PSL format still have this information so <CODE>Bio.SearchIO</CODE>
-can extract them:</P><PRE CLASS="verbatim">>>> blat_hsp.query_span     # length of query match
-61
->>> blat_hsp.hit_span       # length of hit match
-61
-</PRE><P>Other format-specific attributes are still present as well:</P><PRE CLASS="verbatim">>>> blat_hsp.score          # PSL score
-61
->>> blat_hsp.mismatch_num   # the mismatch column
-0
-</PRE><P>So far so good? Things get more interesting when you look at another &#X2018;variant&#X2019;
-of HSP present in our BLAT results. You might recall that in BLAT searches,
-sometimes we get our results separated into &#X2018;blocks&#X2019;. These blocks are
-essentially alignment fragments that may have some intervening sequence between
-them.</P><P>Let&#X2019;s take a look at a BLAT HSP that contains multiple blocks to see how
-<CODE>Bio.SearchIO</CODE> deals with this:</P><PRE CLASS="verbatim">>>> blat_hsp2 = blat_qresult[0][1]      # first hit, second hsp
->>> print(blat_hsp2)
-      Query: mystery_seq <unknown description>
-        Hit: chr19 <unknown description>
-Query range: [0:61] (1)
-  Hit range: [54233104:54264463] (1)
-Quick stats: evalue ?; bitscore ?
-  Fragments: ---  --------------  ----------------------  ----------------------
-               #            Span             Query range               Hit range
-             ---  --------------  ----------------------  ----------------------
-               0               ?                  [0:18]     [54233104:54233122]
-               1               ?                 [18:61]     [54264420:54264463]
-</PRE><P>What&#X2019;s happening here? We still some essential details covered: the IDs and
-descriptions, the coordinates, and the quick statistics are similar to what
-you&#X2019;ve seen before. But the fragments detail is all different. Instead of
-showing &#X2018;Fragments: 1&#X2019;, we now have a table with two data rows.</P><P>This is how <CODE>Bio.SearchIO</CODE> deals with HSPs having multiple fragments. As
-mentioned before, an HSP alignment may be separated by intervening sequences
-into fragments. The intervening sequences are not part of the query-hit match,
-so they should not be considered part of query nor hit sequence. However, they
-do affect how we deal with sequence coordinates, so we can&#X2019;t ignore them.</P><P>Take a look at the hit coordinate of the HSP above. In the <CODE>Hit range:</CODE> field,
-we see that the coordinate is <CODE>[54233104:54264463]</CODE>. But looking at the
-table rows, we see that not the entire region spanned by this coordinate matches
-our query. Specifically, the intervening region spans from <CODE>54233122</CODE> to
-<CODE>54264420</CODE>.</P><P>Why then, is the query coordinates seem to be contiguous, you ask? This is
-perfectly fine. In this case it means that the query match is contiguous (no
-intervening regions), while the hit match is not.</P><P>All these attributes are accessible from the HSP directly, by the way:</P><PRE CLASS="verbatim">>>> blat_hsp2.hit_range         # hit start and end coordinates of the entire HSP
-(54233104, 54264463)
->>> blat_hsp2.hit_range_all     # hit start and end coordinates of each fragment
-[(54233104, 54233122), (54264420, 54264463)]
->>> blat_hsp2.hit_span          # hit span of the entire HSP
-31359
->>> blat_hsp2.hit_span_all      # hit span of each fragment
-[18, 43]
->>> blat_hsp2.hit_inter_ranges  # start and end coordinates of intervening regions in the hit sequence
-[(54233122, 54264420)]
->>> blat_hsp2.hit_inter_spans   # span of intervening regions in the hit sequence
-[31298]
-</PRE><P>Most of these attributes are not readily available from the PSL file we have,
-but <CODE>Bio.SearchIO</CODE> calculates them for you on the fly when you parse the
-PSL file. All it needs are the start and end coordinates of each fragment.</P><P>What about the <CODE>query</CODE>, <CODE>hit</CODE>, and <CODE>aln</CODE> attributes? If the
-HSP has multiple fragments, you won&#X2019;t be able to use these attributes as they
-only fetch single <CODE>SeqRecord</CODE> or <CODE>MultipleSeqAlignment</CODE> objects.
-However, you can use their <CODE>*_all</CODE> counterparts: <CODE>query_all</CODE>,
-<CODE>hit_all</CODE>, and <CODE>aln_all</CODE>. These properties will return a list containing
-<CODE>SeqRecord</CODE> or <CODE>MultipleSeqAlignment</CODE> objects from each of the HSP
-fragment. There are other attributes that behave similarly, i.e. they only work
-for HSPs with one fragment. Check out the <CODE>HSP</CODE> <A HREF="http://biopython.org/DIST/docs/api/Bio.SearchIO._model.hsp-module.html">documentation</A>
-for a full list.</P><P>Finally, to check whether you have multiple fragments or not, you can use the
-<CODE>is_fragmented</CODE> property like so:</P><PRE CLASS="verbatim">>>> blat_hsp2.is_fragmented     # BLAT HSP with 2 fragments
-True
->>> blat_hsp.is_fragmented      # BLAT HSP from earlier, with one fragment
-False
-</PRE><P>Before we move on, you should also know that we can use the slice notation on
-<CODE>HSP</CODE> objects, just like <CODE>QueryResult</CODE> or <CODE>Hit</CODE> objects. When
-you use this notation, you&#X2019;ll get an <CODE>HSPFragment</CODE> object in return, the
-last component of the object model.</P><!--TOC subsection HSPFragment-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc103">8.1.4</A>&#XA0;&#XA0;HSPFragment</H3><!--SEC END --><P>
-<A NAME="sec:searchio-hspfragment"></A></P><P><CODE>HSPFragment</CODE> represents a single, contiguous match between the query and
-hit sequences. You could consider it the core of the object model and search
-result, since it is the presence of these fragments that determine whether your
-search have results or not.</P><P>In most cases, you don&#X2019;t have to deal with <CODE>HSPFragment</CODE> objects directly
-since not that many sequence search tools fragment their HSPs. When you do have
-to deal with them, what you should remember is that <CODE>HSPFragment</CODE> objects
-were written with to be as compact as possible. In most cases, they only contain
-attributes directly related to sequences: strands, reading frames, alphabets,
-coordinates, the sequences themselves, and their IDs and descriptions.</P><P>These attributes are readily shown when you invoke <CODE>print</CODE> on an
-<CODE>HSPFragment</CODE>. Here&#X2019;s an example, taken from our BLAST search:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
->>> blast_frag = blast_qresult[0][0][0]    # first hit, first hsp, first fragment
->>> print(blast_frag)
-      Query: 42291 mystery_seq
-        Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
-Query range: [0:61] (1)
-  Hit range: [0:61] (1)
-  Fragments: 1 (61 columns)
-     Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-             |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
-       Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
-</PRE><P>At this level, the BLAT fragment looks quite similar to the BLAST fragment, save
-for the query and hit sequences which are not present:</P><PRE CLASS="verbatim">>>> blat_qresult = SearchIO.read('my_blat.psl', 'blat-psl')
->>> blat_frag = blat_qresult[0][0][0]    # first hit, first hsp, first fragment
->>> print(blat_frag)
-      Query: mystery_seq <unknown description>
-        Hit: chr19 <unknown description>
-Query range: [0:61] (1)
-  Hit range: [54204480:54204541] (1)
-  Fragments: 1 (? columns)
-</PRE><P>In all cases, these attributes are accessible using our favorite dot notation.
-Some examples:</P><PRE CLASS="verbatim">>>> blast_frag.query_start      # query start coordinate
-0
->>> blast_frag.hit_strand       # hit sequence strand
-1
->>> blast_frag.hit              # hit sequence, as a SeqRecord object
-SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])
-</PRE><!--TOC section A note about standards and conventions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc104">8.2</A>&#XA0;&#XA0;A note about standards and conventions</H2><!--SEC END --><P>
-<A NAME="sec:searchio-standards"></A></P><P>Before we move on to the main functions, there is something you ought to know
-about the standards <CODE>Bio.SearchIO</CODE> uses. If you&#X2019;ve worked with multiple
-sequence search tools, you might have had to deal with the many different ways
-each program deals with things like sequence coordinates. It might not have been
-a pleasant experience as these search tools usually have their own standards.
-For example, one tools might use one-based coordinates, while the other uses
-zero-based coordinates. Or, one program might reverse the start and end
-coordinates if the strand is minus, while others don&#X2019;t. In short, these often
-creates unnecessary mess must be dealt with.</P><P>We realize this problem ourselves and we intend to address it in
-<CODE>Bio.SearchIO</CODE>. After all, one of the goals of <CODE>Bio.SearchIO</CODE> is to
-create a common, easy to use interface to deal with various search output files.
-This means creating standards that extend beyond the object model you just saw.</P><P>Now, you might complain, "Not another standard!". Well, eventually we have to
-choose one convention or the other, so this is necessary. Plus, we&#X2019;re not
-creating something entirely new here; just adopting a standard we think is best
-for a Python programmer (it is Biopython, after all).</P><P>There are three implicit standards that you can expect when working with
-<CODE>Bio.SearchIO</CODE>:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The first one pertains to sequence coordinates. In <CODE>Bio.SearchIO</CODE>,
-all sequence coordinates follows Python&#X2019;s coordinate style: zero-based and
-half open. For example, if in a BLAST XML output file the start and end
-coordinates of an HSP are 10 and 28, they would become 9 and 28 in
-<CODE>Bio.SearchIO</CODE>. The start coordinate becomes 9 because Python indices
-start from zero, while the end coordinate remains 28 as Python slices omit
-the last item in an interval.
-</LI><LI CLASS="li-itemize">The second is on sequence coordinate orders. In <CODE>Bio.SearchIO</CODE>, start
-coordinates are always less than or equal to end coordinates. This isn&#X2019;t
-always the case with all sequence search tools, as some of them have larger
-start coordinates when the sequence strand is minus.
-</LI><LI CLASS="li-itemize">The last one is on strand and reading frame values. For strands, there are
-only four valid choices: <CODE>1</CODE> (plus strand), <CODE>-1</CODE> (minus strand),
-<CODE>0</CODE> (protein sequences), and <CODE>None</CODE> (no strand). For reading
-frames, the valid choices are integers from <CODE>-3</CODE> to <CODE>3</CODE> and
-<CODE>None</CODE>.
-</LI></UL><P>Note that these standards only exist in <CODE>Bio.SearchIO</CODE> objects. If you
-write <CODE>Bio.SearchIO</CODE> objects into an output format, <CODE>Bio.SearchIO</CODE>
-will use the format&#X2019;s standard for the output. It does not force its standard
-over to your output file.</P><!--TOC section Reading search output files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc105">8.3</A>&#XA0;&#XA0;Reading search output files</H2><!--SEC END --><P>
-<A NAME="sec:searchio-input"></A></P><P>There are two functions you can use for reading search output files into
-<CODE>Bio.SearchIO</CODE> objects: <CODE>read</CODE> and <CODE>parse</CODE>. They&#X2019;re essentially
-similar to <CODE>read</CODE> and <CODE>parse</CODE> functions in other submodules like
-<CODE>Bio.SeqIO</CODE> or <CODE>Bio.AlignIO</CODE>. In both cases, you need to supply the
-search output file name and the file format name, both as Python strings. You
-can check the documentation for a list of format names <CODE>Bio.SearchIO</CODE>
-recognizes.</P><P><CODE>Bio.SearchIO.read</CODE> is used for reading search output files with only one
-query and returns a <CODE>QueryResult</CODE> object. You&#X2019;ve seen <CODE>read</CODE> used in
-our previous examples. What you haven&#X2019;t seen is that <CODE>read</CODE> may also accept
-additional keyword arguments, depending on the file format.</P><P>Here are some examples. In the first one, we use <CODE>read</CODE> just like
-previously to read a BLAST tabular output file. In the second one, we use a
-keyword argument to modify so it parses the BLAST tabular variant with comments
-in it:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> qresult = SearchIO.read('tab_2226_tblastn_003.txt', 'blast-tab')
->>> qresult
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> qresult2 = SearchIO.read('tab_2226_tblastn_007.txt', 'blast-tab', comments=True)
->>> qresult2
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
-</PRE><P>These keyword arguments differs among file formats. Check the format
-documentation to see if it has keyword arguments that modifies its parser&#X2019;s
-behavior.</P><P>As for the <CODE>Bio.SearchIO.parse</CODE>, it is used for reading search output
-files with any number of queries. The function returns a generator object that
-yields a <CODE>QueryResult</CODE> object in each iteration. Like
-<CODE>Bio.SearchIO.read</CODE>, it also accepts format-specific keyword arguments:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> qresults = SearchIO.parse('tab_2226_tblastn_001.txt', 'blast-tab')
->>> for qresult in qresults:
-...     print(qresult.id)
-gi|16080617|ref|NP_391444.1|
-gi|11464971:4-101
->>> qresults2 = SearchIO.parse('tab_2226_tblastn_005.txt', 'blast-tab', comments=True)
->>> for qresult in qresults2:
-...     print(qresult.id)
-random_s00
-gi|16080617|ref|NP_391444.1|
-gi|11464971:4-101
-</PRE><!--TOC section Dealing with large search output files with indexing-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc106">8.4</A>&#XA0;&#XA0;Dealing with large search output files with indexing</H2><!--SEC END --><P>
-<A NAME="sec:searchio-index"></A></P><P>Sometimes, you&#X2019;re handed a search output file containing hundreds or thousands
-of queries that you need to parse. You can of course use
-<CODE>Bio.SearchIO.parse</CODE> for this file, but that would be grossly inefficient
-if you need to access only a few of the queries. This is because <CODE>parse</CODE>
-will parse all queries it sees before it fetches your query of interest.</P><P>In this case, the ideal choice would be to index the file using
-<CODE>Bio.SearchIO.index</CODE> or <CODE>Bio.SearchIO.index_db</CODE>. If the names sound
-familiar, it&#X2019;s because you&#X2019;ve seen them before in Section&#XA0;<A HREF="#sec:SeqIO-index">5.4.2</A>.
-These functions also behave similarly to their <CODE>Bio.SeqIO</CODE> counterparts,
-with the addition of format-specific keyword arguments.</P><P>Here are some examples. You can use <CODE>index</CODE> with just the filename and
-format name:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab')
->>> sorted(idx.keys())
-['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|']
->>> idx['gi|16080617|ref|NP_391444.1|']
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> idx.close()
-</PRE><P>Or also with the format-specific keyword argument:</P><PRE CLASS="verbatim">>>> idx = SearchIO.index('tab_2226_tblastn_005.txt', 'blast-tab', comments=True)
->>> sorted(idx.keys())
-['gi|11464971:4-101', 'gi|16080617|ref|NP_391444.1|', 'random_s00']
->>> idx['gi|16080617|ref|NP_391444.1|']
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> idx.close()
-</PRE><P>Or with the <CODE>key_function</CODE> argument, as in <CODE>Bio.SeqIO</CODE>:</P><PRE CLASS="verbatim">>>> key_function = lambda id: id.upper()    # capitalizes the keys
->>> idx = SearchIO.index('tab_2226_tblastn_001.txt', 'blast-tab', key_function=key_function)
->>> sorted(idx.keys())
-['GI|11464971:4-101', 'GI|16080617|REF|NP_391444.1|']
->>> idx['GI|16080617|REF|NP_391444.1|']
-QueryResult(id='gi|16080617|ref|NP_391444.1|', 3 hits)
->>> idx.close()
-</PRE><P><CODE>Bio.SearchIO.index_db</CODE> works like as <CODE>index</CODE>, only it writes the
-query offsets into an SQLite database file.</P><!--TOC section Writing and converting search output files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc107">8.5</A>&#XA0;&#XA0;Writing and converting search output files</H2><!--SEC END --><P>
-<A NAME="sec:searchio-write"></A></P><P>It is occasionally useful to be able to manipulate search results from an output
-file and write it again to a new file. <CODE>Bio.SearchIO</CODE> provides a
-<CODE>write</CODE> function that lets you do exactly this. It takes as its arguments
-an iterable returning <CODE>QueryResult</CODE> objects, the output filename to write
-to, the format name to write to, and optionally some format-specific keyword
-arguments. It returns a four-item tuple, which denotes the number or
-<CODE>QueryResult</CODE>, <CODE>Hit</CODE>, <CODE>HSP</CODE>, and <CODE>HSPFragment</CODE> objects that
-were written.</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> qresults = SearchIO.parse('mirna.xml', 'blast-xml')     # read XML file
->>> SearchIO.write(qresults, 'results.tab', 'blast-tab')    # write to tabular file
-(3, 239, 277, 277)
-</PRE><P>You should note different file formats require different attributes of the
-<CODE>QueryResult</CODE>, <CODE>Hit</CODE>, <CODE>HSP</CODE> and <CODE>HSPFragment</CODE> objects. If
-these attributes are not present, writing won&#X2019;t work. In other words, you can&#X2019;t
-always write to the output format that you want. For example, if you read a
-BLAST XML file, you wouldn&#X2019;t be able to write the results to a PSL file as PSL
-files require attributes not calculated by BLAST (e.g. the number of repeat
-matches). You can always set these attributes manually, if you really want to
-write to PSL, though.</P><P>Like <CODE>read</CODE>, <CODE>parse</CODE>, <CODE>index</CODE>, and <CODE>index_db</CODE>, <CODE>write</CODE>
-also accepts format-specific keyword arguments. Check out the documentation for
-a complete list of formats <CODE>Bio.SearchIO</CODE> can write to and their arguments.</P><P>Finally, <CODE>Bio.SearchIO</CODE> also provides a <CODE>convert</CODE> function, which is
-simply a shortcut for <CODE>Bio.SearchIO.parse</CODE> and <CODE>Bio.SearchIO.write</CODE>.
-Using the convert function, our example above would be:</P><PRE CLASS="verbatim">>>> from Bio import SearchIO
->>> SearchIO.convert('mirna.xml', 'blast-xml', 'results.tab', 'blast-tab')
-(3, 239, 277, 277)
-</PRE><P>As <CODE>convert</CODE> uses <CODE>write</CODE>, it is only limited to format conversions
-that have all the required attributes. Here, the BLAST XML file provides all the
-default values a BLAST tabular file requires, so it works just fine. However,
-other format conversions are less likely to work since you need to manually
-assign the required attributes first.</P><!--TOC chapter Accessing NCBI&#X2019;s Entrez databases-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc108">Chapter&#XA0;9</A>&#XA0;&#XA0;Accessing NCBI&#X2019;s Entrez databases</H1><!--SEC END --><P>
-<A NAME="chapter:entrez"></A></P><P>Entrez (<A HREF="http://www.ncbi.nlm.nih.gov/Entrez"><TT>http://www.ncbi.nlm.nih.gov/Entrez</TT></A>) is a data retrieval system that provides users access to NCBI&#X2019;s databases such as PubMed, GenBank, GEO, and many others. You can access Entrez from a web browser to manually enter queries, or you can use Biopython&#X2019;s <CODE>Bio.Entrez</CODE> module for programmatic access to Entrez. The latter allows you for example to search PubMed or down [...]
-Each of these tools corresponds to one Python function in the <CODE>Bio.Entrez</CODE> module, as described in the sections below. This module makes sure that the correct URL is used for the queries, and that not more than one request is made every three seconds, as required by NCBI.</P><P>The output returned by the Entrez Programming Utilities is typically in XML format. To parse such output, you have several options:
-</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Use <CODE>Bio.Entrez</CODE>&#X2019;s parser to parse the XML output into a Python object;
-</LI><LI CLASS="li-enumerate">Use the DOM (Document Object Model) parser in Python&#X2019;s standard library;
-</LI><LI CLASS="li-enumerate">Use the SAX (Simple API for XML) parser in Python&#X2019;s standard library;
-</LI><LI CLASS="li-enumerate">Read the XML output as raw text, and parse it by string searching and manipulation.
-</LI></OL><P>
-For the DOM and SAX parsers, see the Python documentation. The parser in <CODE>Bio.Entrez</CODE> is discussed below.</P><P>NCBI uses DTD (Document Type Definition) files to describe the structure of the information contained in XML files. Most of the DTD files used by NCBI are included in the Biopython distribution. The <CODE>Bio.Entrez</CODE> parser makes use of the DTD files when parsing an XML file returned by NCBI Entrez.</P><P>Occasionally, you may find that the DTD file associated  [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc109">9.1</A>&#XA0;&#XA0;Entrez Guidelines</H2><!--SEC END --><P>
-<A NAME="sec:entrez-guidelines"></A>
-Before using Biopython to access the NCBI&#X2019;s online resources (via <CODE>Bio.Entrez</CODE> or some of the other modules), please read the
-<A HREF="http://www.ncbi.nlm.nih.gov/books/NBK25497/#chapter2.Usage_Guidelines_and_Requiremen">NCBI&#X2019;s Entrez User Requirements</A>.
-If the NCBI finds you are abusing their systems, they can and will ban your access!</P><P>To paraphrase:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-For any series of more than 100 requests, do this at weekends or outside USA peak times. This is up to you to obey.
-</LI><LI CLASS="li-itemize">Use the <A HREF="http://eutils.ncbi.nlm.nih.gov"><TT>http://eutils.ncbi.nlm.nih.gov</TT></A> address, not the standard NCBI Web address. Biopython uses this web address.
-</LI><LI CLASS="li-itemize">Make no more than three requests every seconds (relaxed from at most one request every three seconds in early 2009). This is automatically enforced by Biopython.
-</LI><LI CLASS="li-itemize">Use the optional email parameter so the NCBI can contact you if there is a problem. You can either explicitly set this as a parameter with each call to Entrez (e.g. include <TT>email="A.N.Other at example.com"</TT> in the argument list), or you can set a global email address:
-<PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"
-</PRE><TT>Bio.Entrez</TT> will then use this email address with each call to Entrez. The <TT>example.com</TT> address is a reserved domain name specifically for documentation (RFC 2606). Please DO NOT use a random email &#X2013; it&#X2019;s better not to give an email at all. The email parameter will be mandatory from June 1, 2010. In case of excessive usage, NCBI will attempt to contact a user at the e-mail address provided prior to blocking access to the E-utilities.
-</LI><LI CLASS="li-itemize">If you are using Biopython within some larger software suite, use the tool parameter to specify this. You can either explicitly set the tool name as a parameter with each call to Entrez (e.g. include <TT>tool="MyLocalScript"</TT> in the argument list), or you can set a global tool name:
-<PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.tool = "MyLocalScript"
-</PRE>The tool parameter will default to Biopython.
-</LI><LI CLASS="li-itemize">For large queries, the NCBI also recommend using their session history feature (the WebEnv session cookie string, see Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>). This is only slightly more complicated.
-</LI></UL><P>In conclusion, be sensible with your usage levels. If you plan to download lots of data, consider other options. For example, if you want easy access to all the human genes, consider fetching each chromosome by FTP as a GenBank file, and importing these into your own BioSQL database (see Section&#XA0;<A HREF="#sec:BioSQL">18.5</A>).</P><!--TOC section EInfo: Obtaining information about the Entrez databases-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc110">9.2</A>&#XA0;&#XA0;EInfo: Obtaining information about the Entrez databases</H2><!--SEC END --><P>
-<A NAME="sec:entrez-einfo"></A>
-EInfo provides field index term counts, last update, and available links for each of NCBI&#X2019;s databases. In addition, you can use EInfo to obtain a list of all database names accessible through the Entrez utilities:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.einfo()
->>> result = handle.read()
-</PRE><P>The variable <CODE>result</CODE> now contains a list of databases in XML format:
-</P><PRE CLASS="verbatim">>>> print(result)
-<?xml version="1.0"?>
-<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN"
- "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
-<eInfoResult>
-<DbList>
-        <DbName>pubmed</DbName>
-        <DbName>protein</DbName>
-        <DbName>nucleotide</DbName>
-        <DbName>nuccore</DbName>
-        <DbName>nucgss</DbName>
-        <DbName>nucest</DbName>
-        <DbName>structure</DbName>
-        <DbName>genome</DbName>
-        <DbName>books</DbName>
-        <DbName>cancerchromosomes</DbName>
-        <DbName>cdd</DbName>
-        <DbName>gap</DbName>
-        <DbName>domains</DbName>
-        <DbName>gene</DbName>
-        <DbName>genomeprj</DbName>
-        <DbName>gensat</DbName>
-        <DbName>geo</DbName>
-        <DbName>gds</DbName>
-        <DbName>homologene</DbName>
-        <DbName>journals</DbName>
-        <DbName>mesh</DbName>
-        <DbName>ncbisearch</DbName>
-        <DbName>nlmcatalog</DbName>
-        <DbName>omia</DbName>
-        <DbName>omim</DbName>
-        <DbName>pmc</DbName>
-        <DbName>popset</DbName>
-        <DbName>probe</DbName>
-        <DbName>proteinclusters</DbName>
-        <DbName>pcassay</DbName>
-        <DbName>pccompound</DbName>
-        <DbName>pcsubstance</DbName>
-        <DbName>snp</DbName>
-        <DbName>taxonomy</DbName>
-        <DbName>toolkit</DbName>
-        <DbName>unigene</DbName>
-        <DbName>unists</DbName>
-</DbList>
-</eInfoResult>
-</PRE><P>Since this is a fairly simple XML file, we could extract the information it contains simply by string searching. Using <CODE>Bio.Entrez</CODE>&#X2019;s parser instead, we can directly parse this XML file into a Python object:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = Entrez.einfo()
->>> record = Entrez.read(handle)
-</PRE><P>Now <CODE>record</CODE> is a dictionary with exactly one key:
-</P><PRE CLASS="verbatim">>>> record.keys()
-[u'DbList']
-</PRE><P>The values stored in this key is the list of database names shown in the XML above:
-</P><PRE CLASS="verbatim">>>> record["DbList"]
-['pubmed', 'protein', 'nucleotide', 'nuccore', 'nucgss', 'nucest',
- 'structure', 'genome', 'books', 'cancerchromosomes', 'cdd', 'gap',
- 'domains', 'gene', 'genomeprj', 'gensat', 'geo', 'gds', 'homologene',
- 'journals', 'mesh', 'ncbisearch', 'nlmcatalog', 'omia', 'omim', 'pmc',
- 'popset', 'probe', 'proteinclusters', 'pcassay', 'pccompound',
- 'pcsubstance', 'snp', 'taxonomy', 'toolkit', 'unigene', 'unists']
-</PRE><P>For each of these databases, we can use EInfo again to obtain more information:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.einfo(db="pubmed")
->>> record = Entrez.read(handle)
->>> record["DbInfo"]["Description"]
-'PubMed bibliographic record'
->>> record["DbInfo"]["Count"]
-'17989604'
->>> record["DbInfo"]["LastUpdate"]
-'2008/05/24 06:45'
-</PRE><P>Try <CODE>record["DbInfo"].keys()</CODE> for other information stored in this record.
-One of the most useful is a list of possible search fields for use with ESearch:</P><PRE CLASS="verbatim">>>> for field in record["DbInfo"]["FieldList"]:
-...     print("%(Name)s, %(FullName)s, %(Description)s" % field)
-ALL, All Fields, All terms from all searchable fields
-UID, UID, Unique number assigned to publication
-FILT, Filter, Limits the records
-TITL, Title, Words in title of publication
-WORD, Text Word, Free text associated with publication
-MESH, MeSH Terms, Medical Subject Headings assigned to publication
-MAJR, MeSH Major Topic, MeSH terms of major importance to publication
-AUTH, Author, Author(s) of publication
-JOUR, Journal, Journal abbreviation of publication
-AFFL, Affiliation, Author's institutional affiliation and address
-...
-</PRE><P>That&#X2019;s a long list, but indirectly this tells you that for the PubMed
-database, you can do things like <TT>Jones[AUTH]</TT> to search the
-author field, or <TT>Sanger[AFFL]</TT> to restrict to authors at the
-Sanger Centre. This can be very handy - especially if you are not so
-familiar with a particular database.</P><!--TOC section ESearch: Searching the Entrez databases-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc111">9.3</A>&#XA0;&#XA0;ESearch: Searching the Entrez databases</H2><!--SEC END --><P>
-<A NAME="sec:entrez-esearch"></A>
-To search any of these databases, we use <CODE>Bio.Entrez.esearch()</CODE>. For example, let&#X2019;s search in PubMed for publications related to Biopython:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="pubmed", term="biopython")
->>> record = Entrez.read(handle)
->>> record["IdList"]
-['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038']
-</PRE><P>In this output, you see seven PubMed IDs (including 19304878 which is the PMID for the Biopython application note), which can be retrieved by EFetch (see section <A HREF="#sec:efetch">9.6</A>).</P><P>You can also use ESearch to search GenBank. Here we&#X2019;ll do a quick
-search for the <EM>matK</EM> gene in <EM>Cypripedioideae</EM> orchids
-(see Section&#XA0;<A HREF="#sec:entrez-einfo">9.2</A> about EInfo for one way to
-find out which fields you can search in each Entrez database):</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae[Orgn] AND matK[Gene]")
->>> record = Entrez.read(handle)
->>> record["Count"]
-'25'
->>> record["IdList"]
-['126789333', '37222967', '37222966', '37222965', ..., '61585492']
-</PRE><P>Each of the IDs (126789333, 37222967, 37222966, &#X2026;) is a GenBank identifier.
-See section&#XA0;<A HREF="#sec:efetch">9.6</A> for information on how to actually download these GenBank records.</P><P>Note that instead of a species name like <TT>Cypripedioideae[Orgn]</TT>, you can restrict the search using an NCBI taxon identifier, here this would be <TT>txid158330[Orgn]</TT>. This isn&#X2019;t currently documented on the ESearch help page - the NCBI explained this in reply to an email query. You can often deduce the search term formatting by playing with the Entrez  [...]
-</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="journals", term="computational")
->>> record = Entrez.read(handle)
->>> record["Count"]
-'16'
->>> record["IdList"]
-['30367', '33843', '33823', '32989', '33190', '33009', '31986',
- '34502', '8799', '22857', '32675', '20258', '33859', '32534',
- '32357', '32249']
-</PRE><P>Again, we could use EFetch to obtain more information for each of these journal IDs.</P><P>ESearch has many useful options &#X2014; see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch_help.html">ESearch help page</A> for more information.</P><!--TOC section EPost: Uploading a list of identifiers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc112">9.4</A>&#XA0;&#XA0;EPost: Uploading a list of identifiers</H2><!--SEC END --><P>
-EPost uploads a list of UIs for use in subsequent search strategies; see the
-<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/epost_help.html">EPost help page</A> for more information. It is available from Biopython through
-the <CODE>Bio.Entrez.epost()</CODE> function.</P><P>To give an example of when this is useful, suppose you have a long list of IDs
-you want to download using EFetch (maybe sequences, maybe citations &#X2013;
-anything). When you make a request with EFetch your list of IDs, the database
-etc, are all turned into a long URL sent to the server. If your list of IDs is
-long, this URL gets long, and long URLs can break (e.g. some proxies don&#X2019;t
-cope well).</P><P>Instead, you can break this up into two steps, first uploading the list of IDs
-using EPost (this uses an &#X201C;HTML post&#X201D; internally, rather than an &#X201C;HTML get&#X201D;,
-getting round the long URL problem). With the history support, you can then
-refer to this long list of IDs, and download the associated data with EFetch.</P><P>Let&#X2019;s look at a simple example to see how EPost works &#X2013; uploading some PubMed identifiers:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
->>> print(Entrez.epost("pubmed", id=",".join(id_list)).read())
-<?xml version="1.0"?>
-<!DOCTYPE ePostResult PUBLIC "-//NLM//DTD ePostResult, 11 May 2002//EN"
- "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/ePost_020511.dtd">
-<ePostResult>
- <QueryKey>1</QueryKey>
- <WebEnv>NCID_01_206841095_130.14.22.101_9001_1242061629</WebEnv>
-</ePostResult>
-</PRE><P>The returned XML includes two important strings, <CODE>QueryKey</CODE> and <CODE>WebEnv</CODE> which together define your history session.
-You would extract these values for use with another Entrez call such as EFetch:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> id_list = ["19304878", "18606172", "16403221", "16377612", "14871861", "14630660"]
->>> search_results = Entrez.read(Entrez.epost("pubmed", id=",".join(id_list)))
->>> webenv = search_results["WebEnv"]
->>> query_key = search_results["QueryKey"]
-</PRE><P>Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A> shows how to use the history feature.</P><!--TOC section ESummary: Retrieving summaries from primary IDs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc113">9.5</A>&#XA0;&#XA0;ESummary: Retrieving summaries from primary IDs</H2><!--SEC END --><P>
-ESummary retrieves document summaries from a list of primary IDs (see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/esummary_help.html">ESummary help page</A> for more information). In Biopython, ESummary is available as <CODE>Bio.Entrez.esummary()</CODE>. Using the search result above, we can for example find out more about the journal with ID 30367:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.esummary(db="journals", id="30367")
->>> record = Entrez.read(handle)
->>> record[0]["Id"]
-'30367'
->>> record[0]["Title"]
-'Computational biology and chemistry'
->>> record[0]["Publisher"]
-'Pergamon,'
-</PRE><!--TOC section EFetch: Downloading full records from Entrez-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc114">9.6</A>&#XA0;&#XA0;EFetch: Downloading full records from Entrez</H2><!--SEC END --><P>
-<A NAME="sec:efetch"></A></P><P>EFetch is what you use when you want to retrieve a full record from Entrez.
-This covers several possible databases, as described on the main <A HREF="http://eutils.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html">EFetch Help page</A>.</P><P>For most of their databases, the NCBI support several different file formats. Requesting a specific file format from Entrez using <CODE>Bio.Entrez.efetch()</CODE> requires specifying the <CODE>rettype</CODE> and/or <CODE>retmode</CODE> optional arguments. The different combinations are described for each database type o [...]
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
->>> print(handle.read())
-LOCUS       EU490707                1302 bp    DNA     linear   PLN 05-MAY-2008
-DEFINITION  Selenipedium aequinoctiale maturase K (matK) gene, partial cds;
-            chloroplast.
-ACCESSION   EU490707
-VERSION     EU490707.1  GI:186972394
-KEYWORDS    .
-SOURCE      chloroplast Selenipedium aequinoctiale
-  ORGANISM  Selenipedium aequinoctiale
-            Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
-            Spermatophyta; Magnoliophyta; Liliopsida; Asparagales; Orchidaceae;
-            Cypripedioideae; Selenipedium.
-REFERENCE   1  (bases 1 to 1302)
-  AUTHORS   Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
-            Endara,C.L., Williams,N.H. and Moore,M.J.
-  TITLE     Phylogenetic utility of ycf1 in orchids
-  JOURNAL   Unpublished
-REFERENCE   2  (bases 1 to 1302)
-  AUTHORS   Neubig,K.M., Whitten,W.M., Carlsward,B.S., Blanco,M.A.,
-            Endara,C.L., Williams,N.H. and Moore,M.J.
-  TITLE     Direct Submission
-  JOURNAL   Submitted (14-FEB-2008) Department of Botany, University of
-            Florida, 220 Bartram Hall, Gainesville, FL 32611-8526, USA
-FEATURES             Location/Qualifiers
-     source          1..1302
-                     /organism="Selenipedium aequinoctiale"
-                     /organelle="plastid:chloroplast"
-                     /mol_type="genomic DNA"
-                     /specimen_voucher="FLAS:Blanco 2475"
-                     /db_xref="taxon:256374"
-     gene            <1..>1302
-                     /gene="matK"
-     CDS             <1..>1302
-                     /gene="matK"
-                     /codon_start=1
-                     /transl_table=11
-                     /product="maturase K"
-                     /protein_id="ACC99456.1"
-                     /db_xref="GI:186972395"
-                     /translation="IFYEPVEIFGYDNKSSLVLVKRLITRMYQQNFLISSVNDSNQKG
-                     FWGHKHFFSSHFSSQMVSEGFGVILEIPFSSQLVSSLEEKKIPKYQNLRSIHSIFPFL
-                     EDKFLHLNYVSDLLIPHPIHLEILVQILQCRIKDVPSLHLLRLLFHEYHNLNSLITSK
-                     KFIYAFSKRKKRFLWLLYNSYVYECEYLFQFLRKQSSYLRSTSSGVFLERTHLYVKIE
-                     HLLVVCCNSFQRILCFLKDPFMHYVRYQGKAILASKGTLILMKKWKFHLVNFWQSYFH
-                     FWSQPYRIHIKQLSNYSFSFLGYFSSVLENHLVVRNQMLENSFIINLLTKKFDTIAPV
-                     ISLIGSLSKAQFCTVLGHPISKPIWTDFSDSDILDRFCRICRNLCRYHSGSSKKQVLY
-                     RIKYILRLSCARTLARKHKSTVRTFMRRLGSGLLEEFFMEEE"
-ORIGIN
-        1 attttttacg aacctgtgga aatttttggt tatgacaata aatctagttt agtacttgtg
-       61 aaacgtttaa ttactcgaat gtatcaacag aattttttga tttcttcggt taatgattct
-      121 aaccaaaaag gattttgggg gcacaagcat tttttttctt ctcatttttc ttctcaaatg
-      181 gtatcagaag gttttggagt cattctggaa attccattct cgtcgcaatt agtatcttct
-      241 cttgaagaaa aaaaaatacc aaaatatcag aatttacgat ctattcattc aatatttccc
-      301 tttttagaag acaaattttt acatttgaat tatgtgtcag atctactaat accccatccc
-      361 atccatctgg aaatcttggt tcaaatcctt caatgccgga tcaaggatgt tccttctttg
-      421 catttattgc gattgctttt ccacgaatat cataatttga atagtctcat tacttcaaag
-      481 aaattcattt acgccttttc aaaaagaaag aaaagattcc tttggttact atataattct
-      541 tatgtatatg aatgcgaata tctattccag tttcttcgta aacagtcttc ttatttacga
-      601 tcaacatctt ctggagtctt tcttgagcga acacatttat atgtaaaaat agaacatctt
-      661 ctagtagtgt gttgtaattc ttttcagagg atcctatgct ttctcaagga tcctttcatg
-      721 cattatgttc gatatcaagg aaaagcaatt ctggcttcaa agggaactct tattctgatg
-      781 aagaaatgga aatttcatct tgtgaatttt tggcaatctt attttcactt ttggtctcaa
-      841 ccgtatagga ttcatataaa gcaattatcc aactattcct tctcttttct ggggtatttt
-      901 tcaagtgtac tagaaaatca tttggtagta agaaatcaaa tgctagagaa ttcatttata
-      961 ataaatcttc tgactaagaa attcgatacc atagccccag ttatttctct tattggatca
-     1021 ttgtcgaaag ctcaattttg tactgtattg ggtcatccta ttagtaaacc gatctggacc
-     1081 gatttctcgg attctgatat tcttgatcga ttttgccgga tatgtagaaa tctttgtcgt
-     1141 tatcacagcg gatcctcaaa aaaacaggtt ttgtatcgta taaaatatat acttcgactt
-     1201 tcgtgtgcta gaactttggc acggaaacat aaaagtacag tacgcacttt tatgcgaaga
-     1261 ttaggttcgg gattattaga agaattcttt atggaagaag aa
-//
-</PRE><P>The arguments <CODE>rettype="gb"</CODE> and <CODE>retmode="text"</CODE> let us download this record in the GenBank format.</P><P>Note that until Easter 2009, the Entrez EFetch API let you use &#X201C;genbank&#X201D; as the
-return type, however the NCBI now insist on using the official return types of
-&#X201C;gb&#X201D; or &#X201C;gbwithparts&#X201D; (or &#X201C;gp&#X201D; for proteins) as described on online.
-Also not that until Feb 2012, the Entrez EFetch API would default to returning
-plain text files, but now defaults to XML.</P><P>Alternatively, you could for example use <CODE>rettype="fasta"</CODE> to get the Fasta-format; see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/efetchseq_help.html">EFetch Sequences Help page</A> for other options. Remember &#X2013; the available formats depend on which database you are downloading from - see the main <A HREF="http://eutils.ncbi.nlm.nih.gov/entrez/query/static/efetch_help.html">EFetch Help page</A>.</P><P>I [...]
->>> handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
->>> record = SeqIO.read(handle, "genbank")
->>> handle.close()
->>> print(record)
-ID: EU490707.1
-Name: EU490707
-Description: Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast.
-Number of features: 3
-...
-Seq('ATTTTTTACGAACCTGTGGAAATTTTTGGTTATGACAATAAATCTAGTTTAGTA...GAA', IUPACAmbiguousDNA())
-</PRE><P>Note that a more typical use would be to save the sequence data to a local file, and <EM>then</EM> parse it with <CODE>Bio.SeqIO</CODE>. This can save you having to re-download the same file repeatedly while working on your script, and places less load on the NCBI&#X2019;s servers. For example:</P><PRE CLASS="verbatim">import os
-from Bio import SeqIO
-from Bio import Entrez
-Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
-filename = "gi_186972394.gbk"
-if not os.path.isfile(filename):
-    # Downloading...
-    net_handle = Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text")
-    out_handle = open(filename, "w")
-    out_handle.write(net_handle.read())
-    out_handle.close()
-    net_handle.close()
-    print("Saved")
-
-print("Parsing...")
-record = SeqIO.read(filename, "genbank")
-print(record)
-</PRE><P>To get the output in XML format, which you can parse using the <CODE>Bio.Entrez.read()</CODE> function, use <CODE>retmode="xml"</CODE>:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = Entrez.efetch(db="nucleotide", id="186972394", retmode="xml")
->>> record = Entrez.read(handle)
->>> handle.close()
->>> record[0]["GBSeq_definition"]
-'Selenipedium aequinoctiale maturase K (matK) gene, partial cds; chloroplast'
->>> record[0]["GBSeq_source"]
-'chloroplast Selenipedium aequinoctiale'
-</PRE><P>So, that dealt with sequences. For examples of parsing file formats specific to the other databases (e.g. the <CODE>MEDLINE</CODE> format used in PubMed), see Section&#XA0;<A HREF="#sec:entrez-specialized-parsers">9.12</A>.</P><P>If you want to perform a search with <CODE>Bio.Entrez.esearch()</CODE>, and then download the records with <CODE>Bio.Entrez.efetch()</CODE>, you should use the WebEnv history feature &#X2013; see Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>.</P><! [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc115">9.7</A>&#XA0;&#XA0;ELink: Searching for related items in NCBI Entrez</H2><!--SEC END --><P>
-<A NAME="sec:elink"></A></P><P>ELink, available from Biopython as <CODE>Bio.Entrez.elink()</CODE>, can be used to find related items in the NCBI Entrez databases. For example, you can us this to find nucleotide entries for an entry in the gene database,
-and other cool stuff.</P><P>Let&#X2019;s use ELink to find articles related to the Biopython application note published in <I>Bioinformatics</I> in 2009. The PubMed ID of this article is 19304878:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"
->>> pmid = "19304878"
->>> record = Entrez.read(Entrez.elink(dbfrom="pubmed", id=pmid))
-</PRE><P>The <CODE>record</CODE> variable consists of a Python list, one for each database in which we searched. Since we specified only one PubMed ID to search for, <CODE>record</CODE> contains only one item. This item is a dictionary containing information about our search term, as well as all the related items that were found:</P><PRE CLASS="verbatim">>>> record[0]["DbFrom"]
-'pubmed'
->>> record[0]["IdList"]
-['19304878']
-</PRE><P>The <CODE>"LinkSetDb"</CODE> key contains the search results, stored as a list consisting of one item for each target database. In our search results, we only find hits in the PubMed database (although sub-divided into categories):</P><PRE CLASS="verbatim">>>> len(record[0]["LinkSetDb"])
-5
->>> for linksetdb in record[0]["LinkSetDb"]:
-...     print(linksetdb["DbTo"], linksetdb["LinkName"], len(linksetdb["Link"]))
-...
-pubmed pubmed_pubmed 110
-pubmed pubmed_pubmed_combined 6
-pubmed pubmed_pubmed_five 6
-pubmed pubmed_pubmed_reviews 5
-pubmed pubmed_pubmed_reviews_five 5
-</PRE><P>The actual search results are stored as under the <CODE>"Link"</CODE> key. In total, 110 items were found under
-standard search.
-Let&#X2019;s now at the first search result:
-</P><PRE CLASS="verbatim">>>> record[0]["LinkSetDb"][0]["Link"][0]
-{u'Id': '19304878'}
-</PRE><P>This is the article we searched for, which doesn&#X2019;t help us much, so let&#X2019;s look at the second search result:</P><PRE CLASS="verbatim">>>> record[0]["LinkSetDb"][0]["Link"][1]
-{u'Id': '14630660'}
-</PRE><P>This paper, with PubMed ID 14630660, is about the Biopython PDB parser.</P><P>We can use a loop to print out all PubMed IDs:
-</P><PRE CLASS="verbatim">>>> for link in record[0]["LinkSetDb"][0]["Link"]:
-...     print(link["Id"])
-19304878
-14630660
-18689808
-17121776
-16377612
-12368254
-......
-</PRE><P>Now that was nice, but personally I am often more interested to find out if a paper has been cited.
-Well, ELink can do that too &#X2013; at least for journals in Pubmed Central (see Section&#XA0;<A HREF="#sec:elink-citations">9.15.3</A>).</P><P>For help on ELink, see the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/elink_help.html">ELink help page</A>.
-There is an entire sub-page just for the <A HREF="http://eutils.ncbi.nlm.nih.gov/corehtml/query/static/entrezlinks.html">link names</A>, describing how different databases can be cross referenced.</P><!--TOC section EGQuery: Global Query - counts for search terms-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc116">9.8</A>&#XA0;&#XA0;EGQuery: Global Query - counts for search terms</H2><!--SEC END --><P>
-EGQuery provides counts for a search term in each of the Entrez databases (i.e. a global query). This is particularly useful to find out how many items your search terms would find in each database without actually performing lots of separate searches with ESearch (see the example in <A HREF="#subsec:entrez_example_genbank">9.14.2</A> below).</P><P>In this example, we use <CODE>Bio.Entrez.egquery()</CODE> to obtain the counts for &#X201C;Biopython&#X201D;:</P><PRE CLASS="verbatim">>&g [...]
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="biopython")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-...     print(row["DbName"], row["Count"])
-...
-pubmed 6
-pmc 62
-journals 0
-...
-</PRE><P>See the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/egquery_help.html">EGQuery help page</A> for more information.</P><!--TOC section ESpell: Obtaining spelling suggestions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc117">9.9</A>&#XA0;&#XA0;ESpell: Obtaining spelling suggestions</H2><!--SEC END --><P>
-ESpell retrieves spelling suggestions. In this example, we use <CODE>Bio.Entrez.espell()</CODE> to obtain the correct spelling of Biopython:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.espell(term="biopythooon")
->>> record = Entrez.read(handle)
->>> record["Query"]
-'biopythooon'
->>> record["CorrectedQuery"]
-'biopython'
-</PRE><P>See the <A HREF="http://www.ncbi.nlm.nih.gov/entrez/query/static/espell_help.html">ESpell help page</A> for more information.
-The main use of this is for GUI tools to provide automatic suggestions for search terms.</P><!--TOC section Parsing huge Entrez XML files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc118">9.10</A>&#XA0;&#XA0;Parsing huge Entrez XML files</H2><!--SEC END --><P>The <CODE>Entrez.read</CODE> function reads the entire XML file returned by Entrez into a single Python object, which is kept in memory. To parse Entrez XML files too large to fit in memory, you can use the function <CODE>Entrez.parse</CODE>. This is a generator function that reads records in the XML file one by one. This function is only useful if the XML file  [...]
-</PRE><P>The resulting XML file has a size of 6.1 GB. Attempting <CODE>Entrez.read</CODE> on this file will result in a <CODE>MemoryError</CODE> on many computers.</P><P>The XML file <CODE>Homo_sapiens.xml</CODE> consists of a list of Entrez gene records, each corresponding to one Entrez gene in human. <CODE>Entrez.parse</CODE> retrieves these gene records one by one. You can then print out or store the relevant information in each record by iterating over the records. For example, this  [...]
->>> handle = open("Homo_sapiens.xml")
->>> records = Entrez.parse(handle)
-
->>> for record in records:
-...     status = record['Entrezgene_track-info']['Gene-track']['Gene-track_status']
-...     if status.attributes['value']=='discontinued':
-...         continue
-...     geneid = record['Entrezgene_track-info']['Gene-track']['Gene-track_geneid']
-...     genename = record['Entrezgene_gene']['Gene-ref']['Gene-ref_locus']
-...     print(geneid, genename)
-</PRE><P>This will print:
-</P><PRE CLASS="verbatim">1 A1BG
-2 A2M
-3 A2MP
-8 AA
-9 NAT1
-10 NAT2
-11 AACP
-12 SERPINA3
-13 AADAC
-14 AAMP
-15 AANAT
-16 AARS
-17 AAVS1
-...
-</PRE><!--TOC section Handling errors-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc119">9.11</A>&#XA0;&#XA0;Handling errors</H2><!--SEC END --><P>Three things can go wrong when parsing an XML file:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-The file may not be an XML file to begin with;
-</LI><LI CLASS="li-itemize">The file may end prematurely or otherwise be corrupted;
-</LI><LI CLASS="li-itemize">The file may be correct XML, but contain items that are not represented in the associated DTD.
-</LI></UL><P>The first case occurs if, for example, you try to parse a Fasta file as if it were an XML file:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = open("NC_005816.fna") # a Fasta file
->>> record = Entrez.read(handle)
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
-    record = handler.read(handle)
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 164, in read
-    raise NotXMLError(e)
-Bio.Entrez.Parser.NotXMLError: Failed to parse the XML data (syntax error: line 1, column 0). Please make sure that the input data are in XML format.
-</PRE><P>Here, the parser didn&#X2019;t find the <CODE><?xml ...</CODE> tag with which an XML file is supposed to start, and therefore decides (correctly) that the file is not an XML file.</P><P>When your file is in the XML format but is corrupted (for example, by ending prematurely), the parser will raise a CorruptedXMLError.
-Here is an example of an XML file that ends prematurely:
-</P><PRE CLASS="verbatim"><?xml version="1.0"?>
-<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
-<eInfoResult>
-<DbList>
-        <DbName>pubmed</DbName>
-        <DbName>protein</DbName>
-        <DbName>nucleotide</DbName>
-        <DbName>nuccore</DbName>
-        <DbName>nucgss</DbName>
-        <DbName>nucest</DbName>
-        <DbName>structure</DbName>
-        <DbName>genome</DbName>
-        <DbName>books</DbName>
-        <DbName>cancerchromosomes</DbName>
-        <DbName>cdd</DbName>
-</PRE><P>which will generate the following traceback:
-</P><PRE CLASS="verbatim">>>> Entrez.read(handle)
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
-    record = handler.read(handle)
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 160, in read
-    raise CorruptedXMLError(e)
-Bio.Entrez.Parser.CorruptedXMLError: Failed to parse the XML data (no element found: line 16, column 0). Please make sure that the input data are not corrupted.
-
->>>
-</PRE><P>Note that the error message tells you at what point in the XML file the error was detected.</P><P>The third type of error occurs if the XML file contains tags that do not have a description in the corresponding DTD file. This is an example of such an XML file:</P><PRE CLASS="verbatim"><?xml version="1.0"?>
-<!DOCTYPE eInfoResult PUBLIC "-//NLM//DTD eInfoResult, 11 May 2002//EN" "http://www.ncbi.nlm.nih.gov/entrez/query/DTD/eInfo_020511.dtd">
-<eInfoResult>
-        <DbInfo>
-        <DbName>pubmed</DbName>
-        <MenuName>PubMed</MenuName>
-        <Description>PubMed bibliographic record</Description>
-        <Count>20161961</Count>
-        <LastUpdate>2010/09/10 04:52</LastUpdate>
-        <FieldList>
-                <Field>
-...
-                </Field>
-        </FieldList>
-        <DocsumList>
-                <Docsum>
-                        <DsName>PubDate</DsName>
-                        <DsType>4</DsType>
-                        <DsTypeName>string</DsTypeName>
-                </Docsum>
-                <Docsum>
-                        <DsName>EPubDate</DsName>
-...
-        </DbInfo>
-</eInfoResult>
-</PRE><P>In this file, for some reason the tag <CODE><DocsumList></CODE> (and several others) are not listed in the DTD file <CODE>eInfo_020511.dtd</CODE>, which is specified on the second line as the DTD for this XML file. By default, the parser will stop and raise a ValidationError if it cannot find some tag in the DTD:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = open("einfo3.xml")
->>> record = Entrez.read(handle)
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/__init__.py", line 257, in read
-    record = handler.read(handle)
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 154, in read
-    self.parser.ParseFile(handle)
-  File "/usr/local/lib/python2.7/site-packages/Bio/Entrez/Parser.py", line 246, in startElementHandler
-    raise ValidationError(name)
-Bio.Entrez.Parser.ValidationError: Failed to find tag 'DocsumList' in the DTD. To skip all tags that are not represented in the DTD, please call Bio.Entrez.read or Bio.Entrez.parse with validate=False.
-</PRE><P>Optionally, you can instruct the parser to skip such tags instead of raising a ValidationError. This is done by calling <CODE>Entrez.read</CODE> or <CODE>Entrez.parse</CODE> with the argument <CODE>validate</CODE> equal to False:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = open("einfo3.xml")
->>> record = Entrez.read(handle, validate=False)
->>>
-</PRE><P>Of course, the information contained in the XML tags that are not in the DTD are not present in the record returned by <CODE>Entrez.read</CODE>.</P><!--TOC section Specialized parsers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc120">9.12</A>&#XA0;&#XA0;Specialized parsers</H2><!--SEC END --><P>
-<A NAME="sec:entrez-specialized-parsers"></A></P><P>The <CODE>Bio.Entrez.read()</CODE> function can parse most (if not all) XML output returned by Entrez. Entrez typically allows you to retrieve records in other formats, which may have some advantages compared to the XML format in terms of readability (or download size).</P><P>To request a specific file format from Entrez using <CODE>Bio.Entrez.efetch()</CODE> requires specifying the <CODE>rettype</CODE> and/or <CODE>retmode</CODE> optio [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc121">9.12.1</A>&#XA0;&#XA0;Parsing Medline records</H3><!--SEC END --><P>
-<A NAME="subsec:entrez-and-medline"></A>
-You can find the Medline parser in <CODE>Bio.Medline</CODE>. Suppose we want to parse the file <CODE>pubmed_result1.txt</CODE>, containing one Medline record. You can find this file in Biopython&#X2019;s <CODE>Tests\Medline</CODE> directory. The file looks like this:</P><PRE CLASS="verbatim">PMID- 12230038
-OWN - NLM
-STAT- MEDLINE
-DA  - 20020916
-DCOM- 20030606
-LR  - 20041117
-PUBM- Print
-IS  - 1467-5463 (Print)
-VI  - 3
-IP  - 3
-DP  - 2002 Sep
-TI  - The Bio* toolkits--a brief overview.
-PG  - 296-302
-AB  - Bioinformatics research is often difficult to do with commercial software. The
-      Open Source BioPerl, BioPython and Biojava projects provide toolkits with
-...
-</PRE><P>We first open the file and then parse it:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> with open("pubmed_result1.txt") as handle:
-...    record = Medline.read(handle)
-...
-</PRE><P>The <CODE>record</CODE> now contains the Medline record as a Python dictionary:
-</P><PRE CLASS="verbatim">>>> record["PMID"]
-'12230038'
-</PRE><PRE CLASS="verbatim">>>> record["AB"]
-'Bioinformatics research is often difficult to do with commercial software.
-The Open Source BioPerl, BioPython and Biojava projects provide toolkits with
-multiple functionality that make it easier to create customised pipelines or
-analysis. This review briefly compares the quirks of the underlying languages
-and the functionality, documentation, utility and relative advantages of the
-Bio counterparts, particularly from the point of view of the beginning
-biologist programmer.'
-</PRE><P>The key names used in a Medline record can be rather obscure; use
-</P><PRE CLASS="verbatim">>>> help(record)
-</PRE><P>for a brief summary.</P><P>To parse a file containing multiple Medline records, you can use the <CODE>parse</CODE> function instead:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> with open("pubmed_result2.txt") as handle:
-...     for record in Medline.parse(handle):
-...         print(record["TI"])
-...
-A high level interface to SCOP and ASTRAL implemented in python.
-GenomeDiagram: a python package for the visualization of large-scale genomic data.
-Open source clustering software.
-PDB file parser and structure class implemented in Python.
-</PRE><P>Instead of parsing Medline records stored in files, you can also parse Medline records downloaded by <CODE>Bio.Entrez.efetch</CODE>. For example, let&#X2019;s look at all Medline records in PubMed related to Biopython:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="pubmed", term="biopython")
->>> record = Entrez.read(handle)
->>> record["IdList"]
-['19304878', '18606172', '16403221', '16377612', '14871861', '14630660', '12230038']
-</PRE><P>We now use <CODE>Bio.Entrez.efetch</CODE> to download these Medline records:
-</P><PRE CLASS="verbatim">>>> idlist = record["IdList"]
->>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="text")
-</PRE><P>Here, we specify <CODE>rettype="medline", retmode="text"</CODE> to obtain the Medline records in plain-text Medline format. Now we use <CODE>Bio.Medline</CODE> to parse these records:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> records = Medline.parse(handle)
->>> for record in records:
-...     print(record["AU"])
-['Cock PJ', 'Antao T', 'Chang JT', 'Chapman BA', 'Cox CJ', 'Dalke A', ..., 'de Hoon MJ']
-['Munteanu CR', 'Gonzalez-Diaz H', 'Magalhaes AL']
-['Casbon JA', 'Crooks GE', 'Saqi MA']
-['Pritchard L', 'White JA', 'Birch PR', 'Toth IK']
-['de Hoon MJ', 'Imoto S', 'Nolan J', 'Miyano S']
-['Hamelryck T', 'Manderick B']
-['Mangalam H']
-</PRE><P>For comparison, here we show an example using the XML format:
-</P><PRE CLASS="verbatim">>>> idlist = record["IdList"]
->>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline", retmode="xml")
->>> records = Entrez.read(handle)
->>> for record in records:
-...     print(record["MedlineCitation"]["Article"]["ArticleTitle"])
-Biopython: freely available Python tools for computational molecular biology and
- bioinformatics.
-Enzymes/non-enzymes classification model complexity based on composition, sequence,
- 3D and topological indices.
-A high level interface to SCOP and ASTRAL implemented in python.
-GenomeDiagram: a python package for the visualization of large-scale genomic data.
-Open source clustering software.
-PDB file parser and structure class implemented in Python.
-The Bio* toolkits--a brief overview.
-</PRE><P>Note that in both of these examples, for simplicity we have naively combined ESearch and EFetch.
-In this situation, the NCBI would expect you to use their history feature,
-as illustrated in Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>.</P><!--TOC subsection Parsing GEO records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc122">9.12.2</A>&#XA0;&#XA0;Parsing GEO records</H3><!--SEC END --><P>GEO (<A HREF="http://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus</A>)
-is a data repository of high-throughput gene expression and hybridization
-array data. The <CODE>Bio.Geo</CODE> module can be used to parse GEO-formatted
-data.</P><P>The following code fragment shows how to parse the example GEO file
-<CODE>GSE16.txt</CODE> into a record and print the record:</P><PRE CLASS="verbatim">>>> from Bio import Geo
->>> handle = open("GSE16.txt")
->>> records = Geo.parse(handle)
->>> for record in records:
-...     print(record)
-</PRE><P>You can search the &#X201C;gds&#X201D; database (GEO datasets) with ESearch:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com" # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="gds", term="GSE16")
->>> record = Entrez.read(handle)
->>> record["Count"]
-2
->>> record["IdList"]
-['200000016', '100000028']
-</PRE><P>From the Entrez website, UID &#X201C;200000016&#X201D; is GDS16 while the other hit
-&#X201C;100000028&#X201D; is for the associated platform, GPL28. Unfortunately, at the
-time of writing the NCBI don&#X2019;t seem to support downloading GEO files using
-Entrez (not as XML, nor in the <I>Simple Omnibus Format in Text</I> (SOFT)
-format).</P><P>However, it is actually pretty straight forward to download the GEO files by FTP
-from <A HREF="ftp://ftp.ncbi.nih.gov/pub/geo/"><TT>ftp://ftp.ncbi.nih.gov/pub/geo/</TT></A> instead. In this case you might want
-<A HREF="ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz"><TT>ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/by_series/GSE16/GSE16_family.soft.gz</TT></A>
-(a compressed file, see the Python module gzip).</P><!--TOC subsection Parsing UniGene records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc123">9.12.3</A>&#XA0;&#XA0;Parsing UniGene records</H3><!--SEC END --><P>UniGene is an NCBI database of the transcriptome, with each UniGene record showing the set of transcripts that are associated with a particular gene in a specific organism. A typical UniGene record looks like this:</P><PRE CLASS="verbatim">ID          Hs.2
-TITLE       N-acetyltransferase 2 (arylamine N-acetyltransferase)
-GENE        NAT2
-CYTOBAND    8p22
-GENE_ID     10
-LOCUSLINK   10
-HOMOL       YES
-EXPRESS      bone| connective tissue| intestine| liver| liver tumor| normal| soft tissue/muscle tissue tumor| adult
-RESTR_EXPR   adult
-CHROMOSOME  8
-STS         ACC=PMC310725P3 UNISTS=272646
-STS         ACC=WIAF-2120 UNISTS=44576
-STS         ACC=G59899 UNISTS=137181
-...
-STS         ACC=GDB:187676 UNISTS=155563
-PROTSIM     ORG=10090; PROTGI=6754794; PROTID=NP_035004.1; PCT=76.55; ALN=288
-PROTSIM     ORG=9796; PROTGI=149742490; PROTID=XP_001487907.1; PCT=79.66; ALN=288
-PROTSIM     ORG=9986; PROTGI=126722851; PROTID=NP_001075655.1; PCT=76.90; ALN=288
-...
-PROTSIM     ORG=9598; PROTGI=114619004; PROTID=XP_519631.2; PCT=98.28; ALN=288
-
-SCOUNT      38
-SEQUENCE    ACC=BC067218.1; NID=g45501306; PID=g45501307; SEQTYPE=mRNA
-SEQUENCE    ACC=NM_000015.2; NID=g116295259; PID=g116295260; SEQTYPE=mRNA
-SEQUENCE    ACC=D90042.1; NID=g219415; PID=g219416; SEQTYPE=mRNA
-SEQUENCE    ACC=D90040.1; NID=g219411; PID=g219412; SEQTYPE=mRNA
-SEQUENCE    ACC=BC015878.1; NID=g16198419; PID=g16198420; SEQTYPE=mRNA
-SEQUENCE    ACC=CR407631.1; NID=g47115198; PID=g47115199; SEQTYPE=mRNA
-SEQUENCE    ACC=BG569293.1; NID=g13576946; CLONE=IMAGE:4722596; END=5'; LID=6989; SEQTYPE=EST; TRACE=44157214
-...
-SEQUENCE    ACC=AU099534.1; NID=g13550663; CLONE=HSI08034; END=5'; LID=8800; SEQTYPE=EST
-//
-</PRE><P>This particular record shows the set of transcripts (shown in the <CODE>SEQUENCE</CODE> lines) that originate from the human gene NAT2, encoding en N-acetyltransferase. The <CODE>PROTSIM</CODE> lines show proteins with significant similarity to NAT2, whereas the <CODE>STS</CODE> lines show the corresponding sequence-tagged sites in the genome.</P><P>To parse UniGene files, use the <CODE>Bio.UniGene</CODE> module:
-</P><PRE CLASS="verbatim">>>> from Bio import UniGene
->>> input = open("myunigenefile.data")
->>> record = UniGene.read(input)
-</PRE><P>The <CODE>record</CODE> returned by <CODE>UniGene.read</CODE> is a Python object with attributes corresponding to the fields in the UniGene record. For example,
-</P><PRE CLASS="verbatim">>>> record.ID
-"Hs.2"
->>> record.title
-"N-acetyltransferase 2 (arylamine N-acetyltransferase)"
-</PRE><P>The <CODE>EXPRESS</CODE> and <CODE>RESTR_EXPR</CODE> lines are stored as Python lists of strings:
-</P><PRE CLASS="verbatim">['bone', 'connective tissue', 'intestine', 'liver', 'liver tumor', 'normal', 'soft tissue/muscle tissue tumor', 'adult']
-</PRE><P>Specialized objects are returned for the <CODE>STS</CODE>, <CODE>PROTSIM</CODE>, and <CODE>SEQUENCE</CODE> lines, storing the keys shown in each line as attributes:
-</P><PRE CLASS="verbatim">>>> record.sts[0].acc
-'PMC310725P3'
->>> record.sts[0].unists
-'272646'
-</PRE><P>and similarly for the <CODE>PROTSIM</CODE> and <CODE>SEQUENCE</CODE> lines.</P><P>To parse a file containing more than one UniGene record, use the <CODE>parse</CODE> function in <CODE>Bio.UniGene</CODE>:</P><PRE CLASS="verbatim">>>> from Bio import UniGene
->>> input = open("unigenerecords.data")
->>> records = UniGene.parse(input)
->>> for record in records:
-...     print(record.ID)
-</PRE><!--TOC section Using a proxy-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc124">9.13</A>&#XA0;&#XA0;Using a proxy</H2><!--SEC END --><P>Normally you won&#X2019;t have to worry about using a proxy, but if this is an issue
-on your network here is how to deal with it. Internally, <CODE>Bio.Entrez</CODE>
-uses the standard Python library <CODE>urllib</CODE> for accessing the NCBI servers.
-This will check an environment variable called <CODE>http_proxy</CODE> to configure
-any simple proxy automatically. Unfortunately this module does not support
-the use of proxies which require authentication.</P><P>You may choose to set the <CODE>http_proxy</CODE> environment variable once (how you
-do this will depend on your operating system). Alternatively you can set this
-within Python at the start of your script, for example:</P><PRE CLASS="verbatim">import os
-os.environ["http_proxy"] = "http://proxyhost.example.com:8080"
-</PRE><P>See the <A HREF="http://www.python.org/doc/lib/module-urllib.html">urllib documentation</A> for more details.</P><!--TOC section Examples-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc125">9.14</A>&#XA0;&#XA0;Examples</H2><!--SEC END --><P>
-<A NAME="sec:entrez_examples"></A></P><!--TOC subsection PubMed and Medline-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc126">9.14.1</A>&#XA0;&#XA0;PubMed and Medline</H3><!--SEC END --><P>
-<A NAME="subsec:pub_med"></A></P><P>If you are in the medical field or interested in human issues (and many times even if you are not!), PubMed (<A HREF="http://www.ncbi.nlm.nih.gov/PubMed/"><TT>http://www.ncbi.nlm.nih.gov/PubMed/</TT></A>) is an excellent source of all kinds of goodies. So like other things, we&#X2019;d like to be able to grab information from it and use it in Python scripts.</P><P>In this example, we will query PubMed for all articles having to do with orchids (see sec [...]
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="orchid")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-...     if row["DbName"]=="pubmed":
-...         print(row["Count"])
-463
-</PRE><P>Now we use the <CODE>Bio.Entrez.efetch</CODE> function to download the PubMed IDs of these 463 articles:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="pubmed", term="orchid", retmax=463)
->>> record = Entrez.read(handle)
->>> idlist = record["IdList"]
->>> print(idlist)
-</PRE><P>This returns a Python list containing all of the PubMed IDs of articles related to orchids:
-</P><PRE CLASS="verbatim">['18680603', '18665331', '18661158', '18627489', '18627452', '18612381',
-'18594007', '18591784', '18589523', '18579475', '18575811', '18575690',
-...
-</PRE><P>Now that we&#X2019;ve got them, we obviously want to get the corresponding Medline records and extract the information from them. Here, we&#X2019;ll download the Medline records in the Medline flat-file format, and use the <CODE>Bio.Medline</CODE> module to parse them:
-</P><PRE CLASS="verbatim">>>> from Bio import Medline
->>> handle = Entrez.efetch(db="pubmed", id=idlist, rettype="medline",
-                           retmode="text")
->>> records = Medline.parse(handle)
-</PRE><P>NOTE - We&#X2019;ve just done a separate search and fetch here, the NCBI much prefer you to take advantage of their history support in this situation. See Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>.</P><P>Keep in mind that <CODE>records</CODE> is an iterator, so you can iterate through the records only once. If you want to save the records, you can convert them to a list:
-</P><PRE CLASS="verbatim">>>> records = list(records)
-</PRE><P>Let&#X2019;s now iterate over the records to print out some information about each record:
-</P><PRE CLASS="verbatim">>>> for record in records:
-...     print("title:", record.get("TI", "?"))
-...     print("authors:", record.get("AU", "?"))
-...     print("source:", record.get("SO", "?"))
-...     print("")
-</PRE><P>The output for this looks like:
-</P><PRE CLASS="verbatim">title: Sex pheromone mimicry in the early spider orchid (ophrys sphegodes):
-patterns of hydrocarbons as the key mechanism for pollination by sexual
-deception [In Process Citation]
-authors: ['Schiestl FP', 'Ayasse M', 'Paulus HF', 'Lofstedt C', 'Hansson BS',
-'Ibarra F', 'Francke W']
-source: J Comp Physiol [A] 2000 Jun;186(6):567-74
-</PRE><P>Especially interesting to note is the list of authors, which is returned as a standard Python list. This makes it easy to manipulate and search using standard Python tools. For instance, we could loop through a whole bunch of entries searching for a particular author with code like the following:
-</P><PRE CLASS="verbatim">>>> search_author = "Waits T"
-
->>> for record in records:
-...     if not "AU" in record:
-...         continue
-...     if search_author in record["AU"]:
-...         print("Author %s found: %s" % (search_author, record["SO"]))
-</PRE><P>Hopefully this section gave you an idea of the power and flexibility of the Entrez and Medline interfaces and how they can be used together.</P><!--TOC subsection Searching, downloading, and parsing Entrez Nucleotide records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc127">9.14.2</A>&#XA0;&#XA0;Searching, downloading, and parsing Entrez Nucleotide records</H3><!--SEC END --><P>
-<A NAME="subsec:entrez_example_genbank"></A></P><P>Here we&#X2019;ll show a simple example of performing a remote Entrez query. In section&#XA0;<A HREF="#sec:orchids">2.3</A> of the parsing examples, we talked about using NCBI&#X2019;s Entrez website to search the NCBI nucleotide databases for info on Cypripedioideae, our friends the lady slipper orchids. Now, we&#X2019;ll look at how to automate that process using a Python script. In this example, we&#X2019;ll just show how to connect,  [...]
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="Cypripedioideae")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-...     if row["DbName"]=="nuccore":
-...         print(row["Count"])
-814
-</PRE><P>So, we expect to find 814 Entrez Nucleotide records (this is the number I obtained in 2008; it is likely to increase in the future). If you find some ridiculously high number of hits, you may want to reconsider if you really want to download all of them, which is our next step:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> handle = Entrez.esearch(db="nucleotide", term="Cypripedioideae", retmax=814)
->>> record = Entrez.read(handle)
-</PRE><P>Here, <CODE>record</CODE> is a Python dictionary containing the search results and some auxiliary information. Just for information, let&#X2019;s look at what is stored in this dictionary:
-</P><PRE CLASS="verbatim">>>> print(record.keys())
-[u'Count', u'RetMax', u'IdList', u'TranslationSet', u'RetStart', u'QueryTranslation']
-</PRE><P>First, let&#X2019;s check how many results were found:
-</P><PRE CLASS="verbatim">>>> print(record["Count"])
-'814'
-</PRE><P>which is the number we expected. The 814 results are stored in <CODE>record['IdList']</CODE>:
-</P><PRE CLASS="verbatim">>>> len(record["IdList"])
-814
-</PRE><P>Let&#X2019;s look at the first five results:
-</P><PRE CLASS="verbatim">>>> record["IdList"][:5]
-['187237168', '187372713', '187372690', '187372688', '187372686']
-</PRE><P><A NAME="sec:entrez-batched-efetch"></A>
-We can download these records using <CODE>efetch</CODE>.
-While you could download these records one by one, to reduce the load on NCBI&#X2019;s servers, it is better to fetch a bunch of records at the same time, shown below.
-However, in this situation you should ideally be using the history feature described later in Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>.</P><PRE CLASS="verbatim">>>> idlist = ",".join(record["IdList"][:5])
->>> print(idlist)
-187237168,187372713,187372690,187372688,187372686
->>> handle = Entrez.efetch(db="nucleotide", id=idlist, retmode="xml")
->>> records = Entrez.read(handle)
->>> len(records)
-5
-</PRE><P>Each of these records corresponds to one GenBank record.
-</P><PRE CLASS="verbatim">>>> print(records[0].keys())
-[u'GBSeq_moltype', u'GBSeq_source', u'GBSeq_sequence',
- u'GBSeq_primary-accession', u'GBSeq_definition', u'GBSeq_accession-version',
- u'GBSeq_topology', u'GBSeq_length', u'GBSeq_feature-table',
- u'GBSeq_create-date', u'GBSeq_other-seqids', u'GBSeq_division',
- u'GBSeq_taxonomy', u'GBSeq_references', u'GBSeq_update-date',
- u'GBSeq_organism', u'GBSeq_locus', u'GBSeq_strandedness']
-
->>> print(records[0]["GBSeq_primary-accession"])
-DQ110336
-
->>> print(records[0]["GBSeq_other-seqids"])
-['gb|DQ110336.1|', 'gi|187237168']
-
->>> print(records[0]["GBSeq_definition"])
-Cypripedium calceolus voucher Davis 03-03 A maturase (matR) gene, partial cds;
-mitochondrial
-
->>> print(records[0]["GBSeq_organism"])
-Cypripedium calceolus
-</PRE><P>You could use this to quickly set up searches &#X2013; but for heavy usage, see Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>.</P><!--TOC subsection Searching, downloading, and parsing GenBank records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc128">9.14.3</A>&#XA0;&#XA0;Searching, downloading, and parsing GenBank records</H3><!--SEC END --><P>
-<A NAME="sec:entrez-search-fetch-genbank"></A></P><P>The GenBank record format is a very popular method of holding information about sequences, sequence features, and other associated sequence information. The format is a good way to get information from the NCBI databases at <A HREF="http://www.ncbi.nlm.nih.gov/"><TT>http://www.ncbi.nlm.nih.gov/</TT></A>.</P><P>In this example we&#X2019;ll show how to query the NCBI databases,to retrieve the records from the query, and then parse them u [...]
-For simplicity, this example <EM>does not</EM> take advantage of the WebEnv history feature &#X2013; see Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A> for this.</P><P>First, we want to make a query and find out the ids of the records to retrieve. Here we&#X2019;ll do a quick search for one of our favorite organisms, <EM>Opuntia</EM> (prickly-pear cacti). We can do quick search and get back the GIs (GenBank identifiers) for all of the corresponding records. First we check how many re [...]
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.egquery(term="Opuntia AND rpl16")
->>> record = Entrez.read(handle)
->>> for row in record["eGQueryResult"]:
-...     if row["DbName"]=="nuccore":
-...         print(row["Count"])
-...
-9
-</PRE><P>Now we download the list of GenBank identifiers:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.esearch(db="nuccore", term="Opuntia AND rpl16")
->>> record = Entrez.read(handle)
->>> gi_list = record["IdList"]
->>> gi_list
-['57240072', '57240071', '6273287', '6273291', '6273290', '6273289', '6273286',
-'6273285', '6273284']
-</PRE><P>Now we use these GIs to download the GenBank records - note that with older versions of Biopython you had to supply a comma separated list of GI numbers to Entrez, as of Biopython 1.59 you can pass a list and this is converted for you:</P><PRE CLASS="verbatim">>>> gi_str = ",".join(gi_list)
->>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
-</PRE><P>If you want to look at the raw GenBank files, you can read from this handle and print out the result:</P><PRE CLASS="verbatim">>>> text = handle.read()
->>> print(text)
-LOCUS       AY851612                 892 bp    DNA     linear   PLN 10-APR-2007
-DEFINITION  Opuntia subulata rpl16 gene, intron; chloroplast.
-ACCESSION   AY851612
-VERSION     AY851612.1  GI:57240072
-KEYWORDS    .
-SOURCE      chloroplast Austrocylindropuntia subulata
-  ORGANISM  Austrocylindropuntia subulata
-            Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
-            Spermatophyta; Magnoliophyta; eudicotyledons; core eudicotyledons;
-            Caryophyllales; Cactaceae; Opuntioideae; Austrocylindropuntia.
-REFERENCE   1  (bases 1 to 892)
-  AUTHORS   Butterworth,C.A. and Wallace,R.S.
-...
-</PRE><P>In this case, we are just getting the raw records. To get the records in a more Python-friendly form, we can use <CODE>Bio.SeqIO</CODE> to parse the GenBank data into <CODE>SeqRecord</CODE> objects, including <CODE>SeqFeature</CODE> objects (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> handle = Entrez.efetch(db="nuccore", id=gi_str, rettype="gb", retmode="text")
->>> records = SeqIO.parse(handle, "gb")
-</PRE><P>We can now step through the records and look at the information we are interested in:
-</P><PRE CLASS="verbatim">>>> for record in records:
->>> ...    print("%s, length %i, with %i features" \
->>> ...           % (record.name, len(record), len(record.features)))
-AY851612, length 892, with 3 features
-AY851611, length 881, with 3 features
-AF191661, length 895, with 3 features
-AF191665, length 902, with 3 features
-AF191664, length 899, with 3 features
-AF191663, length 899, with 3 features
-AF191660, length 893, with 3 features
-AF191659, length 894, with 3 features
-AF191658, length 896, with 3 features
-</PRE><P>Using these automated query retrieval functionality is a big plus over doing things by hand. Although the module should obey the NCBI&#X2019;s max three queries per second rule, the NCBI have other recommendations like avoiding peak hours. See Section&#XA0;<A HREF="#sec:entrez-guidelines">9.1</A>.
-In particular, please note that for simplicity, this example does not use the WebEnv history feature. You should use this for any non-trivial search and download work, see Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>.</P><P>Finally, if plan to repeat your analysis, rather than downloading the files from the NCBI and parsing them immediately (as shown in this example), you should just download the records <EM>once</EM> and save them to your hard disk, and then parse the local file.< [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc129">9.14.4</A>&#XA0;&#XA0;Finding the lineage of an organism</H3><!--SEC END --><P>Staying with a plant example, let&#X2019;s now find the lineage of the Cypripedioideae orchid family. First, we search the Taxonomy database for Cypripedioideae, which yields exactly one NCBI taxonomy identifier:
-</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.esearch(db="Taxonomy", term="Cypripedioideae")
->>> record = Entrez.read(handle)
->>> record["IdList"]
-['158330']
->>> record["IdList"][0]
-'158330'
-</PRE><P>Now, we use <CODE>efetch</CODE> to download this entry in the Taxonomy database, and then parse it:
-</P><PRE CLASS="verbatim">>>> handle = Entrez.efetch(db="Taxonomy", id="158330", retmode="xml")
->>> records = Entrez.read(handle)
-</PRE><P>Again, this record stores lots of information:
-</P><PRE CLASS="verbatim">>>> records[0].keys()
-[u'Lineage', u'Division', u'ParentTaxId', u'PubDate', u'LineageEx',
- u'CreateDate', u'TaxId', u'Rank', u'GeneticCode', u'ScientificName',
- u'MitoGeneticCode', u'UpdateDate']
-</PRE><P>We can get the lineage directly from this record:
-</P><PRE CLASS="verbatim">>>> records[0]["Lineage"]
-'cellular organisms; Eukaryota; Viridiplantae; Streptophyta; Streptophytina;
- Embryophyta; Tracheophyta; Euphyllophyta; Spermatophyta; Magnoliophyta;
- Liliopsida; Asparagales; Orchidaceae'
-</PRE><P>The record data contains much more than just the information shown here - for example look under <TT>"LineageEx"</TT> instead of <TT>"Lineage"</TT> and you&#X2019;ll get the NCBI taxon identifiers of the lineage entries too.</P><!--TOC section Using the history and WebEnv-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc130">9.15</A>&#XA0;&#XA0;Using the history and WebEnv</H2><!--SEC END --><P>
-<A NAME="sec:entrez-webenv"></A></P><P>Often you will want to make a series of linked queries. Most typically,
-running a search, perhaps refining the search, and then retrieving detailed
-search results. You <EM>can</EM> do this by making a series of separate calls
-to Entrez. However, the NCBI prefer you to take advantage of their history
-support - for example combining ESearch and EFetch.</P><P>Another typical use of the history support would be to combine EPost and
-EFetch. You use EPost to upload a list of identifiers, which starts a new
-history session. You then download the records with EFetch by referring
-to the session (instead of the identifiers).</P><!--TOC subsection Searching for and downloading sequences using the history-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc131">9.15.1</A>&#XA0;&#XA0;Searching for and downloading sequences using the history</H3><!--SEC END --><P>
-Suppose we want to search and download all the <I>Opuntia</I> rpl16
-nucleotide sequences, and store them in a FASTA file. As shown in
-Section&#XA0;<A HREF="#sec:entrez-search-fetch-genbank">9.14.3</A>, we can naively combine
-<CODE>Bio.Entrez.esearch()</CODE> to get a list of GI numbers, and then call
-<CODE>Bio.Entrez.efetch()</CODE> to download them all.</P><P>However, the approved approach is to run the search with the history
-feature. Then, we can fetch the results by reference to the search
-results - which the NCBI can anticipate and cache.</P><P>To do this, call <CODE>Bio.Entrez.esearch()</CODE> as normal, but with the
-additional argument of <CODE>usehistory="y"</CODE>,</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "history.user at example.com"
->>> search_handle = Entrez.esearch(db="nucleotide",term="Opuntia[orgn] and rpl16",
-                                   usehistory="y")
->>> search_results = Entrez.read(search_handle)
->>> search_handle.close()
-</PRE><P>When you get the XML output back, it will still include the usual search results:</P><PRE CLASS="verbatim">>>> gi_list = search_results["IdList"]
->>> count = int(search_results["Count"])
->>> assert count == len(gi_list)
-</PRE><P>However, you also get given two additional pieces of information, the <TT>WebEnv</TT> session cookie, and the <TT>QueryKey</TT>:</P><PRE CLASS="verbatim">>>> webenv = search_results["WebEnv"]
->>> query_key = search_results["QueryKey"]
-</PRE><P>Having stored these values in variables <TT>session_cookie</TT> and <TT>query_key</TT> we can use them as parameters to <CODE>Bio.Entrez.efetch()</CODE> instead of giving the GI numbers as identifiers.</P><P>While for small searches you might be OK downloading everything at once, it is better to download in batches. You use the <TT>retstart</TT> and <TT>retmax</TT> parameters to specify which range of search results you want returned (starting entry using zero-based counting, an [...]
-For example,</P><PRE CLASS="verbatim">from Bio import Entrez
-import time
-try:
-    from urllib.error import HTTPError  # for Python 3
-except ImportError:
-    from urllib2 import HTTPError  # for Python 2
-batch_size = 3
-out_handle = open("orchid_rpl16.fasta", "w")
-for start in range(0, count, batch_size):
-    end = min(count, start+batch_size)
-    print("Going to download record %i to %i" % (start+1, end))
-    attempt = 1
-    while attempt <= 3:
-        try:
-            fetch_handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text",
-                                         retstart=start, retmax=batch_size,
-                                         webenv=webenv, query_key=query_key)
-        except HTTPError as err:
-            if 500 <= err.code <= 599:
-                print("Received error from server %s" % err)
-                print("Attempt %i of 3" % attempt)
-                attempt += 1
-                time.sleep(15)
-            else:
-                raise
-    data = fetch_handle.read()
-    fetch_handle.close()
-    out_handle.write(data)
-out_handle.close()
-</PRE><P>For illustrative purposes, this example downloaded the FASTA records in batches of three. Unless you are downloading genomes or chromosomes, you would normally pick a larger batch size.</P><!--TOC subsection Searching for and downloading abstracts using the history-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc132">9.15.2</A>&#XA0;&#XA0;Searching for and downloading abstracts using the history</H3><!--SEC END --><P>
-Here is another history example, searching for papers published in the last year about the <I>Opuntia</I>, and then downloading them into a file in MedLine format:</P><PRE CLASS="verbatim">from Bio import Entrez
-import time
-try:
-    from urllib.error import HTTPError  # for Python 3
-except ImportError:
-    from urllib2 import HTTPError  # for Python 2
-Entrez.email = "history.user at example.com"
-search_results = Entrez.read(Entrez.esearch(db="pubmed",
-                                            term="Opuntia[ORGN]",
-                                            reldate=365, datetype="pdat",
-                                            usehistory="y"))
-count = int(search_results["Count"])
-print("Found %i results" % count)
-
-batch_size = 10
-out_handle = open("recent_orchid_papers.txt", "w")
-for start in range(0,count,batch_size):
-    end = min(count, start+batch_size)
-    print("Going to download record %i to %i" % (start+1, end))
-    attempt = 1
-    while attempt <= 3:
-        try:
-            fetch_handle = Entrez.efetch(db="pubmed",rettype="medline",
-                                         retmode="text",retstart=start,
-                                         retmax=batch_size,
-                                         webenv=search_results["WebEnv"],
-                                         query_key=search_results["QueryKey"])
-        except HTTPError as err:
-            if 500 <= err.code <= 599:
-                print("Received error from server %s" % err)
-                print("Attempt %i of 3" % attempt)
-                attempt += 1
-                time.sleep(15)
-            else:
-                raise
-    data = fetch_handle.read()
-    fetch_handle.close()
-    out_handle.write(data)
-out_handle.close()
-</PRE><P>At the time of writing, this gave 28 matches - but because this is a date dependent search, this will of course vary. As described in Section&#XA0;<A HREF="#subsec:entrez-and-medline">9.12.1</A> above, you can then use <CODE>Bio.Medline</CODE> to parse the saved records.</P><!--TOC subsection Searching for citations-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc133">9.15.3</A>&#XA0;&#XA0;Searching for citations</H3><!--SEC END --><P>
-<A NAME="sec:elink-citations"></A></P><P>Back in Section&#XA0;<A HREF="#sec:elink">9.7</A> we mentioned ELink can be used to search for citations of a given paper.
-Unfortunately this only covers journals indexed for PubMed Central
-(doing it for all the journals in PubMed would mean a lot more work for the NIH).
-Let&#X2019;s try this for the Biopython PDB parser paper, PubMed ID 14630660:</P><PRE CLASS="verbatim">>>> from Bio import Entrez
->>> Entrez.email = "A.N.Other at example.com"
->>> pmid = "14630660"
->>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
-...                                    LinkName="pubmed_pmc_refs", from_uid=pmid))
->>> pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]]
->>> pmc_ids
-['2744707', '2705363', '2682512', ..., '1190160']
-</PRE><P>Great - eleven articles. But why hasn&#X2019;t the Biopython application note been
-found (PubMed ID 19304878)? Well, as you might have guessed from the variable
-names, there are not actually PubMed IDs, but PubMed Central IDs. Our
-application note is the third citing paper in that list, PMCID 2682512.</P><P>So, what if (like me) you&#X2019;d rather get back a list of PubMed IDs? Well we
-can call ELink again to translate them. This becomes a two step process,
-so by now you should expect to use the history feature to accomplish it
-(Section&#XA0;<A HREF="#sec:entrez-webenv">9.15</A>).</P><P>But first, taking the more straightforward approach of making a second
-(separate) call to ELink:</P><PRE CLASS="verbatim">>>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed",
-...                                     from_uid=",".join(pmc_ids)))
->>> pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]]
->>> pubmed_ids
-['19698094', '19450287', '19304878', ..., '15985178']
-</PRE><P>This time you can immediately spot the Biopython application note
-as the third hit (PubMed ID 19304878).</P><P>Now, let&#X2019;s do that all again but with the history &#X2026;<I>TODO</I>.</P><P>And finally, don&#X2019;t forget to include your <EM>own</EM> email address in the Entrez calls.
-</P><!--TOC chapter Swiss-Prot and ExPASy-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc134">Chapter&#XA0;10</A>&#XA0;&#XA0;Swiss-Prot and ExPASy</H1><!--SEC END --><P>
-<A NAME="chapter:swiss_prot"></A></P><!--TOC section Parsing Swiss-Prot files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc135">10.1</A>&#XA0;&#XA0;Parsing Swiss-Prot files</H2><!--SEC END --><P>Swiss-Prot (<A HREF="http://www.expasy.org/sprot"><TT>http://www.expasy.org/sprot</TT></A>) is a hand-curated database of protein sequences. Biopython can parse the &#X201C;plain text&#X201D; Swiss-Prot file format, which is still used for the UniProt Knowledgebase which combined Swiss-Prot, TrEMBL and PIR-PSD. We do not (yet) support the UniProtKB XML file format.</ [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc136">10.1.1</A>&#XA0;&#XA0;Parsing Swiss-Prot records</H3><!--SEC END --><P>In Section&#XA0;<A HREF="#sec:SeqIO_ExPASy_and_SwissProt">5.3.2</A>, we described how to extract the sequence of a Swiss-Prot record as a <CODE>SeqRecord</CODE> object. Alternatively, you can store the Swiss-Prot record in a <CODE>Bio.SwissProt.Record</CODE> object, which in fact stores the complete information contained in the Swiss-Prot record. In this secti [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Open a Swiss-Prot file locally: <BR>
-<CODE>>>> handle = open("myswissprotfile.dat")</CODE>
-</LI><LI CLASS="li-itemize">Open a gzipped Swiss-Prot file:
-<PRE CLASS="verbatim">>>> import gzip
->>> handle = gzip.open("myswissprotfile.dat.gz")
-</PRE></LI><LI CLASS="li-itemize">Open a Swiss-Prot file over the internet:
-<PRE CLASS="verbatim">>>> import urllib
->>> handle = urllib.urlopen("http://www.somelocation.org/data/someswissprotfile.dat")
-</PRE></LI><LI CLASS="li-itemize">Open a Swiss-Prot file over the internet from the ExPASy database
-(see section <A HREF="#subsec:expasy_swissprot">10.5.1</A>):
-<PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> handle = ExPASy.get_sprot_raw(myaccessionnumber)
-</PRE></LI></UL><P>
-The key point is that for the parser, it doesn&#X2019;t matter how the handle was created, as long as it points to data in the Swiss-Prot format.</P><P>We can use <CODE>Bio.SeqIO</CODE> as described in Section&#XA0;<A HREF="#sec:SeqIO_ExPASy_and_SwissProt">5.3.2</A> to get file format agnostic <CODE>SeqRecord</CODE> objects. Alternatively, we can use <CODE>Bio.SwissProt</CODE> get <CODE>Bio.SwissProt.Record</CODE> objects, which are a much closer match to the underlying file format.</P>< [...]
-</P><PRE CLASS="verbatim">>>> from Bio import SwissProt
->>> record = SwissProt.read(handle)
-</PRE><P>This function should be used if the handle points to exactly one Swiss-Prot record. It raises a <CODE>ValueError</CODE> if no Swiss-Prot record was found, and also if more than one record was found.</P><P>We can now print out some information about this record:
-</P><PRE CLASS="verbatim">>>> print(record.description)
-'RecName: Full=Chalcone synthase 3; EC=2.3.1.74; AltName: Full=Naringenin-chalcone synthase 3;'
->>> for ref in record.references:
-...     print("authors:", ref.authors)
-...     print("title:", ref.title)
-...
-authors: Liew C.F., Lim S.H., Loh C.S., Goh C.J.;
-title: "Molecular cloning and sequence analysis of chalcone synthase cDNAs of
-Bromheadia finlaysoniana.";
->>> print(record.organism_classification)
-['Eukaryota', 'Viridiplantae', 'Streptophyta', 'Embryophyta', ..., 'Bromheadia']
-</PRE><P>To parse a file that contains more than one Swiss-Prot record, we use the <CODE>parse</CODE> function instead. This function allows us to iterate over the records in the file.</P><P>For example, let&#X2019;s parse the full Swiss-Prot database and collect all the descriptions.
-You can download this from the <A HREF="ftp://ftp.expasy.org/databases/uniprot/current_release/knowledgebase/complete/uniprot_sprot.dat.gz">ExPAYs FTP site</A> as a single gzipped-file <CODE>uniprot_sprot.dat.gz</CODE> (about 300MB). This is a compressed file containing a single file, <CODE>uniprot_sprot.dat</CODE> (over 1.5GB).</P><P>As described at the start of this section, you can use the Python library <CODE>gzip</CODE> to open and uncompress a <TT>.gz</TT> file, like this:</P><PRE  [...]
->>> handle = gzip.open("uniprot_sprot.dat.gz")
-</PRE><P>However, uncompressing a large file takes time, and each time you open the file for reading in this way, it has to be decompressed on the fly. So, if you can spare the disk space you&#X2019;ll save time in the long run if you first decompress the file to disk, to get the <CODE>uniprot_sprot.dat</CODE> file inside. Then you can open the file for reading as usual:</P><PRE CLASS="verbatim">>>> handle = open("uniprot_sprot.dat")
-</PRE><P>As of June 2009, the full Swiss-Prot database downloaded from ExPASy contained 468851 Swiss-Prot records. One concise way to build up a list of the record descriptions is with a list comprehension:
-</P><PRE CLASS="verbatim">>>> from Bio import SwissProt
->>> handle = open("uniprot_sprot.dat")
->>> descriptions = [record.description for record in SwissProt.parse(handle)]
->>> len(descriptions)
-468851
->>> descriptions[:5]
-['RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-1R;',
- 'RecName: Full=Protein MGF 100-2L;']
-
-</PRE><P>Or, using a for loop over the record iterator:
-</P><PRE CLASS="verbatim">>>> from Bio import SwissProt
->>> descriptions = []
->>> handle = open("uniprot_sprot.dat")
->>> for record in SwissProt.parse(handle):
-...     descriptions.append(record.description)
-...
->>> len(descriptions)
-468851
-</PRE><P>Because this is such a large input file, either way takes about eleven minutes on my new desktop computer (using the uncompressed <CODE>uniprot_sprot.dat</CODE> file as input).</P><P>It is equally easy to extract any kind of information you&#X2019;d like from Swiss-Prot records. To see the members of a Swiss-Prot record, use
-</P><PRE CLASS="verbatim">>>> dir(record)
-['__doc__', '__init__', '__module__', 'accessions', 'annotation_update',
-'comments', 'created', 'cross_references', 'data_class', 'description',
-'entry_name', 'features', 'gene_name', 'host_organism', 'keywords',
-'molecule_type', 'organelle', 'organism', 'organism_classification',
-'references', 'seqinfo', 'sequence', 'sequence_length',
-'sequence_update', 'taxonomy_id']
-</PRE><!--TOC subsection Parsing the Swiss-Prot keyword and category list-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc137">10.1.2</A>&#XA0;&#XA0;Parsing the Swiss-Prot keyword and category list</H3><!--SEC END --><P>Swiss-Prot also distributes a file <CODE>keywlist.txt</CODE>, which lists the keywords and categories used in Swiss-Prot. The file contains entries in the following form:</P><PRE CLASS="verbatim">ID   2Fe-2S.
-AC   KW-0001
-DE   Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron
-DE   atoms complexed to 2 inorganic sulfides and 4 sulfur atoms of
-DE   cysteines from the protein.
-SY   Fe2S2; [2Fe-2S] cluster; [Fe2S2] cluster; Fe2/S2 (inorganic) cluster;
-SY   Di-mu-sulfido-diiron; 2 iron, 2 sulfur cluster binding.
-GO   GO:0051537; 2 iron, 2 sulfur cluster binding
-HI   Ligand: Iron; Iron-sulfur; 2Fe-2S.
-HI   Ligand: Metal-binding; 2Fe-2S.
-CA   Ligand.
-//
-ID   3D-structure.
-AC   KW-0002
-DE   Protein, or part of a protein, whose three-dimensional structure has
-DE   been resolved experimentally (for example by X-ray crystallography or
-DE   NMR spectroscopy) and whose coordinates are available in the PDB
-DE   database. Can also be used for theoretical models.
-HI   Technical term: 3D-structure.
-CA   Technical term.
-//
-ID   3Fe-4S.
-...
-</PRE><P>The entries in this file can be parsed by the <CODE>parse</CODE> function in the <CODE>Bio.SwissProt.KeyWList</CODE> module. Each entry is then stored as a <CODE>Bio.SwissProt.KeyWList.Record</CODE>, which is a Python dictionary.</P><PRE CLASS="verbatim">>>> from Bio.SwissProt import KeyWList
->>> handle = open("keywlist.txt")
->>> records = KeyWList.parse(handle)
->>> for record in records:
-...     print(record['ID'])
-...     print(record['DE'])
-</PRE><P>This prints
-</P><PRE CLASS="verbatim">2Fe-2S.
-Protein which contains at least one 2Fe-2S iron-sulfur cluster: 2 iron atoms
-complexed to 2 inorganic sulfides and 4 sulfur atoms of cysteines from the
-protein.
-...
-</PRE><!--TOC section Parsing Prosite records-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc138">10.2</A>&#XA0;&#XA0;Parsing Prosite records</H2><!--SEC END --><P>Prosite is a database containing protein domains, protein families, functional sites, as well as the patterns and profiles to recognize them. Prosite was developed in parallel with Swiss-Prot. In Biopython, a Prosite record is represented by the <CODE>Bio.ExPASy.Prosite.Record</CODE> class, whose members correspond to the different fields in a Prosite record.</P><P>In [...]
->>> handle = open("myprositefile.dat")
->>> records = Prosite.parse(handle)
-</PRE><P>We can now take the records one at a time and print out some information. For example, using the file containing the complete Prosite database, we&#X2019;d find
-</P><PRE CLASS="verbatim">>>> from Bio.ExPASy import Prosite
->>> handle = open("prosite.dat")
->>> records = Prosite.parse(handle)
->>> record = next(records)
->>> record.accession
-'PS00001'
->>> record.name
-'ASN_GLYCOSYLATION'
->>> record.pdoc
-'PDOC00001'
->>> record = next(records)
->>> record.accession
-'PS00004'
->>> record.name
-'CAMP_PHOSPHO_SITE'
->>> record.pdoc
-'PDOC00004'
->>> record = next(records)
->>> record.accession
-'PS00005'
->>> record.name
-'PKC_PHOSPHO_SITE'
->>> record.pdoc
-'PDOC00005'
-</PRE><P>and so on. If you&#X2019;re interested in how many Prosite records there are, you could use
-</P><PRE CLASS="verbatim">>>> from Bio.ExPASy import Prosite
->>> handle = open("prosite.dat")
->>> records = Prosite.parse(handle)
->>> n = 0
->>> for record in records: n+=1
-...
->>> n
-2073
-</PRE><P>To read exactly one Prosite from the handle, you can use the <CODE>read</CODE> function:
-</P><PRE CLASS="verbatim">>>> from Bio.ExPASy import Prosite
->>> handle = open("mysingleprositerecord.dat")
->>> record = Prosite.read(handle)
-</PRE><P>This function raises a ValueError if no Prosite record is found, and also if more than one Prosite record is found.</P><!--TOC section Parsing Prosite documentation records-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc139">10.3</A>&#XA0;&#XA0;Parsing Prosite documentation records</H2><!--SEC END --><P>In the Prosite example above, the <CODE>record.pdoc</CODE> accession numbers <CODE>'PDOC00001'</CODE>, <CODE>'PDOC00004'</CODE>, <CODE>'PDOC00005'</CODE> and so on refer to Prosite documentation. The Prosite documentation records are available from ExPASy as individual files, and as one file (<CODE>prosite.doc</CODE>) containing all Prosite documentation [...]
->>> handle = open("prosite.doc")
->>> records = Prodoc.parse(handle)
->>> accessions = [record.accession for record in records]
-</PRE><P>Again a <CODE>read()</CODE> function is provided to read exactly one Prosite documentation record from the handle.</P><!--TOC section Parsing Enzyme records-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc140">10.4</A>&#XA0;&#XA0;Parsing Enzyme records</H2><!--SEC END --><P>ExPASy&#X2019;s Enzyme database is a repository of information on enzyme nomenclature. A typical Enzyme record looks as follows:</P><PRE CLASS="verbatim">ID   3.1.1.34
-DE   Lipoprotein lipase.
-AN   Clearing factor lipase.
-AN   Diacylglycerol lipase.
-AN   Diglyceride lipase.
-CA   Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.
-CC   -!- Hydrolyzes triacylglycerols in chylomicrons and very low-density
-CC       lipoproteins (VLDL).
-CC   -!- Also hydrolyzes diacylglycerol.
-PR   PROSITE; PDOC00110;
-DR   P11151, LIPL_BOVIN ;  P11153, LIPL_CAVPO ;  P11602, LIPL_CHICK ;
-DR   P55031, LIPL_FELCA ;  P06858, LIPL_HUMAN ;  P11152, LIPL_MOUSE ;
-DR   O46647, LIPL_MUSVI ;  P49060, LIPL_PAPAN ;  P49923, LIPL_PIG   ;
-DR   Q06000, LIPL_RAT   ;  Q29524, LIPL_SHEEP ;
-//
-</PRE><P>In this example, the first line shows the EC (Enzyme Commission) number of lipoprotein lipase (second line). Alternative names of lipoprotein lipase are "clearing factor lipase", "diacylglycerol lipase", and "diglyceride lipase" (lines 3 through 5). The line starting with "CA" shows the catalytic activity of this enzyme. Comment lines start with "CC". The "PR" line shows references to the Prosite Documentation records, and the "DR" lines show references to Swiss-Prot records. No [...]
->>> with open("lipoprotein.txt") as handle:
-...     record = Enzyme.read(handle)
-...
->>> record["ID"]
-'3.1.1.34'
->>> record["DE"]
-'Lipoprotein lipase.'
->>> record["AN"]
-['Clearing factor lipase.', 'Diacylglycerol lipase.', 'Diglyceride lipase.']
->>> record["CA"]
-'Triacylglycerol + H(2)O = diacylglycerol + a carboxylate.'
->>> record["PR"]
-['PDOC00110']
-</PRE><PRE CLASS="verbatim">>>> record["CC"]
-['Hydrolyzes triacylglycerols in chylomicrons and very low-density lipoproteins
-(VLDL).', 'Also hydrolyzes diacylglycerol.']
->>> record["DR"]
-[['P11151', 'LIPL_BOVIN'], ['P11153', 'LIPL_CAVPO'], ['P11602', 'LIPL_CHICK'],
-['P55031', 'LIPL_FELCA'], ['P06858', 'LIPL_HUMAN'], ['P11152', 'LIPL_MOUSE'],
-['O46647', 'LIPL_MUSVI'], ['P49060', 'LIPL_PAPAN'], ['P49923', 'LIPL_PIG'],
-['Q06000', 'LIPL_RAT'], ['Q29524', 'LIPL_SHEEP']]
-</PRE><P>The <CODE>read</CODE> function raises a ValueError if no Enzyme record is found, and also if more than one Enzyme record is found.</P><P>The full set of Enzyme records can be downloaded as a single file (<CODE>enzyme.dat</CODE>) from the <A HREF="ftp://ftp.expasy.org/databases/enzyme/enzyme.dat">ExPASy FTP site</A>, containing 4877 records (release of 3 March 2009). To parse such a file containing multiple Enzyme records, use the <CODE>parse</CODE> function in <CODE>Bio.ExPASy.E [...]
->>> handle = open("enzyme.dat")
->>> records = Enzyme.parse(handle)
-</PRE><P>We can now iterate over the records one at a time. For example, we can make a list of all EC numbers for which an Enzyme record is available:
-</P><PRE CLASS="verbatim">>>> ecnumbers = [record["ID"] for record in records]
-</PRE><!--TOC section Accessing the ExPASy server-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc141">10.5</A>&#XA0;&#XA0;Accessing the ExPASy server</H2><!--SEC END --><P>Swiss-Prot, Prosite, and Prosite documentation records can be downloaded from the ExPASy web server at <A HREF="http://www.expasy.org"><TT>http://www.expasy.org</TT></A>. Six kinds of queries are available from ExPASy:
-</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>get_prodoc_entry</B></DT><DD CLASS="dd-description">To download a Prosite documentation record in HTML format
-</DD><DT CLASS="dt-description"><B>get_prosite_entry</B></DT><DD CLASS="dd-description">To download a Prosite record in HTML format
-</DD><DT CLASS="dt-description"><B>get_prosite_raw</B></DT><DD CLASS="dd-description">To download a Prosite or Prosite documentation record in raw format
-</DD><DT CLASS="dt-description"><B>get_sprot_raw</B></DT><DD CLASS="dd-description">To download a Swiss-Prot record in raw format
-</DD><DT CLASS="dt-description"><B>sprot_search_ful</B></DT><DD CLASS="dd-description">To search for a Swiss-Prot record
-</DD><DT CLASS="dt-description"><B>sprot_search_de</B></DT><DD CLASS="dd-description">To search for a Swiss-Prot record
-</DD></DL><P>
-To access this web server from a Python script, we use the <CODE>Bio.ExPASy</CODE> module.</P><!--TOC subsection Retrieving a Swiss-Prot record-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc142">10.5.1</A>&#XA0;&#XA0;Retrieving a Swiss-Prot record</H3><!--SEC END --><P>
-<A NAME="subsec:expasy_swissprot"></A></P><P>Let&#X2019;s say we are looking at chalcone synthases for Orchids (see section&#XA0;<A HREF="#sec:orchids">2.3</A> for some justification for looking for interesting things about orchids). Chalcone synthase is involved in flavanoid biosynthesis in plants, and flavanoids make lots of cool things like pigment colors and UV protectants.</P><P>If you do a search on Swiss-Prot, you can find three orchid proteins for Chalcone Synthase, id numbers O2 [...]
->>> from Bio import SwissProt
-
->>> accessions = ["O23729", "O23730", "O23731"]
->>> records = []
-
->>> for accession in accessions:
-...     handle = ExPASy.get_sprot_raw(accession)
-...     record = SwissProt.read(handle)
-...     records.append(record)
-</PRE><P>If the accession number you provided to <CODE>ExPASy.get_sprot_raw</CODE> does not exist, then <CODE>SwissProt.read(handle)</CODE> will raise a <CODE>ValueError</CODE>. You can catch <CODE>ValueException</CODE> exceptions to detect invalid accession numbers:</P><PRE CLASS="verbatim">>>> for accession in accessions:
-...     handle = ExPASy.get_sprot_raw(accession)
-...     try:
-...         record = SwissProt.read(handle)
-...     except ValueException:
-...         print("WARNING: Accession %s not found" % accession)
-...     records.append(record)
-</PRE><!--TOC subsection Searching Swiss-Prot-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc143">10.5.2</A>&#XA0;&#XA0;Searching Swiss-Prot</H3><!--SEC END --><P>Now, you may remark that I knew the records&#X2019; accession numbers
-beforehand. Indeed, <CODE>get_sprot_raw()</CODE> needs either the entry name
-or an accession number. When you don&#X2019;t have them handy, you can use
-one of the <CODE>sprot_search_de()</CODE> or <CODE>sprot_search_ful()</CODE>
-functions.</P><P><CODE>sprot_search_de()</CODE> searches in the ID, DE, GN, OS and OG lines;
-<CODE>sprot_search_ful()</CODE> searches in (nearly) all the fields. They
-are detailed on
-<A HREF="http://www.expasy.org/cgi-bin/sprot-search-de"><TT>http://www.expasy.org/cgi-bin/sprot-search-de</TT></A> and
-<A HREF="http://www.expasy.org/cgi-bin/sprot-search-ful"><TT>http://www.expasy.org/cgi-bin/sprot-search-ful</TT></A>
-respectively. Note that they don&#X2019;t search in TrEMBL by default
-(argument <CODE>trembl</CODE>). Note also that they return HTML pages;
-however, accession numbers are quite easily extractable:</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> import re
-
->>> handle = ExPASy.sprot_search_de("Orchid Chalcone Synthase")
->>> # or:
->>> # handle = ExPASy.sprot_search_ful("Orchid and {Chalcone Synthase}")
->>> html_results = handle.read()
->>> if "Number of sequences found" in html_results:
-...     ids = re.findall(r'HREF="/uniprot/(\w+)"', html_results)
-... else:
-...     ids = re.findall(r'href="/cgi-bin/niceprot\.pl\?(\w+)"', html_results)
-</PRE><!--TOC subsection Retrieving Prosite and Prosite documentation records-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc144">10.5.3</A>&#XA0;&#XA0;Retrieving Prosite and Prosite documentation records</H3><!--SEC END --><P>Prosite and Prosite documentation records can be retrieved either in HTML format, or in raw format. To parse Prosite and Prosite documentation records with Biopython, you should retrieve the records in raw format. For other purposes, however, you may be interested in these records in HTML format.</P><P>To retrieve a Prosite or Prosite [...]
->>> handle = ExPASy.get_prosite_raw('PS00001')
->>> text = handle.read()
->>> print(text)
-</PRE><P>To retrieve a Prosite record and parse it into a <CODE>Bio.Prosite.Record</CODE> object, use</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> from Bio import Prosite
->>> handle = ExPASy.get_prosite_raw('PS00001')
->>> record = Prosite.read(handle)
-</PRE><P>The same function can be used to retrieve a Prosite documentation record and parse it into a <CODE>Bio.ExPASy.Prodoc.Record</CODE> object:</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> from Bio.ExPASy import Prodoc
->>> handle = ExPASy.get_prosite_raw('PDOC00001')
->>> record = Prodoc.read(handle)
-</PRE><P>For non-existing accession numbers, <CODE>ExPASy.get_prosite_raw</CODE> returns a handle to an emptry string. When faced with an empty string, <CODE>Prosite.read</CODE> and <CODE>Prodoc.read</CODE> will raise a ValueError. You can catch these exceptions to detect invalid accession numbers.</P><P>The functions <CODE>get_prosite_entry()</CODE> and <CODE>get_prodoc_entry()</CODE> are used to download Prosite and Prosite documentation records in HTML format. To create a web page sho [...]
->>> handle = ExPASy.get_prosite_entry('PS00001')
->>> html = handle.read()
->>> output = open("myprositerecord.html", "w")
->>> output.write(html)
->>> output.close()
-</PRE><P>and similarly for a Prosite documentation record:</P><PRE CLASS="verbatim">>>> from Bio import ExPASy
->>> handle = ExPASy.get_prodoc_entry('PDOC00001')
->>> html = handle.read()
->>> output = open("myprodocrecord.html", "w")
->>> output.write(html)
->>> output.close()
-</PRE><P>For these functions, an invalid accession number returns an error message in HTML format.</P><!--TOC section Scanning the Prosite database-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc145">10.6</A>&#XA0;&#XA0;Scanning the Prosite database</H2><!--SEC END --><P><A HREF="http://www.expasy.org/tools/scanprosite/">ScanProsite</A> allows you to scan protein sequences online against the Prosite database by providing a UniProt or PDB sequence identifier or the sequence itself. For more information about ScanProsite, please see the <A HREF="http://www.expasy.org/tools/scanprosite/scanprosite-doc.html">ScanProsite documentatio [...]
-CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN
-</PRE><P>you can use the following code:</P><PRE CLASS="verbatim">>>> sequence = "MEHKEVVLLLLLFLKSGQGEPLDDYVNTQGASLFSVTKKQLGAGSIEECAAKCEEDEEFT
-CRAFQYHSKEQQCVIMAENRKSSIIIRMRDVVLFEKKVYLSECKTGNGKNYRGTMSKTKN"
->>> from Bio.ExPASy import ScanProsite
->>> handle = ScanProsite.scan(seq=sequence)
-</PRE><P>By executing <CODE>handle.read()</CODE>, you can obtain the search results in raw XML format. Instead, let&#X2019;s use <CODE>Bio.ExPASy.ScanProsite.read</CODE> to parse the raw XML into a Python object:</P><PRE CLASS="verbatim">>>> result = ScanProsite.read(handle)
->>> type(result)
-<class 'Bio.ExPASy.ScanProsite.Record'>
-</PRE><P>A <CODE>Bio.ExPASy.ScanProsite.Record</CODE> object is derived from a list, with each element in the list storing one ScanProsite hit. This object also stores the number of hits, as well as the number of search sequences, as returned by ScanProsite. This ScanProsite search resulted in six hits:</P><PRE CLASS="verbatim">>>> result.n_seq
-1
->>> result.n_match
-6
->>> len(result)
-6
->>> result[0]
-{'signature_ac': u'PS50948', 'level': u'0', 'stop': 98, 'sequence_ac': u'USERSEQ1', 'start': 16, 'score': u'8.873'}
->>> result[1]
-{'start': 37, 'stop': 39, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'}
->>> result[2]
-{'start': 45, 'stop': 48, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00006'}
->>> result[3]
-{'start': 60, 'stop': 62, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00005'}
->>> result[4]
-{'start': 80, 'stop': 83, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00004'}
->>> result[5]
-{'start': 106, 'stop': 111, 'sequence_ac': u'USERSEQ1', 'signature_ac': u'PS00008'}
-</PRE><P>Other ScanProsite parameters can be passed as keyword arguments; see the <A HREF="http://www.expasy.org/tools/scanprosite/ScanPrositeREST.html">documentation for programmatic access of ScanProsite</A> for more information. As an example, passing <CODE>lowscore=1</CODE> to include matches with low level scores lets use find one additional hit:</P><PRE CLASS="verbatim">>>> handle = ScanProsite.scan(seq=sequence, lowscore=1)
->>> result = ScanProsite.read(handle)
->>> result.n_match
-7
-</PRE><!--TOC chapter Bio.PopGen: Population genetics-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc146">Chapter&#XA0;11</A>&#XA0;&#XA0;Bio.PopGen: Population genetics</H1><!--SEC END --><P>Bio.PopGen is a Biopython module supporting population genetics,
-available in Biopython 1.44 onwards.</P><P>The medium term objective for the module is to support widely used data
-formats, applications and databases. This module is currently under intense
-development and support for new features should appear at a rather fast pace.
-Unfortunately this might also entail some instability on the API, especially
-if you are using a development version. APIs that are made available on
-our official public releases should be much more stable.</P><!--TOC section GenePop-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc147">11.1</A>&#XA0;&#XA0;GenePop</H2><!--SEC END --><P>GenePop (<A HREF="http://genepop.curtin.edu.au/"><TT>http://genepop.curtin.edu.au/</TT></A>) is a popular population
-genetics software package supporting Hardy-Weinberg tests, linkage
-desiquilibrium, population diferentiation, basic statistics, <I>F</I><SUB><I>st</I></SUB> and
-migration estimates, among others. GenePop does not supply sequence
-based statistics as it doesn&#X2019;t handle sequence data.
-The GenePop file format is supported by a wide range of other population
-genetic software applications, thus making it a relevant format in the
-population genetics field.</P><P>Bio.PopGen provides a parser and generator of GenePop file format.
-Utilities to manipulate the content of a record are also provided.
-Here is an example on how to read a GenePop file (you can find
-example GenePop data files in the Test/PopGen directory of Biopython):</P><PRE CLASS="verbatim">from Bio.PopGen import GenePop
-
-handle = open("example.gen")
-rec = GenePop.read(handle)
-handle.close()
-</PRE><P>This will read a file called example.gen and parse it. If you
-do print rec, the record will be output again, in GenePop format.</P><P>The most important information in rec will be the loci names and
-population information (but there is more &#X2013; use help(GenePop.Record)
-to check the API documentation). Loci names can be found on rec.loci_list.
-Population information can be found on rec.populations.
-Populations is a list with one element per population. Each element is itself
-a list of individuals, each individual is a pair composed by individual
-name and a list of alleles (2 per marker), here is an example for
-rec.populations:</P><PRE CLASS="verbatim">[
-    [
-        ('Ind1', [(1, 2),    (3, 3), (200, 201)],
-        ('Ind2', [(2, None), (3, 3), (None, None)],
-    ],
-    [
-        ('Other1', [(1, 1),  (4, 3), (200, 200)],
-    ]
-]
-</PRE><P>So we have two populations, the first with two individuals, the
-second with only one. The first individual of the first
-population is called Ind1, allelic information for each of
-the 3 loci follows. Please note that for any locus, information
-might be missing (see as an example, Ind2 above).</P><P>A few utility functions to manipulate GenePop records are made
-available, here is an example:</P><PRE CLASS="verbatim">from Bio.PopGen import GenePop
-
-#Imagine that you have loaded rec, as per the code snippet above...
-
-rec.remove_population(pos)
-#Removes a population from a record, pos is the population position in
-#  rec.populations, remember that it starts on position 0.
-#  rec is altered.
-
-rec.remove_locus_by_position(pos)
-#Removes a locus by its position, pos is the locus position in
-#  rec.loci_list, remember that it starts on position 0.
-#  rec is altered.
-
-rec.remove_locus_by_name(name)
-#Removes a locus by its name, name is the locus name as in
-#  rec.loci_list. If the name doesn't exist the function fails
-#  silently.
-#  rec is altered.
-
-rec_loci = rec.split_in_loci()
-#Splits a record in loci, that is, for each loci, it creates a new
-#  record, with a single loci and all populations.
-#  The result is returned in a dictionary, being each key the locus name.
-#  The value is the GenePop record.
-#  rec is not altered.
-
-rec_pops =  rec.split_in_pops(pop_names)
-#Splits a record in populations, that is, for each population, it creates
-#  a new record, with a single population and all loci.
-#  The result is returned in a dictionary, being each key
-#  the population name. As population names are not available in GenePop,
-#  they are passed in array (pop_names).
-#  The value of each dictionary entry is the GenePop record.
-#  rec is not altered.
-</PRE><P>GenePop does not support population names, a limitation which can be
-cumbersome at times. Functionality to enable population names is currently
-being planned for Biopython. These extensions won&#X2019;t break compatibility in
-any way with the standard format. In the medium term, we would also like to
-support the GenePop web service.</P><!--TOC section Coalescent simulation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc148">11.2</A>&#XA0;&#XA0;Coalescent simulation</H2><!--SEC END --><P>A coalescent simulation is a backward model of population genetics with relation to
-time. A simulation of ancestry is done until the Most Recent Common Ancestor (MRCA) is found.
-This ancestry relationship starting on the MRCA and ending on the current generation
-sample is sometimes called a genealogy. Simple cases assume a population of constant
-size in time, haploidy, no population structure, and simulate the alleles of a single
-locus under no selection pressure.</P><P>Coalescent theory is used in many fields like selection detection, estimation of
-demographic parameters of real populations or disease gene mapping.</P><P>The strategy followed in the Biopython implementation of the coalescent was not
-to create a new, built-in, simulator from scratch but to use an existing one,
-Fastsimcoal2 (<A HREF="http://cmpg.unibe.ch/software/fastsimcoal2/"><TT>http://cmpg.unibe.ch/software/fastsimcoal2/</TT></A>). Fastsimcoal2 allows for,
-among others, population structure, multiple demographic events, simulation
-of multiple types of loci (SNPs, sequences, STRs/microsatellites and RFLPs)
-with recombination, diploidy multiple chromosomes or ascertainment bias. Notably
-Fastsimcoal2 doesn&#X2019;t support any selection model. We recommend reading
-Fastsimcoal2&#X2019;s
-documentation, available in the link above.</P><P>The input for Fastsimcoal2 is a file specifying the desired demography and genome,
-the output is a set of files (typically around 1000) with the simulated genomes
-of a sample of individuals per subpopulation. This set of files can be used
-in many ways, like to compute confidence intervals where which certain
-statistics (e.g., <I>F</I><SUB><I>st</I></SUB> or Tajima D) are expected to lie. Real population
-genetics datasets statistics can then be compared to those confidence intervals.</P><P>Biopython coalescent code allows to create demographic scenarios and genomes and
-to run Fastsimcoal2.</P><!--TOC subsection Creating scenarios-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc149">11.2.1</A>&#XA0;&#XA0;Creating scenarios</H3><!--SEC END --><P>Creating a scenario involves both creating a demography and a chromosome structure.
-In many cases (e.g. when doing Approximate Bayesian Computations &#X2013; ABC) it is
-important to test many parameter variations (e.g. vary the effective population size,
-<I>N</I><SUB><I>e</I></SUB>, between 10, 50, 500 and 1000 individuals). The code provided allows for
-the simulation of scenarios with different demographic parameters very easily.</P><P>Below we see how we can create scenarios and then how simulate them.</P><!--TOC subsubsection Demography-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.2.1.1&#XA0;&#XA0;Demography</H4><!--SEC END --><P>A few predefined demographies are built-in, all have two shared parameters: sample size
-(called sample_size on the template, see below for its use) per deme and deme size, i.e.
-subpopulation size (pop_size). All demographies are available as templates where all
-parameters can be varied, each template has a system name. The prefedined
-demographies/templates are:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>Single population, constant size</B></DT><DD CLASS="dd-description"> The standard parameters are enough to specify
-it. Template name: simple.
-</DD><DT CLASS="dt-description"><B>Single population, bottleneck</B></DT><DD CLASS="dd-description"> As seen on figure <A HREF="#fig:bottle">11.2.1.1</A>. The parameters
-are current population size (pop_size on template ne3 on figure), time of expansion,
-given as the generation in the past when it occurred (expand_gen),
-effective population size during bottleneck (ne2), time of contraction
-(contract_gen) and original size in the remote past (ne3). Template name: bottle.
-</DD><DT CLASS="dt-description"><B>Island model</B></DT><DD CLASS="dd-description"> The typical island model. The total number of demes is specified
-by total_demes and the migration rate by mig. Template name island.
-</DD><DT CLASS="dt-description"><B>Stepping stone model - 1 dimension</B></DT><DD CLASS="dd-description"> The stepping stone model in 1 dimension,
-extremes disconnected. The total number of demes is total_demes, migration rate
-is mig. Template name is ssm_1d.
-</DD><DT CLASS="dt-description"><B>Stepping stone model - 2 dimensions</B></DT><DD CLASS="dd-description"> The stepping stone model in 2 dimensions,
-extremes disconnected. The parameters are x for the horizontal dimension and y
-for the vertical (being the total number of demes x times y), migration rate is mig.
-Template name is ssm_2d.
-</DD></DL><P>
-<A NAME="fig:bottle"></A>
-<IMG SRC="images/bottle.png">
-</P><P>In our first example, we will generate a template for a single population, constant size
-model with a sample size of 30 and a deme size of 500. The code for this is:</P><PRE CLASS="verbatim">from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
-
-generate_simcoal_from_template('simple',
-    [(1, [('SNP', [24, 0.0005, 0.0])])],
-    [('sample_size', [30]),
-    ('pop_size', [100])])
-</PRE><P>Executing this code snippet will generate a file on the current directory called
-simple_100_300.par this file can be given as input to Fastsimcoal2 to simulate the
-demography (below we will see how Biopython can take care of calling
-Fastsimcoal2).</P><P>This code consists of a single function call, let&#X2019;s discuss it parameter by parameter.</P><P>The first parameter is the template id (from the list above). We are using the id
-&#X2019;simple&#X2019; which is the template for a single population of constant size along time.</P><P>The second parameter is the chromosome structure. Please ignore it for now, it will be
-explained in the next section.</P><P>The third parameter is a list of all required parameters (recall that the simple model
-only needs sample_size and pop_size) and possible values (in this case each
-parameter only has a possible value).</P><P>Now, let&#X2019;s consider an example where we want to generate several island models, and we
-are interested in varying the number of demes: 10, 50 and 100 with a migration
-rate of 1%. Sample size and deme
-size will be the same as before. Here is the code:</P><PRE CLASS="verbatim">from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
-
-generate_simcoal_from_template('island',
-    [(1, [('SNP', [24, 0.0005, 0.0])])],
-    [('sample_size', [30]),
-    ('pop_size', [100]),
-    ('mig', [0.01]),
-    ('total_demes', [10, 50, 100])])
-</PRE><P>In this case, 3 files will be generated: island_100_0.01_100_30.par,
-island_10_0.01_100_30.par and island_50_0.01_100_30.par. Notice the
-rule to make file names: template name, followed by parameter values in
-reverse order.</P><P>A few, arguably more esoteric template demographies exist (please check the
-Bio/PopGen/SimCoal/data directory on Biopython source tree). Furthermore it is possible
-for the user to create new templates. That functionality will be discussed in a future
-version of this document.</P><!--TOC subsubsection Chromosome structure-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.2.1.2&#XA0;&#XA0;Chromosome structure</H4><!--SEC END --><P>We strongly recommend reading Fastsimcoal2 documentation to understand the full potential
-available in modeling chromosome structures. In this subsection we only discuss how
-to implement chromosome structures using the Biopython interface, not the underlying
-Fastsimcoal2 capabilities.</P><P>We will start by implementing a single chromosome, with 24 SNPs with
-a recombination rate immediately on the right of each locus of 0.0005 and a
-minimum frequency of the minor allele of 0. This will be specified by the
-following list (to be passed as second parameter to the function
-generate_simcoal_from_template):</P><PRE CLASS="verbatim">[(1, [('SNP', [24, 0.0005, 0.0])])]
-</PRE><P>This is actually the chromosome structure used in the above examples.</P><P>The chromosome structure is represented by a list of chromosomes,
-each chromosome (i.e., each element in the list)
-is composed by a tuple (a pair): the first element
-is the number of times the chromosome is to be repeated (as there
-might be interest in repeating the same chromosome many times).
-The second element is a list of the actual components of the chromosome.
-Each element is again a pair, the first member is the locus type and
-the second element the parameters for that locus type. Confused?
-Before showing more examples let&#X2019;s review the example above: We have
-a list with one element (thus one chromosome), the chromosome is
-a single instance (therefore not to be repeated), it is composed
-of 24 SNPs, with a recombination rate of 0.0005 between each
-consecutive SNP, the minimum frequency of the minor allele is
-0.0 (i.e, it can be absent from a certain population).</P><P>Let&#X2019;s see a more complicated example:</P><PRE CLASS="verbatim">[
-  (5, [
-       ('SNP', [24, 0.0005, 0.0])
-      ]
-  ),
-  (2, [
-       ('DNA', [10, 0.0, 0.00005, 0.33]),
-       ('RFLP', [1, 0.0, 0.0001]),
-       ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0])
-      ]
-  )
-]
-</PRE><P>We start by having 5 chromosomes with the same structure as
-above (i.e., 24 SNPs). We then have 2 chromosomes which
-have a DNA sequence with 10 nucleotides, 0.0 recombination rate,
-0.0005 mutation rate, and a transition rate of 0.33. Then we
-have an RFLP with 0.0 recombination rate to the next locus and
-a 0.0001 mutation rate. Finally we have a microsatellite (or STR),
-with 0.0 recombination rate to the next locus (note, that as this
-is a single microsatellite which has no loci following, this
-recombination rate here is irrelevant), with a mutation rate
-of 0.001, geometric parameter of 0.0 and a range constraint
-of 0.0 (for information about this parameters please consult
-the Fastsimcoal2 documentation, you can use them to simulate
-various mutation models, including the typical &#X2013; for microsatellites &#X2013;
-stepwise mutation model among others).</P><!--TOC subsection Running Fastsimcoal2-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc150">11.2.2</A>&#XA0;&#XA0;Running Fastsimcoal2</H3><!--SEC END --><P>We now discuss how to run Fastsimcoal2 from inside Biopython. It is required
-that the binary for Fastsimcoal2 is called fastsimcoal21 (or fastsimcoal21.exe on Windows
-based platforms), please note that the typical name when downloading the
-program is in the format fastsimcoal2_x_y. As such, when installing
-Fastsimcoal2
-you will need to rename of the downloaded executable so that Biopython can
-find it.</P><P>It is possible to run Fastsimcoal2 on files that were not generated using the method
-above (e.g., writing a parameter file by hand), but we will show an
-example by creating a model using the framework presented above.</P><PRE CLASS="verbatim">from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
-from Bio.PopGen.SimCoal.Controller import FastSimCoalController
-
-
-generate_simcoal_from_template('simple',
-    [
-      (5, [
-           ('SNP', [24, 0.0005, 0.0])
-          ]
-      ),
-      (2, [
-           ('DNA', [10, 0.0, 0.00005, 0.33]),
-           ('RFLP', [1, 0.0, 0.0001]),
-           ('MICROSAT', [1, 0.0, 0.001, 0.0, 0.0])
-          ]
-      )
-    ],
-    [('sample_size', [30]),
-    ('pop_size', [100])])
-
-ctrl = FastSimCoalController()
-ctrl.run_fastsimcoal('simple_100_30.par', 50)
-</PRE><P>The lines of interest are the last two (plus the new import).
-Firstly a controller for the
-application is created. The directory where the binary is located has
-to be specified.</P><P>The simulator is then run on the last line: we know, from the rules explained
-above, that the input file name is simple_100_30.par for the
-simulation parameter file created. We then specify
-that we want to run 50 independent simulations, by default Biopython
-requests a simulation of diploid data, but a third parameter can
-be added to simulate haploid data (adding as a parameter the
-string &#X2019;0&#X2019;). Fastsimcoal2 will now run (please
-note that this can take quite a lot of time) and will create a directory
-with the simulation results. The results can now be analysed (typically
-studying the data with Arlequin3). In the future Biopython might support
-reading the Arlequin3 format and thus allowing for the analysis of
-Fastsimcoal2
-data inside Biopython.</P><!--TOC section Other applications-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc151">11.3</A>&#XA0;&#XA0;Other applications</H2><!--SEC END --><P>Here we discuss interfaces and utilities to deal with population genetics&#X2019;
-applications which arguably have a smaller user base.</P><!--TOC subsection FDist: Detecting selection and molecular adaptation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc152">11.3.1</A>&#XA0;&#XA0;FDist: Detecting selection and molecular adaptation</H3><!--SEC END --><P>FDist is a selection detection application suite based on computing
-(i.e. simulating) a &#X201C;neutral&#X201D; confidence interval based on <I>F</I><SUB><I>st</I></SUB> and
-heterozygosity. Markers (which can be SNPs, microsatellites, AFLPs
-among others) which lie outside the &#X201C;neutral&#X201D; interval are to be
-considered as possible candidates for being under selection.</P><P>FDist is mainly used when the number of markers is considered enough
-to estimate an average <I>F</I><SUB><I>st</I></SUB>, but not enough to either have outliers
-calculated from the dataset directly or, with even more markers for
-which the relative positions in the genome are known, to use
-approaches based on, e.g., Extended Haplotype Heterozygosity (EHH).</P><P>The typical usage pattern for FDist is as follows:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Import a dataset from an external format into FDist format.
-</LI><LI CLASS="li-enumerate">Compute average <I>F</I><SUB><I>st</I></SUB>. This is done by datacal inside FDist.
-</LI><LI CLASS="li-enumerate">Simulate &#X201C;neutral&#X201D; markers based on the
-average <I>F</I><SUB><I>st</I></SUB> and expected number of total populations.
-This is the core operation, done by fdist inside FDist.
-</LI><LI CLASS="li-enumerate">Calculate the confidence interval, based on the desired
-confidence boundaries (typically 95% or 99%). This is done by
-cplot and is mainly used to plot the interval.
-</LI><LI CLASS="li-enumerate">Assess each marker status against the simulation &#X201C;neutral&#X201D;
-confidence interval. Done
-by pv. This is used to detect the outlier status of each marker
-against the simulation.
-</LI></OL><P>We will now discuss each step with illustrating example code
-(for this example to work FDist binaries have to be on the
-executable PATH).</P><P>The FDist data format is application specific and is not used at
-all by other applications, as such you will probably have to convert
-your data for use with FDist. Biopython can help you do this.
-Here is an example converting from GenePop format to FDist format
-(along with imports that will be needed on examples further below):</P><PRE CLASS="verbatim">from Bio.PopGen import GenePop
-from Bio.PopGen import FDist
-from Bio.PopGen.FDist import Controller
-from Bio.PopGen.FDist.Utils import convert_genepop_to_fdist
-
-gp_rec = GenePop.read(open("example.gen"))
-fd_rec = convert_genepop_to_fdist(gp_rec)
-in_file = open("infile", "w")
-in_file.write(str(fd_rec))
-in_file.close()
-</PRE><P>In this code we simply parse a GenePop file and convert it to a FDist
-record.</P><P>Printing an FDist record will generate
-a string that can be directly saved to a file and supplied to FDist. FDist
-requires the input file to be called infile, therefore we save the record on
-a file with that name.</P><P>The most important fields on a FDist record are: num_pops, the number of
-populations; num_loci, the number of loci and loci_data with the marker
-data itself. Most probably the details of the record are of no interest
-to the user, as the record only purpose is to be passed to FDist.</P><P>The next step is to calculate the average <I>F</I><SUB><I>st</I></SUB> of the dataset (along
-with the sample size):</P><PRE CLASS="verbatim">ctrl = Controller.FDistController()
-fst, samp_size = ctrl.run_datacal()
-</PRE><P>On the first line we create an object to control the call of FDist
-suite, this object will be used further on in order to call other
-suite applications.</P><P>On the second line we call the datacal application which computes the
-average <I>F</I><SUB><I>st</I></SUB>
-and the sample size. It is worth noting that the <I>F</I><SUB><I>st</I></SUB> computed by
-datacal is a <EM>variation</EM> of Weir and Cockerham&#X2019;s &#X3B8;.</P><P>We can now call the main fdist application in order to simulate neutral
-markers.</P><PRE CLASS="verbatim">sim_fst = ctrl.run_fdist(npops = 15, nsamples = fd_rec.num_pops, fst = fst,
-    sample_size = samp_size, mut = 0, num_sims = 40000)
-</PRE><DL CLASS="description"><DT CLASS="dt-description">
-<B>npops</B></DT><DD CLASS="dd-description"> Number of populations existing in nature. This is really a
-&#X201C;guestimate&#X201D;. Has to be lower than 100.
-</DD><DT CLASS="dt-description"><B>nsamples</B></DT><DD CLASS="dd-description"> Number of populations sampled, has to be lower than npops.
-</DD><DT CLASS="dt-description"><B>fst</B></DT><DD CLASS="dd-description"> Average <I>F</I><SUB><I>st</I></SUB>.
-</DD><DT CLASS="dt-description"><B>sample_size</B></DT><DD CLASS="dd-description"> Average number of individuals sampled on each population.
-</DD><DT CLASS="dt-description"><B>mut</B></DT><DD CLASS="dd-description"> Mutation model: 0 - Infinite alleles; 1 - Stepwise mutations
-</DD><DT CLASS="dt-description"><B>num_sims</B></DT><DD CLASS="dd-description"> Number of simulations to perform. Typically a number around
-40000 will be OK, but if you get a confidence interval that looks sharp
-(this can be detected when plotting the confidence interval computed
-below) the value can be increased (a suggestion would be steps of 10000
-simulations).
-</DD></DL><P>The confusion in wording between number of samples and sample size
-stems from the original application.</P><P>A file named out.dat will be created with the simulated heterozygosities
-and <I>F</I><SUB><I>st</I></SUB>s, it will have as many lines as the number of simulations
-requested.</P><P>Note that fdist returns the average <I>F</I><SUB><I>st</I></SUB> that it was <EM>capable</EM> of
-simulating, for more details about this issue please read below the paragraph
-on approximating the desired average <I>F</I><SUB><I>st</I></SUB>.</P><P>The next (optional) step is to calculate the confidence interval:</P><PRE CLASS="verbatim">cpl_interval = ctrl.run_cplot(ci=0.99)
-</PRE><P>You can only call cplot after having run fdist.</P><P>This will calculate the confidence intervals (99% in this case)
-for a previous fdist run. A list of quadruples is returned. The
-first element represents the heterozygosity, the second the lower
-bound of <I>F</I><SUB><I>st</I></SUB> confidence interval for that heterozygosity,
-the third the average and the fourth the upper bound. This can
-be used to trace the confidence interval contour. This list
-is also written to a file, out.cpl.</P><P>The main purpose of this step is return a set of points which can
-be easily used to plot a confidence interval. It can be skipped
-if the objective is only to assess the status of each marker against
-the simulation, which is the next step...</P><PRE CLASS="verbatim">pv_data = ctrl.run_pv()
-</PRE><P>You can only call cplot after having run datacal and fdist.</P><P>This will use the simulated markers to assess the status of each
-individual real marker. A list, in the same order than the loci_list
-that is on the FDist record (which is in the same order that the GenePop
-record) is returned. Each element in the list is a quadruple, the
-fundamental member of each quadruple is the last element (regarding the
-other elements, please refer to the pv documentation &#X2013; for the
-sake of simplicity we will not discuss them here) which returns the
-probability of the simulated <I>F</I><SUB><I>st</I></SUB> being lower than the marker <I>F</I><SUB><I>st</I></SUB>.
-Higher values would indicate a stronger candidate for positive selection,
-lower values a candidate for balancing selection, and intermediate values
-a possible neutral marker. What is &#X201C;higher&#X201D;, &#X201C;lower&#X201D; or &#X201C;intermediate&#X201D;
-is really a subjective issue, but taking a &#X201C;confidence interval&#X201D; approach
-and considering a 95% confidence interval, &#X201C;higher&#X201D; would be between 0.95
-and 1.0, &#X201C;lower&#X201D; between 0.0 and 0.05 and &#X201C;intermediate&#X201D; between 0.05 and
-0.95.</P><!--TOC subsubsection Approximating the desired average <I>F</I><SUB><I>st</I></SUB>-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.3.1.1&#XA0;&#XA0;Approximating the desired average <I>F</I><SUB><I>st</I></SUB></H4><!--SEC END --><P>Fdist tries to approximate the desired average <I>F</I><SUB><I>st</I></SUB> by doing a
-coalescent simulation using migration rates based on the formula</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>N</I><SUB><I>m</I></SUB>&#XA0;=&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1&#XA0;&#X2212;&#XA0;<I>F</I><SUB><I>st</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">4<I>F</I><SUB><I>st</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD></TR>
-</TABLE><P>This formula assumes a few premises like an infinite number of populations.</P><P>In practice, when the number of populations is low, the mutation model
-is stepwise and the sample size increases, fdist will not be able to
-simulate an acceptable approximate average <I>F</I><SUB><I>st</I></SUB>.</P><P>To address that, a function is provided to iteratively approach the desired
-value by running several fdists in sequence. This approach is computationally
-more intensive than running a single fdist run, but yields good results.
-The following code runs fdist approximating the desired <I>F</I><SUB><I>st</I></SUB>:</P><PRE CLASS="verbatim">sim_fst = ctrl.run_fdist_force_fst(npops = 15, nsamples = fd_rec.num_pops,
-    fst = fst, sample_size = samp_size, mut = 0, num_sims = 40000,
-    limit = 0.05)
-</PRE><P>The only new optional parameter, when comparing with run_fdist, is limit
-which is the desired maximum error. run_fdist can (and probably should)
-be safely replaced with run_fdist_force_fst.</P><!--TOC subsubsection Final notes-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->11.3.1.2&#XA0;&#XA0;Final notes</H4><!--SEC END --><P>The process to determine the average <I>F</I><SUB><I>st</I></SUB> can be more sophisticated than
-the one presented here. For more information we refer you to the FDist
-README file. Biopython&#X2019;s code can be used to implement more sophisticated
-approaches.</P><!--TOC section Future Developments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc153">11.4</A>&#XA0;&#XA0;Future Developments</H2><!--SEC END --><P>The most desired future developments would be the ones you add yourself ;) .</P><P>That being said, already existing fully functional code is currently being
-incorporated in Bio.PopGen, that code covers the applications FDist and
-SimCoal2, the HapMap and UCSC Table Browser databases and some simple statistics
-like <I>F</I><SUB><I>st</I></SUB>, or allele counts.</P><!--TOC chapter Phylogenetics with Bio.Phylo-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc154">Chapter&#XA0;12</A>&#XA0;&#XA0;Phylogenetics with Bio.Phylo</H1><!--SEC END --><P>
-<A NAME="sec:Phylo"></A></P><P>The Bio.Phylo module was introduced in Biopython 1.54. Following the lead of SeqIO and AlignIO,
-it aims to provide a common way to work with phylogenetic trees independently of the source data
-format, as well as a consistent API for I/O operations.</P><P>Bio.Phylo is described in an open-access journal article [<A HREF="#talevich2012">9</A>, Talevich
-<I>et al.</I>, 2012], which you might also find helpful.</P><!--TOC section Demo: What&#X2019;s in a Tree?-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc155">12.1</A>&#XA0;&#XA0;Demo: What&#X2019;s in a Tree?</H2><!--SEC END --><P>To get acquainted with the module, let&#X2019;s start with a tree that we&#X2019;ve already constructed, and
-inspect it a few different ways. Then we&#X2019;ll colorize the branches, to use a special phyloXML
-feature, and finally save it.</P><P>Create a simple Newick file named <TT>simple.dnd</TT> using your favorite text editor,
-or use <A HREF="http://biopython.org/SRC/biopython/Doc/examples/simple.dnd"><TT>simple.dnd</TT></A>
-provided with the Biopython source code:</P><PRE CLASS="verbatim">(((A,B),(C,D)),(E,F,G));
-</PRE><P>This tree has no branch lengths, only a topology and labelled terminals. (If you have a real
-tree file available, you can follow this demo using that instead.)</P><P>Launch the Python interpreter of your choice:</P><PRE CLASS="verbatim">% ipython -pylab
-</PRE><P>For interactive work, launching the IPython interpreter with the <CODE>-pylab</CODE> flag enables
-<B>matplotlib</B> integration, so graphics will pop up automatically. We&#X2019;ll use that during
-this demo.</P><P>Now, within Python, read the tree file, giving the file name and the name of the format.</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("simple.dnd", "newick")
-</PRE><P>Printing the tree object as a string gives us a look at the entire object hierarchy.</P><PRE CLASS="verbatim">>>> print(tree)
-Tree(rooted=False, weight=1.0)
-    Clade()
-        Clade()
-            Clade()
-                Clade(name='A')
-                Clade(name='B')
-            Clade()
-                Clade(name='C')
-                Clade(name='D')
-        Clade()
-            Clade(name='E')
-            Clade(name='F')
-            Clade(name='G')
-</PRE><P>The <TT>Tree</TT> object contains global information about the tree, such as whether it&#X2019;s
-rooted or unrooted. It has one root clade, and under that, it&#X2019;s nested lists of clades all the
-way down to the tips.</P><P>The function <CODE>draw_ascii</CODE> creates a simple ASCII-art (plain text) dendrogram. This is a
-convenient visualization for interactive exploration, in case better graphical tools aren&#X2019;t
-available.</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("simple.dnd", "newick")
->>> Phylo.draw_ascii(tree)
-                                                    ________________________ A
-                           ________________________|
-                          |                        |________________________ B
-  ________________________|
- |                        |                         ________________________ C
- |                        |________________________|
-_|                                                 |________________________ D
- |
- |                         ________________________ E
- |                        |
- |________________________|________________________ F
-                          |
-                          |________________________ G
-<BLANKLINE>
-</PRE><P>If you have <B>matplotlib</B> or <B>pylab</B> installed, you can create a graphic
-using the <CODE>draw</CODE> function (see Fig. <A HREF="#fig:phylo-simple-draw">12.1</A>):</P><PRE CLASS="verbatim">>>> tree.rooted = True
->>> Phylo.draw(tree)
-</PRE><P>
-<IMG SRC="images/phylo-simple-draw.png" width=666, height=530>
-<A NAME="fig:phylo-simple-draw"></A>
-</P><!--TOC subsection Coloring branches within a tree-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc156">12.1.1</A>&#XA0;&#XA0;Coloring branches within a tree</H3><!--SEC END --><P>The functions <CODE>draw</CODE> and <CODE>draw_graphviz</CODE> support the display of different
-colors and branch widths in a tree.
-As of Biopython 1.59, the <CODE>color</CODE> and <CODE>width</CODE> attributes are available on the
-basic Clade object and there&#X2019;s nothing extra required to use them.
-Both attributes refer to the branch leading the given clade, and apply recursively, so
-all descendent branches will also inherit the assigned width and color values during
-display.</P><P>In earlier versions of Biopython, these were special features of PhyloXML trees, and
-using the attributes required first converting the tree to a subclass of the basic tree
-object called Phylogeny, from the Bio.Phylo.PhyloXML module.</P><P>In Biopython 1.55 and later, this is a convenient tree method:</P><PRE CLASS="verbatim">>>> tree = tree.as_phyloxml()
-</PRE><P>In Biopython 1.54, you can accomplish the same thing with one extra import:</P><PRE CLASS="verbatim">>>> from Bio.Phylo.PhyloXML import Phylogeny
->>> tree = Phylogeny.from_tree(tree)
-</PRE><P>Note that the file formats Newick and Nexus don&#X2019;t support branch colors or widths, so
-if you use these attributes in Bio.Phylo, you will only be able to save the values in
-PhyloXML format. (You can still save a tree as Newick or Nexus, but the color and width
-values will be skipped in the output file.)</P><P>Now we can begin assigning colors.
-First, we&#X2019;ll color the root clade gray. We can do that by assigning the 24-bit color
-value as an RGB triple, an HTML-style hex string, or the name of one of the predefined
-colors.</P><PRE CLASS="verbatim">>>> tree.root.color = (128, 128, 128)
-</PRE><P>Or:</P><PRE CLASS="verbatim">>>> tree.root.color = "#808080"
-</PRE><P>Or:</P><PRE CLASS="verbatim">>>> tree.root.color = "gray"
-</PRE><P>Colors for a clade are treated as cascading down through the entire clade, so when we colorize
-the root here, it turns the whole tree gray. We can override that by assigning a different
-color lower down on the tree.</P><P>Let&#X2019;s target the most recent common ancestor (MRCA) of the nodes named &#X201C;E&#X201D; and &#X201C;F&#X201D;. The
-<CODE>common_ancestor</CODE> method returns a reference to that clade in the original tree, so when
-we color that clade &#X201C;salmon&#X201D;, the color will show up in the original tree.</P><PRE CLASS="verbatim">>>> mrca = tree.common_ancestor({"name": "E"}, {"name": "F"})
->>> mrca.color = "salmon"
-</PRE><P>If we happened to know exactly where a certain clade is in the tree, in terms of nested list
-entries, we can jump directly to that position in the tree by indexing it. Here, the index
-<CODE>[0,1]</CODE> refers to the second child of the first child of the root.</P><PRE CLASS="verbatim">>>> tree.clade[0, 1].color = "blue"
-</PRE><P>Finally, show our work (see Fig. <A HREF="#fig:phylo-color-draw">12.1.1</A>):</P><PRE CLASS="verbatim">>>> Phylo.draw(tree)
-</PRE><P>
-<IMG SRC="images/phylo-color-draw.png" width=666, height=530>
-<A NAME="fig:phylo-color-draw"></A>
-</P><P>Note that a clade&#X2019;s color includes the branch leading to that clade, as well as its
-descendents. The common ancestor of E and F turns out to be just under the root, and with this
-coloring we can see exactly where the root of the tree is.</P><P>My, we&#X2019;ve accomplished a lot! Let&#X2019;s take a break here and save our work. Call the
-<TT>write</TT> function with a file name or handle &#X2014; here we use standard output, to see what
-would be written &#X2014; and the format <TT>phyloxml</TT>. PhyloXML saves the colors we assigned,
-so you can open this phyloXML file in another tree viewer like Archaeopteryx, and the colors
-will show up there, too.</P><PRE CLASS="verbatim">>>> import sys
->>> Phylo.write(tree, sys.stdout, "phyloxml")
-
-<phy:phyloxml xmlns:phy="http://www.phyloxml.org">
-  <phy:phylogeny rooted="true">
-    <phy:clade>
-      <phy:branch_length>1.0</phy:branch_length>
-      <phy:color>
-        <phy:red>128</phy:red>
-        <phy:green>128</phy:green>
-        <phy:blue>128</phy:blue>
-      </phy:color>
-      <phy:clade>
-        <phy:branch_length>1.0</phy:branch_length>
-        <phy:clade>
-          <phy:branch_length>1.0</phy:branch_length>
-          <phy:clade>
-            <phy:name>A</phy:name>
-            ...
-</PRE><P>The rest of this chapter covers the core functionality of Bio.Phylo in greater detail. For more
-examples of using Bio.Phylo, see the cookbook page on Biopython.org:</P><P><A HREF="http://biopython.org/wiki/Phylo_cookbook"><TT>http://biopython.org/wiki/Phylo_cookbook</TT></A></P><!--TOC section I/O functions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc157">12.2</A>&#XA0;&#XA0;I/O functions</H2><!--SEC END --><P>Like SeqIO and AlignIO, Phylo handles file input and output through four functions:
-<CODE>parse</CODE>, <CODE>read</CODE>, <CODE>write</CODE> and <CODE>convert</CODE>,
-all of which support the tree file formats Newick, NEXUS, phyloXML and NeXML, as
-well as the Comparative Data Analysis Ontology (CDAO).</P><P>The <CODE>read</CODE> function parses a single tree in the given file and returns it. Careful; it
-will raise an error if the file contains more than one tree, or no trees.</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("Tests/Nexus/int_node_labels.nwk", "newick")
->>> print(tree)
-</PRE><P>(Example files are available in the <TT>Tests/Nexus/</TT> and <TT>Tests/PhyloXML/</TT>
-directories of the Biopython distribution.)</P><P>To handle multiple (or an unknown number of) trees, use the <CODE>parse</CODE> function iterates
-through each of the trees in the given file:</P><PRE CLASS="verbatim">>>> trees = Phylo.parse("../../Tests/PhyloXML/phyloxml_examples.xml", "phyloxml")
->>> for tree in trees:
-...     print(tree)
-</PRE><P>Write a tree or iterable of trees back to file with the <CODE>write</CODE> function:</P><PRE CLASS="verbatim">>>> trees = list(Phylo.parse("../../Tests/PhyloXML/phyloxml_examples.xml", "phyloxml"))
->>> tree1 = trees[0]
->>> others = trees[1:]
->>> Phylo.write(tree1, "tree1.nwk", "newick")
-1
->>> Phylo.write(others, "other_trees.nwk", "newick")
-12
-</PRE><P>Convert files between any of the supported formats with the <CODE>convert</CODE> function:</P><PRE CLASS="verbatim">>>> Phylo.convert("tree1.nwk", "newick", "tree1.xml", "nexml")
-1
->>> Phylo.convert("other_trees.xml", "phyloxml", "other_trees.nex", "nexus")
-12
-</PRE><P>To use strings as input or output instead of actual files, use <CODE>StringIO</CODE> as you would
-with SeqIO and AlignIO:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> from StringIO import StringIO
->>> handle = StringIO("(((A,B),(C,D)),(E,F,G));")
->>> tree = Phylo.read(handle, "newick")
-</PRE><!--TOC section View and export trees-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc158">12.3</A>&#XA0;&#XA0;View and export trees</H2><!--SEC END --><P>The simplest way to get an overview of a <CODE>Tree</CODE> object is to <CODE>print</CODE> it:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> tree = Phylo.read("PhyloXML/example.xml", "phyloxml")
->>> print(tree)
-Phylogeny(description='phyloXML allows to use either a "branch_length" attribute...', name='example from Prof. Joe Felsenstein's book "Inferring Phyl...', rooted=True)
-    Clade()
-        Clade(branch_length=0.06)
-            Clade(branch_length=0.102, name='A')
-            Clade(branch_length=0.23, name='B')
-        Clade(branch_length=0.4, name='C')
-</PRE><P>This is essentially an outline of the object hierarchy Biopython uses to represent a tree. But
-more likely, you&#X2019;d want to see a drawing of the tree. There are three functions to do this.</P><P>As we saw in the demo, <CODE>draw_ascii</CODE> prints an ascii-art drawing of the tree (a
-rooted phylogram) to standard output, or an open file handle if given. Not all of the
-available information about the tree is shown, but it provides a way to quickly view the
-tree without relying on any external dependencies.</P><PRE CLASS="verbatim">>>> tree = Phylo.read("example.xml", "phyloxml")
->>> Phylo.draw_ascii(tree)
-             __________________ A
-  __________|
-_|          |___________________________________________ B
- |
- |___________________________________________________________________________ C
-
-</PRE><P>The <CODE>draw</CODE> function draws a more attractive image using the matplotlib
-library. See the API documentation for details on the arguments it accepts to
-customize the output.</P><PRE CLASS="verbatim">>>> tree = Phylo.read("example.xml", "phyloxml")
->>> Phylo.draw(tree, branch_labels=lambda c: c.branch_length)
-</PRE><P>
-<IMG SRC="images/phylo-draw-example.png" width=701, height=465>
-<A NAME="fig:phylo-draw-example"></A>
-</P><P><CODE>draw_graphviz</CODE> draws an unrooted cladogram, but requires that you have Graphviz,
-PyDot or PyGraphviz, NetworkX, and matplotlib (or pylab) installed. Using the same example as
-above, and the <CODE>dot</CODE> program included with Graphviz, let&#X2019;s draw a rooted tree (see
-Fig.&#XA0;<A HREF="#fig:phylo-dot">12.3</A>):</P><PRE CLASS="verbatim">>>> tree = Phylo.read("example.xml", "phyloxml")
->>> Phylo.draw_graphviz(tree, prog='dot')
->>> import pylab
->>> pylab.show()                    # Displays the tree in an interactive viewer
->>> pylab.savefig('phylo-dot.png')  # Creates a PNG file of the same graphic
-</PRE><P>
-<IMG SRC="images/phylo-dot.png" width=400, height=312>
-<A NAME="fig:phylo-dot"></A>
-</P><P>(Tip: If you execute IPython with the <CODE>-pylab</CODE> option, calling <CODE>draw_graphviz</CODE> causes
-the matplotlib viewer to launch automatically without manually calling <CODE>show()</CODE>.)</P><P>This exports the tree object to a NetworkX graph, uses Graphviz to lay out the nodes, and
-displays it using matplotlib.
-There are a number of keyword arguments that can modify the resulting diagram, including
-most of those accepted by the NetworkX functions <CODE>networkx.draw</CODE> and
-<CODE>networkx.draw_graphviz</CODE>.</P><P>The display is also affected by the <CODE>rooted</CODE> attribute of the given tree object.
-Rooted trees are shown with a &#X201C;head&#X201D; on each branch indicating direction (see
-Fig.&#XA0;<A HREF="#fig:phylo-rooted">12.3</A>):</P><PRE CLASS="verbatim">>>> tree = Phylo.read("simple.dnd", "newick")
->>> tree.rooted = True
->>> Phylo.draw_graphviz(tree)
-</PRE><P>
-<IMG SRC="images/phylo-rooted.png" width=432, height=368>
-<A NAME="fig:phylo-rooted"></A>
-</P><P>The &#X201C;prog&#X201D; argument specifies the Graphviz engine used for layout. The default,
-<CODE>twopi</CODE>, behaves well for any size tree, reliably avoiding crossed branches. The
-<CODE>neato</CODE> program may draw more attractive moderately-sized trees, but sometimes will
-cross branches (see Fig.&#XA0;<A HREF="#fig:phylo-color">12.3</A>). The <CODE>dot</CODE> program may be useful
-with small trees, but tends to do surprising things with the layout of larger trees.</P><PRE CLASS="verbatim">>>> Phylo.draw_graphviz(tree, prog="neato")
-</PRE><P>
-<IMG SRC="images/phylo-color.png" width=499, height=348>
-<A NAME="fig:phylo-color"></A>
-</P><P>This viewing mode is particularly handy for exploring larger trees, because the matplotlib
-viewer can zoom in on a selected region, thinning out a cluttered graphic.
-</P><PRE CLASS="verbatim">>>> tree = Phylo.read("apaf.xml", "phyloxml")
->>> Phylo.draw_graphviz(tree, prog="neato", node_size=0)
-</PRE><P>
-<IMG SRC="images/phylo-apaf.png" width=519, height=400>
-<A NAME="fig:phylo-apaf"></A>
-<IMG SRC="images/phylo-apaf-zoom.png" width=506, height=400>
-<A NAME="fig:phylo-apaf-zoom"></A>
-</P><P>Note that branch lengths are not displayed accurately, because Graphviz ignores them when
-creating the node layouts. The branch lengths are retained when exporting a tree as a NetworkX
-graph object (<CODE>to_networkx</CODE>), however.</P><P>See the Phylo page on the Biopython wiki (<A HREF="http://biopython.org/wiki/Phylo"><TT>http://biopython.org/wiki/Phylo</TT></A>) for
-descriptions and examples of the more advanced functionality in <CODE>draw_ascii</CODE>,
-<CODE>draw_graphviz</CODE> and <CODE>to_networkx</CODE>.</P><!--TOC section Using Tree and Clade objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc159">12.4</A>&#XA0;&#XA0;Using Tree and Clade objects</H2><!--SEC END --><P>The <CODE>Tree</CODE> objects produced by <CODE>parse</CODE> and <CODE>read</CODE> are containers for recursive
-sub-trees, attached to the <CODE>Tree</CODE> object at the <CODE>root</CODE> attribute (whether or not the
-phylogenic tree is actually considered rooted). A <CODE>Tree</CODE> has globally applied information
-for the phylogeny, such as rootedness, and a reference to a single <CODE>Clade</CODE>; a
-<CODE>Clade</CODE> has node- and clade-specific information, such as branch length, and a list of
-its own descendent <CODE>Clade</CODE> instances, attached at the <CODE>clades</CODE> attribute.</P><P>So there is a distinction between <CODE>tree</CODE> and <CODE>tree.root</CODE>. In practice, though, you
-rarely need to worry about it. To smooth over the difference, both <CODE>Tree</CODE> and
-<CODE>Clade</CODE> inherit from <CODE>TreeMixin</CODE>, which contains the implementations for methods
-that would be commonly used to search, inspect or modify a tree or any of its clades. This
-means that almost all of the methods supported by <CODE>tree</CODE> are also available on
-<CODE>tree.root</CODE> and any clade below it. (<CODE>Clade</CODE> also has a <CODE>root</CODE> property, which
-returns the clade object itself.)</P><!--TOC subsection Search and traversal methods-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc160">12.4.1</A>&#XA0;&#XA0;Search and traversal methods</H3><!--SEC END --><P>For convenience, we provide a couple of simplified methods that return all external or internal
-nodes directly as a list:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>get_terminals</TT></B></DT><DD CLASS="dd-description"> makes a list of all of this tree&#X2019;s terminal (leaf) nodes.
-</DD><DT CLASS="dt-description"><B><TT>get_nonterminals</TT></B></DT><DD CLASS="dd-description"> makes a list of all of this tree&#X2019;s nonterminal (internal)
-nodes.
-</DD></DL><P>These both wrap a method with full control over tree traversal, <CODE>find_clades</CODE>. Two more
-traversal methods, <CODE>find_elements</CODE> and <CODE>find_any</CODE>, rely on the same core
-functionality and accept the same arguments, which we&#X2019;ll call a &#X201C;target specification&#X201D; for
-lack of a better description. These specify which objects in the tree will be matched and
-returned during iteration. The first argument can be any of the following types:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-A <B>TreeElement instance</B>, which tree elements will match by identity &#X2014; so
-searching with a Clade instance as the target will find that clade in the tree;</LI><LI CLASS="li-itemize">A <B>string</B>, which matches tree elements&#X2019; string representation &#X2014; in
-particular, a clade&#X2019;s <CODE>name</CODE> <I>(added in Biopython 1.56)</I>;</LI><LI CLASS="li-itemize">A <B>class</B> or <B>type</B>, where every tree element of the same type (or
-sub-type) will be matched;</LI><LI CLASS="li-itemize">A <B>dictionary</B> where keys are tree element attributes and values are matched to the
-corresponding attribute of each tree element. This one gets even more elaborate:<UL CLASS="itemize"><LI CLASS="li-itemize">
-If an <TT>int</TT> is given, it matches numerically equal attributes, e.g. 1 will
-match 1 or 1.0</LI><LI CLASS="li-itemize">If a boolean is given (True or False), the corresponding attribute value is
-evaluated as a boolean and checked for the same</LI><LI CLASS="li-itemize"><TT>None</TT> matches <TT>None</TT></LI><LI CLASS="li-itemize">If a string is given, the value is treated as a regular expression (which must
-match the whole string in the corresponding element attribute, not just a prefix). A
-given string without special regex characters will match string attributes exactly, so
-if you don&#X2019;t use regexes, don&#X2019;t worry about it. For example, in a tree with clade
-names Foo1, Foo2 and Foo3, <CODE>tree.find_clades({"name": "Foo1"})</CODE> matches Foo1,
-<CODE>{"name": "Foo.*"}</CODE> matches all three clades, and <CODE>{"name": "Foo"}</CODE> doesn&#X2019;t
-match anything.</LI></UL><P>Since floating-point arithmetic can produce some strange behavior, we don&#X2019;t support
-matching <TT>float</TT>s directly. Instead, use the boolean <TT>True</TT> to match every
-element with a nonzero value in the specified attribute, then filter on that attribute
-manually with an inequality (or exact number, if you like living dangerously).</P><P>If the dictionary contains multiple entries, a matching element must match each of the
-given attribute values &#X2014; think &#X201C;and&#X201D;, not &#X201C;or&#X201D;.</P></LI><LI CLASS="li-itemize">A <B>function</B> taking a single argument (it will be applied to each element in the
-tree), returning True or False. For convenience, LookupError, AttributeError and ValueError
-are silenced, so this provides another safe way to search for floating-point values in the
-tree, or some more complex characteristic.</LI></UL><P>After the target, there are two optional keyword arguments:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>terminal</B></DT><DD CLASS="dd-description"> &#X2014; A boolean value to select for or against terminal clades (a.k.a. leaf
-nodes): True searches for only terminal clades, False for non-terminal (internal) clades,
-and the default, None, searches both terminal and non-terminal clades, as well as any tree
-elements lacking the <CODE>is_terminal</CODE> method.</DD><DT CLASS="dt-description"><B>order</B></DT><DD CLASS="dd-description"> &#X2014; Tree traversal order: <TT>"preorder"</TT> (default) is depth-first search,
-<TT>"postorder"</TT> is DFS with child nodes preceding parents, and <TT>"level"</TT> is
-breadth-first search.</DD></DL><P>Finally, the methods accept arbitrary keyword arguments which are treated the same way as a
-dictionary target specification: keys indicate the name of the element attribute to search for,
-and the argument value (string, integer, None or boolean) is compared to the value of each
-attribute found. If no keyword arguments are given, then any TreeElement types are matched.
-The code for this is generally shorter than passing a dictionary as the target specification:
-<CODE>tree.find_clades({"name": "Foo1"})</CODE> can be shortened to
-<CODE>tree.find_clades(name="Foo1")</CODE>.</P><P>(In Biopython 1.56 or later, this can be even shorter: <CODE>tree.find_clades("Foo1")</CODE>)</P><P>Now that we&#X2019;ve mastered target specifications, here are the methods used to traverse a tree:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>find_clades</TT></B></DT><DD CLASS="dd-description">
-Find each clade containing a matching element. That is, find each element as with
-<CODE>find_elements</CODE>, but return the corresponding clade object. (This is usually what you
-want.)<P>The result is an iterable through all matching objects, searching depth-first by default.
-This is not necessarily the same order as the elements appear in the Newick, Nexus or XML
-source file!</P></DD><DT CLASS="dt-description"><B><TT>find_elements</TT></B></DT><DD CLASS="dd-description">
-Find all tree elements matching the given attributes, and return the matching elements
-themselves. Simple Newick trees don&#X2019;t have complex sub-elements, so this behaves the same
-as <CODE>find_clades</CODE> on them. PhyloXML trees often do have complex objects attached to
-clades, so this method is useful for extracting those.</DD><DT CLASS="dt-description"><B><TT>find_any</TT></B></DT><DD CLASS="dd-description">
-Return the first element found by <CODE>find_elements()</CODE>, or None. This is also useful for
-checking whether any matching element exists in the tree, and can be used in a conditional.</DD></DL><P>Two more methods help navigating between nodes in the tree:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>get_path</TT></B></DT><DD CLASS="dd-description">
-List the clades directly between the tree root (or current clade) and the given target.
-Returns a list of all clade objects along this path, ending with the given target, but
-excluding the root clade.</DD><DT CLASS="dt-description"><B><TT>trace</TT></B></DT><DD CLASS="dd-description">
-List of all clade object between two targets in this tree. Excluding start, including
-finish.</DD></DL><!--TOC subsection Information methods-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc161">12.4.2</A>&#XA0;&#XA0;Information methods</H3><!--SEC END --><P>These methods provide information about the whole tree (or any clade).</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>common_ancestor</TT></B></DT><DD CLASS="dd-description">
-Find the most recent common ancestor of all the given targets. (This will be a Clade object).
-If no target is given, returns the root of the current clade (the one this method is called
-from); if 1 target is given, this returns the target itself. However, if any of the
-specified targets are not found in the current tree (or clade), an exception is raised.</DD><DT CLASS="dt-description"><B><TT>count_terminals</TT></B></DT><DD CLASS="dd-description">
-Counts the number of terminal (leaf) nodes within the tree.</DD><DT CLASS="dt-description"><B><TT>depths</TT></B></DT><DD CLASS="dd-description">
-Create a mapping of tree clades to depths. The result is a dictionary where the keys are
-all of the Clade instances in the tree, and the values are the distance from the root to
-each clade (including terminals). By default the distance is the cumulative branch length
-leading to the clade, but with the <CODE>unit_branch_lengths=True</CODE> option, only the number
-of branches (levels in the tree) is counted.</DD><DT CLASS="dt-description"><B><TT>distance</TT></B></DT><DD CLASS="dd-description">
-Calculate the sum of the branch lengths between two targets. If only one target is
-specified, the other is the root of this tree.</DD><DT CLASS="dt-description"><B><TT>total_branch_length</TT></B></DT><DD CLASS="dd-description">
-Calculate the sum of all the branch lengths in this tree. This is usually just called the
-&#X201C;length&#X201D; of the tree in phylogenetics, but we use a more explicit name to avoid confusion
-with Python terminology.</DD></DL><P>The rest of these methods are boolean checks:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>is_bifurcating</TT></B></DT><DD CLASS="dd-description">
-True if the tree is strictly bifurcating; i.e. all nodes have either 2 or 0 children
-(internal or external, respectively). The root may have 3 descendents and still be
-considered part of a bifurcating tree.</DD><DT CLASS="dt-description"><B><TT>is_monophyletic</TT></B></DT><DD CLASS="dd-description">
-Test if all of the given targets comprise a complete subclade &#X2014; i.e., there
-exists a clade such that its terminals are the same set as the given targets. The targets
-should be terminals of the tree. For convenience, this method returns the common ancestor
-(MCRA) of the targets if they are monophyletic (instead of the value <CODE>True</CODE>), and
-<CODE>False</CODE> otherwise.</DD><DT CLASS="dt-description"><B><TT>is_parent_of</TT></B></DT><DD CLASS="dd-description"> True if target is a descendent of this tree &#X2014; not required
-to be a direct descendent. To check direct descendents of a clade, simply use list
-membership testing: <CODE>if subclade in clade: ...</CODE></DD><DT CLASS="dt-description"><B><TT>is_preterminal</TT></B></DT><DD CLASS="dd-description"> True if all direct descendents are terminal; False if any
-direct descendent is not terminal.</DD></DL><!--TOC subsection Modification methods-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc162">12.4.3</A>&#XA0;&#XA0;Modification methods</H3><!--SEC END --><P>These methods modify the tree in-place. If you want to keep the original tree intact, make a
-complete copy of the tree first, using Python&#X2019;s <TT>copy</TT> module:</P><PRE CLASS="verbatim">tree = Phylo.read('example.xml', 'phyloxml')
-import copy
-newtree = copy.deepcopy(tree)
-</PRE><DL CLASS="description"><DT CLASS="dt-description">
-<B><TT>collapse</TT></B></DT><DD CLASS="dd-description">
-Deletes the target from the tree, relinking its children to its parent.</DD><DT CLASS="dt-description"><B><TT>collapse_all</TT></B></DT><DD CLASS="dd-description">
-Collapse all the descendents of this tree, leaving only terminals. Branch lengths are
-preserved, i.e. the distance to each terminal stays the same. With a target specification
-(see above), collapses only the internal nodes matching the specification.</DD><DT CLASS="dt-description"><B><TT>ladderize</TT></B></DT><DD CLASS="dd-description">
-Sort clades in-place according to the number of terminal nodes. Deepest clades are placed
-last by default. Use <CODE>reverse=True</CODE> to sort clades deepest-to-shallowest.</DD><DT CLASS="dt-description"><B><TT>prune</TT></B></DT><DD CLASS="dd-description">
-Prunes a terminal clade from the tree. If taxon is from a bifurcation, the connecting node
-will be collapsed and its branch length added to remaining terminal node. This might no
-longer be a meaningful value.</DD><DT CLASS="dt-description"><B><TT>root_with_outgroup</TT></B></DT><DD CLASS="dd-description">
-Reroot this tree with the outgroup clade containing the given targets, i.e. the common
-ancestor of the outgroup. This method is only available on Tree objects, not Clades.<P>If the outgroup is identical to self.root, no change occurs. If the outgroup clade is
-terminal (e.g. a single terminal node is given as the outgroup), a new bifurcating root
-clade is created with a 0-length branch to the given outgroup. Otherwise, the internal node
-at the base of the outgroup becomes a trifurcating root for the whole tree. If the original
-root was bifurcating, it is dropped from the tree.</P><P>In all cases, the total branch length of the tree stays the same.</P></DD><DT CLASS="dt-description"><B><TT>root_at_midpoint</TT></B></DT><DD CLASS="dd-description">
-Reroot this tree at the calculated midpoint between the two most distant
-tips of the tree. (This uses <CODE>root_with_outgroup</CODE> under the hood.)</DD><DT CLASS="dt-description"><B><TT>split</TT></B></DT><DD CLASS="dd-description">
-Generate <I>n</I> (default 2) new descendants. In a species tree, this is a speciation
-event. New clades have the given <CODE>branch_length</CODE> and the same name as this clade&#X2019;s
-root plus an integer suffix (counting from 0) &#X2014; for example, splitting a clade named
-&#X201C;A&#X201D; produces the sub-clades &#X201C;A0&#X201D; and &#X201C;A1&#X201D;.</DD></DL><P>See the Phylo page on the Biopython wiki (<A HREF="http://biopython.org/wiki/Phylo"><TT>http://biopython.org/wiki/Phylo</TT></A>) for
-more examples of using the available methods.</P><!--TOC subsection Features of PhyloXML trees-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc163">12.4.4</A>&#XA0;&#XA0;Features of PhyloXML trees</H3><!--SEC END --><P>
-<A NAME="sec:PhyloXML"></A></P><P>The phyloXML file format includes fields for annotating trees with additional data types and
-visual cues.</P><P>See the PhyloXML page on the Biopython wiki (<A HREF="http://biopython.org/wiki/PhyloXML"><TT>http://biopython.org/wiki/PhyloXML</TT></A>) for
-descriptions and examples of using the additional annotation features provided by PhyloXML.</P><!--TOC section Running external applications-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc164">12.5</A>&#XA0;&#XA0;Running external applications</H2><!--SEC END --><P>
-<A NAME="sec:PhyloApps"></A></P><P>While Bio.Phylo doesn&#X2019;t infer trees from alignments itself, there are third-party
-programs available that do. These are supported through the module
-<TT>Bio.Phylo.Applications</TT>, using the same general framework as
-<TT>Bio.Emboss.Applications</TT>, <TT>Bio.Align.Applications</TT> and others.</P><P>Biopython 1.58 introduced a wrapper for PhyML
-(<A HREF="http://www.atgc-montpellier.fr/phyml/"><TT>http://www.atgc-montpellier.fr/phyml/</TT></A>). The program accepts an input alignment in
-<TT>phylip-relaxed</TT> format (that&#X2019;s Phylip format, but without the 10-character limit
-on taxon names) and a variety of options. A quick example:</P><PRE CLASS="verbatim">>>> from Bio import Phylo
->>> from Bio.Phylo.Applications import PhymlCommandline
->>> cmd = PhymlCommandline(input='Tests/Phylip/random.phy')
->>> out_log, err_log = cmd()
-</PRE><P>This generates a tree file and a stats file with the names
-[<I>input&#XA0;filename</I>]<CODE>_phyml_tree.txt</CODE> and
-[<I>input&#XA0;filename</I>]<CODE>_phyml_stats.txt</CODE>. The tree file is in Newick format:</P><PRE CLASS="verbatim">>>> tree = Phylo.read('Tests/Phylip/random.phy_phyml_tree.txt', 'newick')
->>> Phylo.draw_ascii(tree)
-</PRE><P>A similar wrapper for RAxML (<A HREF="http://sco.h-its.org/exelixis/software.html"><TT>http://sco.h-its.org/exelixis/software.html</TT></A>)
-was added in Biopython 1.60, and FastTree
-(<A HREF="http://www.microbesonline.org/fasttree/"><TT>http://www.microbesonline.org/fasttree/</TT></A>) in Biopython 1.62.</P><P>Note that some popular Phylip programs, including <TT>dnaml</TT> and <TT>protml</TT>,
-are already available through the EMBOSS wrappers in <TT>Bio.Emboss.Applications</TT> if
-you have the Phylip extensions to EMBOSS installed on your system.
-See Section&#XA0;<A HREF="#sec:alignment-tools">6.4</A> for some examples and clues on how to use
-programs like these.</P><!--TOC section PAML integration-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc165">12.6</A>&#XA0;&#XA0;PAML integration</H2><!--SEC END --><P>
-<A NAME="sec:PhyloPAML"></A></P><P>Biopython 1.58 brought support for PAML
-(<A HREF="http://abacus.gene.ucl.ac.uk/software/paml.html"><TT>http://abacus.gene.ucl.ac.uk/software/paml.html</TT></A>), a suite of programs for
-phylogenetic analysis by maximum likelihood. Currently the programs codeml, baseml and
-yn00 are implemented. Due to PAML&#X2019;s usage of control files rather than command line
-arguments to control runtime options, usage of this wrapper strays from the format of
-other application wrappers in Biopython.</P><P>A typical workflow would be to initialize a PAML object, specifying an alignment file, a
-tree file, an output file and a working directory. Next, runtime options are set via the
-<TT>set_options()</TT> method or by reading an existing control file. Finally, the
-program is run via the <TT>run()</TT> method and the output file is automatically parsed
-to a results dictionary.</P><P>Here is an example of typical usage of codeml:
-</P><PRE CLASS="verbatim">>>> from Bio.Phylo.PAML import codeml
->>> cml = codeml.Codeml()
->>> cml.alignment = "Tests/PAML/alignment.phylip"
->>> cml.tree = "Tests/PAML/species.tree"
->>> cml.out_file = "results.out"
->>> cml.working_dir = "./scratch"
->>> cml.set_options(seqtype=1,
-...         verbose=0,
-...         noisy=0,
-...         RateAncestor=0,
-...         model=0,
-...         NSsites=[0, 1, 2],
-...         CodonFreq=2,
-...         cleandata=1,
-...         fix_alpha=1,
-...         kappa=4.54006)
->>> results = cml.run()
->>> ns_sites = results.get("NSsites")
->>> m0 = ns_sites.get(0)
->>> m0_params = m0.get("parameters")
->>> print(m0_params.get("omega"))
-</PRE><P>Existing output files may be parsed as well using a module&#X2019;s <TT>read()</TT> function:
-</P><PRE CLASS="verbatim">>>> results = codeml.read("Tests/PAML/Results/codeml/codeml_NSsites_all.out")
->>> print(results.get("lnL max"))
-</PRE><P>Detailed documentation for this new module currently lives on the Biopython wiki:
-<A HREF="http://biopython.org/wiki/PAML"><TT>http://biopython.org/wiki/PAML</TT></A></P><!--TOC section Future plans-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc166">12.7</A>&#XA0;&#XA0;Future plans</H2><!--SEC END --><P>
-<A NAME="sec:PhyloFuture"></A></P><P>Bio.Phylo is under active development. Here are some features we might add in future
-releases:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>New methods</B></DT><DD CLASS="dd-description">
-Generally useful functions for operating on Tree or Clade objects appear on the Biopython
-wiki first, so that casual users can test them and decide if they&#X2019;re useful before we add
-them to Bio.Phylo:<P><A HREF="http://biopython.org/wiki/Phylo_cookbook"><TT>http://biopython.org/wiki/Phylo_cookbook</TT></A></P></DD><DT CLASS="dt-description"><B>Bio.Nexus port</B></DT><DD CLASS="dd-description">
-Much of this module was written during Google Summer of Code 2009, under the auspices of
-NESCent, as a project to implement Python support for the phyloXML data format (see
-<A HREF="#sec:PhyloXML">12.4.4</A>). Support for Newick and Nexus formats was added by porting part of the
-existing Bio.Nexus module to the new classes used by Bio.Phylo.<P>Currently, Bio.Nexus contains some useful features that have not yet been ported to
-Bio.Phylo classes &#X2014; notably, calculating a consensus tree. If you find some functionality
-lacking in Bio.Phylo, try poking throught Bio.Nexus to see if it&#X2019;s there instead.</P></DD></DL><P>We&#X2019;re open to any suggestions for improving the functionality and usability of this module;
-just let us know on the mailing list or our bug database.</P><P>Finally, if you need additional functionality not yet included in the Phylo
-module, check if it&#X2019;s available in another of the high-quality Python libraries
-for phylogenetics such as DendroPy (<A HREF="http://pythonhosted.org/DendroPy/"><TT>http://pythonhosted.org/DendroPy/</TT></A>) or
-PyCogent (<A HREF="http://pycogent.org/"><TT>http://pycogent.org/</TT></A>). Since these libraries also support
-standard file formats for phylogenetic trees, you can easily transfer data
-between libraries by writing to a temporary file or StringIO object.</P><!--TOC chapter Sequence motif analysis using Bio.motifs-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc167">Chapter&#XA0;13</A>&#XA0;&#XA0;Sequence motif analysis using Bio.motifs</H1><!--SEC END --><P>This chapter gives an overview of the functionality of the
-<CODE>Bio.motifs</CODE> package included in Biopython. It is intended
-for people who are involved in the analysis of sequence motifs, so I&#X2019;ll
-assume that you are familiar with basic notions of motif analysis. In
-case something is unclear, please look at Section&#XA0;<A HREF="#sec:links">13.10</A>
-for some relevant links.</P><P>Most of this chapter describes the new <CODE>Bio.motifs</CODE> package included
-in Biopython 1.61 onwards, which is replacing the older <CODE>Bio.Motif</CODE> package
-introduced with Biopython 1.50, which was in turn based on two older former
-Biopython modules, <CODE>Bio.AlignAce</CODE> and <CODE>Bio.MEME</CODE>. It provides
-most of their functionality with a unified motif object implementation.</P><P>Speaking of other libraries, if you are reading this you might be
-interested in <A HREF="http://fraenkel.mit.edu/TAMO/">TAMO</A>, another python library
-designed to deal with sequence motifs. It supports more <EM>de-novo</EM>
-motif finders, but it is not a part of Biopython and has some restrictions
-on commercial use.</P><!--TOC section Motif objects-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc168">13.1</A>&#XA0;&#XA0;Motif objects</H2><!--SEC END --><P>
-<A NAME="sec:object"></A></P><P>Since we are interested in motif analysis, we need to take a look at
-<CODE>Motif</CODE> objects in the first place. For that we need to import
-the Bio.motifs library:
-</P><PRE CLASS="verbatim">>>> from Bio import motifs
-</PRE><P>and we can start creating our first motif objects. We can either create
-a <CODE>Motif</CODE> object from a list of instances of the motif, or we can
-obtain a <CODE>Motif</CODE> object by parsing a file from a motif database
-or motif finding software.</P><!--TOC subsection Creating a motif from instances-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc169">13.1.1</A>&#XA0;&#XA0;Creating a motif from instances</H3><!--SEC END --><P>Suppose we have these instances of a DNA motif:
-</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> instances = [Seq("TACAA"),
-...              Seq("TACGC"),
-...              Seq("TACAC"),
-...              Seq("TACCC"),
-...              Seq("AACCC"),
-...              Seq("AATGC"),
-...              Seq("AATGC"),
-...             ]
-</PRE><P>then we can create a Motif object as follows:
-</P><PRE CLASS="verbatim">>>> m = motifs.create(instances)
-</PRE><P>The instances are saved in an attribute <CODE>m.instances</CODE>, which is essentially a Python list with some added functionality, as described below.
-Printing out the Motif object shows the instances from which it was constructed:
-</P><PRE CLASS="verbatim">>>> print(m)
-TACAA
-TACGC
-TACAC
-TACCC
-AACCC
-AATGC
-AATGC
-<BLANKLINE>
-</PRE><P>The length of the motif is defined as the sequence length, which should be the same for all instances:
-</P><PRE CLASS="verbatim">>>> len(m)
-5
-</PRE><P>The Motif object has an attribute <CODE>.counts</CODE> containing the counts of each
-nucleotide at each position. Printing this counts matrix shows it in an easily readable format:
-</P><PRE CLASS="verbatim">>>> print(m.counts)
-        0      1      2      3      4
-A:   3.00   7.00   0.00   2.00   1.00
-C:   0.00   0.00   5.00   2.00   6.00
-G:   0.00   0.00   0.00   3.00   0.00
-T:   4.00   0.00   2.00   0.00   0.00
-<BLANKLINE>
-</PRE><P>You can access these counts as a dictionary:
-</P><PRE CLASS="verbatim">>>> m.counts['A']
-[3, 7, 0, 2, 1]
-</PRE><P>but you can also think of it as a 2D array with the nucleotide as the first
-dimension and the position as the second dimension:
-</P><PRE CLASS="verbatim">>>> m.counts['T', 0]
-4
->>> m.counts['T', 2]
-2
->>> m.counts['T', 3]
-0
-</PRE><P>You can also directly access columns of the counts matrix
-</P><PRE CLASS="verbatim">>>> m.counts[:, 3]
-{'A': 2, 'C': 2, 'T': 0, 'G': 3}
-</PRE><P>Instead of the nucleotide itself, you can also use the index of the nucleotide
-in the sorted letters in the alphabet of the motif:
-</P><PRE CLASS="verbatim">>>> m.alphabet
-IUPACUnambiguousDNA()
->>> m.alphabet.letters
-'GATC'
->>> sorted(m.alphabet.letters)
-['A', 'C', 'G', 'T']
->>> m.counts['A',:]
-(3, 7, 0, 2, 1)
->>> m.counts[0,:]
-(3, 7, 0, 2, 1)
-</PRE><P>The motif has an associated consensus sequence, defined as the sequence of
-letters along the positions of the motif for which the largest value in the
-corresponding columns of the <CODE>.counts</CODE> matrix is obtained:
-</P><PRE CLASS="verbatim">>>> m.consensus
-Seq('TACGC', IUPACUnambiguousDNA())
-</PRE><P>as well as an anticonsensus sequence, corresponding to the smallest values in
-the columns of the <CODE>.counts</CODE> matrix:
-</P><PRE CLASS="verbatim">>>> m.anticonsensus
-Seq('GGGTG', IUPACUnambiguousDNA())
-</PRE><P>You can also ask for a degenerate consensus sequence, in which ambiguous
-nucleotides are used for positions where there are multiple nucleotides with
-high counts:
-</P><PRE CLASS="verbatim">>>> m.degenerate_consensus
-Seq('WACVC', IUPACAmbiguousDNA())
-</PRE><P>Here, W and R follow the IUPAC nucleotide ambiguity codes: W is either A or T,
-and V is A, C, or G [<A HREF="#cornish1985">10</A>]. The degenerate consensus sequence is
-constructed following the rules specified by Cavener [<A HREF="#cavener1987">11</A>].</P><P>We can also get the reverse complement of a motif:
-</P><PRE CLASS="verbatim">>>> r = m.reverse_complement()
->>> r.consensus
-Seq('GCGTA', IUPACUnambiguousDNA())
->>> r.degenerate_consensus
-Seq('GBGTW', IUPACAmbiguousDNA())
->>> print(r)
-TTGTA
-GCGTA
-GTGTA
-GGGTA
-GGGTT
-GCATT
-GCATT
-<BLANKLINE>
-</PRE><P>The reverse complement and the degenerate consensus sequence are
-only defined for DNA motifs.</P><!--TOC subsection Creating a sequence logo-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc170">13.1.2</A>&#XA0;&#XA0;Creating a sequence logo</H3><!--SEC END --><P>
-If we have internet access, we can create a <A HREF="http://weblogo.berkeley.edu">weblogo</A>:
-</P><PRE CLASS="verbatim">>>> m.weblogo("mymotif.png")
-</PRE><P>We should get our logo saved as a PNG in the specified file.</P><!--TOC section Reading motifs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc171">13.2</A>&#XA0;&#XA0;Reading motifs</H2><!--SEC END --><P>
-<A NAME="sec:io"></A></P><P>Creating motifs from instances by hand is a bit boring, so it&#X2019;s
-useful to have some I/O functions for reading and writing
-motifs. There are not any really well established standards for storing
-motifs, but there are a couple of formats that are more used than
-others.</P><!--TOC subsection JASPAR-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc172">13.2.1</A>&#XA0;&#XA0;JASPAR</H3><!--SEC END --><P>
-One of the most popular motif databases is <A HREF="http://jaspar.genereg.net">JASPAR</A>. In addition to the motif sequence information, the JASPAR database stores a lot of meta-information for each motif. The module <CODE>Bio.motifs</CODE> contains a specialized class <CODE>jaspar.Motif</CODE> in which this meta-information is represented as attributes:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>matrix_id</CODE> - the unique JASPAR motif ID, e.g. &#X2019;MA0004.1&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>name</CODE> - the name of the TF, e.g. &#X2019;Arnt&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>collection</CODE> - the JASPAR collection to which the motif belongs, e.g. &#X2019;CORE&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>tf_class</CODE> - the structual class of this TF, e.g. &#X2019;Zipper-Type&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>tf_family</CODE> - the family to which this TF belongs, e.g. &#X2019;Helix-Loop-Helix&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>species</CODE> - the species to which this TF belongs, may have multiple values, these are specified as taxonomy IDs, e.g. 10090
-</LI><LI CLASS="li-itemize"><CODE>tax_group</CODE> - the taxonomic supergroup to which this motif belongs, e.g. &#X2019;vertebrates&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>acc</CODE> - the accession number of the TF protein, e.g. &#X2019;P53762&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>data_type</CODE> - the type of data used to construct this motif, e.g. &#X2019;SELEX&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>medline</CODE> - the Pubmed ID of literature supporting this motif, may be multiple values, e.g. 7592839
-</LI><LI CLASS="li-itemize"><CODE>pazar_id</CODE> - external reference to the TF in the <A HREF="http://pazar.info">PAZAR</A> database, e.g. &#X2019;TF0000003&#X2019;
-</LI><LI CLASS="li-itemize"><CODE>comment</CODE> - free form text containing notes about the construction of the motif
-</LI></UL><P>The <CODE>jaspar.Motif</CODE> class inherits from the generic <CODE>Motif</CODE> class and therefore provides all the facilities of any of the motif formats &#X2014; reading motifs, writing motifs, scanning sequences for motif instances etc.</P><P>JASPAR stores motifs in several different ways including three different flat file formats and as an SQL database. All of these formats facilitate the construction of a counts matrix. However, the amount of meta information describ [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->The JASPAR <TT>sites</TT> format</H4><!--SEC END --><P>The first of the three flat file formats contains a list of instances. As an example, these are the beginning and ending lines of the JASPAR <CODE>Arnt.sites</CODE> file showing known binding sites of the mouse helix-loop-helix transcription factor Arnt.
-</P><PRE CLASS="verbatim">>MA0004 ARNT 1
-CACGTGatgtcctc
->MA0004 ARNT 2
-CACGTGggaggtac
->MA0004 ARNT 3
-CACGTGccgcgcgc
-...
->MA0004 ARNT 18
-AACGTGacagccctcc
->MA0004 ARNT 19
-AACGTGcacatcgtcc
->MA0004 ARNT 20
-aggaatCGCGTGc
-</PRE><P>The parts of the sequence in capital letters are the motif instances that were found to align to each other.</P><P>We can create a <CODE>Motif</CODE> object from these instances as follows:
-</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("Arnt.sites") as handle:
-...     arnt = motifs.read(handle, "sites")
-...
-</PRE><P>The instances from which this motif was created is stored in the <CODE>.instances</CODE> property:
-</P><PRE CLASS="verbatim">>>> print(arnt.instances[:3])
-[Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA()), Seq('CACGTG', IUPACUnambiguousDNA())]
->>> for instance in arnt.instances:
-...     print(instance)
-...
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-CACGTG
-AACGTG
-AACGTG
-AACGTG
-AACGTG
-CGCGTG
-</PRE><P>The counts matrix of this motif is automatically calculated from the instances:
-</P><PRE CLASS="verbatim">>>> print(arnt.counts)
-        0      1      2      3      4      5
-A:   4.00  19.00   0.00   0.00   0.00   0.00
-C:  16.00   0.00  20.00   0.00   0.00   0.00
-G:   0.00   1.00   0.00  20.00   0.00  20.00
-T:   0.00   0.00   0.00   0.00  20.00   0.00
-<BLANKLINE>
-</PRE><P>This format does not store any meta information.</P><!--TOC subsubsection The JASPAR <TT>pfm</TT> format-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->The JASPAR <TT>pfm</TT> format</H4><!--SEC END --><P>JASPAR also makes motifs available directly as a count matrix,
-without the instances from which it was created. This <CODE>pfm</CODE> format only
-stores the counts matrix for a single motif.
-For example, this is the JASPAR file <CODE>SRF.pfm</CODE> containing the counts matrix for the human SRF transcription factor:
-</P><PRE CLASS="verbatim"> 2 9 0 1 32 3 46 1 43 15 2 2
- 1 33 45 45 1 1 0 0 0 1 0 1
-39 2 1 0 0 0 0 0 0 0 44 43
- 4 2 0 0 13 42 0 45 3 30 0 0
-</PRE><P>We can create a motif for this count matrix as follows:
-</P><PRE CLASS="verbatim">>>> with open("SRF.pfm") as handle:
-...     srf = motifs.read(handle, "pfm")
-...
->>> print(srf.counts)
-        0      1      2      3      4      5      6      7      8      9     10     11
-A:   2.00   9.00   0.00   1.00  32.00   3.00  46.00   1.00  43.00  15.00   2.00   2.00
-C:   1.00  33.00  45.00  45.00   1.00   1.00   0.00   0.00   0.00   1.00   0.00   1.00
-G:  39.00   2.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00  44.00  43.00
-T:   4.00   2.00   0.00   0.00  13.00  42.00   0.00  45.00   3.00  30.00   0.00   0.00
-<BLANKLINE>
-</PRE><P>As this motif was created from the counts matrix directly, it has no instances associated with it:
-</P><PRE CLASS="verbatim">>>> print(srf.instances)
-None
-</PRE><P>We can now ask for the consensus sequence of these two motifs:
-</P><PRE CLASS="verbatim">>>> print(arnt.counts.consensus)
-CACGTG
->>> print(srf.counts.consensus)
-GCCCATATATGG
-</PRE><P>As with the instances file, no meta information is stored in this format.</P><!--TOC subsubsection The JASPAR format <TT>jaspar</TT>-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->The JASPAR format <TT>jaspar</TT></H4><!--SEC END --><P>The <CODE>jaspar</CODE> file format allows multiple motifs to be specified in a single file. In this format each of the motif records consist of a header line followed by four lines defining the counts matrix. The header line begins with a <CODE>></CODE> character (similar to the Fasta file format) and is followed by the unique JASPAR matrix ID and the TF name. The following example sho [...]
-</P><PRE CLASS="verbatim">>MA0004.1 Arnt
-A  [ 4 19  0  0  0  0 ]
-C  [16  0 20  0  0  0 ]
-G  [ 0  1  0 20  0 20 ]
-T  [ 0  0  0  0 20  0 ]
->MA0002.1 RUNX1
-A  [10 12  4  1  2  2  0  0  0  8 13 ]
-C  [ 2  2  7  1  0  8  0  0  1  2  2 ]
-G  [ 3  1  1  0 23  0 26 26  0  0  4 ]
-T  [11 11 14 24  1 16  0  0 25 16  7 ]
->MA0052.1 MEF2A
-A  [ 1  0 57  2  9  6 37  2 56  6 ]
-C  [50  0  1  1  0  0  0  0  0  0 ]
-G  [ 0  0  0  0  0  0  0  0  2 50 ]
-T  [ 7 58  0 55 49 52 21 56  0  2 ]
-</PRE><P>The motifs are read as follows:
-</P><PRE CLASS="verbatim">>>> fh = open("jaspar_motifs.txt")
->>> for m in motifs.parse(fh, "jaspar"))
-...     print(m)
-TF name  Arnt
-Matrix ID MA0004.1
-Matrix:
-        0      1      2      3      4      5
-A:   4.00  19.00   0.00   0.00   0.00   0.00
-C:  16.00   0.00  20.00   0.00   0.00   0.00
-G:   0.00   1.00   0.00  20.00   0.00  20.00
-T:   0.00   0.00   0.00   0.00  20.00   0.00
-
-
-
-TF name  RUNX1
-Matrix ID MA0002.1
-Matrix:
-        0      1      2      3      4      5      6      7      8      9     10
-A:  10.00  12.00   4.00   1.00   2.00   2.00   0.00   0.00   0.00   8.00  13.00
-C:   2.00   2.00   7.00   1.00   0.00   8.00   0.00   0.00   1.00   2.00   2.00
-G:   3.00   1.00   1.00   0.00  23.00   0.00  26.00  26.00   0.00   0.00   4.00
-T:  11.00  11.00  14.00  24.00   1.00  16.00   0.00   0.00  25.00  16.00   7.00
-
-
-
-TF name  MEF2A
-Matrix ID MA0052.1
-Matrix:
-        0      1      2      3      4      5      6      7      8      9
-A:   1.00   0.00  57.00   2.00   9.00   6.00  37.00   2.00  56.00   6.00
-C:  50.00   0.00   1.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00
-G:   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   2.00  50.00
-T:   7.00  58.00   0.00  55.00  49.00  52.00  21.00  56.00   0.00   2.00
-</PRE><P>Note that printing a JASPAR motif yields both the counts data and the available meta-information.</P><!--TOC subsubsection Accessing the JASPAR database-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->Accessing the JASPAR database</H4><!--SEC END --><P>In addition to parsing these flat file formats, we can also retrieve motifs from a JASPAR SQL database. Unlike the flat file formats, a JASPAR database allows storing of all possible meta information defined in the JASPAR <CODE>Motif</CODE> class. It is beyond the scope of this document to describe how to set up a JASPAR database (please see the main <A HREF="http://jaspar.genereg.net">JASPAR< [...]
-</P><PRE CLASS="verbatim">>>> from Bio.motifs.jaspar.db import JASPAR5
->>>
->>> JASPAR_DB_HOST = <hostname>
->>> JASPAR_DB_NAME = <db_name>
->>> JASPAR_DB_USER = <user>
->>> JASPAR_DB_PASS = <passord>
->>>
->>> jdb = JASPAR5(
-...     host=JASPAR_DB_HOST,
-...     name=JASPAR_DB_NAME,
-...     user=JASPAR_DB_USER,
-...     password=JASPAR_DB_PASS
-... )
-</PRE><P>Now we can fetch a single motif by its unique JASPAR ID with the <CODE>fetch_motif_by_id</CODE> method. Note that a JASPAR ID conists of a base ID and a version number seperated by a decimal point, e.g. &#X2019;MA0004.1&#X2019;. The <CODE>fetch_motif_by_id</CODE> method allows you to use either the fully specified ID or just the base ID. If only the base ID is provided, the latest version of the motif is returned.
-</P><PRE CLASS="verbatim">>>> arnt = jdb.fetch_motif_by_id("MA0004")
-</PRE><P>Printing the motif reveals that the JASPAR SQL database stores much more meta-information than the flat files:
-</P><PRE CLASS="verbatim">>>> print(arnt)
-TF name Arnt
-Matrix ID MA0004.1
-Collection CORE
-TF class Zipper-Type
-TF family Helix-Loop-Helix
-Species 10090
-Taxonomic group vertebrates
-Accession ['P53762']
-Data type used SELEX
-Medline 7592839
-PAZAR ID TF0000003
-Comments -
-Matrix:
- 0      1      2      3      4      5
-A:   4.00  19.00   0.00   0.00   0.00   0.00
-C:  16.00   0.00  20.00   0.00   0.00   0.00
-G:   0.00   1.00   0.00  20.00   0.00  20.00
-T:   0.00   0.00   0.00   0.00  20.00   0.00
-
-
-</PRE><P>We can also fetch motifs by name. The name must be an exact match (partial matches or database wildcards are not currently supported). Note that as the name is not guaranteed to be unique, the <CODE>fetch_motifs_by_name</CODE> method actually returns a list.
-</P><PRE CLASS="verbatim">>>> motifs = jdb.fetch_motifs_by_name("Arnt")
->>> print(motifs[0])
-TF name Arnt
-Matrix ID MA0004.1
-Collection CORE
-TF class Zipper-Type
-TF family Helix-Loop-Helix
-Species 10090
-Taxonomic group vertebrates
-Accession ['P53762']
-Data type used SELEX
-Medline 7592839
-PAZAR ID TF0000003
-Comments -
-Matrix:
- 0      1      2      3      4      5
-A:   4.00  19.00   0.00   0.00   0.00   0.00
-C:  16.00   0.00  20.00   0.00   0.00   0.00
-G:   0.00   1.00   0.00  20.00   0.00  20.00
-T:   0.00   0.00   0.00   0.00  20.00   0.00
-
-
-</PRE><P>The <CODE>fetch_motifs</CODE> method allows you to fetch motifs which match a specified set of criteria. These criteria include any of the above described meta information as well as certain matrix properties such as the minimum information content (<CODE>min_ic</CODE> in the example below), the minimum length of the matrix or the minimum number of sites used to construct the matrix. Only motifs which pass ALL the specified criteria are returned. Note that selection criteria whi [...]
-</P><PRE CLASS="verbatim">>>> motifs = jdb.fetch_motifs(
-...     collection = 'CORE',
-...     tax_group = ['vertebrates', 'insects'],
-...     tf_class = 'Winged Helix-Turn-Helix',
-...     tf_family = ['Forkhead', 'Ets'],
-...     min_ic = 12
-... )
->>> for motif in motifs:
-...     pass # do something with the motif
-</PRE><!--TOC subsubsection Compatibility with Perl TFBS modules-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->Compatibility with Perl TFBS modules</H4><!--SEC END --><P>An important thing to note is that the JASPAR <CODE>Motif</CODE> class was designed to be compatible with the popular <A HREF="http://tfbs.genereg.net/">Perl TFBS modules</A>. Therefore some specifics about the choice of defaults for background and pseudocounts as well as how information content is computed and sequences searched for instances is based on this compatibility criteria. Th [...]
-<B>Choice of background:</B><BR>
-The Perl <CODE>TFBS</CODE> modules appear to allow a choice of custom background probabilities (although the documentation states that uniform background is assumed). However the default is to use a uniform background. Therefore it is recommended that you use a uniform background for computing the position-specific scoring matrix (PSSM). This is the default when using the Biopython <CODE>motifs</CODE> module.
-</LI><LI CLASS="li-itemize"><B>Choice of pseudocounts:</B><BR>
-By default, the Perl <CODE>TFBS</CODE> modules use a pseudocount equal to &#X221A;<SPAN style="text-decoration:overline"><I>N</I></SPAN> * bg[nucleotide], where <I>N</I> represents the total number of sequences used to construct the matrix. To apply this same pseudocount formula, set the motif <CODE>pseudocounts</CODE> attribute using the <CODE>jaspar.calculate\_pseudcounts()</CODE> function:
-<PRE CLASS="verbatim">>>> motif.pseudocounts = motifs.jaspar.calculate_pseudocounts(motif)
-</PRE>Note that it is possible for the counts matrix to have an unequal number of sequences making up the columns. The pseudocount computation uses the average number of sequences making up the matrix. However, when <CODE>normalize</CODE> is called on the counts matrix, each count value in a column is divided by the total number of sequences making up that specific column, not by the average number of sequences. This differs from the Perl <CODE>TFBS</CODE> modules because the normalizati [...]
-</LI><LI CLASS="li-itemize"><B>Computation of matrix information content:</B><BR>
-The information content (IC) or specificity of a matrix is computed using the <CODE>mean</CODE> method of the <CODE>PositionSpecificScoringMatrix</CODE> class. However of note, in the Perl <CODE>TFBS</CODE> modules the default behaviour is to compute the IC without first applying pseudocounts, even though by default the PSSMs are computed using pseudocounts as described above.
-</LI><LI CLASS="li-itemize"><B>Searching for instances:</B><BR>
-Searching for instances with the Perl <CODE>TFBS</CODE> motifs was usually performed using a relative score threshold, i.e. a score in the range 0 to 1. In order to compute the absolute PSSM score corresponding to a relative score one can use the equation:
-<PRE CLASS="verbatim">>>> abs_score =  (pssm.max - pssm.min) * rel_score + pssm.min
-</PRE>To convert the absolute score of an instance back to a relative score, one can use the equation:
-<PRE CLASS="verbatim">>>> rel_score = (abs_score - pssm.min) / (pssm.max - pssm.min)
-</PRE>For example, using the Arnt motif before, let&#X2019;s search a sequence with a relative score threshold of 0.8.
-<PRE CLASS="verbatim">>>> test_seq=Seq("TAAGCGTGCACGCGCAACACGTGCATTA", unambiguous_dna)
->>> arnt.pseudocounts = motifs.jaspar.calculate_pseudocounts(arnt)
->>> pssm = arnt.pssm
->>> max_score = pssm.max
->>> min_score = pssm.min
->>> abs_score_threshold = (max_score - min_score) * 0.8 + min_score
->>> for position, score in pssm.search(test_seq,
-                                       threshold=abs_score_threshold):
-...     rel_score = (score - min_score) / (max_score - min_score)
-...     print("Position %d: score = %5.3f, rel. score = %5.3f" % (
-            position, score, rel_score))
-...
-Position 2: score = 5.362, rel. score = 0.801
-Position 8: score = 6.112, rel. score = 0.831
-Position -20: score = 7.103, rel. score = 0.870
-Position 17: score = 10.351, rel. score = 1.000
-Position -11: score = 10.351, rel. score = 1.000
-</PRE></LI></UL><!--TOC subsection MEME-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc173">13.2.2</A>&#XA0;&#XA0;MEME</H3><!--SEC END --><P>MEME [<A HREF="#bailey1994">12</A>] is a tool for discovering motifs in a group of related
-DNA or protein sequences. It takes as input a group of DNA or protein sequences
-and outputs as many motifs as requested. Therefore, in contrast to JASPAR
-files, MEME output files typically contain multiple motifs. This is an example.</P><P>At the top of an output file generated by MEME shows some background information
-about the MEME and the version of MEME used:
-</P><PRE CLASS="verbatim">********************************************************************************
-MEME - Motif discovery tool
-********************************************************************************
-MEME version 3.0 (Release date: 2004/08/18 09:07:01)
-...
-</PRE><P>Further down, the input set of training sequences is recapitulated:
-</P><PRE CLASS="verbatim">********************************************************************************
-TRAINING SET
-********************************************************************************
-DATAFILE= INO_up800.s
-ALPHABET= ACGT
-Sequence name            Weight Length  Sequence name            Weight Length
--------------            ------ ------  -------------            ------ ------
-CHO1                     1.0000    800  CHO2                     1.0000    800
-FAS1                     1.0000    800  FAS2                     1.0000    800
-ACC1                     1.0000    800  INO1                     1.0000    800
-OPI3                     1.0000    800
-********************************************************************************
-</PRE><P>and the exact command line that was used:
-</P><PRE CLASS="verbatim">********************************************************************************
-COMMAND LINE SUMMARY
-********************************************************************************
-This information can also be useful in the event you wish to report a
-problem with the MEME software.
-
-command: meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s
-...
-</PRE><P>Next is detailed information on each motif that was found:
-</P><PRE CLASS="verbatim">********************************************************************************
-MOTIF  1        width =   12   sites =   7   llr = 95   E-value = 2.0e-001
-********************************************************************************
---------------------------------------------------------------------------------
-        Motif 1 Description
---------------------------------------------------------------------------------
-Simplified        A  :::9:a::::3:
-pos.-specific     C  ::a:9:11691a
-probability       G  ::::1::94:4:
-matrix            T  aa:1::9::11:
-</PRE><P>To parse this file (stored as <CODE>meme.dna.oops.txt</CODE>), use
-</P><PRE CLASS="verbatim">>>> handle = open("meme.dna.oops.txt")
->>> record = motifs.parse(handle, "meme")
->>> handle.close()
-</PRE><P>The <CODE>motifs.parse</CODE> command reads the complete file directly, so you can
-close the file after calling <CODE>motifs.parse</CODE>.
-The header information is stored in attributes:
-</P><PRE CLASS="verbatim">>>> record.version
-'3.0'
->>> record.datafile
-'INO_up800.s'
->>> record.command
-'meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s'
->>> record.alphabet
-IUPACUnambiguousDNA()
->>> record.sequences
-['CHO1', 'CHO2', 'FAS1', 'FAS2', 'ACC1', 'INO1', 'OPI3']
-</PRE><P>The record is an object of the <CODE>Bio.motifs.meme.Record</CODE> class.
-The class inherits from list, and you can think of <CODE>record</CODE> as a list of Motif objects:
-</P><PRE CLASS="verbatim">>>> len(record)
-2
->>> motif = record[0]
->>> print(motif.consensus)
-TTCACATGCCGC
->>> print(motif.degenerate_consensus)
-TTCACATGSCNC
-</PRE><P>In addition to these generic motif attributes, each motif also stores its
-specific information as calculated by MEME. For example,
-</P><PRE CLASS="verbatim">>>> motif.num_occurrences
-7
->>> motif.length
-12
->>> evalue = motif.evalue
->>> print("%3.1g" % evalue)
-0.2
->>> motif.name
-'Motif 1'
-</PRE><P>In addition to using an index into the record, as we did above,
-you can also find it by its name:
-</P><PRE CLASS="verbatim">>>> motif = record['Motif 1']
-</PRE><P>Each motif has an attribute <CODE>.instances</CODE> with the sequence instances
-in which the motif was found, providing some information on each instance:
-</P><PRE CLASS="verbatim">>>> len(motif.instances)
-7
->>> motif.instances[0]
-Instance('TTCACATGCCGC', IUPACUnambiguousDNA())
->>> motif.instances[0].motif_name
-'Motif 1'
->>> motif.instances[0].sequence_name
-'INO1'
->>> motif.instances[0].start
-620
->>> motif.instances[0].strand
-'-'
->>> motif.instances[0].length
-12
->>> pvalue = motif.instances[0].pvalue
->>> print("%5.3g" % pvalue)
-1.85e-08
-</PRE><!--TOC subsubsection MAST-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR -->MAST</H4><!--SEC END --><!--TOC subsection TRANSFAC-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc174">13.2.3</A>&#XA0;&#XA0;TRANSFAC</H3><!--SEC END --><P>TRANSFAC is a manually curated database of transcription factors, together
-with their genomic binding sites and DNA binding profiles [<A HREF="#matys2003">27</A>].
-While the file format used in the TRANSFAC database is nowadays also used
-by others, we will refer to it as the TRANSFAC file format.</P><P>A minimal file in the TRANSFAC format looks as follows:
-</P><PRE CLASS="verbatim">ID  motif1
-P0      A      C      G      T
-01      1      2      2      0      S
-02      2      1      2      0      R
-03      3      0      1      1      A
-04      0      5      0      0      C
-05      5      0      0      0      A
-06      0      0      4      1      G
-07      0      1      4      0      G
-08      0      0      0      5      T
-09      0      0      5      0      G
-10      0      1      2      2      K
-11      0      2      0      3      Y
-12      1      0      3      1      G
-//
-</PRE><P>This file shows the frequency matrix of motif <CODE>motif1</CODE> of 12 nucleotides.
-In general, one file in the TRANSFAC format can contain multiple motifs. For
-example, this is the contents of the example TRANSFAC file <CODE>transfac.dat</CODE>:
-</P><PRE CLASS="verbatim">VV  EXAMPLE January 15, 2013
-XX
-//
-ID  motif1
-P0      A      C      G      T
-01      1      2      2      0      S
-02      2      1      2      0      R
-03      3      0      1      1      A
-...
-11      0      2      0      3      Y
-12      1      0      3      1      G
-//
-ID  motif2
-P0      A      C      G      T
-01      2      1      2      0      R
-02      1      2      2      0      S
-...
-09      0      0      0      5      T
-10      0      2      0      3      Y
-//
-</PRE><P>To parse a TRANSFAC file, use
-</P><PRE CLASS="verbatim">>>> handle = open("transfac.dat")
->>> record = motifs.parse(handle, "TRANSFAC")
->>> handle.close()
-</PRE><P>The overall version number, if available, is stored as <CODE>record.version</CODE>:
-</P><PRE CLASS="verbatim">>>> record.version
-'EXAMPLE January 15, 2013'
-</PRE><P>Each motif in <CODE>record</CODE> is in instance of the <CODE>Bio.motifs.transfac.Motif</CODE>
-class, which inherits both from the <CODE>Bio.motifs.Motif</CODE> class and
-from a Python dictionary. The dictionary uses the two-letter keys to
-store any additional information about the motif:
-</P><PRE CLASS="verbatim">>>> motif = record[0]
->>> motif.degenerate_consensus # Using the Bio.motifs.Motif method
-Seq('SRACAGGTGKYG', IUPACAmbiguousDNA())
->>> motif['ID'] # Using motif as a dictionary
-'motif1'
-</PRE><P>TRANSFAC files are typically much more elaborate than this example, containing
-lots of additional information about the motif. Table <A HREF="#table:transfaccodes">13.2.3</A>
-lists the two-letter field codes that are commonly found in TRANSFAC files:
-</P><BLOCKQUOTE CLASS="table"><DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV>
-<A NAME="table:transfaccodes"></A>
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 13.1: Fields commonly found in TRANSFAC files</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=left NOWRAP><CODE>AC</CODE></TD><TD ALIGN=left NOWRAP>Accession number</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>AS</CODE></TD><TD ALIGN=left NOWRAP>Accession numbers, secondary</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>BA</CODE></TD><TD ALIGN=left NOWRAP>Statistical basis</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>BF</CODE></TD><TD ALIGN=left NOWRAP>Binding factors</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>BS</CODE></TD><TD ALIGN=left NOWRAP>Factor binding sites underlying the matrix</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>CC</CODE></TD><TD ALIGN=left NOWRAP>Comments</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>CO</CODE></TD><TD ALIGN=left NOWRAP>Copyright notice</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>DE</CODE></TD><TD ALIGN=left NOWRAP>Short factor description</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>DR</CODE></TD><TD ALIGN=left NOWRAP>External databases</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>DT</CODE></TD><TD ALIGN=left NOWRAP>Date created/updated</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>HC</CODE></TD><TD ALIGN=left NOWRAP>Subfamilies</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>HP</CODE></TD><TD ALIGN=left NOWRAP>Superfamilies</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>ID</CODE></TD><TD ALIGN=left NOWRAP>Identifier</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>NA</CODE></TD><TD ALIGN=left NOWRAP>Name of the binding factor</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>OC</CODE></TD><TD ALIGN=left NOWRAP>Taxonomic classification</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>OS</CODE></TD><TD ALIGN=left NOWRAP>Species/Taxon</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>OV</CODE></TD><TD ALIGN=left NOWRAP>Older version</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>PV</CODE></TD><TD ALIGN=left NOWRAP>Preferred version</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>TY</CODE></TD><TD ALIGN=left NOWRAP>Type</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>XX</CODE></TD><TD ALIGN=left NOWRAP>Empty line; these are not stored in the Record.</TD></TR>
-</TABLE>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>Each motif also has an attribute <CODE>.references</CODE> containing the
-references associated with the motif, using these two-letter keys:</P><BLOCKQUOTE CLASS="table"><DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV>
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 13.2: Fields used to store references in TRANSFAC files</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=left NOWRAP><CODE>RN</CODE></TD><TD ALIGN=left NOWRAP>Reference number</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RA</CODE></TD><TD ALIGN=left NOWRAP>Reference authors</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RL</CODE></TD><TD ALIGN=left NOWRAP>Reference data</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RT</CODE></TD><TD ALIGN=left NOWRAP>Reference title</TD></TR>
-<TR><TD ALIGN=left NOWRAP><CODE>RX</CODE></TD><TD ALIGN=left NOWRAP>PubMed ID</TD></TR>
-</TABLE>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>Printing the motifs writes them out in their native TRANSFAC format:
-</P><PRE CLASS="verbatim">>>> print(record)
-VV  EXAMPLE January 15, 2013
-XX
-//
-ID  motif1
-XX
-P0      A      C      G      T
-01      1      2      2      0      S
-02      2      1      2      0      R
-03      3      0      1      1      A
-04      0      5      0      0      C
-05      5      0      0      0      A
-06      0      0      4      1      G
-07      0      1      4      0      G
-08      0      0      0      5      T
-09      0      0      5      0      G
-10      0      1      2      2      K
-11      0      2      0      3      Y
-12      1      0      3      1      G
-XX
-//
-ID  motif2
-XX
-P0      A      C      G      T
-01      2      1      2      0      R
-02      1      2      2      0      S
-03      0      5      0      0      C
-04      3      0      1      1      A
-05      0      0      4      1      G
-06      5      0      0      0      A
-07      0      1      4      0      G
-08      0      0      5      0      G
-09      0      0      0      5      T
-10      0      2      0      3      Y
-XX
-//
-<BLANKLINE>
-</PRE><P>You can export the motifs in the TRANSFAC format by capturing this output
-in a string and saving it in a file:
-</P><PRE CLASS="verbatim">>>> text = str(record)
->>> handle = open("mytransfacfile.dat", 'w')
->>> handle.write(text)
->>> handle.close()
-</PRE><!--TOC section Writing motifs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc175">13.3</A>&#XA0;&#XA0;Writing motifs</H2><!--SEC END --><P>Speaking of exporting, let&#X2019;s look at export functions in general.
-We can use the <CODE>format</CODE> method to write the motif in the simple JASPAR <CODE>pfm</CODE> format:
-</P><PRE CLASS="verbatim">>>> print(arnt.format("pfm"))
-  4.00  19.00   0.00   0.00   0.00   0.00
- 16.00   0.00  20.00   0.00   0.00   0.00
-  0.00   1.00   0.00  20.00   0.00  20.00
-  0.00   0.00   0.00   0.00  20.00   0.00
-</PRE><P>Similarly, we can use <CODE>format</CODE> to write the motif in the JASPAR <CODE>jaspar</CODE> format:
-</P><PRE CLASS="verbatim">>>> print(arnt.format("jaspar"))
->MA0004.1  Arnt
-A [  4.00  19.00   0.00   0.00   0.00   0.00]
-C [ 16.00   0.00  20.00   0.00   0.00   0.00]
-G [  0.00   1.00   0.00  20.00   0.00  20.00]
-T [  0.00   0.00   0.00   0.00  20.00   0.00]
-</PRE><P>To write the motif in a TRANSFAC-like matrix format, use
-</P><PRE CLASS="verbatim">>>> print(m.format("transfac"))
-P0      A      C      G      T
-01      3      0      0      4      W
-02      7      0      0      0      A
-03      0      5      0      2      C
-04      2      2      3      0      V
-05      1      6      0      0      C
-XX
-//
-<BLANKLINE>
-</PRE><P>To write out multiple motifs, you can use <CODE>motifs.write</CODE>.
-This function can be used regardless of whether the motifs originated from a TRANSFAC file. For example,
-</P><PRE CLASS="verbatim">>>> two_motifs = [arnt, srf]
->>> print(motifs.write(two_motifs, 'transfac'))
-P0      A      C      G      T
-01      4     16      0      0      C
-02     19      0      1      0      A
-03      0     20      0      0      C
-04      0      0     20      0      G
-05      0      0      0     20      T
-06      0      0     20      0      G
-XX
-//
-P0      A      C      G      T
-01      2      1     39      4      G
-02      9     33      2      2      C
-03      0     45      1      0      C
-04      1     45      0      0      C
-05     32      1      0     13      A
-06      3      1      0     42      T
-07     46      0      0      0      A
-08      1      0      0     45      T
-09     43      0      0      3      A
-10     15      1      0     30      T
-11      2      0     44      0      G
-12      2      1     43      0      G
-XX
-//
-<BLANKLINE>
-</PRE><P>Or, to write multiple motifs in the <CODE>jaspar</CODE> format:
-</P><PRE CLASS="verbatim">>>> two_motifs = [arnt, mef2a]
->>> print(motifs.write(two_motifs, "jaspar"))
->MA0004.1  Arnt
-A [  4.00  19.00   0.00   0.00   0.00   0.00]
-C [ 16.00   0.00  20.00   0.00   0.00   0.00]
-G [  0.00   1.00   0.00  20.00   0.00  20.00]
-T [  0.00   0.00   0.00   0.00  20.00   0.00]
->MA0052.1  MEF2A
-A [  1.00   0.00  57.00   2.00   9.00   6.00  37.00   2.00  56.00   6.00]
-C [ 50.00   0.00   1.00   1.00   0.00   0.00   0.00   0.00   0.00   0.00]
-G [  0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   2.00  50.00]
-T [  7.00  58.00   0.00  55.00  49.00  52.00  21.00  56.00   0.00   2.00]
-</PRE><!--TOC section Position-Weight Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc176">13.4</A>&#XA0;&#XA0;Position-Weight Matrices</H2><!--SEC END --><P>The <CODE>.counts</CODE> attribute of a Motif object shows how often each
-nucleotide appeared at each position along the alignment. We can
-normalize this matrix by dividing by the number of instances in the
-alignment, resulting in the probability of each nucleotide at each
-position along the alignment. We refer to these probabilities as
-the position-weight matrix. However, beware that in the literature
-this term may also be used to refer to the position-specific scoring
-matrix, which we discuss below.</P><P>Usually, pseudocounts are added to each position before normalizing.
-This avoids overfitting of the position-weight matrix to the limited
-number of motif instances in the alignment, and can also prevent
-probabilities from becoming zero. To add a fixed pseudocount to all
-nucleotides at all positions, specify a number for the
-<CODE>pseudocounts</CODE> argument:
-</P><PRE CLASS="verbatim">>>> pwm = m.counts.normalize(pseudocounts=0.5)
->>> print(pwm)
-        0      1      2      3      4
-A:   0.39   0.83   0.06   0.28   0.17
-C:   0.06   0.06   0.61   0.28   0.72
-G:   0.06   0.06   0.06   0.39   0.06
-T:   0.50   0.06   0.28   0.06   0.06
-<BLANKLINE>
-</PRE><P>Alternatively, <CODE>pseudocounts</CODE> can be a dictionary specifying the
-pseudocounts for each nucleotide. For example, as the GC content of
-the human genome is about 40%, you may want to choose the
-pseudocounts accordingly:
-</P><PRE CLASS="verbatim">>>> pwm = m.counts.normalize(pseudocounts={'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6})
->>> print(pwm)
-        0      1      2      3      4
-A:   0.40   0.84   0.07   0.29   0.18
-C:   0.04   0.04   0.60   0.27   0.71
-G:   0.04   0.04   0.04   0.38   0.04
-T:   0.51   0.07   0.29   0.07   0.07
-<BLANKLINE>
-</PRE><P>The position-weight matrix has its own methods to calculate the
-consensus, anticonsensus, and degenerate consensus sequences:
-</P><PRE CLASS="verbatim">>>> pwm.consensus
-Seq('TACGC', IUPACUnambiguousDNA())
->>> pwm.anticonsensus
-Seq('GGGTG', IUPACUnambiguousDNA())
->>> pwm.degenerate_consensus
-Seq('WACNC', IUPACAmbiguousDNA())
-</PRE><P>Note that due to the pseudocounts, the degenerate consensus sequence
-calculated from the position-weight matrix is slightly different
-from the degenerate consensus sequence calculated from the instances
-in the motif:
-</P><PRE CLASS="verbatim">>>> m.degenerate_consensus
-Seq('WACVC', IUPACAmbiguousDNA())
-</PRE><P>The reverse complement of the position-weight matrix can be calculated directly from the <CODE>pwm</CODE>:
-</P><PRE CLASS="verbatim">>>> rpwm = pwm.reverse_complement()
->>> print(rpwm)
-        0      1      2      3      4
-A:   0.07   0.07   0.29   0.07   0.51
-C:   0.04   0.38   0.04   0.04   0.04
-G:   0.71   0.27   0.60   0.04   0.04
-T:   0.18   0.29   0.07   0.84   0.40
-<BLANKLINE>
-</PRE><!--TOC section Position-Specific Scoring Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc177">13.5</A>&#XA0;&#XA0;Position-Specific Scoring Matrices</H2><!--SEC END --><P>Using the background distribution and PWM with pseudo-counts added,
-it&#X2019;s easy to compute the log-odds ratios, telling us what are the log
-odds of a particular symbol to be coming from a motif against the
-background. We can use the <CODE>.log_odds()</CODE> method on the position-weight
-matrix:
-</P><PRE CLASS="verbatim">>>> pssm = pwm.log_odds()
->>> print(pssm)
-        0      1      2      3      4
-A:   0.68   1.76  -1.91   0.21  -0.49
-C:  -2.49  -2.49   1.26   0.09   1.51
-G:  -2.49  -2.49  -2.49   0.60  -2.49
-T:   1.03  -1.91   0.21  -1.91  -1.91
-<BLANKLINE>
-</PRE><P>Here we can see positive values for symbols more frequent in the motif
-than in the background and negative for symbols more frequent in the
-background. 0.0 means that it&#X2019;s equally likely to see a symbol in the
-background and in the motif.</P><P>This assumes that A, C, G, and T are equally likely in the background. To
-calculate the position-specific scoring matrix against a background with
-unequal probabilities for A, C, G, T, use the <CODE>background</CODE> argument.
-For example, against a background with a 40% GC content, use
-</P><PRE CLASS="verbatim">>>> background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3}
->>> pssm = pwm.log_odds(background)
->>> print(pssm)
-        0      1      2      3      4
-A:   0.42   1.49  -2.17  -0.05  -0.75
-C:  -2.17  -2.17   1.58   0.42   1.83
-G:  -2.17  -2.17  -2.17   0.92  -2.17
-T:   0.77  -2.17  -0.05  -2.17  -2.17
-<BLANKLINE>
-</PRE><P>The maximum and minimum score obtainable from the PSSM are stored in the
-<CODE>.max</CODE> and <CODE>.min</CODE> properties:
-</P><PRE CLASS="verbatim">>>> print("%4.2f" % pssm.max)
-6.59
->>> print("%4.2f" % pssm.min)
--10.85
-</PRE><P>The mean and standard deviation of the PSSM scores with respect to a specific
-background are calculated by the <CODE>.mean</CODE> and <CODE>.std</CODE> methods.
-</P><PRE CLASS="verbatim">>>> mean = pssm.mean(background)
->>> std = pssm.std(background)
->>> print("mean = %0.2f, standard deviation = %0.2f" % (mean, std))
-mean = 3.21, standard deviation = 2.59
-</PRE><P>A uniform background is used if <CODE>background</CODE> is not specified.
-The mean is particularly important, as its value is equal to the
-Kullback-Leibler divergence or relative entropy, and is a measure for the
-information content of the motif compared to the background. As in Biopython
-the base-2 logarithm is used in the calculation of the log-odds scores, the
-information content has units of bits.</P><P>The <CODE>.reverse_complement</CODE>, <CODE>.consensus</CODE>, <CODE>.anticonsensus</CODE>, and
-<CODE>.degenerate_consensus</CODE> methods can be applied directly to PSSM objects.</P><!--TOC section Searching for instances-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc178">13.6</A>&#XA0;&#XA0;Searching for instances</H2><!--SEC END --><P>
-<A NAME="sec:search"></A></P><P>The most frequent use for a motif is to find its instances in some
-sequence. For the sake of this section, we will use an artificial sequence like this:</P><PRE CLASS="verbatim">>>> test_seq=Seq("TACACTGCATTACAACCCAAGCATTA", m.alphabet)
->>> len(test_seq)
-26
-</PRE><!--TOC subsection Searching for exact matches-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc179">13.6.1</A>&#XA0;&#XA0;Searching for exact matches</H3><!--SEC END --><P>The simplest way to find instances, is to look for exact matches of
-the true instances of the motif:
-</P><PRE CLASS="verbatim">>>> for pos, seq in m.instances.search(test_seq):
-...     print("%i %s" % (pos, seq))
-...
-0 TACAC
-10 TACAA
-13 AACCC
-</PRE><P>We can do the same with the reverse complement (to find instances on the complementary strand):
-</P><PRE CLASS="verbatim">>>> for pos, seq in r.instances.search(test_seq):
-...     print("%i %s" % (pos, seq))
-...
-6 GCATT
-20 GCATT
-</PRE><!--TOC subsection Searching for matches using the PSSM score-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc180">13.6.2</A>&#XA0;&#XA0;Searching for matches using the PSSM score</H3><!--SEC END --><P>It&#X2019;s just as easy to look for positions, giving rise to high log-odds scores against our motif:
-</P><PRE CLASS="verbatim">>>> for position, score in pssm.search(test_seq, threshold=3.0):
-...     print("Position %d: score = %5.3f" % (position, score))
-...
-Position 0: score = 5.622
-Position -20: score = 4.601
-Position 10: score = 3.037
-Position 13: score = 5.738
-Position -6: score = 4.601
-</PRE><P>The negative positions refer to instances of the motif found on the
-reverse strand of the test sequence, and follow the Python convention
-on negative indices. Therefore, the instance of the motif at <CODE>pos</CODE>
-is located at <CODE>test_seq[pos:pos+len(m)]</CODE> both for positive and for
-negative values of <CODE>pos</CODE>.</P><P>You may notice the threshold parameter, here set arbitrarily to
-3.0. This is in <I>log</I><SUB>2</SUB>, so we are now looking only for words, which
-are eight times more likely to occur under the motif model than in the
-background. The default threshold is 0.0, which selects everything
-that looks more like the motif than the background.</P><P>You can also calculate the scores at all positions along the sequence:
-</P><PRE CLASS="verbatim">>>> pssm.calculate(test_seq)
-array([  5.62230396,  -5.6796999 ,  -3.43177247,   0.93827754,
-        -6.84962511,  -2.04066086, -10.84962463,  -3.65614533,
-        -0.03370807,  -3.91102552,   3.03734159,  -2.14918518,
-        -0.6016975 ,   5.7381525 ,  -0.50977498,  -3.56422281,
-        -8.73414803,  -0.09919716,  -0.6016975 ,  -2.39429784,
-       -10.84962463,  -3.65614533], dtype=float32)
-</PRE><P>In general, this is the fastest way to calculate PSSM scores.
-The scores returned by <CODE>pssm.calculate</CODE> are for the forward strand
-only. To obtain the scores on the reverse strand, you can take the reverse
-complement of the PSSM:
-</P><PRE CLASS="verbatim">>>> rpssm = pssm.reverse_complement()
->>> rpssm.calculate(test_seq)
-array([ -9.43458748,  -3.06172252,  -7.18665981,  -7.76216221,
-        -2.04066086,  -4.26466274,   4.60124254,  -4.2480607 ,
-        -8.73414803,  -2.26503372,  -6.49598789,  -5.64668512,
-        -8.73414803, -10.84962463,  -4.82356262,  -4.82356262,
-        -5.64668512,  -8.73414803,  -4.15613794,  -5.6796999 ,
-         4.60124254,  -4.2480607 ], dtype=float32)
-</PRE><!--TOC subsection Selecting a score threshold-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc181">13.6.3</A>&#XA0;&#XA0;Selecting a score threshold</H3><!--SEC END --><P>If you want to use a less arbitrary way of selecting thresholds, you
-can explore the distribution of PSSM scores. Since the space for a score
-distribution grows exponentially with motif length, we are using an
-approximation with a given precision to keep computation cost manageable:
-</P><PRE CLASS="verbatim">>>> distribution = pssm.distribution(background=background, precision=10**4)
-</PRE><P>The <CODE>distribution</CODE> object can be used to determine a number of different thresholds.
-We can specify the requested false-positive rate (probability of &#X201C;finding&#X201D; a motif instance in background generated sequence):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_fpr(0.01)
->>> print("%5.3f" % threshold)
-4.009
-</PRE><P>or the false-negative rate (probability of &#X201C;not finding&#X201D; an instance generated from the motif):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_fnr(0.1)
->>> print("%5.3f" % threshold)
--0.510
-</PRE><P>or a threshold (approximately) satisfying some relation between the false-positive rate and the false-negative rate (fnr/fpr&#X2243; <I>t</I>):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_balanced(1000)
->>> print("%5.3f" % threshold)
-6.241
-</PRE><P>or a threshold satisfying (roughly) the equality between the
-false-positive rate and the &#X2212;<I>log</I> of the information content (as used
-in patser software by Hertz and Stormo):
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_patser()
->>> print("%5.3f" % threshold)
-0.346
-</PRE><P>For example, in case of our motif, you can get the threshold giving
-you exactly the same results (for this sequence) as searching for
-instances with balanced threshold with rate of 1000.
-</P><PRE CLASS="verbatim">>>> threshold = distribution.threshold_fpr(0.01)
->>> print("%5.3f" % threshold)
-4.009
->>> for position, score in pssm.search(test_seq, threshold=threshold):
-...     print("Position %d: score = %5.3f" % (position, score))
-...
-Position 0: score = 5.622
-Position -20: score = 4.601
-Position 13: score = 5.738
-Position -6: score = 4.601
-</PRE><!--TOC section Each motif object has an associated Position-Specific Scoring Matrix-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc182">13.7</A>&#XA0;&#XA0;Each motif object has an associated Position-Specific Scoring Matrix</H2><!--SEC END --><P>To facilitate searching for potential TFBSs using PSSMs, both the position-weight matrix and the position-specific scoring matrix are associated with each motif. Using the Arnt motif as an example:
-</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("Arnt.sites") as handle:
-...     motif = motifs.read(handle, 'sites')
-...
->>> print(motif.counts)
-        0      1      2      3      4      5
-A:   4.00  19.00   0.00   0.00   0.00   0.00
-C:  16.00   0.00  20.00   0.00   0.00   0.00
-G:   0.00   1.00   0.00  20.00   0.00  20.00
-T:   0.00   0.00   0.00   0.00  20.00   0.00
-<BLANKLINE>
->>> print(motif.pwm)
-        0      1      2      3      4      5
-A:   0.20   0.95   0.00   0.00   0.00   0.00
-C:   0.80   0.00   1.00   0.00   0.00   0.00
-G:   0.00   0.05   0.00   1.00   0.00   1.00
-T:   0.00   0.00   0.00   0.00   1.00   0.00
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(motif.pssm)
-        0      1      2      3      4      5
-A:  -0.32   1.93   -inf   -inf   -inf   -inf
-C:   1.68   -inf   2.00   -inf   -inf   -inf
-G:   -inf  -2.32   -inf   2.00   -inf   2.00
-T:   -inf   -inf   -inf   -inf   2.00   -inf
-<BLANKLINE>
-</PRE><P>The negative infinities appear here because the corresponding entry in the frequency matrix is 0, and we are using zero pseudocounts by default:
-</P><PRE CLASS="verbatim">>>> for letter in "ACGT":
-...     print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
-...
-A: 0.00
-C: 0.00
-G: 0.00
-T: 0.00
-</PRE><P>If you change the <CODE>.pseudocounts</CODE> attribute, the position-frequency matrix and the position-specific scoring matrix are recalculated automatically:
-</P><PRE CLASS="verbatim">>>> motif.pseudocounts = 3.0
->>> for letter in "ACGT":
-...     print("%s: %4.2f" % (letter, motif.pseudocounts[letter]))
-...
-A: 3.00
-C: 3.00
-G: 3.00
-T: 3.00
-</PRE><PRE CLASS="verbatim">>>> print(motif.pwm)
-        0      1      2      3      4      5
-A:   0.22   0.69   0.09   0.09   0.09   0.09
-C:   0.59   0.09   0.72   0.09   0.09   0.09
-G:   0.09   0.12   0.09   0.72   0.09   0.72
-T:   0.09   0.09   0.09   0.09   0.72   0.09
-<BLANKLINE>
-</PRE><PRE CLASS="verbatim">>>> print(motif.pssm)
-        0      1      2      3      4      5
-A:  -0.19   1.46  -1.42  -1.42  -1.42  -1.42
-C:   1.25  -1.42   1.52  -1.42  -1.42  -1.42
-G:  -1.42  -1.00  -1.42   1.52  -1.42   1.52
-T:  -1.42  -1.42  -1.42  -1.42   1.52  -1.42
-<BLANKLINE>
-</PRE><P>You can also set the <CODE>.pseudocounts</CODE> to a dictionary over the four nucleotides if you want to use different pseudocounts for them. Setting <CODE>motif.pseudocounts</CODE> to <CODE>None</CODE> resets it to its default value of zero.</P><P>The position-specific scoring matrix depends on the background distribution, which is uniform by default:
-</P><PRE CLASS="verbatim">>>> for letter in "ACGT":
-...     print("%s: %4.2f" % (letter, motif.background[letter]))
-...
-A: 0.25
-C: 0.25
-G: 0.25
-T: 0.25
-</PRE><P>Again, if you modify the background distribution, the position-specific scoring matrix is recalculated:
-</P><PRE CLASS="verbatim">>>> motif.background = {'A': 0.2, 'C': 0.3, 'G': 0.3, 'T': 0.2}
->>> print(motif.pssm)
-        0      1      2      3      4      5
-A:   0.13   1.78  -1.09  -1.09  -1.09  -1.09
-C:   0.98  -1.68   1.26  -1.68  -1.68  -1.68
-G:  -1.68  -1.26  -1.68   1.26  -1.68   1.26
-T:  -1.09  -1.09  -1.09  -1.09   1.85  -1.09
-<BLANKLINE>
-</PRE><P>Setting <CODE>motif.background</CODE> to <CODE>None</CODE> resets it to a uniform distribution:
-</P><PRE CLASS="verbatim">>>> motif.background = None
->>> for letter in "ACGT":
-...     print("%s: %4.2f" % (letter, motif.background[letter]))
-...
-A: 0.25
-C: 0.25
-G: 0.25
-T: 0.25
-</PRE><P>If you set <CODE>motif.background</CODE> equal to a single value, it will be interpreted as the GC content:
-</P><PRE CLASS="verbatim">>>> motif.background = 0.8
->>> for letter in "ACGT":
-...     print("%s: %4.2f" % (letter, motif.background[letter]))
-...
-A: 0.10
-C: 0.40
-G: 0.40
-T: 0.10
-</PRE><P>Note that you can now calculate the mean of the PSSM scores over the background against which it was computed:
-</P><PRE CLASS="verbatim">>>> print("%f" % motif.pssm.mean(motif.background))
-4.703928
-</PRE><P>as well as its standard deviation:
-</P><PRE CLASS="verbatim">>>> print("%f" % motif.pssm.std(motif.background))
-3.290900
-</PRE><P>and its distribution:
-</P><PRE CLASS="verbatim">>>> distribution = motif.pssm.distribution(background=motif.background)
->>> threshold = distribution.threshold_fpr(0.01)
->>> print("%f" % threshold)
-3.854375
-</PRE><P>Note that the position-weight matrix and the position-specific scoring matrix are recalculated each time you call <CODE>motif.pwm</CODE> or <CODE>motif.pssm</CODE>, respectively. If speed is an issue and you want to use the PWM or PSSM repeatedly, you can save them as a variable, as in
-</P><PRE CLASS="verbatim">>>> pssm = motif.pssm
-</PRE><!--TOC section Comparing motifs-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc183">13.8</A>&#XA0;&#XA0;Comparing motifs</H2><!--SEC END --><P>
-<A NAME="sec:comp"></A>
-Once we have more than one motif, we might want to compare them.</P><P>Before we start comparing motifs, I should point out that motif
-boundaries are usually quite arbitrary. This means we often need
-to compare motifs of different lengths, so comparison needs to involve
-some kind of alignment. This means we have to take into account two things:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-alignment of motifs
-</LI><LI CLASS="li-itemize">some function to compare aligned motifs
-</LI></UL><P>
-To align the motifs, we use ungapped alignment of PSSMs and substitute zeros
-for any missing columns at the beginning and end of the matrices. This means
-that effectively we are using the background distribution for columns missing
-from the PSSM.
-The distance function then returns the minimal distance between motifs, as
-well as the corresponding offset in their alignment.</P><P>To give an example, let us first load another motif,
-which is similar to our test motif <CODE>m</CODE>:
-</P><PRE CLASS="verbatim">>>> with open("REB1.pfm") as handle:
-...    m_reb1 = motifs.read(handle, "pfm")
-...
->>> m_reb1.consensus
-Seq('GTTACCCGG', IUPACUnambiguousDNA())
->>> print(m_reb1.counts)
-        0      1      2      3      4      5      6      7      8
-A:  30.00   0.00   0.00 100.00   0.00   0.00   0.00   0.00  15.00
-C:  10.00   0.00   0.00   0.00 100.00 100.00 100.00   0.00  15.00
-G:  50.00   0.00   0.00   0.00   0.00   0.00   0.00  60.00  55.00
-T:  10.00 100.00 100.00   0.00   0.00   0.00   0.00  40.00  15.00
-<BLANKLINE>
-</PRE><P>To make the motifs comparable, we choose the same values for the pseudocounts and the background distribution as our motif <CODE>m</CODE>:
-</P><PRE CLASS="verbatim">>>> m_reb1.pseudocounts = {'A':0.6, 'C': 0.4, 'G': 0.4, 'T': 0.6}
->>> m_reb1.background = {'A':0.3,'C':0.2,'G':0.2,'T':0.3}
->>> pssm_reb1 = m_reb1.pssm
->>> print(pssm_reb1)
-        0      1      2      3      4      5      6      7      8
-A:   0.00  -5.67  -5.67   1.72  -5.67  -5.67  -5.67  -5.67  -0.97
-C:  -0.97  -5.67  -5.67  -5.67   2.30   2.30   2.30  -5.67  -0.41
-G:   1.30  -5.67  -5.67  -5.67  -5.67  -5.67  -5.67   1.57   1.44
-T:  -1.53   1.72   1.72  -5.67  -5.67  -5.67  -5.67   0.41  -0.97
-<BLANKLINE>
-</PRE><P>We&#X2019;ll compare these motifs using the Pearson correlation.
-Since we want it to resemble a distance measure, we actually take
-1&#X2212;<I>r</I>, where <I>r</I> is the Pearson correlation coefficient (PCC):
-</P><PRE CLASS="verbatim">>>> distance, offset = pssm.dist_pearson(pssm_reb1)
->>> print("distance = %5.3g" % distance)
-distance = 0.239
->>> print(offset)
--2
-</PRE><P>This means that the best PCC between motif <CODE>m</CODE> and <CODE>m_reb1</CODE> is obtained with the following alignment:
-</P><PRE CLASS="verbatim">m:      bbTACGCbb
-m_reb1: GTTACCCGG
-</PRE><P>where <CODE>b</CODE> stands for background distribution. The PCC itself is
-roughly 1&#X2212;0.239=0.761.</P><!--TOC section <EM>De novo</EM> motif finding-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc184">13.9</A>&#XA0;&#XA0;<EM>De novo</EM> motif finding</H2><!--SEC END --><P>
-<A NAME="sec:find"></A></P><P>Currently, Biopython has only limited support for <EM>de novo</EM> motif
-finding. Namely, we support running and parsing of AlignAce and
-MEME. Since the number of motif finding tools is growing rapidly,
-contributions of new parsers are welcome.</P><!--TOC subsection MEME-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc185">13.9.1</A>&#XA0;&#XA0;MEME</H3><!--SEC END --><P>
-<A NAME="sec:meme"></A></P><P>Let&#X2019;s assume, you have run MEME on sequences of your choice with your
-favorite parameters and saved the output in the file
-<CODE>meme.out</CODE>. You can retrieve the motifs reported by MEME by
-running the following piece of code:</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("meme.out") as handle:
-...     motifsM = motifs.parse(handle, "meme")
-...
-</PRE><PRE CLASS="verbatim">>>> motifsM
-[<Bio.motifs.meme.Motif object at 0xc356b0>]
-</PRE><P>Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>.alphabet</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.datafile</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.sequence_names</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.version</CODE>
-</LI><LI CLASS="li-itemize"><CODE>.command</CODE>
-</LI></UL><P>The motifs returned by the MEME Parser can be treated exactly like regular
-Motif objects (with instances), they also provide some extra
-functionality, by adding additional information about the instances.</P><PRE CLASS="verbatim">>>> motifsM[0].consensus
-Seq('CTCAATCGTA', IUPACUnambiguousDNA())
->>> motifsM[0].instances[0].sequence_name
-'SEQ10;'
->>> motifsM[0].instances[0].start
-3
->>> motifsM[0].instances[0].strand
-'+'
-</PRE><PRE CLASS="verbatim">>>> motifsM[0].instances[0].pvalue
-8.71e-07
-</PRE><!--TOC subsection AlignAce-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc186">13.9.2</A>&#XA0;&#XA0;AlignAce</H3><!--SEC END --><P>
-<A NAME="sec:alignace"></A></P><P>We can do very similar things with the AlignACE program. Assume, you have
-your output in the file <CODE>alignace.out</CODE>. You can parse your output
-with the following code:</P><PRE CLASS="verbatim">>>> from Bio import motifs
->>> with open("alignace.out") as handle:
-...     motifsA = motifs.parse(handle, "alignace")
-...
-</PRE><P>Again, your motifs behave as they should:
-</P><PRE CLASS="verbatim">>>> motifsA[0].consensus
-Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-</PRE><P>In fact you can even see, that AlignAce found a very similar motif as
-MEME. It is just a longer version of a reverse complement of the MEME
-motif:
-</P><PRE CLASS="verbatim">>>> motifsM[0].reverse_complement().consensus
-Seq('TACGATTGAG', IUPACUnambiguousDNA())
-</PRE><P>If you have AlignAce installed on the same machine, you can also run
-it directly from Biopython. A short example of how this can be done is
-shown below (other parameters can be specified as keyword parameters):</P><PRE CLASS="verbatim">>>> command="/opt/bin/AlignACE"
->>> input_file="test.fa"
->>> from Bio.motifs.applications import AlignAceCommandline
->>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10)
->>> stdout, stderr= cmd()
-</PRE><P>Since AlignAce prints all of its output to standard output, you can get
-to your motifs by parsing the first part of the result:
-</P><PRE CLASS="verbatim">>>> motifs = motifs.parse(stdout, "alignace")
-</PRE><!--TOC section Useful links -->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc187">13.10</A>&#XA0;&#XA0;Useful links </H2><!--SEC END --><P>
-<A NAME="sec:links"></A></P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="http://en.wikipedia.org/wiki/Sequence_motif">Sequence motif</A> in wikipedia
-</LI><LI CLASS="li-itemize"><A HREF="http://en.wikipedia.org/wiki/Position_weight_matrix">PWM</A> in wikipedia
-</LI><LI CLASS="li-itemize"><A HREF="http://en.wikipedia.org/wiki/Consensus_sequence">Consensus sequence</A> in wikipedia
-</LI><LI CLASS="li-itemize"><A HREF="http://bio.cs.washington.edu/assessment/">Comparison of different motif finding programs</A>
-</LI></UL><!--TOC chapter Cluster analysis-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc188">Chapter&#XA0;14</A>&#XA0;&#XA0;Cluster analysis</H1><!--SEC END --><P>Cluster analysis is the grouping of items into clusters based on the similarity of the items to each other. In bioinformatics, clustering is widely used in gene expression data analysis to find groups of genes with similar gene expression profiles. This may identify functionally related genes, as well as suggest the function of presently unknown genes.</P><P>The B [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Hierarchical clustering (pairwise centroid-, single-, complete-, and average-linkage);
-</LI><LI CLASS="li-itemize"><I>k</I>-means, <I>k</I>-medians, and <I>k</I>-medoids clustering;
-</LI><LI CLASS="li-itemize">Self-Organizing Maps;
-</LI><LI CLASS="li-itemize">Principal Component Analysis.
-</LI></UL><!--TOC subsection Data representation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Data representation</H3><!--SEC END --><P>The data to be clustered are represented by a <I>n</I> &#XD7; <I>m</I> Numerical Python array <CODE>data</CODE>. Within the context of gene expression data clustering, typically the rows correspond to different genes whereas the columns correspond to different experimental conditions. The clustering algorithms in <CODE>Bio.Cluster</CODE> can be applied both to rows (genes) and to columns (experiments).</P> [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Missing values</H3><!--SEC END --><P>Often in microarray experiments, some of the data values are missing, which is indicated by an additional <I>n</I> &#XD7; <I>m</I> Numerical Python integer array <CODE>mask</CODE>. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing and is ignored in the analysis.</P><!--TOC subsection Random number generator-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Random number generator</H3><!--SEC END --><P>The <I>k</I>-means/medians/medoids clustering algorithms and Self-Organizing Maps (SOMs) include the use of a random number generator. The uniform random number generator in <CODE>Bio.Cluster</CODE> is based on the algorithm by L&#X2019;Ecuyer [<A HREF="#lecuyer1988">25</A>], while random numbers following the binomial distribution are generated using the BTPE algorithm by Kachitvichyanukul and Schmeis [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc189">14.1</A>&#XA0;&#XA0;Distance functions</H2><!--SEC END --><P>
-<A NAME="sec:distancefunctions"></A></P><P>In order to cluster items into groups based on their similarity, we should first define what exactly we mean by <EM>similar</EM>. <CODE>Bio.Cluster</CODE> provides eight distance functions, indicated by a single character, to measure similarity, or conversely, distance:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>'e'</CODE>:
-Euclidean distance;
-</LI><LI CLASS="li-itemize"><CODE>'b'</CODE>:
-City-block distance.
-</LI><LI CLASS="li-itemize"><CODE>'c'</CODE>:
-Pearson correlation coefficient;
-</LI><LI CLASS="li-itemize"><CODE>'a'</CODE>:
-Absolute value of the Pearson correlation coefficient;
-</LI><LI CLASS="li-itemize"><CODE>'u'</CODE>:
-Uncentered Pearson correlation (equivalent to the cosine of the angle between two data vectors);
-</LI><LI CLASS="li-itemize"><CODE>'x'</CODE>:
-Absolute uncentered Pearson correlation;
-</LI><LI CLASS="li-itemize"><CODE>'s'</CODE>:
-Spearman&#X2019;s rank correlation;
-</LI><LI CLASS="li-itemize"><CODE>'k'</CODE>:
-Kendall&#X2019;s &#X3C4;.
-</LI></UL><P>
-The first two are true distance functions that satisfy the triangle inequality:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I></TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">&#X2264;&#XA0;<I>d</I></TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">+&#XA0;<I>d</I></TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell"> for all &#XA0;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,&#XA0;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,&#XA0;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-and are therefore refered to as <EM>metrics</EM>. In everyday language, this means that the shortest distance between two points is a straight line.</P><P>The remaining six distance measures are related to the correlation coefficient, where the distance <I>d</I> is defined in terms of the correlation <I>r</I> by <I>d</I>=1&#X2212;<I>r</I>. Note that these distance functions are <EM>semi-metrics</EM> that do not satisfy the triangle inequality. For example, for
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>u</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">=</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239D;</TD><TD CLASS="dcell">1,0,&#X2212;1</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X23A0;</TD><TD CLASS="dcell">;</TD></TR>
-</TABLE><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>v</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">=</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239D;</TD><TD CLASS="dcell">1,1,0</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X23A0;</TD><TD CLASS="dcell">;</TD></TR>
-</TABLE><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>w</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell">=</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239D;</TD><TD CLASS="dcell">0,1,1</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X23A0;</TD><TD CLASS="dcell">;</TD></TR>
-</TABLE><P>
-we find a Pearson distance
-<I>d</I>(<U><I>u</I></U>,<U><I>w</I></U>) = 1.8660, while
-<I>d</I>(<U><I>u</I></U>,<U><I>v</I></U>)+<I>d</I>(<U><I>v</I></U>,<U><I>w</I></U>) = 1.6340.</P><!--TOC subsection Euclidean distance-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Euclidean distance</H3><!--SEC END --><P>In <CODE>Bio.Cluster</CODE>, we define the Euclidean distance as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I>&#XA0;=&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1&#XA0;</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239D;</TD><TD CLASS="dcell"><I>x</I><SUB><I>i</I></SUB>&#X2212;<I>y</I><SUB><I>i</I></SUB></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X23A0;</TD><TD CLASS="dcell"><SUP>2</SUP>.</TD></TR>
-</TABLE><P>
-Only those terms are included in the summation for which both
-<I>x</I><SUB><I>i</I></SUB> and <I>y</I><SUB><I>i</I></SUB> are present, and the denominator <I>n</I> is chosen accordingly.
-As the expression data <I>x</I><SUB><I>i</I></SUB> and <I>y</I><SUB><I>i</I></SUB> are subtracted directly from each other, we should make sure that the expression data are properly normalized when using the Euclidean distance.</P><!--TOC subsection City-block distance-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->City-block distance</H3><!--SEC END --><P>The city-block distance, alternatively known as the Manhattan distance, is related to the Euclidean distance. Whereas the Euclidean distance corresponds to the length of the shortest path between two points, the city-block distance is the sum of distances along each dimension. As gene expression data tend to have missing values, in <CODE>Bio.Cluster</CODE> we define the city-block distance as the sum of di [...]
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I>&#XA0;=&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1&#XA0;</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X23AA;<BR>
-&#X23AA;</TD><TD CLASS="dcell"><I>x</I><SUB><I>i</I></SUB>&#X2212;<I>y</I><SUB><I>i</I></SUB></TD><TD CLASS="dcell">&#X23AA;<BR>
-&#X23AA;</TD><TD CLASS="dcell">.</TD></TR>
-</TABLE><P>
-This is equal to the distance you would have to walk between two points in a city, where you have to walk along city blocks. As for the Euclidean distance,
-the expression data are subtracted directly from each other, and we should therefore make sure that they are properly normalized.</P><!--TOC subsection The Pearson correlation coefficient-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->The Pearson correlation coefficient</H3><!--SEC END --><P>The Pearson correlation coefficient is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>r</I>&#XA0;=&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>x</I><SUB><I>i</I></SUB>&#XA0;&#X2212;<SPAN style="text-decoration:overline">x</SPAN></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">&#X3C3;<SUB><I>x</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>y</I><SUB><I>i</I></SUB>&#XA0;&#X2212;&#X233;</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">&#X3C3;<SUB><I>y</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-in which
-<SPAN style="text-decoration:overline">x</SPAN>, &#X233;
-are the sample mean of <I>x</I> and <I>y</I> respectively, and
-&#X3C3;<SUB><I>x</I></SUB>, &#X3C3;<SUB><I>y</I></SUB>
-are the sample standard deviation of <I>x</I> and <I>y</I>.
-The Pearson correlation coefficient is a measure for how well a straight line can be fitted to a scatterplot of <I>x</I> and <I>y</I>.
-If all the points in the scatterplot lie on a straight line, the Pearson correlation coefficient is either +1 or -1, depending on whether the slope of line is positive or negative. If the Pearson correlation coefficient is equal to zero, there is no correlation between <I>x</I> and <I>y</I>.</P><P>The <EM>Pearson distance</EM> is then defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>P</SUB>&#XA0;&#X2261;&#XA0;1&#XA0;&#X2212;&#XA0;<I>r</I>.</TD></TR>
-</TABLE><P>
-As the Pearson correlation coefficient lies between -1 and 1, the Pearson distance lies between 0 and 2.</P><!--TOC subsection Absolute Pearson correlation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Absolute Pearson correlation</H3><!--SEC END --><P>By taking the absolute value of the Pearson correlation, we find a number between 0 and 1. If the absolute value is 1, all the points in the scatter plot lie on a straight line with either a positive or a negative slope. If the absolute value is equal to zero, there is no correlation between <I>x</I> and <I>y</I>.</P><P>The corresponding distance is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>A</SUB>&#XA0;&#X2261;&#XA0;1&#XA0;&#X2212;&#XA0;</TD><TD CLASS="dcell">&#X23AA;<BR>
-&#X23AA;</TD><TD CLASS="dcell"><I>r</I></TD><TD CLASS="dcell">&#X23AA;<BR>
-&#X23AA;</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where <I>r</I> is the Pearson correlation coefficient. As the absolute value of the Pearson correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well.</P><P>In the context of gene expression experiments, the absolute correlation is equal to 1 if the gene expression profiles of two genes are either exactly the same or exactly opposite. The absolute correlation coefficient should therefore be used with care.</P><!--TOC subsection Uncentered corre [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Uncentered correlation (cosine of the angle)</H3><!--SEC END --><P>In some cases, it may be preferable to use the <EM>uncentered correlation</EM> instead of the regular Pearson correlation coefficient. The uncentered correlation is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>r</I><SUB>U</SUB>&#XA0;=&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>x</I><SUB><I>i</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">&#X3C3;<SUB><I>x</I></SUB><SUP>(0)</SUP></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>y</I><SUB><I>i</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">&#X3C3;<SUB><I>y</I></SUB><SUP>(0)</SUP></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-
-
-&#XA0;&#XA0;&#XA0;&#XA0;&#XA0;
-
-</TD><TD CLASS="dcell"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=right NOWRAP>&#X3C3;<SUB><I>x</I></SUB><SUP>(0)</SUP></TD><TD ALIGN=center NOWRAP>&#XA0;=</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CELLSPACING=0 CELLPADDING=0><TR><TD ALIGN="right"><DIV CLASS="vbar" STYLE="height:2em;"></DIV></TD></TR>
-<TR><TD><FONT SIZE=7>&#X221A;</FONT></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD CLASS="hbar"></TD></TR>
-<TR><TD ALIGN=center NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"><I>x</I><SUB><I>i</I></SUB><SUP>2</SUP></TD></TR>
-</TABLE></TD></TR>
-</TABLE></TD><TD CLASS="dcell">;&#XA0;&#XA0;</TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP> </TD></TR>
-<TR><TD ALIGN=right NOWRAP>&#X3C3;<SUB><I>y</I></SUB><SUP>(0)</SUP></TD><TD ALIGN=center NOWRAP>&#XA0;=</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CELLSPACING=0 CELLPADDING=0><TR><TD ALIGN="right"><DIV CLASS="vbar" STYLE="height:2em;"></DIV></TD></TR>
-<TR><TD><FONT SIZE=7>&#X221A;</FONT></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD CLASS="hbar"></TD></TR>
-<TR><TD ALIGN=center NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell"><I>y</I><SUB><I>i</I></SUB><SUP>2</SUP></TD></TR>
-</TABLE></TD></TR>
-</TABLE></TD><TD CLASS="dcell">.&#XA0;
-</TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP> </TD></TR>
-</TABLE></TD></TR>
-</TABLE><P>
-This is the same expression as for the regular Pearson correlation coefficient, except that the sample means
-<SPAN style="text-decoration:overline">x</SPAN>, &#X233;
-are set equal to zero. The uncentered correlation may be appropriate if there is a zero reference state. For instance, in the case of gene expression data given in terms of log-ratios, a log-ratio equal to zero corresponds to the green and red signal being equal, which means that the experimental manipulation did not affect the gene expression.</P><P>The distance corresponding to the uncentered correlation coefficient is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>U</SUB>&#XA0;&#X2261;&#XA0;1&#XA0;&#X2212;&#XA0;<I>r</I><SUB>U</SUB>,</TD></TR>
-</TABLE><P>
-where
-<I>r</I><SUB>U</SUB>
-is the uncentered correlation.
-As the uncentered correlation coefficient lies between -1 and 1, the corresponding distance lies between 0 and 2.</P><P>The uncentered correlation is equal to the cosine of the angle of the two data vectors in <I>n</I>-dimensional space, and is often referred to as such.</P><!--TOC subsection Absolute uncentered correlation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Absolute uncentered correlation</H3><!--SEC END --><P>As for the regular Pearson correlation, we can define a distance measure using the absolute value of the uncentered correlation:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>AU</SUB>&#XA0;&#X2261;&#XA0;1&#XA0;&#X2212;&#XA0;</TD><TD CLASS="dcell">&#X23AA;<BR>
-&#X23AA;</TD><TD CLASS="dcell"><I>r</I><SUB>U</SUB></TD><TD CLASS="dcell">&#X23AA;<BR>
-&#X23AA;</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where
-<I>r</I><SUB>U</SUB>
-is the uncentered correlation coefficient. As the absolute value of the uncentered correlation coefficient lies between 0 and 1, the corresponding distance lies between 0 and 1 as well.</P><P>Geometrically, the absolute value of the uncentered correlation is equal to the cosine between the supporting lines of the two data vectors (i.e., the angle without taking the direction of the vectors into consideration).</P><!--TOC subsection Spearman rank correlation-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Spearman rank correlation</H3><!--SEC END --><P>The Spearman rank correlation is an example of a non-parametric similarity measure, and tends to be more robust against outliers than the Pearson correlation.</P><P>To calculate the Spearman rank correlation, we replace each data value by their rank if we would order the data in each vector by their value. We then calculate the Pearson correlation between the two rank vectors instead of the data vect [...]
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>S</SUB>&#XA0;&#X2261;&#XA0;1&#XA0;&#X2212;&#XA0;<I>r</I><SUB>S</SUB>,</TD></TR>
-</TABLE><P>
-where
-<I>r</I><SUB>S</SUB>
-is the Spearman rank correlation.</P><!--TOC subsection Kendall&#X2019;s &#X3C4;-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Kendall&#X2019;s &#X3C4;</H3><!--SEC END --><P>Kendall&#X2019;s &#X3C4;
-is another example of a non-parametric similarity measure. It is similar to the Spearman rank correlation, but instead of the ranks themselves only the relative ranks are used to calculate &#X3C4; (see Snedecor & Cochran [<A HREF="#snedecor1989">29</A>]).</P><P>We can define a distance measure corresponding to Kendall&#X2019;s &#X3C4;
-as </P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>d</I><SUB>K</SUB>&#XA0;&#X2261;&#XA0;1&#XA0;&#X2212;&#XA0;&#X3C4;.</TD></TR>
-</TABLE><P>
-As Kendall&#X2019;s &#X3C4; is always between -1 and 1, the corresponding distance will be between 0 and 2.</P><!--TOC subsection Weighting-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Weighting</H3><!--SEC END --><P>For most of the distance functions available in <CODE>Bio.Cluster</CODE>, a weight vector can be applied. The weight vector contains weights for the items in the data vector. If the weight for item <I>i</I> is <I>w</I><SUB><I>i</I></SUB>, then that item is treated as if it occurred <I>w</I><SUB><I>i</I></SUB> times in the data. The weight do not have to be integers.
-For the Spearman rank correlation and Kendall&#X2019;s
-&#X3C4;,
-weights do not have a well-defined meaning and are therefore not implemented.</P><!--TOC subsection Calculating the distance matrix-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance matrix</H3><!--SEC END --><P>
-<A NAME="subsec:distancematrix"></A></P><P>The distance matrix is a square matrix with all pairwise distances between the items in <CODE>data</CODE>, and can be calculated by the function <CODE>distancematrix</CODE> in the <CODE>Bio.Cluster</CODE> module:
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import distancematrix
->>> matrix = distancematrix(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the distances between the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or between the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>To save memory, the distance matrix is returned as a list of 1D arrays.
-The number of columns in each row is equal to the row number. Hence, the first row has zero elements. An example of the return value is
-</P><PRE CLASS="verbatim">[array([]),
- array([1.]),
- array([7., 3.]),
- array([4., 2., 6.])]
-</PRE><P>This corresponds to the distance matrix
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell">
-</TD><TD CLASS="dcell"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=center NOWRAP>0</TD><TD ALIGN=center NOWRAP>1</TD><TD ALIGN=center NOWRAP>7</TD><TD ALIGN=center NOWRAP>4&#XA0;&#XA0;</TD></TR>
-<TR><TD ALIGN=center NOWRAP>1</TD><TD ALIGN=center NOWRAP>0</TD><TD ALIGN=center NOWRAP>3</TD><TD ALIGN=center NOWRAP>2&#XA0;&#XA0;</TD></TR>
-<TR><TD ALIGN=center NOWRAP>7</TD><TD ALIGN=center NOWRAP>3</TD><TD ALIGN=center NOWRAP>0</TD><TD ALIGN=center NOWRAP>6&#XA0;&#XA0;</TD></TR>
-<TR><TD ALIGN=center NOWRAP>4</TD><TD ALIGN=center NOWRAP>2</TD><TD ALIGN=center NOWRAP>6</TD><TD ALIGN=center NOWRAP>0</TD></TR>
-</TABLE></TD><TD CLASS="dcell">
-</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">.
-</TD></TR>
-</TABLE><!--TOC section Calculating cluster properties-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc190">14.2</A>&#XA0;&#XA0;Calculating cluster properties</H2><!--SEC END --><!--TOC subsection Calculating the cluster centroids-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the cluster centroids</H3><!--SEC END --><P>
-<A NAME="subsec:clustercentroids"></A></P><P>The centroid of a cluster can be defined either as the mean or as the median of each dimension over all cluster items. The function <CODE>clustercentroids</CODE> in <CODE>Bio.Cluster</CODE> can be used to calculate either:</P><PRE CLASS="verbatim">>>> from Bio.Cluster import clustercentroids
->>> cdata, cmask = clustercentroids(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>clusterid</CODE> (default: <CODE>None</CODE>)<BR>
-Vector of integers showing to which cluster each item belongs. If <CODE>clusterid</CODE> is <CODE>None</CODE>, then all items are assumed to belong to the same cluster.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies whether the arithmetic mean (<CODE>method=='a'</CODE>) or the median (<CODE>method=='m'</CODE>) is used to calculate the cluster center.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the centroids of the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or the centroids of the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI></UL><P>This function returns the tuple <CODE>(cdata, cmask)</CODE>. The centroid data are stored in the 2D Numerical Python array <CODE>cdata</CODE>, with missing data indicated by the 2D Numerical Python integer array <CODE>cmask</CODE>. The dimensions of these arrays are (number of clusters, number of columns) if <CODE>transpose</CODE> is <CODE>0</CODE>, or (number of rows, number of clusters) if <CODE>transpose</CODE> is <CODE>1</CODE>. Each row (if <CODE>transpose</CODE> is <CO [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance between clusters</H3><!--SEC END --><P>Given a distance function between <EM>items</EM>, we can define the distance between two <EM>clusters</EM> in several ways. The distance between the arithmetic means of the two clusters is used in pairwise centroid-linkage clustering and in <I>k</I>-means clustering. In <I>k</I>-medoids clustering, the distance between the medians of the two clusters is used instead. The shortest pair [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import clusterdistance
->>> distance = clusterdistance(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>index1</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the first cluster. A cluster containing only one item <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>index2</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the second cluster. A cluster containing only one items <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies how the distance between clusters is defined:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>'a'</CODE>: Distance between the two cluster centroids (arithmetic mean);
-</LI><LI CLASS="li-itemize"><CODE>'m'</CODE>: Distance between the two cluster centroids (median);
-</LI><LI CLASS="li-itemize"><CODE>'s'</CODE>: Shortest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'x'</CODE>: Longest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'v'</CODE>: Average over the pairwise distances between items in the two clusters.
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-If <CODE>transpose==0</CODE>, calculate the distance between the rows of <CODE>data</CODE>. If <CODE>transpose==1</CODE>, calculate the distance between the columns of <CODE>data</CODE>.
-</LI></UL><!--TOC section Partitioning algorithms-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc191">14.3</A>&#XA0;&#XA0;Partitioning algorithms</H2><!--SEC END --><P>Partitioning algorithms divide items into <I>k</I> clusters such that the sum of distances over the items to their cluster centers is minimal.
-The number of clusters <I>k</I> is specified by the user.
-Three partitioning algorithms are available in <CODE>Bio.Cluster</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<I>k</I>-means clustering
-</LI><LI CLASS="li-itemize"><I>k</I>-medians clustering
-</LI><LI CLASS="li-itemize"><I>k</I>-medoids clustering
-</LI></UL><P>
-These algorithms differ in how the cluster center is defined. In <I>k</I>-means clustering, the cluster center is defined as the mean data vector averaged over all items in the cluster. Instead of the mean, in <I>k</I>-medians clustering the median is calculated for each dimension in the data vector. Finally, in <I>k</I>-medoids clustering the cluster center is defined as the item which has the smallest sum of distances to the other items in the cluster. This clustering algorithm is suit [...]
-In the initialization of the EM algorithm, we randomly assign items to clusters. To ensure that no empty clusters are produced, we use the binomial distribution to randomly choose the number of items in each cluster to be one or more. We then randomly permute the cluster assignments to items such that each item has an equal probability to be in any cluster. Each cluster is thus guaranteed to contain at least one item.</P><P>We then iterate:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Calculate the centroid of each cluster, defined as either the mean, the median, or the medoid of the cluster;
-</LI><LI CLASS="li-itemize">Calculate the distances of each item to the cluster centers;
-</LI><LI CLASS="li-itemize">For each item, determine which cluster centroid is closest;
-</LI><LI CLASS="li-itemize">Reassign each item to its closest cluster, or stop the iteration if no further item reassignments take place.
-</LI></UL><P>To avoid clusters becoming empty during the iteration, in <I>k</I>-means and <I>k</I>-medians clustering the algorithm keeps track of the number of items in each cluster, and prohibits the last remaining item in a cluster from being reassigned to a different cluster. For <I>k</I>-medoids clustering, such a check is not needed, as the item that functions as the cluster centroid has a zero distance to itself, and will therefore never be closer to a different cluster.</P><P>As  [...]
-To find the optimal clustering solution, the <I>k</I>-means algorithm is repeated many times, each time starting from a different initial random clustering. The sum of distances of the items to their cluster center is saved for each run, and the solution with the smallest value of this sum will be returned as the overall clustering solution.</P><P>How often the EM algorithm should be run depends on the number of items being clustered. As a rule of thumb, we can consider how often the opt [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><I>k</I>-means and <I>k</I>-medians</H3><!--SEC END --><P>The <I>k</I>-means and <I>k</I>-medians algorithms are implemented as the function <CODE>kcluster</CODE> in <CODE>Bio.Cluster</CODE>:</P><PRE CLASS="verbatim">>>> from Bio.Cluster import kcluster
->>> clusterid, error, nfound = kcluster(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>nclusters</CODE> (default: <CODE>2</CODE>)<BR>
-The number of clusters <I>k</I>.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>npass</CODE> (default: <CODE>1</CODE>)<BR>
-The number of times the <I>k</I>-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If <CODE>initialid</CODE> is given, the value of <CODE>npass</CODE> is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>a</CODE>)<BR>
-describes how the center of a cluster is found:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='a'</CODE>: arithmetic mean (<I>k</I>-means clustering);
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: median (<I>k</I>-medians clustering).
-</LI></UL>
-For other values of <CODE>method</CODE>, the arithmetic mean is used.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-Whereas all eight distance measures are accepted by <CODE>kcluster</CODE>, from a theoretical viewpoint it is best to use the Euclidean distance for the <I>k</I>-means algorithm, and the city-block distance for <I>k</I>-medians.
-</LI><LI CLASS="li-itemize"><CODE>initialid</CODE> (default: <CODE>None</CODE>)<BR>
-Specifies the initial clustering to be used for the EM algorithm. If <CODE>initialid==None</CODE>, then a different random initial clustering is used for each of the <CODE>npass</CODE> runs of the EM algorithm. If <CODE>initialid</CODE> is not <CODE>None</CODE>, then it should be equal to a 1D array containing the cluster number (between <CODE>0</CODE> and <CODE>nclusters-1</CODE>) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM [...]
-</LI></UL><P>This function returns a tuple <CODE>(clusterid, error, nfound)</CODE>, where <CODE>clusterid</CODE> is an integer array containing the number of the cluster to which each row or cluster was assigned, <CODE>error</CODE> is the within-cluster sum of distances for the optimal clustering solution, and <CODE>nfound</CODE> is the number of times this optimal solution was found.</P><!--TOC subsection <I>k</I>-medoids clustering-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><I>k</I>-medoids clustering</H3><!--SEC END --><P>The <CODE>kmedoids</CODE> routine performs <I>k</I>-medoids clustering on a given set of items, using the distance matrix and the number of clusters passed by the user:
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import kmedoids
->>> clusterid, error, nfound = kmedoids(distance)
-</PRE><P>where the following arguments are defined:
-, nclusters=2, npass=1, initialid=None)|</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>distance</CODE> (required)<BR>
-The matrix containing the distances between the items; this matrix can be specified in three ways:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed):
-<PRE CLASS="verbatim">distance = array([[0.0, 1.1, 2.3],
-                  [1.1, 0.0, 4.5],
-                  [2.3, 4.5, 0.0]])
-</PRE></LI><LI CLASS="li-itemize">as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = array([1.1, 2.3, 4.5])
-</PRE></LI><LI CLASS="li-itemize">as a list containing the rows of the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = [array([]|,
-            array([1.1]),
-            array([2.3, 4.5])
-           ]
-</PRE></LI></UL>
-These three expressions correspond to the same distance matrix.
-</LI><LI CLASS="li-itemize"><CODE>nclusters</CODE> (default: <CODE>2</CODE>)<BR>
-The number of clusters <I>k</I>.
-</LI><LI CLASS="li-itemize"><CODE>npass</CODE> (default: <CODE>1</CODE>)<BR>
-The number of times the <I>k</I>-medoids clustering algorithm is performed, each time with a different (random) initial condition. If <CODE>initialid</CODE> is given, the value of <CODE>npass</CODE> is ignored, as the clustering algorithm behaves deterministically in that case.
-</LI><LI CLASS="li-itemize"><CODE>initialid</CODE> (default: <CODE>None</CODE>)<BR>
-Specifies the initial clustering to be used for the EM algorithm. If <CODE>initialid==None</CODE>, then a different random initial clustering is used for each of the <CODE>npass</CODE> runs of the EM algorithm. If <CODE>initialid</CODE> is not <CODE>None</CODE>, then it should be equal to a 1D array containing the cluster number (between <CODE>0</CODE> and <CODE>nclusters-1</CODE>) for each item. Each cluster should contain at least one item. With the initial clustering specified, the EM [...]
-</LI></UL><P>This function returns a tuple <CODE>(clusterid, error, nfound)</CODE>, where <CODE>clusterid</CODE> is an array containing the number of the cluster to which each item was assigned, <CODE>error</CODE> is the within-cluster sum of distances for the optimal <I>k</I>-medoids clustering solution, and <CODE>nfound</CODE> is the number of times the optimal solution was found. Note that the cluster number in <CODE>clusterid</CODE> is defined as the item number of the item represent [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc192">14.4</A>&#XA0;&#XA0;Hierarchical clustering</H2><!--SEC END --><P>Hierarchical clustering methods are inherently different from the <I>k</I>-means clustering method. In hierarchical clustering, the similarity in the expression profile between genes or experimental conditions are represented in the form of a tree structure. This tree structure can be shown graphically by programs such as Treeview and Java Treeview, which has contribu [...]
-In pairwise single-linkage clustering, the distance between two nodes is defined as the shortest distance among the pairwise distances between the members of the two nodes.
-</LI><LI CLASS="li-itemize">In pairwise maximum-linkage clustering, alternatively known as pairwise complete-linkage clustering, the distance between two nodes is defined as the longest distance among the pairwise distances between the members of the two nodes.
-</LI><LI CLASS="li-itemize">In pairwise average-linkage clustering, the distance between two nodes is defined as the average over all pairwise distances between the items of the two nodes.
-</LI><LI CLASS="li-itemize">In pairwise centroid-linkage clustering, the distance between two nodes is defined as the distance between their centroids. The centroids are calculated by taking the mean over all the items in a cluster. As the distance from each newly formed node to existing nodes and items need to be calculated at each step, the computing time of pairwise centroid-linkage clustering may be significantly longer than for the other hierarchical clustering methods. Another pecu [...]
-</LI></UL><P>For pairwise single-, complete-, and average-linkage clustering, the distance between two nodes can be found directly from the distances between the individual items. Therefore, the clustering algorithm does not need access to the original gene expression data, once the distance matrix is known. For pairwise centroid-linkage clustering, however, the centroids of newly formed subnodes can only be calculated from the original data and not from the distance matrix.</P><P>The im [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Representing a hierarchical clustering solution</H3><!--SEC END --><P>The result of hierarchical clustering consists of a tree of nodes, in which each node joins two items or subnodes. Usually, we are not only interested in which items or subnodes are joined at each node, but also in their similarity (or distance) as they are joined. To store one node in the hierarchical clustering tree, we make use of the class <CODE>Node</CODE>, which defined in [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>left</CODE>
-</LI><LI CLASS="li-itemize"><CODE>right</CODE>
-</LI><LI CLASS="li-itemize"><CODE>distance</CODE>
-</LI></UL><P>
-Here, <CODE>left</CODE> and <CODE>right</CODE> are integers referring to the two items or subnodes that are joined at this node, and <CODE>distance</CODE> is the distance between them. The items being clustered are numbered from 0 to (number of items &#X2212; 1), while clusters are numbered from -1 to &#X2212;(number of items&#X2212;1). Note that the number of nodes is one less than the number of items.</P><P>To create a new <CODE>Node</CODE> object, we need to specify <CODE>left</CODE>  [...]
->>> Node(2, 3)
-(2, 3): 0
->>> Node(2, 3, 0.91)
-(2, 3): 0.91
-</PRE><P>The attributes <CODE>left</CODE>, <CODE>right</CODE>, and <CODE>distance</CODE> of an existing <CODE>Node</CODE> object can be modified directly:</P><PRE CLASS="verbatim">>>> node = Node(4, 5)
->>> node.left = 6
->>> node.right = 2
->>> node.distance = 0.73
->>> node
-(6, 2): 0.73
-</PRE><P>An error is raised if <CODE>left</CODE> and <CODE>right</CODE> are not integers, or if <CODE>distance</CODE> cannot be converted to a floating-point value.</P><P>The Python class <CODE>Tree</CODE> represents a full hierarchical clustering solution. A <CODE>Tree</CODE> object can be created from a list of <CODE>Node</CODE> objects:</P><PRE CLASS="verbatim">>>> from Bio.Cluster import Node, Tree
->>> nodes = [Node(1, 2, 0.2), Node(0, 3, 0.5), Node(-2, 4, 0.6), Node(-1, -3, 0.9)]
->>> tree = Tree(nodes)
->>> print(tree)
-(1, 2): 0.2
-(0, 3): 0.5
-(-2, 4): 0.6
-(-1, -3): 0.9
-</PRE><P>The <CODE>Tree</CODE> initializer checks if the list of nodes is a valid hierarchical clustering result:</P><PRE CLASS="verbatim">>>> nodes = [Node(1, 2, 0.2), Node(0, 2, 0.5)]
->>> Tree(nodes)
-Traceback (most recent call last):
-  File "<stdin>", line 1, in ?
-ValueError: Inconsistent tree
-</PRE><P>Individual nodes in a <CODE>Tree</CODE> object can be accessed using square brackets:</P><PRE CLASS="verbatim">>>> nodes = [Node(1, 2, 0.2), Node(0, -1, 0.5)]
->>> tree = Tree(nodes)
->>> tree[0]
-(1, 2): 0.2
->>> tree[1]
-(0, -1): 0.5
->>> tree[-1]
-(0, -1): 0.5
-</PRE><P>As a <CODE>Tree</CODE> object is read-only, we cannot change individual nodes in a <CODE>Tree</CODE> object. However, we can convert the tree to a list of nodes, modify this list, and create a new tree from this list:</P><PRE CLASS="verbatim">>>> tree = Tree([Node(1, 2, 0.1), Node(0, -1, 0.5), Node(-2, 3, 0.9)])
->>> print(tree)
-(1, 2): 0.1
-(0, -1): 0.5
-(-2, 3): 0.9
->>> nodes = tree[:]
->>> nodes[0] = Node(0, 1, 0.2)
->>> nodes[1].left = 2
->>> tree = Tree(nodes)
->>> print(tree)
-(0, 1): 0.2
-(2, -1): 0.5
-(-2, 3): 0.9
-</PRE><P>This guarantees that any <CODE>Tree</CODE> object is always well-formed.</P><P>To display a hierarchical clustering solution with visualization programs such as Java Treeview, it is better to scale all node distances such that they are between zero and one. This can be accomplished by calling the <CODE>scale</CODE> method on an existing <CODE>Tree</CODE> object:
-</P><PRE CLASS="verbatim">>>> tree.scale()
-</PRE><P>This method takes no arguments, and returns <CODE>None</CODE>.</P><P>After hierarchical clustering, the items can be grouped into <I>k</I> clusters based on the tree structure stored in the <CODE>Tree</CODE> object by cutting the tree:
-</P><PRE CLASS="verbatim">>>> clusterid = tree.cut(nclusters=1)
-</PRE><P>where <CODE>nclusters</CODE> (defaulting to <CODE>1</CODE>) is the desired number of clusters <I>k</I>.
-This method ignores the top <I>k</I>&#X2212;1 linking events in the tree structure, resulting in <I>k</I> separated clusters of items. The number of clusters <I>k</I> should be positive, and less than or equal to the number of items.
-This method returns an array <CODE>clusterid</CODE> containing the number of the cluster to which each item is assigned.</P><!--TOC subsection Performing hierarchical clustering-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Performing hierarchical clustering</H3><!--SEC END --><P>To perform hierarchical clustering, use the <CODE>treecluster</CODE> function in <CODE>Bio.Cluster</CODE>.
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import treecluster
->>> tree = treecluster(data)
-</PRE><P>where the following arguments are defined:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE><BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-The weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose==0</CODE>) or columns (<CODE>transpose==1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'m'</CODE>)<BR>
-defines the linkage method to be used:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='s'</CODE>: pairwise single-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: pairwise maximum- (or complete-) linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='c'</CODE>: pairwise centroid-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='a'</CODE>: pairwise average-linkage clustering
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>To apply hierarchical clustering on a precalculated distance matrix, specify the <CODE>distancematrix</CODE> argument when calling <CODE>treecluster</CODE> function instead of the <CODE>data</CODE> argument:
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import treecluster
->>> tree = treecluster(distancematrix=distance)
-</PRE><P>In this case, the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>distancematrix</CODE><BR>
-The distance matrix, which can be specified in three ways:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-as a 2D Numerical Python array (in which only the left-lower part of the array will be accessed):
-<PRE CLASS="verbatim">distance = array([[0.0, 1.1, 2.3],
-                  [1.1, 0.0, 4.5],
-                  [2.3, 4.5, 0.0]])
-</PRE></LI><LI CLASS="li-itemize">as a 1D Numerical Python array containing consecutively the distances in the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = array([1.1, 2.3, 4.5])
-</PRE></LI><LI CLASS="li-itemize">as a list containing the rows of the left-lower part of the distance matrix:
-<PRE CLASS="verbatim">distance = [array([]),
-            array([1.1]),
-            array([2.3, 4.5])
-</PRE></LI></UL>
-These three expressions correspond to the same distance matrix.
-As <CODE>treecluster</CODE> may shuffle the values in the distance matrix as part of the clustering algorithm, be sure to save this array in a different variable before calling <CODE>treecluster</CODE> if you need it later.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE><BR>
-The linkage method to be used:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='s'</CODE>: pairwise single-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: pairwise maximum- (or complete-) linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='a'</CODE>: pairwise average-linkage clustering
-</LI></UL>
-While pairwise single-, maximum-, and average-linkage clustering can be calculated from the distance matrix alone, pairwise centroid-linkage cannot.
-</LI></UL><P>When calling <CODE>treecluster</CODE>, either <CODE>data</CODE> or <CODE>distancematrix</CODE> should be <CODE>None</CODE>.</P><P>This function returns a <CODE>Tree</CODE> object. This object contains (number of items &#X2212; 1) nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes <CODE>left</CODE> and <CODE>right</CODE> each  [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc193">14.5</A>&#XA0;&#XA0;Self-Organizing Maps</H2><!--SEC END --><P>Self-Organizing Maps (SOMs) were invented by Kohonen to describe neural networks (see for instance Kohonen, 1997 [<A HREF="#kohonen1997">24</A>]). Tamayo (1999) first applied Self-Organizing Maps to gene expression data [<A HREF="#tamayo1999">30</A>].</P><P>SOMs organize items into clusters that are situated in some topology. Usually a rectangular topology is chosen. The [...]
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">&#X394;&#XA0;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>x</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><SUB>cell</SUB>&#XA0;=&#XA0;&#X3C4;&#XA0;&#XB7;&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>x</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><SUB>row</SUB>&#XA0;&#X2212;&#XA0;</TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD ALIGN=center NOWRAP><I>x</I></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-</TABLE></TD><TD CLASS="dcell"><SUB>cell</SUB>&#XA0;</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">.</TD></TR>
-</TABLE><P>
-The parameter
-&#X3C4;
-is a parameter that decreases at each iteration step. We have used a simple linear function of the iteration step:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">&#X3C4;&#XA0;=&#XA0;&#X3C4;<SUB>init</SUB>&#XA0;&#XB7;&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell">1&#XA0;&#X2212;&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>i</I>&#XA0;</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-&#X3C4;<SUB>init</SUB>
-is the initial value of &#X3C4; as specified by the user, <I>i</I> is the number of the current iteration step, and <I>n</I> is the total number of iteration steps to be performed. While changes are made rapidly in the beginning of the iteration, at the end of iteration only small changes are made.</P><P>All clusters within a radius <I>R</I> are adjusted to the gene under consideration. This radius decreases as the calculation progresses as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>R</I>&#XA0;=&#XA0;<I>R</I><SUB>max</SUB>&#XA0;&#XB7;&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell">1&#XA0;&#X2212;&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>i</I>&#XA0;</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>n</I></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-in which the maximum radius is defined as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell"><I>R</I><SUB>max</SUB>&#XA0;=&#XA0;</TD><TD CLASS="dcell"><FONT SIZE=5>&#X221A;</FONT></TD><TD CLASS="dcell"><TABLE border=0 cellspacing=1 cellpadding=0><TR><TD CLASS="hbar"></TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>N</I><SUB><I>x</I></SUB><SUP>2</SUP>&#XA0;+&#XA0;<I>N</I><SUB><I>y</I></SUB><SUP>2</SUP></TD></TR>
-</TABLE></TD><TD CLASS="dcell">,</TD></TR>
-</TABLE><P>
-where
-(<I>N</I><SUB><I>x</I></SUB>, <I>N</I><SUB><I>y</I></SUB>)
-are the dimensions of the rectangle defining the topology.</P><P>The function <CODE>somcluster</CODE> implements the complete algorithm to calculate a Self-Organizing Map on a rectangular grid. First it initializes the random number generator. The node data are then initialized using the random number generator. The order in which genes or microarrays are used to modify the SOM is also randomized. The total number of iterations in the SOM algorithm is specified by the user.</P><P>To run  [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import somcluster
->>> clusterid, celldata = somcluster(data)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>data</CODE> (required)<BR>
-Array containing the data for the items.
-</LI><LI CLASS="li-itemize"><CODE>mask</CODE> (default: <CODE>None</CODE>)<BR>
-Array of integers showing which data are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If <CODE>mask==None</CODE>, then all data are present.
-</LI><LI CLASS="li-itemize"><CODE>weight</CODE> (default: <CODE>None</CODE>)<BR>
-contains the weights to be used when calculating distances. If <CODE>weight==None</CODE>, then equal weights are assumed.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>nxgrid, nygrid</CODE> (default: <CODE>2, 1</CODE>)<BR>
-The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated.
-</LI><LI CLASS="li-itemize"><CODE>inittau</CODE> (default: <CODE>0.02</CODE>)<BR>
-The initial value for the parameter &#X3C4; that is used in the SOM algorithm. The default value for <CODE>inittau</CODE> is 0.02, which was used in Michael Eisen&#X2019;s Cluster/TreeView program.
-</LI><LI CLASS="li-itemize"><CODE>niter</CODE> (default: <CODE>1</CODE>)<BR>
-The number of iterations to be performed.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns the tuple <CODE>(clusterid, celldata)</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>clusterid</CODE>:<BR>
-An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the <I>x</I> and <I>y</I> coordinates of the cell in the rectangular SOM grid to which the item was assigned.
-</LI><LI CLASS="li-itemize"><CODE>celldata</CODE>:<BR>
-An array with dimensions (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of columns) if rows are being clustered, or (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of rows) if columns are being clustered. Each element <CODE>[ix][iy]</CODE> of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates <CODE>[ix][iy]</CODE>.
-</LI></UL><!--TOC section Principal Component Analysis-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc194">14.6</A>&#XA0;&#XA0;Principal Component Analysis</H2><!--SEC END --><P>Principal Component Analysis (PCA) is a widely used technique for analyzing multivariate data. A practical example of applying Principal Component Analysis to gene expression data is presented by Yeung and Ruzzo (2001) [<A HREF="#yeung2001">33</A>].</P><P>In essence, PCA is a coordinate transformation in which each row in the data matrix is written as a linear su [...]
-</P><PRE CLASS="verbatim">>>> from Bio.Cluster import pca
->>> columnmean, coordinates, components, eigenvalues = pca(data)
-</PRE><P>This function returns a tuple <CODE>columnmean, coordinates, components, eigenvalues</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>columnmean</CODE><BR>
-Array containing the mean over each column in <CODE>data</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>coordinates</CODE><BR>
-The coordinates of each row in <CODE>data</CODE> with respect to the principal components.
-</LI><LI CLASS="li-itemize"><CODE>components</CODE><BR>
-The principal components.
-</LI><LI CLASS="li-itemize"><CODE>eigenvalues</CODE><BR>
-The eigenvalues corresponding to each of the principal components.
-</LI></UL><P>
-The original matrix <CODE>data</CODE> can be recreated by calculating <CODE>columnmean +  dot(coordinates, components)</CODE>.</P><!--TOC section Handling Cluster/TreeView-type files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc195">14.7</A>&#XA0;&#XA0;Handling Cluster/TreeView-type files</H2><!--SEC END --><P>Cluster/TreeView are GUI-based codes for clustering gene expression data. They were originally written by <A HREF="http://rana.lbl.gov">Michael Eisen</A> while at Stanford University. <CODE>Bio.Cluster</CODE> contains functions for reading and writing data files that correspond to the format specified for Cluster/TreeView. In particular, by saving a clust [...]
->>> handle = open("mydatafile.txt")
->>> record = Cluster.read(handle)
->>> handle.close()
-</PRE><P>This two-step process gives you some flexibility in the source of the data.
-For example, you can use</P><PRE CLASS="verbatim">>>> import gzip # Python standard library
->>> handle = gzip.open("mydatafile.txt.gz")
-</PRE><P>to open a gzipped file, or
-</P><PRE CLASS="verbatim">>>> import urllib # Python standard library
->>> handle = urllib.urlopen("http://somewhere.org/mydatafile.txt")
-</PRE><P>to open a file stored on the Internet before calling <CODE>read</CODE>.</P><P>The <CODE>read</CODE> command reads the tab-delimited text file <CODE>mydatafile.txt</CODE> containing gene expression data in the format specified for Michael Eisen&#X2019;s Cluster/TreeView program. For a description of this file format, see the manual to Cluster/TreeView. It is available at <A HREF="http://rana.lbl.gov/manuals/ClusterTreeView.pdf">Michael Eisen&#X2019;s lab website</A> and at <A HRE [...]
-<CODE>data</CODE><BR>
-The data array containing the gene expression data. Genes are stored row-wise, while microarrays are stored column-wise.</LI><LI CLASS="li-itemize"><CODE>mask</CODE><BR>
-This array shows which elements in the <CODE>data</CODE> array, if any, are missing. If <CODE>mask[i,j]==0</CODE>, then <CODE>data[i,j]</CODE> is missing. If no data were found to be missing, <CODE>mask</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>geneid</CODE><BR>
-This is a list containing a unique description for each gene (i.e., ORF numbers).</LI><LI CLASS="li-itemize"><CODE>genename</CODE><BR>
-This is a list containing a description for each gene (i.e., gene name). If not present in the data file, <CODE>genename</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>gweight</CODE><BR>
-The weights that are to be used to calculate the distance in expression profile between genes. If not present in the data file, <CODE>gweight</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>gorder</CODE><BR>
-The preferred order in which genes should be stored in an output file. If not present in the data file, <CODE>gorder</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>expid</CODE><BR>
-This is a list containing a description of each microarray, e.g. experimental condition.</LI><LI CLASS="li-itemize"><CODE>eweight</CODE><BR>
-The weights that are to be used to calculate the distance in expression profile between microarrays. If not present in the data file, <CODE>eweight</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>eorder</CODE><BR>
-The preferred order in which microarrays should be stored in an output file. If not present in the data file, <CODE>eorder</CODE> is set to <CODE>None</CODE>.</LI><LI CLASS="li-itemize"><CODE>uniqid</CODE><BR>
-The string that was used instead of UNIQID in the data file.
-</LI></UL><P>After loading a <CODE>Record</CODE> object, each of these attributes can be accessed and modified directly. For example, the data can be log-transformed by taking the logarithm of <CODE>record.data</CODE>.</P><!--TOC subsection Calculating the distance matrix-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance matrix</H3><!--SEC END --><P>To calculate the distance matrix between the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> matrix = record.distancematrix()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the distances between the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or between the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns the distance matrix as a list of rows, where the number of columns of each row is equal to the row number (see section <A HREF="#subsec:distancematrix">14.1</A>).</P><!--TOC subsection Calculating the cluster centroids-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the cluster centroids</H3><!--SEC END --><P>To calculate the centroids of clusters of items stored in the record, use
-</P><PRE CLASS="verbatim">>>> cdata, cmask = record.clustercentroids()
-</PRE><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>clusterid</CODE> (default: <CODE>None</CODE>)<BR>
-Vector of integers showing to which cluster each item belongs. If <CODE>clusterid</CODE> is not given, then all items are assumed to belong to the same cluster.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies whether the arithmetic mean (<CODE>method=='a'</CODE>) or the median (<CODE>method=='m'</CODE>) is used to calculate the cluster center.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if the centroids of the rows of <CODE>data</CODE> are to be calculated (<CODE>transpose==0</CODE>), or the centroids of the columns of <CODE>data</CODE> (<CODE>transpose==1</CODE>).
-</LI></UL><P>This function returns the tuple <CODE>cdata, cmask</CODE>; see section <A HREF="#subsec:clustercentroids">14.2</A> for a description.</P><!--TOC subsection Calculating the distance between clusters-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating the distance between clusters</H3><!--SEC END --><P>
-To calculate the distance between clusters of items stored in the record, use
-</P><PRE CLASS="verbatim">>>> distance = record.clusterdistance()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>index1</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the first cluster. A cluster containing only one item <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>index2</CODE> (default: <CODE>0</CODE>)<BR>
-A list containing the indices of the items belonging to the second cluster. A cluster containing only one item <I>i</I> can be represented either as a list <CODE>[i]</CODE>, or as an integer <CODE>i</CODE>.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'a'</CODE>)<BR>
-Specifies how the distance between clusters is defined:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>'a'</CODE>: Distance between the two cluster centroids (arithmetic mean);
-</LI><LI CLASS="li-itemize"><CODE>'m'</CODE>: Distance between the two cluster centroids (median);
-</LI><LI CLASS="li-itemize"><CODE>'s'</CODE>: Shortest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'x'</CODE>: Longest pairwise distance between items in the two clusters;
-</LI><LI CLASS="li-itemize"><CODE>'v'</CODE>: Average over the pairwise distances between items in the two clusters.
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-If <CODE>transpose==0</CODE>, calculate the distance between the rows of <CODE>data</CODE>. If <CODE>transpose==1</CODE>, calculate the distance between the columns of <CODE>data</CODE>.
-</LI></UL><!--TOC subsection Performing hierarchical clustering-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Performing hierarchical clustering</H3><!--SEC END --><P>To perform hierarchical clustering on the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> tree = record.treecluster()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose==0</CODE>) or columns (<CODE>transpose==1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>'m'</CODE>)<BR>
-defines the linkage method to be used:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='s'</CODE>: pairwise single-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: pairwise maximum- (or complete-) linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='c'</CODE>: pairwise centroid-linkage clustering
-</LI><LI CLASS="li-itemize"><CODE>method=='a'</CODE>: pairwise average-linkage clustering
-</LI></UL>
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE><BR>
-Determines if genes or microarrays are being clustered. If <CODE>transpose==0</CODE>, genes (rows) are being clustered. If <CODE>transpose==1</CODE>, microarrays (columns) are clustered.
-</LI></UL><P>This function returns a <CODE>Tree</CODE> object. This object contains (number of items &#X2212; 1) nodes, where the number of items is the number of rows if rows were clustered, or the number of columns if columns were clustered. Each node describes a pairwise linking event, where the node attributes <CODE>left</CODE> and <CODE>right</CODE> each contain the number of one item or subnode, and <CODE>distance</CODE> the distance between them. Items are numbered from 0 to (numb [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Performing <I>k</I>-means or <I>k</I>-medians clustering</H3><!--SEC END --><P>To perform <I>k</I>-means or <I>k</I>-medians clustering on the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> clusterid, error, nfound = record.kcluster()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>nclusters</CODE> (default: <CODE>2</CODE>)<BR>
-The number of clusters <I>k</I>.
-</LI><LI CLASS="li-itemize"><CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>npass</CODE> (default: <CODE>1</CODE>)<BR>
-The number of times the <I>k</I>-means/-medians clustering algorithm is performed, each time with a different (random) initial condition. If <CODE>initialid</CODE> is given, the value of <CODE>npass</CODE> is ignored and the clustering algorithm is run only once, as it behaves deterministically in that case.
-</LI><LI CLASS="li-itemize"><CODE>method</CODE> (default: <CODE>a</CODE>)<BR>
-describes how the center of a cluster is found:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>method=='a'</CODE>: arithmetic mean (<I>k</I>-means clustering);
-</LI><LI CLASS="li-itemize"><CODE>method=='m'</CODE>: median (<I>k</I>-medians clustering).
-</LI></UL>
-For other values of <CODE>method</CODE>, the arithmetic mean is used.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns a tuple <CODE>(clusterid, error, nfound)</CODE>, where <CODE>clusterid</CODE> is an integer array containing the number of the cluster to which each row or cluster was assigned, <CODE>error</CODE> is the within-cluster sum of distances for the optimal clustering solution, and <CODE>nfound</CODE> is the number of times this optimal solution was found.</P><!--TOC subsection Calculating a Self-Organizing Map-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Calculating a Self-Organizing Map</H3><!--SEC END --><P>To calculate a Self-Organizing Map of the items stored in the record, use
-</P><PRE CLASS="verbatim">>>> clusterid, celldata = record.somcluster()
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>transpose</CODE> (default: <CODE>0</CODE>)<BR>
-Determines if rows (<CODE>transpose</CODE> is <CODE>0</CODE>) or columns (<CODE>transpose</CODE> is <CODE>1</CODE>) are to be clustered.
-</LI><LI CLASS="li-itemize"><CODE>nxgrid, nygrid</CODE> (default: <CODE>2, 1</CODE>)<BR>
-The number of cells horizontally and vertically in the rectangular grid on which the Self-Organizing Map is calculated.
-</LI><LI CLASS="li-itemize"><CODE>inittau</CODE> (default: <CODE>0.02</CODE>)<BR>
-The initial value for the parameter &#X3C4; that is used in the SOM algorithm. The default value for <CODE>inittau</CODE> is 0.02, which was used in Michael Eisen&#X2019;s Cluster/TreeView program.
-</LI><LI CLASS="li-itemize"><CODE>niter</CODE> (default: <CODE>1</CODE>)<BR>
-The number of iterations to be performed.
-</LI><LI CLASS="li-itemize"><CODE>dist</CODE> (default: <CODE>'e'</CODE>, Euclidean distance)<BR>
-Defines the distance function to be used (see <A HREF="#sec:distancefunctions">14.1</A>).
-</LI></UL><P>This function returns the tuple <CODE>(clusterid, celldata)</CODE>:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>clusterid</CODE>:<BR>
-An array with two columns, where the number of rows is equal to the number of items that were clustered. Each row contains the <I>x</I> and <I>y</I> coordinates of the cell in the rectangular SOM grid to which the item was assigned.
-</LI><LI CLASS="li-itemize"><CODE>celldata</CODE>:<BR>
-An array with dimensions (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of columns) if rows are being clustered, or (<CODE>nxgrid</CODE>, <CODE>nygrid</CODE>, number of rows) if columns are being clustered. Each element <CODE>[ix][iy]</CODE> of this array is a 1D vector containing the gene expression data for the centroid of the cluster in the grid cell with coordinates <CODE>[ix][iy]</CODE>.
-</LI></UL><!--TOC subsection Saving the clustering result-->
-<H3 CLASS="subsection"><!--SEC ANCHOR -->Saving the clustering result</H3><!--SEC END --><P>To save the clustering result, use
-</P><PRE CLASS="verbatim">>>> record.save(jobname, geneclusters, expclusters)
-</PRE><P>where the following arguments are defined:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>jobname</CODE><BR>
-The string <CODE>jobname</CODE> is used as the base name for names of the files that are to be saved.
-</LI><LI CLASS="li-itemize"><CODE>geneclusters</CODE><BR>
-This argument describes the gene (row-wise) clustering result. In case of <I>k</I>-means clustering, this is a 1D array containing the number of the cluster each gene belongs to. It can be calculated using <CODE>kcluster</CODE>. In case of hierarchical clustering, <CODE>geneclusters</CODE> is a <CODE>Tree</CODE> object.
-</LI><LI CLASS="li-itemize"><CODE>expclusters</CODE><BR>
-This argument describes the (column-wise) clustering result for the experimental conditions. In case of <I>k</I>-means clustering, this is a 1D array containing the number of the cluster each experimental condition belongs to. It can be calculated using <CODE>kcluster</CODE>. In case of hierarchical clustering, <CODE>expclusters</CODE> is a <CODE>Tree</CODE> object.
-</LI></UL><P>This method writes the text file <CODE>jobname.cdt</CODE>, <CODE>jobname.gtr</CODE>, <CODE>jobname.atr</CODE>, <CODE>jobname*.kgg</CODE>, and/or <CODE>jobname*.kag</CODE> for subsequent reading by the Java TreeView program. If <CODE>geneclusters</CODE> and <CODE>expclusters</CODE> are both <CODE>None</CODE>, this method only writes the text file <CODE>jobname.cdt</CODE>; this file can subsequently be read into a new <CODE>Record</CODE> object.
-</P><!--TOC section Example calculation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc196">14.8</A>&#XA0;&#XA0;Example calculation</H2><!--SEC END --><P>This is an example of a hierarchical clustering calculation, using single linkage clustering for genes and maximum linkage clustering for experimental conditions. As the Euclidean distance is being used for gene clustering, it is necessary to scale the node distances <CODE>genetree</CODE> such that they are all between zero and one. This is needed for the Java TreeView co [...]
->>> handle = open("cyano.txt")
->>> record = Cluster.read(handle)
->>> handle.close()
->>> genetree = record.treecluster(method='s')
->>> genetree.scale()
->>> exptree = record.treecluster(dist='u', transpose=1)
->>> record.save("cyano_result", genetree, exptree)
-</PRE><P>This will create the files <CODE>cyano_result.cdt</CODE>, <CODE>cyano_result.gtr</CODE>, and <CODE>cyano_result.atr</CODE>.</P><P>Similarly, we can save a <I>k</I>-means clustering solution:</P><PRE CLASS="verbatim">>>> from Bio import Cluster
->>> handle = open("cyano.txt")
->>> record = Cluster.read(handle)
->>> handle.close()
->>> (geneclusters, error, ifound) = record.kcluster(nclusters=5, npass=1000)
->>> (expclusters, error, ifound) = record.kcluster(nclusters=2, npass=100, transpose=1)
->>> record.save("cyano_result", geneclusters, expclusters)
-</PRE><P>This will create the files <CODE>cyano_result_K_G2_A2.cdt</CODE>, <CODE>cyano_result_K_G2.kgg</CODE>, and <CODE>cyano_result_K_A2.kag</CODE>.</P><!--TOC section Auxiliary functions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc197">14.9</A>&#XA0;&#XA0;Auxiliary functions</H2><!--SEC END --><P><CODE>median(data)</CODE>
-returns the median of the 1D array <CODE>data</CODE>.</P><P><CODE>mean(data)</CODE>
-returns the mean of the 1D array <CODE>data</CODE>.</P><P><CODE>version()</CODE>
-returns the version number of the underlying C Clustering Library as a string.</P><!--TOC chapter Supervised learning methods-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc198">Chapter&#XA0;15</A>&#XA0;&#XA0;Supervised learning methods</H1><!--SEC END --><P>Note the supervised learning methods described in this chapter all require Numerical Python (numpy) to be installed.</P><!--TOC section The Logistic Regression Model-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc199">15.1</A>&#XA0;&#XA0;The Logistic Regression Model</H2><!--SEC END --><P>
-<A NAME="sec:LogisticRegression"></A></P><!--TOC subsection Background and Purpose-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc200">15.1.1</A>&#XA0;&#XA0;Background and Purpose</H3><!--SEC END --><P>Logistic regression is a supervised learning approach that attempts to distinguish <I>K</I> classes from each other using a weighted sum of some predictor variables <I>x</I><SUB><I>i</I></SUB>. The logistic regression model is used to calculate the weights &#X3B2;<SUB><I>i</I></SUB> of the predictor variables. In Biopython, the logistic regression model is current [...]
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<I>x</I><SUB>1</SUB>: the number of base pairs between them;
-</LI><LI CLASS="li-itemize"><I>x</I><SUB>2</SUB>: their similarity in expression profile.
-</LI></UL><P>In a logistic regression model, we use a weighted sum of these two predictors to calculate a joint score <I>S</I>:
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-<I>S</I>&#XA0;=&#XA0;&#X3B2;<SUB>0</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>1</SUB>&#XA0;<I>x</I><SUB>1</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>2</SUB>&#XA0;<I>x</I><SUB>2</SUB>.
-&#XA0;&#XA0;&#XA0;&#XA0;(15.1)</TD></TR>
-</TABLE><P>
-The logistic regression model gives us appropriate values for the parameters &#X3B2;<SUB>0</SUB>, &#X3B2;<SUB>1</SUB>, &#X3B2;<SUB>2</SUB> using two sets of example genes:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-OP: Adjacent genes, on the same strand of DNA, known to belong to the same operon;
-</LI><LI CLASS="li-itemize">NOP: Adjacent genes, on the same strand of DNA, known to belong to different operons.
-</LI></UL><P>In the logistic regression model, the probability of belonging to a class depends on the score via the logistic function. For the two classes OP and NOP, we can write this as
-</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-
-
-&#XA0;&#XA0;&#XA0;&#XA0;&#XA0;
-
-</TD><TD CLASS="dcell"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD ALIGN=right NOWRAP>Pr(<I>OP</I>|<I>x</I><SUB>1</SUB>,&#XA0;<I>x</I><SUB>2</SUB>)</TD><TD ALIGN=center NOWRAP>&#XA0;=</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">exp(&#X3B2;<SUB>0</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>1</SUB>&#XA0;<I>x</I><SUB>1</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>2</SUB>&#XA0;<I>x</I><SUB>2 [...]
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">1+exp(&#X3B2;<SUB>0</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>1</SUB>&#XA0;<I>x</I><SUB>1</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>2</SUB>&#XA0;<I>x</I><SUB>2</SUB>)</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;<A NAME="eq:OP">&#XA0;</A>&#XA0;</TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP>&#XA0;&#XA0;&#XA0;&#XA0;(15.2)</TD></TR>
-<TR><TD ALIGN=right NOWRAP>Pr(<I>NOP</I>|<I>x</I><SUB>1</SUB>,&#XA0;<I>x</I><SUB>2</SUB>)</TD><TD ALIGN=center NOWRAP>&#XA0;=</TD><TD ALIGN=left NOWRAP><TABLE CLASS="display"><TR VALIGN="middle"><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center">1</TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">1+exp(&#X3B2;<SUB>0</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>1</SUB>&#XA0;<I>x</I><SUB>1</SUB>&#XA0;+&#XA0;&#X3B2;<SUB>2</SUB>&#XA0;<I>x</I><SUB>2</SUB>)</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;<A NAME="eq:NOP">&#XA0;</A>
-</TD></TR>
-</TABLE></TD><TD ALIGN=right NOWRAP>&#XA0;&#XA0;&#XA0;&#XA0;(15.3)</TD></TR>
-</TABLE></TD></TR>
-</TABLE><P>
-Using a set of gene pairs for which it is known whether they belong to the same operon (class OP) or to different operons (class NOP), we can calculate the weights &#X3B2;<SUB>0</SUB>, &#X3B2;<SUB>1</SUB>, &#X3B2;<SUB>2</SUB> by maximizing the log-likelihood corresponding to the probability functions (<A HREF="#eq:OP">15.2</A>) and (<A HREF="#eq:NOP">15.3</A>).</P><!--TOC subsection Training the logistic regression model-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc201">15.1.2</A>&#XA0;&#XA0;Training the logistic regression model</H3><!--SEC END --><P>
-<A NAME="subsec:LogisticRegressionTraining"></A></P><BLOCKQUOTE CLASS="table"><DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV>
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 15.1: Adjacent gene pairs known to belong to the same operon (class OP) or to different operons (class NOP). Intergene distances are negative if the two genes overlap.</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=center NOWRAP>Gene pair</TD><TD ALIGN=center NOWRAP>Intergene distance (<I>x</I><SUB>1</SUB>)</TD><TD ALIGN=center NOWRAP>Gene expression score (<I>x</I><SUB>2</SUB>)</TD><TD ALIGN=center NOWRAP>Class</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>cotJA</I> &#X2014; <I>cotJB</I></TD><TD ALIGN=center NOWRAP>-53</TD><TD ALIGN=center NOWRAP>-200.78</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yesK</I> &#X2014; <I>yesL</I></TD><TD ALIGN=center NOWRAP>117</TD><TD ALIGN=center NOWRAP>-267.14</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplA</I> &#X2014; <I>lplB</I></TD><TD ALIGN=center NOWRAP>57</TD><TD ALIGN=center NOWRAP>-163.47</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplB</I> &#X2014; <I>lplC</I></TD><TD ALIGN=center NOWRAP>16</TD><TD ALIGN=center NOWRAP>-190.30</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplC</I> &#X2014; <I>lplD</I></TD><TD ALIGN=center NOWRAP>11</TD><TD ALIGN=center NOWRAP>-220.94</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lplD</I> &#X2014; <I>yetF</I></TD><TD ALIGN=center NOWRAP>85</TD><TD ALIGN=center NOWRAP>-193.94</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfmT</I> &#X2014; <I>yfmS</I></TD><TD ALIGN=center NOWRAP>16</TD><TD ALIGN=center NOWRAP>-182.71</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfmF</I> &#X2014; <I>yfmE</I></TD><TD ALIGN=center NOWRAP>15</TD><TD ALIGN=center NOWRAP>-180.41</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>citS</I> &#X2014; <I>citT</I></TD><TD ALIGN=center NOWRAP>-26</TD><TD ALIGN=center NOWRAP>-181.73</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>citM</I> &#X2014; <I>yflN</I></TD><TD ALIGN=center NOWRAP>58</TD><TD ALIGN=center NOWRAP>-259.87</TD><TD ALIGN=center NOWRAP>OP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfiI</I> &#X2014; <I>yfiJ</I></TD><TD ALIGN=center NOWRAP>126</TD><TD ALIGN=center NOWRAP>-414.53</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>lipB</I> &#X2014; <I>yfiQ</I></TD><TD ALIGN=center NOWRAP>191</TD><TD ALIGN=center NOWRAP>-249.57</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfiU</I> &#X2014; <I>yfiV</I></TD><TD ALIGN=center NOWRAP>113</TD><TD ALIGN=center NOWRAP>-265.28</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yfhH</I> &#X2014; <I>yfhI</I></TD><TD ALIGN=center NOWRAP>145</TD><TD ALIGN=center NOWRAP>-312.99</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>cotY</I> &#X2014; <I>cotX</I></TD><TD ALIGN=center NOWRAP>154</TD><TD ALIGN=center NOWRAP>-213.83</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yjoB</I> &#X2014; <I>rapA</I></TD><TD ALIGN=center NOWRAP>147</TD><TD ALIGN=center NOWRAP>-380.85</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>ptsI</I> &#X2014; <I>splA</I></TD><TD ALIGN=center NOWRAP>93</TD><TD ALIGN=center NOWRAP>-291.13</TD><TD ALIGN=center NOWRAP>NOP</TD></TR>
-</TABLE>
-<A NAME="table:training"></A>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>Table <A HREF="#table:training">15.1</A> lists some of the <I>Bacillus subtilis</I> gene pairs for which the operon structure is known.
-Let&#X2019;s calculate the logistic regression model from these data:</P><PRE CLASS="verbatim">>>> from Bio import LogisticRegression
->>> xs = [[-53, -200.78],
-          [117, -267.14],
-          [57, -163.47],
-          [16, -190.30],
-          [11, -220.94],
-          [85, -193.94],
-          [16, -182.71],
-          [15, -180.41],
-          [-26, -181.73],
-          [58, -259.87],
-          [126, -414.53],
-          [191, -249.57],
-          [113, -265.28],
-          [145, -312.99],
-          [154, -213.83],
-          [147, -380.85],
-          [93, -291.13]]
->>> ys = [1,
-          1,
-          1,
-          1,
-          1,
-          1,
-          1,
-          1,
-          1,
-          1,
-          0,
-          0,
-          0,
-          0,
-          0,
-          0,
-          0]
->>> model = LogisticRegression.train(xs, ys)
-</PRE><P>Here, <CODE>xs</CODE> and <CODE>ys</CODE> are the training data: <CODE>xs</CODE> contains the predictor variables for each gene pair, and <CODE>ys</CODE> specifies if the gene pair belongs to the same operon (<CODE>1</CODE>, class OP) or different operons (<CODE>0</CODE>, class NOP). The resulting logistic regression model is stored in <CODE>model</CODE>, which contains the weights &#X3B2;<SUB>0</SUB>, &#X3B2;<SUB>1</SUB>, and &#X3B2;<SUB>2</SUB>:</P><PRE CLASS="verbatim">>&g [...]
-[8.9830290157144681, -0.035968960444850887, 0.02181395662983519]
-</PRE><P>Note that &#X3B2;<SUB>1</SUB> is negative, as gene pairs with a shorter intergene distance have a higher probability of belonging to the same operon (class OP). On the other hand, &#X3B2;<SUB>2</SUB> is positive, as gene pairs belonging to the same operon typically have a higher similarity score of their gene expression profiles.
-The parameter &#X3B2;<SUB>0</SUB> is positive due to the higher prevalence of operon gene pairs than non-operon gene pairs in the training data.</P><P>The function <CODE>train</CODE> has two optional arguments: <CODE>update_fn</CODE> and <CODE>typecode</CODE>. The <CODE>update_fn</CODE> can be used to specify a callback function, taking as arguments the iteration number and the log-likelihood. With the callback function, we can for example track the progress of the model calculation (whi [...]
-        print("Iteration:", iteration, "Log-likelihood function:", loglikelihood)
->>>
->>> model = LogisticRegression.train(xs, ys, update_fn=show_progress)
-Iteration: 0 Log-likelihood function: -11.7835020695
-Iteration: 1 Log-likelihood function: -7.15886767672
-Iteration: 2 Log-likelihood function: -5.76877209868
-Iteration: 3 Log-likelihood function: -5.11362294338
-Iteration: 4 Log-likelihood function: -4.74870642433
-Iteration: 5 Log-likelihood function: -4.50026077146
-Iteration: 6 Log-likelihood function: -4.31127773737
-Iteration: 7 Log-likelihood function: -4.16015043396
-Iteration: 8 Log-likelihood function: -4.03561719785
-Iteration: 9 Log-likelihood function: -3.93073282192
-Iteration: 10 Log-likelihood function: -3.84087660929
-Iteration: 11 Log-likelihood function: -3.76282560605
-Iteration: 12 Log-likelihood function: -3.69425027154
-Iteration: 13 Log-likelihood function: -3.6334178602
-Iteration: 14 Log-likelihood function: -3.57900855837
-Iteration: 15 Log-likelihood function: -3.52999671386
-Iteration: 16 Log-likelihood function: -3.48557145163
-Iteration: 17 Log-likelihood function: -3.44508206139
-Iteration: 18 Log-likelihood function: -3.40799948447
-Iteration: 19 Log-likelihood function: -3.3738885624
-Iteration: 20 Log-likelihood function: -3.3423876581
-Iteration: 21 Log-likelihood function: -3.31319343769
-Iteration: 22 Log-likelihood function: -3.2860493346
-Iteration: 23 Log-likelihood function: -3.2607366863
-Iteration: 24 Log-likelihood function: -3.23706784091
-Iteration: 25 Log-likelihood function: -3.21488073614
-Iteration: 26 Log-likelihood function: -3.19403459259
-Iteration: 27 Log-likelihood function: -3.17440646052
-Iteration: 28 Log-likelihood function: -3.15588842703
-Iteration: 29 Log-likelihood function: -3.13838533947
-Iteration: 30 Log-likelihood function: -3.12181293595
-Iteration: 31 Log-likelihood function: -3.10609629966
-Iteration: 32 Log-likelihood function: -3.09116857282
-Iteration: 33 Log-likelihood function: -3.07696988017
-Iteration: 34 Log-likelihood function: -3.06344642288
-Iteration: 35 Log-likelihood function: -3.05054971191
-Iteration: 36 Log-likelihood function: -3.03823591619
-Iteration: 37 Log-likelihood function: -3.02646530573
-Iteration: 38 Log-likelihood function: -3.01520177394
-Iteration: 39 Log-likelihood function: -3.00441242601
-Iteration: 40 Log-likelihood function: -2.99406722296
-Iteration: 41 Log-likelihood function: -2.98413867259
-</PRE><P>The iteration stops once the increase in the log-likelihood function is less than 0.01. If no convergence is reached after 500 iterations, the <CODE>train</CODE> function returns with an <CODE>AssertionError</CODE>.</P><P>The optional keyword <CODE>typecode</CODE> can almost always be ignored. This keyword allows the user to choose the type of Numeric matrix to use. In particular, to avoid memory problems for very large problems, it may be necessary to use single-precision float [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc202">15.1.3</A>&#XA0;&#XA0;Using the logistic regression model for classification</H3><!--SEC END --><P>Classification is performed by calling the <CODE>classify</CODE> function. Given a logistic regression model and the values for <I>x</I><SUB>1</SUB> and <I>x</I><SUB>2</SUB> (e.g. for a gene pair of unknown operon structure), the <CODE>classify</CODE> function returns <CODE>1</CODE> or <CODE>0</CODE>, corresponding to class OP and c [...]
-<DIV CLASS="center">
-<DIV CLASS="caption"><TABLE CELLSPACING=6 CELLPADDING=0><TR><TD VALIGN=top ALIGN=left>Table 15.2: Adjacent gene pairs of unknown operon status.</TD></TR>
-</TABLE></DIV>
-<TABLE BORDER=1 CELLSPACING=0 CELLPADDING=1><TR><TD ALIGN=center NOWRAP>Gene pair</TD><TD ALIGN=center NOWRAP>Intergene distance <I>x</I><SUB>1</SUB></TD><TD ALIGN=center NOWRAP>Gene expression score <I>x</I><SUB>2</SUB></TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yxcE</I> &#X2014; <I>yxcD</I></TD><TD ALIGN=center NOWRAP>6</TD><TD ALIGN=center NOWRAP>-173.143442352</TD></TR>
-<TR><TD ALIGN=center NOWRAP><I>yxiB</I> &#X2014; <I>yxiA</I></TD><TD ALIGN=center NOWRAP>309</TD><TD ALIGN=center NOWRAP>-271.005880394</TD></TR>
-</TABLE>
-</DIV>
-<DIV CLASS="center"><HR WIDTH="80%" SIZE=2></DIV></BLOCKQUOTE><P>The logistic regression model classifies <I>yxcE</I>, <I>yxcD</I> as belonging to the same operon (class OP), while <I>yxiB</I>, <I>yxiA</I> are predicted to belong to different operons:
-</P><PRE CLASS="verbatim">>>> print("yxcE, yxcD:", LogisticRegression.classify(model, [6, -173.143442352]))
-yxcE, yxcD: 1
->>> print("yxiB, yxiA:", LogisticRegression.classify(model, [309, -271.005880394]))
-yxiB, yxiA: 0
-</PRE><P>(which, by the way, agrees with the biological literature).</P><P>To find out how confident we can be in these predictions, we can call the <CODE>calculate</CODE> function to obtain the probabilities (equations (<A HREF="#eq:OP">15.2</A>) and <A HREF="#eq:NOP">15.3</A>) for class OP and NOP. For <I>yxcE</I>, <I>yxcD</I> we find
-</P><PRE CLASS="verbatim">>>> q, p = LogisticRegression.calculate(model, [6, -173.143442352])
->>> print("class OP: probability =", p, "class NOP: probability =", q)
-class OP: probability = 0.993242163503 class NOP: probability = 0.00675783649744
-</PRE><P>and for <I>yxiB</I>, <I>yxiA</I>
-</P><PRE CLASS="verbatim">>>> q, p = LogisticRegression.calculate(model, [309, -271.005880394])
->>> print("class OP: probability =", p, "class NOP: probability =", q)
-class OP: probability = 0.000321211251817 class NOP: probability = 0.999678788748
-</PRE><P>To get some idea of the prediction accuracy of the logistic regression model, we can apply it to the training data:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
-        print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-</PRE><P>showing that the prediction is correct for all but one of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
-        model = LogisticRegression.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
-        print("True:", ys[i], "Predicted:", LogisticRegression.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-</PRE><P>The leave-one-out analysis shows that the prediction of the logistic regression model is incorrect for only two of the gene pairs, which corresponds to a prediction accuracy of 88%.</P><!--TOC subsection Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc203">15.1.4</A>&#XA0;&#XA0;Logistic Regression, Linear Discriminant Analysis, and Support Vector Machines</H3><!--SEC END --><P>The logistic regression model is similar to linear discriminant analysis. In linear discriminant analysis, the class probabilities also follow equations (<A HREF="#eq:OP">15.2</A>) and (<A HREF="#eq:NOP">15.3</A>). However, instead of estimating the coefficients &#X3B2; directly, we first fit a normal distrib [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc204">15.2</A>&#XA0;&#XA0;<I>k</I>-Nearest Neighbors</H2><!--SEC END --><!--TOC subsection Background and purpose-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc205">15.2.1</A>&#XA0;&#XA0;Background and purpose</H3><!--SEC END --><P>The <I>k</I>-nearest neighbors method is a supervised learning approach that does not need to fit a model to the data. Instead, data points are classified based on the categories of the <I>k</I> nearest neighbors in the training data set.</P><P>In Biopython, the <I>k</I>-nearest neighbors method is available in <CODE>Bio.kNN</CODE>. To illustrate the use of the <I [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc206">15.2.2</A>&#XA0;&#XA0;Initializing a <I>k</I>-nearest neighbors model</H3><!--SEC END --><P>Using the data in Table <A HREF="#table:training">15.1</A>, we create and initialize a <I>k</I>-nearest neighbors model as follows:</P><PRE CLASS="verbatim">>>> from Bio import kNN
->>> k = 3
->>> model = kNN.train(xs, ys, k)
-</PRE><P>where <CODE>xs</CODE> and <CODE>ys</CODE> are the same as in Section <A HREF="#subsec:LogisticRegressionTraining">15.1.2</A>. Here, <CODE>k</CODE> is the number of neighbors <I>k</I> that will be considered for the classification. For classification into two classes, choosing an odd number for <I>k</I> lets you avoid tied votes. The function name <CODE>train</CODE> is a bit of a misnomer, since no model training is done: this function simply stores <CODE>xs</CODE>, <CODE>ys</COD [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc207">15.2.3</A>&#XA0;&#XA0;Using a <I>k</I>-nearest neighbors model for classification</H3><!--SEC END --><P>To classify new data using the <I>k</I>-nearest neighbors model, we use the <CODE>classify</CODE> function. This function takes a data point (<I>x</I><SUB>1</SUB>,<I>x</I><SUB>2</SUB>) and finds the <I>k</I>-nearest neighbors in the training data set <CODE>xs</CODE>. The data point (<I>x</I><SUB>1</SUB>, <I>x</I><SUB>2</SUB>) i [...]
-</P><PRE CLASS="verbatim">>>> x = [6, -173.143442352]
->>> print("yxcE, yxcD:", kNN.classify(model, x))
-yxcE, yxcD: 1
->>> x = [309, -271.005880394]
->>> print("yxiB, yxiA:", kNN.classify(model, x))
-yxiB, yxiA: 0
-</PRE><P>In agreement with the logistic regression model, <I>yxcE</I>, <I>yxcD</I> are classified as belonging to the same operon (class OP), while <I>yxiB</I>, <I>yxiA</I> are predicted to belong to different operons.</P><P>The <CODE>classify</CODE> function lets us specify both a distance function and a weight function as optional arguments. The distance function affects which <I>k</I> neighbors are chosen as the nearest neighbors, as these are defined as the neighbors with the smalles [...]
-...    assert len(x1)==2
-...    assert len(x2)==2
-...    distance = abs(x1[0]-x2[0]) + abs(x1[1]-x2[1])
-...    return distance
-...
->>> x = [6, -173.143442352]
->>> print("yxcE, yxcD:", kNN.classify(model, x, distance_fn = cityblock))
-yxcE, yxcD: 1
-</PRE><P>The weight function can be used for weighted voting. For example, we may want to give closer neighbors a higher weight than neighbors that are further away:</P><PRE CLASS="verbatim">>>> def weight(x1, x2):
-...    assert len(x1)==2
-...    assert len(x2)==2
-...    return exp(-abs(x1[0]-x2[0]) - abs(x1[1]-x2[1]))
-...
->>> x = [6, -173.143442352]
->>> print("yxcE, yxcD:", kNN.classify(model, x, weight_fn = weight))
-yxcE, yxcD: 1
-</PRE><P>By default, all neighbors are given an equal weight.</P><P>To find out how confident we can be in these predictions, we can call the <CODE>calculate</CODE> function, which will calculate the total weight assigned to the classes OP and NOP. For the default weighting scheme, this reduces to the number of neighbors in each category. For <I>yxcE</I>, <I>yxcD</I>, we find
-</P><PRE CLASS="verbatim">>>> x = [6, -173.143442352]
->>> weight = kNN.calculate(model, x)
->>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1])
-class OP: weight = 0.0 class NOP: weight = 3.0
-</PRE><P>which means that all three neighbors of <CODE>x1</CODE>, <CODE>x2</CODE> are in the NOP class. As another example, for <I>yesK</I>, <I>yesL</I> we find</P><PRE CLASS="verbatim">>>> x = [117, -267.14]
->>> weight = kNN.calculate(model, x)
->>> print("class OP: weight =", weight[0], "class NOP: weight =", weight[1])
-class OP: weight = 2.0 class NOP: weight = 1.0
-</PRE><P>which means that two neighbors are operon pairs and one neighbor is a non-operon pair.</P><P>To get some idea of the prediction accuracy of the <I>k</I>-nearest neighbors approach, we can apply it to the training data:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
-        print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-</PRE><P>showing that the prediction is correct for all but two of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted:
-</P><PRE CLASS="verbatim">>>> for i in range(len(ys)):
-        model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
-        print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 1
-True: 1 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 1
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 0
-True: 0 Predicted: 1
-</PRE><P>The leave-one-out analysis shows that <I>k</I>-nearest neighbors model is correct for 13 out of 17 gene pairs, which corresponds to a prediction accuracy of 76%.</P><!--TOC section Na&#XEF;ve Bayes-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc208">15.3</A>&#XA0;&#XA0;Na&#XEF;ve Bayes</H2><!--SEC END --><P>This section will describe the <CODE>Bio.NaiveBayes</CODE> module.</P><!--TOC section Maximum Entropy-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc209">15.4</A>&#XA0;&#XA0;Maximum Entropy</H2><!--SEC END --><P>This section will describe the <CODE>Bio.MaximumEntropy</CODE> module.</P><!--TOC section Markov Models-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc210">15.5</A>&#XA0;&#XA0;Markov Models</H2><!--SEC END --><P>This section will describe the <CODE>Bio.MarkovModel</CODE> and/or <CODE>Bio.HMM.MarkovModel</CODE> modules.</P><!--TOC chapter Graphics including GenomeDiagram-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc211">Chapter&#XA0;16</A>&#XA0;&#XA0;Graphics including GenomeDiagram</H1><!--SEC END --><P>
-<A NAME="chapter:graphics"></A></P><P>The <CODE>Bio.Graphics</CODE> module depends on the third party Python library
-<A HREF="http://www.reportlab.org">ReportLab</A>. Although focused on producing PDF files,
-ReportLab can also create encapsulated postscript (EPS) and (SVG) files. In addition
-to these vector based images, provided certain further dependencies such as the
-<A HREF="http://www.pythonware.com/products/pil/">Python Imaging Library (PIL)</A> are
-installed, ReportLab can also output bitmap images (including JPEG, PNG, GIF, BMP
-and PICT formats).</P><!--TOC section GenomeDiagram-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc212">16.1</A>&#XA0;&#XA0;GenomeDiagram</H2><!--SEC END --><P>
-<A NAME="sec:genomediagram"></A>
-</P><!--TOC subsection Introduction-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc213">16.1.1</A>&#XA0;&#XA0;Introduction</H3><!--SEC END --><P>The <CODE>Bio.Graphics.GenomeDiagram</CODE> module was added to Biopython 1.50,
-having previously been available as a separate Python module dependent on Biopython.
-GenomeDiagram is described in the Bioinformatics journal publication by Pritchard et al. (2006) [<A HREF="#pritchard2006">2</A>],
-which includes some examples images. There is a PDF copy of the old manual here,
-<A HREF="http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf"><TT>http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf</TT></A> which has some
-more examples.
-</P><P>As the name might suggest, GenomeDiagram was designed for drawing whole genomes, in
-particular prokaryotic genomes, either as linear diagrams (optionally broken up into
-fragments to fit better) or as circular wheel diagrams. Have a look at Figure 2 in
-Toth <I>et al.</I> (2006) [<A HREF="#toth2006">3</A>]
-for a good example. It proved also well suited to drawing quite detailed figures for
-smaller genomes such as phage, plasmids or mitochrondia, for example see Figures 1
-and 2 in Van der Auwera <I>et al.</I> (2009) [<A HREF="#vanderauwera2009">4</A>]
-(shown with additional manual editing).</P><P>This module is easiest to use if you have your genome loaded as a <CODE>SeqRecord</CODE>
-object containing lots of <CODE>SeqFeature</CODE> objects - for example as loaded from a
-GenBank file (see Chapters&#XA0;<A HREF="#chapter:SeqRecord">4</A> and&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).</P><!--TOC subsection Diagrams, tracks, feature-sets and features-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc214">16.1.2</A>&#XA0;&#XA0;Diagrams, tracks, feature-sets and features</H3><!--SEC END --><P>GenomeDiagram uses a nested set of objects. At the top level, you have a diagram
-object representing a sequence (or sequence region) along the horizontal axis (or
-circle). A diagram can contain one or more tracks, shown stacked vertically (or
-radially on circular diagrams). These will typically all have the same length
-and represent the same sequence region. You might use one track to show the gene
-locations, another to show regulatory regions, and a third track to show the GC
-percentage. </P><P>The most commonly used type of track will contain features, bundled together in
-feature-sets. You might choose to use one feature-set for all your CDS features,
-and another for tRNA features. This isn&#X2019;t required - they can all go in the same
-feature-set, but it makes it easier to update the properties of just selected
-features (e.g. make all the tRNA features red).</P><P>There are two main ways to build up a complete diagram. Firstly, the top down
-approach where you create a diagram object, and then using its methods add
-track(s), and use the track methods to add feature-set(s), and use their
-methods to add the features. Secondly, you can create the individual objects
-separately (in whatever order suits your code), and then combine them.</P><!--TOC subsection A top down example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc215">16.1.3</A>&#XA0;&#XA0;A top down example</H3><!--SEC END --><P>
-<A NAME="sec:gd_top_down"></A></P><P>We&#X2019;re going to draw a whole genome from a <CODE>SeqRecord</CODE> object read in from
-a GenBank file (see Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>). This example uses the
-pPCP1 plasmid from <I>Yersinia pestis biovar Microtus</I>, the file is
-included with the Biopython unit tests under the GenBank folder, or online
-<A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb"><TT>NC_005816.gb</TT></A> from our website.</P><PRE CLASS="verbatim">from reportlab.lib import colors
-from reportlab.lib.units import cm
-from Bio.Graphics import GenomeDiagram
-from Bio import SeqIO
-record = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><P>We&#X2019;re using a top down approach, so after loading in our sequence we next
-create an empty diagram, then add an (empty) track, and to that add an
-(empty) feature set:</P><PRE CLASS="verbatim">gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1")
-gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features")
-gd_feature_set = gd_track_for_features.new_set()
-</PRE><P>Now the fun part - we take each gene <CODE>SeqFeature</CODE> object in our
-<CODE>SeqRecord</CODE>, and use it to generate a feature on the diagram. We&#X2019;re
-going to color them blue, alternating between a dark blue and a light blue.
-</P><PRE CLASS="verbatim">for feature in record.features:
-    if feature.type != "gene":
-        #Exclude this feature
-        continue
-    if len(gd_feature_set) % 2 == 0:
-        color = colors.blue
-    else:
-        color = colors.lightblue
-    gd_feature_set.add_feature(feature, color=color, label=True)
-</PRE><P>Now we come to actually making the output file. This happens in two steps,
-first we call the <CODE>draw</CODE> method, which creates all the shapes using
-ReportLab objects. Then we call the <CODE>write</CODE> method which renders these
-to the requested file format. Note you can output in multiple file formats:</P><PRE CLASS="verbatim">gd_diagram.draw(format="linear", orientation="landscape", pagesize='A4',
-                fragments=4, start=0, end=len(record))
-gd_diagram.write("plasmid_linear.pdf", "PDF")
-gd_diagram.write("plasmid_linear.eps", "EPS")
-gd_diagram.write("plasmid_linear.svg", "SVG")
-</PRE><P>Also, provided you have the dependencies installed, you can also do bitmaps,
-for example:</P><PRE CLASS="verbatim">gd_diagram.write("plasmid_linear.png", "PNG")
-</PRE><P>
-<IMG SRC="images/plasmid_linear.png" width=550, height=400></P><P>
-
-Notice that the <CODE>fragments</CODE> argument which we set to four controls how
-many pieces the genome gets broken up into.</P><P>If you want to do a circular figure, then try this:</P><PRE CLASS="verbatim">gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm),
-                start=0, end=len(record), circle_core=0.7)
-gd_diagram.write("plasmid_circular.pdf", "PDF")
-</PRE><P>
-<IMG SRC="images/plasmid_circular.png" width=400, height=400></P><P>
-
-These figures are not very exciting, but we&#X2019;ve only just got started.</P><!--TOC subsection A bottom up example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc216">16.1.4</A>&#XA0;&#XA0;A bottom up example</H3><!--SEC END --><P>
-Now let&#X2019;s produce exactly the same figures, but using the bottom up approach.
-This means we create the different objects directly (and this can be done in
-almost any order) and then combine them.</P><PRE CLASS="verbatim">from reportlab.lib import colors
-from reportlab.lib.units import cm
-from Bio.Graphics import GenomeDiagram
-from Bio import SeqIO
-record = SeqIO.read("NC_005816.gb", "genbank")
-
-#Create the feature set and its feature objects,
-gd_feature_set = GenomeDiagram.FeatureSet()
-for feature in record.features:
-    if feature.type != "gene":
-        #Exclude this feature
-        continue
-    if len(gd_feature_set) % 2 == 0:
-        color = colors.blue
-    else:
-        color = colors.lightblue
-    gd_feature_set.add_feature(feature, color=color, label=True)
-#(this for loop is the same as in the previous example)
-
-#Create a track, and a diagram
-gd_track_for_features = GenomeDiagram.Track(name="Annotated Features")
-gd_diagram = GenomeDiagram.Diagram("Yersinia pestis biovar Microtus plasmid pPCP1")
-
-#Now have to glue the bits together...
-gd_track_for_features.add_set(gd_feature_set)
-gd_diagram.add_track(gd_track_for_features, 1)
-</PRE><P>You can now call the <CODE>draw</CODE> and <CODE>write</CODE> methods as before to produce
-a linear or circular diagram, using the code at the end of the top-down example
-above. The figures should be identical.</P><!--TOC subsection Features without a SeqFeature-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc217">16.1.5</A>&#XA0;&#XA0;Features without a SeqFeature</H3><!--SEC END --><P>
-<A NAME="sec:gd_features_without_seqfeatures"></A></P><P>In the above example we used a <CODE>SeqRecord</CODE>&#X2019;s <CODE>SeqFeature</CODE> objects
-to build our diagram (see also Section&#XA0;<A HREF="#sec:seq_features">4.3</A>).
-Sometimes you won&#X2019;t have <CODE>SeqFeature</CODE> objects,
-but just the coordinates for a feature you want to draw. You have to create
-minimal <CODE>SeqFeature</CODE> object, but this is easy:</P><PRE CLASS="verbatim">from Bio.SeqFeature import SeqFeature, FeatureLocation
-my_seq_feature = SeqFeature(FeatureLocation(50,100),strand=+1)
-</PRE><P>For strand, use <TT>+1</TT> for the forward strand, <TT>-1</TT> for the
-reverse strand, and <TT>None</TT> for both. Here is a short self contained
-example:</P><PRE CLASS="verbatim">from Bio.SeqFeature import SeqFeature, FeatureLocation
-from Bio.Graphics import GenomeDiagram
-from reportlab.lib.units import cm
-
-gdd = GenomeDiagram.Diagram('Test Diagram')
-gdt_features = gdd.new_track(1, greytrack=False)
-gds_features = gdt_features.new_set()
-
-#Add three features to show the strand options,
-feature = SeqFeature(FeatureLocation(25, 125), strand=+1)
-gds_features.add_feature(feature, name="Forward", label=True)
-feature = SeqFeature(FeatureLocation(150, 250), strand=None)
-gds_features.add_feature(feature, name="Strandless", label=True)
-feature = SeqFeature(FeatureLocation(275, 375), strand=-1)
-gds_features.add_feature(feature, name="Reverse", label=True)
-
-gdd.draw(format='linear', pagesize=(15*cm,4*cm), fragments=1,
-         start=0, end=400)
-gdd.write("GD_labels_default.pdf", "pdf")
-</PRE><P>
-The top part of the image in the next subsection shows the output
-
-
-(in the default feature color, pale green).</P><P>Notice that we have used the <TT>name</TT> argument here to specify the
-caption text for these features. This is discussed in more detail next.</P><!--TOC subsection Feature captions-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc218">16.1.6</A>&#XA0;&#XA0;Feature captions</H3><!--SEC END --><P>
-<A NAME="sec:gd_feature_captions"></A></P><P>Recall we used the following (where <TT>feature</TT> was a
-<CODE>SeqFeature</CODE> object) to add a feature to the diagram:</P><PRE CLASS="verbatim">gd_feature_set.add_feature(feature, color=color, label=True)
-</PRE><P>In the example above the <CODE>SeqFeature</CODE> annotation was used to pick a
-sensible caption for the features. By default the following possible entries
-under the <CODE>SeqFeature</CODE> object&#X2019;s qualifiers dictionary are used:
-<TT>gene</TT>, <TT>label</TT>, <TT>name</TT>, <TT>locus_tag</TT>, and
-<TT>product</TT>. More simply, you can specify a name directly:</P><PRE CLASS="verbatim">gd_feature_set.add_feature(feature, color=color, label=True, name="My Gene")
-</PRE><P>In addition to the caption text for each feature&#X2019;s label, you can also choose
-the font, position (this defaults to the start of the sigil, you can also
-choose the middle or at the end) and orientation (for linear diagrams only,
-where this defaults to rotated by 45 degrees):</P><PRE CLASS="verbatim">#Large font, parallel with the track
-gd_feature_set.add_feature(feature, label=True, color="green",
-                           label_size=25, label_angle=0)
-
-#Very small font, perpendicular to the track (towards it)
-gd_feature_set.add_feature(feature, label=True, color="purple",
-                           label_position="end",
-                           label_size=4, label_angle=90)
-
-#Small font, perpendicular to the track (away from it)
-gd_feature_set.add_feature(feature, label=True, color="blue",
-                           label_position="middle",
-                           label_size=6, label_angle=-90)
-</PRE><P>Combining each of these three fragments with the complete example
-in the previous section should give something like
-
-this:</P><P><IMG SRC="images/GD_sigil_labels.png" width=600, height=700>
-<A NAME="fig:gd_sigil_labels"></A></P><P>We&#X2019;ve not shown it here, but you can also set <TT>label_color</TT> to
-control the label&#X2019;s color (used in Section&#XA0;<A HREF="#sec:gd_nice_example">16.1.9</A>).</P><P>You&#X2019;ll notice the default font is quite small - this makes sense because
-you will usually be drawing many (small) features on a page, not just a
-few large ones as shown here.</P><!--TOC subsection Feature sigils-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc219">16.1.7</A>&#XA0;&#XA0;Feature sigils</H3><!--SEC END --><P>
-<A NAME="sec:gd_sigils"></A></P><P>The examples above have all just used the default sigil for the feature, a
-plain box, which was all that was available in the last publicly released standalone version of GenomeDiagram. Arrow sigils were included when
-GenomeDiagram was added to Biopython 1.50:</P><PRE CLASS="verbatim">#Default uses a BOX sigil
-gd_feature_set.add_feature(feature)
-
-#You can make this explicit:
-gd_feature_set.add_feature(feature, sigil="BOX")
-
-#Or opt for an arrow:
-gd_feature_set.add_feature(feature, sigil="ARROW")
-</PRE><P>Biopython 1.61 added three more sigils,</P><PRE CLASS="verbatim">#Box with corners cut off (making it an octagon)
-gd_feature_set.add_feature(feature, sigil="OCTO")
-
-#Box with jagged edges (useful for showing breaks in contains)
-gd_feature_set.add_feature(feature, sigil="JAGGY")
-
-#Arrow which spans the axis with strand used only for direction
-gd_feature_set.add_feature(feature, sigil="BIGARROW")
-</PRE><P>These are shown
-
-below.
-
-
-Most sigils fit into a bounding box (as given by the default BOX sigil),
-either above or below the axis for the forward or reverse strand, or
-straddling it (double the height) for strand-less features.
-The BIGARROW sigil is different, always straddling the axis with the
-direction taken from the feature&#X2019;s stand.</P><P>
-<IMG SRC="images/GD_sigils.png" width=425, height=600>
-
-</P><!--TOC subsection Arrow sigils-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc220">16.1.8</A>&#XA0;&#XA0;Arrow sigils</H3><!--SEC END --><P>
-<A NAME="sec:gd_arrow_sigils"></A></P><P>We introduced the arrow sigils in the previous section.
-There are two additional options to adjust the shapes of the arrows, firstly
-the thickness of the arrow shaft, given as a proportion of the height of the
-bounding box:</P><PRE CLASS="verbatim">#Full height shafts, giving pointed boxes:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="brown",
-                           arrowshaft_height=1.0)
-#Or, thin shafts:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="teal",
-                           arrowshaft_height=0.2)
-#Or, very thin shafts:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="darkgreen",
-                           arrowshaft_height=0.1)
-</PRE><P>
-The results are shown below:</P><P><IMG SRC="images/GD_sigil_arrow_shafts.png" width=600, height=700></P><P>Secondly, the length of the arrow head - given as a proportion of the height
-of the bounding box (defaulting to 0.5, or 50%):</P><PRE CLASS="verbatim">#Short arrow heads:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="blue",
-                           arrowhead_length=0.25)
-#Or, longer arrow heads:
-gd_feature_set.add_feature(feature, sigil="ARROW", color="orange",
-                           arrowhead_length=1)
-#Or, very very long arrow heads (i.e. all head, no shaft, so triangles):
-gd_feature_set.add_feature(feature, sigil="ARROW", color="red",
-                           arrowhead_length=10000)
-</PRE><P>
-The results are shown below:</P><P><IMG SRC="images/GD_sigil_arrow_heads.png" width=600, height=700></P><P>Biopython 1.61 adds a new <CODE>BIGARROW</CODE> sigil which always stradles
-the axis, pointing left for the reverse strand or right otherwise:</P><PRE CLASS="verbatim">#A large arrow straddling the axis:
-gd_feature_set.add_feature(feature, sigil="BIGARROW")
-</PRE><P>All the shaft and arrow head options shown above for the
-<CODE>ARROW</CODE> sigil can be used for the <CODE>BIGARROW</CODE> sigil too.</P><!--TOC subsection A nice example-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc221">16.1.9</A>&#XA0;&#XA0;A nice example</H3><!--SEC END --><P>
-<A NAME="sec:gd_nice_example"></A></P><P>Now let&#X2019;s return to the pPCP1 plasmid from <I>Yersinia pestis biovar
-Microtus</I>, and the top down approach used in Section&#XA0;<A HREF="#sec:gd_top_down">16.1.3</A>,
-but take advantage of the sigil options we&#X2019;ve now discussed. This time
-we&#X2019;ll use arrows for the genes, and overlay them with strand-less features
-(as plain boxes) showing the position of some restriction digest sites.</P><PRE CLASS="verbatim">from reportlab.lib import colors
-from reportlab.lib.units import cm
-from Bio.Graphics import GenomeDiagram
-from Bio import SeqIO
-from Bio.SeqFeature import SeqFeature, FeatureLocation
-
-record = SeqIO.read("NC_005816.gb", "genbank")
-
-gd_diagram = GenomeDiagram.Diagram(record.id)
-gd_track_for_features = gd_diagram.new_track(1, name="Annotated Features")
-gd_feature_set = gd_track_for_features.new_set()
-
-for feature in record.features:
-    if feature.type != "gene":
-        #Exclude this feature
-        continue
-    if len(gd_feature_set) % 2 == 0:
-        color = colors.blue
-    else:
-        color = colors.lightblue
-    gd_feature_set.add_feature(feature, sigil="ARROW",
-                               color=color, label=True,
-                               label_size = 14, label_angle=0)
-
-#I want to include some strandless features, so for an example
-#will use EcoRI recognition sites etc.
-for site, name, color in [("GAATTC","EcoRI",colors.green),
-                          ("CCCGGG","SmaI",colors.orange),
-                          ("AAGCTT","HindIII",colors.red),
-                          ("GGATCC","BamHI",colors.purple)]:
-    index = 0
-    while True:
-        index  = record.seq.find(site, start=index)
-        if index == -1 : break
-        feature = SeqFeature(FeatureLocation(index, index+len(site)))
-        gd_feature_set.add_feature(feature, color=color, name=name,
-                                   label=True, label_size = 10,
-                                   label_color=color)
-        index += len(site)
-
-gd_diagram.draw(format="linear", pagesize='A4', fragments=4,
-                start=0, end=len(record))
-gd_diagram.write("plasmid_linear_nice.pdf", "PDF")
-gd_diagram.write("plasmid_linear_nice.eps", "EPS")
-gd_diagram.write("plasmid_linear_nice.svg", "SVG")
-
-gd_diagram.draw(format="circular", circular=True, pagesize=(20*cm,20*cm),
-                start=0, end=len(record), circle_core = 0.5)
-gd_diagram.write("plasmid_circular_nice.pdf", "PDF")
-gd_diagram.write("plasmid_circular_nice.eps", "EPS")
-gd_diagram.write("plasmid_circular_nice.svg", "SVG")
-</PRE><P>
-And the output:</P><P><IMG SRC="images/plasmid_linear_nice.png" width=550, height=400></P><P><IMG SRC="images/plasmid_circular_nice.png" width=591, height=591></P><!--TOC subsection Multiple tracks-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc222">16.1.10</A>&#XA0;&#XA0;Multiple tracks</H3><!--SEC END --><P>
-<A NAME="sec:gd_multiple_tracks"></A></P><P>All the examples so far have used a single track, but you can have more than
-one track &#X2013; for example show the genes on one, and repeat regions on another.
-In this example we&#X2019;re going to show three phage genomes side by side to scale,
-inspired by Figure 6 in Proux <I>e</I>t al. (2002) [<A HREF="#proux2002">5</A>].
-We&#X2019;ll need the GenBank files for the following three phage:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>NC_002703</CODE> &#X2013; Lactococcus phage Tuc2009, complete genome (38347 bp)
-</LI><LI CLASS="li-itemize"><CODE>AF323668</CODE> &#X2013; Bacteriophage bIL285, complete genome (35538 bp)
-</LI><LI CLASS="li-itemize"><CODE>NC_003212</CODE> &#X2013; <I>Listeria innocua</I> Clip11262, complete genome,
-of which we are focussing only on integrated prophage 5 (similar length).
-</LI></UL><P>You can download these using Entrez if you like, see Section&#XA0;<A HREF="#sec:efetch">9.6</A>
-for more details. For the third record we&#X2019;ve worked out where the phage is
-integrated into the genome, and slice the record to extract it (with the
-features preserved, see Section&#XA0;<A HREF="#sec:SeqRecord-slicing">4.6</A>), and must also
-reverse complement to match the orientation of the first two phage (again
-preserving the features, see Section&#XA0;<A HREF="#sec:SeqRecord-reverse-complement">4.8</A>):</P><PRE CLASS="verbatim">from Bio import SeqIO
-
-A_rec = SeqIO.read("NC_002703.gbk", "gb")
-B_rec = SeqIO.read("AF323668.gbk", "gb")
-C_rec = SeqIO.read("NC_003212.gbk", "gb")[2587879:2625807].reverse_complement(name=True)
-</PRE><P>The figure we are imitating used different colors for different gene functions.
-One way to do this is to edit the GenBank file to record color preferences for
-each feature - something <A HREF="http://www.sanger.ac.uk/resources/software/artemis/">Sanger&#X2019;s Artemis editor</A> does, and which GenomeDiagram should understand. Here
-however, we&#X2019;ll just hard code three lists of colors.</P><P>Note that the annotation in the GenBank files doesn&#X2019;t exactly match that shown
-in Proux <I>et al.</I>, they have drawn some unannotated genes.</P><PRE CLASS="verbatim">from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple
-
-A_colors = [red]*5 + [grey]*7 + [orange]*2 + [grey]*2 + [orange] + [grey]*11 + [green]*4 \
-         + [grey] + [green]*2 + [grey, green] + [brown]*5 + [blue]*4 + [lightblue]*5 \
-         + [grey, lightblue] + [purple]*2 + [grey]
-B_colors = [red]*6 + [grey]*8 + [orange]*2 + [grey] + [orange] + [grey]*21 + [green]*5 \
-         + [grey] + [brown]*4 + [blue]*3 + [lightblue]*3 + [grey]*5 + [purple]*2
-C_colors = [grey]*30 + [green]*5 + [brown]*4 + [blue]*2 + [grey, blue] + [lightblue]*2 \
-         + [grey]*5
-</PRE><P>Now to draw them &#X2013; this time we add three tracks to the diagram, and also notice they
-are given different start/end values to reflect their different lengths (this requires
-Biopython 1.59 or later).</P><PRE CLASS="verbatim">from Bio.Graphics import GenomeDiagram
-
-name = "Proux Fig 6"
-gd_diagram = GenomeDiagram.Diagram(name)
-max_len = 0
-for record, gene_colors in zip([A_rec, B_rec, C_rec], [A_colors, B_colors, C_colors]):
-    max_len = max(max_len, len(record))
-    gd_track_for_features = gd_diagram.new_track(1,
-                            name=record.name,
-                            greytrack=True,
-                            start=0, end=len(record))
-    gd_feature_set = gd_track_for_features.new_set()
-
-    i = 0
-    for feature in record.features:
-        if feature.type != "gene":
-            #Exclude this feature
-            continue
-        gd_feature_set.add_feature(feature, sigil="ARROW",
-                                   color=gene_colors[i], label=True,
-                                   name = str(i+1),
-                                   label_position="start",
-                                   label_size = 6, label_angle=0)
-        i+=1
-
-gd_diagram.draw(format="linear", pagesize='A4', fragments=1,
-                start=0, end=max_len)
-gd_diagram.write(name + ".pdf", "PDF")
-gd_diagram.write(name + ".eps", "EPS")
-gd_diagram.write(name + ".svg", "SVG")
-</PRE><P>
-The result:</P><P><IMG SRC="images/three_track_simple.png" width=565, height=400></P><P>
-
-I did wonder why in the original manuscript there were no red or orange genes
-marked in the bottom phage. Another important point is here the phage are
-shown with different lengths - this is because they are all drawn to the same
-scale (they <EM>are</EM> different lengths).</P><P>The key difference from the published figure is they have color-coded links
-between similar proteins &#X2013; which is what we will do in the next section.</P><!--TOC subsection Cross-Links between tracks-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc223">16.1.11</A>&#XA0;&#XA0;Cross-Links between tracks</H3><!--SEC END --><P>
-<A NAME="sec:gd_cross_links"></A></P><P>Biopython 1.59 added the ability to draw cross links between tracks - both
-simple linear diagrams as we will show here, but also linear diagrams split
-into fragments and circular diagrams.</P><P>Continuing the example from the previous section inspired by Figure 6 from
-Proux <I>et al.</I> 2002 [<A HREF="#proux2002">5</A>],
-we would need a list of cross links between pairs of genes, along with a score
-or color to use. Realistically you might extract this from a BLAST file
-computationally, but here I have manually typed them in.</P><P>My naming convention continues to refer to the three phage as A, B and C.
-Here are the links we want to show between A and B, given as a list of
-tuples (percentage similarity score, gene in A, gene in B).</P><PRE CLASS="verbatim">#Tuc2009 (NC_002703) vs bIL285 (AF323668)
-A_vs_B = [
-    (99, "Tuc2009_01", "int"),
-    (33, "Tuc2009_03", "orf4"),
-    (94, "Tuc2009_05", "orf6"),
-    (100,"Tuc2009_06", "orf7"),
-    (97, "Tuc2009_07", "orf8"),
-    (98, "Tuc2009_08", "orf9"),
-    (98, "Tuc2009_09", "orf10"),
-    (100,"Tuc2009_10", "orf12"),
-    (100,"Tuc2009_11", "orf13"),
-    (94, "Tuc2009_12", "orf14"),
-    (87, "Tuc2009_13", "orf15"),
-    (94, "Tuc2009_14", "orf16"),
-    (94, "Tuc2009_15", "orf17"),
-    (88, "Tuc2009_17", "rusA"),
-    (91, "Tuc2009_18", "orf20"),
-    (93, "Tuc2009_19", "orf22"),
-    (71, "Tuc2009_20", "orf23"),
-    (51, "Tuc2009_22", "orf27"),
-    (97, "Tuc2009_23", "orf28"),
-    (88, "Tuc2009_24", "orf29"),
-    (26, "Tuc2009_26", "orf38"),
-    (19, "Tuc2009_46", "orf52"),
-    (77, "Tuc2009_48", "orf54"),
-    (91, "Tuc2009_49", "orf55"),
-    (95, "Tuc2009_52", "orf60"),
-]
-</PRE><P>Likewise for B and C:</P><PRE CLASS="verbatim">#bIL285 (AF323668) vs Listeria innocua prophage 5 (in NC_003212)
-B_vs_C = [
-    (42, "orf39", "lin2581"),
-    (31, "orf40", "lin2580"),
-    (49, "orf41", "lin2579"), #terL
-    (54, "orf42", "lin2578"), #portal
-    (55, "orf43", "lin2577"), #protease
-    (33, "orf44", "lin2576"), #mhp
-    (51, "orf46", "lin2575"),
-    (33, "orf47", "lin2574"),
-    (40, "orf48", "lin2573"),
-    (25, "orf49", "lin2572"),
-    (50, "orf50", "lin2571"),
-    (48, "orf51", "lin2570"),
-    (24, "orf52", "lin2568"),
-    (30, "orf53", "lin2567"),
-    (28, "orf54", "lin2566"),
-]
-</PRE><P>For the first and last phage these identifiers are locus tags, for the middle
-phage there are no locus tags so I&#X2019;ve used gene names instead. The following
-little helper function lets us lookup a feature using either a locus tag or
-gene name:</P><PRE CLASS="verbatim">def get_feature(features, id, tags=["locus_tag", "gene"]):
-    """Search list of SeqFeature objects for an identifier under the given tags."""
-    for f in features:
-        for key in tags:
-            #tag may not be present in this feature
-            for x in f.qualifiers.get(key, []):
-                if x == id:
-                     return f
-    raise KeyError(id)
-</PRE><P>We can now turn those list of identifier pairs into SeqFeature pairs, and thus
-find their location co-ordinates. We can now add all that code and the following
-snippet to the previous example (just before the <CODE>gd_diagram.draw(...)</CODE>
-line &#X2013; see the finished example script
-<A HREF="http://biopython.org/SRC/biopython/Doc/examples/Proux_et_al_2002_Figure_6.py">Proux_et_al_2002_Figure_6.py</A>
-included in the <TT>Doc/examples</TT> folder of the Biopython source code)
-to add cross links to the figure:</P><PRE CLASS="verbatim">from Bio.Graphics.GenomeDiagram import CrossLink
-from reportlab.lib import colors
-#Note it might have been clearer to assign the track numbers explicitly...
-for rec_X, tn_X, rec_Y, tn_Y, X_vs_Y in [(A_rec, 3, B_rec, 2, A_vs_B),
-                                         (B_rec, 2, C_rec, 1, B_vs_C)]:
-    track_X = gd_diagram.tracks[tn_X]
-    track_Y = gd_diagram.tracks[tn_Y]
-    for score, id_X, id_Y in X_vs_Y:
-        feature_X = get_feature(rec_X.features, id_X)
-        feature_Y = get_feature(rec_Y.features, id_Y)
-        color = colors.linearlyInterpolatedColor(colors.white, colors.firebrick, 0, 100, score)
-        link_xy = CrossLink((track_X, feature_X.location.start, feature_X.location.end),
-                            (track_Y, feature_Y.location.start, feature_Y.location.end),
-                            color, colors.lightgrey)
-        gd_diagram.cross_track_links.append(link_xy)
-</PRE><P>There are several important pieces to this code. First the <CODE>GenomeDiagram</CODE> object
-has a <CODE>cross_track_links</CODE> attribute which is just a list of <CODE>CrossLink</CODE> objects.
-Each <CODE>CrossLink</CODE> object takes two sets of track-specific co-ordinates (here given
-as tuples, you can alternatively use a <CODE>GenomeDiagram.Feature</CODE> object instead).
-You can optionally supply a colour, border color, and say if this link should be drawn
-flipped (useful for showing inversions).</P><P>You can also see how we turn the BLAST percentage identity score into a colour,
-interpolating between white (0%) and a dark red (100%). In this example
-we don&#X2019;t have any problems with overlapping cross-links. One way to tackle that
-is to use transparency in ReportLab, by using colors with their alpha channel set.
-However, this kind of shaded color scheme combined with overlap transparency
-would be difficult to interpret.
-
-The result:</P><P><IMG SRC="images/three_track_cl.png" width=565, height=400></P><P>There is still a lot more that can be done within Biopython to help
-improve this figure. First of all, the cross links in this case are
-between proteins which are drawn in a strand specific manor. It can
-help to add a background region (a feature using the &#X2018;BOX&#X2019; sigil) on the
-feature track to extend the cross link. Also, we could reduce the vertical
-height of the feature tracks to allocate more to the links instead &#X2013; one
-way to do that is to allocate space for empty tracks. Furthermore,
-in cases like this where there are no large gene overlaps, we can use
-the axis-straddling <CODE>BIGARROW</CODE> sigil, which allows us to further
-reduce the vertical space needed for the track. These improvements
-are demonstrated in the example script
-<A HREF="http://biopython.org/SRC/biopython/Doc/examples/Proux_et_al_2002_Figure_6.py">Proux_et_al_2002_Figure_6.py</A>
-included in the <TT>Doc/examples</TT> folder of the Biopython source code.
-
-The result:</P><P><IMG SRC="images/three_track_cl2a.png" width=565, height=400></P><P>Beyond that, finishing touches you might want to do manually in a vector
-image editor include fine tuning the placement of gene labels, and adding
-other custom annotation such as highlighting particular regions.</P><P>Although not really necessary in this example since none of the cross-links
-overlap, using a transparent color in ReportLab is a very useful technique
-for superimposing multiple links. However, in this case a shaded color
-scheme should be avoided.</P><!--TOC subsection Further options-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc224">16.1.12</A>&#XA0;&#XA0;Further options</H3><!--SEC END --><P>You can control the tick marks to show the scale &#X2013; after all every graph
-should show its units, and the number of the grey-track labels.</P><P>Also, we have only used the <CODE>FeatureSet</CODE> so far. GenomeDiagram also has
-a <CODE>GraphSet</CODE> which can be used for show line graphs, bar charts and heat
-plots (e.g. to show plots of GC% on a track parallel to the features).</P><P>These options are not covered here yet, so for now we refer you to the
-<A HREF="http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf">User Guide (PDF)</A> included with the standalone version of GenomeDiagram (but
-please read the next section first), and the docstrings.</P><!--TOC subsection Converting old code-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc225">16.1.13</A>&#XA0;&#XA0;Converting old code</H3><!--SEC END --><P>If you have old code written using the standalone version of GenomeDiagram, and
-you want to switch it over to using the new version included with Biopython then
-you will have to make a few changes - most importantly to your import statements.</P><P>Also, the older version of GenomeDiagram used only the UK spellings of color and
-center (colour and centre). You will need to change to the American spellings,
-although for several years the Biopython version of GenomeDiagram supported both.</P><P>For example, if you used to have:
-</P><PRE CLASS="verbatim">from GenomeDiagram import GDFeatureSet, GDDiagram
-gdd = GDDiagram("An example")
-...
-</PRE><P>you could just switch the import statements like this:
-</P><PRE CLASS="verbatim">from Bio.Graphics.GenomeDiagram import FeatureSet as GDFeatureSet, Diagram as GDDiagram
-gdd = GDDiagram("An example")
-...
-</PRE><P>and hopefully that should be enough. In the long term you might want to
-switch to the new names, but you would have to change more of your code:
-</P><PRE CLASS="verbatim">from Bio.Graphics.GenomeDiagram import FeatureSet, Diagram
-gdd = Diagram("An example")
-...
-</PRE><P>or:
-</P><PRE CLASS="verbatim">from Bio.Graphics import GenomeDiagram
-gdd = GenomeDiagram.Diagram("An example")
-...
-</PRE><P>If you run into difficulties, please ask on the Biopython mailing list for
-advice. One catch is that we have not included the old module
-<CODE>GenomeDiagram.GDUtilities</CODE> yet. This included a number of
-GC% related functions, which will probably be merged under
-<CODE>Bio.SeqUtils</CODE> later on.
-</P><!--TOC section Chromosomes-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc226">16.2</A>&#XA0;&#XA0;Chromosomes</H2><!--SEC END --><P>The <CODE>Bio.Graphics.BasicChromosome</CODE> module allows drawing of chromosomes.
-There is an example in Jupe <I>et al.</I> (2012) [<A HREF="#jupe2012">6</A>]
-(open access) using colors to highlight different gene families.</P><!--TOC subsection Simple Chromosomes-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc227">16.2.1</A>&#XA0;&#XA0;Simple Chromosomes</H3><!--SEC END --><P>
-Here is a very simple example - for which we&#X2019;ll use <I>Arabidopsis thaliana</I>.</P><P>You can skip this bit, but first I downloaded the five sequenced chromosomes
-from the NCBI&#X2019;s FTP site
-<A HREF="ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana"><TT>ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana</TT></A> and then parsed
-them with <CODE>Bio.SeqIO</CODE> to find out their lengths. You could use the
-GenBank files for this, but it is faster to use the FASTA files for the
-whole chromosomes:</P><PRE CLASS="verbatim">from Bio import SeqIO
-entries = [("Chr I", "CHR_I/NC_003070.fna"),
-           ("Chr II", "CHR_II/NC_003071.fna"),
-           ("Chr III", "CHR_III/NC_003074.fna"),
-           ("Chr IV", "CHR_IV/NC_003075.fna"),
-           ("Chr V", "CHR_V/NC_003076.fna")]
-for (name, filename) in entries:
-   record = SeqIO.read(filename,"fasta")
-   print(name, len(record))
-</PRE><P>This gave the lengths of the five chromosomes, which we&#X2019;ll now use in
-the following short demonstration of the <CODE>BasicChromosome</CODE> module:</P><PRE CLASS="verbatim">from reportlab.lib.units import cm
-from Bio.Graphics import BasicChromosome
-
-entries = [("Chr I", 30432563),
-           ("Chr II", 19705359),
-           ("Chr III", 23470805),
-           ("Chr IV", 18585042),
-           ("Chr V", 26992728)]
-
-max_len = 30432563 #Could compute this
-telomere_length = 1000000 #For illustration
-
-chr_diagram = BasicChromosome.Organism()
-chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape
-
-for name, length in entries:
-    cur_chromosome = BasicChromosome.Chromosome(name)
-    #Set the scale to the MAXIMUM length plus the two telomeres in bp,
-    #want the same scale used on all five chromosomes so they can be
-    #compared to each other
-    cur_chromosome.scale_num = max_len + 2 * telomere_length
-
-    #Add an opening telomere
-    start = BasicChromosome.TelomereSegment()
-    start.scale = telomere_length
-    cur_chromosome.add(start)
-
-    #Add a body - using bp as the scale length here.
-    body = BasicChromosome.ChromosomeSegment()
-    body.scale = length
-    cur_chromosome.add(body)
-
-    #Add a closing telomere
-    end = BasicChromosome.TelomereSegment(inverted=True)
-    end.scale = telomere_length
-    cur_chromosome.add(end)
-
-    #This chromosome is done
-    chr_diagram.add(cur_chromosome)
-
-chr_diagram.draw("simple_chrom.pdf", "Arabidopsis thaliana")
-</PRE><P>This should create a very simple PDF file, shown
-
-here:</P><P><IMG SRC="images/simple_chrom.png" width=650, height=460></P><P>
-
-This example is deliberately short and sweet. The next example shows the
-location of features of interest.</P><!--TOC subsection Annotated Chromosomes-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc228">16.2.2</A>&#XA0;&#XA0;Annotated Chromosomes</H3><!--SEC END --><P>Continuing from the previous example, let&#X2019;s also show the tRNA genes.
-We&#X2019;ll get their locations by parsing the GenBank files for the five
-<I>Arabidopsis thaliana</I> chromosomes. You&#X2019;ll need to download these
-files from the NCBI FTP site
-<A HREF="ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana"><TT>ftp://ftp.ncbi.nlm.nih.gov/genomes/Arabidopsis_thaliana</TT></A>,
-and preserve the subdirectory names or edit the paths below:</P><PRE CLASS="verbatim">from reportlab.lib.units import cm
-from Bio import SeqIO
-from Bio.Graphics import BasicChromosome
-
-entries = [("Chr I", "CHR_I/NC_003070.gbk"),
-           ("Chr II", "CHR_II/NC_003071.gbk"),
-           ("Chr III", "CHR_III/NC_003074.gbk"),
-           ("Chr IV", "CHR_IV/NC_003075.gbk"),
-           ("Chr V", "CHR_V/NC_003076.gbk")]
-
-max_len = 30432563 #Could compute this
-telomere_length = 1000000 #For illustration
-
-chr_diagram = BasicChromosome.Organism()
-chr_diagram.page_size = (29.7*cm, 21*cm) #A4 landscape
-
-for index, (name, filename) in enumerate(entries):
-    record = SeqIO.read(filename,"genbank")
-    length = len(record)
-    features = [f for f in record.features if f.type=="tRNA"]
-    #Record an Artemis style integer color in the feature's qualifiers,
-    #1 = Black, 2 = Red, 3 = Green, 4 = blue, 5 =cyan, 6 = purple
-    for f in features: f.qualifiers["color"] = [index+2]
-
-    cur_chromosome = BasicChromosome.Chromosome(name)
-    #Set the scale to the MAXIMUM length plus the two telomeres in bp,
-    #want the same scale used on all five chromosomes so they can be
-    #compared to each other
-    cur_chromosome.scale_num = max_len + 2 * telomere_length
-
-    #Add an opening telomere
-    start = BasicChromosome.TelomereSegment()
-    start.scale = telomere_length
-    cur_chromosome.add(start)
-
-    #Add a body - again using bp as the scale length here.
-    body = BasicChromosome.AnnotatedChromosomeSegment(length, features)
-    body.scale = length
-    cur_chromosome.add(body)
-
-    #Add a closing telomere
-    end = BasicChromosome.TelomereSegment(inverted=True)
-    end.scale = telomere_length
-    cur_chromosome.add(end)
-
-    #This chromosome is done
-    chr_diagram.add(cur_chromosome)
-
-chr_diagram.draw("tRNA_chrom.pdf", "Arabidopsis thaliana")
-</PRE><P>It might warn you about the labels being too close together - have a look
-at the forward strand (right hand side) of Chr I, but it should create a
-colorful PDF file, shown
-
-here:</P><P><IMG SRC="images/tRNA_chrom.png" width=650, height=460></P><!--TOC chapter KEGG-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc229">Chapter&#XA0;17</A>&#XA0;&#XA0;KEGG</H1><!--SEC END --><P>
-<A NAME="chap:kegg"></A></P><P>KEGG (<A HREF="http://www.kegg.jp/"><TT>http://www.kegg.jp/</TT></A>) is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies.</P><P>Please note that the KEGG parser implementation in Biopython is incomplet [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc230">17.1</A>&#XA0;&#XA0;Parsing KEGG records</H2><!--SEC END --><P>
-Parsing a KEGG record is as simple as using any other file format parser in Biopython. 
-(Before running the following codes, please open <A HREF="http://rest.kegg.jp/get/ec:5.4.2.2"><TT>http://rest.kegg.jp/get/ec:5.4.2.2</TT></A> with your web browser and save it as <CODE>ec_5.4.2.2.txt</CODE>.)</P><PRE CLASS="verbatim">>>> from Bio.KEGG import Enzyme
->>> records = Enzyme.parse(open("ec_5.4.2.2.txt"))
->>> record = list(records)[0]
->>> record.classname
-['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases (phosphomutases)']
->>> record.entry
-'5.4.2.2'
-</PRE><P>The following section will shows how to download the above enzyme using the KEGG api as well as how to use the generic parser with data that does not have a custom parser implemented.</P><!--TOC section Querying the KEGG API-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc231">17.2</A>&#XA0;&#XA0;Querying the KEGG API</H2><!--SEC END --><P>Biopython has full support for the querying of the KEGG api. Querying all KEGG endpoints are supported; all methods documented by KEGG (<A HREF="http://www.kegg.jp/kegg/rest/keggapi.html"><TT>http://www.kegg.jp/kegg/rest/keggapi.html</TT></A>) are supported. The interface has some validation of queries which follow rules defined on the KEGG site. However, invalid querie [...]
->>> from Bio.KEGG import Enzyme
->>> request = REST.kegg_get("ec:5.4.2.2")
->>> open("ec_5.4.2.2.txt", 'w').write(request.read())
->>> records = Enzyme.parse(open("ec_5.4.2.2.txt"))
->>> record = list(records)[0]
->>> record.classname
-['Isomerases;', 'Intramolecular transferases;', 'Phosphotransferases (phosphomutases)']
->>> record.entry
-'5.4.2.2'
-</PRE><P>Now, here&#X2019;s a more realistic example which shows a combination of querying the KEGG API. This will demonstrate how to extract a unique set of all human pathway gene symbols which relate to DNA repair. The steps that need to be taken to do so are as follows. First, we need to get a list of all human pathways. Secondly, we need to filter those for ones which relate to "repair". Lastly, we need to get a list of all the gene symbols in all repair pathways.</P><PRE CLASS="verb [...]
-
-human_pathways = REST.kegg_list("pathway", "hsa").read()
-
-# Filter all human pathways for repair pathways
-repair_pathways = []
-for line in human_pathways.rstrip().split("\n"):
-    entry, description = line.split("\t")
-    if "repair" in description:
-        repair_pathways.append(entry)
-
-# Get the genes for pathways and add them to a list
-repair_genes = [] 
-for pathway in repair_pathways:
-    pathway_file = REST.kegg_get(pathway).read()  # query and read each pathway
-
-    # iterate through each KEGG pathway file, keeping track of which section
-    # of the file we're in, only read the gene in each pathway
-    current_section = None
-    for line in pathway_file.rstrip().split("\n"):
-        section = line[:12].strip()  # section names are within 12 columns
-        if not section == "":
-            current_section = section
-        
-        if current_section == "GENE":
-            gene_identifiers, gene_description = line[12:].split("; ")
-            gene_id, gene_symbol = gene_identifiers.split()
-
-            if not gene_symbol in repair_genes:
-                repair_genes.append(gene_symbol)
-
-print "There are %d repair pathways and %d repair genes. The genes are:" % \
-      (len(repair_pathways), len(repair_genes))
-print ", ".join(repair_genes)
-</PRE><P>The KEGG API wrapper is compatible with all endpoints. Usage is essentially replacing all slashes in the url with commas and using that list as arguments to the corresponding method in the KEGG module. Here are a few examples from the api documentation (<A HREF="http://www.kegg.jp/kegg/docs/keggapi.html"><TT>http://www.kegg.jp/kegg/docs/keggapi.html</TT></A>).</P><PRE CLASS="verbatim">/list/hsa:10458+ece:Z5100          -> REST.kegg_list(["hsa:10458", "ece:Z5100"])
-/find/compound/300-310/mol_weight  -> REST.kegg_find("compound", "300-310", "mol_weight")
-/get/hsa:10458+ece:Z5100/aaseq     -> REST.kegg_get(["hsa:10458", "ece:Z5100"], "aaseq")
-</PRE><!--TOC chapter Cookbook &#X2013; Cool things to do with it-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc232">Chapter&#XA0;18</A>&#XA0;&#XA0;Cookbook &#X2013; Cool things to do with it</H1><!--SEC END --><P>
-<A NAME="chapter:cookbook"></A></P><P>Biopython now has two collections of &#X201C;cookbook&#X201D; examples &#X2013; this chapter
-(which has been included in this tutorial for many years and has gradually
-grown), and <A HREF="http://biopython.org/wiki/Category:Cookbook"><TT>http://biopython.org/wiki/Category:Cookbook</TT></A> which is a
-user contributed collection on our wiki.</P><P>We&#X2019;re trying to encourage Biopython users to contribute their own examples
-to the wiki. In addition to helping the community, one direct benefit of
-sharing an example like this is that you could also get some feedback on
-the code from other Biopython users and developers - which could help you
-improve all your Python code.</P><P>In the long term, we may end up moving all of the examples in this chapter
-to the wiki, or elsewhere within the tutorial.</P><!--TOC section Working with sequence files-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc233">18.1</A>&#XA0;&#XA0;Working with sequence files</H2><!--SEC END --><P>
-<A NAME="seq:cookbook-sequences"></A></P><P>This section shows some more examples of sequence input/output, using the
-<CODE>Bio.SeqIO</CODE> module described in Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>.</P><!--TOC subsection Filtering a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc234">18.1.1</A>&#XA0;&#XA0;Filtering a sequence file</H3><!--SEC END --><P>Often you&#X2019;ll have a large file with many sequences in it (e.g. FASTA file
-or genes, or a FASTQ or SFF file of reads), a separate shorter list of
-the IDs for a subset of sequences of interest, and want to make a new
-sequence file for this subset.</P><P>Let&#X2019;s say the list of IDs is in a simple text file, as the first word on
-each line. This could be a tabular file where the first column is the ID.
-Try something like this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-input_file = "big_file.sff"
-id_file = "short_list.txt"
-output_file = "short_list.sff"
-wanted = set(line.rstrip("\n").split(None,1)[0] for line in open(id_file))
-print("Found %i unique identifiers in %s" % (len(wanted), id_file))
-records = (r for r in SeqIO.parse(input_file, "sff") if r.id in wanted)
-count = SeqIO.write(records, output_file, "sff")
-print("Saved %i records from %s to %s" % (count, input_file, output_file))
-if count < len(wanted):
-    print("Warning %i IDs not found in %s" % (len(wanted)-count, input_file))
-</PRE><P>Note that we use a Python <CODE>set</CODE> rather than a <CODE>list</CODE>, this makes
-testing membership faster.</P><!--TOC subsection Producing randomised genomes-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc235">18.1.2</A>&#XA0;&#XA0;Producing randomised genomes</H3><!--SEC END --><P>Let&#X2019;s suppose you are looking at genome sequence, hunting for some sequence
-feature &#X2013; maybe extreme local GC% bias, or possible restriction digest sites.
-Once you&#X2019;ve got your Python code working on the real genome it may be sensible
-to try running the same search on randomised versions of the same genome for
-statistical analysis (after all, any &#X201C;features&#X201D; you&#X2019;ve found could just be
-there just by chance).</P><P>For this discussion, we&#X2019;ll use the GenBank file for the pPCP1 plasmid from
-<I>Yersinia pestis biovar Microtus</I>. The file is included with the
-Biopython unit tests under the GenBank folder, or you can get it from our
-website, <A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.gb"><TT>NC_005816.gb</TT></A>.
-This file contains one and only one record, so we can read it in as a
-<CODE>SeqRecord</CODE> using the <CODE>Bio.SeqIO.read()</CODE> function:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> original_rec = SeqIO.read("NC_005816.gb", "genbank")
-</PRE><P>So, how can we generate a shuffled versions of the original sequence? I would
-use the built in Python <CODE>random</CODE> module for this, in particular the function
-<CODE>random.shuffle</CODE> &#X2013; but this works on a Python list. Our sequence is a
-<CODE>Seq</CODE> object, so in order to shuffle it we need to turn it into a list:</P><PRE CLASS="verbatim">>>> import random
->>> nuc_list = list(original_rec.seq)
->>> random.shuffle(nuc_list) #acts in situ!
-</PRE><P>Now, in order to use <CODE>Bio.SeqIO</CODE> to output the shuffled sequence, we need
-to construct a new <CODE>SeqRecord</CODE> with a new <CODE>Seq</CODE> object using this
-shuffled list. In order to do this, we need to turn the list of nucleotides
-(single letter strings) into a long string &#X2013; the standard Python way to do
-this is with the string object&#X2019;s join method.</P><PRE CLASS="verbatim">>>> from Bio.Seq import Seq
->>> from Bio.SeqRecord import SeqRecord
->>> shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet),
-...                          id="Shuffled", description="Based on %s" % original_rec.id)
-</PRE><P>Let&#X2019;s put all these pieces together to make a complete Python script which
-generates a single FASTA file containing 30 randomly shuffled versions of
-the original sequence.</P><P>This first version just uses a big for loop and writes out the records one by one
-(using the <CODE>SeqRecord</CODE>&#X2019;s format method described in
-Section&#XA0;<A HREF="#sec:Bio.SeqIO-and-StringIO">5.5.4</A>):</P><PRE CLASS="verbatim">import random
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio import SeqIO
-
-original_rec = SeqIO.read("NC_005816.gb","genbank")
-
-handle = open("shuffled.fasta", "w")
-for i in range(30):
-    nuc_list = list(original_rec.seq)
-    random.shuffle(nuc_list)
-    shuffled_rec = SeqRecord(Seq("".join(nuc_list), original_rec.seq.alphabet), \
-                             id="Shuffled%i" % (i+1), \
-                             description="Based on %s" % original_rec.id)
-    handle.write(shuffled_rec.format("fasta"))
-handle.close()
-</PRE><P>Personally I prefer the following version using a function to shuffle the record
-and a generator expression instead of the for loop:</P><PRE CLASS="verbatim">import random
-from Bio.Seq import Seq
-from Bio.SeqRecord import SeqRecord
-from Bio import SeqIO
-
-def make_shuffle_record(record, new_id):
-    nuc_list = list(record.seq)
-    random.shuffle(nuc_list)
-    return SeqRecord(Seq("".join(nuc_list), record.seq.alphabet), \
-           id=new_id, description="Based on %s" % original_rec.id)
-
-original_rec = SeqIO.read("NC_005816.gb","genbank")
-shuffled_recs = (make_shuffle_record(original_rec, "Shuffled%i" % (i+1)) \
-                 for i in range(30))
-handle = open("shuffled.fasta", "w")
-SeqIO.write(shuffled_recs, handle, "fasta")
-handle.close()
-</PRE><!--TOC subsection Translating a FASTA file of CDS entries-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc236">18.1.3</A>&#XA0;&#XA0;Translating a FASTA file of CDS entries</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-translate"></A>
-Suppose you&#X2019;ve got an input file of CDS entries for some organism, and you
-want to generate a new FASTA file containing their protein sequences. i.e.
-Take each nucleotide sequence from the original file, and translate it.
-Back in Section&#XA0;<A HREF="#sec:translation">3.9</A> we saw how to use the <CODE>Seq</CODE>
-object&#X2019;s <CODE>translate method</CODE>, and the optional <CODE>cds</CODE> argument
-which enables correct translation of alternative start codons.</P><P>We can combine this with <CODE>Bio.SeqIO</CODE> as
-shown in the reverse complement example in Section&#XA0;<A HREF="#sec:SeqIO-reverse-complement">5.5.3</A>.
-The key point is that for each nucleotide <CODE>SeqRecord</CODE>, we need to create
-a protein <CODE>SeqRecord</CODE> - and take care of naming it.</P><P>You can write you own function to do this, choosing suitable protein identifiers
-for your sequences, and the appropriate genetic code. In this example we just
-use the default table and add a prefix to the identifier:</P><PRE CLASS="verbatim">from Bio.SeqRecord import SeqRecord
-def make_protein_record(nuc_record):
-    """Returns a new SeqRecord with the translated sequence (default table)."""
-    return SeqRecord(seq = nuc_record.seq.translate(cds=True), \
-                     id = "trans_" + nuc_record.id, \
-                     description = "translation of CDS, using default table")
-</PRE><P>We can then use this function to turn the input nucleotide records into protein
-records ready for output. An elegant way and memory efficient way to do this
-is with a generator expression:</P><PRE CLASS="verbatim">from Bio import SeqIO
-proteins = (make_protein_record(nuc_rec) for nuc_rec in \
-            SeqIO.parse("coding_sequences.fasta", "fasta"))
-SeqIO.write(proteins, "translations.fasta", "fasta")
-</PRE><P>This should work on any FASTA file of complete coding sequences.
-If you are working on partial coding sequences, you may prefer to use
-<CODE>nuc_record.seq.translate(to_stop=True)</CODE> in the example above, as
-this wouldn&#X2019;t check for a valid start codon etc.</P><!--TOC subsection Making the sequences in a FASTA file upper case-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc237">18.1.4</A>&#XA0;&#XA0;Making the sequences in a FASTA file upper case</H3><!--SEC END --><P>Often you&#X2019;ll get data from collaborators as FASTA files, and sometimes the
-sequences can be in a mixture of upper and lower case. In some cases this is
-deliberate (e.g. lower case for poor quality regions), but usually it is not
-important. You may want to edit the file to make everything consistent (e.g.
-all upper case), and you can do this easily using the <CODE>upper()</CODE> method
-of the <CODE>SeqRecord</CODE> object (added in Biopython 1.55):</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = (rec.upper() for rec in SeqIO.parse("mixed.fas", "fasta"))
-count = SeqIO.write(records, "upper.fas", "fasta")
-print("Converted %i records to upper case" % count)
-</PRE><P>How does this work? The first line is just importing the <CODE>Bio.SeqIO</CODE>
-module. The second line is the interesting bit &#X2013; this is a Python
-generator expression which gives an upper case version of each record
-parsed from the input file (<TT>mixed.fas</TT>). In the third line we give
-this generator expression to the <CODE>Bio.SeqIO.write()</CODE> function and it
-saves the new upper cases records to our output file (<TT>upper.fas</TT>).</P><P>The reason we use a generator expression (rather than a list or list
-comprehension) is this means only one record is kept in memory at a time.
-This can be really important if you are dealing with large files with
-millions of entries.</P><!--TOC subsection Sorting a sequence file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc238">18.1.5</A>&#XA0;&#XA0;Sorting a sequence file</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-sort"></A></P><P>Suppose you wanted to sort a sequence file by length (e.g. a set of
-contigs from an assembly), and you are working with a file format like
-FASTA or FASTQ which <CODE>Bio.SeqIO</CODE> can read, write (and index).</P><P>If the file is small enough, you can load it all into memory at once
-as a list of <CODE>SeqRecord</CODE> objects, sort the list, and save it:</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = list(SeqIO.parse("ls_orchid.fasta","fasta"))
-records.sort(cmp=lambda x,y: cmp(len(x),len(y)))
-SeqIO.write(records, "sorted_orchids.fasta", "fasta")
-</PRE><P>The only clever bit is specifying a comparison function for how to
-sort the records (here we sort them by length). If you wanted the
-longest records first, you could flip the comparison or use the
-reverse argument:</P><PRE CLASS="verbatim">from Bio import SeqIO
-records = list(SeqIO.parse("ls_orchid.fasta","fasta"))
-records.sort(cmp=lambda x,y: cmp(len(y),len(x)))
-SeqIO.write(records, "sorted_orchids.fasta", "fasta")
-</PRE><P>Now that&#X2019;s pretty straight forward - but what happens if you have a
-very large file and you can&#X2019;t load it all into memory like this?
-For example, you might have some next-generation sequencing reads
-to sort by length. This can be solved using the
-<CODE>Bio.SeqIO.index()</CODE> function.</P><PRE CLASS="verbatim">from Bio import SeqIO
-#Get the lengths and ids, and sort on length
-len_and_ids = sorted((len(rec), rec.id) for rec in \
-                     SeqIO.parse("ls_orchid.fasta","fasta"))
-ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids #free this memory
-record_index = SeqIO.index("ls_orchid.fasta", "fasta")
-records = (record_index[id] for id in ids)
-SeqIO.write(records, "sorted.fasta", "fasta")
-</PRE><P>First we scan through the file once using <CODE>Bio.SeqIO.parse()</CODE>,
-recording the record identifiers and their lengths in a list of tuples.
-We then sort this list to get them in length order, and discard the lengths.
-Using this sorted list of identifiers <CODE>Bio.SeqIO.index()</CODE> allows us to
-retrieve the records one by one, and we pass them to <CODE>Bio.SeqIO.write()</CODE>
-for output.</P><P>These examples all use <CODE>Bio.SeqIO</CODE> to parse the records into
-<CODE>SeqRecord</CODE> objects which are output using <CODE>Bio.SeqIO.write()</CODE>.
-What if you want to sort a file format which <CODE>Bio.SeqIO.write()</CODE> doesn&#X2019;t
-support, like the plain text SwissProt format? Here is an alternative
-solution using the <CODE>get_raw()</CODE> method added to <CODE>Bio.SeqIO.index()</CODE>
-in Biopython 1.54 (see Section&#XA0;<A HREF="#sec:seqio-index-getraw">5.4.2.2</A>).</P><PRE CLASS="verbatim">from Bio import SeqIO
-#Get the lengths and ids, and sort on length
-len_and_ids = sorted((len(rec), rec.id) for rec in \
-                     SeqIO.parse("ls_orchid.fasta","fasta"))
-ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids #free this memory
-record_index = SeqIO.index("ls_orchid.fasta", "fasta")
-handle = open("sorted.fasta", "w")
-for id in ids:
-    handle.write(record_index.get_raw(id))
-handle.close()
-</PRE><P>As a bonus, because it doesn&#X2019;t parse the data into <CODE>SeqRecord</CODE> objects
-a second time it should be faster.</P><!--TOC subsection Simple quality filtering for FASTQ files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc239">18.1.6</A>&#XA0;&#XA0;Simple quality filtering for FASTQ files</H3><!--SEC END --><P>
-<A NAME="sec:FASTQ-filtering-example"></A></P><P>The FASTQ file format was introduced at Sanger and is now widely used for
-holding nucleotide sequencing reads together with their quality scores.
-FASTQ files (and the related QUAL files) are an excellent example of
-per-letter-annotation, because for each nucleotide in the sequence there is
-an associated quality score. Any per-letter-annotation is held in a
-<CODE>SeqRecord</CODE> in the <CODE>letter_annotations</CODE> dictionary as a list,
-tuple or string (with the same number of elements as the sequence length).</P><P>One common task is taking a large set of sequencing reads and filtering them
-(or cropping them) based on their quality scores.
-The following example is very simplistic, but should illustrate the basics of
-working with quality data in a <CODE>SeqRecord</CODE> object. All we are going to
-do here is read in a file of FASTQ data, and filter it to pick out only those
-records whose PHRED quality scores are all above some threshold (here 20).</P><P>For this example we&#X2019;ll use some real data downloaded from the ENA sequence
-read archive,
-<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>
-(2MB) which unzips to a 19MB file <TT>SRR020192.fastq</TT>. This is some
-Roche 454 GS FLX single end data from virus infected California sea lions
-(see <A HREF="http://www.ebi.ac.uk/ena/data/view/SRS004476"><TT>http://www.ebi.ac.uk/ena/data/view/SRS004476</TT></A> for details).</P><P>First, let&#X2019;s count the reads:</P><PRE CLASS="verbatim">from Bio import SeqIO
-count = 0
-for rec in SeqIO.parse("SRR020192.fastq", "fastq"):
-    count += 1
-print("%i reads" % count)
-</PRE><P>Now let&#X2019;s do a simple filtering for a minimum PHRED quality of 20:</P><PRE CLASS="verbatim">from Bio import SeqIO
-good_reads = (rec for rec in \
-              SeqIO.parse("SRR020192.fastq", "fastq") \
-              if min(rec.letter_annotations["phred_quality"]) >= 20)
-count = SeqIO.write(good_reads, "good_quality.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>This pulled out only 14580 reads out of the 41892 present.
-A more sensible thing to do would be to quality trim the reads, but this
-is intended as an example only.</P><P>FASTQ files can contain millions of entries, so it is best to avoid loading
-them all into memory at once. This example uses a generator expression, which
-means only one <CODE>SeqRecord</CODE> is created at a time - avoiding any memory
-limitations.</P><!--TOC subsection Trimming off primer sequences-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc240">18.1.7</A>&#XA0;&#XA0;Trimming off primer sequences</H3><!--SEC END --><P>
-<A NAME="sec:FASTQ-slicing-off-primer"></A></P><P>For this example we&#X2019;re going to pretend that <TT>GATGACGGTGT</TT> is a 5&#X2019; primer
-sequence we want to look for in some FASTQ formatted read data. As in the example
-above, we&#X2019;ll use the <TT>SRR020192.fastq</TT> file downloaded from the ENA
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>).
-The same approach would work with any other supported file format (e.g. FASTA files).</P><P>This code uses <CODE>Bio.SeqIO</CODE> with a generator expression (to avoid loading
-all the sequences into memory at once), and the <CODE>Seq</CODE> object&#X2019;s
-<CODE>startswith</CODE> method to see if the read starts with the primer sequence:</P><PRE CLASS="verbatim">from Bio import SeqIO
-primer_reads = (rec for rec in \
-                SeqIO.parse("SRR020192.fastq", "fastq") \
-                if rec.seq.startswith("GATGACGGTGT"))
-count = SeqIO.write(primer_reads, "with_primer.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>That should find 13819 reads from <TT>SRR014849.fastq</TT> and save them to
-a new FASTQ file, <TT>with_primer.fastq</TT>.</P><P>Now suppose that instead you wanted to make a FASTQ file containing these reads
-but with the primer sequence removed? That&#X2019;s just a small change as we can slice the
-<CODE>SeqRecord</CODE> (see Section&#XA0;<A HREF="#sec:SeqRecord-slicing">4.6</A>) to remove the first eleven
-letters (the length of our primer):</P><PRE CLASS="verbatim">from Bio import SeqIO
-trimmed_primer_reads = (rec[11:] for rec in \
-                        SeqIO.parse("SRR020192.fastq", "fastq") \
-                        if rec.seq.startswith("GATGACGGTGT"))
-count = SeqIO.write(trimmed_primer_reads, "with_primer_trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>Again, that should pull out the 13819 reads from <TT>SRR020192.fastq</TT>,
-but this time strip off the first ten characters, and save them to another new
-FASTQ file, <TT>with_primer_trimmed.fastq</TT>.</P><P>Finally, suppose you want to create a new FASTQ file where these reads have
-their primer removed, but all the other reads are kept as they were?
-If we want to still use a generator expression, it is probably clearest to
-define our own trim function:</P><PRE CLASS="verbatim">from Bio import SeqIO
-def trim_primer(record, primer):
-    if record.seq.startswith(primer):
-        return record[len(primer):]
-    else:
-        return record
-
-trimmed_reads = (trim_primer(record, "GATGACGGTGT") for record in \
-                 SeqIO.parse("SRR020192.fastq", "fastq"))
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>This takes longer, as this time the output file contains all 41892 reads.
-Again, we&#X2019;re used a generator expression to avoid any memory problems.
-You could alternatively use a generator function rather than a generator
-expression.</P><PRE CLASS="verbatim">from Bio import SeqIO
-def trim_primers(records, primer):
-    """Removes perfect primer sequences at start of reads.
-
-    This is a generator function, the records argument should
-    be a list or iterator returning SeqRecord objects.
-    """
-    len_primer = len(primer) #cache this for later
-    for record in records:
-        if record.seq.startswith(primer):
-            yield record[len_primer:]
-        else:
-            yield record
-
-original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
-trimmed_reads = trim_primers(original_reads, "GATGACGGTGT")
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>This form is more flexible if you want to do something more complicated
-where only some of the records are retained &#X2013; as shown in the next example.</P><!--TOC subsection Trimming off adaptor sequences-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc241">18.1.8</A>&#XA0;&#XA0;Trimming off adaptor sequences</H3><!--SEC END --><P>
-<A NAME="sec:FASTQ-slicing-off-adaptor"></A></P><P>This is essentially a simple extension to the previous example. We are going
-to going to pretend <TT>GATGACGGTGT</TT> is an adaptor sequence in some FASTQ
-formatted read data, again the <TT>SRR020192.fastq</TT> file from the NCBI
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>).</P><P>This time however, we will look for the sequence <EM>anywhere</EM> in the reads,
-not just at the very beginning:</P><PRE CLASS="verbatim">from Bio import SeqIO
-
-def trim_adaptors(records, adaptor):
-    """Trims perfect adaptor sequences.
-
-    This is a generator function, the records argument should
-    be a list or iterator returning SeqRecord objects.
-    """
-    len_adaptor = len(adaptor) #cache this for later
-    for record in records:
-        index = record.seq.find(adaptor)
-        if index == -1:
-            #adaptor not found, so won't trim
-            yield record
-        else:
-            #trim off the adaptor
-            yield record[index+len_adaptor:]
-
-original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
-trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT")
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>Because we are using a FASTQ input file in this example, the <CODE>SeqRecord</CODE>
-objects have per-letter-annotation for the quality scores. By slicing the
-<CODE>SeqRecord</CODE> object the appropriate scores are used on the trimmed
-records, so we can output them as a FASTQ file too.</P><P>Compared to the output of the previous example where we only looked for
-a primer/adaptor at the start of each read, you may find some of the
-trimmed reads are quite short after trimming (e.g. if the adaptor was
-found in the middle rather than near the start). So, let&#X2019;s add a minimum
-length requirement as well:</P><PRE CLASS="verbatim">from Bio import SeqIO
-
-def trim_adaptors(records, adaptor, min_len):
-    """Trims perfect adaptor sequences, checks read length.
-
-    This is a generator function, the records argument should
-    be a list or iterator returning SeqRecord objects.
-    """
-    len_adaptor = len(adaptor) #cache this for later
-    for record in records:
-        len_record = len(record) #cache this for later
-        if len(record) < min_len:
-           #Too short to keep
-           continue
-        index = record.seq.find(adaptor)
-        if index == -1:
-            #adaptor not found, so won't trim
-            yield record
-        elif len_record - index - len_adaptor >= min_len:
-            #after trimming this will still be long enough
-            yield record[index+len_adaptor:]
-
-original_reads = SeqIO.parse("SRR020192.fastq", "fastq")
-trimmed_reads = trim_adaptors(original_reads, "GATGACGGTGT", 100)
-count = SeqIO.write(trimmed_reads, "trimmed.fastq", "fastq")
-print("Saved %i reads" % count)
-</PRE><P>By changing the format names, you could apply this to FASTA files instead.
-This code also could be extended to do a fuzzy match instead of an exact
-match (maybe using a pairwise alignment, or taking into account the read
-quality scores), but that will be much slower.</P><!--TOC subsection Converting FASTQ files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc242">18.1.9</A>&#XA0;&#XA0;Converting FASTQ files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-fastq-conversion"></A></P><P>Back in Section&#XA0;<A HREF="#sec:SeqIO-conversion">5.5.2</A> we showed how to use
-<CODE>Bio.SeqIO</CODE> to convert between two file formats. Here we&#X2019;ll go into a
-little more detail regarding FASTQ files which are used in second generation
-DNA sequencing. Please refer to Cock <I>et al.</I> (2009) [<A HREF="#cock2010">7</A>]
-for a longer description. FASTQ files store both the DNA sequence (as a string)
-and the associated read qualities.</P><P>PHRED scores (used in most FASTQ files, and also in QUAL files, ACE files
-and SFF files) have become a <I>de facto</I> standard for representing
-the probability of a sequencing error (here denoted by <I>P</I><SUB><I>e</I></SUB>) at a given
-base using a simple base ten log transformation:</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-<I>Q</I><SUB>PHRED</SUB>&#XA0;=&#XA0;&#X2212;&#XA0;10&#XA0;&#XD7;&#XA0;log<SUB>10</SUB>&#XA0;(&#XA0;<I>P</I><SUB><I>e</I></SUB>&#XA0;)
-&#XA0;&#XA0;&#XA0;&#XA0;(18.1)</TD></TR>
-</TABLE><P>This means a wrong read (<I>P</I><SUB><I>e</I></SUB> = 1) gets a PHRED quality of 0, while a very
-good read like <I>P</I><SUB><I>e</I></SUB> = 0.00001 gets a PHRED quality of 50. While for raw
-sequencing data qualities higher than this are rare, with post processing
-such as read mapping or assembly, qualities of up to about 90 are possible
-(indeed, the MAQ tool allows for PHRED scores in the range 0 to 93 inclusive).</P><P>The FASTQ format has the potential to become a <I>de facto</I> standard for
-storing the letters and quality scores for a sequencing read in a single plain
-text file. The only fly in the ointment is that there are at least three
-versions of the FASTQ format which are incompatible and difficult to
-distinguish...</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-The original Sanger FASTQ format uses PHRED qualities encoded with an
-ASCII offset of 33. The NCBI are using this format in their Short Read
-Archive. We call this the <TT>fastq</TT> (or <TT>fastq-sanger</TT>) format
-in <CODE>Bio.SeqIO</CODE>.
-</LI><LI CLASS="li-enumerate">Solexa (later bought by Illumina) introduced their own version using
-Solexa qualities encoded with an ASCII offset of 64. We call this the
-<TT>fastq-solexa</TT> format.
-</LI><LI CLASS="li-enumerate">Illumina pipeline 1.3 onwards produces FASTQ files with PHRED qualities
-(which is more consistent), but encoded with an ASCII offset of 64. We call
-this the <TT>fastq-illumina</TT> format.
-</LI></OL><P>The Solexa quality scores are defined using a different log transformation:</P><TABLE CLASS="display dcenter"><TR VALIGN="middle"><TD CLASS="dcell">
-<I>Q</I><SUB>Solexa</SUB>&#XA0;=&#XA0;&#X2212;&#XA0;10&#XA0;&#XD7;&#XA0;log<SUB>10</SUB>&#XA0;</TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>P</I><SUB><I>e</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center">1&#X2212;<I>P</I><SUB><I>e</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;</TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD><TD CLASS="dcell">
-&#XA0;&#XA0;&#XA0;&#XA0;(18.2)</TD></TR>
-</TABLE><P>Given Solexa/Illumina have now moved to using PHRED scores in version 1.3 of
-their pipeline, the Solexa quality scores will gradually fall out of use.
-If you equate the error estimates (<I>P</I><SUB><I>e</I></SUB>) these two equations allow conversion
-between the two scoring systems - and Biopython includes functions to do this
-in the <CODE>Bio.SeqIO.QualityIO</CODE> module, which are called if you use
-<CODE>Bio.SeqIO</CODE> to convert an old Solexa/Illumina file into a standard Sanger
-FASTQ file:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("solexa.fastq", "fastq-solexa", "standard.fastq", "fastq")
-</PRE><P>If you want to convert a new Illumina 1.3+ FASTQ file, all that gets changed
-is the ASCII offset because although encoded differently the scores are all
-PHRED qualities:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("illumina.fastq", "fastq-illumina", "standard.fastq", "fastq")
-</PRE><P>Note that using <CODE>Bio.SeqIO.convert()</CODE> like this is <EM>much</EM> faster
-than combining <CODE>Bio.SeqIO.parse()</CODE> and <CODE>Bio.SeqIO.write()</CODE>
-because optimised code is used for converting between FASTQ variants
-(and also for FASTQ to FASTA conversion).</P><P>For good quality reads, PHRED and Solexa scores are approximately equal,
-which means since both the <TT>fasta-solexa</TT> and <TT>fastq-illumina</TT>
-formats use an ASCII offset of 64 the files are almost the same. This was a
-deliberate design choice by Illumina, meaning applications expecting the old
-<TT>fasta-solexa</TT> style files will probably be OK using the newer
-<TT>fastq-illumina</TT> files (on good data). Of course, both variants are
-very different from the original FASTQ standard as used by Sanger,
-the NCBI, and elsewhere (format name <TT>fastq</TT> or <TT>fastq-sanger</TT>).</P><P>For more details, see the built in help (also <A HREF="http://www.biopython.org/DIST/docs/api/Bio.SeqIO.QualityIO-module.html">online</A>):</P><PRE CLASS="verbatim">>>> from Bio.SeqIO import QualityIO
->>> help(QualityIO)
-...
-</PRE><!--TOC subsection Converting FASTA and QUAL files into FASTQ files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc243">18.1.10</A>&#XA0;&#XA0;Converting FASTA and QUAL files into FASTQ files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-fasta-qual-conversion"></A></P><P>FASTQ files hold <EM>both</EM> sequences and their quality strings.
-FASTA files hold <EM>just</EM> sequences, while QUAL files hold <EM>just</EM>
-the qualities. Therefore a single FASTQ file can be converted to or from
-<EM>paired</EM> FASTA and QUAL files.</P><P>Going from FASTQ to FASTA is easy:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("example.fastq", "fastq", "example.fasta", "fasta")
-</PRE><P>Going from FASTQ to QUAL is also easy:</P><PRE CLASS="verbatim">from Bio import SeqIO
-SeqIO.convert("example.fastq", "fastq", "example.qual", "qual")
-</PRE><P>However, the reverse is a little more tricky. You can use <CODE>Bio.SeqIO.parse()</CODE>
-to iterate over the records in a <EM>single</EM> file, but in this case we have
-two input files. There are several strategies possible, but assuming that the
-two files are really paired the most memory efficient way is to loop over both
-together. The code is a little fiddly, so we provide a function called
-<CODE>PairedFastaQualIterator</CODE> in the <CODE>Bio.SeqIO.QualityIO</CODE> module to do
-this. This takes two handles (the FASTA file and the QUAL file) and returns
-a <CODE>SeqRecord</CODE> iterator:</P><PRE CLASS="verbatim">from Bio.SeqIO.QualityIO import PairedFastaQualIterator
-for record in PairedFastaQualIterator(open("example.fasta"), open("example.qual")):
-   print(record)
-</PRE><P>This function will check that the FASTA and QUAL files are consistent (e.g.
-the records are in the same order, and have the same sequence length).
-You can combine this with the <CODE>Bio.SeqIO.write()</CODE> function to convert a
-pair of FASTA and QUAL files into a single FASTQ files:</P><PRE CLASS="verbatim">from Bio import SeqIO
-from Bio.SeqIO.QualityIO import PairedFastaQualIterator
-handle = open("temp.fastq", "w") #w=write
-records = PairedFastaQualIterator(open("example.fasta"), open("example.qual"))
-count = SeqIO.write(records, handle, "fastq")
-handle.close()
-print("Converted %i records" % count)
-</PRE><!--TOC subsection Indexing a FASTQ file-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc244">18.1.11</A>&#XA0;&#XA0;Indexing a FASTQ file</H3><!--SEC END --><P>
-<A NAME="sec:fastq-indexing"></A></P><P>FASTQ files are often very large, with millions of reads in them. Due to the
-sheer amount of data, you can&#X2019;t load all the records into memory at once.
-This is why the examples above (filtering and trimming) iterate over the file
-looking at just one <CODE>SeqRecord</CODE> at a time.</P><P>However, sometimes you can&#X2019;t use a big loop or an iterator - you may need
-random access to the reads. Here the <CODE>Bio.SeqIO.index()</CODE> function
-may prove very helpful, as it allows you to access any read in the FASTQ file
-by its name (see Section&#XA0;<A HREF="#sec:SeqIO-index">5.4.2</A>).</P><P>Again we&#X2019;ll use the <TT>SRR020192.fastq</TT> file from the ENA
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR020/SRR020192/SRR020192.fastq.gz</TT></A>),
-although this is actually quite a small FASTQ file with less than 50,000 reads:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> fq_dict = SeqIO.index("SRR020192.fastq", "fastq")
->>> len(fq_dict)
-41892
->>> fq_dict.keys()[:4]
-['SRR020192.38240', 'SRR020192.23181', 'SRR020192.40568', 'SRR020192.23186']
->>> fq_dict["SRR020192.23186"].seq
-Seq('GTCCCAGTATTCGGATTTGTCTGCCAAAACAATGAAATTGACACAGTTTACAAC...CCG', SingleLetterAlphabet())
-</PRE><P>When testing this on a FASTQ file with seven million reads,
-indexing took about a minute, but record access was almost instant.</P><P>The example in Section&#XA0;<A HREF="#sec:SeqIO-sort">18.1.5</A> show how you can use the
-<CODE>Bio.SeqIO.index()</CODE> function to sort a large FASTA file &#X2013; this
-could also be used on FASTQ files.</P><!--TOC subsection Converting SFF files-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc245">18.1.12</A>&#XA0;&#XA0;Converting SFF files</H3><!--SEC END --><P>
-<A NAME="sec:SeqIO-sff-conversion"></A></P><P>If you work with 454 (Roche) sequence data, you will probably have access
-to the raw data as a Standard Flowgram Format (SFF) file. This contains
-the sequence reads (called bases) with quality scores and the original
-flow information.</P><P>A common task is to convert from SFF to a pair of FASTA and QUAL files,
-or to a single FASTQ file. These operations are trivial using the
-<CODE>Bio.SeqIO.convert()</CODE> function (see Section&#XA0;<A HREF="#sec:SeqIO-conversion">5.5.2</A>):</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fasta", "fasta")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.qual", "qual")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff", "reads.fastq", "fastq")
-10
-</PRE><P>Remember the convert function returns the number of records, in
-this example just ten. This will give you the <EM>untrimmed</EM> reads, where
-the leading and trailing poor quality sequence or adaptor will be in lower
-case. If you want the <EM>trimmed</EM> reads (using the clipping information
-recorded within the SFF file) use this:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fasta", "fasta")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.qual", "qual")
-10
->>> SeqIO.convert("E3MFGYR02_random_10_reads.sff", "sff-trim", "trimmed.fastq", "fastq")
-10
-</PRE><P>If you run Linux, you could ask Roche for a copy of their &#X201C;off instrument&#X201D;
-tools (often referred to as the Newbler tools). This offers an alternative way to
-do SFF to FASTA or QUAL conversion at the command line (but currently FASTQ output
-is not supported), e.g.</P><PRE CLASS="verbatim">$ sffinfo -seq -notrim E3MFGYR02_random_10_reads.sff > reads.fasta
-$ sffinfo -qual -notrim E3MFGYR02_random_10_reads.sff > reads.qual
-$ sffinfo -seq -trim E3MFGYR02_random_10_reads.sff > trimmed.fasta
-$ sffinfo -qual -trim E3MFGYR02_random_10_reads.sff > trimmed.qual
-</PRE><P>The way Biopython uses mixed case sequence strings to represent
-the trimming points deliberately mimics what the Roche tools do.</P><P>For more information on the Biopython SFF support, consult the built in help:</P><PRE CLASS="verbatim">>>> from Bio.SeqIO import SffIO
->>> help(SffIO)
-...
-</PRE><!--TOC subsection Identifying open reading frames-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc246">18.1.13</A>&#XA0;&#XA0;Identifying open reading frames</H3><!--SEC END --><P>A very simplistic first step at identifying possible genes is to look for
-open reading frames (ORFs). By this we mean look in all six frames for long
-regions without stop codons &#X2013; an ORF is just a region of nucleotides with
-no in frame stop codons.</P><P>Of course, to find a gene you would also need to worry about locating a start
-codon, possible promoters &#X2013; and in Eukaryotes there are introns to worry about
-too. However, this approach is still useful in viruses and Prokaryotes.</P><P>To show how you might approach this with Biopython, we&#X2019;ll need a sequence to
-search, and as an example we&#X2019;ll again use the bacterial plasmid &#X2013; although
-this time we&#X2019;ll start with a plain FASTA file with no pre-marked genes:
-<A HREF="http://biopython.org/SRC/biopython/Tests/GenBank/NC_005816.fna"><TT>NC_005816.fna</TT></A>. This is a bacterial sequence, so we&#X2019;ll want to use
-NCBI codon table 11 (see Section&#XA0;<A HREF="#sec:translation">3.9</A> about translation).</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> record = SeqIO.read("NC_005816.fna", "fasta")
->>> table = 11
->>> min_pro_len = 100
-</PRE><P>Here is a neat trick using the <CODE>Seq</CODE> object&#X2019;s <CODE>split</CODE> method to
-get a list of all the possible ORF translations in the six reading frames:</P><PRE CLASS="verbatim">>>> for strand, nuc in [(+1, record.seq), (-1, record.seq.reverse_complement())]:
-...     for frame in range(3):
-...         length = 3 * ((len(record)-frame) // 3) #Multiple of three
-...         for pro in nuc[frame:frame+length].translate(table).split("*"):
-...             if len(pro) >= min_pro_len:
-...                 print("%s...%s - length %i, strand %i, frame %i" \
-...                       % (pro[:30], pro[-3:], len(pro), strand, frame))
-GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, frame 0
-KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, frame 1
-GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, frame 1
-VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, frame 1
-NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, frame 2
-RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, frame 2
-TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, frame 2
-QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, frame 0
-IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, frame 0
-WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, frame 1
-RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, frame 1
-WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, frame 1
-LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, frame 2
-RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, frame 2
-</PRE><P>Note that here we are counting the frames from the 5&#X2019; end (start) of
-<EM>each</EM> strand. It is sometimes easier to always count from the 5&#X2019; end
-(start) of the <EM>forward</EM> strand.</P><P>You could easily edit the above loop based code to build up a list of the
-candidate proteins, or convert this to a list comprehension. Now, one thing
-this code doesn&#X2019;t do is keep track of where the proteins are.</P><P>You could tackle this in several ways. For example, the following code tracks
-the locations in terms of the protein counting, and converts back to the
-parent sequence by multiplying by three, then adjusting for the frame and
-strand:</P><PRE CLASS="verbatim">from Bio import SeqIO
-record = SeqIO.read("NC_005816.gb","genbank")
-table = 11
-min_pro_len = 100
-
-def find_orfs_with_trans(seq, trans_table, min_protein_length):
-    answer = []
-    seq_len = len(seq)
-    for strand, nuc in [(+1, seq), (-1, seq.reverse_complement())]:
-        for frame in range(3):
-            trans = str(nuc[frame:].translate(trans_table))
-            trans_len = len(trans)
-            aa_start = 0
-            aa_end = 0
-            while aa_start < trans_len:
-                aa_end = trans.find("*", aa_start)
-                if aa_end == -1:
-                    aa_end = trans_len
-                if aa_end-aa_start >= min_protein_length:
-                    if strand == 1:
-                        start = frame+aa_start*3
-                        end = min(seq_len,frame+aa_end*3+3)
-                    else:
-                        start = seq_len-frame-aa_end*3-3
-                        end = seq_len-frame-aa_start*3
-                    answer.append((start, end, strand,
-                                   trans[aa_start:aa_end]))
-                aa_start = aa_end+1
-    answer.sort()
-    return answer
-
-orf_list = find_orfs_with_trans(record.seq, table, min_pro_len)
-for start, end, strand, pro in orf_list:
-    print("%s...%s - length %i, strand %i, %i:%i" \
-          % (pro[:30], pro[-3:], len(pro), strand, start, end))
-</PRE><P>And the output:</P><PRE CLASS="verbatim">NQIQGVICSPDSGEFMVTFETVMEIKILHK...GVA - length 355, strand 1, 41:1109
-WDVKTVTGVLHHPFHLTFSLCPEGATQSGR...VKR - length 111, strand -1, 491:827
-KSGELRQTPPASSTLHLRLILQRSGVMMEL...NPE - length 285, strand 1, 1030:1888
-RALTGLSAPGIRSQTSCDRLRELRYVPVSL...PLQ - length 119, strand -1, 2830:3190
-RRKEHVSKKRRPQKRPRRRRFFHRLRPPDE...PTR - length 128, strand 1, 3470:3857
-GLNCSFFSICNWKFIDYINRLFQIIYLCKN...YYH - length 176, strand 1, 4249:4780
-RGIFMSDTMVVNGSGGVPAFLFSGSTLSSY...LLK - length 361, strand -1, 4814:5900
-VKKILYIKALFLCTVIKLRRFIFSVNNMKF...DLP - length 165, strand 1, 5923:6421
-LSHTVTDFTDQMAQVGLCQCVNVFLDEVTG...KAA - length 107, strand -1, 5974:6298
-GCLMKKSSIVATIITILSGSANAASSQLIP...YRF - length 315, strand 1, 6654:7602
-IYSTSEHTGEQVMRTLDEVIASRSPESQTR...FHV - length 111, strand -1, 7788:8124
-WGKLQVIGLSMWMVLFSQRFDDWLNEQEDA...ESK - length 125, strand -1, 8087:8465
-TGKQNSCQMSAIWQLRQNTATKTRQNRARI...AIK - length 100, strand 1, 8741:9044
-QGSGYAFPHASILSGIAMSHFYFLVLHAVK...CSD - length 114, strand -1, 9264:9609
-</PRE><P>If you comment out the sort statement, then the protein sequences will be
-shown in the same order as before, so you can check this is doing the same
-thing. Here we have sorted them by location to make it easier to compare
-to the actual annotation in the GenBank file (as visualised in
-Section&#XA0;<A HREF="#sec:gd_nice_example">16.1.9</A>).</P><P>If however all you want to find are the locations of the open reading frames,
-then it is a waste of time to translate every possible codon, including doing
-the reverse complement to search the reverse strand too. All you need to do
-is search for the possible stop codons (and their reverse complements). Using
-regular expressions is an obvious approach here (see the Python module
-<CODE>re</CODE>). These are an extremely powerful (but rather complex) way of
-describing search strings, which are supported in lots of programming
-languages and also command line tools like <TT>grep</TT> as well). You can
-find whole books about this topic!</P><!--TOC section Sequence parsing plus simple plots-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc247">18.2</A>&#XA0;&#XA0;Sequence parsing plus simple plots</H2><!--SEC END --><P>
-<A NAME="seq:sequence-parsing-plus-pylab"></A></P><P>This section shows some more examples of sequence parsing, using the <CODE>Bio.SeqIO</CODE>
-module described in Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>, plus the Python library matplotlib&#X2019;s <CODE>pylab</CODE> plotting interface (see <A HREF="http://matplotlib.sourceforge.net/">the matplotlib website for a tutorial</A>). Note that to follow these examples you will need matplotlib installed - but without it you can still try the data parsing bits.</P><!--TOC subsection Histogram of sequence lengths-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc248">18.2.1</A>&#XA0;&#XA0;Histogram of sequence lengths</H3><!--SEC END --><P>There are lots of times when you might want to visualise the distribution of sequence
-lengths in a dataset &#X2013; for example the range of contig sizes in a genome assembly
-project. In this example we&#X2019;ll reuse our orchid FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A> which has only 94 sequences.</P><P>First of all, we will use <CODE>Bio.SeqIO</CODE> to parse the FASTA file and compile a list
-of all the sequence lengths. You could do this with a for loop, but I find a list
-comprehension more pleasing:</P><PRE CLASS="verbatim">>>> from Bio import SeqIO
->>> sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
->>> len(sizes), min(sizes), max(sizes)
-(94, 572, 789)
->>> sizes
-[740, 753, 748, 744, 733, 718, 730, 704, 740, 709, 700, 726, ..., 592]
-</PRE><P>Now that we have the lengths of all the genes (as a list of integers), we can use the
-matplotlib histogram function to display it.</P><PRE CLASS="verbatim">from Bio import SeqIO
-sizes = [len(rec) for rec in SeqIO.parse("ls_orchid.fasta", "fasta")]
-
-import pylab
-pylab.hist(sizes, bins=20)
-pylab.title("%i orchid sequences\nLengths %i to %i" \
-            % (len(sizes),min(sizes),max(sizes)))
-pylab.xlabel("Sequence length (bp)")
-pylab.ylabel("Count")
-pylab.show()
-</PRE><P>
-That should pop up a new window containing the following graph:</P><P><IMG SRC="images/hist_plot.png" width=600, height=450></P><P>
-
-Notice that most of these orchid sequences are about 740 bp long, and there could be
-two distinct classes of sequence here with a subset of shorter sequences.</P><P><EM>Tip:</EM> Rather than using <CODE>pylab.show()</CODE> to show the plot in a window, you can also use <CODE>pylab.savefig(...)</CODE> to save the figure to a file (e.g. as a PNG or PDF).</P><!--TOC subsection Plot of sequence GC%-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc249">18.2.2</A>&#XA0;&#XA0;Plot of sequence GC%</H3><!--SEC END --><P>Another easily calculated quantity of a nucleotide sequence is the GC%. You might
-want to look at the GC% of all the genes in a bacterial genome for example, and
-investigate any outliers which could have been recently acquired by horizontal gene
-transfer. Again, for this example we&#X2019;ll reuse our orchid FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A>.</P><P>First of all, we will use <CODE>Bio.SeqIO</CODE> to parse the FASTA file and compile a list
-of all the GC percentages. Again, you could do this with a for loop, but I prefer this:</P><PRE CLASS="verbatim">from Bio import SeqIO
-from Bio.SeqUtils import GC
-
-gc_values = sorted(GC(rec.seq) for rec in SeqIO.parse("ls_orchid.fasta", "fasta"))
-</PRE><P>Having read in each sequence and calculated the GC%, we then sorted them into ascending
-order. Now we&#X2019;ll take this list of floating point values and plot them with matplotlib:</P><PRE CLASS="verbatim">import pylab
-pylab.plot(gc_values)
-pylab.title("%i orchid sequences\nGC%% %0.1f to %0.1f" \
-            % (len(gc_values),min(gc_values),max(gc_values)))
-pylab.xlabel("Genes")
-pylab.ylabel("GC%")
-pylab.show()
-</PRE><P>
-As in the previous example, that should pop up a new window containing a graph:</P><P><IMG SRC="images/gc_plot.png" width=600, height=450></P><P>
-
-If you tried this on the full set of genes from one organism, you&#X2019;d probably get a much
-smoother plot than this.</P><!--TOC subsection Nucleotide dot plots-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc250">18.2.3</A>&#XA0;&#XA0;Nucleotide dot plots</H3><!--SEC END --><P>
-A dot plot is a way of visually comparing two nucleotide sequences for similarity to
-each other. A sliding window is used to compare short sub-sequences to each other,
-often with a mis-match threshold. Here for simplicity we&#X2019;ll only look for perfect
-matches (shown in black
-
-
-in the plot below).
-</P><P>To start off, we&#X2019;ll need two sequences. For the sake of argument, we&#X2019;ll just take
-the first two from our orchid FASTA file <A HREF="http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta"><TT>ls_orchid.fasta</TT></A>:</P><PRE CLASS="verbatim">from Bio import SeqIO
-handle = open("ls_orchid.fasta")
-record_iterator = SeqIO.parse(handle, "fasta")
-rec_one = next(record_iterator)
-rec_two = next(record_iterator)
-handle.close()
-</PRE><P>We&#X2019;re going to show two approaches. Firstly, a simple naive implementation
-which compares all the window sized sub-sequences to each other to compiles a
-similarity matrix. You could construct a matrix or array object, but here we
-just use a list of lists of booleans created with a nested list
-comprehension:</P><PRE CLASS="verbatim">window = 7
-seq_one = str(rec_one.seq).upper()
-seq_two = str(rec_two.seq).upper()
-data = [[(seq_one[i:i+window] <> seq_two[j:j+window]) \
-        for j in range(len(seq_one)-window)] \
-       for i in range(len(seq_two)-window)]
-</PRE><P>Note that we have <EM>not</EM> checked for reverse complement matches here.
-Now we&#X2019;ll use the matplotlib&#X2019;s <CODE>pylab.imshow()</CODE> function to display this
-data, first requesting the gray color scheme so this is done in black and
-white:</P><PRE CLASS="verbatim">import pylab
-pylab.gray()
-pylab.imshow(data)
-pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
-pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
-pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window)
-pylab.show()
-</PRE><P>
-That should pop up a new window containing a graph like this:</P><P><IMG SRC="images/dot_plot.png" width=600, height=450></P><P>
-
-As you might have expected, these two sequences are very similar with a
-partial line of window sized matches along the diagonal. There are no off
-diagonal matches which would be indicative of inversions or other interesting
-events.</P><P>The above code works fine on small examples, but there are two problems
-applying this to larger sequences, which we will address below.
-First off all, this brute force approach to the all against all comparisons
-is very slow. Instead, we&#X2019;ll compile dictionaries mapping the window sized
-sub-sequences to their locations, and then take the set intersection to find
-those sub-sequences found in both sequences. This uses more memory, but is
-<EM>much</EM> faster. Secondly, the <CODE>pylab.imshow()</CODE> function is limited
-in the size of matrix it can display. As an alternative, we&#X2019;ll use the
-<CODE>pylab.scatter()</CODE> function.</P><P>We start by creating dictionaries mapping the window-sized sub-sequences to locations:
-</P><PRE CLASS="verbatim">window = 7
-dict_one = {}
-dict_two = {}
-for (seq, section_dict) in [(str(rec_one.seq).upper(), dict_one),
-                            (str(rec_two.seq).upper(), dict_two)]:
-    for i in range(len(seq)-window):
-        section = seq[i:i+window]
-        try:
-            section_dict[section].append(i)
-        except KeyError:
-            section_dict[section] = [i]
-#Now find any sub-sequences found in both sequences
-#(Python 2.3 would require slightly different code here)
-matches = set(dict_one).intersection(dict_two)
-print("%i unique matches" % len(matches))
-</PRE><P>In order to use the <CODE>pylab.scatter()</CODE> we need separate lists for the <I>x</I> and <I>y</I> co-ordinates:
-</P><PRE CLASS="verbatim">#Create lists of x and y co-ordinates for scatter plot
-x = []
-y = []
-for section in matches:
-    for i in dict_one[section]:
-        for j in dict_two[section]:
-            x.append(i)
-            y.append(j)
-</PRE><P>We are now ready to draw the revised dot plot as a scatter plot:
-</P><PRE CLASS="verbatim">import pylab
-pylab.cla() #clear any prior graph
-pylab.gray()
-pylab.scatter(x,y)
-pylab.xlim(0, len(rec_one)-window)
-pylab.ylim(0, len(rec_two)-window)
-pylab.xlabel("%s (length %i bp)" % (rec_one.id, len(rec_one)))
-pylab.ylabel("%s (length %i bp)" % (rec_two.id, len(rec_two)))
-pylab.title("Dot plot using window size %i\n(allowing no mis-matches)" % window)
-pylab.show()
-</PRE><P>
-That should pop up a new window containing a graph like this:</P><P><IMG SRC="images/dot_plot_scatter.png" width=600, height=450></P><P>
-
-Personally I find this second plot much easier to read!
-Again note that we have <EM>not</EM> checked for reverse complement matches here
-&#X2013; you could extend this example to do this, and perhaps plot the forward
-matches in one color and the reverse matches in another.</P><!--TOC subsection Plotting the quality scores of sequencing read data-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc251">18.2.4</A>&#XA0;&#XA0;Plotting the quality scores of sequencing read data</H3><!--SEC END --><P>If you are working with second generation sequencing data, you may want to try plotting
-the quality data. Here is an example using two FASTQ files containing paired end reads,
-<TT>SRR001666_1.fastq</TT> for the forward reads, and <TT>SRR001666_2.fastq</TT> for
-the reverse reads. These were downloaded from the ENA sequence read archive FTP site
-(<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_1.fastq.gz</TT></A> and
-<A HREF="ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz"><TT>ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR001/SRR001666/SRR001666_2.fastq.gz</TT></A>), and
-are from <I>E. coli</I> &#X2013; see <A HREF="http://www.ebi.ac.uk/ena/data/view/SRR001666"><TT>http://www.ebi.ac.uk/ena/data/view/SRR001666</TT></A>
-for details.
-</P><P>In the following code the <CODE>pylab.subplot(...)</CODE> function is used in order to show
-the forward and reverse qualities on two subplots, side by side. There is also a little
-bit of code to only plot the first fifty reads.</P><PRE CLASS="verbatim">import pylab
-from Bio import SeqIO
-for subfigure in [1,2]:
-    filename = "SRR001666_%i.fastq" % subfigure
-    pylab.subplot(1, 2, subfigure)
-    for i,record in enumerate(SeqIO.parse(filename, "fastq")):
-        if i >= 50 : break #trick!
-        pylab.plot(record.letter_annotations["phred_quality"])
-    pylab.ylim(0,45)
-    pylab.ylabel("PHRED quality score")
-    pylab.xlabel("Position")
-pylab.savefig("SRR001666.png")
-print("Done")
-</PRE><P>You should note that we are using the <CODE>Bio.SeqIO</CODE> format name <TT>fastq</TT>
-here because the NCBI has saved these reads using the standard Sanger FASTQ format
-with PHRED scores. However, as you might guess from the read lengths, this data was
-from an Illumina Genome Analyzer and was probably originally in one of the two
-Solexa/Illumina FASTQ variant file formats instead.</P><P>This example uses the <CODE>pylab.savefig(...)</CODE> function instead of
-<CODE>pylab.show(...)</CODE>, but as mentioned before both are useful.
-
-
-Here is the result:</P><P><IMG SRC="images/SRR001666.png" width=600, height=600></P><!--TOC section Dealing with alignments-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc252">18.3</A>&#XA0;&#XA0;Dealing with alignments</H2><!--SEC END --><P>This section can been seen as a follow on to Chapter&#XA0;<A HREF="#chapter:Bio.AlignIO">6</A>.</P><!--TOC subsection Calculating summary information-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc253">18.3.1</A>&#XA0;&#XA0;Calculating summary information</H3><!--SEC END --><P>
-<A NAME="sec:summary_info"></A></P><P>Once you have an alignment, you are very likely going to want to find out information about it. Instead of trying to have all of the functions that can generate information about an alignment in the alignment object itself, we&#X2019;ve tried to separate out the functionality into separate classes, which act on the alignment.</P><P>Getting ready to calculate summary information about an object is quick to do. Let&#X2019;s say we&#X2019;ve got an alig [...]
-summary_align = AlignInfo.SummaryInfo(alignment)
-</PRE><P>The <CODE>summary_align</CODE> object is very useful, and will do the following neat things for you:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Calculate a quick consensus sequence &#X2013; see section&#XA0;<A HREF="#sec:consensus">18.3.2</A>
-</LI><LI CLASS="li-enumerate">Get a position specific score matrix for the alignment &#X2013; see section&#XA0;<A HREF="#sec:pssm">18.3.3</A>
-</LI><LI CLASS="li-enumerate">Calculate the information content for the alignment &#X2013; see section&#XA0;<A HREF="#sec:getting_info_content">18.3.4</A>
-</LI><LI CLASS="li-enumerate">Generate information on substitutions in the alignment &#X2013; section&#XA0;<A HREF="#sec:sub_matrix">18.4</A> details using this to generate a substitution matrix.
-</LI></OL><!--TOC subsection Calculating a quick consensus sequence-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc254">18.3.2</A>&#XA0;&#XA0;Calculating a quick consensus sequence</H3><!--SEC END --><P>
-<A NAME="sec:consensus"></A></P><P>The <CODE>SummaryInfo</CODE> object, described in section&#XA0;<A HREF="#sec:summary_info">18.3.1</A>, provides functionality to calculate a quick consensus of an alignment. Assuming we&#X2019;ve got a <CODE>SummaryInfo</CODE> object called <CODE>summary_align</CODE> we can calculate a consensus by doing:</P><PRE CLASS="verbatim">consensus = summary_align.dumb_consensus()
-</PRE><P>As the name suggests, this is a really simple consensus calculator, and will just add up all of the residues at each point in the consensus, and if the most common value is higher than some threshold value will add the common residue to the consensus. If it doesn&#X2019;t reach the threshold, it adds an ambiguity character to the consensus. The returned consensus object is Seq object whose alphabet is inferred from the alphabets of the sequences making up the consensus. So doing [...]
-...', IUPACAmbiguousDNA())
-</PRE><P>You can adjust how <CODE>dumb_consensus</CODE> works by passing optional parameters:</P><DL CLASS="description"><DT CLASS="dt-description">
-<B>the threshold</B></DT><DD CLASS="dd-description"> This is the threshold specifying how common a particular residue has to be at a position before it is added. The default is 0.7 (meaning 70%).</DD><DT CLASS="dt-description"><B>the ambiguous character</B></DT><DD CLASS="dd-description"> This is the ambiguity character to use. The default is &#X2019;N&#X2019;.</DD><DT CLASS="dt-description"><B>the consensus alphabet</B></DT><DD CLASS="dd-description"> This is the alphabet to use for the [...]
-</DD></DL><!--TOC subsection Position Specific Score Matrices-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc255">18.3.3</A>&#XA0;&#XA0;Position Specific Score Matrices</H3><!--SEC END --><P>
-<A NAME="sec:pssm"></A></P><P>Position specific score matrices (PSSMs) summarize the alignment information in a different way than a consensus, and may be useful for different tasks. Basically, a PSSM is a count matrix. For each column in the alignment, the number of each alphabet letters is counted and totaled. The totals are displayed relative to some representative sequence along the left axis. This sequence may be the consesus sequence, but can also be any sequence in the alignment.  [...]
-AT--C
-CTGTC
-</PRE><P>the PSSM is:</P><PRE CLASS="verbatim">      G A T C
-    G 1 1 0 1
-    T 0 0 3 0
-    A 1 1 0 0
-    T 0 0 2 0
-    C 0 0 0 3
-</PRE><P>Let&#X2019;s assume we&#X2019;ve got an alignment object called <CODE>c_align</CODE>. To get a PSSM with the consensus sequence along the side we first get a summary object and calculate the consensus sequence:</P><PRE CLASS="verbatim">summary_align = AlignInfo.SummaryInfo(c_align)
-consensus = summary_align.dumb_consensus()
-</PRE><P>Now, we want to make the PSSM, but ignore any <CODE>N</CODE> ambiguity residues when calculating this:</P><PRE CLASS="verbatim">my_pssm = summary_align.pos_specific_score_matrix(consensus,
-                                                  chars_to_ignore = ['N'])
-</PRE><P>Two notes should be made about this:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-To maintain strictness with the alphabets, you can only include characters along the top of the PSSM that are in the alphabet of the alignment object. Gaps are not included along the top axis of the PSSM.</LI><LI CLASS="li-enumerate">The sequence passed to be displayed along the left side of the axis does not need to be the consensus. For instance, if you wanted to display the second sequence in the alignment along this axis, you would need to do:<PRE CLASS="verbatim">second_seq = alignm [...]
-my_pssm = summary_align.pos_specific_score_matrix(second_seq
-                                                  chars_to_ignore = ['N'])
-</PRE></LI></OL><P>The command above returns a <CODE>PSSM</CODE> object.
-To print out the PSSM as shown above,
-we simply need to do a <CODE>print(my_pssm)</CODE>, which gives:</P><PRE CLASS="verbatim">    A   C   G   T
-T  0.0 0.0 0.0 7.0
-A  7.0 0.0 0.0 0.0
-T  0.0 0.0 0.0 7.0
-A  7.0 0.0 0.0 0.0
-C  0.0 7.0 0.0 0.0
-A  7.0 0.0 0.0 0.0
-T  0.0 0.0 0.0 7.0
-T  1.0 0.0 0.0 6.0
-...
-</PRE><P>You can access any element of the PSSM by subscripting like <CODE>your_pssm[sequence_number][residue_count_name]</CODE>. For instance, to get the counts for the &#X2019;A&#X2019; residue in the second element of the above PSSM you would do:</P><PRE CLASS="verbatim">>>> print(my_pssm[1]["A"])
-7.0
-</PRE><P>The structure of the PSSM class hopefully makes it easy both to access elements and to pretty print the matrix.</P><!--TOC subsection Information Content-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc256">18.3.4</A>&#XA0;&#XA0;Information Content</H3><!--SEC END --><P>
-<A NAME="sec:getting_info_content"></A></P><P>A potentially useful measure of evolutionary conservation is the information content of a sequence.</P><P>A useful introduction to information theory targeted towards molecular biologists can be found at <A HREF="http://www.lecb.ncifcrf.gov/~toms/paper/primer/"><TT>http://www.lecb.ncifcrf.gov/~toms/paper/primer/</TT></A>. For our purposes, we will be looking at the information content of a consesus sequence, or a portion of a consensus sequen [...]
-<TR><TD CLASS="dcell" ALIGN="center"><FONT SIZE=6>&#X2211;</FONT></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>i</I>=1</TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#XA0;<I>P</I><SUB><I>ij</I></SUB>&#XA0;<I>log</I></TD><TD CLASS="dcell">&#X239B;<BR>
-&#X239C;<BR>
-&#X239C;<BR>
-&#X239D;</TD><TD CLASS="dcell"><TABLE CLASS="display"><TR><TD CLASS="dcell" ALIGN="center"><I>P</I><SUB><I>ij</I></SUB></TD></TR>
-<TR><TD CLASS="hbar"></TD></TR>
-<TR><TD CLASS="dcell" ALIGN="center"><I>Q</I><SUB><I>i</I></SUB></TD></TR>
-</TABLE></TD><TD CLASS="dcell">&#X239E;<BR>
-&#X239F;<BR>
-&#X239F;<BR>
-&#X23A0;</TD></TR>
-</TABLE><P>where:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<I>IC</I><SUB><I>j</I></SUB> &#X2013; The information content for the <I>j</I>-th column in an alignment.
-</LI><LI CLASS="li-itemize"><I>N</I><SUB><I>a</I></SUB> &#X2013; The number of letters in the alphabet.
-</LI><LI CLASS="li-itemize"><I>P</I><SUB><I>ij</I></SUB> &#X2013; The frequency of a particular letter <I>i</I> in the <I>j</I>-th column (i.&#XA0;e.&#XA0;if G occurred 3 out of 6 times in an aligment column, this would be 0.5)
-</LI><LI CLASS="li-itemize"><I>Q</I><SUB><I>i</I></SUB> &#X2013; The expected frequency of a letter <I>i</I>. This is an
-optional argument, usage of which is left at the user&#X2019;s
-discretion. By default, it is automatically assigned to 0.05 = 1/20 for a
-protein alphabet, and 0.25 = 1/4 for a nucleic acid alphabet. This is for
-geting the information content without any assumption of prior
-distributions. When assuming priors, or when using a non-standard
-alphabet, you should supply the values for <I>Q</I><SUB><I>i</I></SUB>.
-</LI></UL><P>Well, now that we have an idea what information content is being calculated in Biopython, let&#X2019;s look at how to get it for a particular region of the alignment.</P><P>First, we need to use our alignment to get an alignment summary object, which we&#X2019;ll assume is called <CODE>summary_align</CODE> (see section&#XA0;<A HREF="#sec:summary_info">18.3.1</A>) for instructions on how to get this. Once we&#X2019;ve got this object, calculating the information content for a [...]
-                                                 chars_to_ignore = ['N'])
-</PRE><P>Wow, that was much easier then the formula above made it look! The variable <CODE>info_content</CODE> now contains a float value specifying the information content over the specified region (from 5 to 30 of the alignment). We specifically ignore the ambiguity residue &#X2019;N&#X2019; when calculating the information content, since this value is not included in our alphabet (so we shouldn&#X2019;t be interested in looking at it!).</P><P>As mentioned above, we can also calculate  [...]
-    'A' : .3,
-    'G' : .2,
-    'T' : .3,
-    'C' : .2}
-</PRE><P>The expected should not be passed as a raw dictionary, but instead by passed as a <CODE>SubsMat.FreqTable</CODE> object (see section&#XA0;<A HREF="#sec:freq_table">20.2.2</A> for more information about FreqTables). The FreqTable object provides a standard for associating the dictionary with an Alphabet, similar to how the Biopython Seq class works.</P><P>To create a FreqTable object, from the frequency dictionary you just need to do:</P><PRE CLASS="verbatim">from Bio.Alphabet im [...]
-from Bio.SubsMat import FreqTable
-
-e_freq_table = FreqTable.FreqTable(expect_freq, FreqTable.FREQ,
-                                   IUPAC.unambiguous_dna)
-</PRE><P>Now that we&#X2019;ve got that, calculating the relative information content for our region of the alignment is as simple as:</P><PRE CLASS="verbatim">info_content = summary_align.information_content(5, 30,
-                                                 e_freq_table = e_freq_table,
-                                                 chars_to_ignore = ['N'])
-</PRE><P>Now, <CODE>info_content</CODE> will contain the relative information content over the region in relation to the expected frequencies.</P><P>The value return is calculated using base 2 as the logarithm base in the formula above. You can modify this by passing the parameter <CODE>log_base</CODE> as the base you want:</P><PRE CLASS="verbatim">info_content = summary_align.information_content(5, 30, log_base = 10,
-                                                 chars_to_ignore = ['N'])
-</PRE><P>Well, now you are ready to calculate information content. If you want to try applying this to some real life problems, it would probably be best to dig into the literature on information content to get an idea of how it is used. Hopefully your digging won&#X2019;t reveal any mistakes made in coding this function!</P><!--TOC section Substitution Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc257">18.4</A>&#XA0;&#XA0;Substitution Matrices</H2><!--SEC END --><P>
-<A NAME="sec:sub_matrix"></A></P><P>Substitution matrices are an extremely important part of everyday bioinformatics work. They provide the scoring terms for classifying how likely two different residues are to substitute for each other. This is essential in doing sequence comparisons. The book &#X201C;Biological Sequence Analysis&#X201D; by Durbin et al. provides a really nice introduction to Substitution Matrices and their uses. Some famous substitution matrices are the PAM and BLOSUM  [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc258">18.4.1</A>&#XA0;&#XA0;Using common substitution matrices</H3><!--SEC END --><!--TOC subsection Creating your own substitution matrix from an alignment-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc259">18.4.2</A>&#XA0;&#XA0;Creating your own substitution matrix from an alignment</H3><!--SEC END --><P>
-<A NAME="sec:subs_mat_ex"></A></P><P>A very cool thing that you can do easily with the substitution matrix
-classes is to create your own substitution matrix from an
-alignment. In practice, this is normally done with protein
-alignments. In this example, we&#X2019;ll first get a Biopython alignment
-object and then get a summary object to calculate info about the
-alignment. The file containing <A HREF="examples/protein.aln">protein.aln</A>
-(also available online
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/protein.aln">here</A>)
-contains the Clustalw alignment output.</P><PRE CLASS="verbatim">>>> from Bio import AlignIO
->>> from Bio import Alphabet
->>> from Bio.Alphabet import IUPAC
->>> from Bio.Align import AlignInfo
->>> filename = "protein.aln"
->>> alpha = Alphabet.Gapped(IUPAC.protein)
->>> c_align = AlignIO.read(filename, "clustal", alphabet=alpha)
->>> summary_align = AlignInfo.SummaryInfo(c_align)
-</PRE><P>Sections&#XA0;<A HREF="#sec:align_clustal">6.4.1</A> and&#XA0;<A HREF="#sec:summary_info">18.3.1</A> contain
-more information on doing this.</P><P>Now that we&#X2019;ve got our <CODE>summary_align</CODE> object, we want to use it
-to find out the number of times different residues substitute for each
-other. To make the example more readable, we&#X2019;ll focus on only amino
-acids with polar charged side chains. Luckily, this can be done easily
-when generating a replacement dictionary, by passing in all of the
-characters that should be ignored. Thus we&#X2019;ll create a dictionary of
-replacements for only charged polar amino acids using:</P><PRE CLASS="verbatim">>>> replace_info = summary_align.replacement_dictionary(["G", "A", "V", "L", "I",
-...                                                      "M", "P", "F", "W", "S",
-...                                                      "T", "N", "Q", "Y", "C"])
-</PRE><P>This information about amino acid replacements is represented as a
-python dictionary which will look something like (the order can vary):</P><PRE CLASS="verbatim">{('R', 'R'): 2079.0, ('R', 'H'): 17.0, ('R', 'K'): 103.0, ('R', 'E'): 2.0,
-('R', 'D'): 2.0, ('H', 'R'): 0, ('D', 'H'): 15.0, ('K', 'K'): 3218.0,
-('K', 'H'): 24.0, ('H', 'K'): 8.0, ('E', 'H'): 15.0, ('H', 'H'): 1235.0,
-('H', 'E'): 18.0, ('H', 'D'): 0, ('K', 'D'): 0, ('K', 'E'): 9.0,
-('D', 'R'): 48.0, ('E', 'R'): 2.0, ('D', 'K'): 1.0, ('E', 'K'): 45.0,
-('K', 'R'): 130.0, ('E', 'D'): 241.0, ('E', 'E'): 3305.0,
-('D', 'E'): 270.0, ('D', 'D'): 2360.0}
-</PRE><P>This information gives us our accepted number of replacements, or how
-often we expect different things to substitute for each other. It
-turns out, amazingly enough, that this is all of the information we
-need to go ahead and create a substitution matrix. First, we use the
-replacement dictionary information to create an Accepted Replacement
-Matrix (ARM):</P><PRE CLASS="verbatim">>>> from Bio import SubsMat
->>> my_arm = SubsMat.SeqMat(replace_info)
-</PRE><P>With this accepted replacement matrix, we can go right ahead and
-create our log odds matrix (i.&#XA0;e.&#XA0;a standard type Substitution Matrix):</P><PRE CLASS="verbatim">>>> my_lom = SubsMat.make_log_odds_matrix(my_arm)
-</PRE><P>The log odds matrix you create is customizable with the following
-optional arguments:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<CODE>exp_freq_table</CODE> &#X2013; You can pass a table of expected
-frequencies for each alphabet. If supplied, this will be used
-instead of the passed accepted replacement matrix when calculate
-expected replacments.</LI><LI CLASS="li-itemize"><CODE>logbase</CODE> - The base of the logarithm taken to create the
-log odd matrix. Defaults to base 10.</LI><LI CLASS="li-itemize"><CODE>factor</CODE> - The factor to multiply each matrix entry
-by. This defaults to 10, which normally makes the matrix numbers
-easy to work with.</LI><LI CLASS="li-itemize"><CODE>round_digit</CODE> - The digit to round to in the matrix. This
-defaults to 0 (i.&#XA0;e.&#XA0;no digits).</LI></UL><P>Once you&#X2019;ve got your log odds matrix, you can display it prettily
-using the function <CODE>print_mat</CODE>. Doing this on our created matrix
-gives:</P><PRE CLASS="verbatim">>>> my_lom.print_mat()
-D   2
-E  -1   1
-H  -5  -4   3
-K -10  -5  -4   1
-R  -4  -8  -4  -2   2
-   D   E   H   K   R
-</PRE><P>Very nice. Now we&#X2019;ve got our very own substitution matrix to play with!</P><!--TOC section BioSQL &#X2013; storing sequences in a relational database-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc260">18.5</A>&#XA0;&#XA0;BioSQL &#X2013; storing sequences in a relational database</H2><!--SEC END --><P>
-<A NAME="sec:BioSQL"></A>
-<A HREF="http://www.biosql.org/">BioSQL</A> is a joint effort between the
-<A HREF="http://open-bio.org/">OBF</A> projects (BioPerl, BioJava etc) to support a
-shared database schema for storing sequence data. In theory, you could load a
-GenBank file into the database with BioPerl, then using Biopython extract this
-from the database as a record object with features - and get more or less the same
-thing as if you had loaded the GenBank file directly as a SeqRecord using
-<CODE>Bio.SeqIO</CODE> (Chapter&#XA0;<A HREF="#chapter:Bio.SeqIO">5</A>).</P><P>Biopython&#X2019;s BioSQL module is currently documented at
-<A HREF="http://biopython.org/wiki/BioSQL"><TT>http://biopython.org/wiki/BioSQL</TT></A> which is part of our wiki pages.</P><!--TOC chapter The Biopython testing framework-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc261">Chapter&#XA0;19</A>&#XA0;&#XA0;The Biopython testing framework</H1><!--SEC END --><P>
-<A NAME="sec:regr_test"></A></P><P>Biopython has a regression testing framework (the file
-<CODE>run_tests.py</CODE>) based on
-<A HREF="http://docs.python.org/library/unittest.html">unittest</A>,
-the standard unit testing framework for Python. Providing comprehensive
-tests for modules is one of the most important aspects of making sure that
-the Biopython code is as bug-free as possible before going out.
-It also tends to be one of the most undervalued aspects of contributing.
-This chapter is designed to make running the Biopython tests and
-writing good test code as easy as possible.
-Ideally, every module that goes into Biopython
-should have a test (and should also have documentation!).
-All our developers, and anyone installing Biopython from source,
-are strongly encouraged to run the unit tests.</P><!--TOC section Running the tests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc262">19.1</A>&#XA0;&#XA0;Running the tests</H2><!--SEC END --><P>When you download the Biopython source code, or check it out from
-our source code repository, you should find a subdirectory call
-<CODE>Tests</CODE>. This contains the key script <CODE>run_tests.py</CODE>,
-lots of individual scripts named <CODE>test_XXX.py</CODE>, a subdirectory
-called <CODE>output</CODE> and lots of other subdirectories which
-contain input files for the test suite.</P><P>As part of building and installing Biopython you will typically
-run the full test suite at the command line from the Biopython
-source top level directory using the following:
-</P><PRE CLASS="verbatim">python setup.py test
-</PRE><P>This is actually equivalent to going to the <CODE>Tests</CODE>
-subdirectory and running:
-</P><PRE CLASS="verbatim">python run_tests.py
-</PRE><P>You&#X2019;ll often want to run just some of the tests, and this is done
-like this:
-</P><PRE CLASS="verbatim">python run_tests.py test_SeqIO.py test_AlignIO.py
-</PRE><P>When giving the list of tests, the <CODE>.py</CODE> extension is optional,
-so you can also just type:
-</P><PRE CLASS="verbatim">python run_tests.py test_SeqIO test_AlignIO
-</PRE><P>To run the docstring tests (see section <A HREF="#section:doctest">19.3</A>), you can use
-</P><PRE CLASS="verbatim">python run_tests.py doctest
-</PRE><P>By default, <CODE>run_tests.py</CODE> runs all tests, including the docstring tests.</P><P>If an individual test is failing, you can also try running it
-directly, which may give you more information.</P><P>Importantly, note that the individual unit tests come in two types:
-</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Simple print-and-compare scripts. These unit tests are
-essentially short example Python programs, which print out
-various output text. For a test file named <CODE>test_XXX.py</CODE>
-there will be a matching text file called <CODE>test_XXX</CODE> under
-the <CODE>output</CODE> subdirectory which contains the expected
-output. All that the test framework does to is run the script,
-and check the output agrees.
-</LI><LI CLASS="li-itemize">Standard <CODE>unittest</CODE>- based tests. These will <CODE>import unittest</CODE>
-and then define <CODE>unittest.TestCase</CODE> classes, each with one
-or more sub-tests as methods starting with <CODE>test_</CODE> which
-check some specific aspect of the code.
-These tests should not print any output directly.
-</LI></UL><P>
-Currently, about half of the Biopython tests are <CODE>unittest</CODE>-style tests, and half are print-and-compare tests.</P><P>Running a simple print-and-compare test directly will usually give lots
-of output on screen, but does not check the output matches the expected
-output. If the test is failing with an exception error, it should be
-very easy to locate where exactly the script is failing.
-For an example of a print-and-compare test, try:
-</P><PRE CLASS="verbatim">python test_SeqIO.py
-</PRE><P>The <CODE>unittest</CODE>-based tests instead show you exactly which sub-section(s) of
-the test are failing. For example,
-</P><PRE CLASS="verbatim">python test_Cluster.py
-</PRE><!--TOC subsection Running the tests using Tox-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc263">19.1.1</A>&#XA0;&#XA0;Running the tests using Tox</H3><!--SEC END --><P>Like most Python projects, you can also use
-<A HREF="http://tox.readthedocs.org/en/latest/">Tox</A> to run the tests on multiple
-Python versions, provided they are already installed in your system.</P><P>We do not provide the configuration <TT>tox.ini</TT> file in our code base because
-of difficulties pinning down user-specific settings (e.g. executable names of the
-Python versions). You may also only be interested in testing Biopython only against
-a subset of the Python versions that we support.</P><P>If you are interested in using Tox, you may start with the example <TT>tox.ini</TT>
-shown below:</P><PRE CLASS="verbatim">[tox]
-envlist = py26, py27, pypy, py33, py34, jython
-
-[testenv]
-changedir = Tests
-commands = {envpython} run_tests.py --offline
-deps =
-    numpy
-</PRE><P>Using the template above, executing <TT>tox</TT> will test your Biopython code against
-Python2.6, Python2.7, PyPy, Python3.3, Python3.4, and Jython. It assumes that those
-Pythons&#X2019; executables are named accordingly: python2.6 for Python2.6, and so on.</P><!--TOC section Writing tests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc264">19.2</A>&#XA0;&#XA0;Writing tests</H2><!--SEC END --><P>Let&#X2019;s say you want to write some tests for a module called <CODE>Biospam</CODE>.
-This can be a module you wrote, or an existing module that doesn&#X2019;t have
-any tests yet. In the examples below, we assume that
-<CODE>Biospam</CODE> is a module that does simple math.</P><P>Each Biopython test can have three important files and directories involved with it:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-<CODE>test_Biospam.py</CODE> &#X2013; The actual test code for your module.
-</LI><LI CLASS="li-enumerate"><CODE>Biospam</CODE> [optional]&#X2013; A directory where any necessary input files
-will be located. Any output files that will be generated should also
-be written here (and preferably cleaned up after the tests are
-done) to prevent clogging up the main Tests directory.
-</LI><LI CLASS="li-enumerate"><CODE>output/Biospam</CODE> &#X2013; [for print-and-compare tests only] This
-file contains the expected output from running <CODE>test_Biospam.py</CODE>.
-This file is not needed for <CODE>unittest</CODE>-style tests, since there
-the validation is done in the test script <CODE>test_Biospam.py</CODE> itself.
-</LI></OL><P>It&#X2019;s up to you to decide whether you want to write a print-and-compare test script or a <CODE>unittest</CODE>-style test script. The important thing is that you cannot mix these two styles in a single test script. Particularly, don&#X2019;t use <CODE>unittest</CODE> features in a print-and-compare test.</P><P>Any script with a <CODE>test_</CODE> prefix in the <CODE>Tests</CODE> directory will be found and run by <CODE>run_tests.py</CODE>. Below, we show an example tes [...]
-</P><PRE CLASS="verbatim">$ python run_tests.py
-test_Ace ... ok
-test_AlignIO ... ok
-test_BioSQL ... ok
-test_BioSQL_SeqIO ... ok
-test_Biospam ... ok
-test_CAPS ... ok
-test_Clustalw ... ok
-</PRE><P>&#X2026;</P><PRE CLASS="verbatim">----------------------------------------------------------------------
-Ran 107 tests in 86.127 seconds
-</PRE><!--TOC subsection Writing a print-and-compare test-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc265">19.2.1</A>&#XA0;&#XA0;Writing a print-and-compare test</H3><!--SEC END --><P>A print-and-compare style test should be much simpler for beginners
-or novices to write - essentially it is just an example script using
-your new module.</P><P>Here is what you should do to make a print-and-compare test for the
-<CODE>Biospam</CODE> module.</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Write a script called <CODE>test_Biospam.py</CODE><UL CLASS="itemize"><LI CLASS="li-itemize">This script should live in the Tests directory</LI><LI CLASS="li-itemize">The script should test all of the important functionality
-of the module (the more you test the better your test is, of course!).</LI><LI CLASS="li-itemize">Try to avoid anything which might be platform specific,
-such as printing floating point numbers without using an explicit
-formatting string to avoid having too many decimal places
-(different platforms can give very slightly different values).</LI></UL></LI><LI CLASS="li-enumerate">If the script requires files to do the testing, these should go in
-the directory Tests/Biospam (if you just need something generic, like
-a FASTA sequence file, or a GenBank record, try and use an existing
-sample input file instead).</LI><LI CLASS="li-enumerate">Write out the test output and verify the output to be correct.<P>There are two ways to do this:</P><OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-The long way:<UL CLASS="itemize"><LI CLASS="li-itemize">Run the script and write its output to a file. On UNIX (including
-Linux and Mac OS X) machines, you would do something like:
-<CODE>python test_Biospam.py > test_Biospam</CODE> which would write the
-output to the file <CODE>test_Biospam</CODE>.</LI><LI CLASS="li-itemize">Manually look at the file <CODE>test_Biospam</CODE> to make sure the output is correct. When you are sure it is all right and there are no bugs, you need to quickly edit the <CODE>test_Biospam</CODE> file so that the first line is: &#X2018;<CODE>test_Biospam</CODE>&#X2019; (no quotes).</LI><LI CLASS="li-itemize">copy the <CODE>test_Biospam</CODE> file to the directory Tests/output</LI></UL></LI><LI CLASS="li-enumera [...]
-Run <CODE>python run_tests.py -g test_Biospam.py</CODE>. The
-regression testing framework is nifty enough that it&#X2019;ll put
-the output in the right place in just the way it likes it.</LI><LI CLASS="li-itemize">Go to the output (which should be in <CODE>Tests/output/test_Biospam</CODE>) and double check the output to make sure it is all correct.</LI></UL></LI></OL></LI><LI CLASS="li-enumerate">Now change to the Tests directory and run the regression tests
-with <CODE>python run_tests.py</CODE>. This will run all of the tests, and
-you should see your test run (and pass!).</LI><LI CLASS="li-enumerate">That&#X2019;s it! Now you&#X2019;ve got a nice test for your module ready to check in,
-or submit to Biopython. Congratulations!
-</LI></OL><P>As an example, the <CODE>test_Biospam.py</CODE> test script to test the
-<CODE>addition</CODE> and <CODE>multiplication</CODE> functions in the <CODE>Biospam</CODE>
-module could look as follows:</P><PRE CLASS="verbatim">from __future__ import print_function
-from Bio import Biospam
-
-print("2 + 3 =", Biospam.addition(2, 3))
-print("9 - 1 =", Biospam.addition(9, -1))
-print("2 * 3 =", Biospam.multiplication(2, 3))
-print("9 * (- 1) =", Biospam.multiplication(9, -1))
-</PRE><P>We generate the corresponding output with <CODE>python run_tests.py -g test_Biospam.py</CODE>, and check the output file <CODE>output/test_Biospam</CODE>:</P><PRE CLASS="verbatim">test_Biospam
-2 + 3 = 5
-9 - 1 = 8
-2 * 3 = 6
-9 * (- 1) = -9
-</PRE><P>Often, the difficulty with larger print-and-compare tests is to keep track which line in the output corresponds to which command in the test script. For this purpose, it is important to print out some markers to help you match lines in the input script with the generated output.</P><!--TOC subsection Writing a unittest-based test-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc266">19.2.2</A>&#XA0;&#XA0;Writing a unittest-based test</H3><!--SEC END --><P>We want all the modules in Biopython to have unit tests, and a simple
-print-and-compare test is better than no test at all. However, although
-there is a steeper learning curve, using the <CODE>unittest</CODE> framework
-gives a more structured result, and if there is a test failure this can
-clearly pinpoint which part of the test is going wrong. The sub-tests can
-also be run individually which is helpful for testing or debugging.</P><P>The <CODE>unittest</CODE>-framework has been included with Python since version
-2.1, and is documented in the Python Library Reference (which I know you
-are keeping under your pillow, as recommended). There is also
-<A HREF="http://docs.python.org/library/unittest.html">online documentaion
-for unittest</A>.
-If you are familiar with the <CODE>unittest</CODE> system (or something similar
-like the nose test framework), you shouldn&#X2019;t have any trouble. You may
-find looking at the existing example within Biopython helpful too.</P><P>Here&#X2019;s a minimal <CODE>unittest</CODE>-style test script for <CODE>Biospam</CODE>,
-which you can copy and paste to get started:</P><PRE CLASS="verbatim">import unittest
-from Bio import Biospam
-
-class BiospamTestAddition(unittest.TestCase):
-
-    def test_addition1(self):
-        result = Biospam.addition(2, 3)
-        self.assertEqual(result, 5)
-
-    def test_addition2(self):
-        result = Biospam.addition(9, -1)
-        self.assertEqual(result, 8)
-
-class BiospamTestDivision(unittest.TestCase):
-
-    def test_division1(self):
-        result = Biospam.division(3.0, 2.0)
-        self.assertAlmostEqual(result, 1.5)
-
-    def test_division2(self):
-        result = Biospam.division(10.0, -2.0)
-        self.assertAlmostEqual(result, -5.0)
-
-
-if __name__ == "__main__":
-    runner = unittest.TextTestRunner(verbosity = 2)
-    unittest.main(testRunner=runner)
-</PRE><P>In the division tests, we use <CODE>assertAlmostEqual</CODE> instead of <CODE>assertEqual</CODE> to avoid tests failing due to roundoff errors; see the <CODE>unittest</CODE> chapter in the Python documentation for details and for other functionality available in <CODE>unittest</CODE> (<A HREF="http://docs.python.org/library/unittest.html">online reference</A>).</P><P>These are the key points of <CODE>unittest</CODE>-based tests:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-Test cases are stored in classes that derive from
-<CODE>unittest.TestCase</CODE> and cover one basic aspect of your code</LI><LI CLASS="li-itemize">You can use methods <CODE>setUp</CODE> and <CODE>tearDown</CODE> for any repeated
-code which should be run before and after each test method. For example,
-the <CODE>setUp</CODE> method might be used to create an instance of the object
-you are testing, or open a file handle. The <CODE>tearDown</CODE> should do any
-&#X201C;tidying up&#X201D;, for example closing the file handle.</LI><LI CLASS="li-itemize">The tests are prefixed with <CODE>test_</CODE> and each test should cover
-one specific part of what you are trying to test. You can have as
-many tests as you want in a class.</LI><LI CLASS="li-itemize">At the end of the test script, you can use
-<PRE CLASS="verbatim">if __name__ == "__main__":
-    runner = unittest.TextTestRunner(verbosity = 2)
-    unittest.main(testRunner=runner)
-</PRE>to execute the tests when the script is run by itself (rather than
-imported from <CODE>run_tests.py</CODE>).
-If you run this script, then you&#X2019;ll see something like the following:<PRE CLASS="verbatim">$ python test_BiospamMyModule.py
-test_addition1 (__main__.TestAddition) ... ok
-test_addition2 (__main__.TestAddition) ... ok
-test_division1 (__main__.TestDivision) ... ok
-test_division2 (__main__.TestDivision) ... ok
-
-----------------------------------------------------------------------
-Ran 4 tests in 0.059s
-
-OK
-</PRE></LI><LI CLASS="li-itemize">To indicate more clearly what each test is doing, you can add
-docstrings to each test. These are shown when running the tests,
-which can be useful information if a test is failing.<PRE CLASS="verbatim">import unittest
-from Bio import Biospam
-
-class BiospamTestAddition(unittest.TestCase):
-
-    def test_addition1(self):
-        """An addition test"""
-        result = Biospam.addition(2, 3)
-        self.assertEqual(result, 5)
-
-    def test_addition2(self):
-        """A second addition test"""
-        result = Biospam.addition(9, -1)
-        self.assertEqual(result, 8)
-
-class BiospamTestDivision(unittest.TestCase):
-
-    def test_division1(self):
-        """Now let's check division"""
-        result = Biospam.division(3.0, 2.0)
-        self.assertAlmostEqual(result, 1.5)
-
-    def test_division2(self):
-        """A second division test"""
-        result = Biospam.division(10.0, -2.0)
-        self.assertAlmostEqual(result, -5.0)
-
-
-if __name__ == "__main__":
-    runner = unittest.TextTestRunner(verbosity = 2)
-    unittest.main(testRunner=runner)
-</PRE><P>Running the script will now show you:</P><PRE CLASS="verbatim">$ python test_BiospamMyModule.py
-An addition test ... ok
-A second addition test ... ok
-Now let's check division ... ok
-A second division test ... ok
-
-----------------------------------------------------------------------
-Ran 4 tests in 0.001s
-
-OK
-</PRE></LI></UL><P>If your module contains docstring tests (see section <A HREF="#section:doctest">19.3</A>),
-you may want to include those in the tests to be run. You can do so as
-follows by modifying the code under <CODE>if __name__ == "__main__":</CODE>
-to look like this:</P><PRE CLASS="verbatim">if __name__ == "__main__":
-    unittest_suite = unittest.TestLoader().loadTestsFromName("test_Biospam")
-    doctest_suite = doctest.DocTestSuite(Biospam)
-    suite = unittest.TestSuite((unittest_suite, doctest_suite))
-    runner = unittest.TextTestRunner(sys.stdout, verbosity = 2)
-    runner.run(suite)
-</PRE><P>This is only relevant if you want to run the docstring tests when you
-execute <CODE>python test_Biospam.py</CODE>; with
-<CODE>python run_tests.py</CODE>, the docstring tests are run automatically
-(assuming they are included in the list of docstring tests in
-<CODE>run_tests.py</CODE>, see the section below).</P><!--TOC section Writing doctests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc267">19.3</A>&#XA0;&#XA0;Writing doctests</H2><!--SEC END --><P>
-<A NAME="section:doctest"></A></P><P>Python modules, classes and functions support built in documentation using
-docstrings. The <A HREF="http://docs.python.org/library/doctest.html">doctest
-framework</A> (included with Python) allows the developer to embed working
-examples in the docstrings, and have these examples automatically tested.</P><P>Currently only a small part of Biopython includes doctests. The
-<CODE>run_tests.py</CODE> script takes care of running the doctests.
-For this purpose, at the top of the <CODE>run_tests.py</CODE> script is a
-manually compiled list of modules to test, which
-allows us to skip modules with optional external dependencies which may
-not be installed (e.g. the Reportlab and NumPy libraries). So, if you&#X2019;ve
-added some doctests to the docstrings in a Biopython module, in order to
-have them included in the Biopython test suite, you must update
-<CODE>run_tests.py</CODE> to include your module. Currently, the relevant part
-of <CODE>run_tests.py</CODE> looks as follows:</P><PRE CLASS="verbatim"># This is the list of modules containing docstring tests.
-# If you develop docstring tests for other modules, please add
-# those modules here.
-DOCTEST_MODULES = ["Bio.Seq",
-                   "Bio.SeqRecord",
-                   "Bio.SeqIO",
-                   "...",
-                  ]
-#Silently ignore any doctests for modules requiring numpy!
-try:
-    import numpy
-    DOCTEST_MODULES.extend(["Bio.Statistics.lowess"])
-except ImportError:
-    pass
-</PRE><P>Note that we regard doctests primarily as documentation, so you should
-stick to typical usage. Generally complicated examples dealing with error
-conditions and the like would be best left to a dedicated unit test.</P><P>Note that if you want to write doctests involving file parsing, defining
-the file location complicates matters. Ideally use relative paths assuming
-the code will be run from the <CODE>Tests</CODE> directory, see the
-<CODE>Bio.SeqIO</CODE> doctests for an example of this.</P><P>To run the docstring tests only, use
-</P><PRE CLASS="verbatim">$ python run_tests.py doctest
-</PRE><!--TOC chapter Advanced-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc268">Chapter&#XA0;20</A>&#XA0;&#XA0;Advanced</H1><!--SEC END --><P>
-<A NAME="chapter:advanced"></A></P><!--TOC section Parser Design-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc269">20.1</A>&#XA0;&#XA0;Parser Design</H2><!--SEC END --><P>Many of the older Biopython parsers were built around an event-oriented
-design that includes Scanner and Consumer objects.</P><P>Scanners take input from a data source and analyze it line by line,
-sending off an event whenever it recognizes some information in the
-data. For example, if the data includes information about an organism
-name, the scanner may generate an <CODE>organism_name</CODE> event whenever it
-encounters a line containing the name.</P><P>Consumers are objects that receive the events generated by Scanners.
-Following the previous example, the consumer receives the
-<CODE>organism_name</CODE> event, and the processes it in whatever manner
-necessary in the current application.</P><P>This is a very flexible framework, which is advantageous if you want to
-be able to parse a file format into more than one representation. For
-example, the <CODE>Bio.GenBank</CODE> module uses this to construct either
-<CODE>SeqRecord</CODE> objects or file-format-specific record objects.</P><P>More recently, many of the parsers added for <CODE>Bio.SeqIO</CODE> and
-<CODE>Bio.AlignIO</CODE> take a much simpler approach, but only generate a
-single object representation (<CODE>SeqRecord</CODE> and
-<CODE>MultipleSeqAlignment</CODE> objects respectively). In some cases the
-<CODE>Bio.SeqIO</CODE> parsers actually wrap
-another Biopython parser - for example, the <CODE>Bio.SwissProt</CODE> parser
-produces SwissProt format specific record objects, which get converted
-into <CODE>SeqRecord</CODE> objects.</P><!--TOC section Substitution Matrices-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc270">20.2</A>&#XA0;&#XA0;Substitution Matrices</H2><!--SEC END --><!--TOC subsection SubsMat-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc271">20.2.1</A>&#XA0;&#XA0;SubsMat</H3><!--SEC END --><P>This module provides a class and a few routines for generating substitution matrices, similar to BLOSUM or PAM matrices, but based on user-provided data. Additionally, you may select a matrix from MatrixInfo.py, a collection of established substitution matrices. The <CODE>SeqMat</CODE> class derives from a dictionary:
-</P><PRE CLASS="verbatim">class SeqMat(dict)
-</PRE><P>The dictionary is of the form <CODE>{(i1,j1):n1, (i1,j2):n2,...,(ik,jk):nk}</CODE> where i, j are alphabet letters, and n is a value.</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-Attributes
-<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>self.alphabet</CODE>: a class as defined in Bio.Alphabet</LI><LI CLASS="li-enumerate"><CODE>self.ab_list</CODE>: a list of the alphabet&#X2019;s letters, sorted. Needed mainly for internal purposes
-</LI></OL></LI><LI CLASS="li-enumerate">Methods<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate"><PRE CLASS="verbatim">__init__(self,data=None,alphabet=None, mat_name='', build_later=0):
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate"><CODE>data</CODE>: can be either a dictionary, or another SeqMat instance.
-</LI><LI CLASS="li-enumerate"><CODE>alphabet</CODE>: a Bio.Alphabet instance. If not provided, construct an alphabet from data.</LI><LI CLASS="li-enumerate"><CODE>mat_name</CODE>: matrix name, such as "BLOSUM62" or "PAM250"</LI><LI CLASS="li-enumerate"><CODE>build_later</CODE>: default false. If true, user may supply only alphabet and empty dictionary, if intending to build the matrix later. this skips the sanity check of alphabet size vs. matrix size.</LI></OL></LI><LI CLASS="li-enumera [...]
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-<CODE>obs_freq_mat</CODE>: an observed frequency matrix. Returns the matrix&#X2019;s entropy, based on the frequency in <CODE>obs_freq_mat</CODE>. The matrix instance should be LO or SUBS.
-</LI></OL></LI><LI CLASS="li-enumerate"><PRE CLASS="verbatim">sum(self)
-</PRE>Calculates the sum of values for each letter in the matrix&#X2019;s alphabet, and returns it as a dictionary of the form <CODE>{i1: s1, i2: s2,...,in:sn}</CODE>, where:
-<UL CLASS="itemize"><LI CLASS="li-itemize">
-i: an alphabet letter;
-</LI><LI CLASS="li-itemize">s: sum of all values in a half-matrix for that letter;
-</LI><LI CLASS="li-itemize">n: number of letters in alphabet.
-</LI></UL></LI><LI CLASS="li-enumerate"><PRE CLASS="verbatim">print_mat(self,f,format="%4d",bottomformat="%4s",alphabet=None)
-</PRE><P>prints the matrix to file handle f. <CODE>format</CODE> is the format field for the matrix values; <CODE>bottomformat</CODE> is the format field for the bottom row, containing matrix letters. Example output for a 3-letter alphabet matrix:</P><PRE CLASS="verbatim">A 23
-B 12 34
-C 7  22  27
-  A   B   C
-</PRE><P>The <CODE>alphabet</CODE> optional argument is a string of all characters in the alphabet. If supplied, the order of letters along the axes is taken from the string, rather than by alphabetical order.</P></LI></OL></LI><LI CLASS="li-enumerate">Usage<P>The following section is laid out in the order by which most people wish to generate a log-odds matrix. Of course, interim matrices can be generated and
-investigated. Most people just want a log-odds matrix, that&#X2019;s all.</P><OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">Generating an Accepted Replacement Matrix<P>Initially, you should generate an accepted replacement matrix (ARM) from your data. The values in ARM are the counted number of replacements according to your data. The data could be a set of pairs or multiple alignments. So for instance if Alanine was replaced by Cysteine 10 times, and Cysteine by Alanine 12 times, [...]
-</PRE><P>as order doesn&#X2019;t matter, user can already provide only one entry:</P><PRE CLASS="verbatim">('A','C'): 22
-</PRE><P>A SeqMat instance may be initialized with either a full (first method of counting: 10, 12) or half (the latter method, 22) matrices. A full protein
-alphabet matrix would be of the size 20x20 = 400. A half matrix of that alphabet would be 20x20/2 + 20/2 = 210. That is because same-letter entries don&#X2019;t
-change. (The matrix diagonal). Given an alphabet size of N:</P><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-Full matrix size: N*N</LI><LI CLASS="li-enumerate">Half matrix size: N(N+1)/2
-</LI></OL><P>The SeqMat constructor automatically generates a half-matrix, if a full matrix is passed. If a half matrix is passed, letters in the key should be provided in alphabetical order: (&#X2019;A&#X2019;,&#X2019;C&#X2019;) and not (&#X2019;C&#X2019;,A&#X2019;).</P><P>At this point, if all you wish to do is generate a log-odds matrix, please go to the section titled Example of Use. The following text describes the nitty-gritty of internal functions, to be used by people who wish to [...]
-</P><PRE CLASS="verbatim">OFM = SubsMat._build_obs_freq_mat(ARM)
-</PRE><P>The OFM is generated from the ARM, only instead of replacement counts, it contains replacement frequencies.</P></LI><LI CLASS="li-enumerate">Generating an expected frequency matrix (EFM)<P>Use:</P><PRE CLASS="verbatim">EFM = SubsMat._build_exp_freq_mat(OFM,exp_freq_table)
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-<CODE>exp_freq_table</CODE>: should be a FreqTable instance. See section&#XA0;<A HREF="#sec:freq_table">20.2.2</A> for detailed information on FreqTable. Briefly, the expected frequency table has the frequencies of appearance for each member of the alphabet. It is
-implemented as a dictionary with the alphabet letters as keys, and each letter&#X2019;s frequency as a value. Values sum to 1.
-</LI></OL><P>The expected frequency table can (and generally should) be generated from the observed frequency matrix. So in most cases you will generate <CODE>exp_freq_table</CODE> using:</P><PRE CLASS="verbatim">>>> exp_freq_table = SubsMat._exp_freq_table_from_obs_freq(OFM)
->>> EFM = SubsMat._build_exp_freq_mat(OFM, exp_freq_table)
-</PRE><P>But you can supply your own <CODE>exp_freq_table</CODE>, if you wish</P></LI><LI CLASS="li-enumerate">Generating a substitution frequency matrix (SFM)<P>Use:</P><PRE CLASS="verbatim">SFM = SubsMat._build_subs_mat(OFM,EFM)
-</PRE><P>Accepts an OFM, EFM. Provides the division product of the corresponding values.</P></LI><LI CLASS="li-enumerate">Generating a log-odds matrix (LOM)<P>Use:
-</P><PRE CLASS="verbatim">LOM=SubsMat._build_log_odds_mat(SFM[,logbase=10,factor=10.0,round_digit=1])
-</PRE><OL CLASS="enumerate" type=i><LI CLASS="li-enumerate">
-Accepts an SFM.</LI><LI CLASS="li-enumerate"><CODE>logbase</CODE>: base of the logarithm used to generate the log-odds values.</LI><LI CLASS="li-enumerate"><CODE>factor</CODE>: factor used to multiply the log-odds values. Each entry is generated by log(LOM[key])*factor And rounded to the <CODE>round_digit</CODE> place after the decimal point, if required.</LI></OL></LI></OL></LI><LI CLASS="li-enumerate">Example of use<P>As most people would want to generate a log-odds matrix, with minimu [...]
-                      factor=10.0,round_digit=0):
-</PRE><OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>acc_rep_mat</CODE>: user provided accepted replacements matrix
-</LI><LI CLASS="li-enumerate"><CODE>exp_freq_table</CODE>: expected frequencies table. Used if provided, if not, generated from the <CODE>acc_rep_mat</CODE>.
-</LI><LI CLASS="li-enumerate"><CODE>logbase</CODE>: base of logarithm for the log-odds matrix. Default base 10.
-</LI><LI CLASS="li-enumerate"><CODE>round_digit</CODE>: number after decimal digit to which result should be rounded. Default zero.
-</LI></OL></LI></OL><!--TOC subsection FreqTable-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc272">20.2.2</A>&#XA0;&#XA0;FreqTable</H3><!--SEC END --><P>
-<A NAME="sec:freq_table"></A></P><PRE CLASS="verbatim">FreqTable.FreqTable(UserDict.UserDict)
-</PRE><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">Attributes:<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>alphabet</CODE>: A Bio.Alphabet instance.
-</LI><LI CLASS="li-enumerate"><CODE>data</CODE>: frequency dictionary
-</LI><LI CLASS="li-enumerate"><CODE>count</CODE>: count dictionary (in case counts are provided).
-</LI></OL></LI><LI CLASS="li-enumerate">Functions:
-<OL CLASS="enumerate" type=a><LI CLASS="li-enumerate">
-<CODE>read_count(f)</CODE>: read a count file from stream f. Then convert to frequencies.
-</LI><LI CLASS="li-enumerate"><CODE>read_freq(f)</CODE>: read a frequency data file from stream f. Of course, we then don&#X2019;t have the counts, but it is usually the letter frequencies which are interesting.
-</LI></OL></LI><LI CLASS="li-enumerate">Example of use:
-The expected count of the residues in the database is sitting in a file, whitespace delimited, in the following format (example given for a 3-letter alphabet):<PRE CLASS="verbatim">A   35
-B   65
-C   100
-</PRE><P>And will be read using the <CODE>FreqTable.read_count(file_handle)</CODE> function.</P><P>An equivalent frequency file:</P><PRE CLASS="verbatim">A  0.175
-B  0.325
-C  0.5
-</PRE><P>Conversely, the residue frequencies or counts can be passed as a dictionary.
-Example of a count dictionary (3-letter alphabet):</P><PRE CLASS="verbatim">{'A': 35, 'B': 65, 'C': 100}
-</PRE><P>Which means that an expected data count would give a 0.5 frequency
-for &#X2019;C&#X2019;, a 0.325 probability of &#X2019;B&#X2019; and a 0.175 probability of &#X2019;A&#X2019;
-out of 200 total, sum of A, B and C)</P><P>A frequency dictionary for the same data would be:</P><PRE CLASS="verbatim">{'A': 0.175, 'B': 0.325, 'C': 0.5}
-</PRE><P>Summing up to 1.</P><P>When passing a dictionary as an argument, you should indicate whether it is a count or a frequency dictionary. Therefore the FreqTable class constructor requires two arguments: the dictionary itself, and FreqTable.COUNT or FreqTable.FREQ indicating counts or frequencies, respectively.</P><P>Read expected counts. readCount will already generate the frequencies
-Any one of the following may be done to geerate the frequency table (ftab):</P><PRE CLASS="verbatim">>>> from SubsMat import *
->>> ftab = FreqTable.FreqTable(my_frequency_dictionary, FreqTable.FREQ)
->>> ftab = FreqTable.FreqTable(my_count_dictionary, FreqTable.COUNT)
->>> ftab = FreqTable.read_count(open('myCountFile'))
->>> ftab = FreqTable.read_frequency(open('myFrequencyFile'))
-</PRE></LI></OL><!--TOC chapter Where to go from here &#X2013; contributing to Biopython-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc273">Chapter&#XA0;21</A>&#XA0;&#XA0;Where to go from here &#X2013; contributing to Biopython</H1><!--SEC END --><!--TOC section Bug Reports + Feature Requests-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc274">21.1</A>&#XA0;&#XA0;Bug Reports + Feature Requests</H2><!--SEC END --><P>Getting feedback on the Biopython modules is very important to us. Open-source projects like this benefit greatly from feedback, bug-reports (and patches!) from a wide variety of contributors.</P><P>The main forums for discussing feature requests and potential bugs are the
-<A HREF="http://biopython.org/wiki/Mailing_lists">Biopython mailing lists</A>:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-<A HREF="mailto:biopython at biopython.org">biopython at biopython.org</A> &#X2013; An unmoderated list for discussion of anything to do with Biopython.</LI><LI CLASS="li-itemize"><A HREF="mailto:biopython-dev at biopython.org">biopython-dev at biopython.org</A> &#X2013; A more development oriented list that is mainly used by developers (but anyone is free to contribute!).
-</LI></UL><P>Additionally, if you think you&#X2019;ve found a new bug, you can submit it to
-our issue tracker at <A HREF="https://github.com/biopython/biopython/issues"><TT>https://github.com/biopython/biopython/issues</TT></A>
-(this has replaced the older tracker hosted at
-<A HREF="http://redmine.open-bio.org/projects/biopython"><TT>http://redmine.open-bio.org/projects/biopython</TT></A>).
-This way, it won&#X2019;t get buried in anyone&#X2019;s Inbox and forgotten about.</P><!--TOC section Mailing lists and helping newcomers-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc275">21.2</A>&#XA0;&#XA0;Mailing lists and helping newcomers</H2><!--SEC END --><P>We encourage all our uses to sign up to the main Biopython mailing list.
-Once you&#X2019;ve got the hang of an area of Biopython, we&#X2019;d encourage you to
-help answer questions from beginners. After all, you were a beginner once.</P><!--TOC section Contributing Documentation-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc276">21.3</A>&#XA0;&#XA0;Contributing Documentation</H2><!--SEC END --><P>We&#X2019;re happy to take feedback or contributions - either via a bug-report or on the Mailing List.
-While reading this tutorial, perhaps you noticed some topics you were interested in which were missing, or not clearly explained. There is also Biopython&#X2019;s built in documentation (the docstrings, these are also
-<A HREF="http://biopython.org/DIST/docs/api">online</A>), where again, you may be able to help fill in any blanks.</P><!--TOC section Contributing cookbook examples-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc277">21.4</A>&#XA0;&#XA0;Contributing cookbook examples</H2><!--SEC END --><P>
-As explained in Chapter&#XA0;<A HREF="#chapter:cookbook">18</A>, Biopython now has a wiki
-collection of user contributed &#X201C;cookbook&#X201D; examples,
-<A HREF="http://biopython.org/wiki/Category:Cookbook"><TT>http://biopython.org/wiki/Category:Cookbook</TT></A> &#X2013; maybe you can add
-to this?</P><!--TOC section Maintaining a distribution for a platform-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc278">21.5</A>&#XA0;&#XA0;Maintaining a distribution for a platform</H2><!--SEC END --><P>
-<A NAME="sec:maintain_dist"></A></P><P>We currently provide source code archives (suitable for any OS, if you have the right build tools installed), and Windows Installers which are just click and run. This covers all the major operating systems.</P><P>Most major Linux distributions have volunteers who take these source code releases, and compile them into packages for Linux users to easily install (taking care of dependencies etc). This is really great and we are of course very grateful [...]
-</PRE><P>Now you&#X2019;ve got a Windows installer. Congrats! At the moment we have no trouble shipping installers built on 32 bit windows. If anyone would like to look into supporting 64 bit Windows that would be great.</P></DD><DT CLASS="dt-description"><B>RPMs</B></DT><DD CLASS="dd-description"> &#X2013; RPMs are pretty popular package systems on some Linux platforms. There is lots of documentation on RPMs available at <A HREF="http://www.rpm.org"><TT>http://www.rpm.org</TT></A> to he [...]
-</PRE><P>This will create an RPM for your specific platform and a source RPM in the directory <CODE>dist</CODE>. This RPM should be good and ready to go, so this is all you need to do! Nice and easy.</P></DD><DT CLASS="dt-description"><B>Macintosh</B></DT><DD CLASS="dd-description"> &#X2013; Since Apple moved to Mac OS X, things have become much easier on the Mac. We generally
-treat it as just another Unix variant, and installing Biopython from source is just as easy as on Linux.
-The easiest way to get all the GCC compilers etc installed is to install Apple&#X2019;s X-Code.
-We might be able to provide click and run installers for Mac OS X, but to date there hasn&#X2019;t been any demand.</DD></DL><P>Once you&#X2019;ve got a package, please test it on your system to make sure it installs everything in a good way and seems to work properly. Once you feel good about it, send it off to one of the Biopython developers (write to our main mailing list at biopython at biopython.org if you&#X2019;re not sure who to send it to) and you&#X2019;ve done it. Thanks!</P><!-- [...]
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc279">21.6</A>&#XA0;&#XA0;Contributing Unit Tests</H2><!--SEC END --><P>Even if you don&#X2019;t have any new functionality to add to Biopython, but you want to write some code, please
-consider extending our unit test coverage. We&#X2019;ve devoted all of Chapter&#XA0;<A HREF="#sec:regr_test">19</A> to this topic.</P><!--TOC section Contributing Code-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc280">21.7</A>&#XA0;&#XA0;Contributing Code</H2><!--SEC END --><P>There are no barriers to joining Biopython code development other
-than an interest in creating biology-related code in Python. The
-best place to express an interest is on the Biopython mailing lists
-&#X2013; just let us know you are interested in coding and what kind of
-stuff you want to work on. Normally, we try to have some discussion
-on modules before coding them, since that helps generate good ideas
-&#X2013; then just feel free to jump right in and start coding!</P><P>The main Biopython release tries to be fairly uniform and interworkable,
-to make it easier for users. You can read about some of (fairly
-informal) coding style guidelines we try to use in Biopython in the
-contributing documentation at
-<A HREF="http://biopython.org/wiki/Contributing"><TT>http://biopython.org/wiki/Contributing</TT></A>. We also try to add code to the distribution along with tests (see Chapter&#XA0;<A HREF="#sec:regr_test">19</A> for more info on the regression testing framework) and documentation, so that everything can stay as workable and well documented as possible (including docstrings). This is, of course, the most ideal situation, under many situations you&#X2019;ll be able to find other people on [...]
-distribution, but that you want to make available, we maintain Script
-Central (<A HREF="http://biopython.org/wiki/Scriptcentral"><TT>http://biopython.org/wiki/Scriptcentral</TT></A>)
-which has pointers to freely available code in Python for bioinformatics.</P><P>Hopefully this documentation has got you excited enough about
-Biopython to try it out (and most importantly, contribute!). Thanks
-for reading all the way through!</P><!--TOC chapter Appendix: Useful stuff about Python-->
-<H1 CLASS="chapter"><!--SEC ANCHOR --><A NAME="htoc281">Chapter&#XA0;22</A>&#XA0;&#XA0;Appendix: Useful stuff about Python</H1><!--SEC END --><P>
-<A NAME="sec:appendix"></A></P><P>If you haven&#X2019;t spent a lot of time programming in Python, many
-questions and problems that come up in using Biopython are often
-related to Python itself. This section tries to present some ideas and
-code that come up often (at least for us!) while using the Biopython
-libraries. If you have any suggestions for useful pointers that could
-go here, please contribute!</P><!--TOC section What the heck is a handle?-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc282">22.1</A>&#XA0;&#XA0;What the heck is a handle?</H2><!--SEC END --><P>
-<A NAME="sec:appendix-handles"></A></P><P>Handles are mentioned quite frequently throughout this documentation,
-and are also fairly confusing (at least to me!). Basically, you can
-think of a handle as being a &#X201C;wrapper&#X201D; around text information.</P><P>Handles provide (at least) two benefits over plain text information:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">
-They provide a standard way to deal with information stored in
-different ways. The text information can be in a file, or in a
-string stored in memory, or the output from a command line program,
-or at some remote website, but the handle provides a common way of
-dealing with information in all of these formats.</LI><LI CLASS="li-enumerate">They allow text information to be read incrementally, instead
-of all at once. This is really important when you are dealing with
-huge text files which would use up all of your memory if you had to
-load them all.
-</LI></OL><P>Handles can deal with text information that is being read (e.&#XA0;g.&#XA0;reading
-from a file) or written (e.&#XA0;g.&#XA0;writing information to a file). In the
-case of a &#X201C;read&#X201D; handle, commonly used functions are <CODE>read()</CODE>,
-which reads the entire text information from the handle, and
-<CODE>readline()</CODE>, which reads information one line at a time. For
-&#X201C;write&#X201D; handles, the function <CODE>write()</CODE> is regularly used.</P><P>The most common usage for handles is reading information from a file,
-which is done using the built-in Python function <CODE>open</CODE>. Here, we open a
-handle to the file <A HREF="examples/m_cold.fasta">m_cold.fasta</A>
-(also available online
-<A HREF="http://biopython.org/DIST/docs/tutorial/examples/m_cold.fasta">here</A>):</P><PRE CLASS="verbatim">>>> handle = open("m_cold.fasta", "r")
->>> handle.readline()
-">gi|8332116|gb|BE037100.1|BE037100 MP14H09 MP Mesembryanthemum ...\n"
-</PRE><P>Handles are regularly used in Biopython for passing information to parsers.
-For example, since Biopython 1.54 the main functions in <CODE>Bio.SeqIO</CODE>
-and <CODE>Bio.AlignIO</CODE> have allowed you to use a filename instead of a
-handle:</P><PRE CLASS="verbatim">from Bio import SeqIO
-for record in SeqIO.parse("m_cold.fasta", "fasta"):
-    print(record.id, len(record))
-</PRE><P>On older versions of Biopython you had to use a handle, e.g.</P><PRE CLASS="verbatim">from Bio import SeqIO
-handle = open("m_cold.fasta", "r")
-for record in SeqIO.parse(handle, "fasta"):
-    print(record.id, len(record))
-handle.close()
-</PRE><P>This pattern is still useful - for example suppose you have a gzip
-compressed FASTA file you want to parse:</P><PRE CLASS="verbatim">import gzip
-from Bio import SeqIO
-handle = gzip.open("m_cold.fasta.gz")
-for record in SeqIO.parse(handle, "fasta"):
-    print(record.id, len(record))
-handle.close()
-</PRE><P>See Section&#XA0;<A HREF="#sec:SeqIO_compressed">5.2</A> for more examples like this,
-including reading bzip2 compressed files.</P><!--TOC subsection Creating a handle from a string-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc283">22.1.1</A>&#XA0;&#XA0;Creating a handle from a string</H3><!--SEC END --><P>One useful thing is to be able to turn information contained in a
-string into a handle. The following example shows how to do this using
-<CODE>cStringIO</CODE> from the Python standard library:</P><PRE CLASS="verbatim">>>> my_info = 'A string\n with multiple lines.'
->>> print(my_info)
-A string
- with multiple lines.
->>> from StringIO import StringIO
->>> my_info_handle = StringIO(my_info)
->>> first_line = my_info_handle.readline()
->>> print(first_line)
-A string
-<BLANKLINE>
->>> second_line = my_info_handle.readline()
->>> print(second_line)
- with multiple lines.
-</PRE><!--TOC chapter References-->
-<H1 CLASS="chapter"><!--SEC ANCHOR -->References</H1><!--SEC END --><DL CLASS="thebibliography"><DT CLASS="dt-thebibliography">
-
-<A NAME="cock2009"><FONT COLOR=purple>[1]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, Michiel J. L. de Hoon: &#X201C;Biopython: freely available Python tools for computational molecular biology and bioinformatics&#X201D;. <I>Bioinformatics</I> <B>25</B> (11), 1422&#X2013;1423 (2009). <A HREF="http://dx.doi.org/10.1093/bioinformatics/btp163">doi:10.1093/bioinformatics/btp163</A>,
-</DD><DT CLASS="dt-thebibliography"><A NAME="pritchard2006"><FONT COLOR=purple>[2]</FONT></A></DT><DD CLASS="dd-thebibliography">
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-<A HREF="http://dx.doi.org/10.1093/bioinformatics/btk021">doi:10.1093/bioinformatics/btk021</A>,
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-<A HREF="http://dx.doi.org/10.1146/annurev.phyto.44.070505.143444">doi:10.1146/annurev.phyto.44.070505.143444</A>,
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-<I>Antonie van Leeuwenhoek</I> <B>96</B> (2): 193&#X2013;204 (2009).
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-Caroline Proux, Douwe van Sinderen, Juan Suarez, Pilar Garcia, Victor Ladero, Gerald F. Fitzgerald, Frank Desiere, Harald Br&#XFC;ssow:
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-Florian Jupe, Leighton Pritchard, Graham J. Etherington, Katrin MacKenzie, Peter JA Cock, Frank Wright, Sanjeev Kumar Sharma1, Dan Bolser, Glenn J Bryan, Jonathan DG Jones, Ingo Hein: &#X201C;Identification and localisation of the NB-LRR gene family within the potato genome&#X201D;. <I>BMC Genomics</I> <B>13</B>: 75 (2012).
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-Peter J. A. Cock, Christopher J. Fields, Naohisa Goto, Michael L. Heuer, Peter M. Rice: &#X201C;The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants&#X201D;. <I>Nucleic Acids Research</I> <B>38</B> (6): 1767&#X2013;1771 (2010). <A HREF="http://dx.doi.org/10.1093/nar/gkp1137">doi:10.1093/nar/gkp1137</A>
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-Eric Talevich, Brandon M. Invergo, Peter J.A. Cock, Brad A. Chapman: &#X201C;Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython&#X201D;. <I>BMC Bioinformatics</I> <B>13</B>: 209 (2012). <A HREF="http://dx.doi.org/10.1186/1471-2105-13-209">doi:10.1186/1471-2105-13-209</A>
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-</DD><DT CLASS="dt-thebibliography"><A NAME="cavener1987"><FONT COLOR=purple>[11]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Douglas R. Cavener: &#X201C;Comparison of the consensus sequence flanking translational start sites in Drosophila and vertebrates.&#X201D; <I>Nucleic Acids Research</I> <B>15</B> (4): 1353&#X2013;1361 (1987). <A HREF="http://dx.doi.org/10.1093/nar/15.4.1353">doi:10.1093/nar/15.4.1353</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="bailey1994"><FONT COLOR=purple>[12]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Timothy L. Bailey and Charles Elkan: &#X201C;Fitting a mixture model by expectation maximization to discover motifs in biopolymers&#X201D;, <I>Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology</I> 28&#X2013;36. AAAI Press, Menlo Park, California (1994).
-</DD><DT CLASS="dt-thebibliography"><A NAME="chapman2000"><FONT COLOR=purple>[13]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Brad Chapman and Jeff Chang: &#X201C;Biopython: Python tools for computational biology&#X201D;. <I>ACM SIGBIO Newsletter</I> <B>20</B> (2): 15&#X2013;19 (August 2000).
-</DD><DT CLASS="dt-thebibliography"><A NAME="dehoon2004"><FONT COLOR=purple>[14]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Michiel J. L. de Hoon, Seiya Imoto, John Nolan, Satoru Miyano: &#X201C;Open source clustering software&#X201D;. <I>Bioinformatics</I> <B>20</B> (9): 1453&#X2013;1454 (2004). <A HREF="http://dx.doi.org/10.1093/bioinformatics/bth078">doi:10.1093/bioinformatics/bth078</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="eisen1998"><FONT COLOR=purple>[15]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Michiel B. Eisen, Paul T. Spellman, Patrick O. Brown, David Botstein: &#X201C;Cluster analysis and display of genome-wide expression patterns&#X201D;. <I>Proceedings of the National Academy of Science USA</I> <B>95</B> (25): 14863&#X2013;14868 (1998). <A HREF="http://dx.doi.org/10.1073/pnas.96.19.10943-c">doi:10.1073/pnas.96.19.10943-c</A>
-</DD><DT CLASS="dt-thebibliography"><A NAME="golub1971"><FONT COLOR=purple>[16]</FONT></A></DT><DD CLASS="dd-thebibliography">
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-implemented in Python&#X201D;. <I>Bioinformatics</I>, <B>19</B> (17): 2308&#X2013;2310 (2003) <A HREF="http://dx.doi.org/10.1093/bioinformatics/btg299">doi: 10.1093/bioinformatics/btg299</A>.
-</DD><DT CLASS="dt-thebibliography"><A NAME="hamelryck2003b"><FONT COLOR=purple>[19]</FONT></A></DT><DD CLASS="dd-thebibliography">
-Thomas Hamelryck: &#X201C;Efficient identification of side-chain patterns using a multidimensional index tree&#X201D;. <I>Proteins</I> <B>51</B> (1): 96&#X2013;108 (2003). <A HREF="http://dx.doi.org/10.1002/prot.10338">doi:10.1002/prot.10338</A>
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-</DD><DT CLASS="dt-thebibliography"><A NAME="hartigan1975"><FONT COLOR=purple>[21]</FONT></A></DT><DD CLASS="dd-thebibliography">
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-<A HREF="http://dx.doi.org/10.1093/bioinformatics/bth349">http://dx.doi.org/10.1093/bioinformatics/bth349</A>
-</DD></DL><!--CUT END -->
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-endobj
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-trailer
-<< /Size 3786
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-/Info 3785 0 R
-/ID [<5170E867C7B1397C94B9D520831636F9> <5170E867C7B1397C94B9D520831636F9>] >>
-startxref
-3420099
-%%EOF
diff --git a/Doc/Tutorial.tex b/Doc/Tutorial.tex
index ac345c8..d6db866 100644
--- a/Doc/Tutorial.tex
+++ b/Doc/Tutorial.tex
@@ -80,7 +80,7 @@ Biopython Tutorial and Cookbook}
 
 \author{Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, \\
 Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski}
-\date{Last Update -- 21 October 2015 (Biopython 1.66)}
+\date{Last Update -- 8 June 2016 (Biopython 1.67)}
 
 %Hack to get the logo at the start of the HTML front page:
 %(hopefully this isn't going to be too wide for most people)
@@ -134,7 +134,7 @@ Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski}
 \include{Tutorial/chapter_uniprot}
 
 %\chapter{Going 3D: The PDB module}
-\include{Tutorial/chapter_pdf}
+\include{Tutorial/chapter_pdb}
 
 %\chapter{Bio.PopGen: Population genetics}
 \include{Tutorial/chapter_popgen}
@@ -160,6 +160,10 @@ Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy\'{n}ski}
 %\label{chap:kegg}
 \include{Tutorial/chapter_kegg}
 
+%\chapter{Bio.phenotype: analyse phenotypic data}
+%\label{chap:phenotype}
+\include{Tutorial/chapter_phenotype}
+
 %\chapter{Cookbook -- Cool things to do with it}
 %\label{chapter:cookbook}
 \include{Tutorial/chapter_cookbook}
diff --git a/Doc/Tutorial/chapter_align.tex b/Doc/Tutorial/chapter_align.tex
index 3c4b47d..b0dbd13 100644
--- a/Doc/Tutorial/chapter_align.tex
+++ b/Doc/Tutorial/chapter_align.tex
@@ -893,7 +893,7 @@ faithful to the actual command line API:
 \end{verbatim}
 
 For the most basic usage, all you need is to have a FASTA input file, such as
-\href{http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta}{opuntia.fasta}
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/opuntia.fasta}{opuntia.fasta}
 (available online or in the Doc/examples subdirectory of the Biopython source
 code). This is a small FASTA file containing seven prickly-pear DNA sequences
 (from the cactus family \textit{Opuntia}).
@@ -1031,7 +1031,7 @@ actual command line API:
 \end{verbatim}
 
 For the most basic usage, all you need is to have a FASTA input file, such as
-\href{http://biopython.org/DIST/docs/tutorial/examples/opuntia.fasta}{opuntia.fasta}
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/opuntia.fasta}{opuntia.fasta}
 (available online or in the Doc/examples subdirectory of the Biopython source
 code). You can then tell MUSCLE to read in this FASTA file, and write the
 alignment to an output file:
@@ -1136,6 +1136,7 @@ with handles instead:
 >>> child = subprocess.Popen(str(muscle_cline),
 ...                          stdout=subprocess.PIPE,
 ...                          stderr=subprocess.PIPE,
+...                          universal_newlines=True,
 ...                          shell=(sys.platform!="win32"))
 >>> from Bio import AlignIO
 >>> align = AlignIO.read(child.stdout, "fasta")
diff --git a/Doc/Tutorial/chapter_appendix.tex b/Doc/Tutorial/chapter_appendix.tex
index 0a41ac4..7795914 100644
--- a/Doc/Tutorial/chapter_appendix.tex
+++ b/Doc/Tutorial/chapter_appendix.tex
@@ -38,10 +38,10 @@ which reads the entire text information from the handle, and
 ``write'' handles, the function \verb|write()| is regularly used.
 
 The most common usage for handles is reading information from a file,
-which is done using the built-in Python function \verb|open|. Here, we open a
-handle to the file \href{examples/m\_cold.fasta}{m\_cold.fasta}
-(also available online
-\href{http://biopython.org/DIST/docs/tutorial/examples/m\_cold.fasta}{here}):
+which is done using the built-in Python function \verb|open|. Here,
+we handle to the file {\tt m\_cold.fasta} which you can download
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/m\_cold.fasta}{here}
+(or find included in the Biopython source code as {\tt Doc/examples/m\_cold.fasta}).
 
 \begin{verbatim}
 >>> handle = open("m_cold.fasta", "r")
@@ -107,86 +107,3 @@ A string
 >>> print(second_line)
  with multiple lines.
 \end{verbatim}
-
-\begin{thebibliography}{99}
-\bibitem{cock2009}
-Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, Michiel J. L. de Hoon: ``Biopython: freely available Python tools for computational molecular biology and bioinformatics''. {\it Bioinformatics} {\bf 25} (11), 1422--1423 (2009). \href{http://dx.doi.org/10.1093/bioinformatics/btp163}{doi:10.1093/bioinformatics/btp163},
-\bibitem{pritchard2006}
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-\end{thebibliography}
-\end{document}
-
diff --git a/Doc/Tutorial/chapter_blast.tex b/Doc/Tutorial/chapter_blast.tex
index 692d16c..66e41b4 100644
--- a/Doc/Tutorial/chapter_blast.tex
+++ b/Doc/Tutorial/chapter_blast.tex
@@ -47,6 +47,10 @@ which are basically analogous to the different parameters you can set
 on the BLAST web page.  We'll just highlight a few of them here:
 
 \begin{itemize}
+\item The argument \verb|url_base| sets the base URL for running BLAST over the
+internet. By default it connects to the NCBI, but one can use this to connect
+to an instance of NCBI BLAST running in the cloud. Please refer to the documentation
+for the \verb|qblast| function for further details.
 \item The \verb|qblast| function can return the BLAST results in various
 formats, which you can choose with the optional \verb|format_type| keyword:
 \verb|"HTML"|, \verb|"Text"|, \verb|"ASN.1"|, or \verb|"XML"|.
diff --git a/Doc/Tutorial/chapter_cluster.tex b/Doc/Tutorial/chapter_cluster.tex
index 9690c9c..fb7684f 100644
--- a/Doc/Tutorial/chapter_cluster.tex
+++ b/Doc/Tutorial/chapter_cluster.tex
@@ -695,7 +695,44 @@ to open a gzipped file, or
 \end{verbatim}
 to open a file stored on the Internet before calling \verb|read|.
 
-The \verb|read| command reads the tab-delimited text file \verb|mydatafile.txt| containing gene expression data in the format specified for Michael Eisen's Cluster/TreeView program. For a description of this file format, see the manual to Cluster/TreeView. It is available at \href{http://rana.lbl.gov/manuals/ClusterTreeView.pdf}{Michael Eisen's lab website} and at \href{http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/cluster3.pdf}{our website}.
+The \verb|read| command reads the tab-delimited text file \verb|mydatafile.txt| containing gene expression data in the format specified for Michael Eisen's Cluster/TreeView program. In this file format, rows represent genes and columns represent samples or observations. For a simple time course, a minimal input file would look like this:
+
+\begin{table}[!htbp]
+\begin{center}
+\begin{tabular}{|l|r|r|r|r|r|}
+\hline
+YORF & 0 minutes & 30 minutes & 1 hour & 2 hours & 4 hours\\
+YAL001C & 1   & 1.3 & 2.4 &  5.8 &  2.4 \\
+YAL002W & 0.9 & 0.8 & 0.7 &  0.5 &  0.2 \\
+YAL003W & 0.8 & 2.1 & 4.2 & 10.1 & 10.1 \\
+YAL005C & 1.1 & 1.3 & 0.8 &      &  0.4 \\
+YAL010C & 1.2 & 1   & 1.1 &  4.5 &  8.3 \\
+\hline
+\end{tabular}
+\end{center}
+\end{table}
+
+\noindent Each row (gene) has an identifier that always goes in the first column. In this example, we are using yeast open reading frame codes. Each column (sample) has a label in the first row. In this example, the labels describe the time at which a sample was taken. The first column of the first row contains a special field that tells the program what kind of objects are in each row. In this case, YORF stands for yeast open reading frame. This field can be any alpha-numeric value. The [...]
+
+The input file may contain additional information. A maximal input file would look like this:
+
+\begin{table}[!htbp]
+\begin{center}
+\begin{tabular}{|l|l|r|r|r|r|r|r|r|}
+\hline
+YORF & NAME & GWEIGHT & GORDER & 0 & 30 & 1 & 2 & 4 \\
+EWEIGHT & & & & 1 & 1 & 1 & 1 & 0 \\
+EORDER  & & & & 5 & 3 & 2 & 1 & 1 \\
+YAL001C & TFIIIC 138 KD SUBUNIT      &   1 & 1 & 1   & 1.3 & 2.4 &  5.8 &  2.4 \\
+YAL002W & UNKNOWN                    & 0.4 & 3 & 0.9 & 0.8 & 0.7 &  0.5 &  0.2 \\
+YAL003W & ELONGATION FACTOR EF1-BETA & 0.4 & 2 & 0.8 & 2.1 & 4.2 & 10.1 & 10.1 \\
+YAL005C & CYTOSOLIC HSP70            & 0.4 & 5 & 1.1 & 1.3 & 0.8 &      &  0.4 \\
+\hline
+\end{tabular}
+\end{center}
+\end{table}
+
+\noindent The added columns NAME, GWEIGHT, and GORDER and rows EWEIGHT and EORDER are optional. The NAME column allows you to specify a label for each gene that is distinct from the ID in column 1.
 
 A \verb|Record| object has the following attributes:
 
diff --git a/Doc/Tutorial/chapter_cookbook.tex b/Doc/Tutorial/chapter_cookbook.tex
index 9716e2d..6ef94e1 100644
--- a/Doc/Tutorial/chapter_cookbook.tex
+++ b/Doc/Tutorial/chapter_cookbook.tex
@@ -258,11 +258,11 @@ to sort by length. This can be solved using the
 
 \begin{verbatim}
 from Bio import SeqIO
-#Get the lengths and ids, and sort on length
+# Get the lengths and ids, and sort on length
 len_and_ids = sorted((len(rec), rec.id) for rec in \
                      SeqIO.parse("ls_orchid.fasta","fasta"))
 ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids	#free this memory
+del len_and_ids  # free this memory
 record_index = SeqIO.index("ls_orchid.fasta", "fasta")
 records = (record_index[id] for id in ids)
 SeqIO.write(records, "sorted.fasta", "fasta")
@@ -284,18 +284,21 @@ in Biopython 1.54 (see Section~\ref{sec:seqio-index-getraw}).
 
 \begin{verbatim}
 from Bio import SeqIO
-#Get the lengths and ids, and sort on length
+# Get the lengths and ids, and sort on length
 len_and_ids = sorted((len(rec), rec.id) for rec in \
                      SeqIO.parse("ls_orchid.fasta","fasta"))
 ids = reversed([id for (length, id) in len_and_ids])
-del len_and_ids	#free this memory
+del len_and_ids  # free this memory
 record_index = SeqIO.index("ls_orchid.fasta", "fasta")
-handle = open("sorted.fasta", "w")
+handle = open("sorted.fasta", "wb")
 for id in ids:
     handle.write(record_index.get_raw(id))
 handle.close()
 \end{verbatim}
 
+Note with Python 3 onwards, we have to open the file for writing in
+binary mode because the \verb|get_raw()| method returns bytes strings.
+
 As a bonus, because it doesn't parse the data into \verb|SeqRecord| objects
 a second time it should be faster.
 
@@ -912,14 +915,21 @@ find whole books about this topic!
 \section{Sequence parsing plus simple plots}
 \label{seq:sequence-parsing-plus-pylab}
 
-This section shows some more examples of sequence parsing, using the \verb|Bio.SeqIO|
-module described in Chapter~\ref{chapter:Bio.SeqIO}, plus the Python library matplotlib's \verb|pylab| plotting interface (see \href{http://matplotlib.sourceforge.net/}{the matplotlib website for a tutorial}).  Note that to follow these examples you will need matplotlib installed - but without it you can still try the data parsing bits.
+This section shows some more examples of sequence parsing, using the
+\verb|Bio.SeqIO| module described in Chapter~\ref{chapter:Bio.SeqIO},
+plus the Python library matplotlib's \verb|pylab| plotting interface
+(see \href{http://matplotlib.sourceforge.net/}{the matplotlib website
+for a tutorial}).  Note that to follow these examples you will need
+matplotlib installed - but without it you can still try the data
+parsing bits.
 
 \subsection{Histogram of sequence lengths}
 
 There are lots of times when you might want to visualise the distribution of sequence
 lengths in a dataset -- for example the range of contig sizes in a genome assembly
-project.  In this example we'll reuse our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} which has only 94 sequences.
+project.  In this example we'll reuse our orchid FASTA file
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}
+which has only 94 sequences.
 
 First of all, we will use \verb|Bio.SeqIO| to parse the FASTA file and compile a list
 of all the sequence lengths.  You could do this with a for loop, but I find a list
@@ -979,14 +989,17 @@ shown in Figure~\ref{fig:seq-len-hist}.
 Notice that most of these orchid sequences are about $740$ bp long, and there could be
 two distinct classes of sequence here with a subset of shorter sequences.
 
-\emph{Tip:} Rather than using \verb|pylab.show()| to show the plot in a window, you can also use \verb|pylab.savefig(...)| to save the figure to a file (e.g. as a PNG or PDF).
+\emph{Tip:} Rather than using \verb|pylab.show()| to show the plot in a window,
+you can also use \verb|pylab.savefig(...)| to save the figure to a file
+(e.g. as a PNG or PDF).
 
 \subsection{Plot of sequence GC\%}
 
 Another easily calculated quantity of a nucleotide sequence is the GC\%.  You might
 want to look at the GC\% of all the genes in a bacterial genome for example, and
 investigate any outliers which could have been recently acquired by horizontal gene
-transfer.  Again, for this example we'll reuse our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}.
+transfer.  Again, for this example we'll reuse our orchid FASTA file
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}.
 
 First of all, we will use \verb|Bio.SeqIO| to parse the FASTA file and compile a list
 of all the GC percentages.  Again, you could do this with a for loop, but I prefer this:
@@ -1065,7 +1078,7 @@ in the plot below).
 \end{latexonly}
 
 To start off, we'll need two sequences.  For the sake of argument, we'll just take
-the first two from our orchid FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}:
+the first two from our orchid FASTA file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta}:
 
 \begin{verbatim}
 from Bio import SeqIO
@@ -1486,7 +1499,7 @@ alignments. In this example, we'll first get a Biopython alignment
 object and then get a summary object to calculate info about the
 alignment. The file containing \href{examples/protein.aln}{protein.aln}
 (also available online
-\href{http://biopython.org/DIST/docs/tutorial/examples/protein.aln}{here})
+\href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/protein.aln}{here})
 contains the Clustalw alignment output.
 
 %doctest examples
diff --git a/Doc/Tutorial/chapter_entrez.tex b/Doc/Tutorial/chapter_entrez.tex
index 20a7324..336fb12 100644
--- a/Doc/Tutorial/chapter_entrez.tex
+++ b/Doc/Tutorial/chapter_entrez.tex
@@ -200,18 +200,19 @@ Note that instead of a species name like \texttt{Cypripedioideae[Orgn]}, you can
 
 As a final example, let's get a list of computational journal titles:
 \begin{verbatim}
->>> handle = Entrez.esearch(db="journals", term="computational")
+>>> handle = Entrez.esearch(db="nlmcatalog", term="computational[Journal]", retmax='20')
 >>> record = Entrez.read(handle)
->>> record["Count"]
-'16'
->>> record["IdList"]
-['30367', '33843', '33823', '32989', '33190', '33009', '31986',
- '34502', '8799', '22857', '32675', '20258', '33859', '32534',
- '32357', '32249']
+>>> print("{} computational journals found".format(record["Count"]))
+117 computational Journals found
+>>> print("The first 20 are\n{}".format(record['IdList']))
+['101660833', '101664671', '101661657', '101659814', '101657941',
+ '101653734', '101669877', '101649614', '101647835', '101639023',
+ '101627224', '101647801', '101589678', '101585369', '101645372',
+ '101586429', '101582229', '101574747', '101564639', '101671907']
 \end{verbatim}
 Again, we could use EFetch to obtain more information for each of these journal IDs.
 
-ESearch has many useful options --- see the \href{http://www.ncbi.nlm.nih.gov/entrez/query/static/esearch\_help.html}{ESearch help page} for more information.
+ESearch has many useful options --- see the \href{https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch}{ESearch help page} for more information.
 
 \section{EPost: Uploading a list of identifiers}
 EPost uploads a list of UIs for use in subsequent search strategies; see the
@@ -261,14 +262,13 @@ ESummary retrieves document summaries from a list of primary IDs (see the  \href
 \begin{verbatim}
 >>> from Bio import Entrez
 >>> Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
->>> handle = Entrez.esummary(db="journals", id="30367")
+>>> handle = Entrez.esummary(db="nlmcatalog", id="101660833")
 >>> record = Entrez.read(handle)
->>> record[0]["Id"]
-'30367'
->>> record[0]["Title"]
-'Computational biology and chemistry'
->>> record[0]["Publisher"]
-'Pergamon,'
+>>> info = record[0]['TitleMainList'][0]
+>>> print("Journal info\nid: {}\nTitle: {}".format(record[0]["Id"], info["Title"]))
+Journal info
+id: 101660833
+Title: IEEE transactions on computational imaging.
 \end{verbatim}
 
 \section{EFetch: Downloading full records from Entrez}
@@ -390,11 +390,11 @@ Note that a more typical use would be to save the sequence data to a local file,
 import os
 from Bio import SeqIO
 from Bio import Entrez
-Entrez.email = "A.N.Other at example.com"     # Always tell NCBI who you are
+Entrez.email = "A.N.Other at example.com"  # Always tell NCBI who you are
 filename = "gi_186972394.gbk"
 if not os.path.isfile(filename):
     # Downloading...
-    net_handle = Entrez.efetch(db="nucleotide",id="186972394",rettype="gb", retmode="text")
+    net_handle = Entrez.efetch(db="nucleotide", id="186972394", rettype="gb", retmode="text")
     out_handle = open(filename, "w")
     out_handle.write(net_handle.read())
     out_handle.close()
@@ -1409,7 +1409,7 @@ Let's try this for the Biopython PDB parser paper, PubMed ID 14630660:
 >>> Entrez.email = "A.N.Other at example.com"
 >>> pmid = "14630660"
 >>> results = Entrez.read(Entrez.elink(dbfrom="pubmed", db="pmc",
-...                                    LinkName="pubmed_pmc_refs", from_uid=pmid))
+...                                    LinkName="pubmed_pmc_refs", id=pmid))
 >>> pmc_ids = [link["Id"] for link in results[0]["LinkSetDb"][0]["Link"]]
 >>> pmc_ids
 ['2744707', '2705363', '2682512', ..., '1190160']
@@ -1430,7 +1430,7 @@ But first, taking the more straightforward approach of making a second
 
 \begin{verbatim}
 >>> results2 = Entrez.read(Entrez.elink(dbfrom="pmc", db="pubmed", LinkName="pmc_pubmed",
-...                                     from_uid=",".join(pmc_ids)))
+...                                     id=",".join(pmc_ids)))
 >>> pubmed_ids = [link["Id"] for link in results2[0]["LinkSetDb"][0]["Link"]]
 >>> pubmed_ids
 ['19698094', '19450287', '19304878', ..., '15985178']
diff --git a/Doc/Tutorial/chapter_graphics.tex b/Doc/Tutorial/chapter_graphics.tex
index 96bf4f2..d2b857e 100644
--- a/Doc/Tutorial/chapter_graphics.tex
+++ b/Doc/Tutorial/chapter_graphics.tex
@@ -867,7 +867,6 @@ plots (e.g. to show plots of GC\% on a track parallel to the features).
 
 These options are not covered here yet, so for now we refer you to the
 \href{http://biopython.org/DIST/docs/GenomeDiagram/userguide.pdf}
-%\href{http://bioinf.scri.ac.uk/lp/downloads/programs/genomediagram/userguide.pdf}
 {User Guide (PDF)} included with the standalone version of GenomeDiagram (but
 please read the next section first), and the docstrings.
 
diff --git a/Doc/Tutorial/chapter_kegg.tex b/Doc/Tutorial/chapter_kegg.tex
index 9301c47..c128352 100644
--- a/Doc/Tutorial/chapter_kegg.tex
+++ b/Doc/Tutorial/chapter_kegg.tex
@@ -74,9 +74,9 @@ for pathway in repair_pathways:
             if not gene_symbol in repair_genes:
                 repair_genes.append(gene_symbol)
 
-print "There are %d repair pathways and %d repair genes. The genes are:" % \
-      (len(repair_pathways), len(repair_genes))
-print ", ".join(repair_genes)
+print("There are %d repair pathways and %d repair genes. The genes are:" % \
+      (len(repair_pathways), len(repair_genes)))
+print(", ".join(repair_genes))
 \end{verbatim}
 
 The KEGG API wrapper is compatible with all endpoints. Usage is essentially replacing all slashes in the url with commas and using that list as arguments to the corresponding method in the KEGG module. Here are a few examples from the api documentation (\url{http://www.kegg.jp/kegg/docs/keggapi.html}).
diff --git a/Doc/Tutorial/chapter_learning.tex b/Doc/Tutorial/chapter_learning.tex
index 9785173..1415d5b 100644
--- a/Doc/Tutorial/chapter_learning.tex
+++ b/Doc/Tutorial/chapter_learning.tex
@@ -379,8 +379,9 @@ True: 0 Predicted: 0
 \end{verbatim}
 showing that the prediction is correct for all but two of the gene pairs. A more reliable estimate of the prediction accuracy can be found from a leave-one-out analysis, in which the model is recalculated from the training data after removing the gene to be predicted:
 \begin{verbatim}
+>>> k = 3
 >>> for i in range(len(ys)):
-        model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:])
+        model = kNN.train(xs[:i]+xs[i+1:], ys[:i]+ys[i+1:], k)
         print("True:", ys[i], "Predicted:", kNN.classify(model, xs[i]))
 True: 1 Predicted: 1
 True: 1 Predicted: 0
diff --git a/Doc/Tutorial/chapter_motifs.tex b/Doc/Tutorial/chapter_motifs.tex
index 4c38fb9..8ff7ec1 100644
--- a/Doc/Tutorial/chapter_motifs.tex
+++ b/Doc/Tutorial/chapter_motifs.tex
@@ -1178,9 +1178,9 @@ or a threshold (approximately) satisfying some relation between the false-positi
 >>> print("%5.3f" % threshold)
 6.241
 \end{verbatim}
-or a threshold satisfying (roughly) the equality between the
-false-positive rate and the $-log$ of the information content (as used
-in patser software by Hertz and Stormo):
+or a threshold satisfying (roughly) the equality between the $-log$ of the
+false-positive rate and the information content (as used in patser software by
+Hertz and Stormo):
 %cont-doctest
 \begin{verbatim}
 >>> threshold = distribution.threshold_patser()
@@ -1434,7 +1434,7 @@ roughly $1-0.239=0.761$.
 \label{sec:find}
 
 Currently, Biopython has only limited support for \emph{de novo} motif
-finding. Namely, we support running and parsing of AlignAce and
+finding. Namely, we support running \verb|xxmotif| and also parsing of
 MEME. Since the number of motif finding tools is growing rapidly,
 contributions of new parsers are welcome.
 
@@ -1488,58 +1488,7 @@ Seq('CTCAATCGTA', IUPACUnambiguousDNA())
 \end{verbatim}
 
 
-\subsection{AlignAce}
-\label{sec:alignace}
-
-We can do very similar things with the AlignACE program. Assume, you have
-your output in the file \verb|alignace.out|. You can parse your output
-with the following code:
-
-%cont-doctest
-\begin{verbatim}
->>> from Bio import motifs
->>> with open("alignace.out") as handle:
-...     motifsA = motifs.parse(handle, "alignace")
-...
-\end{verbatim}
-
-Again, your motifs behave as they should:
-%cont-doctest
-\begin{verbatim}
->>> motifsA[0].consensus
-Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-In fact you can even see, that AlignAce found a very similar motif as
-MEME. It is just a longer version of a reverse complement of the MEME
-motif:
-%cont-doctest
-\begin{verbatim}
->>> motifsM[0].reverse_complement().consensus
-Seq('TACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-If you have AlignAce installed on the same machine, you can also run
-it directly from Biopython. A short example of how this can be done is
-shown below (other parameters can be specified as keyword parameters):
-
-\begin{verbatim}
->>> command="/opt/bin/AlignACE"
->>> input_file="test.fa"
->>> from Bio.motifs.applications import AlignAceCommandline
->>> cmd = AlignAceCommandline(cmd=command, input=input_file, gcback=0.6, numcols=10)
->>> stdout, stderr= cmd()
-\end{verbatim}
-
-Since AlignAce prints all of its output to standard output, you can get
-to your motifs by parsing the first part of the result:
-\begin{verbatim}
->>> motifs = motifs.parse(stdout, "alignace")
-\end{verbatim}
-
-
-
-\section{Useful links }
+\section{Useful links}
 \label{sec:links}
 
 
diff --git a/Doc/Tutorial/chapter_phenotype.tex b/Doc/Tutorial/chapter_phenotype.tex
new file mode 100644
index 0000000..6495fe8
--- /dev/null
+++ b/Doc/Tutorial/chapter_phenotype.tex
@@ -0,0 +1,271 @@
+\chapter{Bio.phenotype: analyse phenotypic data}
+\label{chapter:phenotype}
+
+This chapter gives an overview of the functionalities of the
+\verb|Bio.phenotype| package included in Biopython. The scope of this
+package is the analysis of phenotypic data, which means parsing and
+analysing growth measurements of cell cultures.
+In its current state the package is focused on the analysis of
+high-throughput phenotypic experiments produced by the
+\href{https://en.wikipedia.org/wiki/Phenotype_microarray}{Phenotype Microarray technology},
+but future developments may include other platforms and formats.
+
+\section{Phenotype Microarrays}
+\label{sec:phenotypemicroarrays}
+
+The \href{https://en.wikipedia.org/wiki/Phenotype_microarray}{Phenotype Microarray}
+is a technology that measures the metabolism of bacterial
+and eukaryotic cells on roughly 2000 chemicals, divided in twenty 96-well
+plates.
+The technology measures the reduction of a tetrazolium dye by
+NADH, whose production by the cell is used as a proxy for cell metabolism;
+color development due to the reduction of this dye is typically measured
+once every 15 minutes.
+When cells are grown in a media that sustains cell metabolism, the
+recorded phenotypic data resembles a sigmoid growth curve, from which a
+series of growth parameters can be retrieved.
+
+\subsection{Parsing Phenotype Microarray data}
+
+The \verb|Bio.phenotype| package can parse two different formats of
+Phenotype Microarray data: the
+\href{https://en.wikipedia.org/wiki/Comma-separated_values}{CSV}
+(comma separated values) files produced by the machine's proprietary
+software and \href{https://en.wikipedia.org/wiki/JSON}{JSON}
+files produced by analysis software, like
+\href{https://www.dsmz.de/research/microorganisms/projects/analysis-of-omnilog-phenotype-microarray-data.html}{opm}
+or \href{http://combogenomics.github.io/DuctApe/}{DuctApe}.
+The parser will return one or a generator of PlateRecord objects, depending
+on whether the read or parse method is being used.
+You can test the parse function by using the \href{http://biopython.org/SRC/biopython/Doc/examples/Plates.csv}{\texttt{Plates.csv}} file provided with the Biopython source code.
+
+%doctest examples lib:numpy
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> for record in phenotype.parse("Plates.csv", "pm-csv"):
+...     print("%s %i" % (record.id, len(record)))
+...
+PM01 96
+PM01 96
+PM09 96
+PM09 96
+\end{verbatim}
+
+The parser returns a series of PlateRecord objects, each one containing a series of WellRecord objects
+(holding each well's experimental data) arranged in 8 rows and 12 columns; each row is indicated by
+a uppercase character from A to H, while columns are indicated by a two digit number, from 01 to 12.
+There are several ways to access WellRecord objects from a PlateRecord objects:
+
+\begin{description}
+  \item[Well identifier]
+    If you know the well identifier (row + column identifiers) you can access the desired well directly.
+    \begin{verbatim}
+    >>> record['A02']
+    \end{verbatim}
+
+  \item[Well plate coordinates]
+    The same well can be retrieved by using the row and columns numbers (0-based index).
+
+%doctest examples lib:numpy
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
+>>> print(record[0, 1].id)
+A02
+\end{verbatim}
+
+  \item[Row or column coordinates]
+    A series of WellRecord objects contiguous to each other in the plate can be retrieved in bulk by
+    using the python list slicing syntax on PlateRecord objects; rows and columns are numbered with
+    a 0-based index.
+
+%cont-doctest
+\begin{verbatim}
+>>> print(record[0])
+Plate ID: PM09
+Well: 12
+Rows: 1
+Columns: 12
+PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], ..., WellRecord['A12']')
+>>> print(record[:, 0])
+Plate ID: PM09
+Well: 8
+Rows: 8
+Columns: 1
+PlateRecord('WellRecord['A01'], WellRecord['B01'], WellRecord['C01'], ..., WellRecord['H01']')
+>>> print(record[:3, :3])
+Plate ID: PM09
+Well: 9
+Rows: 3
+Columns: 3
+PlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord['A03'], ..., WellRecord['C03']')
+\end{verbatim}
+
+\end{description}
+
+\subsection{Manipulating Phenotype Microarray data}
+
+\subsubsection{Accessing raw data}
+The raw data extracted from the PM files is comprised of a series of tuples for each well,
+containing the time (in hours) and the colorimetric measure (in arbitrary units).
+Usually the instrument collects data every fifteen minutes, but that can vary between
+experiments. The raw data can be accessed by iterating on a WellRecord object;
+in the example below only the first ten time points are shown.
+
+%doctest examples lib:numpy
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
+>>> well = record['A02']  
+\end{verbatim}
+%rest of code snippet output is truncated
+\begin{verbatim}
+>>> for time, signal in well:
+...    print(time, signal)
+...
+(0.0, 12.0)
+(0.25, 18.0)
+(0.5, 27.0)
+(0.75, 35.0)
+(1.0, 37.0)
+(1.25, 41.0)
+(1.5, 44.0)
+(1.75, 44.0)
+(2.0, 44.0)
+(2.25, 44.0)
+[...]
+\end{verbatim}
+
+This method, while providing a way to access the raw data, doesn't allow a direct
+comparison between different WellRecord objects, which may have measurements at
+different time points.
+
+\subsubsection{Accessing interpolated data}
+To make it easier to compare different experiments and in general to allow a more intuitive handling
+of the phenotypic data, the module allows to define a custom slicing of the time points that are present
+in the WellRecord object. Colorimetric data for time points that have not been directly measured are
+derived through a linear interpolation of the available data, otherwise a NaN is returned.
+This method only works in the time interval where actual data is available.
+Time intervals can be defined with the same syntax as list
+indexing; the default time interval is therefore one hour.
+
+%cont-doctest
+\begin{verbatim}
+>>> well[:10]  
+[12.0, 37.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0]
+\end{verbatim}
+
+Different time intervals can be used, for instance five minutes:
+%Rounding makes this tricky as a doctest...
+\begin{verbatim}
+>>> well[63:64:0.083]
+[12.0, 37.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0, 44.0]
+>>> well[9.55]
+44.0
+>>> well[63.33:73.33]
+[113.31999999999999,
+ 117.0,
+ 120.31999999999999,
+ 128.0,
+ 129.63999999999999,
+ 132.95999999999998,
+ 136.95999999999998,
+ 140.0,
+ 142.0,
+ nan]
+\end{verbatim}
+
+\subsubsection{Control well subtraction}
+Many Phenotype Microarray plates contain a control well (usually A01), that is a well where the media shouldn't support
+any growth; the low signal produced by this well can be subtracted from the other wells.
+The PlateRecord objects have a dedicated function for that, which returns another PlateRecord object
+with the corrected data.
+
+%cont-doctest
+\begin{verbatim}
+>>> corrected = record.subtract_control(control='A01')
+>>> record['A01'][63]
+336.0
+>>> corrected['A01'][63]
+0.0
+\end{verbatim}
+
+\subsubsection{Parameters extraction}
+Those wells where metabolic activity is observed show a sigmoid behavior for the colorimetric data.
+To allow an easier way to compare different experiments a sigmoid curve can be fitted onto the data,
+so that a series of summary parameters can be extracted and used for comparisons.
+The parameters that can be extracted from the curve are:
+
+\begin{itemize}
+  \item Minimum (\textbf{min}) and maximum (\textbf{max}) signal;
+
+  \item Average height (\textbf{average\_height});
+
+  \item Area under the curve (\textbf{area});
+
+  \item Curve plateau point (\textbf{plateau});
+
+  \item Curve slope during exponential metabolic activity (\textbf{slope});
+
+  \item Curve lag time (\textbf{lag}).
+\end{itemize}
+
+All the parameters (except \textbf{min}, \textbf{max} and \textbf{average\_height}) require the
+\href{http://www.scipy.org/}{scipy library} to be installed.
+
+The fit function uses three sigmoid functions:
+
+\begin{description}
+  \item[Gompertz] $Ae^{-e^{(\frac{\mu_{m}e}{A}(\lambda - t) + 1)}} + y0$
+
+  \item[Logistic] $\frac{A}{1+e^{(\frac{4\mu_{m}}{A}(\lambda - t) + 2)}} + y_{0}$
+
+  \item[Richards] $A(1 + ve^{1 + v} + e^{\frac{\mu_{m}}{A}(1 + v)(1 + \frac{1}{v})(\lambda - t)})^{-\frac{1}{v}} + y0$
+
+\end{description}
+
+Where:
+\begin{itemize}
+  \item[\textbf{A}] corresponds to the \textbf{plateau}
+
+  \item[\textbf{$\mu_{m}$}] corresponds to the \textbf{slope}
+
+  \item[\textbf{$\lambda$}] corresponds to the \textbf{lag}
+
+\end{itemize}
+
+These functions have been derived from \href{http://www.ncbi.nlm.nih.gov/pubmed/16348228}{this publication}.
+The fit method by default tries first to fit the gompertz function: if it fails it will then try to fit
+the logistic and then the richards function. The user can also specify one of the three functions to be applied.
+
+%TODO: enable with doctest examples
+\begin{verbatim}
+>>> from Bio import phenotype
+>>> record = list(phenotype.parse("Plates.csv", "pm-csv"))[-1]
+>>> well = record['A02'] 
+>>> well.fit()
+>>> print("Function fitted: %s" % well.model)
+Function fitted: gompertz
+>>> for param in ["area", "average_height", "lag", "max", "min",
+...               "plateau", "slope"]:
+...     print("%s\t%.2f" % (param, getattr(well, param)))
+...
+area    4414.38
+average_height  61.58
+lag     48.60
+max     143.00
+min     12.00
+plateau 120.02
+slope   4.99
+\end{verbatim}
+
+\subsection{Writing Phenotype Microarray data}
+PlateRecord objects can be written to file in the form of
+\href{https://en.wikipedia.org/wiki/JSON}{JSON}
+files, a format compatible with other software packages such as
+\href{https://www.dsmz.de/research/microorganisms/projects/analysis-of-omnilog-phenotype-microarray-data.html}{opm}
+or \href{http://combogenomics.github.io/DuctApe/}{DuctApe}.
+\begin{verbatim}
+>>> phenotype.write(record, "out.json", "pm-json")
+1
+\end{verbatim}
diff --git a/Doc/Tutorial/chapter_quick_start.tex b/Doc/Tutorial/chapter_quick_start.tex
index ce967a3..119fdaa 100644
--- a/Doc/Tutorial/chapter_quick_start.tex
+++ b/Doc/Tutorial/chapter_quick_start.tex
@@ -75,7 +75,7 @@ A large part of much bioinformatics work involves dealing with the many types of
 
 We are now going to briefly introduce the \verb|Bio.SeqIO| module -- you can find out more in Chapter~\ref{chapter:Bio.SeqIO}.  We'll start with an online search for our friends, the lady slipper orchids. To keep this introduction simple, we're just using the NCBI website by hand.  Let's just take a look through the nucleotide databases at NCBI, using an Entrez online search (\url{http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=Nucleotide}) for everything mentioning the text Cypriped [...]
 
-When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file and as a GenBank formatted text file (files \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} and \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk}, also included with the Biopython source code under {\tt docs/tutorial/examples/}).
+When this tutorial was originally written, this search gave us only 94 hits, which we saved as a FASTA formatted text file and as a GenBank formatted text file (files \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} and \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk}, also included with the Biopython source code under {\tt docs/tutorial/examples/}).
 
 % The GenBank version is actually new - it was created by filtering out the original 94 hits from the
 % latest search of over 400 hits.
@@ -85,7 +85,7 @@ If you run the search today, you'll get hundreds of results!  When following the
 \subsection{Simple FASTA parsing example}
 \label{sec:fasta-parsing}
 
-If you open the lady slipper orchids FASTA file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} in your favourite text editor, you'll see that the file starts like this:
+If you open the lady slipper orchids FASTA file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{\tt ls\_orchid.fasta} in your favourite text editor, you'll see that the file starts like this:
 
 \begin{verbatim}
 >gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
@@ -120,7 +120,7 @@ Notice that the FASTA format does not specify the alphabet, so \verb|Bio.SeqIO|
 
 \subsection{Simple GenBank parsing example}
 
-Now let's load the GenBank file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk} instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:
+Now let's load the GenBank file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{\tt ls\_orchid.gbk} instead - notice that the code to do this is almost identical to the snippet used above for the FASTA file - the only difference is we change the filename and the format string:
 
 \begin{verbatim}
 from Bio import SeqIO
diff --git a/Doc/Tutorial/chapter_searchio.tex b/Doc/Tutorial/chapter_searchio.tex
index 1835d69..7d1d552 100644
--- a/Doc/Tutorial/chapter_searchio.tex
+++ b/Doc/Tutorial/chapter_searchio.tex
@@ -35,10 +35,11 @@ BLAST and BLAT. They are used merely for illustrative purposes, and you should
 be able to adapt the workflow to any other search tools supported by
 \verb|Bio.SearchIO| in a breeze. You're very welcome to follow along with the
 search output files we'll be using. The BLAST output file can be downloaded
-\href{http://biopython.org/SRC/Tests/Tutorial/my_blast.xml}{here},
+\href{http://biopython.org/SRC/biopython/Doc/examples/my_blast.xml}{here},
 and the BLAT output file
-\href{http://biopython.org/SRC/Tests/Tutorial/my_blat.psl}{here}.
-Both output files were generated using this sequence:
+\href{http://biopython.org/SRC/biopython/Doc/examples/my_blat.psl}{here}
+or are included with the Biopython source code under the \verb|Doc/examples/|
+folder. Both output files were generated using this sequence:
 
 \begin{verbatim}
 >mystery_seq
diff --git a/Doc/Tutorial/chapter_seq_annot.tex b/Doc/Tutorial/chapter_seq_annot.tex
index 235aaf6..f6da0fe 100644
--- a/Doc/Tutorial/chapter_seq_annot.tex
+++ b/Doc/Tutorial/chapter_seq_annot.tex
@@ -574,6 +574,63 @@ the difference between the start and end positions. However,
 for a \verb|CompoundLocation| the length is the sum of the
 constituent regions.
 
+\section{Comparison}
+
+The \verb|SeqRecord| objects can be very complex, but here's a simple example:
+
+%doctest
+\begin{verbatim}
+>>> from Bio.Seq import Seq
+>>> from Bio.SeqRecord import SeqRecord
+>>> record1 = SeqRecord(Seq("ACGT"), id="test")
+>>> record2 = SeqRecord(Seq("ACGT"), id="test")
+\end{verbatim}
+
+What happens when you try to compare these ``identical'' records?
+
+%this is not a doctest:
+\begin{verbatim}
+>>> record1 == record2
+...
+\end{verbatim}
+
+Perhaps surprisingly older versions of Biopython would use Python's default object
+comparison for the \verb|SeqRecord|, meaning \verb|record1 == record2| would
+only return \verb|True| if these variables pointed at the same object in memory.
+In this example, \verb|record1 == record2| would have returned \verb|False|
+here!
+
+%this is not a doctest:
+\begin{verbatim}
+>>> record1 == record2  # on old versions of Biopython!
+False
+\end{verbatim}
+
+As of Biopython 1.67, \verb|SeqRecord| comparison like \verb|record1 == record2|
+will instead raise an explicit error to avoid people being caught out by this:
+
+%cont-doctest
+\begin{verbatim}
+>>> record1 == record2
+Traceback (most recent call last):
+...
+NotImplementedError: SeqRecord comparison is deliberately not implemented. Explicitly compare the attributes of interest.
+\end{verbatim}
+
+Instead you should check the attributes you are interested in, for example the
+identifier and the sequence:
+
+%cont-doctest
+\begin{verbatim}
+>>> record1.id == record2.id
+True
+>>> record1.seq == record2.seq
+True
+\end{verbatim}
+
+Beware that comparing complex objects quickly gets complicated (see also
+Section~\ref{sec:seq-comparison}).
+
 \section{References}
 
 Another common annotation related to a sequence is a reference to a journal or other published work dealing with the sequence. We have a fairly simple way of representing a Reference in Biopython -- we have a \verb|Bio.SeqFeature.Reference| class that stores the relevant information about a reference as attributes of an object.
@@ -724,8 +781,9 @@ qualifiers:
     Key: locus_tag, Value: ['YP_pPCP05']
 <BLANKLINE>
 \end{verbatim}
+%As above, output is truncated so cannot test this as a doctest:
 \begin{verbatim}
->>> print(sub_record.features[20])
+>>> print(sub_record.features[1])
 type: CDS
 location: [42:480](+)
 qualifiers:
diff --git a/Doc/Tutorial/chapter_seqio.tex b/Doc/Tutorial/chapter_seqio.tex
index dd7990c..e544878 100644
--- a/Doc/Tutorial/chapter_seqio.tex
+++ b/Doc/Tutorial/chapter_seqio.tex
@@ -40,7 +40,7 @@ for seq_record in SeqIO.parse("ls_orchid.fasta", "fasta"):
     print(len(seq_record))
 \end{verbatim}
 
-The above example is repeated from the introduction in Section~\ref{sec:sequence-parsing}, and will load the orchid DNA sequences in the FASTA format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{ls\_orchid.fasta}.  If instead you wanted to load a GenBank format file like \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} then all you need to do is change the filename and the format string:
+The above example is repeated from the introduction in Section~\ref{sec:sequence-parsing}, and will load the orchid DNA sequences in the FASTA format file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{ls\_orchid.fasta}.  If instead you wanted to load a GenBank format file like \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk} then all you need to do is change the filename and t [...]
 
 \begin{verbatim}
 from Bio import SeqIO
@@ -143,7 +143,7 @@ You can of course still use a for loop with a list of \verb|SeqRecord| objects.
 \subsection{Extracting data}
 
 The \verb|SeqRecord| object and its annotation structures are described more fully in
-Chapter~\ref{chapter:SeqRecord}.  As an example of how annotations are stored, we'll look at the output from parsing the first record in the GenBank file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk}.
+Chapter~\ref{chapter:SeqRecord}.  As an example of how annotations are stored, we'll look at the output from parsing the first record in the GenBank file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk}.
 
 \begin{verbatim}
 from Bio import SeqIO
@@ -190,7 +190,7 @@ print(first_record.annotations.values())
 
 In general, the annotation values are strings, or lists of strings.  One special case is any references in the file get stored as reference objects.
 
-Suppose you wanted to extract a list of the species from the \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} GenBank file.  The information we want, \emph{Cypripedium irapeanum}, is held in the annotations dictionary under `source' and `organism', which we can access like this:
+Suppose you wanted to extract a list of the species from the \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk} GenBank file.  The information we want, \emph{Cypripedium irapeanum}, is held in the annotations dictionary under `source' and `organism', which we can access like this:
 
 \begin{verbatim}
 >>> print(first_record.annotations["source"])
@@ -236,7 +236,7 @@ print(all_species)
 
 Great.  That was pretty easy because GenBank files are annotated in a standardised way.
 
-Now, let's suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file.  The bad news is you will have to write some code to extract the data you want from the record's description line - if the information is in the file in the first place!  Our example FASTA format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.fasta}{ls\_orchid.fasta} starts like this:
+Now, let's suppose you wanted to extract a list of the species from a FASTA file, rather than the GenBank file.  The bad news is you will have to write some code to extract the data you want from the record's description line - if the information is in the file in the first place!  Our example FASTA format file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta}{ls\_orchid.fasta} starts like this:
 
 \begin{verbatim}
 >gi|2765658|emb|Z78533.1|CIZ78533 C.irapeanum 5.8S rRNA gene and ITS1 and ITS2 DNA
@@ -428,13 +428,12 @@ so we must use the \verb|Bio.SeqIO.parse()| function:
 from Bio import Entrez
 from Bio import SeqIO
 Entrez.email = "A.N.Other at example.com"
-handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text", \
+handle = Entrez.efetch(db="nucleotide", rettype="gb", retmode="text",
                        id="6273291,6273290,6273289")
 for seq_record in SeqIO.parse(handle, "gb"):
-    print seq_record.id, seq_record.description[:50] + "..."
-    print "Sequence length %i," % len(seq_record),
-    print "%i features," % len(seq_record.features),
-    print "from: %s" % seq_record.annotations["source"]
+    print("%s %s..." % (seq_record.id, seq_record.description[:50]))
+    print("Sequence length %i, %i features, from: %s"
+          % (len(seq_record), len(seq_record.features), seq_record.annotations["source"]))
 handle.close()
 \end{verbatim}
 
@@ -758,8 +757,8 @@ The dictionary-like object from \verb|Bio.SeqIO.index()| gives you each
 entry as a \verb|SeqRecord| object. However, it is sometimes useful to
 be able to get the original raw data straight from the file. For this
 use the \verb|get_raw()| method which takes a
-single argument (the record identifier) and returns a string (extracted
-from the file without modification).
+single argument (the record identifier) and returns a bytes string
+(extracted from the file without modification).
 
 A motivating example is extracting a subset of a records from a large
 file where either \verb|Bio.SeqIO.write()| does not (yet) support the
@@ -777,12 +776,15 @@ want to extract just a few records from it:
 \begin{verbatim}
 >>> from Bio import SeqIO
 >>> uniprot = SeqIO.index("uniprot_sprot.dat", "swiss")
->>> handle = open("selected.dat", "w")
+>>> handle = open("selected.dat", "wb")
 >>> for acc in ["P33487", "P19801", "P13689", "Q8JZQ5", "Q9TRC7"]:
 ...     handle.write(uniprot.get_raw(acc))
 >>> handle.close()
 \end{verbatim}
 
+Note with Python 3 onwards, we have to open the file for writing in
+binary mode because the \verb|get_raw()| method returns bytes strings.
+
 There is a longer example in Section~\ref{sec:SeqIO-sort} using the
 \verb|SeqIO.index()| function to sort a large sequence file (without
 loading everything into memory at once).
@@ -807,40 +809,77 @@ This index file is actually an SQLite3 database.
 
 As an example, consider the GenBank flat file releases from the NCBI FTP site,
 \url{ftp://ftp.ncbi.nih.gov/genbank/}, which are gzip compressed GenBank files.
-As of GenBank release $182$, there are $16$ files making up the viral sequences,
-\texttt{gbvrl1.seq}, \ldots, \texttt{gbvrl16.seq}, containing in total almost
-one million records. You can index them like this:
+
+As of GenBank release $210$, there are $38$ files making up the viral sequences,
+\texttt{gbvrl1.seq}, \ldots, \texttt{gbvrl38.seq}, taking about 8GB on disk once
+decompressed, and containing in total nearly two million records.
+
+If you were interested in the viruses, you could download all the virus files
+from the command line very easily with the \texttt{rsync} command, and then
+decompress them with \texttt{gunzip}:
+
+\begin{verbatim}
+# For illustration only, see reduced example below
+$ rsync -avP "ftp.ncbi.nih.gov::genbank/gbvrl*.seq.gz" .
+$ gunzip gbvrl*.seq.gz
+\end{verbatim}
+
+Unless you care about viruses, that's a lot of data to download just for this
+example - so let's download \emph{just} the first four chunks (about 25MB each
+compressed), and decompress them (taking in all about 1GB of space):
 
 \begin{verbatim}
+# Reduced example, download only the first four chunks
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl1.seq.gz
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl2.seq.gz
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl3.seq.gz
+$ curl -O ftp://ftp.ncbi.nih.gov/genbank/gbvrl4.seq.gz
+$ gunzip gbvrl*.seq.gz
+\end{verbatim}
+
+Now, in Python, index these GenBank files as follows:
+
+\begin{verbatim}
+>>> import glob
 >>> from Bio import SeqIO
->>> files = ["gbvrl%i.seq" % (i+1) for i in range(16)]
+>>> files = glob.glob("gbvrl*.seq")
+>>> print("%i files to index" % len(files))
+4
 >>> gb_vrl = SeqIO.index_db("gbvrl.idx", files, "genbank")
 >>> print("%i sequences indexed" % len(gb_vrl))
-958086 sequences indexed
+272960 sequences indexed
 \end{verbatim}
 
-That takes about two minutes to run on my machine. If you rerun it then the
-index file (here \texttt{gbvrl.idx}) is reloaded in under a second. You can
-use the index as a read only Python dictionary - without having to worry
+Indexing the full set of virus GenBank files took about ten minutes on my machine,
+just the first four files took about a minute or so.
+
+However, once done, repeating this will reload the index file \verb|gbvrl.idx|
+in a fraction of a second.
+
+You can use the index as a read only Python dictionary - without having to worry
 about which file the sequence comes from, e.g.
 
 \begin{verbatim}
->>> print(gb_vrl["GQ333173.1"].description)
-HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial cds.
+>>> print(gb_vrl[``AB811634.1''].description)
+Equine encephalosis virus NS3 gene, complete cds, isolate: Kimron1.
 \end{verbatim}
 
 \subsubsection{Getting the raw data for a record}
 
 Just as with the \verb|Bio.SeqIO.index()| function discussed above in
 Section~\ref{sec:seqio-index-getraw}, the dictionary like object also lets you
-get at the raw text of each record:
-
-\begin{verbatim}
->>> print(gb_vrl.get_raw("GQ333173.1"))
-LOCUS       GQ333173                 459 bp    DNA     linear   VRL 21-OCT-2009
-DEFINITION  HIV-1 isolate F12279A1 from Uganda gag protein (gag) gene, partial
-            cds.
-ACCESSION   GQ333173
+get at the raw bytes of each record:
+
+% TODO - Under Python 3 you'd get the bytes string representation with
+% leading b single-quote, escaped newlines, and closing single-quote:
+%
+% >>> print(gb_vrl.get_raw(``GQ333173.1''))
+% b'LOCUS       GQ333173                 459 bp    DNA     linear   VRL 21-OCT-2009\nDEFINITION...'
+\begin{verbatim}
+>>> print(gb_vrl.get_raw(``AB811634.1''))
+LOCUS       AB811634                 723 bp    RNA     linear   VRL 17-JUN-2015
+DEFINITION  Equine encephalosis virus NS3 gene, complete cds, isolate: Kimron1.
+ACCESSION   AB811634
 ...
 //
 \end{verbatim}
@@ -1065,7 +1104,7 @@ solution.
 
 In previous example we used a list of \verb|SeqRecord| objects as input to the \verb|Bio.SeqIO.write()| function, but it will also accept a \verb|SeqRecord| iterator like we get from \verb|Bio.SeqIO.parse()| -- this lets us do file conversion by combining these two functions.
 
-For this example we'll read in the GenBank format file \href{http://biopython.org/DIST/docs/tutorial/examples/ls_orchid.gbk}{ls\_orchid.gbk} and write it out in FASTA format:
+For this example we'll read in the GenBank format file \href{https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.gbk}{ls\_orchid.gbk} and write it out in FASTA format:
 
 \begin{verbatim}
 from Bio import SeqIO
diff --git a/Doc/biopdb_faq.lyx b/Doc/biopdb_faq.lyx
new file mode 100644
index 0000000..3f8a79c
--- /dev/null
+++ b/Doc/biopdb_faq.lyx
@@ -0,0 +1,2724 @@
+#LyX 1.3 created this file. For more info see http://www.lyx.org/
+\lyxformat 221
+\textclass article
+\begin_preamble
+% header
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\lhead{Structural Biopython FAQ}
+\rhead{}
+
+% remove date
+\date{}
+
+% make everything have section numbers
+% Make links between references
+\usepackage{hyperref}
+
+\newif\ifpdf
+\ifx\pdfoutput\undefined
+  \pdffalse
+\else
+  \pdfoutput=1
+  \pdftrue
+\fi
+\ifpdf
+  \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue}
+\fi
+\end_preamble
+\language english
+\inputencoding auto
+\fontscheme bookman
+\graphics default
+\paperfontsize default
+\spacing single 
+\papersize a4paper
+\paperpackage a4
+\use_geometry 1
+\use_amsmath 0
+\use_natbib 0
+\use_numerical_citations 0
+\paperorientation portrait
+\topmargin 20mm
+\bottommargin 20mm
+\secnumdepth 3
+\tocdepth 3
+\paragraph_separation indent
+\defskip medskip
+\quotes_language english
+\quotes_times 1
+\papercolumns 1
+\papersides 1
+\paperpagestyle default
+
+\layout Title
+
+
+\size huge 
+The Biopython
+\newline 
+Structural Bioinformatics FAQ
+\layout Author
+
+Thomas Hamelryck
+\layout Author
+
+
+\size normal 
+Bioinformatics center
+\newline 
+Institute of Molecular Biology
+\newline 
+University of Copenhagen 
+\newline 
+Universitetsparken 15, Bygning 10
+\newline 
+DK-2100 K�benhavn �
+\newline 
+Denmark 
+\newline 
+thamelry at binf.ku.dk
+\size default 
+
+\newline 
+
+\begin_inset LatexCommand \url{http://www.binf.ku.dk/users/thamelry/}
+
+\end_inset 
+
+
+\layout Section
+
+Introduction
+\layout Standard
+
+The Biopython Project is an international association of developers of freely
+ available Python (
+\begin_inset LatexCommand \url{http://www.python.org}
+
+\end_inset 
+
+) tools for computational molecular biology.
+ Python is an object oriented, interpreted, flexible language that is becoming
+ increasingly popular for scientific computing.
+ Python is easy to learn, has a very clear syntax and can easily be extended
+ with modules written in C, C++ or FORTRAN.
+\layout Standard
+
+The Biopython web site (
+\begin_inset LatexCommand \url{http://www.biopython.org}
+
+\end_inset 
+
+) provides an online resource for modules, scripts, and web links for developers
+ of Python-based software for bioinformatics use and research.
+ Basically, the goal of biopython is to make it as easy as possible to use
+ python for bioinformatics by creating high-quality, reusable modules and
+ classes.
+ Biopython features include parsers for various Bioinformatics file formats
+ (BLAST, Clustalw, FASTA, Genbank,...), access to online services (NCBI, Expasy,...),
+ interfaces to common and not-so-common programs (Clustalw, DSSP, MSMS...),
+ a standard sequence class, various clustering modules, a KD tree data structure
+ etc.
+ and even documentation.
+ 
+\layout Standard
+
+Bio.PDB is a biopython module that focuses on working with crystal structures
+ of biological macromolecules.
+ This document gives a fairly complete overview of Bio.PDB.
+\layout Section
+
+Bio.PDB's installation
+\layout Standard
+
+Bio.PDB is automatically installed as part of Biopython.
+ Biopython can be obtained from 
+\begin_inset LatexCommand \url{http://www.biopython.org}
+
+\end_inset 
+
+.
+ It runs on many platforms (Linux/Unix, windows, Mac,...).
+\layout Section
+
+Who's using Bio.PDB?
+\layout Standard
+
+Bio.PDB was used in the construction of DISEMBL, a web server that predicts
+ disordered regions in proteins (
+\begin_inset LatexCommand \url{http://dis.embl.de/}
+
+\end_inset 
+
+), and COLUMBA, a website that provides annotated protein structures (
+\begin_inset LatexCommand \url{http://www.columba-db.de/}
+
+\end_inset 
+
+).
+ Bio.PDB has also been used to perform a large scale search for active sites
+ similarities between protein structures in the PDB (see 
+\shape italic 
+Proteins Struct.
+ Func.
+ Gen.
+\shape default 
+, 
+\series bold 
+2003
+\series default 
+, 51, 96-108), and to develop a new algorithm that identifies linear secondary
+ structure elements (
+\emph on 
+BMC Bioinformatics
+\emph default 
+, 
+\series bold 
+2005
+\series default 
+, 6, 202, 
+\begin_inset LatexCommand \url{http://www.biomedcentral.com/1471-2105/6/202}
+
+\end_inset 
+
+).
+ 
+\layout Standard
+
+Judging from requests for features and information, Bio.PDB is also used
+ by several LPCs (Large Pharmaceutical Companies :-).
+\layout Section
+
+Is there a Bio.PDB reference?
+\layout Standard
+
+Yes, and I'd appreciate it if you would refer to Bio.PDB in publications
+ if you make use of it.
+ The reference is:
+\layout Quote
+
+Hamelryck, T., Manderick, B.
+ (2003) PDB parser and structure class implemented in Python.
+ 
+\shape italic 
+Bioinformatics
+\shape default 
+, 
+\series bold 
+19
+\series default 
+, 2308-2310.
+ 
+\layout Standard
+
+The article can be freely downloaded via the Bioinformatics journal website
+ (
+\begin_inset LatexCommand \url{http://www.binf.ku.dk/users/thamelry/references.html}
+
+\end_inset 
+
+).
+ I welcome e-mails telling me what you are using Bio.PDB for.
+ Feature requests are welcome too.
+\layout Section
+
+How well tested is Bio.PDB?
+\layout Standard
+
+Pretty well, actually.
+ Bio.PDB has been extensively tested on nearly 5500 structures from the PDB
+ - all structures seemed to be parsed correctly.
+ More details can be found in the Bio.PDB Bioinformatics article.
+ Bio.PDB has been used/is being used in many research projects as a reliable
+ tool.
+ In fact, I'm using Bio.PDB almost daily for research purposes and continue
+ working on improving it and adding new features.
+\layout Section
+
+How fast is it?
+\layout Standard
+
+The 
+\family typewriter 
+PDBParser
+\family default 
+ performance was tested on about 800 structures (each belonging to a unique
+ SCOP superfamily).
+ This takes about 20 minutes, or on average 1.5 seconds per structure.
+ Parsing the structure of the large ribosomal subunit (1FKK), which contains
+ about 64000 atoms, takes 10 seconds on a 1000 MHz PC.
+ In short: it's more than fast enough for many applications.
+\layout Section
+
+Why should I use Bio.PDB?
+\layout Standard
+
+Bio.PDB might be exactly what you want, and then again it might not.
+ If you are interested in data mining the PDB header, you might want to
+ look elsewhere because there is only limited support for this.
+ If you look for a powerful, complete data structure to access the atomic
+ data Bio.PDB is probably for you.
+ 
+\layout Section
+
+Usage
+\layout Subsection
+
+General questions
+\layout Subsubsection*
+
+Importing Bio.PDB
+\layout Standard
+
+That's simple:
+\layout LyX-Code
+
+from Bio.PDB import *
+\layout Subsubsection*
+
+Is there support for molecular graphics?
+\layout Standard
+
+Not directly, mostly since there are quite a few Python based/Python aware
+ solutions already, that can potentially be used with Bio.PDB.
+ My choice is Pymol, BTW (I've used this successfully with Bio.PDB, and there
+ will probably be specific PyMol modules in Bio.PDB soon/some day).
+ Python based/aware molecular graphics solutions include:
+\layout Itemize
+
+PyMol: 
+\begin_inset LatexCommand \url{http://pymol.sourceforge.net/}
+
+\end_inset 
+
+
+\layout Itemize
+
+Chimera: 
+\begin_inset LatexCommand \url{http://www.cgl.ucsf.edu/chimera/}
+
+\end_inset 
+
+
+\layout Itemize
+
+PMV: 
+\begin_inset LatexCommand \url{http://www.scripps.edu/~sanner/python/}
+
+\end_inset 
+
+
+\layout Itemize
+
+Coot: 
+\begin_inset LatexCommand \url{http://www.ysbl.york.ac.uk/~emsley/coot/}
+
+\end_inset 
+
+
+\layout Itemize
+
+CCP4mg: 
+\begin_inset LatexCommand \url{http://www.ysbl.york.ac.uk/~lizp/molgraphics.html}
+
+\end_inset 
+
+
+\layout Itemize
+
+mmLib: 
+\begin_inset LatexCommand \url{http://pymmlib.sourceforge.net/}
+
+\end_inset 
+
+ 
+\layout Itemize
+
+VMD: 
+\begin_inset LatexCommand \url{http://www.ks.uiuc.edu/Research/vmd/}
+
+\end_inset 
+
+
+\layout Itemize
+
+MMTK: 
+\begin_inset LatexCommand \url{http://starship.python.net/crew/hinsen/MMTK/}
+
+\end_inset 
+
+
+\layout Standard
+
+I'd be crazy to write another molecular graphics application (been there
+ - done that, actually :-).
+\layout Subsection
+
+Input/output
+\layout Subsubsection*
+
+How do I create a structure object from a PDB file?
+\layout Standard
+
+First, create a
+\family typewriter 
+ PDBParser 
+\family default 
+object:
+\layout LyX-Code
+
+parser=PDBParser()
+\layout Standard
+
+Then, create a structure object from a PDB file in the following way (the
+ PDB file in this case is called '1FAT.pdb', 'PHA-L' is a user defined name
+ for the structure):
+\layout LyX-Code
+
+structure=parser.get_structure('PHA-L', '1FAT.pdb')
+\layout Subsubsection*
+
+How do I create a structure object from an mmCIF file?
+\layout Standard
+
+Similarly to the case the case of PDB files, first create an 
+\family typewriter 
+MMCIFParser
+\family default 
+ object:
+\layout LyX-Code
+
+parser=MMCIFParser()
+\layout Standard
+
+Then use this parser to create a structure object from the mmCIF file:
+\layout LyX-Code
+
+structure=parser.get_structure('PHA-L', '1FAT.cif')
+\layout Subsubsection*
+
+...and what about the new PDB XML format?
+\layout Standard
+
+That's not yet supported, but I'm definitely planning to support that in
+ the future (it's not a lot of work).
+ Contact me if you need this, it might encourage me :-).
+\layout Subsubsection*
+
+I'd like to have some more low level access to an mmCIF file...
+\layout Standard
+
+You got it.
+ You can create a python dictionary that maps all mmCIF tags in an mmCIF
+ file to their values.
+ If there are multiple values (like in the case of tag 
+\family typewriter 
+_atom_site.Cartn_y
+\family default 
+, which holds the y coordinates of all atoms), the tag is mapped to a list
+ of values.
+ The dictionary is created from the mmCIF file as follows:
+\layout LyX-Code
+
+mmcif_dict=MMCIF2Dict('1FAT.cif')
+\layout Standard
+
+Example: get the solvent content from an mmCIF file:
+\layout LyX-Code
+
+sc=mmcif_dict['_exptl_crystal.density_percent_sol']
+\layout Standard
+
+Example: get the list of the y coordinates of all atoms
+\layout LyX-Code
+
+y_list=mmcif_dict['_atom_site.Cartn_y']
+\layout Subsubsection*
+
+Can I access the header information?
+\layout Standard
+
+Thanks to Christian Rother you can access some information from the PDB
+ header.
+ Note however that many PDB files contain headers with incomplete or erroneous
+ information.
+ Many of the errors have been fixed in the equivalent mmCIF files.
+ 
+\emph on 
+Hence, if you are interested in the header information, it is a good idea
+ to extract information from mmCIF files using the 
+\family typewriter 
+MMCIF2Dict
+\family default 
+ tool described above, instead of parsing the PDB header.
+ 
+\layout Standard
+
+Now that is clarified, let's return to parsing the PDB header.
+ The structure object has an attribute called 
+\family typewriter 
+header
+\family default 
+ which is a python dictionary that maps header records to their values.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+resolution=structure.header['resolution']
+\layout LyX-Code
+
+keywords=structure.header['keywords']
+\layout Standard
+
+The available keys are 
+\family typewriter 
+name, head, deposition_\SpecialChar \-
+date, release_\SpecialChar \-
+date, structure_\SpecialChar \-
+method, resolution,
+ structure_\SpecialChar \-
+reference 
+\family default 
+(maps to a list of references), 
+\family typewriter 
+journal_\SpecialChar \-
+reference, author
+\family default 
+ and 
+\family typewriter 
+compound
+\family default 
+ (maps to a dictionary with various information about the crystallized compound).
+\layout Standard
+
+The dictionary can also be created without creating a 
+\family typewriter 
+Structure
+\family default 
+ object, ie.
+ directly from the PDB file:
+\layout LyX-Code
+
+file=open(filename,'r')
+\layout LyX-Code
+
+header_dict=parse_pdb_header(file)
+\layout LyX-Code
+
+file.close()
+\layout Subsubsection*
+
+Can I use Bio.PDB with NMR structures (ie.
+ with more than one model)?
+\layout Standard
+
+Sure.
+ Many PDB parsers assume that there is only one model, making them all but
+ useless for NMR structures.
+ The design of the 
+\family typewriter 
+Structure
+\family default 
+ object makes it easy to handle PDB files with more than one model (see
+ section 
+\begin_inset LatexCommand \ref{sub:The-Structure-object}
+
+\end_inset 
+
+).
+ 
+\layout Subsubsection*
+
+How do I download structures from the PDB?
+\layout Standard
+
+This can be done using the 
+\family typewriter 
+PDBList
+\family default 
+ object, using the 
+\family typewriter 
+retrieve_pdb_file
+\family default 
+ method.
+ The argument for this method is the PDB identifier of the structure.
+\layout LyX-Code
+
+pdbl=PDBList()
+\layout LyX-Code
+
+pdbl.retrieve_pdb_file('1FAT')
+\layout Standard
+
+The 
+\family typewriter 
+PDBList
+\family default 
+ class can also be used as a command-line tool: 
+\layout LyX-Code
+
+python PDBList.py 1fat
+\layout Standard
+
+The downloaded file will be called 
+\family typewriter 
+pdb1fat.ent
+\family default 
+ and stored in the current working directory.
+ Note that the 
+\family typewriter 
+retrieve_pdb_file
+\family default 
+ method also has an optional argument 
+\family typewriter 
+pdir
+\family default 
+ that specifies a specific directory in which to store the downloaded PDB
+ files.
+ 
+\layout Standard
+
+The 
+\family typewriter 
+retrieve_pdb_file
+\family default 
+ method also has some options to specify the compression format used for
+ the download, and the program used for local decompression (default 
+\family typewriter 
+.Z
+\family default 
+ format and 
+\family typewriter 
+gunzip
+\family default 
+).
+ In addition, the PDB ftp site can be specified upon creation of the 
+\family typewriter 
+PDBList
+\family default 
+ object.
+ By default, the RCSB PDB server (
+\begin_inset LatexCommand \url{ftp://ftp.rcsb.org/pub/pdb/data/structures/divided/pdb/}
+
+\end_inset 
+
+) is used.
+ See the API documentation for more details.
+ Thanks again to Kristian Rother for donating this module.
+\layout Subsubsection*
+
+How do I download the entire PDB?
+\layout Standard
+
+The following commands will store all PDB files in the 
+\family typewriter 
+/data/pdb
+\family default 
+ directory: 
+\layout LyX-Code
+
+python PDBList.py all /data/pdb 
+\layout LyX-Code
+
+python PDBList.py all /data/pdb -d 
+\layout Standard
+\noindent 
+The API method for this is called 
+\family typewriter 
+download_entire_pdb
+\family default 
+.
+ Adding the 
+\family typewriter 
+-d
+\family default 
+ option will store all files in the same directory.
+ Otherwise, they are sorted into PDB-style subdirectories according to their
+ PDB ID's.
+ Depending on the traffic, a complete download will take 2-4 days.
+ 
+\layout Subsubsection*
+
+How do I keep a local copy of the PDB up-to-date?
+\layout Standard
+
+This can also be done using the 
+\family typewriter 
+PDBList
+\family default 
+ object.
+ One simply creates a 
+\family typewriter 
+PDBList
+\family default 
+ object (specifying the directory where the local copy of the PDB is present)
+ and calls the 
+\family typewriter 
+update_pdb
+\family default 
+ method:
+\layout LyX-Code
+
+pl=PDBList(pdb='/data/pdb')
+\layout LyX-Code
+
+pl.update_pdb()
+\layout Standard
+
+One can of course make a weekly 
+\family typewriter 
+cronjob 
+\family default 
+out of this to keep the local copy automatically up-to-date.
+ The PDB ftp site can also be specified (see API documentation).
+\layout Standard
+
+
+\family typewriter 
+PDBList
+\family default 
+ has some additional methods that can be of use.
+ The 
+\family typewriter 
+get_all_obsolete
+\family default 
+ method can be used to get a list of all obsolete PDB entries.
+ The 
+\family typewriter 
+changed_this_week
+\family default 
+ method can be used to obtain the entries that were added, modified or obsoleted
+ during the current week.
+ For more info on the possibilities of 
+\family typewriter 
+PDBList
+\family default 
+, see the API documentation.
+\layout Subsubsection*
+
+What about all those buggy PDB files?
+\layout Standard
+
+It is well known that many PDB files contain semantic errors (I'm not talking
+ about the structures themselves know, but their representation in PDB files).
+ Bio.PDB tries to handle this in two ways.
+ The PDBParser object can behave in two ways: a restrictive way and a permissive
+ way (THIS IS NOW THE DEFAULT).
+ The restrictive way used to be the default, but people seemed to think
+ that Bio.PDB 'crashed' due to a bug (hah!), so I changed it.
+ If you ever encounter a real bug, please tell me immediately!
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+# Permissive parser
+\layout LyX-Code
+
+parser=PDBParser(PERMISSIVE=1)
+\layout LyX-Code
+
+parser=PDBParser() # The same (default)
+\layout LyX-Code
+
+# Strict parser
+\layout LyX-Code
+
+strict_parser=PDBParser(PERMISSIVE=0)
+\layout Standard
+
+In the permissive state (DEFAULT), PDB files that obviously contain errors
+ are 'corrected' (ie.
+ some residues or atoms are left out).
+ These errors include:
+\layout Itemize
+
+Multiple residues with the same identifier
+\layout Itemize
+
+Multiple atoms with the same identifier (taking into account the altloc
+ identifier)
+\layout Standard
+
+These errors indicate real problems in the PDB file (for details see the
+ Bioinformatics article).
+ In the restrictive state, PDB files with errors cause an exception to occur.
+ This is useful to find errors in PDB files.
+\layout Standard
+
+Some errors however are automatically corrected.
+ Normally each disordered atom should have a non-blanc altloc identifier.
+ However, there are many structures that do not follow this convention,
+ and have a blank and a non-blank identifier for two disordered positions
+ of the same atom.
+ This is automatically interpreted in the right way.
+\layout Standard
+
+Sometimes a structure contains a list of residues belonging to chain A,
+ followed by residues belonging to chain B, and again followed by residues
+ belonging to chain A, i.e.
+ the chains are 'broken'.
+ This is also correctly interpreted.
+\layout Subsubsection*
+
+Can I write PDB files?
+\layout Standard
+
+Use the PDBIO class for this.
+ It's easy to write out specific parts of a structure too, of course.
+\layout Standard
+
+Example: saving a structure
+\layout LyX-Code
+
+io=PDBIO()
+\layout LyX-Code
+
+io.set_structure(s)
+\layout LyX-Code
+
+io.save('out.pdb')
+\layout Standard
+
+If you want to write out a part of the structure, make use of the 
+\family typewriter 
+Select 
+\family default 
+class (also in 
+\family typewriter 
+PDBIO
+\family default 
+).
+ Select has four methods:
+\layout LyX-Code
+
+accept_model(model)
+\layout LyX-Code
+
+accept_chain(chain)
+\layout LyX-Code
+
+accept_residue(residue)
+\layout LyX-Code
+
+accept_atom(atom)
+\layout Standard
+
+By default, every method returns 1 (which means the model/\SpecialChar \-
+chain/\SpecialChar \-
+residue/\SpecialChar \-
+atom
+ is included in the output).
+ By subclassing 
+\family typewriter 
+Select
+\family default 
+ and returning 0 when appropriate you can exclude models, chains, etc.
+ from the output.
+ Cumbersome maybe, but very powerful.
+ The following code only writes out glycine residues:
+\layout LyX-Code
+
+class GlySelect(Select):
+\layout LyX-Code
+
+    def accept_residue(self, residue):
+\layout LyX-Code
+
+        if residue.get_name()=='GLY':
+\layout LyX-Code
+
+            return 1
+\layout LyX-Code
+
+        else:
+\layout LyX-Code
+
+            return 0
+\layout LyX-Code
+
+\layout LyX-Code
+
+io=PDBIO()
+\layout LyX-Code
+
+io.set_structure(s)
+\layout LyX-Code
+
+io.save('gly_only.pdb', GlySelect())
+\layout Standard
+
+If this is all too complicated for you, the 
+\family typewriter 
+Dice 
+\family default 
+module contains a handy 
+\family typewriter 
+extract
+\family default 
+ function that writes out all residues in a chain between a start and end
+ residue.
+ 
+\layout Subsubsection*
+
+Can I write mmCIF files?
+\layout Standard
+
+No, and I also don't have plans to add that functionality soon (or ever
+ - I don't need it at all, and it's a lot of work, plus no-one has ever
+ asked for it).
+ People who want to add this can contact me.
+\layout Subsection
+
+The Structure object
+\begin_inset LatexCommand \label{sub:The-Structure-object}
+
+\end_inset 
+
+
+\layout Subsubsection*
+
+What's the overall layout of a Structure object?
+\layout Standard
+
+The 
+\family typewriter 
+Structure
+\family default 
+ object follows the so-called 
+\family typewriter 
+SMCRA
+\family default 
+ (Structure/\SpecialChar \-
+Model/\SpecialChar \-
+Chain/\SpecialChar \-
+Residue/\SpecialChar \-
+Atom) architecture : 
+\layout Itemize
+
+A structure consists of models
+\layout Itemize
+
+A model consists of chains
+\layout Itemize
+
+A chain consists of residues
+\layout Itemize
+
+A residue consists of atoms
+\layout Standard
+
+This is the way many structural biologists/bioinformaticians think about
+ structure, and provides a simple but efficient way to deal with structure.
+ Additional stuff is essentially added when needed.
+ A UML diagram of the 
+\family typewriter 
+Structure
+\family default 
+ object (forget about the 
+\family typewriter 
+Disordered
+\family default 
+ classes for now) is shown in Fig.
+ 
+\begin_inset LatexCommand \ref{cap:SMCRA}
+
+\end_inset 
+
+.
+\layout Standard
+
+
+\begin_inset Float figure
+placement tbh
+wide false
+collapsed false
+
+\layout Standard
+\align center 
+
+\begin_inset Graphics
+	filename images/smcra.png
+	lyxscale 50
+	width 100mm
+	keepAspectRatio
+
+\end_inset 
+
+
+\layout Caption
+
+
+\begin_inset LatexCommand \label{cap:SMCRA}
+
+\end_inset 
+
+UML diagram of SMCRA architecture of the 
+\family typewriter 
+Structure 
+\family default 
+object.
+ Full lines with diamonds denote aggregation, full lines with arrows denote
+ referencing, full lines with triangles denote inheritance and dashed lines
+ with triangles denote interface realization.
+ 
+\end_inset 
+
+
+\layout Subsubsection*
+
+How do I navigate through a Structure object?
+\layout Standard
+
+The following code iterates through all atoms of a structure:
+\layout LyX-Code
+
+p=PDBParser()
+\layout LyX-Code
+
+structure=p.get_structure('X', 'pdb1fat.ent')
+\layout LyX-Code
+
+for model in structure:
+\layout LyX-Code
+
+  for chain in model: 
+\layout LyX-Code
+
+    for residue in chain:
+\layout LyX-Code
+
+      for atom in residue:
+\layout LyX-Code
+
+        print atom
+\layout Standard
+
+There are also some shortcuts:
+\layout LyX-Code
+
+# Iterate over all atoms in a structure
+\layout LyX-Code
+
+for atom in structure.get_atoms():
+\layout LyX-Code
+
+    print atom
+\layout LyX-Code
+
+# Iterate over all residues in a model
+\layout LyX-Code
+
+for residue in model.get_residues():
+\layout LyX-Code
+
+    print residue
+\layout Standard
+
+Structures, models, chains, residues and atoms are called 
+\family typewriter 
+Entities
+\family default 
+ in Biopython.
+ You can always get a parent 
+\family typewriter 
+Entity
+\family default 
+ from a child 
+\family typewriter 
+Entity
+\family default 
+, eg.:
+\layout LyX-Code
+
+residue=atom.get_parent()
+\layout LyX-Code
+
+chain=residue.get_parent()
+\layout Standard
+
+You can also test wether an 
+\family typewriter 
+Entity
+\family default 
+ has a certain child using the 
+\family typewriter 
+has_id
+\family default 
+ method.
+\layout Subsubsection*
+
+Can I do that a bit more conveniently?
+\layout Standard
+
+You can do things like:
+\layout LyX-Code
+
+atoms=structure.get_atoms()
+\layout LyX-Code
+
+residue=structure.get_residues()
+\layout LyX-Code
+
+atoms=chain.get_atoms()
+\layout Standard
+
+You can also use the 
+\family typewriter 
+Selection.unfold_entities
+\family default 
+ function:
+\layout LyX-Code
+
+# Get all residues from a structure
+\layout LyX-Code
+
+res_list=Selection.unfold_entities(structure, 'R')
+\layout LyX-Code
+
+# Get all atoms from a chain
+\layout LyX-Code
+
+atom_list=Selection.unfold_entities(chain, 'A')
+\layout Standard
+
+Obviously, 
+\family typewriter 
+A=atom, R=residue, C=chain, M=model, S=structure
+\family default 
+.
+ You can use this to go up in the hierarchy, eg.
+\begin_inset ERT
+status Collapsed
+
+\layout Standard
+
+\backslash 
+
+\end_inset 
+
+ to get a list of (unique) 
+\family typewriter 
+Residue
+\family default 
+ or 
+\family typewriter 
+Chain
+\family default 
+ parents from a list of 
+\family typewriter 
+Atoms
+\family default 
+:
+\layout LyX-Code
+
+residue_list=Selection.unfold_entities(atom_list, 'R')
+\layout LyX-Code
+
+chain_list=Selection.unfold_entities(atom_list, 'C')
+\layout Standard
+
+For more info, see the API documentation.
+\layout Subsubsection*
+
+How do I extract a specific 
+\family typewriter 
+Atom/\SpecialChar \-
+Residue/\SpecialChar \-
+Chain/\SpecialChar \-
+Model
+\family default 
+ from a Structure?
+\layout Standard
+
+Easy.
+ Here are some examples:
+\layout LyX-Code
+
+model=structure[0]
+\layout LyX-Code
+
+chain=model['A']
+\layout LyX-Code
+
+residue=chain[100]
+\layout LyX-Code
+
+atom=residue['CA']
+\layout Standard
+
+Note that you can use a shortcut:
+\layout LyX-Code
+
+atom=structure[0]['A'][100]['CA']
+\layout Subsubsection*
+
+What is a model id?
+\layout Standard
+
+The model id is an integer which denotes the rank of the model in the PDB/mmCIF
+ file.
+ The model is starts at 0.
+ Crystal structures generally have only one model (with id 0), while NMR
+ files usually have several models.
+\layout Subsubsection*
+
+What is a chain id?
+\layout Standard
+
+The chain id is specified in the PDB/mmCIF file, and is a single character
+ (typically a letter).
+ 
+\layout Subsubsection*
+
+What is a residue id?
+\layout Standard
+
+This is a bit more complicated, due to the clumsy PDB format.
+ A residue id is a tuple with three elements:
+\layout Itemize
+
+The 
+\series bold 
+hetero-flag
+\series default 
+: this is
+\family typewriter 
+ 'H_'
+\family default 
+ plus the name of the hetero-residue (eg.
+
+\family typewriter 
+ 'H_GLC'
+\family default 
+ in the case of a glucose molecule), or 
+\family typewriter 
+'W'
+\family default 
+ in the case of a water molecule.
+\layout Itemize
+
+The 
+\series bold 
+sequence identifier
+\series default 
+ in the chain, eg.
+ 100
+\layout Itemize
+
+The 
+\series bold 
+insertion code
+\series default 
+, eg.
+ 'A'.
+ The insertion code is sometimes used to preserve a certain desirable residue
+ numbering scheme.
+ A Ser 80 insertion mutant (inserted e.g.
+ between a Thr 80 and an Asn 81 residue) could e.g.
+ have sequence identifiers and insertion codes as follows: Thr 80 A, Ser
+ 80 B, Asn 81.
+ In this way the residue numbering scheme stays in tune with that of the
+ wild type structure.
+\layout Standard
+
+The id of the above glucose residue would thus be 
+\family typewriter 
+('H_GLC', 100, 'A')
+\family default 
+.
+ If the hetero-flag and insertion code are blanc, the sequence identifier
+ alone can be used:
+\layout LyX-Code
+
+# Full id
+\layout LyX-Code
+
+residue=chain[(' ', 100, ' ')]
+\layout LyX-Code
+
+# Shortcut id
+\layout LyX-Code
+
+residue=chain[100]
+\layout Standard
+
+The reason for the hetero-flag is that many, many PDB files use the same
+ sequence identifier for an amino acid and a hetero-residue or a water,
+ which would create obvious problems if the hetero-flag was not used.
+ 
+\layout Subsubsection*
+
+What is an atom id?
+\layout Standard
+
+The atom id is simply the atom name (eg.
+ 
+\family typewriter 
+'CA'
+\family default 
+).
+ In practice, the atom name is created by stripping all spaces from the
+ atom name in the PDB file.
+ 
+\layout Standard
+
+However, in PDB files, a space can be part of an atom name.
+ Often, calcium atoms are called 
+\family typewriter 
+'CA..'
+\family default 
+ in order to distinguish them from C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ atoms (which are called 
+\family typewriter 
+'.CA.'
+\family default 
+).
+ In cases were stripping the spaces would create problems (ie.
+ two atoms called
+\family typewriter 
+ 'CA'
+\family default 
+ in the same residue) the spaces are kept.
+\layout Subsubsection*
+
+How is disorder handled?
+\layout Standard
+
+This is one of the strong points of Bio.PDB.
+ It can handle both disordered atoms and point mutations (ie.
+ a Gly and an Ala residue in the same position).
+ 
+\layout Standard
+
+Disorder should be dealt with from two points of view: the atom and the
+ residue points of view.
+ In general, I have tried to encapsulate all the complexity that arises
+ from disorder.
+ If you just want to loop over all C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ atoms, you do not care that some residues have a disordered side chain.
+ On the other hand it should also be possible to represent disorder completely
+ in the data structure.
+ Therefore, disordered atoms or residues are stored in special objects that
+ behave as if there is no disorder.
+ This is done by only representing a subset of the disordered atoms or residues.
+ Which subset is picked (e.g.
+ which of the two disordered OG side chain atom positions of a Ser residue
+ is used) can be specified by the user.
+\layout Standard
+
+
+\series bold 
+Disordered atom positions
+\series default 
+ are represented by ordinary 
+\family typewriter 
+Atom
+\family default 
+ objects, but all 
+\family typewriter 
+Atom
+\family default 
+ objects that represent the same physical atom are stored in a 
+\family typewriter 
+Disordered\SpecialChar \-
+Atom
+\family default 
+ object (see Fig.
+ 
+\begin_inset LatexCommand \ref{cap:SMCRA}
+
+\end_inset 
+
+).
+ Each 
+\family typewriter 
+Atom
+\family default 
+ object in a 
+\family typewriter 
+Disordered\SpecialChar \-
+Atom
+\family default 
+ object can be uniquely indexed using its altloc specifier.
+ The 
+\family typewriter 
+Disordered\SpecialChar \-
+Atom
+\family default 
+ object forwards all uncaught method calls to the selected Atom object,
+ by default the one that represents the atom with the highest occupancy.
+ The user can of course change the selected 
+\family typewriter 
+Atom 
+\family default 
+object, making use of its altloc specifier.
+ In this way atom disorder is represented correctly without much additional
+ complexity.
+ In other words, if you are not interested in atom disorder, you will not
+ be bothered by it.
+\layout Standard
+
+Each disordered atom has a characteristic altloc identifier.
+ You can specify that a 
+\family typewriter 
+Disordered\SpecialChar \-
+Atom
+\family default 
+ object should behave like the 
+\family typewriter 
+Atom
+\family default 
+ object associated with a specific altloc identifier:
+\layout LyX-Code
+
+atom.disordered_select('A') # select altloc A atom
+\layout LyX-Code
+
+atom.disordered_select('B') # select altloc B atom 
+\layout Standard
+
+A special case arises when disorder is due to 
+\series bold 
+point mutations
+\series default 
+, i.e.
+ when two or more point mutants of a polypeptide are present in the crystal.
+ An example of this can be found in PDB structure 1EN2.
+\layout Standard
+
+Since these residues belong to a different residue type (e.g.
+ let's say Ser 60 and Cys 60) they should not be stored in a single 
+\family typewriter 
+Residue
+\family default 
+ object as in the common case.
+ In this case, each residue is represented by one 
+\family typewriter 
+Residue
+\family default 
+ object, and both 
+\family typewriter 
+Residue
+\family default 
+ objects are stored in a single 
+\family typewriter 
+Disordered\SpecialChar \-
+Residue
+\family default 
+ object (see Fig.
+ 
+\begin_inset LatexCommand \ref{cap:SMCRA}
+
+\end_inset 
+
+).
+\layout Standard
+
+The 
+\family typewriter 
+Dis\SpecialChar \-
+ordered\SpecialChar \-
+Residue
+\family default 
+ object forwards all un\SpecialChar \-
+caught methods to the selected 
+\family typewriter 
+Residue
+\family default 
+ object (by default the last 
+\family typewriter 
+Residue
+\family default 
+ object added), and thus behaves like an ordinary residue.
+ Each 
+\family typewriter 
+Residue
+\family default 
+ object in a 
+\family typewriter 
+Disordered\SpecialChar \-
+Residue
+\family default 
+ object can be uniquely identified by its residue name.
+ In the above example, residue Ser 60 would have id 'SER' in the 
+\family typewriter 
+Disordered\SpecialChar \-
+Residue
+\family default 
+ object, while residue Cys 60 would have id 'CYS'.
+ The user can select the active 
+\family typewriter 
+Residue
+\family default 
+ object in a 
+\family typewriter 
+Disordered\SpecialChar \-
+Residue
+\family default 
+ object via this id.
+\layout Standard
+
+Example: suppose that a chain has a point mutation at position 10, consisting
+ of a Ser and a Cys residue.
+ Make sure that residue 10 of this chain behaves as the Cys residue.
+\layout LyX-Code
+
+residue=chain[10]
+\layout LyX-Code
+
+residue.disordered_select('CYS')
+\layout Standard
+
+In addition, you can get a list of all 
+\family typewriter 
+Atom
+\family default 
+ objects (ie.
+ all 
+\family typewriter 
+DisorderedAtom
+\family default 
+ objects are 'unpacked' to their individual 
+\family typewriter 
+Atom
+\family default 
+ objects) using the 
+\family typewriter 
+get_unpacked_list
+\family default 
+ method of a 
+\family typewriter 
+(Disordered)\SpecialChar \-
+Residue
+\family default 
+ object.
+\layout Subsubsection*
+
+Can I sort residues in a chain somehow?
+\layout Standard
+
+Yes, kinda, but I'm waiting for a request for this feature to finish it
+ :-).
+\layout Subsubsection*
+
+How are ligands and solvent handled?
+\layout Standard
+
+See 'What is a residue id?'.
+\layout Subsubsection*
+
+What about B factors?
+\layout Standard
+
+Well, yes! Bio.PDB supports isotropic and anisotropic B factors, and also
+ deals with standard deviations of anisotropic B factor if present (see
+ 
+\begin_inset LatexCommand \ref{sub:Analysis}
+
+\end_inset 
+
+).
+\layout Subsubsection*
+
+What about standard deviation of atomic positions?
+\layout Standard
+
+Yup, supported.
+ See section 
+\begin_inset LatexCommand \ref{sub:Analysis}
+
+\end_inset 
+
+.
+\layout Subsubsection*
+
+I think the SMCRA data structure is not flexible/\SpecialChar \-
+sexy/\SpecialChar \-
+whatever enough...
+\layout Standard
+
+Sure, sure.
+ Everybody is always coming up with (mostly vaporware or partly implemented)
+ data structures that handle all possible situations and are extensible
+ in all thinkable (and unthinkable) ways.
+ The prosaic truth however is that 99.9% of people using (and I mean really
+ using!) crystal structures think in terms of models, chains, residues and
+ atoms.
+ The philosophy of Bio.PDB is to provide a reasonably fast, clean, simple,
+ but complete data structure to access structure data.
+ The proof of the pudding is in the eating.
+\layout Standard
+
+Moreover, it is quite easy to build more specialised data structures on
+ top of the 
+\family typewriter 
+Structure
+\family default 
+ class (eg.
+ there's a 
+\family typewriter 
+Polypeptide
+\family default 
+ class).
+ On the other hand, the 
+\family typewriter 
+Structure 
+\family default 
+object is built using a Parser/\SpecialChar \-
+Consumer approach (called 
+\family typewriter 
+PDBParser/\SpecialChar \-
+MMCIFParser
+\family default 
+ and 
+\family typewriter 
+Structure\SpecialChar \-
+Builder
+\family default 
+, respectively).
+ One can easily re-use the PDB/mmCIF parsers by implementing a specialised
+ 
+\family typewriter 
+Structure\SpecialChar \-
+Builder
+\family default 
+ class.
+ It is of course also trivial to add support for new file formats by writing
+ new parsers.
+\layout Subsection
+
+
+\begin_inset LatexCommand \label{sub:Analysis}
+
+\end_inset 
+
+Analysis
+\layout Subsubsection*
+
+How do I extract information from an 
+\family typewriter 
+Atom
+\family default 
+ object?
+\layout Standard
+
+Using the following methods:
+\layout LyX-Code
+
+a.get_name() # atom name (spaces stripped, e.g.
+ 'CA')
+\layout LyX-Code
+
+a.get_id() # id (equals atom name)
+\layout LyX-Code
+
+a.get_coord() # atomic coordinates
+\layout LyX-Code
+
+a.get_vector() # atomic coordinates as Vector object
+\layout LyX-Code
+
+a.get_bfactor() # isotropic B factor
+\layout LyX-Code
+
+a.get_occupancy() # occupancy
+\layout LyX-Code
+
+a.get_altloc() # alternative location specifier
+\layout LyX-Code
+
+a.get_sigatm() # std.
+ dev.
+ of atomic parameters
+\layout LyX-Code
+
+a.get_siguij() # std.
+ dev.
+ of anisotropic B factor
+\layout LyX-Code
+
+a.get_anisou() # anisotropic B factor
+\layout LyX-Code
+
+a.get_fullname() # atom name (with spaces, e.g.
+ '.CA.')
+\layout Subsubsection*
+
+How do I extract information from a 
+\family typewriter 
+Residue
+\family default 
+ object?
+\layout Standard
+
+Using the following methods:
+\layout LyX-Code
+
+r.get_resname() # return the residue name (eg.
+ 'GLY')
+\layout LyX-Code
+
+r.is_disordered() # 1 if the residue has disordered atoms
+\layout LyX-Code
+
+r.get_segid() # return the SEGID
+\layout LyX-Code
+
+r.has_id(name) # test if a residue has a certain atom
+\layout Subsubsection*
+
+How do I measure distances?
+\layout Standard
+
+That's simple: the minus operator for atoms has been overloaded to return
+ the distance between two atoms.
+ 
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+# Get some atoms
+\layout LyX-Code
+
+ca1=residue1['CA']
+\layout LyX-Code
+
+ca2=residue2['CA']
+\layout LyX-Code
+
+# Simply subtract the atoms to get their distance
+\layout LyX-Code
+
+distance=ca1-ca2
+\layout Subsubsection*
+
+How do I measure angles?
+\layout Standard
+
+This can easily be done via the vector representation of the atomic coordinates,
+ and the 
+\family typewriter 
+calc_angle
+\family default 
+ function from the 
+\family typewriter 
+Vector
+\family default 
+ module:
+\layout LyX-Code
+
+vector1=atom1.get_vector()
+\layout LyX-Code
+
+vector2=atom2.get_vector()
+\layout LyX-Code
+
+vector3=atom3.get_vector()
+\layout LyX-Code
+
+angle=calc_angle(vector1, vector2, vector3)
+\layout Subsubsection*
+
+How do I measure torsion angles?
+\layout Standard
+
+Again, this can easily be done via the vector representation of the atomic
+ coordinates, this time using the 
+\family typewriter 
+calc_dihedral
+\family default 
+ function from the 
+\family typewriter 
+Vector
+\family default 
+ module:
+\layout LyX-Code
+
+vector1=atom1.get_vector()
+\layout LyX-Code
+
+vector2=atom2.get_vector()
+\layout LyX-Code
+
+vector3=atom3.get_vector()
+\layout LyX-Code
+
+vector4=atom4.get_vector()
+\layout LyX-Code
+
+angle=calc_dihedral(vector1, vector2, vector3, vector4)
+\layout Subsubsection*
+
+How do I determine atom-atom contacts?
+\layout Standard
+
+Use 
+\family typewriter 
+NeighborSearch
+\family default 
+.
+ This uses a KD tree data structure coded in C++ behind the screens, so
+ it's pretty darn fast (see 
+\family typewriter 
+Bio.KDTree
+\family default 
+).
+\layout Subsubsection*
+
+How do I extract polypeptides from a 
+\family typewriter 
+Structure
+\family default 
+ object?
+\layout Standard
+
+Use 
+\family typewriter 
+PolypeptideBuilder
+\family default 
+.
+ You can use the resulting 
+\family typewriter 
+Polypeptide
+\family default 
+ object to get the sequence as a 
+\family typewriter 
+Seq
+\family default 
+ object or to get a list of C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ atoms as well.
+ Polypeptides can be built using a C-N or a C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+-C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ distance criterion.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+# Using C-N 
+\layout LyX-Code
+
+ppb=PPBuilder()
+\layout LyX-Code
+
+for pp in ppb.build_peptides(structure): 
+\layout LyX-Code
+
+    print pp.get_sequence()
+\layout LyX-Code
+
+# Using CA-CA
+\layout LyX-Code
+
+ppb=CaPPBuilder()
+\layout LyX-Code
+
+for pp in ppb.build_peptides(structure): 
+\layout LyX-Code
+
+    print pp.get_sequence()
+\layout Standard
+
+Note that in the above case only model 0 of the structure is considered
+ by 
+\family typewriter 
+PolypeptideBuilder
+\family default 
+.
+ However, it is possible to use 
+\family typewriter 
+PolypeptideBuilder
+\family default 
+ to build 
+\family typewriter 
+Polypeptide
+\family default 
+ objects from 
+\family typewriter 
+Model
+\family default 
+ and 
+\family typewriter 
+Chain
+\family default 
+ objects as well.
+\layout Subsubsection*
+
+How do I get the sequence of a structure?
+\layout Standard
+
+The first thing to do is to extract all polypeptides from the structure
+ (see previous entry).
+ The sequence of each polypeptide can then easily be obtained from the 
+\family typewriter 
+Polypeptide 
+\family default 
+objects.
+ The sequence is represented as a Biopython 
+\family typewriter 
+Seq 
+\family default 
+object, and its alphabet is defined by a 
+\family typewriter 
+ProteinAlphabet 
+\family default 
+object.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+>>> seq=polypeptide.get_sequence()
+\layout LyX-Code
+
+>>> print seq
+\layout LyX-Code
+
+Seq('SNVVE...', <class Bio.Alphabet.ProteinAlphabet>)
+\layout Subsubsection*
+
+How do I determine secondary structure?
+\layout Standard
+
+For this functionality, you need to install DSSP (and obtain a license for
+ it - free for academic use, see 
+\begin_inset LatexCommand \url{http://www.cmbi.kun.nl/gv/dssp/}
+
+\end_inset 
+
+).
+ Then use the 
+\family typewriter 
+DSSP
+\family default 
+ class, which maps 
+\family typewriter 
+Residue
+\family default 
+ objects to their secondary structure (and accessible surface area).
+ The DSSP codes are listed in Table 
+\begin_inset LatexCommand \ref{cap:DSSP-codes}
+
+\end_inset 
+
+.
+ Note that DSSP (the program, and thus by consequence the class) cannot
+ handle multiple models!
+\layout Standard
+
+
+\begin_inset Float table
+wide false
+collapsed false
+
+\layout Subsubsection*
+
+
+\begin_inset  Tabular
+<lyxtabular version="3" rows="9" columns="2">
+<features>
+<column alignment="center" valignment="top" leftline="true" width="0">
+<column alignment="center" valignment="top" leftline="true" rightline="true" width="0">
+<row topline="true" bottomline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+Code
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+Secondary structure
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+H
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\begin_inset Formula $\alpha$
+\end_inset 
+
+-helix
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+B
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+Isolated 
+\family default 
+\series default 
+\shape default 
+\size default 
+\emph default 
+\bar default 
+\noun default 
+\color default
+
+\begin_inset Formula $\beta$
+\end_inset 
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+-bridge resid
+\family default 
+\series default 
+\shape default 
+\size default 
+\emph default 
+\bar default 
+\noun default 
+\color default
+ue
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+E
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+Strand 
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+G
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+3-10 helix 
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+I
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\begin_inset Formula $\Pi$
+\end_inset 
+
+-
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+helix 
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+T
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+Turn
+\end_inset 
+</cell>
+</row>
+<row topline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+S
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+Bend 
+\end_inset 
+</cell>
+</row>
+<row topline="true" bottomline="true">
+<cell alignment="center" valignment="top" topline="true" leftline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+-
+\end_inset 
+</cell>
+<cell alignment="center" valignment="top" topline="true" leftline="true" rightline="true" usebox="none">
+\begin_inset Text
+
+\layout Standard
+
+
+\family roman 
+\series medium 
+\shape up 
+\size normal 
+\emph off 
+\bar no 
+\noun off 
+\color none
+Other
+\end_inset 
+</cell>
+</row>
+</lyxtabular>
+
+\end_inset 
+
+
+\layout Caption
+
+
+\begin_inset LatexCommand \label{cap:DSSP-codes}
+
+\end_inset 
+
+DSSP codes in Bio.PDB.
+\end_inset 
+
+
+\layout Subsubsection*
+
+How do I calculate the accessible surface area of a residue?
+\layout Standard
+
+Use the 
+\family typewriter 
+DSSP 
+\family default 
+class (see also previous entry).
+ But see also next entry.
+\layout Subsubsection*
+
+How do I calculate residue depth?
+\layout Standard
+
+Residue depth is the average distance of a residue's atoms from the solvent
+ accessible surface.
+ It's a fairly new and very powerful parameterization of solvent accessibility.
+ For this functionality, you need to install Michel Sanner's MSMS program
+ (
+\begin_inset LatexCommand \url{http://www.scripps.edu/pub/olson-web/people/sanner/html/msms_home.html}
+
+\end_inset 
+
+).
+ Then use the 
+\family typewriter 
+ResidueDepth
+\family default 
+ class.
+ This class behaves as a dictionary which maps 
+\family typewriter 
+Residue
+\family default 
+ objects to corresponding (residue depth, C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ depth) tuples.
+ The C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ depth is the distance of a residue's C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ atom to the solvent accessible surface.
+ 
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+model=structure[0]
+\layout LyX-Code
+
+rd=ResidueDepth(model, pdb_file)
+\layout LyX-Code
+
+residue_depth, ca_depth=rd[some_residue]
+\layout Standard
+
+You can also get access to the molecular surface itself (via the 
+\family typewriter 
+get_surface
+\family default 
+ function), in the form of a Numeric python array with the surface points.
+\layout Subsubsection*
+
+How do I calculate Half Sphere Exposure?
+\layout Standard
+
+Half Sphere Exposure (HSE) is a new, 2D measure of solvent exposure.
+ Basically, it counts the number of C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ atoms around a residue in the direction of its side chain, and in the opposite
+ direction (within a radius of 13 �).
+ Despite its simplicity, it outperforms many other measures of solvent exposure.
+ An article describing this novel 2D measure has been submitted.
+\layout Standard
+
+HSE comes in two flavors: HSE
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ and HSE
+\begin_inset Formula $\beta$
+\end_inset 
+
+.
+ The former only uses the C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ atom positions, while the latter uses the C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+ and C
+\begin_inset Formula $\beta$
+\end_inset 
+
+ atom positions.
+ The HSE measure is calculated by the 
+\family typewriter 
+HSExposure
+\family default 
+ class, which can also calculate the contact number.
+ The latter class has methods which return dictionaries that map a 
+\family typewriter 
+Residue
+\family default 
+ object to its corresponding HSE
+\begin_inset Formula $\alpha$
+\end_inset 
+
+, HSE
+\begin_inset Formula $\beta$
+\end_inset 
+
+ and contact number values.
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+model=structure[0]
+\layout LyX-Code
+
+hse=HSExposure()
+\layout LyX-Code
+
+# Calculate HSEalpha
+\layout LyX-Code
+
+exp_ca=hse.calc_hs_exposure(model, option='CA3')
+\layout LyX-Code
+
+# Calculate HSEbeta
+\layout LyX-Code
+
+exp_cb=hse.calc_hs_exposure(model, option='CB')
+\layout LyX-Code
+
+# Calculate classical coordination number exp_fs=hse.calc_fs_exposure(model)
+\layout LyX-Code
+
+# Print HSEalpha for a residue
+\layout LyX-Code
+
+print exp_ca[some_residue]
+\layout Subsubsection*
+
+How do I map the residues of two related structures onto each other?
+\layout Standard
+
+First, create an alignment file in FASTA format, then use the 
+\family typewriter 
+StructureAlignment
+\family default 
+ class.
+ This class can also be used for alignments with more than two structures.
+\layout Subsubsection*
+
+How do I test if a Residue object is an amino acid?
+\layout Standard
+
+Use 
+\family typewriter 
+is_aa(residue)
+\family default 
+.
+\layout Subsubsection*
+
+Can I do vector operations on atomic coordinates?
+\layout Standard
+
+
+\family typewriter 
+Atom
+\family default 
+ objects return a 
+\family typewriter 
+Vector
+\family default 
+ object representation of the coordinates with the 
+\family typewriter 
+get_vector
+\family default 
+ method.
+ 
+\family typewriter 
+Vector
+\family default 
+ implements the full set of 3D vector operations, matrix multiplication
+ (left and right) and some advanced rotation-related operations as well.
+ See also next question.
+\layout Subsubsection*
+
+How do I put a virtual C
+\begin_inset Formula $\beta$
+\end_inset 
+
+ on a Gly residue?
+\layout Standard
+
+OK, I admit, this example is only present to show off the possibilities
+ of Bio.PDB's 
+\family typewriter 
+Vector
+\family default 
+ module (though this code is actually used in the 
+\family typewriter 
+HSExposure
+\family default 
+ module, which contains a novel way to parametrize residue exposure - publicatio
+n underway).
+ Suppose that you would like to find the position of a Gly residue's C
+\begin_inset Formula $\beta$
+\end_inset 
+
+ atom, if it had one.
+ How would you do that? Well, rotating the N atom of the Gly residue along
+ the C
+\begin_inset Formula $\alpha$
+\end_inset 
+
+-C bond over -120 degrees roughly puts it in the position of a virtual C
+\begin_inset Formula $\beta$
+\end_inset 
+
+ atom.
+ Here's how to do it, making use of the 
+\family typewriter 
+rotaxis
+\family default 
+ method (which can be used to construct a rotation around a certain axis)
+ of the 
+\family typewriter 
+Vector
+\family default 
+ module:
+\layout LyX-Code
+
+# get atom coordinates as vectors
+\layout LyX-Code
+
+n=residue['N'].get_vector() 
+\layout LyX-Code
+
+c=residue['C'].get_vector() 
+\layout LyX-Code
+
+ca=residue['CA'].get_vector()
+\layout LyX-Code
+
+# center at origin
+\layout LyX-Code
+
+n=n-ca 
+\layout LyX-Code
+
+c=c-ca 
+\layout LyX-Code
+
+# find rotation matrix that rotates n 
+\layout LyX-Code
+
+# -120 degrees along the ca-c vector
+\layout LyX-Code
+
+rot=rotaxis(-pi*120.0/180.0, c)
+\layout LyX-Code
+
+# apply rotation to ca-n vector
+\layout LyX-Code
+
+cb_at_origin=n.left_multiply(rot)
+\layout LyX-Code
+
+# put on top of ca atom
+\layout LyX-Code
+
+cb=cb_at_origin+ca
+\layout Standard
+
+This example shows that it's possible to do some quite nontrivial vector
+ operations on atomic data, which can be quite useful.
+ In addition to all the usual vector operations (cross (use 
+\family typewriter 
+**
+\family default 
+), and dot (use 
+\family typewriter 
+*
+\family default 
+) product, angle, norm, etc.) and the above mentioned 
+\family typewriter 
+rotaxis
+\family default 
+ function, the 
+\family typewriter 
+Vector
+\family default 
+ module also has methods to rotate (
+\family typewriter 
+rotmat
+\family default 
+) or reflect (
+\family typewriter 
+refmat
+\family default 
+) one vector on top of another.
+\layout Subsection
+
+Manipulating the structure
+\layout Subsubsection*
+
+How do I superimpose two structures?
+\layout Standard
+
+Surprisingly, this is done using the 
+\family typewriter 
+Superimposer
+\family default 
+ object.
+ This object calculates the rotation and translation matrix that rotates
+ two lists of atoms on top of each other in such a way that their RMSD is
+ minimized.
+ Of course, the two lists need to contain the same amount of atoms.
+ The 
+\family typewriter 
+Superimposer
+\family default 
+ object can also apply the rotation/translation to a list of atoms.
+ The rotation and translation are stored as a tuple in the 
+\family typewriter 
+rotran
+\family default 
+ attribute of the 
+\family typewriter 
+Superimposer
+\family default 
+ object (note that the rotation is right multiplying!).
+ The RMSD is stored in the 
+\family typewriter 
+rmsd
+\family default 
+ attribute.
+\layout Standard
+
+The algorithm used by 
+\family typewriter 
+Superimposer
+\family default 
+ comes from 
+\shape italic 
+Matrix computations, 2nd ed.
+ Golub, G.
+ & Van Loan (1989)
+\shape default 
+ and makes use of singular value decomposition (this is implemented in the
+ general 
+\family typewriter 
+Bio.\SpecialChar \-
+SVDSuperimposer
+\family default 
+ module).
+\layout Standard
+
+Example:
+\layout LyX-Code
+
+sup=Superimposer()
+\layout LyX-Code
+
+# Specify the atom lists
+\layout LyX-Code
+
+# 'fixed' and 'moving' are lists of Atom objects
+\layout LyX-Code
+
+# The moving atoms will be put on the fixed atoms
+\layout LyX-Code
+
+sup.set_atoms(fixed, moving)
+\layout LyX-Code
+
+# Print rotation/translation/rmsd
+\layout LyX-Code
+
+print sup.rotran
+\layout LyX-Code
+
+print sup.rms 
+\layout LyX-Code
+
+# Apply rotation/translation to the moving atoms
+\layout LyX-Code
+
+sup.apply(moving)
+\layout Subsubsection*
+
+How do I superimpose two structures based on their active sites?
+\layout Standard
+
+Pretty easily.
+ Use the active site atoms to calculate the rotation/translation matrices
+ (see above), and apply these to the whole molecule.
+\layout Subsubsection*
+
+Can I manipulate the atomic coordinates?
+\layout Standard
+
+Yes, using the 
+\family typewriter 
+transform
+\family default 
+ method of the 
+\family typewriter 
+Atom
+\family default 
+ object, or directly using the 
+\family typewriter 
+set_coord
+\family default 
+ method.
+\layout Section
+
+Other Structural Bioinformatics modules
+\layout Subsubsection*
+
+Bio.SCOP
+\layout Standard
+
+Info coming soon.
+\layout Subsubsection*
+
+Bio.FSSP
+\layout Standard
+
+Info coming soon.
+\layout Section
+
+You haven't answered my question yet!
+\layout Standard
+
+Woah! It's late and I'm tired, and a glass of excellent 
+\shape italic 
+Pedro Ximenez
+\shape default 
+ sherry is waiting for me.
+ Just drop me a mail, and I'll answer you in the morning (with a bit of
+ luck...).
+\layout Section
+
+Contributors
+\layout Standard
+
+The main author/maintainer of Bio.PDB is yours truly.
+ Kristian Rother donated code to interact with the PDB database, and to
+ parse the PDB header.
+ Indraneel Majumdar sent in some bug reports and assisted in coding the
+ 
+\family typewriter 
+Polypeptide 
+\family default 
+module.
+ Many thanks to Brad Chapman, Jeffrey Chang, Andrew Dalke and Iddo Friedberg
+ for suggestions, comments, help and/or biting criticism :-).
+\layout Section
+
+Can I contribute?
+\layout Standard
+
+Yes, yes, yes! Just send me an e-mail (thamelry at binf.ku.dk) if you have something
+ useful to contribute! Eternal fame awaits!
+\layout Section
+
+Biopython License Agreement
+\layout Standard
+
+Permission to use, copy, modify, and distribute this software and its documentat
+ion with or without modifications and for any purpose and without fee is
+ hereby granted, provided that any copyright notices appear in all copies
+ and that both those copyright notices and this permission notice appear
+ in supporting documentation, and that the names of the contributors or
+ copyright holders not be used in advertising or publicity pertaining to
+ distribution of the software without specific prior permission.
+\layout Standard
+
+THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DISCLAIM ALL WARRANTIES
+ WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILI
+TY AND FITNESS, IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT HOLDERS
+ BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+ WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION
+ OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
+ CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+ 
+\the_end
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index 0000000..926ad6b
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+++ b/Doc/cookbook/README
@@ -0,0 +1,13 @@
+This is a directory to hold "cookbook" style documentation -- small,
+single-purpose documents that describe a particular aspect of
+Biopython. This is meant as an alternative to the large Tutorial
+style documentation which can be daunting to maintain and keep up
+to date.
+
+Since these are all independent -- they can be written in any
+style you find convenient, as long as it can be used to produce
+something displayable on the web (HTML or PDF).
+
+We welcome contributions about any aspect of Biopython. Please feel
+free to discuss on our mailing list (biopython at biopython.org).
+
diff --git a/Doc/cookbook/Restriction/Restriction.html b/Doc/cookbook/Restriction/Restriction.html
index e957159..ecb7b4d 100644
--- a/Doc/cookbook/Restriction/Restriction.html
+++ b/Doc/cookbook/Restriction/Restriction.html
@@ -1,854 +1,1096 @@
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 </head>
-<body vlink="#ff0000" alink="#000088" link="#0000ff"
- style="color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">
-<h2><a class="mozTocH2" name="mozTocId419365"></a><span class="mozTocH2"></span> 
-Working
-with restriction enzymes</h2>
-<big style="font-weight: bold;"><br>
-Table of content<br>
-</big>
-<ul id="mozToc">
-<!--mozToc h1 1 h2 2 h3 3 h4 4 h5 5 h6 6--><li><a href="#mozTocId419365"> 
-Working
-with restriction enzymes</a>
-    <ul>
-      <li>
-        <ul>
-          <li><a href="#mozTocId101269">1  The
-restriction enzymes classes </a>
-            <ul>
-              <li><a href="#mozTocId59051">1.1 
-Importing the enzymes</a></li>
-              <li><a href="#mozTocId868359">1.2 
-Naming convention </a></li>
-              <li><a href="#mozTocId447698">1.3 
-Searching for restriction sites</a></li>
-              <li><a href="#mozTocId29937">1.4 
-Retrieving the sequences produced by a digestion</a></li>
-              <li><a href="#mozTocId255275">1.5 
-Analysing circular sequences</a></li>
-              <li><a href="#mozTocId25220">1.6 Comparing enzymes with
-each others </a></li>
-              <li><a href="#mozTocId724099">1.7
-Other
-facilities provided by the enzyme classes</a></li>
-            </ul>
-          </li>
-          <li><a href="#mozTocId343319">2 
-The
-RestrictionBatch class : a class to deal with several enzymes </a>
-            <ul>
-              <li><a href="#mozTocId32850">2.1
-Creating a RestrictionBatch </a></li>
-              <li><a href="#mozTocId672810">2.2
-Restricting a RestrictionBatch to a particular supplier</a></li>
-              <li><a href="#mozTocId436678">2.3
-Adding enzymes to a RestrictionBatch </a></li>
-              <li><a href="#mozTocId432981">2.4
-Removing enzymes from a RestrictionBatch</a></li>
-              <li><a href="#mozTocId479498">2.5
-Manipulating RestrictionBatch</a></li>
-              <li><a href="#mozTocId919079">2.6
-Analysing sequences with a RestrictionBatch </a></li>
-              <li><a href="#mozTocId114148">2.7
-Other
-RestrictionBatch methods </a></li>
-            </ul>
-          </li>
-          <li><a href="#mozTocId569237">3  AllEnzymes
-and CommOnly : two
-preconfigured RestrictionBatches </a></li>
-          <li><a href="#mozTocId960354">4  The Analysis
-class : even simpler
-restriction analysis </a>
-            <ul>
-              <li><a href="#mozTocId994414">4.1 Setting up an
-Analysis</a></li>
-              <li><a href="#mozTocId129244">4.2 Full restriction
-analysis </a></li>
-              <li><a href="#mozTocId441271">4.3 Changing the title </a></li>
-              <li><a href="#mozTocId450266">4.4 Customising the
-output </a></li>
-              <li><a href="#mozTocId864311">4.5 Fancier
-restriction analysis </a></li>
-              <li><a href="#mozTocId289477">4.6 More complex
-analysis </a></li>
-            </ul>
-          </li>
-          <li><a href="#mozTocId343319">5 
-Advanced features : the FormattedSeq class </a>
-            <ul>
-              <li><a href="#mozTocId74092">5.1 Creating a
-FormattedSeq </a></li>
-              <li><a href="#mozTocId143837">5.2 Unlike Bio.Seq,
-FormattedSeq retains information about their shape </a></li>
-              <li><a href="#mozTocId123270">5.3 Changing the
-shape of a FormattedSeq </a></li>
-              <li><a href="#mozTocId710376">5.4 Using / and //
-operators with FormattedSeq </a></li>
-            </ul>
-          </li>
-          <li><a href="#mozTocId80946">6  More advanced
-features </a>
-            <ul>
-              <li><a href="#mozTocId546339">6.1 Updating the
-enzymes from Rebase : rebase_update.py </a></li>
-              <li><a href="#mozTocId49285">6.2 Compiling a new
-dictionary : the script ranacompiler.py </a></li>
-              <li><a href="#mozTocId968583">6.3 Subclassing
-the class Analysis </a></li>
-            </ul>
-          </li>
-          <li><a href="#mozTocId114111">7  Limitation
-and caveat </a>
-            <ul>
-              <li><a href="#mozTocId218594">7.1 All DNA are non
-methylated </a></li>
-              <li><a href="#mozTocId790484">7.2 No support for
-star activity </a></li>
-              <li><a href="#mozTocId218334">7.3 Safe to use with
-degenerated DNA </a></li>
-              <li><a href="#mozTocId49392">7.4 Non standard bases
-in DNA are not
-allowed </a></li>
-              <li><a href="#mozTocId395117">7.5 Sites found at
-the edge of linear
-DNA might not be accessible in a real digestion </a></li>
-              <li><a href="#mozTocId112206">7.6 Restriction
-reports cutting sites
-not enzyme recognition sites </a></li>
-            </ul>
-          </li>
-          <li><a href="#mozTocId386540">8  Annexe :
-modifying dir() to use
-with from Bio.Restriction import * </a></li>
-        </ul>
-      </li>
-    </ul>
-  </li>
-</ul>
-<big style="font-weight: bold;"><br>
-</big><br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId101269"></a>1  The
-restriction enzymes classes<br>
-</h3>
-<br>
-The restriction enzyme
-package is situated in Bio/Restriction. This package will allow you to
-work with restriction enzymes and realise restriction analysis on your
-sequence. Restriction make use of the facilities offered by Rebase and
-contains classes for more than 600 restriction enzymes. This chapter
-will lead you through a quick overview of the facilities offered by the
-Restriction package of biopython. The chapter is constructed as an
-interactive python session and the best way to read it is with a python
-shell open alongside you.<br>
-<h4><a class="mozTocH4" name="mozTocId59051"></a><span class="mozTocH4"></span>1.1 
-Importing the enzymes</h4>
-To import the enzymes, open a python shell and type :<br>
-<pre>>>> from Bio import Restriction<br>>>> dir()<br>['Restriction', '__builtins__', '__doc__', '__name__']<br>>>> Restriction.EcoRI<br>EcoRI<br>>>> Restriction.EcoRI.site<br>'GAATTC'<br>>>><br></pre>
-You will certainly notice that the package is quite slow to load. This
-is normal as each enzyme possess its own class and there is a lot of
-them. This will not affect the speed of python after the initial
-import. <br>
-<br>
-I don't know for you but I find it quite cumbersome to have to prefix
-each operation with <span style="font-family: monospace;">Restriction.</span>,
-so here is another way to import
-the package.<br>
-<pre>>>> from Bio.Restriction import *<br>>>> EcoRI<br>EcoRI<br>>>> EcoRI.site<br>'GAATTC'<br>>>></pre>
-However, this method has one big disadvantage :<br>
-It
-is almost impossible to use the command 'dir()' anymore as there is
-so much enzymes the results is hardly readable. A workaround is
-provided at the end of this tutorial. I let you decide which method you
-prefer. But in this tutorial I
-will use the second. If you prefer the first method you will need to
-prefix each call to a restriction enzyme with 'Restriction.' in the
-remaining of the tutorial.<br>
-<h4><a class="mozTocH4" name="mozTocId868359"></a><span class="mozTocH4"></span>1.2 
-Naming convention<br>
-</h4>
-To access an Enzyme simply enter
-it's name. You must respect the usual naming convention with the upper
-case letters and Latin numbering (in upper case as well):<br>
-<pre>>>> EcoRI <br>EcoRI<br>>>> ecori<br><br>Traceback (most recent call last):<br>  File "<pyshell#25>", line 1, in -toplevel-<br>    ecori<br>NameError: name 'ecori' is not defined<br>>>> EcoR1<br><br>Traceback (most recent call last):<br>  File "<pyshell#26>", line 1, in -toplevel-<br>    EcoR1<br>NameError: name 'EcoR1' is not defined<br>>>> KpnI<br>KpnI<br>>>><br></pre>
-ecori or EcoR1 are not enzymes, EcoRI and KpnI are.<br>
-<h4><a class="mozTocH4" name="mozTocId447698"></a><span class="mozTocH4"></span>1.3 
-Searching for restriction sites</h4>
-So what can we do with these restriction enzymes. To see that we
-will
-need a DNA sequence. Restriction enzymes support both <span
- style="font-family: monospace;">Bio.Seq.MutableSeq</span> and <span
- style="font-family: monospace;">Bio.Seq.Seq</span> objects. You can
-use any DNA
-alphabet which complies with the IUPAC
-alphabet.
-<pre>>>> from Bio.Seq import Seq<br>>>> from Bio.Alphabet.IUPAC import IUPACAmbiguousDNA<br>>>> amb = IUPACAmbiguousDNA()<br>>>> my_seq = Seq('AAAAAAAAAAAAAA', amb)<br>>>> my_seq<br>Seq('AAAAAAAAAAAAAA', IUPACAmbiguousDNA())<br></pre>
-Searching a sequence for the presence of restriction site for your
-preferred enzyme is as simple as :<br>
-<pre>>>> EcoRI.search(my_seq)<br>[]<br></pre>
-The results is a list. Here the list is empty since there is
-obviously no site EcoRI site in my_seq.  Let's try to get a
-sequence
-with a EcoRI site.<br>
-<pre>>>> ecoseq = my_seq + Seq(EcoRI.site, amb) + my_seq<br>>>> ecoseq<br>Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())<br>>>> EcoRI.search(ecoseq)<br>[16]<br></pre>
-We therefore have a site at position 16 of the sequence ecoseq. The
-position returned by the method search is the first base of the
-downstream segment produced by a restriction (i.e. the first base after
-the position where the enzyme will cut). The Restriction
-package follows biological convention (the first base of a sequence is
-base 1). No need to make difficult conversions between your recorded
-biological data and the results produced by the enzymes in this
-package.<br>
-<h4><a class="mozTocH4" name="mozTocId29937"></a><span class="mozTocH4"></span>1.4 
-Retrieving the sequences produced by a digestion</h4>
-Seq objects as all python sequence, have different conventions and the
-first base of a sequence is base 0. Therefore to get the sequences
-produced by an EcoRI digestion of ecoseq, one should do the following :<br>
-<pre>>>> ecoseq[:15], ecoseq[15:] <br>(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())) <br></pre>
-I hear you thinking "this is a cumbersome and error prone method to get
-these sequences".  To simplify your life, enzymes provide another
-method to get theses sequences
-without hassle : <span style="font-family: monospace;">catalyse</span>.
-This method will return a tuple containing
-all the fragments produced by a complete digestion of the sequence.
-Using it is as simple as before :<br>
-<pre>>>> EcoRI.catalyse(ecoseq)<br>(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA()))</pre>
-<h4><a class="mozTocH4" name="mozTocId255275"></a><span class="mozTocH4"></span>1.5 
-Analysing circular sequences</h4>
-Now, if you have entered the previous command in your shell you may
-have noticed that both 'search' and 'catalyse' can take a second
-argument 'linear' which default to True. Using this will allow you to
-simulate circular sequences such as plasmids. Setting linear to False
-inform the enzyme to make the search over a circular sequence and to
-search for potential sites spanning over the boundaries of the sequence.<br>
-<pre>>>> EcoRI.search(ecoseq, linear=False)<br>[16]<br>>>> EcoRI.catalyse(ecoseq, linear=False)<br>(Seq('AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG', IUPACAmbiguousDNA()),)<br>>>> ecoseq  # for memory<br>Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())</pre>
-OK, this is quite a difference, we only get one fragment, which
-correspond to the linearised sequence. The beginning sequence has been
-shifted to take this fact into account. Moreover we can see another
-difference :<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> EcoRI.search(new_seq)<br>[]<br>>>> EcoRI.search(new_seq, linear=False)<br>[33]<br></pre>
-As you can see using 'linear=False', make a site appears in the
-sequence new_seq. This site does not exist in a linear sequence as the
-EcoRI site is split into two halves at the start and the end of the
-sequence. In a circular sequence however, the site is effectively
-present when the beginning and end of the sequence are joined.<br>
-<h4><a class="mozTocH4" name="mozTocId25220"></a>1.6 Comparing enzymes
-with each others<br>
-</h4>
-Restriction Enzymes define 4 comparative operators ==, !=, >> and
-%. All these operator compares two enzymes together and either return
-True or False.<br>
-<br>
-== :<br>
-<br>
-It will return True if the two sides of the operator are the same. same
-is defined as : same name, same site, same overhang (i.e. the only
-thing which is equal to EcoRI is EcoRI).<br>
-<br>
-!= :<br>
-<br>
-It will return True if the two sides of the operator are different. Two
-enzymes are not different if the result produced by one enzyme will
-always be the same as the result produced by the other (i.e. True
-isoschizomers will not being the same enzymes, are not different since
-they are interchangeable).<br>
-<br>
->> :<br>
-<br>
-True if the enzymes recognise the same site, but cut it in a different
-way (i.e. the enzymes are neoschizomers).<br>
-<br>
-% :<br>
-<br>
-Test the compatibility of the ending produced by the enzymes. (will be
-True if the fragments produced with one of the enzyme can directly be
-ligated to fragments produced by the other).<br>
-<br>
-Let's use Acc65I and its isoschizomers as example :<br>
-<pre>>>> Acc65I.isoschizomers()<br>[Asp718I, KpnI]<br>>>> Acc65I.elucidate()<br>'G^GTAC_C'<br>>>> Asp718I.elucidate()<br>'G^GTAC_C'<br>>>> KpnI.elucidate()<br>'G_GTAC^C'<br>>>> # Asp718I and Acc65I are True isoschizomers, <br>>>> # they recognise the same site and cut it the <br>>>> # same way.<br>>>> # KpnI is a neoschizomers of the 2  others.<br>>>> # here is the results of the 4 operator<br>>>> # [...]
-<h4><a class="mozTocH4" name="mozTocId724099"></a>1.7
-Other
-facilities provided by the enzyme classes</h4>
-The restriction enzymes class provided quite a number of others
-methods. We will not go through all of them, but only have a quick look
-to the most useful ones. <br>
-<br>
-Not all enzymes possess the same properties when it comes to the way
-they digest a DNA. If you want to know more about the way a particular
-enzyme cut you can use the three following methods. They are fairly
-straightforward to understand and refer to the ends that the enzyme
-produces blunt, 5' overhanging (also called 3' recessed) sticky end and
-3' overhanging (or 5' recessed) sticky end.<br>
-<pre>>>> EcoRI.is_blunt()<br>False<br>>>> EcoRI.is_5overhang()<br>True<br>>>> EcoRI.is_3overhang()<br>False<br></pre>
-A more detailled view of the restriction site can be produced using the
-<span style="font-family: monospace;">elucidate()</span> method. the
-"^" refers to the position of the cut in the
-sense strand of the sequence. "_" to the cut on the antisense or
-complementary strand.  "^_" means blunt.
-<pre>>>> EcoRI.elucidate()<br>'G^AATT_C'<br>>>> KpnI.elucidate()<br>'G_GTAC^C'<br>>>> EcoRV.elucidate()<br>'GAT^_ATC'<br></pre>
-The method frequency will give you the statiscal <span
- style="font-family: monospace;">frequency()</span> of the
-enzyme site. <br>
-<pre>>>> EcoRI.frequency()<br>4096<br>>>> XhoII.elucidate()<br>'R^GATC_Y'<br>>>> XhoII.frequency()<br>1024 </pre>
-To get the length of a the recognition sequence of an enzyme use the
-built-in function <span style="font-family: monospace;">len()</span> :<br>
-<pre>>>> len(EcoRI)<br>6<br>>>> BstXI.elucidate()<br>'CCAN_NNNN^NTGG'<br>>>> len(BstXI)<br>12<br>>>> FokI.site<br>'GGATG'<br>>>> FokI.elucidate()  	# FokI cut well outside its recognition site<br>'GGATGNNNNNNNNN^NNNN_N'<br>>>> len(FokI)	   	# its length is the length of the recognition site	<br>5</pre>
-Also interesting are the methods dealing with isoschizomers.
-For memory, two enzymes are isoschizomers if they share a same
-recognition site. <br>
-A further division is made between isoschizomers (same name, recognise
-the same
-sequence and cut the same way) and neoschizomers which cut at different
-positions. equischizomer is an arbitrary choice to design
-"isoschizomers_that_are_not_neoschizomers" as this last one was a bit
-long.<br>
-Another set of method <span style="font-family: monospace;">one_enzyme.is_*schizomers(one_other_enzyme)</span>,
-allow to test 2
-enzymes against each other.<br>
-<pre>>>> Acc65I.isoschizomers()<br>[Asp718I, KpnI]<br>>>> Acc65I.neoschizomers()<br>[KpnI]<br>>>> Acc65I.equischizomers()<br>[Asp718I]<br>>>> KpnI.elucidate()<br>'G_GTAC^C'<br>>>> Acc65I.elucidate()<br>'G^GTAC_C'<br>>>> KpnI.is_neoschizomer(Acc65I)<br>True<br>>>> KpnI.is_neoschizomer(KpnI)<br>False<br>>>> KpnI.is_isoschizomer(Acc65I)<br>True<br>>>> KpnI.is_isoschizomer(KpnI)<br>True<br>>>> KpnI.is_e [...]
-<span style="font-family: monospace;">suppliers()</span> will get you
-the list of all the suppliers of the enzyme. <span
- style="font-family: monospace;">all_suppliers()</span> will give you
-all the suppliers in the database.<br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId343319"></a><span class="mozTocH3"></span>2 
-The
-RestrictionBatch class : a class to deal with several enzymes<br>
-</h3>
-If you want to
-make a restriction map of a sequence, using individual enzymes can
-become tedious and will endures a big overhead due to the repetitive
-conversion of the sequence to a FormattedSeq. Restriction provides a
-class to make easier the use of
-large number of enzymes in one go : RestrictionBatch.<br>
-RestrictionBatch will help you to manipulate lots of enzymes with a
-single command. Moreover all the enzymes in the restrictionBatch will
-share the same converted sequence, reducing the overhead to <br>
-<h4><a class="mozTocH4" name="mozTocId32850"></a><span class="mozTocH4"></span>2.1
-Creating a RestrictionBatch<br>
-</h4>
-You can initiate a
-restriction batch by passing it a list of enzymes or enzymes name as
-argument.
-<pre>>>> rb = RestrictionBatch([EcoRI])<br>>>> rb<br>RestrictionBatch(['EcoRI'])<br>>>> rb2 = RestrictionBatch(['EcoRI'])<br>RestrictionBatch(['EcoRI'])<br>>>> rb == rb2<br>True<br></pre>
-Adding a new enzyme to a restriction batch is easy :<br>
-<pre>>>> rb.add(KpnI)<br>>>> rb<br>RestrictionBatch(['EcoRI', 'KpnI'])<br>>>> rb += EcoRV<br>>>> rb<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])])<br></pre>
-Another way to create a RestrictionBatch is by simply adding
-restriction enzymes together, this is particularly useful for small
-batches :<br>
-<pre>>>> rb3 = EcoRI + KpnI + EcoRV<br>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br></pre>
-<h4><a class="mozTocH4" name="mozTocId672810"></a><span class="mozTocH4"></span>2.2
-Restricting a RestrictionBatch to a particular supplier</h4>
-The Restriction package is based upon the Rebase database. This
-database gives a list of suppliers for each enzyme. It would be a shame
-not to make use of this facility. You can Produce a RestrictionBatch
-containing only enzymes from one or a few supplier(s). Here is how to
-do it :<br>
-<pre>>>> rb_supp = RestrictionBatch(first=[], suppliers=['A','C','E','G','F','I','H','K','J','M','O','N','Q','P','S','R','U','V','X'])<br>>>> # This will create a RestrictionBatch with the all enzymes which possess a supplier. </pre>
-The argument 'suppliers' take a list of one or several single letter
-codes corresponding to the supplier(s). The codes are the same as
-defined in Rebase. As it would be a pain to have to remember each
-supplier code, RestrictionBatch provides a method which show the pair
-code <=> supplier :<br>
-<pre>>>> RestrictionBatch.show_codes()	# as of july 2004 Rebase release.<br>A = Amersham Pharmacia Biotech<br>C = Minotech Biotechnology<br>E = Stratagene<br>G = Qbiogene<br>F = Fermentas AB<br>I = SibEnzyme Ltd.<br>H = American Allied Biochemical, Inc.<br>K = Takara Shuzo Co. Ltd.<br>J = Nippon Gene Co., Ltd.<br>M = Roche Applied Science<br>O = Toyobo Biochemicals<br>N = New England Biolabs<br>Q = CHIMERx<br>P = Megabase Research Products<br>S = Sigma Chemical Corporation<br>R  [...]
-This way of producing a RestrictionBatch can drastically reduce the
-amount of useless output from a restriction analysis, limiting the
-search to enzymes that you can get hold of and limiting the risks of
-nervous breakdown. Nothing is more frustrating than to get the perfect
-enzyme for a sub-cloning only to find it's not commercially available.<br>
-<h4><a class="mozTocH4" name="mozTocId436678"></a><span class="mozTocH4"></span>2.3
-Adding enzymes to a RestrictionBatch<br>
-</h4>
-Adding an enzyme to a batch if the enzyme is already present will not
-raise an exception, but will have no effects. Sometimes you want to get
-an enzyme from a RestrictionBatch or add it to the batch if it is not
-present.<br>
-You will use the get method setting the second argument to True.<br>
-<pre>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br>>>> rb3.add(EcoRI)<br>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br>>>> rb3.get(EcoRI)<br>EcoRI<br>>>> rb3.get(SmaI)<br><br>Traceback (most recent call last):<br>  File "<pyshell#4>", line 1, in -toplevel-<br>    rb3.get(SmaI)<br>  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1800, in get<br>    raise ValueError, 'enzyme %s is not [...]
-<h4><a class="mozTocH4" name="mozTocId432981"></a><span class="mozTocH4"></span>2.4
-Removing enzymes from a RestrictionBatch</h4>
-Removing enzymes from a Batch is done using the <span
- style="font-family: monospace;">remove()</span> method. If the
-enzyme is not present in the batch this will raise a KeyError. If the
-value you want to remove is not an enzyme this will raise a ValueError.<br>
-<pre>>>> rb3.remove(EcoRI)<br>>>> rb3<br>RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])<br>>>> rb3.remove(EcoRI)<br><br>Traceback (most recent call last):<br>  File "<pyshell#14>", line 1, in -toplevel-<br>    rb3.remove('EcoRI')<br>  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove<br>    return Set.remove(self, self.format(other))<br>  File "/usr/lib/python2.3/sets.py", line 534, in remove<br>    del self._data[e [...]
-<h4><a class="mozTocH4" name="mozTocId479498"></a><span class="mozTocH4"></span>2.5
-Manipulating RestrictionBatch</h4>
-You can not however add batch together, as they are python sets. You
-must use the operator | instead. You can find the intersection between
-2 batches using & (see the python documentation about sets for more
-information (or import sets; help(sets)).<br>
-<pre>>>> rb3 = EcoRI+KpnI+EcoRV<br>>>> rb3<br>RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])<br>>>> rb4 = SmaI + PstI<br>>>> rb4<br>RestrictionBatch(['PstI', 'SmaI'])<br>>>> rb3 + rb4<br><br>Traceback (most recent call last):<br>  File "<pyshell#23>", line 1, in -toplevel-<br>    rb3 + rb4<br>  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 1829, in __add__<br>    new.add(other)<br>  File "/usr/lib/python2.3/s [...]
-<h4><a class="mozTocH4" name="mozTocId919079"></a><span class="mozTocH4"></span>2.6
-Analysing sequences with a RestrictionBatch<br>
-</h4>
-To analyse a sequence for potential site, you can use the search
-method of the batch, the same way you did for restriction enzymes. The
-results is no longer a list however, but a dictionary. The keys of the
-dictionary are the names of the enzymes and the value a list of
-position site. RestrictionBatch does not implement a catalyse method,
-as it would not have a real meaning when used with large batch.<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> rb.search(new_seq)<br>{'KpnI': [], 'EcoRV': [], 'EcoRI': []}<br>>>> rb.search(new_seq, linear=False)<br>{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}</pre>
-<h4><a class="mozTocH4" name="mozTocId114148"></a><span class="mozTocH4"></span>2.7
-Other
-RestrictionBatch methods<br>
-</h4>
-Amongst the other methods provided by RestrictionBatch <span
- style="font-family: monospace;">elements()</span> which
-return a list of all the element names
-alphabetically sorted is certainly the most useful.<br>
-<pre>>>> rb = EcoRI + KpnI + EcoRV<br>>>> rb.elements()<br>['EcoRI', 'EcoRV', 'KpnI']</pre>
-If you don't care about the alphabetical order use the method <span
- style="font-family: monospace;">as_string()</span>, to get the same
-thing a bit faster. The list is not sorted.
-The order is random as python sets are dictionary.<br>
-<pre>>>> rb = EcoRI + KpnI + EcoRV <br>>>> rb.as_string()<br>['EcoRI', 'KpnI', 'EcoRV']</pre>
-Other RestrictionBatch methods are generally used for particular
-purposes and will not be discussed here. See the source if you are
-interested.<span style="font-family: monospace;"></span><br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId569237"></a>3  AllEnzymes
-and CommOnly : two
-preconfigured RestrictionBatches<br>
-</h3>
-While it is sometime practical to produce a RestrictionBatch of your
-own you will certainly more frequently use the two batches provided
-with the Restriction packages : AllEnzymes and CommOnly. These two
-batches contain respectively all the enzymes in the database and only
-the enzymes which have a commercial supplier. They are rather big, but
-that's what make them useful. With these batch you can produce a full
-description of a sequence with a single command. You can use these two
-batch as any other batch.<br>
-<pre>>>> len(AllEnzymes)<br>671<br>>>> len(CommOnly)<br>589<br>>>> AllEnzymes.search(new_seq) ...</pre>
-There is not a lot to say about them apart the fact that they are
-present. They are really normal batches, and you can use them as any
-other batch.
-<h3><a class="mozTocH3" name="mozTocId960354"></a>4  The Analysis
-class : even simpler
-restriction analysis<br>
-</h3>
-RestrictionBatch can give you a dictionary with the sites for all the
-enzymes in a RestrictionBatch. However, it is sometime nice to
-get something a bit easier to read than a python dictionary. Complex
-restriction analysis are not easy with RestrictionBatch. Some
-refinements in the way to search a sequence for restriction sites will
-help. Analysis
-provides a serie of command to customise the results obtained from a
-pair RestrictionBatch/sequence and some facilities to make the output
-sligthly more human readable.<br>
-<h4><a class="mozTocH4" name="mozTocId994414"></a>4.1 Setting up an
-Analysis</h4>
-To build a Restriction Analysis you will need a
-RestrictionBatch and a sequence and to tell it if the sequence is
-linear or circular. The first argument Analysis take is the
-RestrictionBatch, the second is the sequence. If the third argument is
-not provided Analysis will assume the sequence is linear.<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])<br>>>> Ana = Analysis(rb, new_seq, linear=False)<br>>>> Ana<br>Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)<br></pre>
-<h4><a class="mozTocH4" name="mozTocId129244"></a>4.2 Full restriction
-analysis<br>
-</h4>
-Once you have created your new Analysis, you can use it to get a
-restriction analysis of your sequence. The way to make a full
-restriction analysis of the sequence is :<br>
-<pre>>>> Ana.full()<br>{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}<br></pre>
-This is much the same as the output of a RestrictionBatch.search
-method. You will get a more easy to read output with "print_that" used
-without argument :<br>
-<pre>>>> <br>>>> # let's create a something a bit more complex to analyse.<br>>>><br>>>> rb = RestrictionBatch([], ['A'])	# we will explain the meaning of the<br>>>>					# double list argument later. <br>>>><br>>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\  <br>SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())<br>>>><br>>>> Analong = Analy [...]
-Much clearer, is'nt ? The output is optimised for a shell 80 columns
-wide. If the output seems odd, check that  the width of your shell
-is at least 80 columns.<br>
-<h4><a class="mozTocH4" name="mozTocId441271"></a>4.3 Changing the title<br>
-</h4>
-You can provide a title to the analysis and modify the sentence
-<span style="font-family: monospace;">'Enzymes which do not cut the
-sequence'</span>, by setting the two optional
-arguments of <span style="font-family: monospace;">print_that</span> 
-"title" and "s1". No formating will be
-done on these strings so if you have to include the newline (<span
- style="font-family: monospace;">'\n'</span>) as
-you see fit :<br>
-<pre>>>> Analong.print_that(None, title='sequence = multi_site\n\n')<br><br>sequence = multi_site<br><br>AfaI       :  14.<br>AvaI       :  26.<br>EcoRI      :  5, 47.<br>EcoRV      :  20.<br>FokI       :  47.<br>HapII      :  27.<br>KpnI       :  16.<br>MspI       :  27.<br>SmaI       :  28.<br><br>   Enzymes which do not cut the sequence.<br><br>AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     <br>AvaII     BalI      BamHI     BanII     BbeI      [...]
-<pre>>>> Analong.print_that(None, <br>		       title = 'sequence = multi_site\n\n',<br>		        s1 = '\n no site :\n\n')<br><br>sequence = multi_site<br><br>AfaI       :  14.<br>AvaI       :  26.<br>EcoRI      :  5, 47.<br>EcoRV      :  20.<br>FokI       :  47.<br>HapII      :  27.<br>KpnI       :  16.<br>MspI       :  27.<br>SmaI       :  28.<br><br> no site :<br><br>AatII     AccI      AccII     AflII     AluI      Aor51HI   ApaI      ApaLI     <br>AvaII     BalI      BamHI   [...]
-<h4><a class="mozTocH4" name="mozTocId450266"></a>4.4 Customising the
-output <br>
-</h4>
-You can modify some aspects of the output interactively. There is three
-main type of output,
-two listing types (alphabetically sorted and sorted by number of
-site) and map-like type. To change the output, use the method <span
- style="font-family: monospace;">print_as()</span> of Analysis. The
-change of output is permanent for the
-instance of Analysis (that is
-until the next time you use  <span style="font-family: monospace;">print_as()</span>).
-The argument of <span style="font-family: monospace;">print_as()</span>
-are
-strings : <span style="font-family: monospace;">'map'</span>, <span
- style="font-family: monospace;">'number'</span> or <span
- style="font-family: monospace;">'alpha'</span>. As you have seen
-previously the
-default behaviour is an
-alphabetical list (<span style="font-family: monospace;">'alpha'</span>).<br>
-<pre>>>> Analong.print_as('map')    <br>>>> Analong.print_that()<br><br>    5 EcoRI<br>    |                                                       <br>    |        14 AfaI<br>    |        |                                              <br>    |        | 16 KpnI<br>    |        | |                                            <br>    |        | |   20 EcoRV<br>    |        | |   |                                        <br>    |        | |   |     26 AvaI<br>    |        | [...]
-To come back to the previous behaviour :<br>
-<pre>>>> Analong.print_as('alpha')<br>>>> Analong.print_that()<br><br>AfaI       :  14.<br>AvaI       :  26.<br>EcoRI      :  5, 47.<br>EcoRV      :  20.<br>etc ...</pre>
-<h4><a class="mozTocH4" name="mozTocId864311"></a>4.5 Fancier
-restriction analysis<br>
-</h4>
-I will not go into the detail for each single method, here are all the
-functions that are available. Most are perfectly self explanatory and
-the others are fairly well documented (use <span
- style="font-family: monospace;">help('Analysis.command_name')</span>).
-The methods are :<br>
-<pre>	full(self,linear=True) <br>	blunt(self,dct = None)             <br>	overhang5(self, dct=None) <br>	overhang3(self, dct=None)  <br>	defined(self,dct=None)  <br>	with_sites(self, dct=None)  <br>	without_site(self, dct=None)  <br>	with_N_sites(self, N, dct=None)  <br>	with_number_list(self, list, dct=None)  <br>	with_name(self, names, dct=None)    <br>	with_site_size(self, site_size, dct=None)    [...]
-Using these methods is simple :<br>
-<pre>>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())<br>>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])<br>>>> Ana = Analysis(rb, new_seq, linear=False)<br>>>> Ana<br>Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)<br>>>> Ana.blunt()  		# output only the result for enzymes which cut blunt<br>{'EcoRV': []}<br>>>> Ana.full()			# all  [...]
-To get a nice output, you still use <span
- style="font-family: monospace;">print_that</span> but this time with
-the
-command you want executed as argument.<br>
-<pre>>>> Ana.print_that(Ana.blunt())<br><br><br>   Enzymes which do not cut the sequence.<br><br>EcoRV     <br><br>>>> pt = Ana.print_that<br>>>> pt(Ana.with_sites())<br><br>EcoRI      :  33.<br><br>>>> pt(Ana.without_site())<br><br><br>   Enzymes which do not cut the sequence.<br><br>EcoRV     KpnI      <br><br>>>> # etc ...<br></pre>
-<h4><a class="mozTocH4" name="mozTocId289477"></a>4.6 More complex
-analysis<br>
-</h4>
-All of these methods (except <span style="font-family: monospace;">full()</span>
-which, well ... do a full
-restriction analysis) can be supplied with an additional dictionary. <br>
-If no
-dictionary is supplied a full
-restriction analysis is used as starting point. Otherwise the
-dictionary provided by the argument dct is used. The dictionary must be
-formatted as the result of <span style="font-family: monospace;">RestrictionBatch.search</span>.
-Therefore of
-the form <span style="font-family: monospace;">{'enzyme_name' :
-[position1, position2],...}</span>, where
-position1 and position2 are integer. All methods list previously output
-such dictionaries and can be used as starting point.<br>
-<br>
-Using this method you can build really complex query by
-chaining several method one after the other. For example if
-you want all the enzymes which are 5' overhang and cut the sequence
-only once, you have two ways to go :<br>
-<br>
-The hard way consist to build a
-RestrictionBatch containing only 5' overhang enzymes and use this batch
-to create a new analysis instance and then use the method <span
- style="font-family: monospace;">with_N_sites()</span> as follow :<br>
-<pre>>>> rbov5 = RestrictionBatch([x for x in rb if x.is_5overhang()])<br>>>> Anaov5 = Analysis(rbov5, new_seq, linear=False)<br>>>> Anaov5.with_N_sites(1)<br>{'EcoRI' : [33]}<br></pre>
-The easy solution is to chain several Analysis methods. This is
-possible since each method return a dictionary as results and is able
-to take a dictionary as input:<br>
-<pre>>>> Ana.with_N_sites(1, Ana.overhang5())<br>{'EcoRI': [33]}<br></pre>
-The dictionary is always the last argument whatever the command you use.<br>
-<br>
-The way to prefer certainly depends of the conditions you will use your
-Analysis instance. If
-you are likely to frequently reuse the same batch with different
-sequences, using a dedicated RestrictionBatch might be faster as the
-batch is likely to be smaller. Chaining methods is
-generally quicker when working with an interactive shell.  In a
-script, the extended syntax may be easier to understand in a few
-months.
-<br>
-<br>
-<h3><a class="mozTocH3" name="mozTocId343319"></a><span class="mozTocH3"></span>5 
-Advanced features : the FormattedSeq class<br>
-</h3>
-Restriction enzymes require a much more strict formatting of the DNA
-sequences than Bio.Seq object provides. For example, the restriction
-enzymes expect to find an ungapped (no space) upper-case sequence,
-while Bio.Seq object allow sequences to be in lower-case separated by
-spaces. Therefore when  a restriction enzyme analyse a Bio.Seq
-object (be it a Seq or a MutableSeq), the object undergoes a
-conversion. The class FormattedSeq ensure the smooth conversion from
-a  Bio.Seq object to something which can be safely be used by the
-enzyme.<br>
-<br>
-While this conversion is done automatically by the enzymes if you
-provide them with a Seq  or a MutableSeq, there is time where it
-will be more efficient to realise the conversion before hand. Each time
-a Seq object is passed to an enzyme for analysis you pay a overhead due
-to the conversion. When analysing the same sequence over and over, it
-will be faster to convert the sequence, store the conversion and then
-use only the converted sequence. <br>
-<h4><a class="mozTocH4" name="mozTocId74092"></a>5.1 Creating a
-FormattedSeq<br>
-</h4>
-Creating a FormattedSeq from a Bio.Seq object is simple. The first
-argument of FormattedSeq is the sequence you wish to convert. You can
-specify a shape with the second argument linear, if you don't the
-FormattedSeq will be linear :
-<pre>>>> from Bio.Restriction import *<br>>>> from Bio.Seq import Seq<br>>>> seq = Seq('TTCAAAAAAAAAAGAATTCAAAAGAA')<br>>>> linear_fseq = FormattedSeq(seq, linear=True) <br>>>> default_fseq = FormattedSeq(seq)<br>>>> circular_fseq = FormattedSeq(seq, linear=False)<br>>>> linear_fseq<br>FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)<br>>>> linear_fseq.is_linear()<br>True<br>>>> defau [...]
-<h4><a class="mozTocH4" name="mozTocId143837"></a>5.2 Unlike Bio.Seq,
-FormattedSeq retains information about their shape<br>
-</h4>
-FormattedSeq retains information about the shape of the sequence.
-Therefore unlike with Seq and MutableSeq you don't need to specify the
-shape of the sequence when using <span style="font-family: monospace;">search()</span>
-or <span style="font-family: monospace;">catalyse()</span>:<br>
-<pre>>>> EcoRI.search(linear_fseq)<br>[15]<br>>>> EcoRI.search(circular_fseq)  # no need to specify the shape<br>[15, 25]<br></pre>
-In fact, the shape of a FormattedSeq is not altered by the second
-argument of the commands search() and catalyse() :<br>
-<pre>>>> # In fact the shape is blocked.<br>>>> # The 3 following commands give the same results<br>>>> # which correspond to a circular sequence<br>>>> EcoRI.search(circular_fseq) <br>[15, 25]<br>>>> EcoRI.search(circular_fseq, linear=True)<br>[15, 25]<br>>>> EcoRI.search(circular_fseq, linear=False)<br>[15, 25]<br>>>><br></pre>
-<h4><a class="mozTocH4" name="mozTocId123270"></a>5.3 Changing the
-shape of a FormattedSeq<br>
-</h4>
-You can however change the shape of the FormattedSeq. The command to
-use are :<br>
-<pre>FormattedSeq.to_circular() => new FormattedSeq, shape will be circular. <br>FormattedSeq.to_linear()   => new FormattedSeq, shape will be linear<br>FormattedSeq.circularise() => change the shape of FormattedShape to circular<br>FormattedSeq.linearise()   => change the shape of FormattedShape to linear<br><br>>>> circular_fseq<br>FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)<br>>>> circular_fseq.is_linear()<br>False<br>>> [...]
-<h4><a class="mozTocH4" name="mozTocId710376"></a>5.4 Using / and //
-operators with FormattedSeq<br>
-</h4>
-Not having to specify the shape of the sequence to analyse gives you
-the opportunity to use the shorthand '/' and '//' with restriction
-enzymes :<br>
-<pre>>>> EcoRI/linear_fseq  # <=> EcoRI.search(linear_fseq)<br>[15]<br>>>> linear_fseq/EcoRI  # <=> EcoRI.search(linear_fseq)<br>[15]<br>>>> EcoRI//linear_fseq # <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)<br>(Seq('TTCAAAAAAAAAAG', Alphabet()), Seq('AATTCAAAAGAA', Alphabet()))<br></pre>
-Another way to avoid the overhead due to a repetitive conversion from a
-Seq object to a FormattedSeq is to use a RestrictionBatch. <br>
-<br>
-To conclude, the performance gain achieved when using a FormattedSeq
-instead of a Seq is not huge. The analysis of a 10 kb sequence by all
-the enzymes in AllEnzymes (<span style="font-family: monospace;">for x
-in AllEnzymes : x.search(seq), </span>671 enzymes)<span
- style="font-family: monospace;"></span> is 7 % faster when using a
-FormattedSeq than a Seq. Using a RestrictionBatch (<span
- style="font-family: monospace;">AllEnzymes.search(seq)</span>) is
-about as fast as using a FormattedSeq the first time the search is run.
-This however is dramatically reduced in subsequent runs with the same
-sequence (RestrictionBatch keep in memory the result of their last run
-while the sequence is not changed).<br>
-<h3><a class="mozTocH3" name="mozTocId80946"></a>6  More advanced
-features <br>
-</h3>
-This chapter addresses some more advanced features of the packages,
-most
-users can safely ignore it.<br>
-<h4><a class="mozTocH4" name="mozTocId546339"></a>6.1 Updating the
-enzymes from Rebase : rebase_update.py<br>
-</h4>
-Most people will certainly not need to update the enzymes. The
-restriction enzyme package will be updated in with each new release of
-biopython. But if you wish to get an update in between
-biopython-releases here is how to do it. Each month, Rebase release a
-new compilation of data about restriction enzymes. While the enzymes do
-not change so frequently, you may wish to update the restriction
-enzymes classes. The first thing to do is to get the last rebase file.
-You can find the release of Rebase at <span
- style="font-family: monospace;">http://rebase.neb.com/rebase.files.html</span>.
-The file you are interested in are in the EMBOSS format. You can
-download the files directly from the rebase ftp server using your
-browser. The file are situated at <span style="font-family: monospace;">ftp://ftp.neb.com/pub/rebase</span>.
-<br>
-You will have to download 3 files :<br>
-                <span
- style="font-family: monospace;">emboss_e.###</span><br>
-                <span
- style="font-family: monospace;">emboss_r.###</span><br>
-                <span
- style="font-family: monospace;">emboss_s.###</span><br>
-The <span style="font-family: monospace;">###</span> is a 3 digit
-number corresponding to the year and month of the release. The first
-digit is the year, the two last are the month : so July 2004 will be :
-407; October 2005 : 510, etc... Download the three file corresponding
-to the current month and place them in a folder. <br>
-<br>
-Another way to do the same thing is to use the <span
- style="font-family: monospace;">rebase_update.py</span>. script
-provided in the package. The script is in
-biopython/Bio/Restriction/Scripts. It will connect directly to the
-rebase ftp server and download the last batch of emboss files. From a
-DOS or Unix shell do the following :<br>
-<pre>$ cd path_to_/Scripts<br>$ ls<br>ranacompiler.py  rebase_update.py<br>$ ./rebase_update.py -m your_e_mail at my_address.com -p http://www.somewhere.com:8000<br><br>Please wait, trying to connect to Rebase<br><br><br>copying ftp://ftp.neb.com/pub/rebase/emboss_e.407<br>to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_e.407<br>copying ftp://ftp.neb.com/pub/rebase/emboss_s.407<br>to /cvsroot/biopython/Bio/Restriction/Scripts/emboss_s.407<br>copying ftp://ftp.neb.com/pub/rebase/emboss_ [...]
-Some explanation are needed : <span style="font-family: monospace;">-m</span>
-stands for e-mail, in order to connect to the ftp server you need to
-provide a your e-mail address. So replace <span
- style="font-family: monospace;">your_e_mail at your_address.com</span>
-with your e-mail address. <span style="font-family: monospace;">-p</span>
-is the switch to indicate to the script you are using a proxy. If you
-use a ftp proxy enter its address and the connection port after the '<span
- style="font-family: monospace;">:</span>'.<br>
-<span style="font-family: monospace;"></span>
-<h4><a class="mozTocH4" name="mozTocId49285"></a>6.2 Compiling a new
-dictionary : the script ranacompiler.py<br>
-</h4>
-Once you have got a the last serie of emboss files you can compile a
-new module containing the data necessary to create restriction enzyme.
-You will need to get out of the python shell and open either a DOS
-shell on windows, or your prefered Unix shell for the others.<br>
-<br>
-Note : if the emboss files are not present in the current directory or
-if they are not up to date, <span style="font-family: monospace;">ranacompiler.py</span>
-will invoke the script rebase_update. You will need to use the same
-options as before (ie <span style="font-family: monospace;">-m</span>
-and <span style="font-family: monospace;">-p</span>). See the previous
-paragraph on <span style="font-family: monospace;">rebase_update.py</span>
-for more details.<br>
-<br>
-For
-simplicity let's assume we have put the emboss files in the same folder
-as the files which contains the script <span
- style="font-family: monospace;">ranacompiler.py</span>.<br>
-<pre><br>$ cd path_to_/Scripts<br>$ ls<br>emboss_e.407  emboss_r.407   emboss_s.407 ranacompiler.py  rebase_update.py<br><br></pre>
-We will use the script <span style="font-family: monospace;">ranacompiler.py</span>.
-You may have the change the mode of the file to make it executable :<br>
-<pre>$ chmod '+x' ranacompiler.py<br></pre>
-Now execute the script :<br>
-<pre>$ python ranacompiler.py  # or ./ranacompiler.py</pre>
-You get normally the following message :<br>
-<pre>$ ./ranacompiler.py<br><br> Using the files : emboss_e.407, emboss_r.407, emboss_s.407<br><br>WARNING : HaeIV cut twice with different overhang length each time.<br>	Unable to deal with this behaviour.<br>        This enzyme will not be included in the database. Sorry.<br>        Checking : Anyway, HaeIV is not commercially available.<br><br><br>WARNING : TaqII has two different sites.<br><br>WARNING : It seems that AspCNI is both commercially available<br>        and its characteri [...]
-The first line indicate which emboss files have been used for the
-present compilation. You can safely ignore the warnings as long as the<span
- style="font-family: monospace;">  compilation of the new
-dictionary : OK.</span>
-is present in the last part of the output. They are here for debugging
-purpose. The number of enzymes in the new module is indicated as
-well as a list of the dictionary which have been compiled. The last
-part indicate that the module has been succesfully created but not
-installed. To finish the update you must copy the file<span
- style="font-family: monospace;">
-/cvsroot/biopython/Bio/Restriction/Scripts/Restriction_Dictionary.py</span>
-into the folder <span style="font-family: monospace;">/usr/lib/python2.3/site-packages/Bio/Restriction/</span>
-as indicated by the script. Looking into the present folder, you will
-see to new files : the newly created dictionary <span
- style="font-family: monospace;">Restriction_Dictionary.py </span>and <span
- style="font-family: monospace;">Restriction_Dictionary.old</span>
-. This last file containing the old dictionary to which you can revert
-in case anything the new file is corrupted (this should not happen
-since the script is happy enough the new dictionary is good, but if
-there is a problem it is always nice to know you can revert to the
-previous setting without having to reinstall the whole thing.<br>
-<pre>$ ls<br>emboss_e.407  ranacompiler.py*            Restriction_Dictionary.py<br>emboss_r.407  rebase_update.py*<br>emboss_s.407  Restriction_Dictionary.old<br>$<br>$ # complete the installation by copying the new dictionary to the Bio/Restriction/ folder.<br>$ # You may have to become root to do this :<br>$<br>$ su -c "cp Restriction_Dictionary.py <span
- style="font-family: monospace;">/usr/lib/python2.3/site-packages/Bio/Restriction/"<br>password :<br></span></pre>
-Enter your password and that's it. If you whish, the script may install
-the folder for you as well,
-but you will have to run it as root if your normal user has no write
-access to your python installation (and it should'nt). Use the command <span
- style="font-family: monospace;">ranacompiler.py -i</span> or <span
- style="font-family: monospace;">ranacompiler.py --install</span>.<br>
-<pre>$ su -c "./ranacompiler.py -i"<br>password :<br><br> Using the files : emboss_e.407, emboss_r.407, emboss_s.407<br><br>WARNING : HaeIV cut twice with different overhang length each time.<br>        Unable to deal with this behaviour.<br>        This enzyme will not be included in the database. Sorry.<br>        Checking : Anyway, HaeIV is not commercially available.<br><br><br>WARNING : TaqII has two different sites.<br><br>WARNING : It seems that AspCNI is both commercially availab [...]
-Much of the same really, but this time the module has directly been
-installed with your other python modules, you don't need to do anything
-more. If anything goes wrong (you have no write access to the
-destination folder for example) the script will let you know it did not
-perform the installation. It will however still save the new module in
-the current directory :<br>
-<pre>$ ./ranacompiler.py -i<br><br> Using the files : emboss_e.407, emboss_r.407, emboss_s.407<br><br>WARNING : HaeIV cut twice with different overhang length each time.<br>        Unable to deal with this behaviour.<br>        This enzyme will not be included in the database. Sorry.<br>        Checking : Anyway, HaeIV is not commercially available.<br><br><br>WARNING : TaqII has two different sites.<br><br>WARNING : It seems that AspCNI is both commercially available<br>        and its  [...]
-As you can see the script is not very bright and will redo the
-compilation each time it is invoked, no matter if a previous version of
-the module is already present. <br>
-<h4><a class="mozTocH4" name="mozTocId968583"></a>6.3 Subclassing
-the class Analysis<br>
-</h4>
-As seen previously, you can modify some aspects of the Analysis output
-interactively. However if you want to write your own Analysis class,
-you may wish to provide others output facilities than is given in this
-package. Depending on what you want to do you may get away with simply
-changing the <span style="font-family: monospace;">make_format</span>
-method of your derived class or you will need
-to provide new methods. Rather than get into a long explanation, here
-is the implementation of a rather useless Analysis class :<br>
-<pre>>>> class UselessAnalysis(Analysis) :<br><br>    def __init__(self, rb=RestrictionBatch(), seq=Seq(''), lin=True) :<br>	"""UselessAnalysis -> A class that waste your time"""<br>	#<br>	#    Unless you want to do something more fancy all<br>	#    you need to do here is instantiate Analysis.<br>	#    Don't forget the self in __init__<br>	#<br>        Analysis.__init__(self, rb, seq, lin)<br><br>    def make_format(self, cut=[], t='', nc=[], s='') :<br>	"""not funny"""<br>	# [...]
-<br>
-Using this example, as a template you should now be able to subclass
-Analysis as you wish. You will found more implementation details in the
-module <span style="font-family: monospace;">Bio.Restriction.PrintFormat</span>
-which contains the class providing all the <span
- style="font-family: monospace;">_make_*</span> methods.<br>
-<pre>        </pre>
-<h3><a class="mozTocH3" name="mozTocId114111"></a>7  Limitation
-and caveat <br>
-</h3>
-You must be aware that Restriction is a fairly young package.
-Particularly, the class Analysis is a quick and
-dirty implementation based on the facilities furnished by the package.
-Please check your
-results and report any fault.<br>
-<br>
-On a more general basis, Restriction have some other limitations :<br>
-<h4><a class="mozTocH4" name="mozTocId218594"></a>7.1 All DNA are non
-methylated<br>
-</h4>
-No facility to work with methylated DNA has been implemented yet. As
-far as the enzyme classes are concerned all DNA is non methylated DNA.
-Implementation of methylation sensibility will certainly occur in the
-future. But for now, if your sequence is methylated, you will have to
-check if the site is methylated using other means. <br>
-<h4><a class="mozTocH4" name="mozTocId790484"></a>7.2 No support for
-star activity<br>
-</h4>
-As before no support has been yet implemented to find site
-mis-recognised by enzymes under high salt concentration conditions, the
-so-called star activity. This will be implemented as soon as I can get
-a good source of information for that.<br>
-<h4><a class="mozTocH4" name="mozTocId218334"></a>7.3 Safe to use with
-degenerated DNA<br>
-</h4>
-It is safe to use degenerated DNA as input for the query. You will not
-be flooded with meaningless results. But this come at a price : GAA<span
- style="text-decoration: underline;">N</span>TC
-will not be recognised as a potential EcoRI site for example, in fact
-it will not
-be recognised at all. Degenerated sequences will not be analysed. If
-your sequence is not fully sequenced, you will certainly miss
-restriction sites :<br>
-<pre>>>> a = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr', IUPACAmbiguousDNA())<br>>>> EcoRI.search(a)<br>[36]<br>>>> b = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr', IUPACAmbiguousDNA())<br>>>> EcoRI.search(b)<br>[]<br></pre>
-<h4><a class="mozTocH4" name="mozTocId49392"></a>7.4 Non standard bases
-in DNA are not
-allowed<br>
-</h4>
-While you can use degenerated DNA, using non standard base alphabet
-will make the enzymes choke, even if Bio.Seq.Seq accepts them. However,
-space-like characters (' ', '\n', '\t', ...) and digit will be removed
-but will not stop the enzyme analysing the sequence. You can use them
-but the fragments produced by catalyse will have lost any formatting.
-Catalyse try to keep the original case of the sequence (i.e lower case
-sequences will generate lower case fragments, upper case sequences
-upper case fragments), but mixed case will return upper case fragments :<br>
-<pre>>>> c = Seq('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr', IUPACAmbiguousDNA())<br>>>> EcoRI.search(c)<br><br>Traceback (most recent call last):<br>  File "<pyshell#110>", line 1, in -toplevel-<br>    EcoRI.search(b)<br>  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 396, in search<br>    cls.dna = FormatedSeq(dna, linear)<br>  File "/usr/lib/python2.3/site-packages/Bio/Restriction/Restriction.py", line 137, in __init__< [...]
-Not allowing other letters than IUPAC might seems drastic but this is
-really to limit errors. It is not
-totally fool proof but it does help. <br>
-<br>
-<h4><a class="mozTocH4" name="mozTocId395117"></a>7.5 Sites found at
-the edge of linear
-DNA might not be accessible in a real digestion<br>
-</h4>
-While sites clearly outsides a sequence will not be reported, nothing
-has been done to try to determine if a restriction site at the end of a
-linear sequence is valid :<br>
-<pre>>>> d = Seq('GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG', IUPACAmbiguousDNA())<br>>>> FokI.site			# site present<br>'GGATG'<br>>>> FokI.elucidate()  		# but cut outside the sequence <br>'GGATGNNNNNNNNN^NNNN_N'<br>>>> FokI.search(d)		# therefore no site found<br>[]<br>>>> EcoRI.search(d)<br>[2] <br></pre>
-EcoRI finds a site at position 2 even if it is highly unlikely that
-EcoRI accepts to cut this site in a tube. It is generally considered
-that at about 5 nucleotides must separate the site from the edge of the
-sequence to be reasonably sure the enzyme will work correctly. This
-"security margin" is variable from one enzyme to the other. In doubt
-consult the documentation for the enzyme.<br>
-<h4><a class="mozTocH4" name="mozTocId112206"></a>7.6 Restriction
-reports cutting sites
-not enzyme recognition sites<br>
-</h4>
-Some enzymes will cut twice each time they encounter a restriction
-site. The enzymes in this package report both cut not the site. Other
-software may only reports restriction sites. Therefore the output given
-for some enzymes might seems to be the double when compared with the
-results of these software. It is not a bug.<br>
-<pre>>>> AloI.cut_twice()<br>True<br>>>> AloI.fst5              # first cut<br>-7<br>>>> AloI.scd5		   # second cut	<br>25<br>>>> AloI.site<br>'GAACNNNNNNTCC'<br>>>> b = Seq('AAAAAAAAAAA'+ AloI.site + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')<br>>>> b<br>Seq('AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA', Alphabet())<br>>>> AloI.search(b)	# one site, two cuts -> two positions<br>[5, 37]<br><br><br><br></pre>
-<h3><a class="mozTocH3" name="mozTocId386540"></a>8  Annexe :
-modifying dir() to use
-with from Bio.Restriction import *<br>
-</h3>
-Having all the enzymes imported directly in the shell is useful when
-working in an interactive shell (even if it is not recommended by the
-purists). Here is a little hack to get some sanity back when using
-dir() in those conditions :<br>
-<pre>>>> # we will change the builtin dir() function to get ride of the enzyme names.<br>>>> import sys<br>>>> def dir(object=None) :<br>	"""dir([object]) -> list of string.<br><br>	over-ride the built-in function to get some clarity."""<br>	if object :<br>		# we only want to modify dir(),<br>		# so here we return the result of the builtin function.<br>		return __builtins__.dir(object)<br>	else :<br>		# now the part we want to modify.<br>		# All the enzymes  [...]
+<body>
+<h1 id="working-with-restriction-enzymes">Working with restriction enzymes</h1>
+<h2 id="table-of-contents">Table of contents</h2>
+<ol style="list-style-type: decimal">
+<li><a href="#1">The restriction enzymes classes</a>
+<ol style="list-style-type: decimal">
+<li><a href="#1.1">Importing the enzymes</a></li>
+<li><a href="#1.2">Naming convention</a></li>
+<li><a href="#1.3">Searching for restriction sites</a></li>
+<li><a href="#1.4">Retrieving the sequences produced by a digestion</a></li>
+<li><a href="#1.5">Analysing circular sequences</a></li>
+<li><a href="#1.6">Comparing enzymes with each others</a></li>
+<li><a href="#1.7">Other facilities provided by the enzyme classes</a></li>
+</ol></li>
+<li><a href="#2">The RestrictionBatch class: a class to deal with several enzymes</a>
+<ol style="list-style-type: decimal">
+<li><a href="#2.1">Creating a RestrictionBatch</a></li>
+<li><a href="#2.2">Restricting a RestrictionBatch to a particular supplier</a></li>
+<li><a href="#2.3">Adding enzymes to a RestrictionBatch</a></li>
+<li><a href="#2.4">Removing enzymes from a RestrictionBatch</a></li>
+<li><a href="#2.5">Manipulating RestrictionBatch</a></li>
+<li><a href="#2.6">Analysing sequences with a RestrictionBatch</a></li>
+<li><a href="#2.7">Other RestrictionBatch methods</a></li>
+</ol></li>
+<li><a href="#3">AllEnzymes and CommOnly: two preconfigured RestrictionBatches</a></li>
+<li><a href="#4">The Analysis class: even simpler restriction analysis</a>
+<ol style="list-style-type: decimal">
+<li><a href="#4.1">Setting up an Analysis</a></li>
+<li><a href="#4.2">Full restriction analysis</a></li>
+<li><a href="#4.3">Changing the title</a></li>
+<li><a href="#4.4">Customising the output</a></li>
+<li><a href="#4.5">Fancier restriction analysis</a></li>
+<li><a href="#4.6">More complex analysis</a></li>
+</ol></li>
+<li><a href="#5">Advanced features: the FormattedSeq class</a>
+<ol style="list-style-type: decimal">
+<li><a href="#5.1">Creating a FormattedSeq</a></li>
+<li><a href="#5.2">Unlike Bio.Seq, FormattedSeq retains information about their shape</a></li>
+<li><a href="#5.3">Changing the shape of a FormattedSeq</a></li>
+<li><a href="#5.4">Using / and // operators with FormattedSeq</a></li>
+</ol></li>
+<li><a href="#6">More advanced features</a>
+<ol style="list-style-type: decimal">
+<li><a href="#6.1">Updating the enzymes from Rebase</a>
+<ol style="list-style-type: decimal">
+<li><a href="#6.1.1">Fetching the recent enzyme files manually from Rebase</a></li>
+<li><a href="#6.1.2">Fetching the recent enzyme files with rebase_update.py</a></li>
+<li><a href="#6.1.3">Compiling a new dictionary with ranacompiler.py</a></li>
+</ol></li>
+<li><a href="#6.2">Subclassing the class Analysis</a></li>
+</ol></li>
+<li><a href="#7">Limitation and caveat</a>
+<ol style="list-style-type: decimal">
+<li><a href="#7.1">All DNA are non methylated</a></li>
+<li><a href="#7.2">No support for star activity</a></li>
+<li><a href="#7.3">Safe to use with degenerated DNA</a></li>
+<li><a href="#7.4">Non standard bases in DNA are not allowed</a></li>
+<li><a href="#7.5">Sites found at the edge of linear DNA might not be accessible in a real digestion</a></li>
+<li><a href="#7.6">Restriction reports cutting sites not enzyme recognition sites</a></li>
+</ol></li>
+<li><a href="#8">Annexe: modifying dir() to use with from Bio.Restriction import *</a></li>
+</ol>
+<h3 id="the-restriction-enzymes-classes"><a name="1"></a>1. The restriction enzymes classes</h3>
+<p>The restriction enzyme package is situated in <code class="inline">Bio.Restriction</code>. This package will allow you to work with restriction enzymes and realise restriction analysis on your sequence. Restriction make use of the facilities offered by <strong>REBASE</strong> and contains classes for more than 800 restriction enzymes. This chapter will lead you through a quick overview of the facilities offered by the <code class="inline">Restriction</code> package of Biopython. The c [...]
+<h4 id="importing-the-enzymes"><a name="1.1"></a> 1.1 Importing the enzymes</h4>
+<p>To import the enzymes, open a Python shell and type:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio <span class="im">import</span> Restriction
+<span class="op">>>></span> <span class="bu">dir</span>()
+[<span class="st">'Restriction'</span>, <span class="st">'__builtins__'</span>, <span class="st">'__doc__'</span>, <span class="st">'__name__'</span>, <span class="st">'__package__'</span>]
+<span class="op">>>></span> Restriction.EcoRI
+EcoRI
+<span class="op">>>></span> Restriction.EcoRI.site
+<span class="co">'GAATTC'</span>
+<span class="op">>>></span></code></pre></div>
+<p>You will certainly notice that the package is quite slow to load. This is normal as each enzyme possess its own class and there is a lot of them. This will not affect the speed of Python after the initial import.</p>
+<p>I don't know for you but I find it quite cumbersome to have to prefix each operation with <code class="inline">Restriction.</code>, so here is another way to import the package.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio.Restriction <span class="im">import</span> <span class="op">*</span>
+<span class="op">>>></span> EcoRI
+EcoRI
+<span class="op">>>></span> EcoRI.site
+<span class="co">'GAATTC'</span>
+<span class="op">>>></span></code></pre></div>
+<p>However, this method has one big disadvantage: It is almost impossible to use the command <code class="inline">dir()</code> anymore as there is so much enzymes the results is hardly readable. A workaround is provided at the end of this tutorial. I let you decide which method you prefer. But in this tutorial I will use the second. If you prefer the first method you will need to prefix each call to a restriction enzyme with <code class="inline">Restriction.</code> in the remaining of th [...]
+<h4 id="naming-convention"><a name="1.2"></a>1.2 Naming convention</h4>
+<p>To access an enzyme simply enter it's name. You must respect the usual naming convention with the upper case letters and Latin numbering (in upper case as well):</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI
+EcoRI
+<span class="op">>>></span> ecori
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#25>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    ecori
+<span class="pp">NameError</span>: name <span class="st">'ecori'</span> <span class="op">is</span> <span class="op">not</span> defined
+<span class="op">>>></span> EcoR1
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#26>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    EcoR1
+<span class="pp">NameError</span>: name <span class="st">'EcoR1'</span> <span class="op">is</span> <span class="op">not</span> defined
+<span class="op">>>></span> KpnI
+KpnI
+<span class="op">>>></span></code></pre></div>
+<p><code class="inline">ecori</code> or <code class="inline">EcoR1</code> are not enzymes, <code class="inline">EcoRI</code> and <code class="inline">KpnI</code> are.</p>
+<h4 id="searching-for-restriction-sites"><a name="1.3"></a>1.3 Searching for restriction sites</h4>
+<p>So what can we do with these restriction enzymes? To see that we will need a DNA sequence. Restriction enzymes support both <code class="inline">Bio.Seq.MutableSeq</code>and <code class="inline">Bio.Seq.Seq</code> objects. You can use any DNA alphabet which complies with the IUPAC alphabet.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio.Seq <span class="im">import</span> Seq
+<span class="op">>>></span> <span class="im">from</span> Bio.Alphabet.IUPAC <span class="im">import</span> IUPACAmbiguousDNA
+<span class="op">>>></span> amb <span class="op">=</span> IUPACAmbiguousDNA()
+<span class="op">>>></span> my_seq <span class="op">=</span> Seq(<span class="st">'AAAAAAAAAAAAAA'</span>, amb)
+<span class="op">>>></span> my_seq
+Seq(<span class="st">'AAAAAAAAAAAAAA'</span>, IUPACAmbiguousDNA())</code></pre></div>
+<p>Searching a sequence for the presence of restriction site for your preferred enzyme is as simple as:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.search(my_seq)
+[]</code></pre></div>
+<p>The results is a list. Here the list is empty since there is obviously no EcoRI site in <em>my_seq</em>. Let's try to get a sequence with an EcoRI site.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> ecoseq <span class="op">=</span> my_seq <span class="op">+</span> Seq(EcoRI.site, amb) <span class="op">+</span> my_seq
+<span class="op">>>></span> ecoseq
+Seq(<span class="st">'AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.search(ecoseq)
+[<span class="dv">16</span>]</code></pre></div>
+<p>We therefore have a site at position 16 of the sequence <em>ecoseq</em>. The position returned by the method search is the first base of the downstream segment produced by a restriction (i.e. the first base after the position where the enzyme will cut). The <code class="inline">Restriction</code> package follows biological convention (the first base of a sequence is base 1). No need to make difficult conversions between your recorded biological data and the results produced by the enz [...]
+<h4 id="retrieving-the-sequences-produced-by-a-digestion"><a name="1.4"></a>1.4 Retrieving the sequences produced by a digestion</h4>
+<p><code class="inline">Seq</code> objects as all Python sequences, have different conventions and the first base of a sequence is base 0. Therefore to get the sequences produced by an EcoRI digestion of <em>ecoseq</em>, one should do the following:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> ecoseq[:<span class="dv">15</span>], ecoseq[<span class="dv">15</span>:]
+(Seq(<span class="st">'AAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCAAAAAAAAAAAAAA'</span>, IUPACAm
+biguousDNA()))</code></pre></div>
+<p>I hear you thinking "this is a cumbersome and error prone method to get these sequences". To simplify your life, <code class="inline">Restriction</code> provides another method to get these sequences without hassle: <code class="inline">catalyse</code>. This method will return a tuple containing all the fragments produced by a complete digestion of the sequence. Using it is as simple as before:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.catalyse(ecoseq)
+(Seq(<span class="st">'AAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCAAAAAAAAAAAAAA'</span>, IUPACAm
+biguousDNA()))</code></pre></div>
+<p>BTW, you can also use spell it the American way <code class="inline">catalyze</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.catalyze(ecoseq)
+(Seq(<span class="st">'AAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCAAAAAAAAAAAAAA'</span>, IUPACAm
+biguousDNA()))</code></pre></div>
+<h4 id="analysing-circular-sequences"><a name="1.5"></a>1.5 Analysing circular sequences</h4>
+<p>Now, if you have entered the previous command in your shell you may have noticed that both <code class="inline">search</code> and <code class="inline">catalyse</code> can take a second argument <code class="inline">linear</code> which defaults to <code class="inline">True</code>. Using this will allow you to simulate circular sequences such as plasmids. Setting <code class="inline">linear</code> to <code class="inline">False</code> informs the enzyme to make the search over a circular [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.search(ecoseq, linear<span class="op">=</span><span class="va">False</span>)
+[<span class="dv">16</span>]
+<span class="op">>>></span> EcoRI.catalyse(ecoseq, linear<span class="op">=</span><span class="va">False</span>)
+(Seq(<span class="st">'AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG'</span>, IUPACAmbiguousDNA()),)
+<span class="op">>>></span> ecoseq  <span class="co"># for memory</span>
+Seq(<span class="st">'AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA'</span>, IUPACAmbiguousDNA())</code></pre></div>
+<p>OK, this is quite a difference, we only get one fragment, which correspond to the linearised sequence. The beginning sequence has been shifted to take this fact into account. Moreover we can see another difference:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.search(new_seq)
+[]
+<span class="op">>>></span> EcoRI.search(new_seq, linear<span class="op">=</span><span class="va">False</span>)
+[<span class="dv">33</span>]</code></pre></div>
+<p>As you can see using <code class="inline">linear=False</code>, make a site appearing in the sequence <em>new_seq</em>. This site does not exist in a linear sequence as the EcoRI site is split into two halves at the start and the end of the sequence. In a circular sequence however, the site is effectively present when the beginning and end of the sequence are joined.</p>
+<h4 id="comparing-enzymes-with-each-others"><a name="1.6"></a>1.6 Comparing enzymes with each others</h4>
+<p><code class="inline">Restriction</code> enzymes define 4 comparative operators <code class="inline">==</code>, <code class="inline">!=</code>, <code class="inline">>></code> and <code class="inline">%</code>. All these operator compares two enzymes together and either return <code class="inline">True</code> or <code class="inline">False</code>.</p>
+<dl>
+<dt><code class="inline">==</code> (test identity)</dt>
+<dd>It will return <code class="inline">True</code> if the two sides of the operator are the same. *Same" is defined as: same name, same site, same overhang (i.e. the only thing which is equal to <code class="inline">EcoRI</code> is <code class="inline">EcoRI</code>).
+</dd>
+<dt><code class="inline">!=</code> (test for different site or cutting)</dt>
+<dd>It will return <code class="inline">True</code> if the two sides of the operator are different. Two enzymes are not different if the result produced by one enzyme will always be the same as the result produced by the other (i.e. true isoschizomers will not being the same enzymes, are not different since they are interchangeable).
+</dd>
+<dt><code class="inline">>></code> (test for neoschizomer)</dt>
+<dd><code class="inline">True</code> if the enzymes recognise the same site, but cut it in a different way (i.e. the enzymes are neoschizomers).
+</dd>
+<dt><code class="inline">%</code> (test compatibilty)</dt>
+<dd>Test the compatibility of the ending produced by the enzymes (will be <code class="inline">True</code> if the fragments produced with one of the enzyme can directly be ligated to fragments produced by the other).
+</dd>
+</dl>
+<p>Let's use <code class="inline">Acc65I</code> and its isoschizomers as example:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+<span class="op">>>></span> Acc65I.elucidate()
+<span class="co">'G^GTAC_C'</span>
+<span class="op">>>></span> Asp718I.elucidate()
+<span class="co">'G^GTAC_C'</span>
+<span class="op">>>></span> KpnI.elucidate()
+<span class="co">'G_GTAC^C'</span>
+<span class="op">>>></span> <span class="co"># Asp718I and Acc65I are true isoschizomers,</span>
+<span class="op">>>></span> <span class="co"># they recognise the same site and cut it the</span>
+<span class="op">>>></span> <span class="co"># same way.</span>
+<span class="op">>>></span> <span class="co"># KpnI is a neoschizomers of the 2 others.</span>
+<span class="op">>>></span> <span class="co"># Here are the results of the 4 operators</span>
+<span class="op">>>></span> <span class="co"># for each pair of enzymes:</span>
+<span class="op">>>></span>
+<span class="op">>>></span> <span class="co">############# x == y  (x is y)</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> Acc65I    <span class="co"># same enzyme => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> KpnI      <span class="co"># all other cases => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> Asp718I
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">==</span> EcoRI
+<span class="va">False</span>
+<span class="op">>>></span> <span class="co">############ x != y  (x and y are not true isoschizomers)</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> Acc65I    <span class="co"># same enzyme => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> Asp718I   <span class="co"># different enzymes, but cut same manner => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> KpnI      <span class="co"># all other cases => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">!=</span> EcoRI
+<span class="va">True</span>
+<span class="op">>>></span> <span class="co">###########  x >> y (x is neoschizomer of y)</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> Acc65I    <span class="co"># same enzyme => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> Asp718I   <span class="co"># same site, same cut => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> EcoRI     <span class="co"># different site => False</span>
+<span class="va">False</span>
+<span class="op">>>></span> Acc65I <span class="op">>></span> KpnI      <span class="co"># same site, different cut => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> <span class="co">########### x % y   (fragments produced by x and fragments produced by y</span>
+<span class="op">>>></span> <span class="co">#            can be directly ligated to each other)</span>
+<span class="op">>>></span> Acc65I <span class="op">%</span> Asp718I
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">%</span> Acc65I
+<span class="va">True</span>
+<span class="op">>>></span> Acc65I <span class="op">%</span> KpnI   <span class="co"># KpnI -> '3 overhang, Acc65I-> 5' overhang => False</span>
+<span class="va">False</span>
+<span class="op">>>></span>
+<span class="op">>>></span> SunI.elucidate()
+<span class="co">'C^GTAC_G'</span>
+<span class="op">>>></span> SunI <span class="op">==</span> Acc65I
+<span class="va">False</span>
+<span class="op">>>></span> SunI <span class="op">!=</span> Acc65I
+<span class="va">True</span>
+<span class="op">>>></span> SunI <span class="op">>></span> Acc65I
+<span class="va">False</span>
+<span class="op">>>></span> SunI <span class="op">%</span> Acc65I  <span class="co"># different site, same overhang (5' GTAC) => True</span>
+<span class="va">True</span>
+<span class="op">>>></span> SmaI <span class="op">%</span> EcoRV   <span class="co"># 2 Blunt enzymes, all blunt enzymes are compatible => True</span>
+<span class="va">True</span></code></pre></div>
+<h4 id="other-facilities-provided-by-the-enzyme-classes"><a name="1.7"></a>1.7 Other facilities provided by the enzyme classes</h4>
+<p>The <code class="inline">Restriction</code> class provides quite a number of others methods. We will not go through all of them, but only have a quick look to the most useful ones.</p>
+<p>Not all enzymes possess the same properties when it comes to the way they digest a DNA. If you want to know more about the way a particular enzyme cut you can use the three following methods. They are fairly straightforward to understand and refer to the ends that the enzyme produces: blunt, 5' overhanging (also called 3' recessed) sticky end and 3' overhanging (or 5' recessed) sticky end.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.is_blunt()
+<span class="va">False</span>
+<span class="op">>>></span> EcoRI.is_5overhang()
+<span class="va">True</span>
+<span class="op">>>></span> EcoRI.is_3overhang()
+<span class="va">False</span></code></pre></div>
+<p>A more detailled view of the restriction site can be produced using the <code class="inline">elucidate()</code> method. The <code class="inline">^</code> refers to the position of the cut in the sense strand of the sequence, <code class="inline">_</code> to the cut on the antisense or complementary strand. <code class="inline">^_</code> means blunt.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.elucidate()
+<span class="co">'G^AATT_C'</span>
+<span class="op">>>></span> KpnI.elucidate()
+<span class="co">'G_GTAC^C'</span>
+<span class="op">>>></span> EcoRV.elucidate()
+<span class="co">'GAT^_ATC'</span></code></pre></div>
+<p>The method <code class="inline">frequency()</code> will give you the statistical frequency of the enzyme site.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.frequency()
+<span class="dv">4096</span>
+<span class="op">>>></span> XhoII.elucidate()
+<span class="co">'R^GATC_Y'</span>
+<span class="op">>>></span> XhoII.frequency()
+<span class="dv">1024</span></code></pre></div>
+<p>To get the length of a the recognition sequence of an enzyme use the built-in function <code class="inline">len()</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="bu">len</span>(EcoRI)
+<span class="dv">6</span>
+<span class="op">>>></span> BstXI.elucidate()
+<span class="co">'CCAN_NNNN^NTGG'</span>
+<span class="op">>>></span> <span class="bu">len</span>(BstXI)
+<span class="dv">12</span>
+<span class="op">>>></span> FokI.site
+<span class="co">'GGATG'</span>
+<span class="op">>>></span> FokI.elucidate()    <span class="co"># FokI cut well outside its recognition site</span>
+<span class="co">'GGATGNNNNNNNNN^NNNN_N'</span>
+<span class="op">>>></span> <span class="bu">len</span>(FokI)       <span class="co"># its length is the length of the recognition site</span>
+<span class="dv">5</span></code></pre></div>
+<p>Also interesting are the methods dealing with isoschizomers. For memory, two enzymes are <em>isoschizomers</em> if they share a same recognition site. A further division is made between isoschizomers (same name, recognise the same sequence and cut the same way) and <em>neoschizomers</em> which cut at different positions. <em>Equischizomer</em> is an arbitrary choice to design "isoschizomers_that_are_not_neoschizomers" as this last one was a bit long. Another set of method <c [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+<span class="op">>>></span> Acc65I.neoschizomers()
+[KpnI]
+<span class="op">>>></span> Acc65I.equischizomers()
+[Asp718I]
+<span class="op">>>></span> KpnI.elucidate()
+<span class="co">'G_GTAC^C'</span>
+<span class="op">>>></span> Acc65I.elucidate()
+<span class="co">'G^GTAC_C'</span>
+<span class="op">>>></span> KpnI.is_neoschizomer(Acc65I)
+<span class="va">True</span>
+<span class="op">>>></span> KpnI.is_neoschizomer(KpnI)
+<span class="va">False</span>
+<span class="op">>>></span> KpnI.is_isoschizomer(Acc65I)
+<span class="va">True</span>
+<span class="op">>>></span> KpnI.is_isoschizomer(KpnI)
+<span class="va">True</span>
+<span class="op">>>></span> KpnI.is_equischizomer(Acc65I)
+<span class="va">False</span>
+<span class="op">>>></span> KpnI.is_equischizomer(KpnI)
+<span class="va">True</span></code></pre></div>
+<p><code class="inline">suppliers()</code> will get you the list of all the suppliers of the enzyme. <code class="inline">all_suppliers()</code> will give you all the suppliers in the database.</p>
+<h3 id="the-restrictionbatch-class-a-class-to-deal-with-several-enzymes"><a name="2"></a>2. The RestrictionBatch class: a class to deal with several enzymes</h3>
+<p>If you want to make a restriction map of a sequence, using individual enzymes can become tedious and will endures a big overhead due to the repetitive conversion of the sequence to a <code class="inline">FormattedSeq</code> (see <a href="#5">Chapter 5</a>). <code class="inline">Restriction</code> provides a class to make easier the use of large number of enzymes in one go: <code class="inline">RestrictionBatch</code>. <code class="inline">RestrictionBatch</code> will help you to manip [...]
+<h4 id="creating-a-restrictionbatch"><a name="2.1"></a><span class="mozTocH4"></span>2.1 Creating a RestrictionBatch</h4>
+<p>You can initiate a restriction batch by passing it a list of enzymes or enzyme names as argument.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([EcoRI])
+<span class="op">>>></span> rb
+RestrictionBatch([<span class="st">'EcoRI'</span>])
+<span class="op">>>></span> rb2 <span class="op">=</span> RestrictionBatch([<span class="st">'EcoRI'</span>])
+<span class="op">>>></span> rb2
+RestrictionBatch([<span class="st">'EcoRI'</span>])
+<span class="op">>>></span> rb <span class="op">==</span> rb2
+<span class="va">True</span></code></pre></div>
+<p>Adding a new enzyme to a restriction batch is easy:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb.add(KpnI)
+<span class="op">>>></span> rb
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb <span class="op">+=</span> EcoRV
+<span class="op">>>></span> rb
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])])</code></pre></div>
+<p>Another way to create a RestrictionBatch is by simply adding restriction enzymes together, this is particularly useful for small batches:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3 <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])</code></pre></div>
+<h4 id="restricting-a-restrictionbatch-to-a-particular-supplier"><a name="2.2"></a>2.2 Restricting a RestrictionBatch to a particular supplier</h4>
+<p>The Restriction package is based upon the <strong>REBASE</strong> database. This database gives a list of suppliers for each enzyme. It would be a shame not to make use of this facility. You can produce a <code class="inline">RestrictionBatch</code> containing only enzymes from one or a few supplier(s). Here is how to do it:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb_supp <span class="op">=</span> RestrictionBatch(first<span class="op">=</span>[], suppliers<span class="op">=</span>[<span class="st">'C'</span>,<span class="st">'B'</span>,<span class="st">'E'</span>,<span class="st">'I'</span>,<span class="st">'K'</span>,<span class="st">'J'</span>,<span class="st">'M'</span>,
+<span class="co">'O'</span>,<span class="st">'N'</span>,<span class="st">'Q'</span>,<span class="st">'S'</span>,<span class="st">'R'</span>,<span class="st">'V'</span>,<span class="st">'Y'</span>,<span class="st">'X'</span>])
+<span class="op">>>></span> <span class="co"># This will create a RestrictionBatch with the all enzymes which possess a s</span>
+upplier.
+<span class="op">>>></span> <span class="bu">len</span>(rb_supp)  <span class="co"># May 2016</span>
+<span class="dv">622</span></code></pre></div>
+<p>The argument <code class="inline">suppliers</code> take a list of one or several single letter codes corresponding to the supplier(s). The codes are the same as defined in REBASE. As it would be a pain to have to remember each supplier code, <code class="inline">RestrictionBatch</code> provides a method which show the pair code <=> supplier:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> RestrictionBatch.show_codes()  <span class="co"># as of May 2016 REBASE release.</span>
+C <span class="op">=</span> Minotech Biotechnology
+B <span class="op">=</span> Life Technologies
+E <span class="op">=</span> Agilent Technologies
+I <span class="op">=</span> SibEnzyme Ltd.
+K <span class="op">=</span> Takara Bio Inc.
+J <span class="op">=</span> Nippon Gene Co., Ltd.
+M <span class="op">=</span> Roche Applied Science
+O <span class="op">=</span> Toyobo Biochemicals
+N <span class="op">=</span> New England Biolabs
+Q <span class="op">=</span> Molecular Biology Resources <span class="op">-</span> CHIMERx
+S <span class="op">=</span> Sigma Chemical Corporation
+R <span class="op">=</span> Promega Corporation
+V <span class="op">=</span> Vivantis Technologies
+Y <span class="op">=</span> SinaClon BioScience Co.
+X <span class="op">=</span> EURx Ltd.
+<span class="op">>>></span> <span class="co"># You can now choose a code and built your RestrictionBatch</span></code></pre></div>
+<p>This way of producing a <code class="inline">RestrictionBatch</code> can drastically reduce the amount of useless output from a restriction analysis, limiting the search to enzymes that you can get hold of and limiting the risks of nervous breakdown. Nothing is more frustrating than to get the perfect enzyme for a sub-cloning only to find it's not commercially available.</p>
+<h4 id="adding-enzymes-to-a-restrictionbatch"><a name="2.3"></a>2.3 Adding enzymes to a RestrictionBatch</h4>
+<p>Adding an enzyme to a batch if the enzyme is already present will not raise an exception, but will have no effects. Sometimes you want to get an enzyme from a <code class="inline">RestrictionBatch</code> or add it to the batch if it is not present. You will use the <code class="inline">get</code> method setting the second argument <code class="inline">add</code> to <code class="inline">True</code>.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb3.add(EcoRI)
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb3.get(EcoRI)
+EcoRI
+<span class="op">>>></span> rb3.get(SmaI)
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#4>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    rb3.get(SmaI)
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1800</span>, <span class="op">in</span> get
+    <span class="cf">raise</span> <span class="pp">ValueError</span>, <span class="st">'enzyme </span><span class="sc">%s</span><span class="st"> is not in RestrictionBatch'</span><span class="op">%</span>e.<span class="va">__name__</span>
+<span class="pp">ValueError</span>: enzyme SmaI <span class="op">is</span> <span class="op">not</span> <span class="op">in</span> RestrictionBatch
+<span class="op">>>></span> rb3.get(SmaI, add<span class="op">=</span><span class="va">True</span>)
+SmaI
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])</code></pre></div>
+<h4 id="removing-enzymes-from-a-restrictionbatch"><a name="2.4"></a>2.4 Removing enzymes from a RestrictionBatch</h4>
+<p>Removing enzymes from a batch is done using the <code class="inline">remove()</code> method. If the enzyme is not present in the batch this will raise a <code class="inline">KeyError</code>. If the value you want to remove is not an enzyme this will raise a <code class="inline">ValueError</code>.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3.remove(EcoRI)
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3.remove(EcoRI)
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#14>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    rb3.remove(<span class="st">'EcoRI'</span>)
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1839</span>, <span class="op">in</span> remove
+    <span class="cf">return</span> Set.remove(<span class="va">self</span>, <span class="va">self</span>.<span class="bu">format</span>(other))
+  File <span class="st">"/usr/lib/Python2.3/sets.py"</span>, line <span class="dv">534</span>, <span class="op">in</span> remove
+    <span class="kw">del</span> <span class="va">self</span>._data[element]
+<span class="pp">KeyError</span>: EcoRI
+<span class="op">>>></span> rb3 <span class="op">+=</span> EcoRI
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3.remove(<span class="st">'EcoRI'</span>)
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3.remove(<span class="st">'spam'</span>)
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#18>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    rb3.remove(<span class="st">'spam'</span>)
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1839</span>, <span class="op">in</span> remove
+    <span class="cf">return</span> Set.remove(<span class="va">self</span>, <span class="va">self</span>.<span class="bu">format</span>(other))
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1871</span>, <span class="op">in</span> <span class="bu">format</span>
+    <span class="cf">raise</span> <span class="pp">ValueError</span>, <span class="st">'</span><span class="sc">%s</span><span class="st"> is not a RestrictionType'</span><span class="op">%</span>y.__class__
+<span class="pp">ValueError</span>: <span class="op"><</span><span class="bu">type</span> <span class="st">'str'</span><span class="op">></span> <span class="op">is</span> <span class="op">not</span> a RestrictionType</code></pre></div>
+<h4 id="manipulating-restrictionbatch"><a name="2.5"></a>2.5 Manipulating RestrictionBatch</h4>
+<p>You can not, however, add batches together, as they are Python <code class="inline">sets</code>. You must use the pipe operator <code class="inline">|</code> instead. You can find the intersection between 2 batches using <code class="inline">&</code> (see the Python documentation about <code class="inline">sets</code> for more information.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb3 <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb3
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>])
+<span class="op">>>></span> rb4 <span class="op">=</span> SmaI <span class="op">+</span> PstI
+<span class="op">>>></span> rb4
+RestrictionBatch([<span class="st">'PstI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3 <span class="op">+</span> rb4
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#23>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    rb3 <span class="op">+</span> rb4
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1829</span>, <span class="op">in</span> <span class="fu">__add__</span>
+    new.add(other)
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1848</span>, <span class="op">in</span> add
+    <span class="cf">return</span> Set.add(<span class="va">self</span>, <span class="va">self</span>.<span class="bu">format</span>(other))
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1871</span>, <span class="op">in</span> <span class="bu">format</span>
+    <span class="cf">raise</span> <span class="pp">ValueError</span>, <span class="st">'</span><span class="sc">%s</span><span class="st"> is not a RestrictionType'</span><span class="op">%</span>y.__class__
+<span class="pp">ValueError</span>: <span class="op"><</span><span class="kw">class</span> <span class="st">'Bio.Restriction.Restriction.RestrictionBatch'</span><span class="op">></span> <span class="op">is</span> <span class="op">not</span> a RestrictionType
+<span class="op">>>></span> rb3 <span class="op">|</span> rb4
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>, <span class="st">'PstI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3 <span class="op">&</span> rb4
+RestrictionBatch([])
+<span class="op">>>></span> rb4 <span class="op">+=</span> EcoRI
+<span class="op">>>></span> rb4
+RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'PstI'</span>, <span class="st">'SmaI'</span>])
+<span class="op">>>></span> rb3 <span class="op">&</span> rb4
+RestrictionBatch([<span class="st">'EcoRI'</span>])</code></pre></div>
+<h4 id="analysing-sequences-with-a-restrictionbatch"><a name="2.6"></a>2.6 Analysing sequences with a RestrictionBatch</h4>
+<p>To analyse a sequence for potential site, you can use the <code class="inline">search</code> method of the batch, the same way you did for restriction enzymes. The results is no longer a list however, but a dictionary. The keys of the dictionary are the names of the enzymes and the value a list of position site. <code class="inline">RestrictionBatch</code> does not implement a <code class="inline">catalyse</code> method, as it would not have a real meaning when used with large batch.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> rb.search(new_seq)
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: []}
+<span class="op">>>></span> rb.search(new_seq, linear<span class="op">=</span><span class="va">False</span>)
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}</code></pre></div>
+<h4 id="other-restrictionbatch-methods"><a name="2.7"></a>2.7 Other RestrictionBatch methods</h4>
+<p>Amongst the other methods provided by <code class="inline">RestrictionBatch</code>, <code class="inline">elements()</code> which return a list of all the element names alphabetically sorted, is certainly the most useful.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb.elements()
+[<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>]</code></pre></div>
+<p>If you don't care about the alphabetical order use the method <code class="inline">as_string()</code>, to get the same thing a bit faster. The list is not sorted. The order is random as Python sets are dictionary.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rb <span class="op">=</span> EcoRI <span class="op">+</span> KpnI <span class="op">+</span> EcoRV
+<span class="op">>>></span> rb.as_string()
+[<span class="st">'EcoRI'</span>, <span class="st">'KpnI'</span>, <span class="st">'EcoRV'</span>]</code></pre></div>
+<p>Other <code class="inline">RestrictionBatch</code> methods are generally used for particular purposes and will not be discussed here. See the <a href="https://github.com/biopython/biopython/tree/master/Bio/Restriction">source</a> if you are interested.</p>
+<h3 id="allenzymes-and-commonly-two-preconfigured-restrictionbatches"><a name="3"></a>3. AllEnzymes and CommOnly: two preconfigured RestrictionBatches</h3>
+<p>While it is sometime practical to produce a <code class="inline">RestrictionBatch</code> of your own you will certainly more frequently use the two batches provided with the <code class="inline">Restriction</code> packages: <code class="inline">AllEnzymes</code> and <code class="inline">CommOnly</code>. These two batches contain respectively all the enzymes in the database and only the enzymes which have a commercial supplier. They are rather big, but that's what make them useful. Wit [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="bu">len</span>(AllEnzymes)
+<span class="dv">778</span>
+<span class="op">>>></span> <span class="bu">len</span>(CommOnly)
+<span class="dv">622</span>
+<span class="op">>>></span> AllEnzymes.search(new_seq) ...</code></pre></div>
+<p>There is not a lot to say about them apart the fact that they are present. They are really normal batches, and you can use them as any other batch.</p>
+<h3 id="the-analysis-class-even-simpler-restriction-analysis"><a name="4"></a>4. The Analysis class: even simpler restriction analysis</h3>
+<p><code class="inline">RestrictionBatch</code> can give you a dictionary with the sites for all the enzymes in a batch. However, it is sometime nice to get something a bit easier to read than a Python dictionary. Complex restriction analysis are not easy with <code class="inline">RestrictionBatch</code>. Some refinements in the way to search a sequence for restriction sites will help. <code class="inline">Analysis</code> provides a serie of command to customise the results obtained from [...]
+<h4 id="setting-up-an-analysis"><a name="4.1"></a>4.1 Setting up an Analysis</h4>
+<p>To build a restriction analysis you will need a <code class="inline">RestrictionBatch</code> and a sequence and to tell it if the sequence is linear or circular. The first argument <code class="inline">Analysis</code> takes is the restriction batch, the second is the sequence. If the third argument is not provided, <code class="inline">Analysis</code> will assume the sequence is linear.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([EcoRI, KpnI, EcoRV])
+<span class="op">>>></span> Ana <span class="op">=</span> Analysis(rb, new_seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> Ana
+Analysis(RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>]),Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAA</span>
+<span class="st">AAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA()),<span class="va">False</span>)</code></pre></div>
+<h4 id="full-restriction-analysis"><a name="4.2"></a>4.2 Full restriction analysis</h4>
+<p>Once you have created your new <code class="inline">Analysis</code>, you can use it to get a restriction analysis of your sequence. The way to make a full restriction analysis of the sequence is:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Ana.full()
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}</code></pre></div>
+<p>This is much the same as the output of a <code class="inline">RestrictionBatch.search</code> method. You will get a more easy to read output with <code class="inline">print_that</code> used without argument:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="co"># let's create a something a bit more complex to analyse.</span>
+<span class="op">>>></span>
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([], [<span class="st">'C'</span>])  <span class="co"># we will explain the meaning of the</span>
+<span class="op">>>></span>                               <span class="co"># double list argument later.</span>
+<span class="op">>>></span>
+<span class="op">>>></span> multi_site <span class="op">=</span> Seq.Seq(<span class="st">'AAA'</span> <span class="op">+</span> EcoRI.site <span class="op">+</span> <span class="st">'G'</span> <span class="op">+</span> KpnI.site <span class="op">+</span> EcoRV.site <span class="op">+</span>
+                     <span class="co">'CT'</span> <span class="op">+</span> SmaI.site <span class="op">+</span> <span class="st">'GT'</span> <span class="op">+</span> FokI.site <span class="op">+</span> <span class="st">'GAAAGGGC'</span> <span class="op">+</span>
+                      EcoRI.site <span class="op">+</span> <span class="st">'ACGT'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> Analong <span class="op">=</span> Analysis(rb, multi_site)
+<span class="op">>>></span> Analong.full()
+{BglI: [], BstEII: [], AsuII: [], HinfI: [], SfiI: [], PspPI: [], BsiSI: [<span class="dv">27</span>], S
+alI: [], SlaI: [], NcoI: [], NotI: [], PstI: [], StyI: [], BseBI: [], PvuII: [],
+HindIII: [], BglII: [], ApaLI: [], TaqI: [], BssAI: [], AluI: [], SstI: [], Bse
+CI: [], Sau3AI: [], HpaI: [], SnaBI: [], NheI: [], BclI: [], KpnI: [<span class="dv">16</span>], NruI: [
+], MspCI: [], BshFI: [], CspAI: [], RsaI: [<span class="dv">14</span>], EcoRV: [<span class="dv">20</span>], SphI: [], BamHI: []
+, MboI: [], SgrBI: [], SspI: [], ScaI: [], XbaI: [], SseBI: [], NaeI: [], EcoRI:
+[<span class="dv">5</span>, <span class="dv">47</span>], SmaI: [<span class="dv">28</span>], BseAI: []}
+<span class="op">>>></span>
+<span class="op">>>></span> <span class="co"># The results are here but it is difficult to read. let's try print_that</span>
+<span class="op">>>></span>
+<span class="op">>>></span> Analong.print_that()
+
+BsiSI      :  <span class="dv">27</span>.
+RsaI       :  <span class="dv">14</span>.
+EcoRI      :  <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV      :  <span class="dv">20</span>.
+KpnI       :  <span class="dv">16</span>.
+SmaI       :  <span class="dv">28</span>.
+
+   Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI</code></pre></div>
+<p>Much clearer, is'nt ? The output is optimised for a shell 80 columns wide. If the output seems odd, check that the width of your shell is at least 80 columns.</p>
+<h4 id="changing-the-title"><a name="4.3"></a>4.3 Changing the title</h4>
+<p>You can provide a title to the analysis and modify the sentence 'Enzymes which do not cut the sequence', by setting the two optional arguments of <code class="inline">print_that</code>, <code class="inline">title</code> and <code class="inline">s1</code>. No formatting will be done on these strings so if you have to include the newline (<code class="inline">\n</code>) as you see fit:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Analong.print_that(<span class="va">None</span>, title<span class="op">=</span><span class="st">'sequence = multi_site</span><span class="ch">\n\n</span><span class="st">'</span>)
+
+sequence <span class="op">=</span> multi_site
+
+BsiSI      :  <span class="dv">27</span>.
+RsaI       :  <span class="dv">14</span>.
+EcoRI      :  <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV      :  <span class="dv">20</span>.
+KpnI       :  <span class="dv">16</span>.
+SmaI       :  <span class="dv">28</span>.
+
+   Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+
+<span class="op">>>></span> Analong.print_that(<span class="va">None</span>, title<span class="op">=</span><span class="st">'sequence = multi_site</span><span class="ch">\n\n</span><span class="st">'</span>,
+                   s1<span class="op">=</span><span class="st">'</span><span class="ch">\n</span><span class="st"> no site:</span><span class="ch">\n\n</span><span class="st">'</span>)
+
+sequence <span class="op">=</span> multi_site
+
+BsiSI      :  <span class="dv">27</span>.
+RsaI       :  <span class="dv">14</span>.
+EcoRI      :  <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV      :  <span class="dv">20</span>.
+KpnI       :  <span class="dv">16</span>.
+SmaI       :  <span class="dv">28</span>.
+
+ no site:
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI</code></pre></div>
+<h4 id="customising-the-output"><a name="4.4"></a>4.4 Customising the output</h4>
+<p>You can modify some aspects of the output interactively. There is three main type of output, two listing types (alphabetically sorted and sorted by number of site) and map-like type. To change the output, use the method <code class="inline">print_as()</code> of <code class="inline">Analysis</code>. The change of output is permanent for the instance of <code class="inline">Analysis</code> (that is until the next time you use <code class="inline">print_as()</code>). The argument of <cod [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Analong.print_as(<span class="st">'map'</span>)
+<span class="op">>>></span> Analong.print_that()
+
+    <span class="dv">5</span> EcoRI
+    <span class="op">|</span>
+    <span class="op">|</span>        <span class="dv">14</span> RsaI
+    <span class="op">|</span>        <span class="op">|</span>
+    <span class="op">|</span>        <span class="op">|</span> <span class="dv">16</span> KpnI
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="dv">20</span> EcoRV
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>      <span class="dv">27</span> BsiSI
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>      <span class="op">|</span>
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>      <span class="op">|</span><span class="dv">28</span> SmaI
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>      <span class="op">||</span>
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>      <span class="op">||</span>                  <span class="dv">47</span> EcoRI
+    <span class="op">|</span>        <span class="op">|</span> <span class="op">|</span>   <span class="op">|</span>      <span class="op">||</span>                  <span class="op">|</span>
+AAAGAATTCGGGTACCGATATCCTCCCGGGGTGGATGGAAAGGGCGAATTCACGT
+<span class="op">|||||||||||||||||||||||||||||||||||||||||||||||||||||||</span>
+TTTCTTAAGCCCATGGCTATAGGAGGGCCCCACCTACCTTTCCCGCTTAAGTGCA
+<span class="dv">1</span>                                                    <span class="dv">55</span>
+
+
+   Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+
+<span class="op">>>></span> Analong.print_as(<span class="st">'number'</span>)
+<span class="op">>>></span> Analong.print_that()
+
+
+
+enzymes which cut <span class="dv">1</span> times :
+
+BsiSI      :  <span class="dv">27</span>.
+RsaI       :  <span class="dv">14</span>.
+EcoRV      :  <span class="dv">20</span>.
+KpnI       :  <span class="dv">16</span>.
+SmaI       :  <span class="dv">28</span>.
+
+
+enzymes which cut <span class="dv">2</span> times :
+
+EcoRI      :  <span class="dv">5</span>, <span class="dv">47</span>.
+
+   Enzymes which do <span class="op">not</span> cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+
+<span class="op">>>></span></code></pre></div>
+<p>To come back to the previous behaviour:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Analong.print_as(<span class="st">'alpha'</span>)
+<span class="op">>>></span> Analong.print_that()
+
+BsiSI      :  <span class="dv">27</span>.
+RsaI       :  <span class="dv">14</span>.
+EcoRI      :  <span class="dv">5</span>, <span class="dv">47</span>.
+EcoRV      :  <span class="dv">20</span>.
+etc ...</code></pre></div>
+<h4 id="fancier-restriction-analysis"><a name="4.5"></a>4.5 Fancier restriction analysis</h4>
+<p>I will not go into the detail for each single method, here are all the functions that are available. Most are perfectly self explanatory and the others are fairly well documented (use <code class="inline">help('Analysis.command_name')</code>). The methods are:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">full(<span class="va">self</span>,linear<span class="op">=</span><span class="va">True</span>)
+blunt(<span class="va">self</span>,dct <span class="op">=</span> <span class="va">None</span>)
+overhang5(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+overhang3(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+defined(<span class="va">self</span>,dct<span class="op">=</span><span class="va">None</span>)
+with_sites(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+without_site(<span class="va">self</span>, dct<span class="op">=</span><span class="va">None</span>)
+with_N_sites(<span class="va">self</span>, N, dct<span class="op">=</span><span class="va">None</span>)
+with_number_list(<span class="va">self</span>, <span class="bu">list</span>, dct<span class="op">=</span><span class="va">None</span>)
+with_name(<span class="va">self</span>, names, dct<span class="op">=</span><span class="va">None</span>)
+with_site_size(<span class="va">self</span>, site_size, dct<span class="op">=</span><span class="va">None</span>)
+only_between(<span class="va">self</span>, start, end, dct<span class="op">=</span><span class="va">None</span>)
+between(<span class="va">self</span>,start, end, dct<span class="op">=</span><span class="va">None</span>)
+show_only_between(<span class="va">self</span>, start, end, dct<span class="op">=</span><span class="va">None</span>)
+only_outside(<span class="va">self</span>, start, end, dct <span class="op">=</span><span class="va">None</span>)
+outside(<span class="va">self</span>, start, end, dct<span class="op">=</span><span class="va">None</span>)
+do_not_cut(<span class="va">self</span>, start, end, dct <span class="op">=</span><span class="va">None</span>)</code></pre></div>
+<p>Using these methods is simple:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> new_seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([EcoRI, KpnI, EcoRV])
+<span class="op">>>></span> Ana <span class="op">=</span> Analysis(rb, new_seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> Ana
+Analysis(RestrictionBatch([<span class="st">'EcoRI'</span>, <span class="st">'EcoRV'</span>, <span class="st">'KpnI'</span>]),Seq(<span class="st">'TTCAAAAAAAAAAAAAAAAAA</span>
+<span class="st">AAAAAAAAAAGAA'</span>, IUPACAmbiguousDNA()),<span class="va">False</span>)
+<span class="op">>>></span> Ana.blunt()  <span class="co"># output only the result for enzymes which cut blunt</span>
+{<span class="st">'EcoRV'</span>: []}
+<span class="op">>>></span> Ana.full()  <span class="co"># all the enzymes in the RestrictionBatch</span>
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: [], <span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span> Ana.with_sites()  <span class="co"># output only the result for enzymes which have a site</span>
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span> Ana.without_site()  <span class="co"># output only the enzymes which have no site</span>
+{<span class="st">'KpnI'</span>: [], <span class="st">'EcoRV'</span>: []}
+<span class="op">>>></span> Ana.only_between(<span class="dv">1</span>, <span class="dv">20</span>)  <span class="co"># the enzymes which cut between position 1 and 20</span>
+{}
+<span class="op">>>></span> Ana.only_between(<span class="dv">20</span>, <span class="dv">34</span>)  <span class="co"># etc...</span>
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span> Ana.only_outside(<span class="dv">20</span>, <span class="dv">34</span>)
+{}
+<span class="op">>>></span> Ana.with_name([EcoRI])
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}
+<span class="op">>>></span></code></pre></div>
+<p>To get a nice output, you still use <code class="inline">print_that</code> but this time with the command you want executed as argument.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Ana.print_that(Ana.blunt())
+
+   Enzymes which do <span class="op">not</span> cut the sequence.
+
+EcoRV
+
+<span class="op">>>></span> pt <span class="op">=</span> Ana.print_that
+<span class="op">>>></span> pt(Ana.with_sites())
+
+EcoRI      :  <span class="dv">33</span>.
+
+<span class="op">>>></span> pt(Ana.without_site())
+
+   Enzymes which do <span class="op">not</span> cut the sequence.
+
+EcoRV     KpnI
+
+<span class="op">>>></span> <span class="co"># etc ...</span></code></pre></div>
+<h4 id="more-complex-analysis"><a name="4.6"></a>4.6 More complex analysis</h4>
+<p>All of these methods (except <code class="inline">full()</code> which, well ... do a full restriction analysis) can be supplied with an additional dictionary. If no dictionary is supplied a full restriction analysis is used as starting point. Otherwise the dictionary provided by the argument <code class="inline">dct</code> is used. The dictionary must be formatted as the result of <code class="inline">RestrictionBatch.search</code>. Therefore of the form <code class="inline">{'enzyme_ [...]
+<p>Using this method you can build really complex query by chaining several method one after the other. For example if you want all the enzymes which are 5' overhang and cut the sequence only once, you have two ways to go:</p>
+<p>The hard way consist to build a restriction batch containing only 5' overhang enzymes and use this batch to create a new <code class="inline">Analysis</code> instance and then use the method <code class="inline">with_N_sites()</code> as follow:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> rbov5 <span class="op">=</span> RestrictionBatch([x <span class="cf">for</span> x <span class="op">in</span> rb <span class="cf">if</span> x.is_5overhang()])
+<span class="op">>>></span> Anaov5 <span class="op">=</span> Analysis(rbov5, new_seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> Anaov5.with_N_sites(<span class="dv">1</span>)
+{<span class="st">'EcoRI'</span> : [<span class="dv">33</span>]}</code></pre></div>
+<p>The easy solution is to chain several <code class="inline">Analysis</code> methods. This is possible since each method return a dictionary as results and is able to take a dictionary as input:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> Ana.with_N_sites(<span class="dv">1</span>, Ana.overhang5())
+{<span class="st">'EcoRI'</span>: [<span class="dv">33</span>]}</code></pre></div>
+<p>The dictionary is always the last argument whatever the command you use.</p>
+<p>The way to prefer certainly depends of the conditions you will use your <code class="inline">Analysis</code> instance. If you are likely to frequently reuse the same batch with different sequences, using a dedicated <code class="inline">RestrictionBatch</code> might be faster as the batch is likely to be smaller. Chaining methods is generally quicker when working with an interactive shell. In a script, the extended syntax may be easier to understand in a few months.</p>
+<h3 id="advanced-features-the-formattedseq-class"><a name="5"></a>5. Advanced features: the FormattedSeq class</h3>
+<p>Restriction enzymes require a much more strict formatting of the DNA sequences than <code class="inline">Bio.Seq</code> object provides. For example, the restriction enzymes expect to find an ungapped (no space) upper-case sequence, while <code class="inline">Bio.Seq</code> object allow sequences to be in lower-case separated by spaces. Therefore when a restriction enzyme analyse a <code class="inline">Bio.Seq</code> object (be it a <code class="inline">Seq</code> or a <code class="in [...]
+<p>While this conversion is done automatically by the enzymes if you provide them with a <code class="inline">Seq</code> or a <code class="inline">MutableSeq</code>, there is time where it will be more efficient to realise the conversion before hand. Each time a <code class="inline">Seq</code> object is passed to an enzyme for analysis you pay a overhead due to the conversion. When analysing the same sequence over and over, it will be faster to convert the sequence, store the conversion  [...]
+<h4 id="creating-a-formattedseq"><a name="5.1"></a>5.1 Creating a FormattedSeq</h4>
+<p>Creating a <code class="inline">FormattedSeq</code> from a <code class="inline">Bio.Seq</code> object is simple. The first argument of <code class="inline">FormattedSeq</code> is the sequence you wish to convert. You can specify a shape with the second argument <code class="inline">linear</code>, if you don't the <code class="inline">FormattedSeq</code> will be linear:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="im">from</span> Bio.Restriction <span class="im">import</span> <span class="op">*</span>
+<span class="op">>>></span> <span class="im">from</span> Bio.Seq <span class="im">import</span> Seq
+<span class="op">>>></span> seq <span class="op">=</span> Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>)
+<span class="op">>>></span> linear_fseq <span class="op">=</span> FormattedSeq(seq, linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> default_fseq <span class="op">=</span> FormattedSeq(seq)
+<span class="op">>>></span> circular_fseq <span class="op">=</span> FormattedSeq(seq, linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> linear_fseq
+FormattedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> linear_fseq.is_linear()
+<span class="va">True</span>
+<span class="op">>>></span> default_fseq.is_linear()
+<span class="va">True</span>
+<span class="op">>>></span> circular_fseq.is_linear()
+<span class="va">False</span>
+<span class="op">>>></span> circular_fseq
+FormattedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">False</span>)</code></pre></div>
+<h4 id="unlike-bio.seq-formattedseq-retains-information-about-their-shape"><a name="5.2"></a>5.2 Unlike Bio.Seq, FormattedSeq retains information about their shape</h4>
+<p><code class="inline">FormattedSeq</code> retains information about the shape of the sequence. Therefore unlike with <code class="inline">Seq</code> and <code class="inline">MutableSeq</code> you don't need to specify the shape of the sequence when using <code class="inline">search()</code> or <code class="inline">catalyse()</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI.search(linear_fseq)
+[<span class="dv">15</span>]
+<span class="op">>>></span> EcoRI.search(circular_fseq)  <span class="co"># no need to specify the shape</span>
+[<span class="dv">15</span>, <span class="dv">25</span>]</code></pre></div>
+<p>In fact, the shape of a FormattedSeq is not altered by the second argument of the commands <code class="inline">search()</code> and <code class="inline">catalyse()</code>:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="co"># In fact the shape is blocked.</span>
+<span class="op">>>></span> <span class="co"># The 3 following commands give the same results</span>
+<span class="op">>>></span> <span class="co"># which correspond to a circular sequence</span>
+<span class="op">>>></span> EcoRI.search(circular_fseq)
+[<span class="dv">15</span>, <span class="dv">25</span>]
+<span class="op">>>></span> EcoRI.search(circular_fseq, linear<span class="op">=</span><span class="va">True</span>)
+[<span class="dv">15</span>, <span class="dv">25</span>]
+<span class="op">>>></span> EcoRI.search(circular_fseq, linear<span class="op">=</span><span class="va">False</span>)
+[<span class="dv">15</span>, <span class="dv">25</span>]</code></pre></div>
+<h4 id="changing-the-shape-of-a-formattedseq"><a name="5.3"></a>5.3 Changing the shape of a FormattedSeq</h4>
+<p>You can however change the shape of the <code class="inline">FormattedSeq</code>. The command to use are:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python">FormattedSeq.to_circular() <span class="op">=></span> new FormattedSeq, shape will be circular.
+FormattedSeq.to_linear()   <span class="op">=></span> new FormattedSeq, shape will be linear
+FormattedSeq.circularise() <span class="op">=></span> change the shape of FormattedShape to circular
+FormattedSeq.linearise()   <span class="op">=></span> change the shape of FormattedShape to linear</code></pre></div>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> circular_fseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> circular_fseq.is_linear()
+<span class="va">False</span>
+<span class="op">>>></span> circular_fseq <span class="op">==</span> linear_fseq
+<span class="va">False</span>
+<span class="op">>>></span> newseq <span class="op">=</span> circular_fseq.to_linear()
+<span class="op">>>></span> circular_fseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">False</span>)
+<span class="op">>>></span> newseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> circular_fseq.linearise()
+<span class="op">>>></span> circular_fseq
+FormatedSeq(Seq(<span class="st">'TTCAAAAAAAAAAGAATTCAAAAGAA'</span>, Alphabet()), linear<span class="op">=</span><span class="va">True</span>)
+<span class="op">>>></span> circular_fseq.is_linear()
+<span class="va">True</span>
+<span class="op">>>></span> circular_fseq <span class="op">==</span> linear_fseq
+<span class="va">True</span>
+<span class="op">>>></span> EcoRI.search(circular_fseq) <span class="co"># which is now linear</span>
+[<span class="dv">15</span>]</code></pre></div>
+<h4 id="using-and-operators-with-formattedseq"><a name="5.4"></a>5.4 Using / and // operators with FormattedSeq</h4>
+<p>Not having to specify the shape of the sequence to analyse gives you the opportunity to use the shorthand '/' and '//' with restriction enzymes:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> EcoRI<span class="op">/</span>linear_fseq  <span class="co"># <=> EcoRI.search(linear_fseq)</span>
+[<span class="dv">15</span>]
+<span class="op">>>></span> linear_fseq<span class="op">/</span>EcoRI  <span class="co"># <=> EcoRI.search(linear_fseq)</span>
+[<span class="dv">15</span>]
+<span class="op">>>></span> EcoRI<span class="op">//</span>linear_fseq <span class="co"># <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)</span>
+(Seq(<span class="st">'TTCAAAAAAAAAAG'</span>, Alphabet()), Seq(<span class="st">'AATTCAAAAGAA'</span>, Alphabet()))</code></pre></div>
+<p>Another way to avoid the overhead due to a repetitive conversion from a <code class="inline">Seq</code> object to a <code class="inline">FormattedSeq</code> is to use a <a href="#2"><code class="inline">RestrictionBatch</code></a>.</p>
+<p>To conclude, the performance gain achieved when using a <code class="inline">FormattedSeq</code> instead of a <code class="inline">Seq</code> is not huge. The analysis of a 10 kb sequence by all the enzymes in <code class="inline">AllEnzymes</code> (<code class="inline">for x in AllEnzymes: x.search(seq)</code>, 867 enzymes) is 7 % faster when using a <code class="inline">FormattedSeq</code> than a <code class="inline">Seq</code>. Using a <code class="inline">RestrictionBatch</code> ( [...]
+<h3 id="more-advanced-features"><a name="6"></a>6. More advanced features</h3>
+<p>This chapter addresses some more advanced features of the packages, most users can safely ignore it.</p>
+<h4 id="updating-the-enzymes-from-rebase"><a name="6.1"></a>6.1 Updating the enzymes from REBASE</h4>
+<p>Most people will certainly not need to update the enzymes. The restriction enzyme package will be updated in with each new release of Biopython. But if you wish to get an update in between Biopython-releases here is how to do it.</p>
+<p>First, you have to download the two scripts <code class="inline">rebase_update.py</code> and <code class="inline">ranacompile.py</code>: Go to <a href="https://github.com/biopython/biopython/tree/master/Scripts/Restriction">https://github.com/biopython/biopython/tree/master/Scripts/Restriction</a>, click on the respective file and press the '<strong>Raw</strong>' button on the top right of the code window. Then, with right-click, save the file. Both scripts must be in the same directory.</p>
+<h5 id="fetching-the-recent-enzyme-files-manually-from-rebase"><a name="6.1.1"></a>6.1.1 Fetching the recent enzyme files manually from REBASE</h5>
+<p>Each month, <a href="http://rebase.neb.com/rebase/rebase.html">REBASE</a> release a new compilation of data about restriction enzymes. While the enzymes do not change so frequently, you may wish to update the restriction enzymes classes. The first thing to do is to get the last rebase file. You can find the release of REBASE at <a href="http://rebase.neb.com/rebase/rebase.files.html">http://rebase.neb.com/rebase/rebase.files.html</a>. The file you are interested in are in the EMBOSS f [...]
+<h5 id="fetching-the-recent-enzyme-files-with-rebase_update.py"><a name="6.1.2"></a>6.1.2 Fetching the recent enzyme files with rebase_update.py</h5>
+<p>Another way to do the same thing is to use the <code class="inline">rebase_update.py</code> script. It will connect directly to the rebase ftp server and download the last batch of emboss files. From a DOS or Unix shell do the following:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">cd</span> path_to_the_update_script
+$ <span class="kw">rebase_update.py</span> -p http://www.somewhere.com:8000
+
+<span class="kw">Please</span> wait, trying to connect to Rebase
+
+<span class="kw">copying</span> ftp://ftp.neb.com/pub/rebase/emboss_e.407
+<span class="kw">to</span> /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_e.407
+<span class="kw">copying</span> ftp://ftp.neb.com/pub/rebase/emboss_s.407
+<span class="kw">to</span> /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_s.407
+<span class="kw">copying</span> ftp://ftp.neb.com/pub/rebase/emboss_r.407
+<span class="kw">to</span> /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_r.407</code></pre></div>
+<p>Some explanation are needed: <code class="inline">-p</code> is the switch to indicate to the script you are using a proxy. If you use a ftp proxy enter its address and the connection port after the '<code class="inline">:</code>'.</p>
+<h5 id="compiling-a-new-dictionary-with-ranacompiler.py"><a name="6.1.3"></a>6.1.3 Compiling a new dictionary with ranacompiler.py</h5>
+<p>Once you have got the recent emboss files you can compile a new module containing the data necessary to create restriction enzyme.</p>
+<p>Note: if the emboss files are not present in the current directory or if they are not up to date, <code class="inline">ranacompiler.py</code> will invoke the script <a href="#6.1.2"><code class="inline">rebase_update.py</code></a>, which needs to be installed in the same folder. You will need to use the same options as before (ie <code class="inline">-m</code> and <code class="inline">-p</code>). See the previous paragraph on <a href="#6.1.2"><code class="inline">rebase_update.py</cod [...]
+<p>For simplicity let's assume we have put the emboss files in the same folder as the files which contains the script <code class="inline">ranacompiler.py</code>. You may have the change the mode of the file to make it executable:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">cd</span> path_to_the_ranacompiler_script
+$ <span class="kw">chmod</span> <span class="st">'+x'</span> ranacompiler.py</code></pre></div>
+<p>Now execute the script:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">Python</span> ranacompiler.py  <span class="co"># or ./ranacompiler.py</span></code></pre></div>
+<p>You get normally the following message:</p>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash">$ <span class="kw">./ranacompiler.py</span>
+
+ <span class="kw">Using</span> the files : emboss_e.407, emboss_r.407, emboss_s.407
+
+<span class="kw">WARNING</span> : HaeIV cut twice with different overhang length each time.
+        <span class="kw">Unable</span> to deal with this behaviour.
+        <span class="kw">This</span> enzyme will not be included in the database. Sorry.
+        <span class="kw">Checking</span> :
+<span class="kw">Anyway</span>, HaeIV is not commercially available.
+
+<span class="kw">WARNING</span> : HpyUM037X has two different sites.
+
+
+<span class="kw">The</span> new database contains 867 enzymes.
+
+<span class="kw">Writing</span> the dictionary containing the new Restriction classes...
+<span class="kw">OK.</span>
+
+<span class="kw">Writing</span> the dictionary containing the suppliers datas...
+<span class="kw">OK.</span>
+
+<span class="kw">Writing</span> the dictionary containing the Restriction types....
+<span class="kw">OK.</span>
+
+ <span class="kw">******************************************************************************</span>
+
+                <span class="kw">Compilation</span> of the new dictionary : OK.
+                <span class="kw">Installation</span> : No.
+
+ <span class="kw">You</span> will find the newly created <span class="st">'Restriction_Dictionary.py'</span> file
+ <span class="kw">in</span> <span class="kw">the</span>  :
+
+        <span class="kw">/path/where/you/run/ranacompiler.py</span>
+
+ <span class="kw">Make</span> a copy of <span class="st">'Restriction_Dictionary.py'</span> and place it with
+ <span class="kw">the</span> other Restriction libraries.
+
+ <span class="kw">note</span> :
+ <span class="kw">This</span> folder should be :
+
+        <span class="kw">path_to_python/site-packages/Bio/Restriction</span>
+
+ <span class="kw">******************************************************************************</span></code></pre></div>
+<p>The first line indicate which emboss files have been used for the present compilation. You can safely ignore the warnings as long as the <code class="inline">compilation of the new dictionary : OK.</code> is present in the last part of the output. They are here for debugging purpose. The number of enzymes in the new module is indicated as well as a list of the dictionary which have been compiled. The last part indicate that the module has been succesfully created but not installed. To [...]
+<p>If you whish, the script may install the folder for you as well, but you will have to run it as root if your normal user has no write access to your Python installation (and it should'nt). Use the command <code class="inline">ranacompiler.py -i</code> or <code class="inline">ranacompiler.py --install</code> for this.</p>
+<p>If anything goes wrong (you have no write access to the destination folder for example) the script will let you know it did not perform the installation. It will however still save the new module in the current directory.</p>
+<p>As you can see the script is not very bright and will redo the compilation each time it is invoked, no matter if a previous version of the module is already present.</p>
+<h4 id="subclassing-the-class-analysis"><a name="6.2"></a>6.2 Subclassing the class Analysis</h4>
+<p>As seen previously, you can modify some aspects of the <code class="inline">Analysis</code> output interactively. However if you want to write your own <code class="inline">Analysis</code> class, you may wish to provide others output facilities than is given in this package. Depending on what you want to do you may get away with simply changing the <code class="inline">make_format</code> method of your derived class or you will need to provide new methods. Rather than get into a long  [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="kw">class</span> UselessAnalysis(Analysis):
+
+    <span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, rb<span class="op">=</span>RestrictionBatch(), seq<span class="op">=</span>Seq(<span class="st">''</span>), lin<span class="op">=</span><span class="va">True</span>):
+    <span class="co">"""UselessAnalysis -> A class that waste your time"""</span>
+    <span class="co">#</span>
+    <span class="co">#    Unless you want to do something more fancy all</span>
+    <span class="co">#    you need to do here is instantiate Analysis.</span>
+    <span class="co">#    Don't forget the self in __init__</span>
+    <span class="co">#</span>
+        Analysis.<span class="fu">__init__</span>(<span class="va">self</span>, rb, seq, lin)
+
+    <span class="kw">def</span> make_format(<span class="va">self</span>, cut<span class="op">=</span>[], t<span class="op">=</span><span class="st">''</span>, nc<span class="op">=</span>[], s<span class="op">=</span><span class="st">''</span>):
+    <span class="co">"""not funny"""</span>
+    <span class="co">#</span>
+    <span class="co">#    Generally, you don't need to do anything else here</span>
+    <span class="co">#    This will tell to your new class to default to the</span>
+    <span class="co">#    _make_joke format.</span>
+    <span class="co">#</span>
+        <span class="cf">return</span> <span class="va">self</span>._make_joke(cut, t, nc, s)
+
+    <span class="kw">def</span> print_as(<span class="va">self</span>, what<span class="op">=</span><span class="st">'joke'</span>):
+    <span class="co">"""Never know somebody might want to change the behaviour of</span>
+<span class="co">    this class."""</span>
+    <span class="co">#</span>
+    <span class="co">#    add your new option to print_as</span>
+    <span class="co">#</span>
+        <span class="cf">if</span> what <span class="op">==</span> <span class="st">'joke'</span>:
+        <span class="va">self</span>.make_format <span class="op">=</span> <span class="va">self</span>._make_joke
+            <span class="cf">return</span>
+    <span class="cf">else</span>:
+        <span class="co">#</span>
+        <span class="co">#   The other options will be treated as before</span>
+        <span class="co">#</span>
+        <span class="cf">return</span> Analysis.print_as(<span class="va">self</span>, what)
+
+    <span class="kw">def</span> _make_joke(<span class="va">self</span>, cut<span class="op">=</span>[], title<span class="op">=</span><span class="st">''</span>, nc<span class="op">=</span>[], s1<span class="op">=</span><span class="st">''</span>):
+    <span class="co">"""UA._make_joke(cut, t, nc, s) -> new analysis output"""</span>
+    <span class="co">#</span>
+    <span class="co">#    starting your new method with '_make_'</span>
+    <span class="co">#    will give a hint to what it is suppose to do</span>
+    <span class="co">#</span>
+    <span class="co">#    We will not process the non-cutting enzymes</span>
+    <span class="co">#    Their names are in nc</span>
+    <span class="co">#    s1 is the string printed before them</span>
+    <span class="co">#</span>
+    <span class="cf">if</span> <span class="op">not</span> title:
+        title <span class="op">=</span> <span class="st">'</span><span class="ch">\n</span><span class="st">You have guessed right the following enzymes:</span><span class="ch">\n\n</span><span class="st">'</span>
+    <span class="cf">for</span> name, sites <span class="op">in</span> cut:
+        <span class="co">#</span>
+        <span class="co">#    cut contains:</span>
+        <span class="co">#    - the name of the enzymes which cut the sequence (name)</span>
+        <span class="co">#    - a list of the site positions (sites)</span>
+        <span class="co">#</span>
+        guess <span class="op">=</span> <span class="bu">raw_input</span>(<span class="st">"next enzyme is </span><span class="sc">%s</span><span class="st">, Guess how many sites ?</span><span class="ch">\n</span><span class="st">>>> "</span><span class="op">%</span>name)
+            <span class="cf">try</span>:
+                guess <span class="op">=</span> <span class="bu">int</span>(guess)
+            <span class="cf">except</span>:
+                guess <span class="op">=</span> <span class="va">None</span>
+            <span class="cf">if</span> guess <span class="op">==</span> <span class="bu">len</span>(sites):
+                <span class="bu">print</span> <span class="st">'You did guess right. Good. Next.'</span>
+        result <span class="op">=</span> <span class="st">'</span><span class="sc">%i</span><span class="st"> site'</span> <span class="op">%</span> guess
+        <span class="cf">if</span> guess <span class="op">></span> <span class="dv">1</span>:
+            result <span class="op">+=</span> <span class="st">'s'</span>
+
+        <span class="co">#</span>
+        <span class="co">#    now we format the line. See the PrintFormat module</span>
+        <span class="co">#    for some examples</span>
+        <span class="co">#   PrintFormat.__section_list and _make_map are good start.</span>
+        <span class="co">#</span>
+                title<span class="op">=</span><span class="st">''</span>.join((title, <span class="bu">str</span>(name).ljust(<span class="va">self</span>.NameWidth),
+                <span class="st">' :  '</span>, result, <span class="st">'.</span><span class="ch">\n</span><span class="st">'</span>))
+    <span class="bu">print</span> <span class="st">'</span><span class="ch">\n</span><span class="st">No more enzyme.'</span>
+        <span class="cf">return</span>  title
+    <span class="co">#</span>
+    <span class="co">#    I you want to print the non cutting enzymes use</span>
+    <span class="co">#    the following return instead of the previous one:</span>
+    <span class="co">#</span>
+    <span class="co">#return  title + t + self._make_nocut_only(nc,s1)</span>
+
+<span class="op">>>></span> <span class="co"># You initiate and use it as before</span>
+<span class="op">>>></span> rb <span class="op">=</span> RestrictionBatch([], [<span class="st">'A'</span>])
+<span class="op">>>></span> multi_site <span class="op">=</span> Seq(<span class="st">'AAA'</span> <span class="op">+</span> EcoRI.site <span class="op">+</span><span class="st">'G'</span> <span class="op">+</span> KpnI.site <span class="op">+</span> EcoRV.site <span class="op">+</span> <span class="st">'CT'</span> <span class="op">+\</span>
+SmaI.site <span class="op">+</span> <span class="st">'GT'</span> <span class="op">+</span> FokI.site <span class="op">+</span> <span class="st">'GAAAGGGC'</span> <span class="op">+</span> EcoRI.site <span class="op">+</span> <span class="st">'ACGT'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span>
+<span class="op">>>></span> b <span class="op">=</span> UselessAnalysis(rb, multi_site)
+<span class="op">>>></span> b.print_that() <span class="co"># Well, I let you discover if you haven't already guessed</span></code></pre></div>
+<p>Using this example, as a template you should now be able to subclass <code class="inline">Analysis</code> as you wish. You will found more implementation details in the module <code class="inline">Bio.Restriction.PrintFormat</code> which contains the class providing all the <code class="inline">_make_*</code> methods.</p>
+<h3 id="limitation-and-caveat"><a name="7"></a>7. Limitation and caveat</h3>
+<p>Particularly, the class <code class="inline">Analysis</code> is a quick and dirty implementation based on the facilities furnished by the package. Please check your results and report any fault.</p>
+<p>On a more general basis, <code class="inline">Restriction</code> have some other limitations:</p>
+<h4 id="all-dna-are-non-methylated"><a name="7.1"></a>7.1 All DNA are non methylated</h4>
+<p>No facility to work with methylated DNA has been implemented yet. As far as the enzyme classes are concerned all DNA is non methylated DNA. Implementation of methylation sensibility will possibly occur in the future. But for now, if your sequence is methylated, you will have to check if the site is methylated using other means.</p>
+<h4 id="no-support-for-star-activity"><a name="7.2"></a>7.2 No support for star activity</h4>
+<p>As before no support has been yet implemented to find site mis-recognised by enzymes under high salt concentration conditions, the so-called star activity. This will be implemented as soon as I can get a good source of information for that.</p>
+<h4 id="safe-to-use-with-degenerated-dna"><a name="7.3"></a>7.3 Safe to use with degenerated DNA</h4>
+<p>It is safe to use degenerated DNA as input for the query. You will not be flooded with meaningless results. But this come at a price: GAA<strong><em>N</em></strong>TC will not be recognised as a potential EcoRI site for example, in fact it will not be recognised at all. Degenerated sequences will not be analysed. If your sequence is not fully sequenced, you will certainly miss restriction sites:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> a <span class="op">=</span> Seq(<span class="st">'nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr'</span>, IUPACAmbiguou
+sDNA())
+<span class="op">>>></span> EcoRI.search(a)
+[<span class="dv">36</span>]
+<span class="op">>>></span> b <span class="op">=</span> Seq(<span class="st">'nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr'</span>, IUPACAmbiguou
+sDNA())
+<span class="op">>>></span> EcoRI.search(b)
+[]</code></pre></div>
+<h4 id="non-standard-bases-in-dna-are-not-allowed"><a name="7.4"></a>7.4 Non standard bases in DNA are not allowed</h4>
+<p>While you can use degenerated DNA, using non standard base alphabet will make the enzymes choke, even if <code class="inline">Bio.Seq.Seq</code> accepts them. However, space-like characters (' ', '', '', ...) and digit will be removed but will not stop the enzyme analysing the sequence. You can use them but the fragments produced by <code class="inline">catalyse</code> will have lost any formatting. <code class="inline">catalyse</code> tries to keep the original case of the sequence ( [...]
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> c <span class="op">=</span> Seq(<span class="st">'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr'</span>, IUPACAmbiguou
+sDNA())
+<span class="op">>>></span> EcoRI.search(c)
+
+Traceback (most recent call last):
+  File <span class="st">"<pyshell#110>"</span>, line <span class="dv">1</span>, <span class="op">in</span> <span class="op">-</span>toplevel<span class="op">-</span>
+    EcoRI.search(b)
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">3</span>
+  <span class="dv">96</span>, <span class="op">in</span> search
+    cls.dna <span class="op">=</span> FormatedSeq(dna, linear)
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1</span>
+  <span class="dv">37</span>, <span class="op">in</span> <span class="fu">__init__</span>
+    <span class="va">self</span>.<span class="bu">format</span>()
+  File <span class="st">"/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py"</span>, line <span class="dv">1</span>
+  <span class="dv">53</span>, <span class="op">in</span> <span class="bu">format</span>
+    <span class="cf">raise</span> AlphabetError, <span class="st">" '</span><span class="sc">%s</span><span class="st">' is not in the IUPAC alphabet"</span> <span class="op">%</span> s
+AlphabetError: <span class="st">'X'</span> <span class="op">is</span> <span class="op">not</span> <span class="op">in</span> the IUPAC alphabet
+<span class="op">>>></span> d <span class="op">=</span> Seq(<span class="st">'1 nnnnn nnnnn nnnnn nnnnn nnnnn </span><span class="ch">\n</span><span class="st">\</span>
+<span class="st">26 nnnnn nnnnG AATTC rrrrr rrrrr </span><span class="ch">\n</span><span class="st">\</span>
+<span class="st">51 r'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> d
+Seq(<span class="st">'1 nnnnn nnnnn nnnnn nnnnn nnnnn </span><span class="ch">\n</span><span class="st">26 nnnnn nnnnG AATTC rrrrr rrrrr </span><span class="ch">\n</span><span class="st">51 r'</span>,
+ IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.search(d)
+[<span class="dv">36</span>]
+<span class="op">>>></span> EcoRI.catalyse(d)
+(Seq(<span class="st">'AATTCRRRRRRRRRRR'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'NNNNNNNNNNNNNNNNNNNNNNNNNNNN</span>
+<span class="st">NNNNNNG'</span>, IUPACAmbiguousDNA()))
+<span class="op">>>></span> e <span class="op">=</span> Seq(<span class="st">'nnnnGAATTCrr'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> f <span class="op">=</span> Seq(<span class="st">'NNNNGAATTCRR'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> g <span class="op">=</span> Seq(<span class="st">'nnnngaattcrr'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> EcoRI.catalyse(e)
+(Seq(<span class="st">'NNNNG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCRR'</span>, IUPACAmbiguousDNA()))
+<span class="op">>>></span> EcoRI.catalyse(f)
+(Seq(<span class="st">'NNNNG'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'AATTCRR'</span>, IUPACAmbiguousDNA()))
+<span class="op">>>></span> EcoRI.catalyse(g)
+(Seq(<span class="st">'nnnng'</span>, IUPACAmbiguousDNA()), Seq(<span class="st">'aattcrr'</span>, IUPACAmbiguousDNA()))</code></pre></div>
+<p>Not allowing other letters than IUPAC might seems drastic but this is really to limit errors. It is not totally fool proof but it does help.</p>
+<h4 id="sites-found-at-the-edge-of-linear-dna-might-not-be-accessible-in-a-real-digestion"><a name="7.5"></a>7.5 Sites found at the edge of linear DNA might not be accessible in a real digestion</h4>
+<p>While sites clearly outsides a sequence will not be reported, nothing has been done to try to determine if a restriction site at the end of a linear sequence is valid:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> d <span class="op">=</span> Seq(<span class="st">'GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG'</span>, IUPACAmbiguousDNA())
+<span class="op">>>></span> FokI.site           <span class="co"># site present</span>
+<span class="co">'GGATG'</span>
+<span class="op">>>></span> FokI.elucidate()        <span class="co"># but cut outside the sequence</span>
+<span class="co">'GGATGNNNNNNNNN^NNNN_N'</span>
+<span class="op">>>></span> FokI.search(d)      <span class="co"># therefore no site found</span>
+[]
+<span class="op">>>></span> EcoRI.search(d)
+[<span class="dv">2</span>]</code></pre></div>
+<p><code class="inline">EcoRI</code> finds a site at position 2 even if it is highly unlikely that EcoRI accepts to cut this site in a tube. It is generally considered that at about 5 nucleotides must separate the site from the edge of the sequence to be reasonably sure the enzyme will work correctly. This "security margin" is variable from one enzyme to the other. In doubt consult the documentation for the enzyme.</p>
+<h4 id="restriction-reports-cutting-sites-not-enzyme-recognition-sites"><a name="7.6"></a>7.6 Restriction reports cutting sites not enzyme recognition sites</h4>
+<p>Some enzymes will cut twice each time they encounter a restriction site. The enzymes in this package report both cut not the site. Other software may only reports restriction sites. Therefore the output given for some enzymes might seems to be the double when compared with the results of these software. It is not a bug.</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> AloI.cut_twice()
+<span class="va">True</span>
+<span class="op">>>></span> AloI.fst5              <span class="co"># first cut</span>
+<span class="op">-</span><span class="dv">7</span>
+<span class="op">>>></span> AloI.scd5          <span class="co"># second cut</span>
+<span class="dv">25</span>
+<span class="op">>>></span> AloI.site
+<span class="co">'GAACNNNNNNTCC'</span>
+<span class="op">>>></span> b <span class="op">=</span> Seq(<span class="st">'AAAAAAAAAAA'</span><span class="op">+</span> AloI.site <span class="op">+</span> <span class="st">'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'</span>)
+<span class="op">>>></span> b
+Seq(<span class="st">'AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA'</span>, Alphabet())
+<span class="op">>>></span> AloI.search(b)  <span class="co"># one site, two cuts -> two positions</span>
+[<span class="dv">5</span>, <span class="dv">37</span>]</code></pre></div>
+<h3 id="annex-modifying-dir-to-use-with-from-bio.restriction-import"><a name="8"></a>8. Annex: modifying dir() to use with from Bio.Restriction import *</h3>
+<p>Having all the enzymes imported directly in the shell is useful when working in an interactive shell (even if it is not recommended by the purists). Here is a little hack to get some sanity back when using dir() in those conditions:</p>
+<div class="sourceCode"><pre class="sourceCode python"><code class="sourceCode python"><span class="op">>>></span> <span class="co"># we will change the builtin dir() function to get ride of the enzyme names.</span>
+<span class="op">>>></span> <span class="im">import</span> sys
+<span class="op">>>></span> <span class="kw">def</span> <span class="bu">dir</span>(<span class="bu">object</span><span class="op">=</span><span class="va">None</span>):
+    <span class="co">"""dir([object]) -> list of string.</span>
+
+<span class="co">    over-ride the built-in function to get some clarity."""</span>
+    <span class="cf">if</span> <span class="bu">object</span>:
+        <span class="co"># we only want to modify dir(),</span>
+        <span class="co"># so here we return the result of the builtin function.</span>
+        <span class="cf">return</span> __builtins__.<span class="bu">dir</span>(<span class="bu">object</span>)
+    <span class="cf">else</span>:
+        <span class="co"># now the part we want to modify.</span>
+        <span class="co"># All the enzymes are in a RestrictionBatch (we will talk about</span>
+        <span class="co"># that later, for the moment simply believe me).</span>
+        <span class="co"># So if we remove from the results of dir() everything which is</span>
+        <span class="co"># in AllEnzymes we will get a much shorter list when we do dir()</span>
+        <span class="co">#</span>
+        <span class="co"># the current level is __main__ ie dir() is equivalent to</span>
+        <span class="co"># ask what's in __main__ at the moment.</span>
+        <span class="co"># we can't access __main__ directly.</span>
+        <span class="co"># so we will use sys.modules['__main__'] to reach it.</span>
+        <span class="co"># the following list comprehension remove from the result of</span>
+        <span class="co"># dir() everything which is also present in AllEnzymes.</span>
+        <span class="co">#</span>
+        <span class="cf">return</span> [x <span class="cf">for</span> x <span class="op">in</span> __builtins__.<span class="bu">dir</span>(sys.modules[<span class="st">'__main__'</span>])
+            <span class="cf">if</span> <span class="op">not</span> x <span class="op">in</span> AllEnzymes]
+
+<span class="op">>>></span> <span class="co"># now let's see if it works.</span>
+<span class="op">>>></span> <span class="bu">dir</span>()
+[<span class="st">'AllEnzymes'</span>, <span class="st">'Analysis'</span>, <span class="st">'CommOnly'</span>, <span class="st">'NonComm'</span>, <span class="st">'PrintFormat'</span>, <span class="st">'RanaConfig'</span>,
+ <span class="co">'Restriction'</span>, <span class="st">'RestrictionBatch'</span>, <span class="st">'Restriction_Dictionary'</span>, <span class="st">'__builtins__'</span>,
+ <span class="co">'__doc__'</span>, <span class="st">'__name__'</span>, <span class="st">'dir'</span>, <span class="st">'sys'</span>]
+<span class="op">>>></span> <span class="co"># ok that's much better.</span>
+<span class="op">>>></span> <span class="co"># The enzymes are still there</span>
+<span class="op">>>></span> EcoRI.site
+<span class="co">'GAATTC'</span></code></pre></div>
 </body>
 </html>
diff --git a/Doc/cookbook/Restriction/Restriction.md b/Doc/cookbook/Restriction/Restriction.md
new file mode 100644
index 0000000..15ae168
--- /dev/null
+++ b/Doc/cookbook/Restriction/Restriction.md
@@ -0,0 +1,1625 @@
+Working with restriction enzymes
+================================
+
+## Table of contents
+
+1. [The restriction enzymes classes](#1)
+    1. [Importing the enzymes](#1.1)
+    2. [Naming convention](#1.2)
+    3. [Searching for restriction sites](#1.3)
+    4. [Retrieving the sequences produced by a digestion](#1.4)
+    5. [Analysing circular sequences](#1.5)
+    6. [Comparing enzymes with each others](#1.6)
+    7. [Other facilities provided by the enzyme classes](#1.7)
+2. [The RestrictionBatch class: a class to deal with several enzymes](#2)
+    1. [Creating a RestrictionBatch](#2.1)
+    2. [Restricting a RestrictionBatch to a particular supplier](#2.2)
+    3. [Adding enzymes to a RestrictionBatch](#2.3)
+    4. [Removing enzymes from a RestrictionBatch](#2.4)
+    5. [Manipulating RestrictionBatch](#2.5)
+    6. [Analysing sequences with a RestrictionBatch](#2.6)
+    7. [Other RestrictionBatch methods](#2.7)
+3. [AllEnzymes and CommOnly: two preconfigured RestrictionBatches](#3)
+4. [The Analysis class: even simpler restriction analysis](#4)
+    1. [Setting up an Analysis](#4.1)
+    2. [Full restriction analysis](#4.2)
+    3. [Changing the title](#4.3)
+    4. [Customising the output](#4.4)
+    5. [Fancier restriction analysis](#4.5)
+    6. [More complex analysis](#4.6)
+5. [Advanced features: the FormattedSeq class](#5)
+    1. [Creating a FormattedSeq](#5.1)
+    2. [Unlike Bio.Seq, FormattedSeq retains information about their shape](#5.2)
+    3. [Changing the shape of a FormattedSeq](#5.3)
+    4. [Using / and // operators with FormattedSeq](#5.4)
+6. [More advanced features](#6)
+    1. [Updating the enzymes from Rebase](#6.1)
+        1. [Fetching the recent enzyme files manually from Rebase](#6.1.1)
+        2. [Fetching the recent enzyme files with rebase_update.py](#6.1.2)
+        3. [Compiling a new dictionary with ranacompiler.py](#6.1.3)
+    2. [Subclassing the class Analysis](#6.2)
+7. [Limitation and caveat](#7)
+    1. [All DNA are non methylated](#7.1)
+    2. [No support for star activity](#7.2)
+    3. [Safe to use with degenerated DNA](#7.3)
+    4. [Non standard bases in DNA are not allowed](#7.4)
+    5. [Sites found at the edge of linear DNA might not be accessible in a real digestion](#7.5)
+    6. [Restriction reports cutting sites not enzyme recognition sites](#7.6)
+8. [Annexe: modifying dir() to use with from Bio.Restriction import *](#8)
+
+### <a name="1"></a>1. The restriction enzymes classes
+The restriction enzyme package is situated in `Bio.Restriction`. This package
+will allow you to work with restriction enzymes and realise restriction
+analysis on your sequence. Restriction make use of the facilities offered
+by **REBASE** and contains classes for more than 800 restriction enzymes.
+This chapter will lead you through a quick overview of the facilities offered
+by the `Restriction` package of Biopython. The chapter is constructed as an
+interactive Python session and the best way to read it is with a Python shell
+open alongside you.
+
+#### <a name="1.1"></a> 1.1  Importing the enzymes
+To import the enzymes, open a Python shell and type:
+
+``` python
+>>> from Bio import Restriction
+>>> dir()
+['Restriction', '__builtins__', '__doc__', '__name__', '__package__']
+>>> Restriction.EcoRI
+EcoRI
+>>> Restriction.EcoRI.site
+'GAATTC'
+>>>
+```
+
+You will certainly notice that the package is quite slow to load. This is normal
+as each enzyme possess its own class and there is a lot of them. This will not
+affect the speed of Python after the initial import.
+
+I don't know for you but I find it quite cumbersome to have to prefix each
+operation with `Restriction.`, so here is another way to import the package.
+
+``` python
+>>> from Bio.Restriction import *
+>>> EcoRI
+EcoRI
+>>> EcoRI.site
+'GAATTC'
+>>>
+```
+
+However, this method has one big disadvantage:
+It is almost impossible to use the command `dir()` anymore as there is so much
+enzymes the results is hardly readable. A workaround is provided at the end of
+this tutorial. I let you decide which method you prefer. But in this tutorial I
+will use the second. If you prefer the first method you will need to prefix
+each call to a restriction enzyme with `Restriction.` in the remaining of the
+tutorial.
+
+#### <a name="1.2"></a>1.2  Naming convention
+
+To access an enzyme simply enter it's name. You must respect the usual naming
+convention with the upper case letters and Latin numbering (in upper case as
+well):
+
+``` python
+>>> EcoRI
+EcoRI
+>>> ecori
+
+Traceback (most recent call last):
+  File "<pyshell#25>", line 1, in -toplevel-
+    ecori
+NameError: name 'ecori' is not defined
+>>> EcoR1
+
+Traceback (most recent call last):
+  File "<pyshell#26>", line 1, in -toplevel-
+    EcoR1
+NameError: name 'EcoR1' is not defined
+>>> KpnI
+KpnI
+>>>
+```
+
+`ecori` or `EcoR1` are not enzymes, `EcoRI` and `KpnI` are.
+
+#### <a name="1.3"></a>1.3  Searching for restriction sites
+
+So what can we do with these restriction enzymes? To see that we will need a
+DNA sequence. Restriction enzymes support both `Bio.Seq.MutableSeq`and
+`Bio.Seq.Seq` objects. You can use any DNA alphabet which complies with the
+IUPAC alphabet.
+
+``` python
+>>> from Bio.Seq import Seq
+>>> from Bio.Alphabet.IUPAC import IUPACAmbiguousDNA
+>>> amb = IUPACAmbiguousDNA()
+>>> my_seq = Seq('AAAAAAAAAAAAAA', amb)
+>>> my_seq
+Seq('AAAAAAAAAAAAAA', IUPACAmbiguousDNA())
+```
+
+Searching a sequence for the presence of restriction site for your preferred
+enzyme is as simple as:
+
+``` python
+>>> EcoRI.search(my_seq)
+[]
+```
+
+The results is a list. Here the list is empty since there is obviously no EcoRI
+site in *my_seq*.  Let's try to get a sequence with an EcoRI site.
+
+``` python
+>>> ecoseq = my_seq + Seq(EcoRI.site, amb) + my_seq
+>>> ecoseq
+Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
+>>> EcoRI.search(ecoseq)
+[16]
+```
+
+We therefore have a site at position 16 of the sequence *ecoseq*. The position
+returned by the method search is the first base of the downstream segment
+produced by a restriction (i.e. the first base after the position where the
+enzyme will cut). The `Restriction` package follows biological convention (the
+first base of a sequence is base 1). No need to make difficult conversions
+between your recorded biological data and the results produced by the enzymes
+in this package.
+
+#### <a name="1.4"></a>1.4 Retrieving the sequences produced by a digestion
+
+`Seq` objects as all Python sequences, have different conventions and the first
+base of a sequence is base 0. Therefore to get the sequences produced by an
+EcoRI digestion of *ecoseq*, one should do the following:
+
+``` python
+>>> ecoseq[:15], ecoseq[15:]
+(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
+biguousDNA()))
+```
+
+I hear you thinking "this is a cumbersome and error prone method to get these
+sequences".  To simplify your life, `Restriction` provides another method to get
+these sequences without hassle: `catalyse`. This method will return a tuple
+containing all the fragments produced by a complete digestion of the sequence.
+Using it is as simple as before:
+
+``` python
+>>> EcoRI.catalyse(ecoseq)
+(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
+biguousDNA()))
+```
+
+BTW, you can also use spell it the American way `catalyze`:
+
+``` python
+>>> EcoRI.catalyze(ecoseq)
+(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
+biguousDNA()))
+```
+
+#### <a name="1.5"></a>1.5 Analysing circular sequences
+
+Now, if you have entered the previous command in your shell you may have noticed
+that both `search` and `catalyse` can take a second argument `linear` which
+defaults to `True`. Using this will allow you to simulate circular sequences
+such as plasmids. Setting `linear` to `False` informs the enzyme to make the
+search over a circular sequence and to search for potential sites spanning over
+the boundaries of the sequence.
+
+``` python
+>>> EcoRI.search(ecoseq, linear=False)
+[16]
+>>> EcoRI.catalyse(ecoseq, linear=False)
+(Seq('AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG', IUPACAmbiguousDNA()),)
+>>> ecoseq  # for memory
+Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
+```
+
+OK, this is quite a difference, we only get one fragment, which correspond to
+the linearised sequence. The beginning sequence has been shifted to take this
+fact into account. Moreover we can see another difference:
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> EcoRI.search(new_seq)
+[]
+>>> EcoRI.search(new_seq, linear=False)
+[33]
+```
+
+As you can see using `linear=False`, make a site appearing in the sequence
+*new_seq*. This site does not exist in a linear sequence as the EcoRI site is
+split into two halves at the start and the end of the sequence. In a circular
+sequence however, the site is effectively present when the beginning and end of
+the sequence are joined.
+
+#### <a name="1.6"></a>1.6 Comparing enzymes with each others
+
+`Restriction` enzymes define 4 comparative operators `==`, `!=`, `>>` and `%`.
+All these operator compares two enzymes together and either return `True` or
+`False`.
+
+`==` (test identity)
+: It will return `True` if the two sides of the operator are the same. *Same"
+is defined as: same name, same site, same overhang (i.e. the only thing which
+is equal to `EcoRI` is `EcoRI`).
+
+`!=` (test for different site or cutting)
+: It will return `True` if the two sides of the operator are different. Two
+enzymes are not different if the result produced by one enzyme will always be
+the same as the result produced by the other (i.e. true isoschizomers will not
+being the same enzymes, are not different since they are interchangeable).
+
+`>>` (test for neoschizomer)
+: `True` if the enzymes recognise the same site, but cut it in a different way
+(i.e. the enzymes are neoschizomers).
+
+`%` (test compatibilty)
+: Test the compatibility of the ending produced by the enzymes (will be `True`
+if the fragments produced with one of the enzyme can directly be ligated to
+fragments produced by the other).
+
+Let's use `Acc65I` and its isoschizomers as example:
+
+``` python
+>>> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+>>> Acc65I.elucidate()
+'G^GTAC_C'
+>>> Asp718I.elucidate()
+'G^GTAC_C'
+>>> KpnI.elucidate()
+'G_GTAC^C'
+>>> # Asp718I and Acc65I are true isoschizomers,
+>>> # they recognise the same site and cut it the
+>>> # same way.
+>>> # KpnI is a neoschizomers of the 2 others.
+>>> # Here are the results of the 4 operators
+>>> # for each pair of enzymes:
+>>>
+>>> ############# x == y  (x is y)
+>>> Acc65I == Acc65I    # same enzyme => True
+True
+>>> Acc65I == KpnI      # all other cases => False
+False
+>>> Acc65I == Asp718I
+False
+>>> Acc65I == EcoRI
+False
+>>> ############ x != y  (x and y are not true isoschizomers)
+>>> Acc65I != Acc65I    # same enzyme => False
+False
+>>> Acc65I != Asp718I   # different enzymes, but cut same manner => False
+False
+>>> Acc65I != KpnI      # all other cases => True
+True
+>>> Acc65I != EcoRI
+True
+>>> ###########  x >> y (x is neoschizomer of y)
+>>> Acc65I >> Acc65I    # same enzyme => False
+False
+>>> Acc65I >> Asp718I   # same site, same cut => False
+False
+>>> Acc65I >> EcoRI     # different site => False
+False
+>>> Acc65I >> KpnI      # same site, different cut => True
+True
+>>> ########### x % y   (fragments produced by x and fragments produced by y
+>>> #            can be directly ligated to each other)
+>>> Acc65I % Asp718I
+True
+>>> Acc65I % Acc65I
+True
+>>> Acc65I % KpnI   # KpnI -> '3 overhang, Acc65I-> 5' overhang => False
+False
+>>>
+>>> SunI.elucidate()
+'C^GTAC_G'
+>>> SunI == Acc65I
+False
+>>> SunI != Acc65I
+True
+>>> SunI >> Acc65I
+False
+>>> SunI % Acc65I  # different site, same overhang (5' GTAC) => True
+True
+>>> SmaI % EcoRV   # 2 Blunt enzymes, all blunt enzymes are compatible => True
+True
+```
+
+#### <a name="1.7"></a>1.7 Other facilities provided by the enzyme classes
+
+The `Restriction` class provides quite a number of others methods. We will not
+go through all of them, but only have a quick look to the most useful ones.
+
+Not all enzymes possess the same properties when it comes to the way they digest
+a DNA. If you want to know more about the way a particular enzyme cut you can
+use the three following methods. They are fairly straightforward to understand
+and refer to the ends that the enzyme produces: blunt, 5' overhanging (also
+called 3' recessed) sticky end and 3' overhanging (or 5' recessed) sticky end.
+
+``` python
+>>> EcoRI.is_blunt()
+False
+>>> EcoRI.is_5overhang()
+True
+>>> EcoRI.is_3overhang()
+False
+```
+
+A more detailled view of the restriction site can be produced using the
+ `elucidate()` method. The `^` refers to the position of the cut in the sense
+strand of the sequence, `_` to the cut on the antisense or complementary strand.
+`^_` means blunt.
+
+``` python
+>>> EcoRI.elucidate()
+'G^AATT_C'
+>>> KpnI.elucidate()
+'G_GTAC^C'
+>>> EcoRV.elucidate()
+'GAT^_ATC'
+```
+
+The method `frequency()` will give you the statistical frequency of the enzyme
+site.
+
+``` python
+>>> EcoRI.frequency()
+4096
+>>> XhoII.elucidate()
+'R^GATC_Y'
+>>> XhoII.frequency()
+1024
+```
+
+To get the length of a the recognition sequence of an enzyme use the built-in
+function `len()`:
+
+``` python
+>>> len(EcoRI)
+6
+>>> BstXI.elucidate()
+'CCAN_NNNN^NTGG'
+>>> len(BstXI)
+12
+>>> FokI.site
+'GGATG'
+>>> FokI.elucidate()    # FokI cut well outside its recognition site
+'GGATGNNNNNNNNN^NNNN_N'
+>>> len(FokI)       # its length is the length of the recognition site
+5
+```
+
+Also interesting are the methods dealing with isoschizomers. For memory, two
+enzymes are *isoschizomers* if they share a same recognition site.
+A further division is made between isoschizomers (same name, recognise the same
+sequence and cut the same way) and *neoschizomers* which cut at different
+positions. *Equischizomer* is an arbitrary choice to design
+"isoschizomers_that_are_not_neoschizomers" as this last one was a bit long.
+Another set of method `one_enzyme.is_*schizomers(one_other_enzyme)`, allow to
+test 2 enzymes against each other.
+
+``` python
+>>> Acc65I.isoschizomers()
+[Asp718I, KpnI]
+>>> Acc65I.neoschizomers()
+[KpnI]
+>>> Acc65I.equischizomers()
+[Asp718I]
+>>> KpnI.elucidate()
+'G_GTAC^C'
+>>> Acc65I.elucidate()
+'G^GTAC_C'
+>>> KpnI.is_neoschizomer(Acc65I)
+True
+>>> KpnI.is_neoschizomer(KpnI)
+False
+>>> KpnI.is_isoschizomer(Acc65I)
+True
+>>> KpnI.is_isoschizomer(KpnI)
+True
+>>> KpnI.is_equischizomer(Acc65I)
+False
+>>> KpnI.is_equischizomer(KpnI)
+True
+```
+
+`suppliers()` will get you the list of all the suppliers of the enzyme.
+`all_suppliers()` will give you all the suppliers in the database.
+
+### <a name="2"></a>2. The RestrictionBatch class: a class to deal with several enzymes
+
+If you want to make a restriction map of a sequence, using individual enzymes
+can become tedious and will endures a big overhead due to the repetitive
+conversion of the sequence to a `FormattedSeq` (see [Chapter 5](#5)).
+`Restriction` provides a class to make easier the use of large number of enzymes
+in one go: `RestrictionBatch`.
+`RestrictionBatch` will help you to manipulate lots of enzymes with a single
+command. Moreover all the enzymes in the restriction batch will share the same
+converted sequence, reducing the overhead.
+
+#### <a name="2.1"></a><span class="mozTocH4"></span>2.1 Creating a RestrictionBatch
+
+You can initiate a restriction batch by passing it a list of enzymes or enzyme
+names as argument.
+
+``` python
+>>> rb = RestrictionBatch([EcoRI])
+>>> rb
+RestrictionBatch(['EcoRI'])
+>>> rb2 = RestrictionBatch(['EcoRI'])
+>>> rb2
+RestrictionBatch(['EcoRI'])
+>>> rb == rb2
+True
+```
+
+Adding a new enzyme to a restriction batch is easy:
+
+``` python
+>>> rb.add(KpnI)
+>>> rb
+RestrictionBatch(['EcoRI', 'KpnI'])
+>>> rb += EcoRV
+>>> rb
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])])
+```
+
+Another way to create a RestrictionBatch is by simply adding restriction enzymes
+together, this is particularly useful for small batches:
+
+``` python
+>>> rb3 = EcoRI + KpnI + EcoRV
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+```
+
+#### <a name="2.2"></a>2.2 Restricting a RestrictionBatch to a particular supplier
+
+The Restriction package is based upon the **REBASE** database. This database
+gives a list of suppliers for each enzyme. It would be a shame not to make use
+of this facility. You can produce a `RestrictionBatch` containing only enzymes
+from one or a few supplier(s). Here is how to do it:
+
+``` python
+>>> rb_supp = RestrictionBatch(first=[], suppliers=['C','B','E','I','K','J','M',
+'O','N','Q','S','R','V','Y','X'])
+>>> # This will create a RestrictionBatch with the all enzymes which possess a s
+upplier.
+>>> len(rb_supp)  # May 2016
+622
+```
+
+The argument `suppliers` take a list of one or several single letter codes
+corresponding to the supplier(s). The codes are the same as defined in REBASE.
+As it would be a pain to have to remember each supplier code, `RestrictionBatch`
+provides a method which show the pair code <=> supplier:
+
+``` python
+>>> RestrictionBatch.show_codes()  # as of May 2016 REBASE release.
+C = Minotech Biotechnology
+B = Life Technologies
+E = Agilent Technologies
+I = SibEnzyme Ltd.
+K = Takara Bio Inc.
+J = Nippon Gene Co., Ltd.
+M = Roche Applied Science
+O = Toyobo Biochemicals
+N = New England Biolabs
+Q = Molecular Biology Resources - CHIMERx
+S = Sigma Chemical Corporation
+R = Promega Corporation
+V = Vivantis Technologies
+Y = SinaClon BioScience Co.
+X = EURx Ltd.
+>>> # You can now choose a code and built your RestrictionBatch
+```
+
+This way of producing a `RestrictionBatch` can drastically reduce the amount of
+useless output from a restriction analysis, limiting the search to enzymes that
+you can get hold of and limiting the risks of nervous breakdown. Nothing is more
+frustrating than to get the perfect enzyme for a sub-cloning only to find it's
+not commercially available.
+
+#### <a name="2.3"></a>2.3 Adding enzymes to a RestrictionBatch
+
+Adding an enzyme to a batch if the enzyme is already present will not raise an
+exception, but will have no effects. Sometimes you want to get an enzyme from a
+`RestrictionBatch` or add it to the batch if it is not present.
+You will use the `get` method setting the second argument `add` to `True`.
+
+``` python
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+>>> rb3.add(EcoRI)
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+>>> rb3.get(EcoRI)
+EcoRI
+>>> rb3.get(SmaI)
+
+Traceback (most recent call last):
+  File "<pyshell#4>", line 1, in -toplevel-
+    rb3.get(SmaI)
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1800, in get
+    raise ValueError, 'enzyme %s is not in RestrictionBatch'%e.__name__
+ValueError: enzyme SmaI is not in RestrictionBatch
+>>> rb3.get(SmaI, add=True)
+SmaI
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])
+```
+
+#### <a name="2.4"></a>2.4 Removing enzymes from a RestrictionBatch
+
+Removing enzymes from a batch is done using the `remove()` method. If the enzyme
+is not present in the batch this will raise a `KeyError`. If the value you want
+to remove is not an enzyme this will raise a `ValueError`.
+
+``` python
+>>> rb3.remove(EcoRI)
+>>> rb3
+RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
+>>> rb3.remove(EcoRI)
+
+Traceback (most recent call last):
+  File "<pyshell#14>", line 1, in -toplevel-
+    rb3.remove('EcoRI')
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
+    return Set.remove(self, self.format(other))
+  File "/usr/lib/Python2.3/sets.py", line 534, in remove
+    del self._data[element]
+KeyError: EcoRI
+>>> rb3 += EcoRI
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])
+>>> rb3.remove('EcoRI')
+>>> rb3
+RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
+>>> rb3.remove('spam')
+
+Traceback (most recent call last):
+  File "<pyshell#18>", line 1, in -toplevel-
+    rb3.remove('spam')
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
+    return Set.remove(self, self.format(other))
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
+    raise ValueError, '%s is not a RestrictionType'%y.__class__
+ValueError: <type 'str'> is not a RestrictionType
+```
+
+#### <a name="2.5"></a>2.5 Manipulating RestrictionBatch
+
+You can not, however, add batches together, as they are Python `sets`. You must
+use the pipe operator `|` instead. You can find the intersection between 2
+batches using `&` (see the Python documentation about `sets` for more
+information.
+
+``` python
+>>> rb3 = EcoRI + KpnI + EcoRV
+>>> rb3
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
+>>> rb4 = SmaI + PstI
+>>> rb4
+RestrictionBatch(['PstI', 'SmaI'])
+>>> rb3 + rb4
+
+Traceback (most recent call last):
+  File "<pyshell#23>", line 1, in -toplevel-
+    rb3 + rb4
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1829, in __add__
+    new.add(other)
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1848, in add
+    return Set.add(self, self.format(other))
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
+    raise ValueError, '%s is not a RestrictionType'%y.__class__
+ValueError: <class 'Bio.Restriction.Restriction.RestrictionBatch'> is not a RestrictionType
+>>> rb3 | rb4
+RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'PstI', 'SmaI'])
+>>> rb3 & rb4
+RestrictionBatch([])
+>>> rb4 += EcoRI
+>>> rb4
+RestrictionBatch(['EcoRI', 'PstI', 'SmaI'])
+>>> rb3 & rb4
+RestrictionBatch(['EcoRI'])
+```
+
+#### <a name="2.6"></a>2.6 Analysing sequences with a RestrictionBatch
+
+To analyse a sequence for potential site, you can use the `search` method of the
+batch, the same way you did for restriction enzymes. The results is no longer a
+list however, but a dictionary. The keys of the dictionary are the names of the
+enzymes and the value a list of position site. `RestrictionBatch` does not
+implement a `catalyse` method, as it would not have a real meaning when used
+with large batch.
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> rb.search(new_seq)
+{'KpnI': [], 'EcoRV': [], 'EcoRI': []}
+>>> rb.search(new_seq, linear=False)
+{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
+```
+
+#### <a name="2.7"></a>2.7 Other RestrictionBatch methods
+
+Amongst the other methods provided by `RestrictionBatch`, `elements()` which
+return a list of all the element names alphabetically sorted, is certainly the
+most useful.
+
+``` python
+>>> rb = EcoRI + KpnI + EcoRV
+>>> rb.elements()
+['EcoRI', 'EcoRV', 'KpnI']
+```
+
+If you don't care about the alphabetical order use the method `as_string()`, to
+get the same thing a bit faster. The list is not sorted. The order is random as
+Python sets are dictionary.
+
+``` python
+>>> rb = EcoRI + KpnI + EcoRV
+>>> rb.as_string()
+['EcoRI', 'KpnI', 'EcoRV']
+```
+
+Other `RestrictionBatch` methods are generally used for particular purposes and
+will not be discussed here. See the
+[source](https://github.com/biopython/biopython/tree/master/Bio/Restriction)
+if you are interested.
+
+### <a name="3"></a>3. AllEnzymes and CommOnly: two preconfigured RestrictionBatches
+
+While it is sometime practical to produce a `RestrictionBatch` of your own you
+will certainly more frequently use the two batches provided with the
+`Restriction` packages: `AllEnzymes` and `CommOnly`. These two batches contain
+respectively all the enzymes in the database and only the enzymes which have a
+commercial supplier. They are rather big, but that's what make them useful. With
+these batch you can produce a full description of a sequence with a single
+command. You can use these two batch as any other batch.
+
+``` python
+>>> len(AllEnzymes)
+778
+>>> len(CommOnly)
+622
+>>> AllEnzymes.search(new_seq) ...
+```
+
+There is not a lot to say about them apart the fact that they are present. They
+are really normal batches, and you can use them as any other batch.
+
+### <a name="4"></a>4. The Analysis class: even simpler restriction analysis
+
+`RestrictionBatch` can give you a dictionary with the sites for all the enzymes
+in a batch. However, it is sometime nice to get something a bit easier to read
+than a Python dictionary. Complex restriction analysis are not easy with
+`RestrictionBatch`. Some refinements in the way to search a sequence for
+restriction sites will help. `Analysis` provides a serie of command to customise
+the results obtained from a pair restriction batch/sequence and some facilities
+to make the output sligthly more human readable.
+
+#### <a name="4.1"></a>4.1 Setting up an Analysis
+
+To build a restriction analysis you will need a `RestrictionBatch` and a
+sequence and to tell it if the sequence is linear or circular. The first
+argument `Analysis` takes is the restriction batch, the second is the sequence.
+If the third argument is not provided, `Analysis` will assume the sequence is
+linear.
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
+>>> Ana = Analysis(rb, new_seq, linear=False)
+>>> Ana
+Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAA
+AAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)
+```
+
+#### <a name="4.2"></a>4.2 Full restriction analysis
+
+Once you have created your new `Analysis`, you can use it to get a restriction
+analysis of your sequence. The way to make a full restriction analysis of the
+sequence is:
+
+``` python
+>>> Ana.full()
+{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
+```
+
+This is much the same as the output of a `RestrictionBatch.search` method. You
+will get a more easy to read output with `print_that` used without argument:
+
+``` python
+>>> # let's create a something a bit more complex to analyse.
+>>>
+>>> rb = RestrictionBatch([], ['C'])  # we will explain the meaning of the
+>>>                               # double list argument later.
+>>>
+>>> multi_site = Seq.Seq('AAA' + EcoRI.site + 'G' + KpnI.site + EcoRV.site +
+                     'CT' + SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' +
+                      EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
+>>> Analong = Analysis(rb, multi_site)
+>>> Analong.full()
+{BglI: [], BstEII: [], AsuII: [], HinfI: [], SfiI: [], PspPI: [], BsiSI: [27], S
+alI: [], SlaI: [], NcoI: [], NotI: [], PstI: [], StyI: [], BseBI: [], PvuII: [],
+HindIII: [], BglII: [], ApaLI: [], TaqI: [], BssAI: [], AluI: [], SstI: [], Bse
+CI: [], Sau3AI: [], HpaI: [], SnaBI: [], NheI: [], BclI: [], KpnI: [16], NruI: [
+], MspCI: [], BshFI: [], CspAI: [], RsaI: [14], EcoRV: [20], SphI: [], BamHI: []
+, MboI: [], SgrBI: [], SspI: [], ScaI: [], XbaI: [], SseBI: [], NaeI: [], EcoRI:
+[5, 47], SmaI: [28], BseAI: []}
+>>>
+>>> # The results are here but it is difficult to read. let's try print_that
+>>>
+>>> Analong.print_that()
+
+BsiSI      :  27.
+RsaI       :  14.
+EcoRI      :  5, 47.
+EcoRV      :  20.
+KpnI       :  16.
+SmaI       :  28.
+
+   Enzymes which do not cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+```
+
+Much clearer, is'nt ? The output is optimised for a shell 80 columns wide. If
+the output seems odd, check that the width of your shell is at least 80 columns.
+
+#### <a name="4.3"></a>4.3 Changing the title
+
+You can provide a title to the analysis and modify the sentence 'Enzymes which
+do not cut the sequence', by setting the two optional arguments of `print_that`,
+`title` and `s1`. No formatting will be done on these strings so if you have to
+include the newline (`\n`) as you see fit:
+
+``` python
+>>> Analong.print_that(None, title='sequence = multi_site\n\n')
+
+sequence = multi_site
+
+BsiSI      :  27.
+RsaI       :  14.
+EcoRI      :  5, 47.
+EcoRV      :  20.
+KpnI       :  16.
+SmaI       :  28.
+
+   Enzymes which do not cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+
+>>> Analong.print_that(None, title='sequence = multi_site\n\n',
+                   s1='\n no site:\n\n')
+
+sequence = multi_site
+
+BsiSI      :  27.
+RsaI       :  14.
+EcoRI      :  5, 47.
+EcoRV      :  20.
+KpnI       :  16.
+SmaI       :  28.
+
+ no site:
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+```
+
+#### <a name="4.4"></a>4.4 Customising the output
+
+You can modify some aspects of the output interactively. There is three main
+type of output, two listing types (alphabetically sorted and sorted by number
+of site) and map-like type. To change the output, use the method `print_as()` of
+`Analysis`. The change of output is permanent for the instance of `Analysis`
+(that is until the next time you use `print_as()`). The argument of `print_as()`
+are strings: `'map'`, `'number'` or `'alpha'`. As you have seen previously the
+default behaviour is an alphabetical list (`'alpha'`).
+
+``` python
+>>> Analong.print_as('map')
+>>> Analong.print_that()
+
+    5 EcoRI
+    |
+    |        14 RsaI
+    |        |
+    |        | 16 KpnI
+    |        | |
+    |        | |   20 EcoRV
+    |        | |   |
+    |        | |   |      27 BsiSI
+    |        | |   |      |
+    |        | |   |      |28 SmaI
+    |        | |   |      ||
+    |        | |   |      ||                  47 EcoRI
+    |        | |   |      ||                  |
+AAAGAATTCGGGTACCGATATCCTCCCGGGGTGGATGGAAAGGGCGAATTCACGT
+|||||||||||||||||||||||||||||||||||||||||||||||||||||||
+TTTCTTAAGCCCATGGCTATAGGAGGGCCCCACCTACCTTTCCCGCTTAAGTGCA
+1                                                    55
+
+
+   Enzymes which do not cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+
+>>> Analong.print_as('number')
+>>> Analong.print_that()
+
+
+
+enzymes which cut 1 times :
+
+BsiSI      :  27.
+RsaI       :  14.
+EcoRV      :  20.
+KpnI       :  16.
+SmaI       :  28.
+
+
+enzymes which cut 2 times :
+
+EcoRI      :  5, 47.
+
+   Enzymes which do not cut the sequence.
+
+AluI      BshFI     MboI      Sau3AI    TaqI      BseBI     HinfI     PspPI
+ApaLI     AsuII     BamHI     BclI      BglII     BseAI     BseCI     BssAI
+CspAI     HindIII   HpaI      MspCI     NaeI      NcoI      NheI      NruI
+PstI      PvuII     SalI      ScaI      SgrBI     SlaI      SnaBI     SphI
+SseBI     SspI      SstI      StyI      XbaI      BstEII    NotI      BglI
+SfiI
+
+>>>
+```
+
+To come back to the previous behaviour:
+
+``` python
+>>> Analong.print_as('alpha')
+>>> Analong.print_that()
+
+BsiSI      :  27.
+RsaI       :  14.
+EcoRI      :  5, 47.
+EcoRV      :  20.
+etc ...
+```
+
+#### <a name="4.5"></a>4.5 Fancier restriction analysis
+
+I will not go into the detail for each single method, here are all the functions
+that are available. Most are perfectly self explanatory and the others are
+fairly well documented (use `help('Analysis.command_name')`). The methods are:
+
+``` python
+full(self,linear=True)
+blunt(self,dct = None)
+overhang5(self, dct=None)
+overhang3(self, dct=None)
+defined(self,dct=None)
+with_sites(self, dct=None)
+without_site(self, dct=None)
+with_N_sites(self, N, dct=None)
+with_number_list(self, list, dct=None)
+with_name(self, names, dct=None)
+with_site_size(self, site_size, dct=None)
+only_between(self, start, end, dct=None)
+between(self,start, end, dct=None)
+show_only_between(self, start, end, dct=None)
+only_outside(self, start, end, dct =None)
+outside(self, start, end, dct=None)
+do_not_cut(self, start, end, dct =None)
+```
+
+Using these methods is simple:
+
+``` python
+>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
+>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
+>>> Ana = Analysis(rb, new_seq, linear=False)
+>>> Ana
+Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAA
+AAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)
+>>> Ana.blunt()  # output only the result for enzymes which cut blunt
+{'EcoRV': []}
+>>> Ana.full()  # all the enzymes in the RestrictionBatch
+{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
+>>> Ana.with_sites()  # output only the result for enzymes which have a site
+{'EcoRI': [33]}
+>>> Ana.without_site()  # output only the enzymes which have no site
+{'KpnI': [], 'EcoRV': []}
+>>> Ana.only_between(1, 20)  # the enzymes which cut between position 1 and 20
+{}
+>>> Ana.only_between(20, 34)  # etc...
+{'EcoRI': [33]}
+>>> Ana.only_outside(20, 34)
+{}
+>>> Ana.with_name([EcoRI])
+{'EcoRI': [33]}
+>>>
+```
+
+To get a nice output, you still use `print_that` but this time with the command
+you want executed as argument.
+
+``` python
+>>> Ana.print_that(Ana.blunt())
+
+   Enzymes which do not cut the sequence.
+
+EcoRV
+
+>>> pt = Ana.print_that
+>>> pt(Ana.with_sites())
+
+EcoRI      :  33.
+
+>>> pt(Ana.without_site())
+
+   Enzymes which do not cut the sequence.
+
+EcoRV     KpnI
+
+>>> # etc ...
+```
+
+#### <a name="4.6"></a>4.6 More complex analysis
+
+All of these methods (except `full()` which, well ... do a full restriction
+analysis) can be supplied with an additional dictionary. If no dictionary is
+supplied a full restriction analysis is used as starting point. Otherwise the
+dictionary provided by the argument `dct` is used. The dictionary must be
+formatted as the result of `RestrictionBatch.search`. Therefore of the form
+`{'enzyme_name': [position1, position2],...}`, where *position1* and
+*position2* are integers. All methods list previously output such dictionaries
+and can be used as starting point.
+
+Using this method you can build really complex query by chaining several method
+one after the other. For example if you want all the enzymes which are 5'
+overhang and cut the sequence only once, you have two ways to go:
+
+The hard way consist to build a restriction batch containing only 5' overhang
+enzymes and use this batch to create a new `Analysis` instance and then use the
+method `with_N_sites()` as follow:
+
+``` python
+>>> rbov5 = RestrictionBatch([x for x in rb if x.is_5overhang()])
+>>> Anaov5 = Analysis(rbov5, new_seq, linear=False)
+>>> Anaov5.with_N_sites(1)
+{'EcoRI' : [33]}
+```
+
+The easy solution is to chain several `Analysis` methods. This is possible since
+each method return a dictionary as results and is able to take a dictionary as
+input:
+
+``` python
+>>> Ana.with_N_sites(1, Ana.overhang5())
+{'EcoRI': [33]}
+```
+
+The dictionary is always the last argument whatever the command you use.
+
+The way to prefer certainly depends of the conditions you will use your
+`Analysis` instance. If you are likely to frequently reuse the same batch with
+different sequences, using a dedicated `RestrictionBatch` might be faster as the
+batch is likely to be smaller. Chaining methods is generally quicker when
+working with an interactive shell. In a script, the extended syntax may be
+easier to understand in a few months.
+
+### <a name="5"></a>5. Advanced features: the FormattedSeq class
+
+Restriction enzymes require a much more strict formatting of the DNA sequences
+than `Bio.Seq` object provides. For example, the restriction enzymes expect to
+find an ungapped (no space) upper-case sequence, while `Bio.Seq` object allow
+sequences to be in lower-case separated by spaces. Therefore when a restriction
+enzyme analyse a `Bio.Seq` object (be it a `Seq` or a `MutableSeq`), the object
+undergoes a conversion. The class `FormattedSeq` ensure the smooth conversion
+from a `Bio.Seq` object to something which can be safely be used by the enzyme.
+
+While this conversion is done automatically by the enzymes if you provide them
+with a `Seq` or a `MutableSeq`, there is time where it will be more efficient to
+realise the conversion before hand. Each time a `Seq` object is passed to an
+enzyme for analysis you pay a overhead due to the conversion. When analysing the
+same sequence over and over, it will be faster to convert the sequence, store
+the conversion and then use only the converted sequence.
+
+#### <a name="5.1"></a>5.1 Creating a FormattedSeq
+
+Creating a `FormattedSeq` from a `Bio.Seq` object is simple. The first argument
+of `FormattedSeq` is the sequence you wish to convert. You can specify a shape
+with the second argument `linear`, if you don't the `FormattedSeq` will be
+linear:
+
+``` python
+>>> from Bio.Restriction import *
+>>> from Bio.Seq import Seq
+>>> seq = Seq('TTCAAAAAAAAAAGAATTCAAAAGAA')
+>>> linear_fseq = FormattedSeq(seq, linear=True)
+>>> default_fseq = FormattedSeq(seq)
+>>> circular_fseq = FormattedSeq(seq, linear=False)
+>>> linear_fseq
+FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
+>>> linear_fseq.is_linear()
+True
+>>> default_fseq.is_linear()
+True
+>>> circular_fseq.is_linear()
+False
+>>> circular_fseq
+FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
+```
+
+#### <a name="5.2"></a>5.2 Unlike Bio.Seq, FormattedSeq retains information about their shape
+
+`FormattedSeq` retains information about the shape of the sequence. Therefore
+unlike with `Seq` and `MutableSeq` you don't need to specify the shape of the
+sequence when using `search()` or `catalyse()`:
+
+``` python
+>>> EcoRI.search(linear_fseq)
+[15]
+>>> EcoRI.search(circular_fseq)  # no need to specify the shape
+[15, 25]
+```
+
+In fact, the shape of a FormattedSeq is not altered by the second argument of
+the commands `search()` and `catalyse()`:
+
+``` python
+>>> # In fact the shape is blocked.
+>>> # The 3 following commands give the same results
+>>> # which correspond to a circular sequence
+>>> EcoRI.search(circular_fseq)
+[15, 25]
+>>> EcoRI.search(circular_fseq, linear=True)
+[15, 25]
+>>> EcoRI.search(circular_fseq, linear=False)
+[15, 25]
+```
+
+#### <a name="5.3"></a>5.3 Changing the shape of a FormattedSeq
+
+You can however change the shape of the `FormattedSeq`. The command to use are:
+
+``` python
+FormattedSeq.to_circular() => new FormattedSeq, shape will be circular.
+FormattedSeq.to_linear()   => new FormattedSeq, shape will be linear
+FormattedSeq.circularise() => change the shape of FormattedShape to circular
+FormattedSeq.linearise()   => change the shape of FormattedShape to linear
+```
+
+``` python
+>>> circular_fseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
+>>> circular_fseq.is_linear()
+False
+>>> circular_fseq == linear_fseq
+False
+>>> newseq = circular_fseq.to_linear()
+>>> circular_fseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
+>>> newseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
+>>> circular_fseq.linearise()
+>>> circular_fseq
+FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
+>>> circular_fseq.is_linear()
+True
+>>> circular_fseq == linear_fseq
+True
+>>> EcoRI.search(circular_fseq) # which is now linear
+[15]
+```
+
+#### <a name="5.4"></a>5.4 Using / and // operators with FormattedSeq
+
+Not having to specify the shape of the sequence to analyse gives you the
+opportunity to use the shorthand '/' and '//' with restriction enzymes:
+
+``` python
+>>> EcoRI/linear_fseq  # <=> EcoRI.search(linear_fseq)
+[15]
+>>> linear_fseq/EcoRI  # <=> EcoRI.search(linear_fseq)
+[15]
+>>> EcoRI//linear_fseq # <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)
+(Seq('TTCAAAAAAAAAAG', Alphabet()), Seq('AATTCAAAAGAA', Alphabet()))
+```
+
+Another way to avoid the overhead due to a repetitive conversion from a `Seq`
+object to a `FormattedSeq` is to use a [`RestrictionBatch`](#2).
+
+To conclude, the performance gain achieved when using a `FormattedSeq` instead
+of a `Seq` is not huge. The analysis of a 10 kb sequence by all the enzymes in
+`AllEnzymes` (`for x in AllEnzymes: x.search(seq)`, 867 enzymes) is 7 % faster
+when using a `FormattedSeq` than a `Seq`. Using a `RestrictionBatch`
+(`AllEnzymes.search(seq)`) is about as fast as using a `FormattedSeq` the first
+time the search is run. This however is dramatically reduced in subsequent runs
+with the same sequence (`RestrictionBatch` keeps in memory the result of their
+last run while the sequence is not changed).
+
+### <a name="6"></a>6.  More advanced features
+
+This chapter addresses some more advanced features of the packages, most users
+can safely ignore it.
+
+#### <a name="6.1"></a>6.1 Updating the enzymes from REBASE
+
+Most people will certainly not need to update the enzymes. The restriction
+enzyme package will be updated in with each new release of Biopython. But if you
+wish to get an update in between Biopython-releases here is how to do it.
+
+First, you have to download the two scripts `rebase_update.py` and
+`ranacompile.py`:
+Go to https://github.com/biopython/biopython/tree/master/Scripts/Restriction,
+click on the respective file and press the '**Raw**' button on the top right of
+the code window. Then, with right-click, save the file. Both scripts must be in
+the same directory.
+
+##### <a name="6.1.1"></a>6.1.1 Fetching the recent enzyme files manually from REBASE
+
+Each month, [REBASE](http://rebase.neb.com/rebase/rebase.html) release a new
+compilation of data about restriction enzymes. While the enzymes do not change
+so frequently, you may wish to update the restriction enzymes classes. The first
+thing to do is to get the last rebase file. You can find the release of REBASE
+at http://rebase.neb.com/rebase/rebase.files.html. The file you are interested
+in are in the EMBOSS format. You can download the files directly from the REBASE
+ftp server using your browser. The file are situated at
+ftp://ftp.neb.com/pub/rebase.
+You will have to download 3 files: `emboss_e.###`, `emboss_r.###`, and
+`emboss_s.###`.
+The `###` is a 3 digit number corresponding to the year and month of the
+release. The first digit is the year, the two last are the month: so July 2015
+will be: 507; October 2016: 610, etc... Download the three file corresponding to
+the current month and place them in the same folder as your `rebase_update.py`
+and `ranacompiler.py` scripts.
+
+##### <a name="6.1.2"></a>6.1.2 Fetching the recent enzyme files with rebase_update.py
+
+Another way to do the same thing is to use the `rebase_update.py` script. It
+will connect directly to the rebase ftp server and download the last batch of
+emboss files. From a DOS or Unix shell do the following:
+
+``` bash
+$ cd path_to_the_update_script
+$ rebase_update.py -p http://www.somewhere.com:8000
+
+Please wait, trying to connect to Rebase
+
+copying ftp://ftp.neb.com/pub/rebase/emboss_e.407
+to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_e.407
+copying ftp://ftp.neb.com/pub/rebase/emboss_s.407
+to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_s.407
+copying ftp://ftp.neb.com/pub/rebase/emboss_r.407
+to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_r.407
+```
+
+Some explanation are needed: `-p` is the switch to indicate to the script you
+are using a proxy. If you use a ftp proxy enter its address and the connection
+port after the '`:`'.
+
+
+##### <a name="6.1.3"></a>6.1.3 Compiling a new dictionary with ranacompiler.py
+
+Once you have got the recent emboss files you can compile a new module
+containing the data necessary to create restriction enzyme.
+
+Note: if the emboss files are not present in the current directory or if they
+are not up to date, `ranacompiler.py` will invoke the script
+[`rebase_update.py`](#6.1.2), which needs to be installed in the same folder.
+You will need to use the same options as before (ie `-m` and `-p`). See the
+previous paragraph on [`rebase_update.py`](#6.1.2) for more details.
+
+For simplicity let's assume we have put the emboss files in the same folder as
+the files which contains the script `ranacompiler.py`. You may have the change
+the mode of the file to make it executable:
+
+``` bash
+$ cd path_to_the_ranacompiler_script
+$ chmod '+x' ranacompiler.py
+```
+
+Now execute the script:
+
+``` bash
+$ Python ranacompiler.py  # or ./ranacompiler.py
+```
+
+You get normally the following message:
+
+``` bash
+$ ./ranacompiler.py
+
+ Using the files : emboss_e.407, emboss_r.407, emboss_s.407
+
+WARNING : HaeIV cut twice with different overhang length each time.
+        Unable to deal with this behaviour.
+        This enzyme will not be included in the database. Sorry.
+        Checking :
+Anyway, HaeIV is not commercially available.
+
+WARNING : HpyUM037X has two different sites.
+
+
+The new database contains 867 enzymes.
+
+Writing the dictionary containing the new Restriction classes...
+OK.
+
+Writing the dictionary containing the suppliers datas...
+OK.
+
+Writing the dictionary containing the Restriction types....
+OK.
+
+ ******************************************************************************
+
+                Compilation of the new dictionary : OK.
+                Installation : No.
+
+ You will find the newly created 'Restriction_Dictionary.py' file
+ in the  :
+
+        /path/where/you/run/ranacompiler.py
+
+ Make a copy of 'Restriction_Dictionary.py' and place it with
+ the other Restriction libraries.
+
+ note :
+ This folder should be :
+
+        path_to_python/site-packages/Bio/Restriction
+
+ ******************************************************************************
+```
+
+The first line indicate which emboss files have been used for the present
+compilation. You can safely ignore the warnings as long as the
+`compilation of the new dictionary : OK.` is present in the last part of the
+output. They are here for debugging purpose. The number of enzymes in the new
+module is indicated as well as a list of the dictionary which have been
+compiled. The last part indicate that the module has been succesfully created
+but not installed. To finish the update you must copy the file
+`Restriction_Dictionary.py` into the folder
+`/your_python_path/site-packages/Bio/Restriction/` as indicated by the script.
+Looking into the present folder, you will see to new files: the newly created
+dictionary `Restriction_Dictionary.py` and `Restriction_Dictionary.old`. This
+last file containing the old dictionary to which you can revert in case anything
+the new file is corrupted (this should not happen since the script is happy
+enough the new dictionary is good, but if there is a problem it is always nice
+to know you can revert to the previous setting without having to reinstall the
+whole thing.
+
+If you whish, the script may install the folder for you as well, but you will
+have to run it as root if your normal user has no write access to your Python
+installation (and it should'nt). Use the command `ranacompiler.py -i` or
+`ranacompiler.py --install` for this.
+
+If anything goes wrong (you have no write access to the destination folder for
+example) the script will let you know it did not perform the installation. It
+will however still save the new module in the current directory.
+
+As you can see the script is not very bright and will redo the compilation each
+time it is invoked, no matter if a previous version of the module is already
+present.
+
+#### <a name="6.2"></a>6.2 Subclassing the class Analysis
+
+As seen previously, you can modify some aspects of the `Analysis` output
+interactively. However if you want to write your own `Analysis` class, you may
+wish to provide others output facilities than is given in this package.
+Depending on what you want to do you may get away with simply changing the
+`make_format` method of your derived class or you will need to provide new
+methods. Rather than get into a long explanation, here is the implementation of
+a rather useless `Analysis` class:
+
+``` python
+>>> class UselessAnalysis(Analysis):
+
+    def __init__(self, rb=RestrictionBatch(), seq=Seq(''), lin=True):
+    """UselessAnalysis -> A class that waste your time"""
+    #
+    #    Unless you want to do something more fancy all
+    #    you need to do here is instantiate Analysis.
+    #    Don't forget the self in __init__
+    #
+        Analysis.__init__(self, rb, seq, lin)
+
+    def make_format(self, cut=[], t='', nc=[], s=''):
+    """not funny"""
+    #
+    #    Generally, you don't need to do anything else here
+    #    This will tell to your new class to default to the
+    #    _make_joke format.
+    #
+        return self._make_joke(cut, t, nc, s)
+
+    def print_as(self, what='joke'):
+    """Never know somebody might want to change the behaviour of
+    this class."""
+    #
+    #    add your new option to print_as
+    #
+        if what == 'joke':
+        self.make_format = self._make_joke
+            return
+    else:
+        #
+        #   The other options will be treated as before
+        #
+        return Analysis.print_as(self, what)
+
+    def _make_joke(self, cut=[], title='', nc=[], s1=''):
+    """UA._make_joke(cut, t, nc, s) -> new analysis output"""
+    #
+    #    starting your new method with '_make_'
+    #    will give a hint to what it is suppose to do
+    #
+    #    We will not process the non-cutting enzymes
+    #    Their names are in nc
+    #    s1 is the string printed before them
+    #
+    if not title:
+        title = '\nYou have guessed right the following enzymes:\n\n'
+    for name, sites in cut:
+        #
+        #    cut contains:
+        #    - the name of the enzymes which cut the sequence (name)
+        #    - a list of the site positions (sites)
+        #
+        guess = raw_input("next enzyme is %s, Guess how many sites ?\n>>> "%name)
+            try:
+                guess = int(guess)
+            except:
+                guess = None
+            if guess == len(sites):
+                print 'You did guess right. Good. Next.'
+        result = '%i site' % guess
+        if guess > 1:
+            result += 's'
+
+        #
+        #    now we format the line. See the PrintFormat module
+        #    for some examples
+        #   PrintFormat.__section_list and _make_map are good start.
+        #
+                title=''.join((title, str(name).ljust(self.NameWidth),
+                ' :  ', result, '.\n'))
+    print '\nNo more enzyme.'
+        return  title
+    #
+    #    I you want to print the non cutting enzymes use
+    #    the following return instead of the previous one:
+    #
+    #return  title + t + self._make_nocut_only(nc,s1)
+
+>>> # You initiate and use it as before
+>>> rb = RestrictionBatch([], ['A'])
+>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\
+SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
+>>>
+>>> b = UselessAnalysis(rb, multi_site)
+>>> b.print_that() # Well, I let you discover if you haven't already guessed
+```
+
+Using this example, as a template you should now be able to subclass `Analysis`
+as you wish. You will found more implementation details in the module
+`Bio.Restriction.PrintFormat` which contains the class providing all the
+`_make_*` methods.
+
+### <a name="7"></a>7. Limitation and caveat
+
+Particularly, the class `Analysis` is a quick and dirty implementation based on
+the facilities furnished by the package. Please check your results and report
+any fault.
+
+On a more general basis, `Restriction` have some other limitations:
+
+#### <a name="7.1"></a>7.1 All DNA are non methylated
+
+No facility to work with methylated DNA has been implemented yet. As far as the
+enzyme classes are concerned all DNA is non methylated DNA. Implementation of
+methylation sensibility will possibly occur in the future. But for now, if your
+sequence is methylated, you will have to check if the site is methylated using
+other means.
+
+#### <a name="7.2"></a>7.2 No support for star activity
+
+As before no support has been yet implemented to find site mis-recognised by
+enzymes under high salt concentration conditions, the so-called star activity.
+This will be implemented as soon as I can get a good source of information for
+that.
+
+#### <a name="7.3"></a>7.3 Safe to use with degenerated DNA
+
+It is safe to use degenerated DNA as input for the query. You will not be
+flooded with meaningless results. But this come at a price: GAA***N***TC will
+not be recognised as a potential EcoRI site for example, in fact it will not be
+recognised at all. Degenerated sequences will not be analysed. If your sequence
+is not fully sequenced, you will certainly miss restriction sites:
+
+``` python
+>>> a = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr', IUPACAmbiguou
+sDNA())
+>>> EcoRI.search(a)
+[36]
+>>> b = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr', IUPACAmbiguou
+sDNA())
+>>> EcoRI.search(b)
+[]
+```
+
+#### <a name="7.4"></a>7.4 Non standard bases in DNA are not allowed
+
+While you can use degenerated DNA, using non standard base alphabet will make
+the enzymes choke, even if `Bio.Seq.Seq` accepts them. However, space-like
+characters (' ', '\n', '\t', ...) and digit will be removed but will not stop
+the enzyme analysing the sequence. You can use them but the fragments produced
+by `catalyse` will have lost any formatting. `catalyse` tries to keep the
+original case of the sequence (i.e lower case sequences will generate lower case
+fragments, upper case sequences upper case fragments), but mixed case will
+return upper case fragments:
+
+``` python
+>>> c = Seq('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr', IUPACAmbiguou
+sDNA())
+>>> EcoRI.search(c)
+
+Traceback (most recent call last):
+  File "<pyshell#110>", line 1, in -toplevel-
+    EcoRI.search(b)
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 3
+  96, in search
+    cls.dna = FormatedSeq(dna, linear)
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1
+  37, in __init__
+    self.format()
+  File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1
+  53, in format
+    raise AlphabetError, " '%s' is not in the IUPAC alphabet" % s
+AlphabetError: 'X' is not in the IUPAC alphabet
+>>> d = Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n\
+26 nnnnn nnnnG AATTC rrrrr rrrrr \n\
+51 r', IUPACAmbiguousDNA())
+>>> d
+Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n26 nnnnn nnnnG AATTC rrrrr rrrrr \n51 r',
+ IUPACAmbiguousDNA())
+>>> EcoRI.search(d)
+[36]
+>>> EcoRI.catalyse(d)
+(Seq('AATTCRRRRRRRRRRR', IUPACAmbiguousDNA()), Seq('NNNNNNNNNNNNNNNNNNNNNNNNNNNN
+NNNNNNG', IUPACAmbiguousDNA()))
+>>> e = Seq('nnnnGAATTCrr', IUPACAmbiguousDNA())
+>>> f = Seq('NNNNGAATTCRR', IUPACAmbiguousDNA())
+>>> g = Seq('nnnngaattcrr', IUPACAmbiguousDNA())
+>>> EcoRI.catalyse(e)
+(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
+>>> EcoRI.catalyse(f)
+(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
+>>> EcoRI.catalyse(g)
+(Seq('nnnng', IUPACAmbiguousDNA()), Seq('aattcrr', IUPACAmbiguousDNA()))
+```
+
+Not allowing other letters than IUPAC might seems drastic but this is really to
+limit errors. It is not totally fool proof but it does help.
+
+#### <a name="7.5"></a>7.5 Sites found at the edge of linear DNA might not be accessible in a real digestion
+
+While sites clearly outsides a sequence will not be reported, nothing has been
+done to try to determine if a restriction site at the end of a linear sequence
+is valid:
+
+``` python
+>>> d = Seq('GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG', IUPACAmbiguousDNA())
+>>> FokI.site           # site present
+'GGATG'
+>>> FokI.elucidate()        # but cut outside the sequence
+'GGATGNNNNNNNNN^NNNN_N'
+>>> FokI.search(d)      # therefore no site found
+[]
+>>> EcoRI.search(d)
+[2]
+```
+
+`EcoRI` finds a site at position 2 even if it is highly unlikely that EcoRI
+accepts to cut this site in a tube. It is generally considered that at about 5
+nucleotides must separate the site from the edge of the sequence to be
+reasonably sure the enzyme will work correctly. This "security margin" is
+variable from one enzyme to the other. In doubt consult the documentation for
+the enzyme.
+
+#### <a name="7.6"></a>7.6 Restriction reports cutting sites not enzyme recognition sites
+
+Some enzymes will cut twice each time they encounter a restriction site. The
+enzymes in this package report both cut not the site. Other software may only
+reports restriction sites. Therefore the output given for some enzymes might
+seems to be the double when compared with the results of these software. It is
+not a bug.
+
+``` python
+>>> AloI.cut_twice()
+True
+>>> AloI.fst5              # first cut
+-7
+>>> AloI.scd5          # second cut
+25
+>>> AloI.site
+'GAACNNNNNNTCC'
+>>> b = Seq('AAAAAAAAAAA'+ AloI.site + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
+>>> b
+Seq('AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA', Alphabet())
+>>> AloI.search(b)  # one site, two cuts -> two positions
+[5, 37]
+```
+
+### <a name="8"></a>8.  Annex: modifying dir() to use with from Bio.Restriction import *
+
+Having all the enzymes imported directly in the shell is useful when working in
+an interactive shell (even if it is not recommended by the purists). Here is a
+little hack to get some sanity back when using dir() in those conditions:
+
+``` python
+>>> # we will change the builtin dir() function to get ride of the enzyme names.
+>>> import sys
+>>> def dir(object=None):
+    """dir([object]) -> list of string.
+
+    over-ride the built-in function to get some clarity."""
+    if object:
+        # we only want to modify dir(),
+        # so here we return the result of the builtin function.
+        return __builtins__.dir(object)
+    else:
+        # now the part we want to modify.
+        # All the enzymes are in a RestrictionBatch (we will talk about
+        # that later, for the moment simply believe me).
+        # So if we remove from the results of dir() everything which is
+        # in AllEnzymes we will get a much shorter list when we do dir()
+        #
+        # the current level is __main__ ie dir() is equivalent to
+        # ask what's in __main__ at the moment.
+        # we can't access __main__ directly.
+        # so we will use sys.modules['__main__'] to reach it.
+        # the following list comprehension remove from the result of
+        # dir() everything which is also present in AllEnzymes.
+        #
+        return [x for x in __builtins__.dir(sys.modules['__main__'])
+            if not x in AllEnzymes]
+
+>>> # now let's see if it works.
+>>> dir()
+['AllEnzymes', 'Analysis', 'CommOnly', 'NonComm', 'PrintFormat', 'RanaConfig',
+ 'Restriction', 'RestrictionBatch', 'Restriction_Dictionary', '__builtins__',
+ '__doc__', '__name__', 'dir', 'sys']
+>>> # ok that's much better.
+>>> # The enzymes are still there
+>>> EcoRI.site
+'GAATTC'
+```
diff --git a/Doc/cookbook/motif/Makefile b/Doc/cookbook/motif/Makefile
deleted file mode 100644
index f82d0d7..0000000
--- a/Doc/cookbook/motif/Makefile
+++ /dev/null
@@ -1,30 +0,0 @@
-# create pdf, txt and html documentation
-
-FILENAME=motif
-
-all: ${FILENAME}.html ${FILENAME}.pdf ${FILENAME}.txt
-
-clean:
-	rm -f ${FILENAME}.aux
-	rm -f ${FILENAME}.log
-	rm -f ${FILENAME}.out
-	rm -f ${FILENAME}.toc
-	rm -f ${FILENAME}.haux
-	rm -f ${FILENAME}.htoc
-
-distclean: clean
-	rm -f ${FILENAME}.pdf
-	rm -f ${FILENAME}.html
-	rm -f ${FILENAME}.txt
-
-${FILENAME}.pdf: ${FILENAME}.tex
-	pdflatex ${FILENAME}.tex
-	pdflatex ${FILENAME}.tex
-	pdflatex ${FILENAME}.tex
-
-${FILENAME}.html: ${FILENAME}.tex
-	hevea -fix ${FILENAME}.tex
-
-${FILENAME}.txt: ${FILENAME}.tex
-	hevea -fix -text ${FILENAME}.tex
-
diff --git a/Doc/cookbook/motif/motif.tex b/Doc/cookbook/motif/motif.tex
deleted file mode 100644
index 6b0a426..0000000
--- a/Doc/cookbook/motif/motif.tex
+++ /dev/null
@@ -1,590 +0,0 @@
-% Documentation describing writing Tests for Biopython modules
-\documentclass{article}
-\usepackage{url}
-\usepackage{fullpage}
-\usepackage{hevea}
-\usepackage{graphicx}
-
-% Make links between references
-\usepackage{hyperref}
-\newif\ifpdf
-\ifx\pdfoutput\undefined
-  \pdffalse
-\else
-  \pdfoutput=1
-  \pdftrue
-\fi
-\ifpdf
-  \hypersetup{colorlinks=true, hyperindex=true, citecolor=red, urlcolor=blue}
-\fi
-
-\begin{document}
-
-\title{Sequence motif analysis using  Biopython}
-\author{Bartek Wilczynski (bartek at mimuw.edu.pl)}
-
-\maketitle
-\tableofcontents
-
-\section{Short introduction}
-\label{sec:intro}
-This short tutorial describes some of the functionality of the
-\verb|Bio.Motif| package included in Biopython distribution. It is intended
-for people who are involved in analysis of sequence motif, so I'll
-assume that you are familiar with basic notions of motif analysis. In
-case something is unclear, please look into Section \ref{sec:links}
-for some relevant links.
-
-It should be also noted, that \verb|Bio.Motif| is based on two
-Biopython modules, \verb|Bio.AlignAce| and \verb|Bio.MEME| and is
-meant to replace them. It provides (almost) all of the functionality
-of these modules and unifies the basic motif object implementation.
-
-Speaking of other libraries, if you are reading this you might be
-interested in the TAMO \cite{tamo}
-(\url{http://fraenkel.mit.edu/TAMO/}), which is another python library
-designed to deal with sequence motifs. It supports more \emph{de-novo}
-motif finders, but it is not a part of biopython (so requires a bit of
-work) and has some restrictions on commercial use.
-
-\section{Motif objects}
-\label{sec:object}
-Since we are interested in motif analysis, we need to take a look at
-\verb|Motif| objects in the first place. For that we need to import 
-the Motif library:
-\begin{verbatim}
-from  Bio import Motif
-\end{verbatim}
-and we can start creating our first motif objects. Let's create a DNA motif:
-\begin{verbatim}
-from Bio.Alphabet import IUPAC
-m=Motif.Motif(alphabet=IUPAC.unambiguous_dna)
-\end{verbatim}
-This is for now just an empty container, so let's add some sequences to our newly created motif:
-\begin{verbatim}
-from Bio.Seq import Seq
-m.add_instance(Seq("TATAA", m.alphabet))
-m.add_instance(Seq("TATTA", m.alphabet))
-m.add_instance(Seq("TATAA", m.alphabet))
-m.add_instance(Seq("TATAA", m.alphabet))
-\end{verbatim}
-Now we have a full \verb|Motif| instance, so we can try to get some
-basic information about it. Let's start with length and consensus
-sequence:
-\begin{verbatim}
->>> m.length
-5
->>> m.consensus
-Seq('TATAA', IUPACUnambiguousDNA())
-\end{verbatim}
-In case of DNA motifs, we can also get a reverse complement of a motif:
-\begin{verbatim}
->>> m.reverse_complement()
-<Bio.Motif.Motif.Motif object at 0xb39890>
->>> m.reverse_complement().consensus
-Seq('TTATA', IUPACUnambiguousDNA())
->>> m.reverse_complement().instances  
-[Seq('TTATA', IUPACUnambiguousDNA()), 
- Seq('TAATA', IUPACUnambiguousDNA()), 
- Seq('TTATA', IUPACUnambiguousDNA()), 
- Seq('TTATA', IUPACUnambiguousDNA())]
-\end{verbatim}
-
-We can also calculate the information content of a motif with a simple call:
-\begin{verbatim}
->>> m.ic()
-5.2735010263278932
-\end{verbatim}
-This gives us a number of bits of information provided by the motif,
-which tells us how much we are different from background.
-
-The most common representation of a motif is a PWM (Position Weight
-Matrix). It summarizes the probabilities of finding any symbol (in
-this case nucleotide) in any position of a motif. It can be computed by calling the \verb|.pwm()| method:
-\begin{verbatim}
->>> m.pwm()
-[{'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, 
- {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}, 
- {'A': 0.05, 'C': 0.05, 'T': 0.85, 'G': 0.05}, 
- {'A': 0.65, 'C': 0.05, 'T': 0.25, 'G': 0.05}, 
- {'A': 0.85, 'C': 0.05, 'T': 0.05, 'G': 0.05}]
-\end{verbatim}
-The probabilities in the motif's PWM are based on the counts in the
-instances, but we can see, that even though there were no Gs and no Cs
-in the instances, we still have non-zero probabilities assigned to
-them. These come from pseudo-counts which are, roughly speaking, a
-commonly used way to acknowledge the incompleteness of our knowledge
-and avoid technical problems with calculating logarithms of $0$.
-
-We can control the way that pseudo-counts are added with two
-properties of Motif objects \verb|.background| is the probability
-distribution over all symbols in the alphabet that we assume is found
-in background (usually based on the GC content of the respective
-genome). It is by default set to a uniform distribution upon creation of a motif:
-\begin{verbatim}
->>> m.background  
-{'A': 0.25, 'C': 0.25, 'T': 0.25, 'G': 0.25}
-\end{verbatim}
-The other parameter is \verb|.beta|, which states the amount of
-pseudo-counts we should add to the PWM. By default it is set to $1.0$,
-\begin{verbatim}
->>> m.beta
-1.0
-\end{verbatim}
-so that the total input of pseudo-counts is equal to that of one instance. 
-
-Using the background distribution and pwm with pseudo-counts added,
-it's easy to compute the log-odds ratios, telling us what are the log
-odds of a particular symbol to be coming from a motif against the
-background. We can use the \verb|.log_odds()| method:
-
-\begin{verbatim}
->>> m.log_odds()
-[{'A': -2.3219280948873622, 
-  'C': -2.3219280948873622, 
-  'T': 1.7655347463629771, 
-  'G': -2.3219280948873622}, 
- {'A': 1.7655347463629771, 
-  'C': -2.3219280948873622, 
-  'T': -2.3219280948873622, 
-  'G': -2.3219280948873622}, 
- {'A': -2.3219280948873622, 
-  'C': -2.3219280948873622, 
-  'T': 1.7655347463629771, 
-  'G': -2.3219280948873622}, 
- {'A': 1.3785116232537298, 
-  'C': -2.3219280948873622, 
-  'T': 0.0, 
-  'G': -2.3219280948873622}, 
- {'A': 1.7655347463629771, 
-  'C': -2.3219280948873622, 
-  'T': -2.3219280948873622, 
-  'G': -2.3219280948873622}
-]
-\end{verbatim}
-Here we can see positive values for symbols more frequent in the motif
-than in the background and negative for symbols more frequent in the
-background. $0.0$ means that it's equally likely to see a symbol in
-background and in the motif (e.g. 'T' in the second-last position).
-
-\subsection{Reading and writing}
-\label{sec:io}
-
-Creating motifs from instances by hand is useful but boring, so it's
-useful to have some I/O functions for reading and writing
-motifs. There are no really well established standards for storing
-motifs, but there's a couple of formats which are more used than
-others. The most important distinction is whether the motif
-representation is based on instances or on some version of PWM matrix.
-On of the most popular motif databases JASPAR
-(\url{http://jaspar.genereg.net}) stores motifs in both formats, so
-let's look at how we can import JASPAR motifs from instances:
-\begin{verbatim}
-arnt = Motif.read(open("Arnt.sites"), "jaspar-sites")
-\end{verbatim}
-and from a count matrix:
-\begin{verbatim}
-srf = Motif.read(open("SRF.pfm"), "jaspar-pfm")
-\end{verbatim}
-
-The \verb|arnt| and \verb|srf| motifs can both do the same things for
-us, but they use different internal representations of the motif. We
-can tell that by inspecting the \verb|has_counts| and
-\verb|has_instances| properties:
-\begin{verbatim}
->>> arnt.has_instances
-True
->>> srf.has_instances
-False
->>> srf.has_counts
-True
->>> srf.counts
-{'A': [2, 9, 0, 1, 32, 3, 46, 1, 43, 15, 2, 2],
- 'C': [1, 33, 45, 45, 1, 1, 0, 0, 0, 1, 0, 1],
- 'G': [39, 2, 1, 0, 0, 0, 0, 0, 0, 0, 44, 43],
- 'T': [4, 2, 0, 0, 13, 42, 0, 45, 3, 30, 0, 0]}
-\end{verbatim}
-
-There are conversion functions, which can help us convert between
-different representations:
-\begin{verbatim}
->>> arnt.make_counts_from_instances()
-{'A': [8, 38, 0, 0, 0, 0],
- 'C': [32, 0, 40, 0, 0, 0],
- 'G': [0, 2, 0, 40, 0, 40],
- 'T': [0, 0, 0, 0, 40, 0]}
-
->>> srf.make_instances_from_counts()
-[Seq('GGGAAAAAAAGG', IUPACUnambiguousDNA()),
- Seq('GGCCAAATAAGG', IUPACUnambiguousDNA()),
- Seq('GACCAAATAAGG', IUPACUnambiguousDNA()),
-....
-\end{verbatim}
-The important thing to remember here is that the method
-\verb|make_instances_from_counts()| creates fake instances, because
-usually there are very many possible sets of instances which give rise
-to the same pwm, and if we have only the count matrix, we cannot
-reconstruct the original one. This does not make any difference if we
-are using the PWM as the representation of the motif, but one should
-be careful with exporting instances from count-based motifs.
-
-Speaking of exporting, let's look at export functions. We can export to fasta:
-\begin{verbatim}
->>> print(m.format("fasta"))
-> instance 0
-TATAA
-> instance 1
-TATTA
-> instance 2
-TATAA
-> instance 3
-TATAA
-\end{verbatim}
-or to TRANSFAC-like matrix format (used by some motif processing software)
-\begin{verbatim}
->>> print(m.format("transfac"))
-XX
-TY Motif
-ID 
-BF undef
-P0 G A T C
-01 0 0 4 0
-02 0 4 0 0
-03 0 0 4 0
-04 0 3 1 0
-05 0 4 0 0
-XX
-\end{verbatim}
-
-Finally, if we have internet access, we can create a weblogo using a nice service at \url{http://weblogo.berkeley.edu} by Crooks et al. \cite{crooks2004}:
-\begin{verbatim}
->>> arnt.weblogo("Arnt.png")
-\end{verbatim}
-We should get our logo saved as a png in the specified file.
-
-\subsection{Searching for instances}
-\label{sec:search}
-
-The most frequent use for a motif is to find its instances in some
-sequence. For the sake of this section, we will use an artificial sequence like this:
-
-\begin{verbatim}
-test_seq=Seq("TATGATGTAGTATAATATAATTATAA",m.alphabet)
-\end{verbatim}
-
-The simplest way to find instances, is to look for exact matches of
-the true instances of the motif:
-\begin{verbatim}
->>> for pos, seq in m.search_instances(test_seq):
-...     print("%i %s" % (pos, seq))
-... 
-10 TATAA
-15 TATAA
-21 TATAA
-\end{verbatim}
-We can do the same with the reverse complement (to find instances on the complementary strand):
-\begin{verbatim}
->>> for pos, seq in m.reverse_complement().search_instances(test_seq):
-...     print("%i %s" % (pos, seq))
-... 
-12 TAATA
-20 TTATA
-\end{verbatim}
-
-It's just as easy to look for positions, giving rise to high log-odds scores against our motif:
-\begin{verbatim}
->>> for pos, score in m.search(test_seq,threshold=5.0):
-...     print("%i %f" % (pos, score))
-... 
-10 8.44065060871
--12 7.06213898545
-15 8.44065060871
--20 8.44065060871
-21 8.44065060871
-\end{verbatim}
-You may notice the threshold parameter, here set arbitrarily to
-$5.0$. This is in $log_2$, so we are now looking only for words, which
-are 32 times more likely to occur under the motif model than in the
-background. The default threshold is $0.0$, which selects everything
-that looks more like the motif, than the background.
-
-If you want to use a less arbitrary way of selecting thresholds, you
-can explore the \verb|Motif.score_distribution| class implementing an
-distribution of scores for a given motif. Since the space for a score
-distribution grows exponentially with motif.length, we are using an
-approximation with a given precision to keep computation cost manageable:
-\begin{verbatim}
->>> sd = Motif.score_distribution(m,precision=10**4)
-\end{verbatim}
-The \verb+sd+ object can be used to determine a number of different thresholds.
-
-We can specify the requested false-positive rate (probability of ``finding'' a motif instance in background generated sequence):
-\begin{verbatim}
->>> sd.threshold_fpr(0.01)
-4.3535838726139886
-\end{verbatim}
-
-or the false-negative rate (probability of ``not finding'' an instance generated from the motif):
-\begin{verbatim}
->>> sd.threshold_fnr(0.1)
-0.26651713652234044
-\end{verbatim}
-
-or a threshold (approximately) satisfying some relation between fpr
-and fnr ($\frac{fnr}{fpr}$, as proposed by Rahmann \cite{Rahmann2003}):
-\begin{verbatim}
->>> sd.threshold_balanced(1000)
-8.4406506087056368
-\end{verbatim}
-
-or a threshold satisfying (roughly) the equality between the
-false-positive rate and the $-log$ of the information content (as used
-in patser software by Hertz and Stormo).
-
-For example, in case of our motif, you can get the threshold giving
-you exactly the same results (for this sequence) as searching for
-instances with balanced threshold with rate of $1000$.
-\begin{verbatim}
->>> threshold = sd.threshold_balanced(1000)
->>> for pos, score in m.search(test_seq, threshold=threshold):
-...     print("%s %s" % (pos, score))
-... 
-10 8.44065060871
-15 8.44065060871
--20 8.44065060871
-21 8.44065060871
-\end{verbatim}
-
-\subsection{Comparing motifs}
-\label{sec:comp}
-Once we have more than one motif, we might want to compare them. For
-that, we have currently three different methods of \verb|Bio.Motif|
-objects.
-
-Before we start comparing motifs, I should point out that motif
-boundaries are usually quite arbitrary. This means, that we often need
-to compare motifs of different lengths, so comparison needs to involve
-some kind of alignment.  This means, that we have to take into account two things:
-\begin{itemize}
-\item alignment of motifs
-\item some function to compare aligned motifs
-\end{itemize}
-In \verb|Bio.Motif| we have 3 different functions for motif
-comparison, which are based on the same idea behind motif alignment,
-but use different functions to compare aligned motifs. Briefly
-speaking, we are using ungapped alignment of PWMs and substitute the
-missing columns at the beginning and end of the matrices with
-background distribution. All three comparison functions are written in
-such a way, that they can be interpreted as distance measures, however
-only one (\verb|dist_dpq|) satisfies the triangle inequality. All of
-them return the minimal distance and the corresponding offset between
-motifs.
-
-To show how these functions work, let us first load another motif,
-which is similar to our test motif \verb|m|:
-\begin{verbatim}
->>> ubx=Motif.read(open("Ubx.pfm"), "jaspar-pfm")
-<Bio.Motif.Motif.Motif object at 0xc29b90>
->>> ubx.consensus
-Seq('TAAT', IUPACUnambiguousDNA())
-\end{verbatim}
-
-The first function we'll use to compare these motifs is based on
-Pearson correlation. Since we want it to resemble a distance
-measure. we actually take $1-r$, where r is the Pearson correlation
-coefficient (PCC):
-\begin{verbatim}
->>> m.dist_pearson(ubx)
-(0.41740393308237722, 2)
-\end{verbatim}
-This means, that the best PCC between motif \verb|m| and  \verb|Ubx| is obtained with the following alignment:
-\begin{verbatim}
-bbTAAT
-TATAAb
-\end{verbatim}
-where \verb|b| stands for background distribution. The PCC itself is
-roughly $1-0.42=0.58$. If we try the reverse complement of the Ubx motif:
-
-\begin{verbatim}
->>> m.dist_pearson(ubx.reverse_complement())
-(0.25784180151584823, 1)
-\end{verbatim}
-We can see that the PCC is better (almost $0.75$), and the alignment is also different:
-\begin{verbatim}
-bATTA
-TATAA
-\end{verbatim}
-
-There are two other functions \verb|dist_dpq|, which is a true metric based on the Kullback-Leibler divergence and proposed by Endres and Sendelin \cite{Endres2003}
-\begin{verbatim}
->>> m.dist_dpq(ubx.reverse_complement())
-(0.49292358382899853, 1)
-\end{verbatim}
-
-In case you need yet another way of comparing motifs, you can use the
-\verb|dist_product| method, which is based on the product of
-probabilities which can be interpreted as the probability of
-independently generating the same instance by both motifs.
-
-\begin{verbatim}
->>> m.dist_product(ubx.reverse_complement())
-(0.16224587301064275, 1)
-\end{verbatim}
-
-\section{\emph{De novo} motif finding}
-\label{sec:find}
-
-Currently, biopython has only limited support for \emph{de novo} motif
-finding. Namely, we support running and parsing of AlignAce \cite{Hughes2000} and
-MEME\cite{Bailey1994}. Since the number of motif finding tools is growing rapidly, 
-contributions of new parsers are welcome. 
-
-\subsection{MEME}
-\label{sec:meme}
-
-Let's assume, you have run MEME on sequences of your choice with your
-favorite parameters and saved the output in the file
-\verb|meme.out|. You can retrieve the motifs reported by MEME by
-running the following piece of code:
-
-\begin{verbatim}
->>> motifsM = list(Motif.parse(open("meme.out"), "MEME"))
->>> motifsM
-[<Bio.Motif.MEMEMotif.MEMEMotif object at 0xc356b0>]
-\end{verbatim}
-
-Besides the most wanted list of motifs, the result object contains more useful information, accessible through properties with self-explanatory names:
-\begin{itemize}
-\item \verb|.alphabet|
-\item \verb|.datafile|
-\item \verb|.sequence_names|
-\item \verb|.version|
-\item \verb|.command|
-\end{itemize}
-
-The motifs returned by MEMEParser can be treated exactly like regular
-Motif objects (with instances), they also provide some extra
-functionality, by adding additional information about the instances. 
-
-\begin{verbatim}
->>> motifsM[0].consensus
-Seq('CTCAATCGTA', IUPACUnambiguousDNA())
-
->>> motifsM[0].instances[0].pvalue
-8.71e-07
->>> motifsM[0].instances[0].sequence_name
-'SEQ10;'
->>> motifsM[0].instances[0].start
-3
->>> motifsM[0].instances[0].strand
-'+'
-\end{verbatim}
-
-
-\subsection{AlignAce}
-\label{sec:alignace}
-
-We can do very similar things with AlignACE program. Assume, you have
-your output in the file \verb|alignace.out|. You can parse your output
-with the following code:
-
-\begin{verbatim}
->>> motifsA = list(Motif.parse(open("alignace.out"), "AlignAce"))
-\end{verbatim}
-
-Again, your motifs behave as they should:
-\begin{verbatim}
->>> motifsA[0].consensus
-Seq('TCTACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-In fact you can even see, that AlignAce found a very similar motif as
-MEME, it is just a longer version of a reverse complement of MEME
-motif:
-\begin{verbatim}
->>> motifsM[0].reverse_complement().consensus
-Seq('TACGATTGAG', IUPACUnambiguousDNA())
-\end{verbatim}
-
-If you have AlignAce installed on the same machine, you can also run
-it directly from Biopython. Short example of how this can be done is
-shown below (other parameters can be specified as keyword parameters):
-
-\begin{verbatim}
->>> command = "/opt/bin/AlignACE"
->>> input_file = "test.fa"
->>> result = Motif.AlignAce(input_file, cmd=command, gcback=0.6, numcols=10)
->>> result
-(<Bio.Application.ApplicationResult instance at 0xf49d78>,
- <Bio.File.UndoHandle instance at 0xf49d00>,
- <Bio.File.UndoHandle instance at 0xf49dc8>)
-\end{verbatim}
-
-Since AlignAce prints all its output to standard output, you can get
-to your motifs by parsing the second member of the result:
-\begin{verbatim}
-motifs = list(Motif.parse(result[1], "AlignAce"))
-\end{verbatim}
-
-
-
-\section{Useful links }
-\label{sec:links}
-
-Wikipedia definitions:
-
-\begin{itemize}
-\item \url{http://en.wikipedia.org/wiki/Sequence_motif}
-\item \url{http://en.wikipedia.org/wiki/Position_weight_matrix}
-\item \url{http://en.wikipedia.org/wiki/Consensus_sequence}
-\end{itemize}
-
-
-Motif finding and comparison methods:
-
-\begin{itemize}
-\item AlignAce and CompareAce\url{http://www.psc.edu/general/software/packages/alignace/}
-\item MEME and MAST \url{http://meme.sdsc.edu/meme/}
-\item BioProspector \url{http://bioprospector.stanford.edu/}
-\item MDScan \url{http://ai.stanford.edu/~xsliu/MDscan/}
-\item Weeder
-\item STAMP \url{http://www.benoslab.pitt.edu/stamp/}
-\item WebMotifs \url{http://fraenkel.mit.edu/webmotifs/}
-\item Comparison of different programs \url{http://bio.cs.washington.edu/assessment/}
-\end{itemize}
-
-\begin{thebibliography}{10}
-
-\bibitem{tamo}
-Gordon DB, Nekludova L, McCallum S, Fraenkel E: \textbf{TAMO: a flexible,
-  object-oriented framework for analyzing transcriptional regulation using
-  DNA-sequence motifs.} \emph{Bioinformatics} 2005, \textbf{21}(14):3164--5.
-
-\bibitem{crooks2004}
-Crooks GE, Hon G, Chandonia JM, steven E~Brenner: \textbf{WebLogo: A Sequence
-  Logo Generator}. \emph{Genome Research} 2004, \textbf{14}:1188--1190.
-
-\bibitem{Rahmann2003}
-Rahmann S, Mueller T, Vingron M: \textbf{On the power of profiles for
-  transcription factor binding site detection.} \emph{Stat Appl Genet Mol Biol}
-  2003, \textbf{2}:Article7.
-
-\bibitem{Endres2003}
-Endres D, Schindelin J: \textbf{A new metric for probability distributions}.
-  \emph{IEEE transactions on Information Theory} 2003,
-  \textbf{49}(7):1858--1860.
-
-\bibitem{Bailey1994}
-Bailey TL, Elkan C: \textbf{{Fitting a mixture model by expectation
-  maximization to discover motifs in biopolymers}}. \emph{Proc Int Conf Intell
-  Syst Mol Biol} 1994, \textbf{2}:28--36.
-
-\bibitem{Hughes2000}
-Hughes JD, Estep PW, Tavazoie S, Church GM: \textbf{{Computational
-  identification of cis-regulatory elements associated with groups of
-  functionally related genes in Saccharomyces cerevisiae}}. \emph{J Mol Biol}
-  2000, \textbf{296}(5):1205--1214.
-
-\end{thebibliography}
-
-\end{document}
diff --git a/Doc/doc.rst b/Doc/doc.rst
new file mode 100644
index 0000000..461cbbd
--- /dev/null
+++ b/Doc/doc.rst
@@ -0,0 +1,508 @@
+
+Chapter XXX  Codon Alignment
+==============================================
+
+This chapter is about Codon Alignments, which is a special case of nucleotide
+alignment in which the trinucleotides correspond directly to amino acids in
+the translated protein product. Codon Alignment carries information that can
+be used for many evolutionary analysis.
+
+This chapter has been divided into four parts to explain the codon alignment
+support in Biopython. First, a general introduction about the basic classes
+in ``Bio.CodonAlign`` will be given. Then, a typical procedure of how to
+obtain a codon alignment within Biopython is then discussed. Next, some
+simple applications of codon alignment, such as dN/dS ratio estimation and
+neutrality test and so forth will be covered. Finally, IO support of codon
+alignment will help user to conduct analysis that cannot be done within
+Biopython.
+
+
+X.1  ``CodonSeq`` Class
+-------------------------------------------
+
+``Bio.CodonAlign.CodonSeq`` object is the base object in Codon Alignment. It
+is similar to ``Bio.Seq`` but with some extra attributes. To obtain a simple
+``CodonSeq`` object, you just need to give a ``str`` object of nucleotide
+sequence whose length is a multiple of 3 (This can be violated if you have
+``rf_table`` argument). For example:
+
+.. code:: verbatim
+
+    >>> from Bio.CodonAlign import CodonSeq
+    >>> codon_seq = CodonSeq("AAATTTCCCGGG")
+    >>> codon_seq
+    CodonSeq('AAATTTCCCGGG', Gapped(CodonAlphabet(), '-'))
+
+An error will raise up if the input sequence is not a multiple of 3.
+
+.. code:: verbatim
+
+    >>> codon_seq = CodonSeq("AAATTTCCCGG")
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+      File "/biopython/Bio/CodonAlign/CodonSeq.py", line 81, in __init__
+        assert len(self) % 3 == 0, "Sequence length is not a triple number"
+    AssertionError: Sequence length is not a triple number
+
+By default, ``Bio.CodonAlign.default_codon_alphabet`` will be assigned to
+``CodonSeq`` object if you don't specify any Alphabet. This
+``default_codon_alphabet`` is gapped universal genetic code, which will work
+in most cases. However, if you are analyzing data from mitochondria, for
+instance, and are in need of assigning an special codon alphabet by yourself,
+``Bio.CodonAlign.CodonAlphabet`` also provides you an easy solution. All you
+need is to pick up a ``CodonTable`` object that is correct for your data.
+For example:
+
+.. code:: verbatim
+    
+    >>> from Bio.CodonAlign import CodonSeq
+    >>> from Bio.CodonAlign.CodonAlphabet import get_codon_alphabet
+    >>> from Bio.Data.CodonTable import generic_by_id
+    # vertebrate mitochondria alphabet
+    >>> codon_alphabet = get_codon_alphabet(generic_by_id[2], gap_char="-")
+    >>> codon_seq1 = CodonSeq("AAA---CCCGGG", alphabet=codon_alphabet)
+    >>> codon_seq1
+    CodonSeq('AAA---CCCGGG', CodonAlphabet(Vertebrate Mitochondrial))
+
+The slice of ``CodonSeq`` is exactly the same with ``Seq`` and it will always
+return a ``Seq`` object if you sliced a ``CodonSeq``. For example:
+
+.. code:: verbatim
+
+    >>> codon_seq1
+    CodonSeq('AAA---CCCGGG', CodonAlphabet(Vertebrate Mitochondrial))
+    >>> codon_seq1[:6]
+    Seq('AAA---', DNAAlphabet())
+    >>> codon_seq1[1:5]
+    Seq('AA--', DNAAlphabet())
+
+As you might imagine, ``CodonSeq`` is able to be translated into amino acid
+sequence based on the ``CodonAlphabet`` within it. In fact, ``CodonSeq`` does
+more than this. ``CodonSeq`` object has a ``rf_table`` attribute that dictates
+how the ``CodonSeq`` will be translated (``rf_table`` will indicate the
+starting position of each codon in the sequence). This is useful if you
+sequence is known to have frameshift events or pseudogene that has insertion
+or deletion. You might notice that in the previous example, you haven't
+specify the ``rf_table`` when initiate a ``CodonSeq`` object. In fact,
+``CodonSeq`` object will automatically assign a ``rf_table`` to the
+``CodonSeq`` if you don't say anything about it.
+
+.. code:: verbatim
+
+    >>> codon_seq1 = CodonSeq("AAACCCGGG")
+    >>> codon_seq1
+    CodonSeq('AAACCCGGG', CodonAlphabet(Standard))
+    >>> codon_seq1.rf_table
+    [0, 3, 6]
+    >>> codon_seq1.translate()
+    'KPG'
+    >>> codon_seq2 = CodonSeq("AAACCCGG", rf_table=[0, 3, 5])
+    >>> codon_seq2.rf_table
+    [0, 3, 5]
+    >>> codon_seq2.translate()
+    'KPR'
+
+In the example, we didn't assign ``rf_table`` to ``codon_seq1``. By default,
+``CodonSeq`` will automatically generate a ``rf_table`` to the coding sequence
+assuming no frameshift events. In this case, it is ``[0, 3, 6]``, which means
+the first codon in the sequence starts at position 0, the second codon in the
+sequence starts at position 3, and the third codon in the sequence starts at
+position 6. In ``codon_seq2``, we only have 8 nucleotides in the sequence, but
+with ``rf_table`` option specified. In this case, the third codon starts at
+the 5th position of the sequence rather than the 6th. And the ``translate()``
+function will use the ``rf_table`` to get the translated amino acid sequence.
+
+Another thing to keep in mind is that ``rf_table`` will only be applied to
+ungapped nucleotide sequence. This makes ``rf_table`` to be interchangeable
+between ``CodonSeq`` with the same sequence but different gaps inserted. For
+example,
+
+.. code:: verbatim
+
+    >>> codon_seq1 = CodonSeq("AAACCC---GGG")
+    >>> codon_seq1.rf_table
+    [0, 3, 6]
+    >>> codon_seq1.translate()
+    'KPG'
+    >>> codon_seq1.full_translate()
+    'KP-G'
+
+We can see that the ``rf_table`` of ``codon_seq1`` is still ``[0, 3, 6]``,
+even though we have gaps added. The ``translate()`` function will skip the
+gaps and return the ungapped amino acid sequence. If gapped protein sequence
+is what you need, ``full_translate()`` comes to help.
+
+It is also easy to convert ``Seq`` object to ``CodonSeq`` object, but it is
+the user's responsibility to ensure all the necessary information is correct
+for a ``CodonSeq`` (mainly ``rf_table``).
+
+.. code:: verbatim
+
+    >>> from Bio.Seq import Seq
+    >>> codon_seq = CodonSeq()
+    >>> seq = Seq('AAAAAA')
+    >>> codon_seq.from_seq(seq)
+    CodonSeq('AAAAAA', CodonAlphabet(Standard))
+    >>> seq = Seq('AAAAA')
+    >>> codon_seq.from_seq(seq)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+      File "/biopython/Bio/CodonAlign/CodonSeq.py", line 264, in from_seq
+        return cls(seq._data, alphabet=alphabet)
+      File "/biopython/Bio/CodonAlign/CodonSeq.py", line 80, in __init__
+        assert len(self) % 3 == 0, "Sequence length is not a triple number"
+    AssertionError: Sequence length is not a triple number
+    >>> codon_seq.from_seq(seq, rf_table=(0, 2))
+    CodonSeq('AAAAA', CodonAlphabet(Standard))
+
+
+X.2  ``CodonAlignment`` Class
+-------------------------------------------
+
+The ``CodonAlignment`` class is another new class in ``Codon.Align``. It's
+aim is to store codon alignment data and apply various analysis upon it.
+Similar to ``MultipleSeqAlignment``, you can use numpy style slice to a
+``CodonAlignment``. However, once you sliced, the returned result will
+always be a ``MultipleSeqAlignment`` object.
+
+.. code:: verbatim
+
+    >>> from Bio.CodonAlign import default_codon_alphabet, CodonSeq, CodonAlignment
+    >>> from Bio.Alphabet import generic_dna
+    >>> from Bio.SeqRecord import SeqRecord
+    >>> from Bio.Alphabet import IUPAC, Gapped
+    >>> a = SeqRecord(CodonSeq("AAAACGTCG", alphabet=default_codon_alphabet), id="Alpha")
+    >>> b = SeqRecord(CodonSeq("AAA---TCG", alphabet=default_codon_alphabet), id="Beta")
+    >>> c = SeqRecord(CodonSeq("AAAAGGTGG", alphabet=default_codon_alphabet), id="Gamma")
+    >>> codon_aln = CodonAlignment([a, b, c])
+    >>> print codon_aln
+    CodonAlphabet(Standard) CodonAlignment with 3 rows and 9 columns (3 codons)
+    AAAACGTCG Alpha
+    AAA---TCG Beta
+    AAAAGGTGG Gamma
+    >>> codon_aln[0]
+    ID: Alpha
+    Name: <unknown name>
+    Description: <unknown description>
+    Number of features: 0
+    CodonSeq('AAAACGTCG', CodonAlphabet(Standard))
+    >>> print codon_aln[:, 3]
+    A-A
+    >>> print codon_aln[1:, 3:10]
+    CodonAlphabet(Standard) alignment with 2 rows and 6 columns
+    ---TCG Beta
+    AGGTGG Gamma
+
+You can write out ``CodonAlignment`` object just as what you do with
+``MultipleSeqAlignment``.
+
+.. code:: verbatim
+
+    >>> from Bio import AlignIO
+    >>> AlignIO.write(codon_aln, 'example.aln', 'clustal')
+
+An alignment file called ``example.aln`` can then be found in your current
+working directory. You can write ``CodonAlignment`` out in any MSA format
+that Biopython supports.
+
+Currently, you are not able to read MSA data as a ``CodonAlignment`` object
+directly (because of dealing with ``rf_table`` issue for each sequence).
+However, you can read the alignment data in as a ``MultipleSeqAlignment``
+object and convert them into ``CodonAlignment`` object using ``from_msa()``
+class method. For example,
+
+.. code:: verbatim
+
+    >>> aln = AlignIO.read('example.aln', 'clustal')
+    >>> codon_aln = CodonAlignment()
+    >>> print codon_aln.from_msa(aln)
+    CodonAlphabet(Standard) CodonAlignment with 3 rows and 9 columns (3 codons)
+    AAAACGTCG Alpha
+    AAA---TCG Beta
+    AAAAGGTGG Gamma
+
+Note, the ``from_msa()`` method assume there is no frameshift events occurs
+in your alignment. Its behavior is not guaranteed if your sequence contain
+frameshift events!!
+
+There is a couple of methods that can be applied to ``CodonAlignment`` class
+for evolutionary analysis. We will cover them more in X.4.
+
+
+X.3  Build a Codon Alignment
+-------------------------------------------
+
+Building a codon alignment is the first step of many evolutionary anaysis.
+But how to do that?  ``Bio.CodonAlign`` provides you an easy funciton
+``build()`` to achieve all. The data you need to prepare in advance is a
+protein alignment and a set of DNA sequences that can be translated into the
+protein sequences in the alignment.
+
+``CodonAlign.build`` method requires two mandatory arguments. The first one
+should be a protein ``MultipleSeqAlignment`` object and the second one is a
+list of nucleotide ``SeqRecord`` object. By default, ``CodonAlign.build``
+assumes the order of the alignment and nucleotide sequences are in the same.
+For example:
+
+.. code:: verbatim
+
+    >>> from Bio import CodonAlign
+    >>> from Bio.Alphabet import IUPAC
+    >>> from Bio.Align import MultipleSeqAlignment
+    >>> from Bio.SeqRecord import SeqRecord
+    >>> from Bio.Seq import Seq
+    >>> nucl1 = SeqRecord(Seq('AAATTTCCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl1')
+    >>> nucl2 = SeqRecord(Seq('AAATTACCCGCG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl2')
+    >>> nucl3 = SeqRecord(Seq('ATATTACCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl3')
+    >>> prot1 = SeqRecord(nucl1.seq.translate(), id='prot1')
+    >>> prot2 = SeqRecord(nucl2.seq.translate(), id='prot2')
+    >>> prot3 = SeqRecord(nucl3.seq.translate(), id='prot3')
+    >>> aln = MultipleSeqAlignment([prot1, prot2, prot3])
+    >>> codon_aln = CodonAlign.build(aln, [nucl1, nucl2, nucl3])
+    >>> print codon_aln
+    CodonAlphabet(Standard) CodonAlignment with 3 rows and 12 columns (4 codons)
+    AAATTTCCCGGG nucl1
+    AAATTACCCGCG nucl2
+    ATATTACCCGGG nucl3
+
+In the above example, ``CodonAlign.build`` will try to match ``nucl1`` with
+``prot1``, ``nucl2`` with ``prot2`` and ``nucl3`` with ``prot3``, i.e.,
+assuming the order of records in ``aln`` and ``[nucl1, nucl2, nucl3]`` is the
+same.
+
+``CodonAlign.build`` method is also able to handle key match. In this case,
+records with same id are paired. For example:
+
+.. code:: verbatim
+
+    >>> nucl1 = SeqRecord(Seq('AAATTTCCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl1')
+    >>> nucl2 = SeqRecord(Seq('AAATTACCCGCG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl2')
+    >>> nucl3 = SeqRecord(Seq('ATATTACCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl3')
+    >>> prot1 = SeqRecord(nucl1.seq.translate(), id='prot1')
+    >>> prot2 = SeqRecord(nucl2.seq.translate(), id='prot2')
+    >>> prot3 = SeqRecord(nucl3.seq.translate(), id='prot3')
+    >>> aln = MultipleSeqAlignment([prot1, prot2, prot3])
+    >>> nucl = {'prot1': nucl1, 'prot2': nucl2, 'prot3': nucl3}
+    >>> codon_aln = CodonAlign.build(aln, nucl)
+    >>> print codon_aln
+    CodonAlphabet(Standard) CodonAlignment with 3 rows and 12 columns (4 codons)
+    AAATTTCCCGGG nucl1
+    AAATTACCCGCG nucl2
+    ATATTACCCGGG nucl3
+
+This option is handleful if you read nucleotide sequences using ``SeqIO.index``
+method, in which case the nucleotide dict with be generated automatically.
+
+Sometimes, you are neither not able to ensure the same order or the same id.
+``CodonAlign.build`` method provides you an manual approach to tell the
+program nucleotide sequence and protein sequence correspondance by generating
+a ``corr_dict``. ``corr_dict`` should be a dictionary that uses protein record
+id as key and nucleotide record id as item. Let's look at an example:
+
+.. code:: verbatim
+
+    >>> nucl1 = SeqRecord(Seq('AAATTTCCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl1')
+    >>> nucl2 = SeqRecord(Seq('AAATTACCCGCG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl2')
+    >>> nucl3 = SeqRecord(Seq('ATATTACCCGGG', alphabet=IUPAC.IUPACUnambiguousDNA()), id='nucl3')
+    >>> prot1 = SeqRecord(nucl1.seq.translate(), id='prot1')
+    >>> prot2 = SeqRecord(nucl2.seq.translate(), id='prot2')
+    >>> prot3 = SeqRecord(nucl3.seq.translate(), id='prot3')
+    >>> aln = MultipleSeqAlignment([prot1, prot2, prot3])
+    >>> corr_dict = {'prot1': 'nucl1', 'prot2': 'nucl2', 'prot3': 'nucl3'}
+    >>> codon_aln = CodonAlign.build(aln, [nucl3, nucl1, nucl2], corr_dict=corr_dict)
+    >>> print codon_aln
+    CodonAlphabet(Standard) CodonAlignment with 3 rows and 12 columns (4 codons)
+    AAATTTCCCGGG nucl1
+    AAATTACCCGCG nucl2
+    ATATTACCCGGG nucl3
+
+We can see, even though the second argument of ``CodonAlign.build`` is not in
+the same order with ``aln`` in the above example, the ``corr_dict`` tells the
+program to pair protein records and nucleotide records. And we are still able
+to obtain the correct ``CodonAlignment`` object.
+
+The underlying algorithm of ``CodonAlign.build`` method is very similar to
+``pal2nal`` (a very famous perl script to build codon alignment).
+``CodonAlign.build`` will first translate protein sequences into a long
+degenerate regular expression and tries to find a match in its corresponding
+nucleotide sequence. When translation fails, it divide protein sequence into
+several small anchors and tries to match each anchor to the nucleotide sequence
+to figure out where the mismatch and frameshift events lie. Other options
+available for ``CodonAlign.build`` includes ``anchor_len`` (default 10) and
+``max_score`` (maximum tolerance of unexpected events, default 10). You may
+want to refer the Biopython build-in help to get more information about these
+options.
+
+Now let's look at a real example of building codon alignment. Here we will use
+epidermal growth factor (EGFR) gene to demonstrate how to obtain codon
+alignment. To reduce your effort, we have already collected EGFR sequences for
+`Homo sapiens`, `Bos taurus`, `Rattus norvegicus`, `Sus scrofa` and
+`Drosophila melanogaster`. You can download them from
+`here <http://zruanweb.com/egfr.zip>`_.
+Uncomressing the ``.zip``, you will see three files. ``egfr_nucl.fa`` is
+nucleotide sequences of EGFR and ``egfr_pro.aln`` is EGFR protein sequence
+alignment in ``clustal`` format. The ``egfr_id`` contains id correspondance
+between protein records and nucleotide records. You can then try the following
+code (make sure the files are in your current python working directory):
+
+.. code:: verbatim
+
+    >>> from Bio import SeqIO, AlignIO
+    >>> nucl = SeqIO.parse('egfr_nucl.fa', 'fasta', alphabet=IUPAC.IUPACUnambiguousDNA())
+    >>> prot = AlignIO.read('egfr_pro.aln', 'clustal', alphabet=IUPAC.protein)
+    >>> id_corr = {i.split()[0]: i.split()[1] for i in open('egfr_id').readlines()}
+    >>> aln = CodonAlign.build(prot, nucl, corr_dict=id_corr, alphabet=CodonAlign.default_codon_alphabet)
+    /biopython/Bio/CodonAlign/__init__.py:568: UserWarning: gi|47522840|ref|NP_999172.1|(L 449) does not correspond to gi|47522839|ref|NM_214007.1|(ATG)
+      % (pro.id, aa, aa_num, nucl.id, this_codon))
+    >>> print aln
+    CodonAlphabet(Standard) CodonAlignment with 6 rows and 4446 columns (1482 codons)
+    ATGATGATTATCAGCATGTGGATGAGCATATCGCGAGGATTGTGGGACAGCAGCTCC...GTG gi|24657088|ref|NM_057410.3|
+    ---------------------ATGCTGCTGCGACGGCGCAACGGCCCCTGCCCCTTC...GTG gi|24657104|ref|NM_057411.3|
+    ------------------------------ATGAAAAAGCACGAG------------...GCC gi|302179500|gb|HM749883.1|
+    ------------------------------ATGCGACGCTCCTGGGCGGGCGGCGCC...GCA gi|47522839|ref|NM_214007.1|
+    ------------------------------ATGCGACCCTCCGGGACGGCCGGGGCA...GCA gi|41327737|ref|NM_005228.3|
+    ------------------------------ATGCGACCCTCAGGGACTGCGAGAACC...GCA gi|6478867|gb|M37394.2|RATEGFR
+
+We can see, while building the codon alignment a mismatch event is found. And
+this is shown as a UserWarning.
+
+
+X.4  Codon Alignment Application
+-------------------------------------------
+
+The most important application of codon alignment is to estimate
+nonsynonymous substitutions per site (dN) and synonymous substitutions per
+site (dS). ``CodonAlign`` currently support three counting based methods
+(NG86, LWL85, YN00) and maximum likelihood method to estimate dN and dS.
+The function to conduct dN, dS estimation is called ``cal_dn_ds``. When you
+obtained a codon alignment, it is quite easy to calculate dN and dS. For
+example (assuming you have EGFR codon alignmnet in the python working
+space):
+
+.. code:: verbatim
+
+    >>> from Bio.CodonAlign.CodonSeq import cal_dn_ds
+    >>> print aln
+    CodonAlphabet(Standard) CodonAlignment with 6 rows and 4446 columns (1482 codons)
+    ATGATGATTATCAGCATGTGGATGAGCATATCGCGAGGATTGTGGGACAGCAGCTCC...GTG gi|24657088|ref|NM_057410.3|
+    ---------------------ATGCTGCTGCGACGGCGCAACGGCCCCTGCCCCTTC...GTG gi|24657104|ref|NM_057411.3|
+    ------------------------------ATGAAAAAGCACGAG------------...GCC gi|302179500|gb|HM749883.1|
+    ------------------------------ATGCGACGCTCCTGGGCGGGCGGCGCC...GCA gi|47522839|ref|NM_214007.1|
+    ------------------------------ATGCGACCCTCCGGGACGGCCGGGGCA...GCA gi|41327737|ref|NM_005228.3|
+    ------------------------------ATGCGACCCTCAGGGACTGCGAGAACC...GCA gi|6478867|gb|M37394.2|RATEGFR
+    >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='NG86')
+    >>> print dN, dS
+    0.0209078305058 0.0178371876389
+    >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='LWL95')
+    >>> print dN, dS
+    0.0203061425453 0.0163935691992
+    >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='YN00')
+    >>> print dN, dS
+    0.0198195580321 0.0221560648799
+    >>> dN, dS = cal_dn_ds(aln[0], aln[1], method='ML')
+    >>> print dN, dS
+    0.0193877676103 0.0217247139962
+
+If you are using maximum likelihood methdo to estimate dN and dS, you are
+also able to specify equilibrium codon frequency to ``cfreq`` argument.
+Available options include ``F1x4``, ``F3x4`` and ``F61``.
+
+It is also possible to get dN and dS matrix or a tree from a ``CodonAlignment`` object.
+
+.. code:: verbatim
+
+    >>> dn_matrix, ds_matrix = aln.get_dn_ds_matrxi()
+    >>> print dn_matrix
+    gi|24657088|ref|NM_057410.3|    0
+    gi|24657104|ref|NM_057411.3|    0.0209078305058 0
+    gi|302179500|gb|HM749883.1|     0.611523924924  0.61022032668   0
+    gi|47522839|ref|NM_214007.1|    0.614035083563  0.60401686212   0.0411803504059 0
+    gi|41327737|ref|NM_005228.3|    0.61415325314   0.60182631356   0.0670105144563 0.0614703609541 0
+    gi|6478867|gb|M37394.2|RATEGFR  0.61870883409   0.606868724887  0.0738690303483 0.0735789092792 0.0517984707257 0
+    gi|24657088|ref|NM_057410.3|    gi|24657104|ref|NM_057411.3|    gi|302179500|gb|HM749883.1| gi|47522839|ref|NM_214007.1|    gi|41327737|ref|NM_005228.3|    gi|6478867|gb|M37394.2|RATEGFR
+    >>> dn_tree, ds_tree = aln.get_dn_ds_tree()
+    >>> print dn_tree
+    Tree(rooted=True)
+        Clade(branch_length=0, name='Inner5')
+            Clade(branch_length=0.279185347322, name='Inner4')
+                Clade(branch_length=0.00859186651689, name='Inner3')
+                    Clade(branch_length=0.0258992353629, name='gi|6478867|gb|M37394.2|RATEGFR')
+                    Clade(branch_length=0.0258992353629, name='gi|41327737|ref|NM_005228.3|')
+                Clade(branch_length=0.0139009266768, name='Inner2')
+                    Clade(branch_length=0.020590175203, name='gi|47522839|ref|NM_214007.1|')
+                    Clade(branch_length=0.020590175203, name='gi|302179500|gb|HM749883.1|')
+            Clade(branch_length=0.294630667432, name='Inner1')
+                Clade(branch_length=0.0104539152529, name='gi|24657104|ref|NM_057411.3|')
+                Clade(branch_length=0.0104539152529, name='gi|24657088|ref|NM_057410.3|')
+
+Another application of codon alignment that ``CodonAlign`` supports is
+Mcdonald-Kreitman test. This test compares the within species synonymous
+substitutions and nonsynonymous substitutions and between species synonymous
+substitutions and nonsynonymous substitutions to see if they are from the same
+evolutionary process. The test requires gene sequences sampled from different
+individuals of the same species. In the following example, we will use `Adh`
+gene from fluit fly to demonstrate how to conduct the test. The data includes
+11 individuals from `D. melanogaster`, 4 individuals from `D. simulans` and
+12 individuals from `D. yakuba`. The data is available from 
+`here <http://zruanweb.com/adh.zip>`_. A function called ``mktest`` will be
+used for the test.
+
+.. code:: verbatim
+
+    >>> from Bio import SeqIO, AlignIO
+    >>> from Bio.Alphabet import IUPAC
+    >>> from Bio.CodonAlign import build
+    >>> from Bio.CodonAlign.CodonAlignment import mktest
+
+    >>> pro_aln = AlignIO.read('adh.aln', 'clustal', alphabet=IUPAC.protein)
+    >>> p = SeqIO.index('drosophilla.fasta', 'fasta', alphabet=IUPAC.IUPACUnambiguousDNA())
+    >>> codon_aln = build(pro_aln, p)
+    >>> print codon_aln
+    CodonAlphabet(Standard) CodonAlignment with 27 rows and 768 columns (256 codons)
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9217|emb|X57365.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9219|emb|X57366.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9221|emb|X57367.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9223|emb|X57368.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9225|emb|X57369.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9227|emb|X57370.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9229|emb|X57371.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9231|emb|X57372.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9233|emb|X57373.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9235|emb|X57374.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9237|emb|X57375.1|
+    ATGGCGTTTACCTTGACCAACAAGAACGTGGTTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|9239|emb|X57376.1|
+    ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|9097|emb|X57361.1|
+    ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|9099|emb|X57362.1|
+    ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|9101|emb|X57363.1|
+    ATGGCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATC...ATC gi|9103|emb|X57364.1|
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156879|gb|M17837.1|DROADHCK
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156877|gb|M17836.1|DROADHCJ
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156875|gb|M17835.1|DROADHCI
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156873|gb|M17834.1|DROADHCH
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156871|gb|M17833.1|DROADHCG
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156863|gb|M19547.1|DROADHCC
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156869|gb|M17832.1|DROADHCF
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTGGCCGGTCTGGGAGGCATT...ATC gi|156867|gb|M17831.1|DROADHCE
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156865|gb|M17830.1|DROADHCD
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156861|gb|M17828.1|DROADHCB
+    ATGTCGTTTACTTTGACCAACAAGAACGTGATTTTCGTTGCCGGTCTGGGAGGCATT...ATC gi|156859|gb|M17827.1|DROADHCA
+
+    >>> print mktest([codon_aln[1:12], codon_aln[12:16], codon_aln[16:]])
+    0.00206457257254
+
+In the above example, ``codon_aln[1:12]`` belongs to `D. melanogaster`,
+``codon_aln[12:16]`` belongs to `D. simulans` and ``codon_aln[16:]`` belongs
+to `D. yakuba`. ``mktest`` will return the p-value of the test. We can see
+in this case, 0.00206 << 0.01, therefore, the gene is under strong negative
+selection according to MK test.
+
+X.4  Future Development
+-------------------------------------------
+
+Because of the limited time frame for Google Summer of Code project, some of
+the functions in ``CodonAlign`` is not tested comprehensively. In the
+following days, I will continue perfect the code and several new features
+will be added. I am always welcome to hear your suggestions and feature
+request. You are also highly encouraged to contribute to the existing code.
+Please do not hesitable to email me (zruan1991 at gmail dot com) when you have
+novel ideas that can make the code better.
diff --git a/Doc/examples/ACT_example.py b/Doc/examples/ACT_example.py
index 863f22f..7ed10bf 100644
--- a/Doc/examples/ACT_example.py
+++ b/Doc/examples/ACT_example.py
@@ -7,11 +7,9 @@ from __future__ import print_function
 
 import sys
 import os
-import time
 from reportlab.lib import colors
 from reportlab.lib.units import cm
 
-from Bio.Blast.Applications import NcbitblastxCommandline
 from Bio.Graphics.GenomeDiagram import Diagram, CrossLink
 from Bio.SeqFeature import SeqFeature, FeatureLocation
 from Bio import SeqIO
@@ -40,7 +38,7 @@ genomes = [
 comparisons = [os.path.join(input_folder, file_a_vs_b)]
 
 # Create diagram with tracks, each with a feature set
-assert len(genomes) >= 2 and len(genomes) == len(comparisons)+1
+assert len(genomes) >= 2 and len(genomes) == len(comparisons) + 1
 gd_diagram = Diagram(name, track_size=0.35, circular=False)
 tracks = dict()
 feature_sets = dict()
@@ -55,53 +53,52 @@ for f, format in genomes:
 
 print("Drawing matches...")
 for i, crunch_file in enumerate(comparisons):
-    q = genomes[i+1][0]  # query file
+    q = genomes[i + 1][0]  # query file
     s = genomes[i][0]  # subject file
     q_set = feature_sets[q]
     s_set = feature_sets[s]
-    handle = open(crunch_file)
-    for line in handle:
-        if line[0]=="#":
-            continue
-        parts = line.rstrip("\n").split(None, 7)
-        # 0 = score
-        # 1 = id
-        # 2 = S1
-        # 3 = E1
-        # 4 = seq1
-        # 5 = S2
-        # 6 = E2
-        # 7 = seq2
-        try:
-            q_start, q_end = int(parts[2]), int(parts[3])
-            s_start, s_end = int(parts[5]), int(parts[6])
-        except IndexError:
-            sys.stderr.write(repr(line) + "\n")
-            sys.stderr.write(repr(parts) + "\n")
-            raise
-        flip = False
-        if q_start > q_end:
-            flip = not flip
-            q_start, q_end = q_end, q_start
-        if s_start > s_end:
-            flip = not flip
-            s_start, s_end = s_end, s_start
-        if flip:
-            c = colors.Color(0, 0, 1, alpha=0.25)
-            b = False
-        else:
-            c = colors.Color(1, 0, 0, alpha=0.25)
-            b = False
-        q_feature = q_set.add_feature(SeqFeature(FeatureLocation(q_start-1, q_end)),
-                                                 color=c, border=b)
-        s_feature = s_set.add_feature(SeqFeature(FeatureLocation(s_start-1, s_end)),
-                                                 color=c, border=b)
-        gd_diagram.cross_track_links.append(CrossLink(q_feature, s_feature, c, b))
-        # NOTE: We are using the same colour for all the matches,
-        # with transparency. This means overlayed matches will appear darker.
-        # It also means the drawing order not very important.
-        # Note ACT puts long hits at the back, and colours by hit score
-    handle.close()
+    with open(crunch_file) as handle:
+        for line in handle:
+            if line[0] == "#":
+                continue
+            parts = line.rstrip("\n").split(None, 7)
+            # 0 = score
+            # 1 = id
+            # 2 = S1
+            # 3 = E1
+            # 4 = seq1
+            # 5 = S2
+            # 6 = E2
+            # 7 = seq2
+            try:
+                q_start, q_end = int(parts[2]), int(parts[3])
+                s_start, s_end = int(parts[5]), int(parts[6])
+            except IndexError:
+                sys.stderr.write(repr(line) + "\n")
+                sys.stderr.write(repr(parts) + "\n")
+                raise
+            flip = False
+            if q_start > q_end:
+                flip = not flip
+                q_start, q_end = q_end, q_start
+            if s_start > s_end:
+                flip = not flip
+                s_start, s_end = s_end, s_start
+            if flip:
+                c = colors.Color(0, 0, 1, alpha=0.25)
+                b = False
+            else:
+                c = colors.Color(1, 0, 0, alpha=0.25)
+                b = False
+            q_feature = q_set.add_feature(SeqFeature(FeatureLocation(q_start - 1, q_end)),
+                                                     color=c, border=b)
+            s_feature = s_set.add_feature(SeqFeature(FeatureLocation(s_start - 1, s_end)),
+                                                     color=c, border=b)
+            gd_diagram.cross_track_links.append(CrossLink(q_feature, s_feature, c, b))
+            # NOTE: We are using the same colour for all the matches,
+            # with transparency. This means overlayed matches will appear darker.
+            # It also means the drawing order not very important.
+            # Note ACT puts long hits at the back, and colours by hit score
 
 print("Drawing CDS features...")
 for f, format in genomes:
@@ -116,9 +113,9 @@ for f, format in genomes:
                                 border=colors.blue)
 
 gd_diagram.draw(format="linear", fragments=3,
-                orientation="landscape", pagesize=(20*cm, 10*cm))
+                orientation="landscape", pagesize=(20 * cm, 10 * cm))
 gd_diagram.write(name + ".pdf", "PDF")
 
 gd_diagram.draw(format="circular",
-                orientation="landscape", pagesize=(20*cm, 20*cm))
+                orientation="landscape", pagesize=(20 * cm, 20 * cm))
 gd_diagram.write(name + "_c.pdf", "PDF")
diff --git a/Doc/examples/Plates.csv b/Doc/examples/Plates.csv
new file mode 100644
index 0000000..c5087ab
--- /dev/null
+++ b/Doc/examples/Plates.csv
@@ -0,0 +1,1202 @@
+"Data File    "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___1_30_IDS_207_Octx01x2007_A_25A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time  ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position     ","25-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type   ","PM 1-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type  ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name  ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other        ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File    "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\04\smel_Rmx1021___1_30_IDS_207_Aprx12x2007_B_24A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time  ","Apr 12 2007 12:25 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position     ","24-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type   ","PM 1-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type  ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name  ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm 1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other        ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File    "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___9_30_IDS_207_Octx01x2007_A_23A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time  ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position     ","23-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type   ","PM 9-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type  ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name  ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other        ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File    "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\04\smel_Rmx1021___9_30_IDS_207_Aprx12x2007_B_24B.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time  ","Apr 12 2007 12:25 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position     ","24-B",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type   ","PM 9-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type  ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name  ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm 1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other        ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+69,364,135,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,170,163,60,13,364,352,350,380,5,12,1,11,25,28,255,59,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,121,3,0,31,0,0,4,0,0,0,1,31,488,466,415,422,394,334,156,127,1,3,16,28
+69.25,364,136,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,170,167,60,13,365,356,351,383,5,12,1,11,25,28,259,59,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,121,3,0,31,0,0,4,0,0,0,1,31,488,469,416,422,394,334,159,132,1,3,16,28
+69.5,364,139,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,170,173,61,13,366,358,353,385,5,12,1,11,25,28,262,60,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,122,5,0,31,0,0,4,0,0,0,1,31,488,469,416,423,394,335,162,136,1,3,16,28
+69.75,364,139,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,170,173,63,13,366,358,353,385,5,12,1,11,25,28,263,60,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,122,5,0,31,0,0,4,0,0,0,1,31,488,469,418,425,395,336,164,138,1,3,16,28
+70,366,139,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,176,173,63,13,369,358,354,385,5,12,1,11,25,28,263,60,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,126,5,0,31,0,0,4,0,0,0,1,31,491,469,420,428,396,340,168,141,1,3,16,28
+70.25,366,140,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,176,173,63,13,372,358,358,385,5,12,1,11,25,28,268,60,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,127,5,0,31,0,0,4,0,0,0,1,31,491,469,420,428,397,340,175,146,1,3,16,28
+70.5,366,140,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,26,38,0,16,176,175,64,13,372,359,358,385,5,12,1,11,25,28,268,60,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,131,5,0,31,0,0,4,0,0,0,1,31,492,470,423,428,398,343,179,147,1,3,16,28
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+71,370,142,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,28,38,0,16,180,178,66,13,377,363,360,391,5,12,1,11,25,28,280,61,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,131,6,0,31,0,0,4,0,0,0,1,31,492,471,423,430,402,347,188,154,1,3,16,28
+71.25,371,142,25,60,32,50,48,37,27,67,71,135,52,37,50,61,19,43,24,18,18,25,51,81,23,29,24,22,29,31,29,23,38,23,38,78,28,38,0,16,184,178,66,13,377,363,360,391,5,12,1,11,25,28,286,61,0,0,0,16,2,0,14,0,0,0,9,0,0,0,0,12,133,8,0,31,0,0,4,0,0,0,1,31,494,471,426,430,402,351,192,156,1,3,16,28
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diff --git a/Doc/examples/Proux_et_al_2002_Figure_6.py b/Doc/examples/Proux_et_al_2002_Figure_6.py
index 6bf3a0d..095dbcf 100644
--- a/Doc/examples/Proux_et_al_2002_Figure_6.py
+++ b/Doc/examples/Proux_et_al_2002_Figure_6.py
@@ -12,11 +12,8 @@ which produces a GenomeDiagram figure close to Proux et al 2002 Figure 6.
 
 See http://dx.doi.org/10.1128/JB.184.21.6026-6036.2002
 """
-import os
-
 from reportlab.lib import colors
 from reportlab.lib.colors import red, grey, orange, green, brown, blue, lightblue, purple
-from reportlab.lib.units import cm
 
 from Bio.Graphics import GenomeDiagram
 from Bio.Graphics.GenomeDiagram import CrossLink
@@ -104,7 +101,7 @@ B_vs_C = [
 ]
 
 
-def get_feature(features, id, tags=["locus_tag", "gene", "old_locus_tag"]):
+def get_feature(features, id, tags=("locus_tag", "gene", "old_locus_tag")):
     """Search list of SeqFeature objects for an identifier under the given tags."""
     for f in features:
         for key in tags:
diff --git a/Doc/examples/clustal_run.py b/Doc/examples/clustal_run.py
index b0b8a0c..4ca087e 100644
--- a/Doc/examples/clustal_run.py
+++ b/Doc/examples/clustal_run.py
@@ -7,7 +7,6 @@ and parse the results into an object that can be dealt with easily."""
 
 from __future__ import print_function
 
-import os
 import sys
 import subprocess
 
diff --git a/Doc/examples/getgene.py b/Doc/examples/getgene.py
index ed25cb1..16cc848 100755
--- a/Doc/examples/getgene.py
+++ b/Doc/examples/getgene.py
@@ -42,44 +42,43 @@ except ImportError:
     from dbm import gnu as gdbm  # Python 3
 
 
-class DB_Index:
+class DB_Index(object):
     def __init__(self, open=1):
         if open:
             self.Open()
 
     def Create(self, infile, outfile):
         db = gdbm.open(outfile, 'n')
-        fid = open(infile)
+        with open(infile) as fid:
 
-        db['datafile'] = os.path.abspath(infile)
+            db['datafile'] = os.path.abspath(infile)
 
-        while True:
-            line = fid.readline()
-            if not line or not len(line):
-                break
+            while True:
+                line = fid.readline()
+                if not line or not len(line):
+                    break
+
+                if line[:3] == 'ID ':
+                    id = string.split(line)[1]
+                    start = fid.tell() - len(line)
+
+                elif line[:3] == 'AC ':
+                    acc = string.split(line)[1]
+                    if acc[-1] == ';':
+                        acc = acc[:-1]
+
+                elif line[:2] == '//':
+                    stop = fid.tell()
+                    try:
+                        value = '%d %d' % (start, stop)
+                        db[id] = value
+                        db[acc] = value
+                        id, acc, start, stop = None, None, None, None
+                    except:
+                        print("AARRGGGG %d %d %s %s" % (start, stop, type(start), type(stop)))
+                        print("%s %s" % (id, acc))
 
-            if line[:3] == 'ID ':
-                id = string.split(line)[1]
-                start = fid.tell() - len(line)
-
-            elif line[:3] == 'AC ':
-                acc = string.split(line)[1]
-                if acc[-1] == ';':
-                    acc = acc[:-1]
-
-            elif line[:2] == '//':
-                stop = fid.tell()
-                try:
-                    value = '%d %d' % (start, stop)
-                    db[id] = value
-                    db[acc] = value
-                    id, acc, start, stop = None, None, None, None
-                except:
-                    print("AARRGGGG %d %d %s %s" % (start, stop, type(start), type(stop)))
-                    print("%s %s" % (id, acc))
-
-        db.close()
-        fid.close()
+            db.close()
 
     def Open(self, indexfile=None):
         if not indexfile:
@@ -115,7 +114,7 @@ class DB_Index:
                 return OS
             if line[0:2] == "//":
                 break
-        return OS
+        return None
 
     def FixOS(self, os):
         os = string.split(os, ',')[0]
@@ -138,7 +137,7 @@ class DB_Index:
 
     def Get_Kingdom(self, id):
         res = self.Get_Taxonomy(id)
-        #print("%s %s" % (id, res))
+        # print("%s %s" % (id, res))
         if not res:
             return "U"
         kd = string.strip(string.split(res, ";")[0])
@@ -273,7 +272,7 @@ def help(exit=0):
         sys.exit(0)
 
 if __name__ == '__main__':
-    pyphy_home = os.environ.get('PYPHY', None)
+    pyphy_home = os.environ.get('PYPHY')
 
     if len(sys.argv) == 1:
         help(exit=1)
diff --git a/Doc/examples/nmr/simplepredict.py b/Doc/examples/nmr/simplepredict.py
index 5102210..558937e 100755
--- a/Doc/examples/nmr/simplepredict.py
+++ b/Doc/examples/nmr/simplepredict.py
@@ -45,14 +45,12 @@
 # ***** LOAD MODULES *****
 
 from __future__ import print_function
-import getopt
 import string
-import sys
 
 # -- don't need to modify sys.path with the *tools in Biopython
 # -- just need Biopython installed somewhere in the PYTHONPATH
-#sys.path=[sys.path,"./"]
-#sys.path=[sys.path,"/usr/people/robert/development/xpktools"]
+# sys.path=[sys.path,"./"]
+# sys.path=[sys.path,"/usr/people/robert/development/xpktools"]
 from Bio.NMR import xpktools  # Contains data classes and functions for .xpk files
 from Bio.NMR import NOEtools  # A module specific for generate NOE predictions
 
@@ -67,69 +65,68 @@ detectatom = "H1"           # Directly detected atom
 relayatom = "N15"           # J-coupling from here to detected atom
 fromatom = "15N2"           # The other labelled nucleus
 
-# *-*-*  First the peaklist is read into a data class from xpktools
-# *-*-*  that contains methods for easily extracting information from
-# *-*-*  the peaklist file
+#  First the peaklist is read into a data class from xpktools
+#  that contains methods for easily extracting information from
+#  the peaklist file
 
 peaklist = xpktools.Peaklist(infn)  # infn is the name of the xpk file
 
-# *-*-* The class attribute residue_dict allows the data lines
-# *-*-* to be separated from the header and returned here to
-# *-*-* the dictionary <dict> as a list indexed by the assignment
-# *-*-* of any of the nuclei in the file -- here, the detected atom
-# *-*-* is used
+# The class attribute residue_dict allows the data lines
+# to be separated from the header and returned here to
+# the dictionary <dict> as a list indexed by the assignment
+# of any of the nuclei in the file -- here, the detected atom
+# is used
 
 dict = peaklist.residue_dict(detectatom)
 
-# *-*-* As well as the data, the dictionary contains two other entries,
-# *-*-* corresponding to the maximum and minimum residues indexed
+# As well as the data, the dictionary contains two other entries,
+# corresponding to the maximum and minimum residues indexed
 
 MAXRES = dict["maxres"]
 MINRES = dict["minres"]
 
-#****** CALCULATE AND WRITE CROSSPEAK PEAKLIST *****
+# ****** CALCULATE AND WRITE CROSSPEAK PEAKLIST *****
 
-# *-*-* The peaklist class has a method for writing out the header
-# *-*-* information in a format recognizable by nmrview
+# The peaklist class has a method for writing out the header
+# information in a format recognizable by nmrview
 
 peaklist.write_header(outfn)  # Write the header to the output file
 
-# *-*-* Predict the i->i+inc and i->i-inc noe positions if possible
-# *-*-* Write each one to the output file as they are calculated
+# Predict the i->i+inc and i->i-inc noe positions if possible
+# Write each one to the output file as they are calculated
 
 count = 0     # A counter that number the output data lines in order
 res = MINRES  # minimum residue number in the set
 outlist = []  # Holds the output data
 
 while (res <= MAXRES):
-
-# *-*-* Predicting the NOE positions based on peak assignment data
-# *-*-* is done by supplying the peaklist to and specifying the label
-# *-*-* of the origin and detected atom in the NOE transfer as well as
-# *-*-* the residues between which the NOE transfer takes places.
+    # Predicting the NOE positions based on peak assignment data
+    # is done by supplying the peaklist to and specifying the label
+    # of the origin and detected atom in the NOE transfer as well as
+    # the residues between which the NOE transfer takes places.
 
     noe1 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res + inc)
     noe2 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res - inc)
 
-# *-*-* The output of predictNOE is in the form of an xpk entry line
-# *-*-* suitable for printing to an output file
-# *-*-* Additionally, it is possible to extract information easily from
-# *-*-* these output lines by using the xpktools.XpkEntry class
+    # The output of predictNOE is in the form of an xpk entry line
+    # suitable for printing to an output file
+    # Additionally, it is possible to extract information easily from
+    # these output lines by using the xpktools.XpkEntry class
 
     entry1 = xpktools.XpkEntry(noe1, peaklist.datalabels)
 
     if noe1 != "":
 
-  # *-*-* Here I'm using the XpkEntry class to gain access to
-  # *-*-* specific fields in the that make the information
-  # *-*-* more readable and suitable for creating data tables
-  # *-*-* This output will be printed to the screen.
-  # *-*-* The data table contains the assignment, coordinates and
-  # *-*-* intensity of the resonance.
+        # Here I'm using the XpkEntry class to gain access to
+        # specific fields in the that make the information
+        # more readable and suitable for creating data tables
+        # This output will be printed to the screen.
+        # The data table contains the assignment, coordinates and
+        # intensity of the resonance.
 
-        print(string.split(entry1.fields["15N2.L"], ".")[0], "-->", \
-            string.split(entry1.fields["N15.L"], ".")[0], "\t", \
-            entry1.fields["H1.P"], entry1.fields["N15.P"], \
+        print(string.split(entry1.fields["15N2.L"], ".")[0], "-->",
+            string.split(entry1.fields["N15.L"], ".")[0], "\t",
+            entry1.fields["H1.P"], entry1.fields["N15.P"],
             entry1.fields["15N2.P"], entry1.fields["int"])
 
         noe1 = noe1 + "\012"
@@ -145,6 +142,5 @@ while (res <= MAXRES):
     res += 1
 
 # Open the output file and write the data
-outfile = open(outfn, 'a')
-outfile.writelines(outlist)  # Write the output lines to the file
-outfile.close()
+with open(outfn, 'a') as outfile:
+    outfile.writelines(outlist)  # Write the output lines to the file
diff --git a/Doc/examples/www_blast.py b/Doc/examples/www_blast.py
index 14be037..54d3599 100644
--- a/Doc/examples/www_blast.py
+++ b/Doc/examples/www_blast.py
@@ -24,10 +24,9 @@ print('Doing the BLAST and retrieving the results...')
 result_handle = NCBIWWW.qblast('blastn', 'nr', f_record.format('fasta'))
 
 # save the results for later, in case we want to look at it
-save_file = open('m_cold_blast.out', 'w')
-blast_results = result_handle.read()
-save_file.write(blast_results)
-save_file.close()
+with open('m_cold_blast.out', 'w') as save_file:
+    blast_results = result_handle.read()
+    save_file.write(blast_results)
 
 print('Parsing the results and extracting info...')
 
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diff --git a/Doc/images/BlastRecord.png b/Doc/images/BlastRecord.png
index db323d1..7086e33 100644
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diff --git a/Doc/images/smcra.eps b/Doc/images/smcra.eps
new file mode 100644
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-<!--HEVEA command line is: hevea -fix Installation.tex -->
-<!--CUT DEF section 1 --><TABLE CLASS="title"><TR><TD><H1 CLASS="titlemain">Biopython Installation</H1><H3 CLASS="titlerest">Brad Chapman, with other contributors</H3></TD></TR>
-</TABLE><!--TOC section Contents-->
-<H2 CLASS="section"><!--SEC ANCHOR -->Contents</H2><!--SEC END --><UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc1">1&#XA0;&#XA0;Purpose and Assumptions</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc2">2&#XA0;&#XA0;C Compiler</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc3">2.1&#XA0;&#XA0;Unix</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc4">2.2&#XA0;&#XA0;Mac OS X</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc5">2.3&#XA0;&#XA0;Windows</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc6">3&#XA0;&#XA0;Installing Python</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc7">3.1&#XA0;&#XA0;Python installation on UNIX systems</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc8">3.1.1&#XA0;&#XA0;RPM and other Package Manager Installation</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc9">3.2&#XA0;&#XA0;Python installation on Windows</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc10">3.3&#XA0;&#XA0;Python installation on Mac OS X</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc11">4&#XA0;&#XA0;Installing Biopython dependencies</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc12">4.1&#XA0;&#XA0;Numerical Python (NumPy) (strongly recommended)</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc13">4.1.1&#XA0;&#XA0;UNIX and Mac OS X systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc14">4.1.2&#XA0;&#XA0;Windows systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc15">4.1.3&#XA0;&#XA0;Making sure it installed correctly</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc16">4.2&#XA0;&#XA0;ReportLab (optional)</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc17">4.2.1&#XA0;&#XA0;UNIX and Mac OS X systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc18">4.2.2&#XA0;&#XA0;Windows systems</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc19">4.2.3&#XA0;&#XA0;Making sure it installed correctly</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc20">4.3&#XA0;&#XA0;Database Access (MySQLdb, ...) (optional)</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc21">4.4&#XA0;&#XA0;mxTextTools (no longer needed)</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc22">5&#XA0;&#XA0;Installing Biopython</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc23">5.1&#XA0;&#XA0;Obtaining Biopython</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc24">5.2&#XA0;&#XA0;Installing on UNIX and Mac OS X</A>
-<UL CLASS="toc"><LI CLASS="li-toc">
-<A HREF="#htoc25">5.2.1&#XA0;&#XA0;Installation from source on UNIX and Mac OS X</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc26">5.2.2&#XA0;&#XA0;Using the Python package index</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc27">5.2.3&#XA0;&#XA0;Installation on Mac OS X using the fink package manager</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc28">5.2.4&#XA0;&#XA0;Installation on UNIX systems using RPMs</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc29">5.3&#XA0;&#XA0;Installing with a Windows Installer</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc30">5.4&#XA0;&#XA0;Installing from source on Windows</A>
-</LI></UL>
-</LI><LI CLASS="li-toc"><A HREF="#htoc31">6&#XA0;&#XA0;Making sure everything worked</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc32">7&#XA0;&#XA0;Third Party Tools</A>
-</LI><LI CLASS="li-toc"><A HREF="#htoc33">8&#XA0;&#XA0;Notes for installing with non-administrator permissions</A>
-</LI></UL><!--TOC section Purpose and Assumptions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc1">1</A>&#XA0;&#XA0;Purpose and Assumptions</H2><!--SEC END --><P>For those of you familiar with installing python packages and who don&#X2019;t
-care for following details instructions can try going to
-<A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A>, installing
-the relevant prerequisites, and Biopython.</P><P>This document describes installing Biopython on your computer. To make
-things as simple as possible, it basically assumes you have nothing
-related to Python or Biopython on your computer and want to end up with
-a working installation of Biopython when you are finished following
-through this documentation. </P><P>Biopython should work on just any operating system where Python works,
-so these instructions contain directions for installation on UNIX/Linux,
-Windows and Macintosh machines. The directions assume 
-that you have permission to install programs on the machine
-(root access on UNIX and Administrator privileges on Windows or Mac
-machines). While it is certainly possible to install things without
-these privileges, this is a serious pain and all the tedious workarounds
-aren&#X2019;t something that I&#X2019;ll go into very much in this documentation.</P><P>With all this said, hopefully these directions will make it
-straightforward to get Biopython installed on your machine so you can
-begin using it as quick as possible.</P><!--TOC section C Compiler-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc2">2</A>&#XA0;&#XA0;C Compiler</H2><!--SEC END --><P>Although mostly written in Python, Biopython (and some of its dependencies)
-include C code, which must be compiled to run. If you are going to be
-installing from source you will therefore need a C compiler. However,
-in many cases this can be avoided by using pre-compiled packages (which
-is what we recommend on Windows).</P><!--TOC subsection Unix-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc3">2.1</A>&#XA0;&#XA0;Unix</H3><!--SEC END --><P>
-We recommend GCC as the C compiler, this is usually available as part
-of the standard set of packages on any Unix or Linux system.</P><!--TOC subsection Mac OS X-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc4">2.2</A>&#XA0;&#XA0;Mac OS X</H3><!--SEC END --><P>
-Please install Apple&#X2019;s XCode suite from the App Store, and then from
-the Xcode options also install the optional command line utilities.</P><!--TOC subsection Windows-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc5">2.3</A>&#XA0;&#XA0;Windows</H3><!--SEC END --><P>
-We recommend you install Biopython and its dependencies using the
-provided pre-compiled Windows Installers. i.e. You don&#X2019;t <EM>need</EM>
-a C compiler. See Section&#XA0;<A HREF="#sec:windows_install">5.4</A> for more details.</P><!--TOC section Installing Python-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc6">3</A>&#XA0;&#XA0;Installing Python</H2><!--SEC END --><P>Python is a interpreting, interactive object-oriented programming
-language and the home for all things python is
-<A HREF="http://www.python.org"><TT>http://www.python.org</TT></A>. Presumedly you have some idea of
-python and what it can do if you are interested in Biopython, but if not
-the python website contains tons of documentation and reasons to learn
-to program in python.</P><P>Biopython is designed to work with Python 2.5 to 2.7 inclusive.
-Python 2.7 is the final 2.x series release, and this would be our
-recommended version (assuming all other Python libraries you plan
-to use support it).</P><P>Upgrading bug-fix releases (for example. 2.6.1 to 2.6.2) 
-is incredibly easy and won&#X2019;t require any re-installation of libraries.</P><P>Upgrading between versions (e.g. 2.6 to 2.7) is more time consuming since you
-need to re-install all libraries you have added to python.</P><P>As of Biopython 1.62 we officially support Python 3, specifically Python 3.3.
-Python 3.0, 3.1 and 3.2 will not be supported.</P><P>Let&#X2019;s get started with installation on various platforms.</P><!--TOC subsection Python installation on UNIX systems-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc7">3.1</A>&#XA0;&#XA0;Python installation on UNIX systems</H3><!--SEC END --><P>First, you should go the main python web site and head over to the information
-page for the latest python release. At the time of this writing the
-latest stable Python 2 release is 2.7.5, which is available from
-<A HREF="http://www.python.org/download/releases/2.7.5/"><TT>http://www.python.org/download/releases/2.7.5/</TT></A>. This page contains links
-to all released files for the given release. For UNIX, we&#X2019;ll want to use
-the tarred and gzipped file, which is called <CODE>Python-2.7.5.tgz</CODE> at
-the time of this writing.</P><P>Download this file and then unpack it with the following command:</P><PRE CLASS="verbatim">$ tar -zxvf Python-2.7.5.tar.gz
-</PRE><P>Then enter into the created directory:</P><PRE CLASS="verbatim">$ cd Python-2.7
-</PRE><P>Now, start the build process by configuring everything to your system:</P><PRE CLASS="verbatim">$ ./configure
-</PRE><P>Build all of the files with:</P><PRE CLASS="verbatim">$ make
-</PRE><P>Finally, you&#X2019;ll need to have root permissions on the system and then
-install everything:</P><PRE CLASS="verbatim">$ make install
-</PRE><P>If there were no errors and everything worked correctly, you should now
-be able to type <CODE>python</CODE> at a command prompt and enter into the
-python interpreter:</P><PRE CLASS="verbatim">$ python
-Python 2.7.5 (...)
-...
-Type "help", "copyright", "credits" or "license" for more information.
->>>
-</PRE><P>(The precise version text and details will depend on the version you installed and your operating system.)</P><!--TOC subsubsection RPM and other Package Manager Installation-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc8">3.1.1</A>&#XA0;&#XA0;RPM and other Package Manager Installation</H4><!--SEC END --><P>There are a multitude of package manager systems out there for which
-python is available. One popular one is the RPM (RedHat Package Manager)
-system. Each of these package managing systems has its own quirks and
-tricks and I certainly can&#X2019;t pretend to understand them all so I won&#X2019;t
-try to describe them all here.</P><P>While these package repositories may include Biopython all ready to install,
-you will typically want to install Biopython from source to get the very
-latest version.</P><P>However, there is one general point which it is important to remember
-when installing from any of these systems: you need to download and
-install the development packages for python. A number of distributions
-contain a "basic" python which contains libraries and enough stuff to
-run simple python programs. However, they do not contain the python
-libraries necessary to build third-party python applications (like
-Biopython and it&#X2019;s dependencies). You&#X2019;ll need to install these libraries
-and header files, which are often found in a separate package called
-<CODE>python-devel</CODE> or something similar. </P><!--TOC subsection Python installation on Windows-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc9">3.2</A>&#XA0;&#XA0;Python installation on Windows</H3><!--SEC END --><P>Installation on Windows is most easily done using handy windows
-installers. As described above in the UNIX section, you should go to the
-webpage for the current stable version of Python to download this
-installer. At the current time, you&#X2019;d go to
-<A HREF="http://www.python.org/download/releases/2.7.5/"><TT>http://www.python.org/download/releases/2.7.5/</TT></A> and download
-<CODE>Python-2.7.5.msi</CODE>. </P><P>The installer is an executable program, so you only need to double click
-it to run it. Then just follow the friendly instructions. On all newer Windows
-machines you&#X2019;ll need to have Administrator privileges to do this
-installation.</P><!--TOC subsection Python installation on Mac OS X-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc10">3.3</A>&#XA0;&#XA0;Python installation on Mac OS X</H3><!--SEC END --><P>Apple includes python on Mac OS X, and while you can use this many people have
-preferred to install the latest version of python as well in parallel. We refer
-you to the <A HREF="http://www.python.org"><TT>http://www.python.org</TT></A> for more details, although
-otherwise the UNIX instructions apply.</P><P>(See note above about installing XCode to get the compiler tools.)</P><!--TOC section Installing Biopython dependencies-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc11">4</A>&#XA0;&#XA0;Installing Biopython dependencies</H2><!--SEC END --><P>Once python is installed, the next step is getting the dependencies
-for Biopython installed. Since not all functionality is included in the
-main python installation, Biopython needs some support libraries to save
-us a lot of work re-writing code that already exists. We try to keep
-as few dependencies as possible to make installation as easy as
-possible.</P><!--TOC subsection Numerical Python (NumPy) (strongly recommended)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc12">4.1</A>&#XA0;&#XA0;Numerical Python (NumPy) (strongly recommended)</H3><!--SEC END --><P>The Numerical Python distribution is a fast implementation of arrays and
-associated array functionality. This is important for a number of Biopython
-modules that deal with number processing (e.g. Bio.Cluster and Bio.PDB).</P><P>As of release 1.49, Biopython supports the standard NumPy distribution.
-Previous releases instead used the older Numeric module (which is no
-longer being maintained).</P><P>The main web site for NumPy is:
-<A HREF="http://numpy.scipy.org/"><TT>http://numpy.scipy.org/</TT></A>.</P><!--TOC subsubsection UNIX and Mac OS X systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc13">4.1.1</A>&#XA0;&#XA0;UNIX and Mac OS X systems</H4><!--SEC END --><P>You should download the <CODE>tar.gz</CODE> file, and follow the standard python
-build process. Note you will need a C compiler installed (see above):</P><PRE CLASS="verbatim">$ tar -zxvf numpy-1.7.1.tar.gz
-$ cd numpy-1.7.1/
-$ python setup.py build
-</PRE><P>Once it is built, you should become root, and then install it:</P><PRE CLASS="verbatim">$ python setup.py install
-</PRE><P>One important note if you use an package system and not installing
-NumPy from source: you may also need to
-install the header files which are not included with some
-packages. As with the main python distribution, this means
-you&#X2019;ll need to look for something like <CODE>python-numpy-devel</CODE> 
-and make sure to install this as well as the basic package.</P><!--TOC subsubsection Windows systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc14">4.1.2</A>&#XA0;&#XA0;Windows systems</H4><!--SEC END --><P>We recommend using the NumPy provided windows installers for your installed
-version of python. For Python 2.7, at the current time this would be
-<CODE>numpy-1.7.1-win32-superpack-python2.7.exe</CODE>. You should follow the 
-now-standard procedure of downloading the installer, double
-clicking it and then following the installation instructions. As before,
-you will need to have administrator permissions to do this.</P><!--TOC subsubsection Making sure it installed correctly-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc15">4.1.3</A>&#XA0;&#XA0;Making sure it installed correctly</H4><!--SEC END --><P>To make sure everything went okay during the install, fire up the python
-interpreter and ensure you can import NumPy without any errors:</P><PRE CLASS="verbatim">$ python2.7
-Python 2.7.4 (default, Apr  8 2013, 15:01:09) 
-[GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2
-Type "help", "copyright", "credits" or "license" for more information.
->>> import numpy
->>>
-</PRE><P>Note that for the import statement, NumPy should be in lower case!</P><!--TOC subsection ReportLab (optional)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc16">4.2</A>&#XA0;&#XA0;ReportLab (optional)</H3><!--SEC END --><P>The ReportLab package is a library for generating PDF documents. It is
-used in the Biopython Graphics modules, which contains basic
-functionality for drawing biological objects like chromosomes. If you
-are not planning on using this, installing ReportLab is not necessary.
-ReportLab in itself is very useful for a number of tasks besides just
-Biopython, so you may want to check out
-<A HREF="http://www.reportlab.org"><TT>http://www.reportlab.org</TT></A> before making your decision.</P><P>The main download page for ReportLab is
-<A HREF="http://www.reportlab.org/downloads.html"><TT>http://www.reportlab.org/downloads.html</TT></A>. The ReportLab
-company has some commercial products as well, but just scroll down their
-page to the Open Source software section for the base ReportLab
-downloads.</P><P>If you want to generate bitmap images, you will also need the ReportLab
-module renderPM. This in turn requires the
-<A HREF="http://www.pythonware.com/products/pil/">Python Imaging Library (PIL)</A>.</P><!--TOC subsubsection UNIX and Mac OS X systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc17">4.2.1</A>&#XA0;&#XA0;UNIX and Mac OS X systems</H4><!--SEC END --><P>For UNIX installs, you should download the tarred and gzipped version of
-the ReportLab distribution. At the time of this writing, this is called
-<CODE>ReportLab_2_3.tar.gz</CODE>. First, unpack the distribution and change
-into the created directory:</P><PRE CLASS="verbatim">$ gunzip ReportLab_2_3.tar.gz
-$ tar -xvpf ReportLab_2_3.tar
-$ cd reportlab_2_3/
-</PRE><P>Once again, ReportLab uses the standard python installation system which
-you are probably feeling really comfortable with by now. So, first build
-the package:</P><PRE CLASS="verbatim">$ python setup.py build
-</PRE><P>Now become root, and install it:</P><PRE CLASS="verbatim">$ python setup.py install
-</PRE><!--TOC subsubsection Windows systems-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc18">4.2.2</A>&#XA0;&#XA0;Windows systems</H4><!--SEC END --><P>ReportLab now has graphical windows installers. Nice and easy.</P><!--TOC subsubsection Making sure it installed correctly-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc19">4.2.3</A>&#XA0;&#XA0;Making sure it installed correctly</H4><!--SEC END --><P>If reportlab is installed correctly, you should be able to do the
-following:</P><PRE CLASS="verbatim">$ python2.7
-Python 2.7.4 (default, Apr  8 2013, 15:01:09) 
-[GCC 4.4.6 20120305 (Red Hat 4.4.6-4)] on linux2
-Type "help", "copyright", "credits" or "license" for more information.
->>> from reportlab.graphics import renderPDF
->>>
-</PRE><P>Depending on your version of python and what you have installed, you may
-get the following warning message: 
-<CODE>Warn: Python Imaging Library not available</CODE>. This isn&#X2019;t anything
-to worry about unless you want to produce bitmap images, since the
-Biopython parts that use ReportLab will work just fine without it.</P><!--TOC subsection Database Access (MySQLdb, ...) (optional)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc20">4.3</A>&#XA0;&#XA0;Database Access (MySQLdb, ...) (optional)</H3><!--SEC END --><P>The MySQLdb package is a library for accessing MySQL databases.
-Biopython includes an accessory module, DocSQL, which provides a convenient
-interface to MySQLdb. 
-If you are not planning on using Bio.DocSQL, installing
-MySQLdb is not necessary.</P><P>Additionally, both MySQLdb and psycopg (a PostgreSQL database adaptor)
-can be used for accessing BioSQL databases through Biopython
-(see <A HREF="http://biopython.org/wiki/BioSQL"><TT>http://biopython.org/wiki/BioSQL</TT></A>). Again if
-you are not going to use BioSQL, there shouldn&#X2019;t be any need to install
-these modules.</P><!--TOC subsection mxTextTools (no longer needed)-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc21">4.4</A>&#XA0;&#XA0;mxTextTools (no longer needed)</H3><!--SEC END --><P>Historically this was an important Biopython dependency, used extensively
-in a number of parsers. However, we have gradually phased out its use,
-and as of Biopython 1.50, mxTextTools is no longer used at all.</P><P>mxTextTools is available along with the entire mx-base system (which
-contains a number of other useful utilities as well) and the latest
-version is available for download at:
-<A HREF="http://www.egenix.com/products/python/mxBase/mxTextTools/"><TT>http://www.egenix.com/products/python/mxBase/mxTextTools/</TT></A>.</P><!--TOC section Installing Biopython-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc22">5</A>&#XA0;&#XA0;Installing Biopython</H2><!--SEC END --><!--TOC subsection Obtaining Biopython-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc23">5.1</A>&#XA0;&#XA0;Obtaining Biopython</H3><!--SEC END --><P>
-Biopython&#X2019;s internet home is at, naturally enough, 
-<A HREF="http://www.biopython.org"><TT>http://www.biopython.org</TT></A>. This is the home of all things 
-Biopython, so it is the best place to start looking around. 
-You have two choices for obtaining Biopython:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">Release code &#X2013; We made available releases on the download page 
-(<A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A>). 
-The releases are also available both as source and as installers 
-(windows installers right now), so you have some choices to pick from 
-on releases if you prefer not to deal with source code directly.</LI><LI CLASS="li-enumerate">git &#X2013; The current working copy of the Biopython sources is available
-via git hosted on github &#X2013; <A HREF="http://github.com/biopython/biopython"><TT>http://github.com/biopython/biopython</TT></A>).
-Concise instructions for accessing this copy are available at
-<A HREF="http://biopython.org/wiki/SourceCode"><TT>http://biopython.org/wiki/SourceCode</TT></A>. Our code in git
-is normally quite stable but there is always the caveat that the code
-there is under development.</LI></OL><P>Based on which way you choose, you&#X2019;ll need to follow one of the following installation options. Read on for the platform you are working on.</P><!--TOC subsection Installing on UNIX and Mac OS X-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc24">5.2</A>&#XA0;&#XA0;Installing on UNIX and Mac OS X</H3><!--SEC END --><P>
-<A NAME="sec:unix_install"></A></P><!--TOC subsubsection Installation from source on UNIX and Mac OS X-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc25">5.2.1</A>&#XA0;&#XA0;Installation from source on UNIX and Mac OS X</H4><!--SEC END --><P>Biopython uses Distutils, the standard python installation package, for
-its installation. If you read the install instructions above you are
-already quite familiar with its workings. Distutils comes standard with 
-Python 1.6 and beyond.</P><P>Now that we&#X2019;ve got what we need, let&#X2019;s get into the installation:</P><OL CLASS="enumerate" type=1><LI CLASS="li-enumerate">First you need to unpack the distribution. If you got the git version, you are all set to go and can skip on ahead. Otherwise, you&#X2019;ll need to unpack it. On UN*X machines, a tar.gz package is provided, which you can unpack with <CODE>tar -xzvpf biopython-X.X.tar.gz</CODE>. A zip file is also provided for other platform [...]
-some other packaging system, this means you&#X2019;ll also have to install the
-header files. This requires installing the python development libraries
-as well (normally called something like <CODE>python-devel-2.5.rpm</CODE>).</LI><LI CLASS="li-enumerate">The distutils setup process allows you to do some customization of your install so you don&#X2019;t have to stick everything in the default location (in case you don&#X2019;t have write permissions there, or just want to test Biopython out). You have quite a few choices, which are covered in detail in the distutils installation manual (<A HREF="http://www.python.org/sigs/distutils-sig [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc26">5.2.2</A>&#XA0;&#XA0;Using the Python package index</H4><!--SEC END --><P>Another simple option is to use the Python package index
-(<A HREF="http://pypi.python.org/pypi"><TT>http://pypi.python.org/pypi</TT></A>) with the <CODE>easy_install</CODE>
-command:</P><PRE CLASS="verbatim">$ easy_install -f http://biopython.org/DIST/ biopython
-</PRE><P>If Python is installed in the standard location, you will need administrator
-privileges to do this; the <CODE>sudo</CODE> command works well:</P><PRE CLASS="verbatim">$ sudo easy_install -f http://biopython.org/DIST/ biopython
-</PRE><!--TOC subsubsection Installation on Mac OS X using the fink package manager-->
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc27">5.2.3</A>&#XA0;&#XA0;Installation on Mac OS X using the fink package manager</H4><!--SEC END --><P>Instead of installing from source, on Mac OS X you can also use the fink package manager, see <A HREF="http://fink.sf.net"><TT>http://fink.sf.net</TT></A>. Fink should take care of downloading the source code and installing all needed packages for Biopython, including Python itself. Once you have installed fink, you can install bi [...]
-</PRE><P>where XX is the python version you would like to use. Currently, python 2.4, 2.5, and 2.6 are available through fink on Mac OS X 10.4, so you would have to replace XX with 24, 25, or 26, respectively. Most likely, you will have to enable the unstable tree of fink in order to install the most recent versions of the package, see also this item in the Fink FAQ: <A HREF="http://fink.sourceforge.net/faq/usage-fink.php#unstable"><TT>http://fink.sourceforge.net/faq/usage-fink.php#unsta [...]
-<H4 CLASS="subsubsection"><!--SEC ANCHOR --><A NAME="htoc28">5.2.4</A>&#XA0;&#XA0;Installation on UNIX systems using RPMs</H4><!--SEC END --><P>Warning. Right now we&#X2019;re not making RPMs for biopython (because I
-stopped using an RPM system, basically). If anyone wants to pick this
-up, or feels especially strongly that they&#X2019;d like RPMs, please let us
-know.</P><P>To simplify things for people running RPM-based systems, biopython can
-also be installed via the RPM system. Additionally, this saves the 
-necessity of having a C compiler to install biopython. </P><P>Installing Biopython from a RPM package should be much the same process as used for other RPMs. If you need general information about how RPMs work, the best place to go is <A HREF="http://www.rpm.org"><TT>http://www.rpm.org</TT></A>.</P><P>To install it, you should just need to do:</P><PRE CLASS="verbatim">$ rpm -i your_biopython.rpm
-</PRE><P>To see what you installed try doing <CODE>rpm -qpl your_biopython.rpm</CODE> which will list all of the installed files.</P><P>RPMs do not install the documentation, tests, or example code, so you might want to also grab a source distribution, so you can use these resources (and also look at the source code if you want to).</P><!--TOC subsection Installing with a Windows Installer-->
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc29">5.3</A>&#XA0;&#XA0;Installing with a Windows Installer</H3><!--SEC END --><P>Installing things on Windows with the installer should be really easy (hey, that&#X2019;s why they&#X2019;ve got graphical installers, right?). You should just need to download the <CODE>Biopython-version.exe</CODE> installer from biopython web site. Then you just need to double click and voila, a nice little installer will come up and you can stick the l [...]
-<H3 CLASS="subsection"><!--SEC ANCHOR --><A NAME="htoc30">5.4</A>&#XA0;&#XA0;Installing from source on Windows</H3><!--SEC END --><P>
-<A NAME="sec:windows_install"></A></P><P>This section deals with installing the source (i.&#XA0;e.&#XA0;from git or from a source zip file) on a Windows machine. Much of the information from the UNIX install applies here, so it would be good to read section&#XA0;<A HREF="#sec:unix_install">5.2</A> before starting. You will need a suitable C compiler.
-What you choose may depend on your version of Python.</P><P>For Python 2.6 we currently use Microsoft&#X2019;s free VC++ 2008 Express Edition from <A HREF="http://www.microsoft.com/express/download/"><TT>http://www.microsoft.com/express/download/</TT></A>, installation of this is pretty simple. Then go to the Biopython source directory and run:</P><PRE CLASS="verbatim">c:\python26\python setup.py build
-c:\python26\python setup.py test
-c:\python26\python setup.py install
-</PRE><P>For older versions of Python, we use mingw32 installed from cygwin (<A HREF="http://www.cygwin.com"><TT>http://www.cygwin.com</TT></A>). Once everything is setup (which is a bit complicated), you would again get the source, and from that directory run:</P><PRE CLASS="verbatim">c:\python25\python setup.py build --compiler=mingw32
-c:\python25\python setup.py test
-c:\python25\python setup.py install
-</PRE><P>Previously (back on Python 2.0), Brad has also managed to use Borland&#X2019;s free C++ compiler (available from <A HREF="http://www.inprise.com/bcppbuilder/freecompiler/"><TT>http://www.inprise.com/bcppbuilder/freecompiler/</TT></A>), but this required extra work.</P><P>Now that you&#X2019;ve got everything installed, carry on ahead to section&#XA0;<A HREF="#sec:is_working">6</A> to make sure everything worked.</P><!--TOC section Making sure everything worked-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc31">6</A>&#XA0;&#XA0;Making sure everything worked</H2><!--SEC END --><P>
-<A NAME="sec:is_working"></A></P><P>First, we&#X2019;ll just do a quick test to make sure Biopython is installed correctly. The most important thing is that python can find the biopython installation. Biopython installs into top level <CODE>Bio</CODE> and <CODE>BioSQL</CODE> directories, so you&#X2019;ll want to make sure these directories are located in a directory specified 
-in your<CODE> $PYTHONPATH</CODE> environmental variable. If you used the default install, this shouldn&#X2019;t be a problem, but if not, you&#X2019;ll need to set the <CODE>PYTHONPATH</CODE> with something like <CODE>export PYTHONPATH = $PYTHONPATH':/directory/where/you/put/Biopython'</CODE> (on UNIX). Now that we think we are ready, fire up your python interpreter and follow along with the following code:</P><PRE CLASS="verbatim">$ python
-Python 2.5 (r25:51908, Nov 23 2006, 18:40:28) 
-[GCC 4.1.1 20061011 (Red Hat 4.1.1-30)] on linux2
-Type "help", "copyright", "credits" or "license" for more information.
->>> from Bio.Seq import Seq
->>> from Bio.Alphabet.IUPAC import unambiguous_dna
->>> new_seq = Seq('GATCAGAAG', unambiguous_dna)
->>> new_seq[0:2]
-Seq('GA', IUPACUnambiguousDNA())
->>> new_seq.translate()
-Seq('DQK', IUPACProtein())
->>>
-</PRE><P>If this worked properly, then it looks like Biopython is in a happy place where python can find it, so now you might want to do some more rigorous tests. The <CODE>Tests</CODE> directory inside the distribution contains a number of tests you can run to make sure all of the different parts of biopython are working. These should all work just by running <CODE>python test_WhateverTheTestIs.py</CODE>. </P><P>If you didn&#X2019;t do this earlier, you should also all of our tests. To  [...]
-</PRE><P>You can also run them by typing <CODE>python run_tests.py</CODE> in the Tests sub directory.
-See the main Tutorial for further details (there is a whole chapter on the test framework).</P><P>If you&#X2019;ve made it this far, you&#X2019;ve gotten Biopython installed and running.
-Congratulations!</P><!--TOC section Third Party Tools-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc32">7</A>&#XA0;&#XA0;Third Party Tools</H2><!--SEC END --><P>Note that Biopython includes support for interfacing with or parsing the output from a number of third party command line tools. These are not required to install Biopython, but may be of interest. This includes:</P><UL CLASS="itemize"><LI CLASS="li-itemize">
-NCBI Standalone BLAST, which can used with the <CODE>Bio.Blast</CODE> module and parsed with the <CODE>Bio.SearchIO</CODE> module.
-</LI><LI CLASS="li-itemize">EMBOSS tools, which can be invoked using the <CODE>Bio.Emboss</CODE> module. The <CODE>Bio.AlignIO</CODE> module can also parse some alignment formats output by the EMBOSS suite.
-</LI><LI CLASS="li-itemize">ClustalW, which can be parsed using <CODE>Bio.AlignIO</CODE> and invoked using the <CODE>Bio.Align.Applications</CODE> module.
-</LI><LI CLASS="li-itemize">SIMCOAL2 and FDist tools for population genetics can be used via the <CODE>Bio.PopGen</CODE> module.
-</LI><LI CLASS="li-itemize">Bill Pearson&#X2019;s FASTA tools output can be parsed using the <CODE>Bio.AlignIO</CODE> and <CODE>Bio.SearchIO</CODE> module.
-</LI><LI CLASS="li-itemize">Wise2 includes the useful tool dnal.
-</LI></UL><P>See also the listing on <A HREF="http://biopython.org/wiki/Download"><TT>http://biopython.org/wiki/Download</TT></A> which should include URLs for these tools, and may also be more up to date.</P><!--TOC section Notes for installing with non-administrator permissions-->
-<H2 CLASS="section"><!--SEC ANCHOR --><A NAME="htoc33">8</A>&#XA0;&#XA0;Notes for installing with non-administrator permissions</H2><!--SEC END --><P>Although I mentioned above that I wouldn&#X2019;t go much into installing in
-non-root directories, if you are stuck installing
-Biopython and it&#X2019;s dependencies into your home directory here are a
-few notes and tricks to keep you going:</P><UL CLASS="itemize"><LI CLASS="li-itemize">Building some C modules, such as <CODE>Bio.Cluster</CODE> require that 
-the NumPy include files (normally installed in
-<CODE>your_dir/include/python/Numeric</CODE>) be available. If the
-compiler can&#X2019;t find these directories you&#X2019;ll normally get an error
-like:<PRE CLASS="verbatim">Bio/Cluster/clustermodule.c:2: NumpPy/arrayobject.h: No such file or directory
-    </PRE><P>Followed by a long messy list of syntax errors. To fix this, you&#X2019;ll
-have to edit the <CODE>setup.py</CODE> file to let it know where the
-include directories are located. Look for the line in
-<CODE>setup.py</CODE> that looks like:</P><PRE CLASS="verbatim">    include_dirs=["Bio/Cluster"]
-    </PRE><P>and adjust it so that it includes the include directory where the
-NumPy libraries were installed:</P><PRE CLASS="verbatim">    include_dirs=["Bio/Cluster", "your_dir/include/python"]
-    </PRE><P>Then you should be able to install everything happily.</P></LI></UL><P>Yes, it&#X2019;s a bit of a mess installing lots of packages in non-standard
-locations. The best solution is to talk with your friendly system
-administrator and get them to assist with the installation of at least
-the required packages (they are generally quite useful for any python
-install) before going ahead with Biopython installation.</P><!--CUT END -->
-<!--HTMLFOOT-->
-<!--ENDHTML-->
-<!--FOOTER-->
-<HR SIZE=2><BLOCKQUOTE CLASS="quote"><EM>This document was translated from L<sup>A</sup>T<sub>E</sub>X by
-</EM><A HREF="http://hevea.inria.fr/index.html"><EM>H</EM><EM><FONT SIZE=2><sup>E</sup></FONT></EM><EM>V</EM><EM><FONT SIZE=2><sup>E</sup></FONT></EM><EM>A</EM></A><EM>.</EM></BLOCKQUOTE></BODY>
-</HTML>
diff --git a/Doc/install/Installation.pdf b/Doc/install/Installation.pdf
deleted file mode 100644
index d299ee5..0000000
Binary files a/Doc/install/Installation.pdf and /dev/null differ
diff --git a/MANIFEST.in b/MANIFEST.in
index b28d80c..53f1c4a 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -27,20 +27,22 @@ recursive-include Doc/examples *.bgz
 recursive-include Doc/examples *.xml
 recursive-include Doc/examples *.psl
 recursive-include Doc/examples *.dnd
+recursive-include Doc/examples *.csv
 recursive-include Doc/images *.png
 recursive-include Doc/images *.jpg # e.g. the logo for the Tutorial
 
-# Include Bio.Cluster, Bio.Affy and Bio.KDTree
+# Include Bio.Cluster, Bio.Affy, Bio.KDTree, Bio.phenotype
 # Note setup.py will only install these if Numpy is installed.
 recursive-include Bio/Cluster *.py
 recursive-include Bio/Affy *.py
 recursive-include Bio/KDTree *.py
+recursive-include Bio/phenotype *.py
 
 exclude Tests/Graphics/*   # Exclude test files generated by reportlab.
 include Tests/Graphics/README # But include README file to make directory!
 
-recursive-exclude * .cvsignore
-recursive-exclude * *.pyc # e.g. from running the unit tests
+# e.g. from running the unit tests, or PyPy, etc
+global-exclude *.pyc *.pyo *.py{} *.py-e
 
 # Make sure the required data files are included.
 include Bio/Entrez/DTDs/*.dtd  # Include DTD files for Entrez
diff --git a/NEWS b/NEWS
index 4d95b45..ab69ac9 100644
--- a/NEWS
+++ b/NEWS
@@ -10,8 +10,112 @@ The latest news is at the top of this file.
 
 ===================================================================
 
+8 June 2016: Biopython 1.67
+
+This release of Biopython supports Python 2.6, 2.7, 3.3, 3.4 and 3.5, but
+support for Python 2.6 is considered to be deprecated. It has also been
+tested on PyPy 5.0, PyPy3 version 2.4, and Jython 2.7.
+
+Comparison of SeqRecord objects until now has used the default Python object
+comparison (are they the same instance in memory?). This can be surprising, but
+comparing all of the attributes would be too complex. As of this release
+attempting to compare SeqRecord objects should raise an exception instead. If
+you want the old behaviour, use id(record1) == id(record2) instead.
+
+New experimental module Bio.phenotype is for working with Phenotype Microarray
+plates in JSON and the machine vendor's CSV format (contributed by Marco
+Galardini).
+
+Following the convention used elsewhere in Biopython, there is a new function
+Bio.KEGG.read(...) for parsing KEGG files expected to contain a single record
+only - the existing function Bio.KEGG.parse(...) is intended to be used to
+iterate over multi-record files.
+
+When a gap character is defined, Bio.Seq will now translate gap codons
+(e.g. "---") into a single gap ("-") in the protein sequence. The gap character
+is inferred from the Seq object's alphabet, but it can also be passed as an
+argument to the translate method.
+
+The new NCBI genetic code table 25, covering Candidate Division SR1 and
+Gracilibacteria, has been added to Bio.Data (and the translation
+functionality).
+
+The Bio.Entrez interface will automatically use an HTTP POST rather than
+HTTP GET if the URL would exceed 1000 characters. This is based on NCBI
+guidelines and the fact that very long queries like complex searches can
+otherwise trigger an HTTP Error 414 Request URI too long.
+
+Foreign keys are now used when creating BioSQL databases with SQLite3 (this
+was not possible until SQLite version 3.6.19). The BioSQL taxonomy code now
+updates the taxon table left/right keys when updating the taxonomy.
+
+There have been some fixes to the MMCIF structure parser which now uses
+identifiers which better match results from the PDB structure parse.
+
+The restriction enzyme list in Bio.Restriction has been updated to the
+May 2016 release of REBASE.
+
+The mmCIF parser in Bio.PDB.MMCIFParser has been joined by a second version
+which only looks at the ATOM and HETATM lines and can be much faster.
+
+The Bio.KEGG.REST will now return unicode text-based handles, except for
+images which remain as binary bytes-based handles, making it easier to use
+with the mostly text-based parsers in Biopython.
+
+Note that the BioSQL test configuration information is now in a new file
+Tests/biosql.ini rather than directly in Tests/test_BioSQL_*.py as before.
+You can make a copy of the provided example file Tests/biosql.ini.sample
+as Tests/biosql.ini and edit this if you wish to run the BioSQL tests.
+
+Additionally, a number of small bugs have been fixed with further additions
+to the test suite, and there has been further work to follow the Python PEP8
+standard coding style, and in converting our docstring documentation to use
+the reStructuredText markup style.
+
+Many thanks to the Biopython developers and community for making this release
+possible, especially the following contributors:
+
+Aaron Rosenfeld (first contribution)
+Anders Pitman (first contribution)
+Barbara Mühlemann (first contribution)
+Ben Fulton
+Ben Woodcroft (first contribution)
+Brandon Invergo
+Brian Osborne (first contribution)
+Carlos Pena
+Chaitanya Gupta (first contribution)
+Chris Warth (first contribution)
+Christiam Camacho (first contribution)
+Connor T. Skennerton
+David Koppstein (first contribution)
+Eric Talevich
+Jacek Śmietański (first contribution)
+João D Ferreira (first contribution)
+João Rodrigues
+Joe Cora (first contribution)
+Kai Blin
+Leighton Pritchard
+Lenna Peterson
+Marco Galardini (first contribution)
+Markus Piotrowski
+Matt Ruffalo (first contribution)
+Matteo Sticco (first contribution)
+Nader Morshed (first contribution)
+Owen Solberg (first contribution)
+Peter Cock
+Steve Bond (first contribution)
+Terry Jones (first contribution)
+Vincent Davis
+Zheng Ruan
+
+===================================================================
+
 21 October 2015: Biopython 1.66
 
+This release of Biopython supports Python 2.6, 2.7, 3.3, 3.4 and 3.5, but
+support for Python 2.6 is considered to be deprecated. It has also been
+tested on PyPy 2.4 to 2.6, PyPy3 version 2.4, and Jython 2.7.
+
 Further work on the Bio.KEGG and Bio.Graphics modules now allows drawing KGML
 pathways with transparency.
 
@@ -71,6 +175,7 @@ Leighton Pritchard
 Lucas Sinclair
 Michiel de Hoon
 Peter Cock
+Saket Choudhary
 Sunhwan Jo (first contribution)
 Tarcisio Fedrizzi (first contribution)
 Tiago Antao
@@ -404,6 +509,10 @@ The Bio.Motif module has been updated and reorganized. To allow for a clean
 deprecation of the old code, the new motif code is stored in a new module
 Bio.motifs, and a PendingDeprecationWarning was added to Bio.Motif.
 
+A faster low level string FASTA based parser SimpleFastaParser has been added
+to Bio.SeqIO.FastaIO which like its sister function for FASTQ files does not
+have the overhead of constructing SeqRecord objects.
+
 Additionally there have been other minor bug fixes and more unit tests.
 
 Finally, we are phasing out support for Python 2.5. We will continue support
diff --git a/PKG-INFO b/PKG-INFO
deleted file mode 100644
index 2e8ddf5..0000000
--- a/PKG-INFO
+++ /dev/null
@@ -1,11 +0,0 @@
-Metadata-Version: 1.1
-Name: biopython
-Version: 1.66
-Summary: Freely available tools for computational molecular biology.
-Home-page: http://www.biopython.org/
-Author: The Biopython Contributors
-Author-email: biopython at biopython.org
-License: UNKNOWN
-Download-URL: http://biopython.org/DIST/
-Description: UNKNOWN
-Platform: UNKNOWN
diff --git a/README b/README
index 69e9b6d..133390d 100644
--- a/README
+++ b/README
@@ -30,15 +30,23 @@ http://dx.doi.org/10.1093/bioinformatics/btp163 pmid:19304878
 For the impatient
 =================
 
-To build and install Biopython, download and unzip the source code, go to this
-directory at the command line, and type::
+Windows users are currently recommended to use the installation packages provided
+on our website, http://biopython.org -- further instructions are given below.
+
+Python 2.7.9 onwards, and Python 3.4 onwards, include the package management
+system "pip" which should allow you to install Biopython with just::
+
+    pip install numpy
+    pip install biopython
+
+Otherwise you may have to build and install Biopython. Download and unzip the
+source code, go to this directory at the command line, and type::
 
     python setup.py build
     python setup.py test
     sudo python setup.py install
 
-Windows users are instead recommended to use the installation packages provided
-on our website, http://biopython.org -- further instructions are given below.
+Here you can replace ``python`` with a specific version, e.g. ``python3.5``.
 
 
 Python Requirements
@@ -53,7 +61,7 @@ implementations:
 
   This is the primary development platform for Biopython.
 
-- PyPy 2.4, 2.5, 2.6 and also PyPy3 2.4 -- see http://www.pypy.org
+- PyPy 5.0.1 and also PyPy3 2.4 -- see http://www.pypy.org
 
   Aside from modules with C code or dependent on NumPy, everything should
   work. PyPy's NumPy reimplementation NumPyPy is still in progress.
@@ -64,7 +72,8 @@ implementations:
   or dependent on SQLite3 or NumPy, everything should work. There are some
   known issues with test failures which have not yet been resolved.
 
-Please note that support for Python 2.6 is deprecated as of Biopython 1.66.
+Please note that support for Python 3.3 is deprecated as of Biopython 1.67,
+and that support for Python 2.6 is deprecated as of Biopython 1.66.
 Biopython 1.62 was our final release to support Python 2.5 and Jython 2.5.
 
 
diff --git a/README.rst b/README.rst
new file mode 120000
index 0000000..100b938
--- /dev/null
+++ b/README.rst
@@ -0,0 +1 @@
+README
\ No newline at end of file
diff --git a/Scripts/GenBank/check_output.py b/Scripts/GenBank/check_output.py
index 9431d56..18a9b23 100644
--- a/Scripts/GenBank/check_output.py
+++ b/Scripts/GenBank/check_output.py
@@ -16,9 +16,9 @@ import os
 import gzip
 
 try:
-    from StringIO import StringIO # Python 2
+    from StringIO import StringIO  # Python 2
 except ImportError:
-    from io import StringIO # Python 3
+    from io import StringIO  # Python 3
 
 # biopython
 from Bio import GenBank
diff --git a/Scripts/GenBank/check_output_simple.py b/Scripts/GenBank/check_output_simple.py
index ff009aa..c4153a6 100644
--- a/Scripts/GenBank/check_output_simple.py
+++ b/Scripts/GenBank/check_output_simple.py
@@ -17,38 +17,37 @@ import sys
 # GenBank stuff to test
 from Bio import GenBank
 
+
 if len(sys.argv) != 2:
     print("Usage ./check_output.py <GenBank file to parse>")
     sys.exit()
 
 parser = GenBank.FeatureParser(debug_level=2)
 
-handle = open(sys.argv[1], 'r')
-
-iterator = GenBank.Iterator(handle, parser)
-
-while True:
-    cur_record = next(iterator)
-
-    if not(cur_record):
-        break
-
-    print("***Record")
-    print("Seq: %s" % cur_record.seq)
-    print("Id: %s" % cur_record.id)
-    print("Name: %s" % cur_record.name)
-    print("Description: %s" % cur_record.description)
-    print("Annotations****")
-    for annotation_key in cur_record.annotations:
-        if annotation_key != 'references':
-            print("Key: %s" % annotation_key)
-            print("Value: %s" % cur_record.annotations[annotation_key])
-        else:
-            print("References*")
-            for reference in cur_record.annotations[annotation_key]:
-                print(str(reference))
-    print("Feaures")
-    for feature in cur_record.features:
-        print(feature)
-
-handle.close()
+with open(sys.argv[1], 'r') as handle:
+
+    iterator = GenBank.Iterator(handle, parser)
+
+    while True:
+        cur_record = next(iterator)
+
+        if not cur_record:
+            break
+
+        print("***Record")
+        print("Seq: %s" % cur_record.seq)
+        print("Id: %s" % cur_record.id)
+        print("Name: %s" % cur_record.name)
+        print("Description: %s" % cur_record.description)
+        print("Annotations****")
+        for annotation_key in cur_record.annotations:
+            if annotation_key != 'references':
+                print("Key: %s" % annotation_key)
+                print("Value: %s" % cur_record.annotations[annotation_key])
+            else:
+                print("References*")
+                for reference in cur_record.annotations[annotation_key]:
+                    print(str(reference))
+        print("Feaures")
+        for feature in cur_record.features:
+            print(feature)
diff --git a/Scripts/Performance/biosql_performance_load.py b/Scripts/Performance/biosql_performance_load.py
index 663342d..464f2a6 100644
--- a/Scripts/Performance/biosql_performance_load.py
+++ b/Scripts/Performance/biosql_performance_load.py
@@ -7,6 +7,9 @@ import time
 # set up the connection
 from Bio import GenBank
 from BioSQL import BioSeqDatabase
+
+__docformat__ = "restructuredtext en"
+
 server = BioSeqDatabase.open_database(host="192.168.0.192", user="root",
                                        passwd="", db="pythonloadtest")
 
@@ -30,5 +33,5 @@ num_records = db.load(iterator)
 end_time = time.time()
 elapsed_time = end_time - start_time
 print("Loading")
-print("\tDid %s records in %s seconds for\n\t%f records per second" % \
+print("\tDid %s records in %s seconds for\n\t%f records per second" %
       (num_records, elapsed_time, float(num_records) / float(elapsed_time)))
diff --git a/Scripts/Performance/biosql_performance_read.py b/Scripts/Performance/biosql_performance_read.py
index b5c89d3..db4668b 100644
--- a/Scripts/Performance/biosql_performance_read.py
+++ b/Scripts/Performance/biosql_performance_read.py
@@ -6,6 +6,9 @@ from __future__ import print_function
 import time
 # set up the connection
 from BioSQL import BioSeqDatabase
+
+__docformat__ = "restructuredtext en"
+
 server = BioSeqDatabase.open_database(host="192.168.0.192", user="root",
                                        passwd="", db="test_biosql")
 db = server["embl_rod"]
@@ -23,7 +26,7 @@ for junk_id, record in db.items():
 end_time = time.time()
 elapsed_time = end_time - start_time
 print("Fasta")
-print("\tDid %s records in %s seconds for\n\t%f records per second" % \
+print("\tDid %s records in %s seconds for\n\t%f records per second" %
       (num_records, elapsed_time, float(num_records) / float(elapsed_time)))
 
 # -- do the "EMBL" timing part
@@ -43,5 +46,5 @@ for junk_id, record in db.items():
 end_time = time.time()
 elapsed_time = end_time - start_time
 print("EMBL")
-print("\tDid %s records in %s seconds for\n\t%f records per second" % \
+print("\tDid %s records in %s seconds for\n\t%f records per second" %
       (num_records, elapsed_time, float(num_records) / float(elapsed_time)))
diff --git a/Scripts/Restriction/ranacompiler.py b/Scripts/Restriction/ranacompiler.py
index 8893497..b380ea6 100644
--- a/Scripts/Restriction/ranacompiler.py
+++ b/Scripts/Restriction/ranacompiler.py
@@ -6,25 +6,20 @@
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
-#
-#   this script is used to produce the dictionary which will contains the data
-#   about the restriction enzymes from the Emboss/Rebase data files
-#   namely
+# This script is used to produce the dictionary which will contains the data
+# about the restriction enzymes from the Emboss/Rebase data files, namely:
 #   emboss_e.### (description of the sites),
 #   emboss_r.### (origin, methylation, references)
 #   emboss_s.### (suppliers)
-#   where ### is a number of three digits : 1 for the year two for the month
-#
-#   very dirty implementation but it does the job, so...
-#   Not very quick either but you are not supposed to use it frequently.
-#
+# Where ### is a number of three digits : 1 for the year two for the month
 #   The results are stored in
 #   path/to/site-packages/Bio/Restriction/Restriction_Dictionary.py
 #   the file contains two dictionary:
 #   'rest_dict' which contains the data for the enzymes
 #   and
 #   'suppliers' which map the name of the suppliers to their abbreviation.
-#
+# very dirty implementation but it does the job, so...
+# Not very quick either but you are not supposed to use it frequently.
 
 """Convert a series of Rebase files into a Restriction_Dictionary.py module.
 
@@ -43,8 +38,8 @@ should allow automated fetching (the the update code and RanaConfig.py).
 In addition there are links on this HTML page which requires manual download
 and renaming of the files: http://rebase.neb.com/rebase/rebase.f37.html
 
-This Python file is intended to be used via the scripts Scripts/Restriction/*.py
-only.
+This Python file is intended to be used via the scripts in
+Scripts/Restriction/*.py only.
 """
 
 from __future__ import print_function
@@ -61,33 +56,23 @@ from functools import reduce
 
 from Bio.Seq import Seq
 from Bio.Alphabet import generic_dna
+from Bio.Data.IUPACData import ambiguous_dna_values as amb_dna
 
 import Bio.Restriction.Restriction
-from Bio.Restriction.Restriction import AbstractCut, RestrictionType, NoCut, OneCut
-from Bio.Restriction.Restriction import TwoCuts, Meth_Dep, Meth_Undep, Palindromic
-from Bio.Restriction.Restriction import NonPalindromic, Unknown, Blunt, Ov5, Ov3
+from Bio.Restriction.Restriction import AbstractCut, RestrictionType, NoCut
+from Bio.Restriction.Restriction import OneCut, TwoCuts, Meth_Dep, Meth_Undep
+from Bio.Restriction.Restriction import Palindromic, NonPalindromic, Unknown
+from Bio.Restriction.Restriction import Blunt, Ov5, Ov3
 from Bio.Restriction.Restriction import NotDefined, Defined, Ambiguous
 from Bio.Restriction.Restriction import Commercially_available, Not_available
 
 import Bio.Restriction.RanaConfig as config
-from Bio.Restriction._Update.Update import RebaseUpdate
+from rebase_update import RebaseUpdate
 from Bio.Restriction.Restriction import *
 
-dna_alphabet = {'A':'A', 'C':'C', 'G':'G', 'T':'T',
-                'R':'AG', 'Y':'CT', 'W':'AT', 'S':'CG', 'M':'AC', 'K':'GT',
-                'H':'ACT', 'B':'CGT', 'V':'ACG', 'D':'AGT',
-                'N':'ACGT',
-                'a': 'a', 'c': 'c', 'g': 'g', 't': 't',
-                'r':'ag', 'y':'ct', 'w':'at', 's':'cg', 'm':'ac', 'k':'gt',
-                'h':'act', 'b':'cgt', 'v':'acg', 'd':'agt',
-                'n':'acgt'}
-
-
-complement_alphabet = {'A':'T', 'T':'A', 'C':'G', 'G':'C','R':'Y', 'Y':'R',
-                       'W':'W', 'S':'S', 'M':'K', 'K':'M', 'H':'D', 'D':'H',
-                       'B':'V', 'V':'B', 'N':'N','a':'t', 'c':'g', 'g':'c',
-                       't':'a', 'r':'y', 'y':'r', 'w':'w', 's':'s','m':'k',
-                       'k':'m', 'h':'d', 'd':'h', 'b':'v', 'v':'b', 'n':'n'}
+__docformat__ = "restructuredtext en"
+
+
 enzymedict = {}
 suppliersdict = {}
 classdict = {}
@@ -99,25 +84,13 @@ class OverhangError(ValueError):
     pass
 
 
-def BaseExpand(base):
-    """BaseExpand(base) -> string.
-
-    given a degenerated base, returns its meaning in IUPAC alphabet.
-
-    i.e:
-        b= 'A' -> 'A'
-        b= 'N' -> 'ACGT'
-        etc..."""
-    base = base.upper()
-    return dna_alphabet[base]
-
-
 def regex(site):
     """regex(site) -> string.
 
     Construct a regular expression from a DNA sequence.
     i.e.:
-        site = 'ABCGN'   -> 'A[CGT]CG.'"""
+        site = 'ABCGN'   -> 'A[CGT]CG.'
+    """
     reg_ex = str(site)
     for base in reg_ex:
         if base in ('A', 'T', 'C', 'G', 'a', 'c', 'g', 't'):
@@ -126,7 +99,7 @@ def regex(site):
             reg_ex = '.'.join(reg_ex.split('N'))
             reg_ex = '.'.join(reg_ex.split('n'))
         if base in ('R', 'Y', 'W', 'M', 'S', 'K', 'H', 'D', 'B', 'V'):
-            expand = '['+ str(BaseExpand(base))+']'
+            expand = '[' + amb_dna[base.upper()] + ']'
             reg_ex = expand.join(reg_ex.split(base))
     return reg_ex
 
@@ -135,7 +108,8 @@ def is_palindrom(sequence):
     """is_palindrom(sequence) -> bool.
 
     True is the sequence is a palindrom.
-    sequence is a Seq object."""
+    sequence is a Seq object.
+    """
     return str(sequence) == str(sequence.reverse_complement())
 
 
@@ -143,13 +117,14 @@ def LocalTime():
     """LocalTime() -> string.
 
     LocalTime calculate the extension for emboss file for the current year and
-    month."""
+    month.
+    """
     t = time.gmtime()
     year = str(t.tm_year)[-1]
     month = str(t.tm_mon)
     if len(month) == 1:
         month = '0' + month
-    return year+month
+    return year + month
 
 
 class newenzyme(object):
@@ -325,7 +300,7 @@ class TypeCompiler(object):
         types = [(p, c, o, d, m, co, baT[0], baT[1])
                  for p in paT for c in cuT for o in ovT
                  for d in deT for m in meT for co in coT]
-        n= 1
+        n = 1
         for ty in types:
             dct = {}
             for t in ty:
@@ -333,8 +308,9 @@ class TypeCompiler(object):
                 #
                 #   here we need to customize the dictionary.
                 #   i.e. types deriving from OneCut have always scd5 and scd3
-                #   equal to None. No need therefore to store that in a specific
-                #   enzyme of this type. but it then need to be in the type.
+                #   equal to None. No need therefore to store that in a
+                #   specific enzyme of this type. but it then need to be in the
+                #   type.
                 #
                 dct['results'] = []
                 dct['substrat'] = 'DNA'
@@ -348,55 +324,55 @@ class TypeCompiler(object):
 
             class klass(type):
                 def __new__(cls):
-                    return type.__new__(cls, 'type%i'%n, ty, dct)
+                    return type.__new__(cls, 'type%i' % n, ty, dct)
 
                 def __init__(cls):
-                    super(klass, cls).__init__('type%i'%n, ty, dct)
+                    super(klass, cls).__init__('type%i' % n, ty, dct)
 
             yield klass()
-            n+=1
+            n += 1
 
-start = '\n\
-#!/usr/bin/env python\n\
-#\n\
-#      Restriction Analysis Libraries.\n\
-#      Copyright (C) 2004. Frederic Sohm.\n\
-#\n\
-# This code is part of the Biopython distribution and governed by its\n\
-# license.  Please see the LICENSE file that should have been included\n\
+start = """#!/usr/bin/env python
+#
+#      Restriction Analysis Libraries.
+#      Copyright (C) 2004. Frederic Sohm.
+#
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
 # as part of this package.\n\
 #\n\
-# This file is automatically generated - do not edit it by hand! Instead,\n\
-# use the tool Scripts/Restriction/ranacompiler.py which in turn uses\n\
-# Bio/Restriction/_Update/RestrictionCompiler.py\n\
+# This file is automatically generated - do not edit it by hand! Instead,
+# use the tool Scripts/Restriction/ranacompiler.py which in turn uses
+# Bio/Restriction/_Update/RestrictionCompiler.py
 #\n\
-# The following dictionaries used to be defined in one go, but that does\n\
-# not work on Jython due to JVM limitations. Therefore we break this up\n\
-# into steps, using temporary functions to avoid the JVM limits.\n\
-\n\n'
+# The following dictionaries used to be defined in one go, but that does
+# not work on Jython due to JVM limitations. Therefore we break this up
+# into steps, using temporary functions to avoid the JVM limits.
+#
+# Used REBASE emboss files version {} ({}).
+
+""".format(LocalTime(), time.gmtime().tm_year)
 
 
 class DictionaryBuilder(object):
 
-    def __init__(self, e_mail='', ftp_proxy=''):
-        """DictionaryBuilder([e_mail[, ftp_proxy]) -> DictionaryBuilder instance.
+    def __init__(self, ftp_proxy=''):
+        """DictionaryBuilder([ftp_proxy])->DictionaryBuilder instance.
 
         If the emboss files used for the construction need to be updated this
         class will download them if the ftp connection is correctly set.
         either in RanaConfig.py or given at run time.
 
-        e_mail is the e-mail address used as password for the anonymous
-        ftp connection.
-
-        proxy is the ftp_proxy to use if any."""
-        self.rebase_pass = e_mail or config.Rebase_password
+        proxy is the ftp_proxy to use if any.
+        """
         self.proxy = ftp_proxy or config.ftp_proxy
 
     def build_dict(self):
         """DB.build_dict() -> None.
 
         Construct the dictionary and build the files containing the new
-        dictionaries."""
+        dictionaries.
+        """
         #
         #   first parse the emboss files.
         #
@@ -413,8 +389,8 @@ class DictionaryBuilder(object):
         #
         tdct = {}
         for klass in TypeCompiler().buildtype():
-            exec(klass.__name__ +'= klass')
-            exec("tdct['"+klass.__name__+"'] = klass")
+            exec(klass.__name__ + '= klass')
+            exec("tdct['" + klass.__name__ + "'] = klass")
 
         #
         #   Now we build the enzymes from enzymedict
@@ -448,7 +424,7 @@ class DictionaryBuilder(object):
             dct = dict(cls.__dict__)
             del dct['bases']
             del dct['__bases__']
-            del dct['__name__']# no need to keep that, it's already in the type.
+            del dct['__name__']  # no need to keep, it's already in the type.
             classdict[name] = dct
 
             commonattr = ['fst5', 'fst3', 'scd5', 'scd3', 'substrat',
@@ -456,9 +432,10 @@ class DictionaryBuilder(object):
             if typename in typedict:
                 typedict[typename][1].append(name)
             else:
-                enzlst= []
+                enzlst = []
                 tydct = dict(typestuff.__dict__)
-                tydct = dict([(k, v) for k, v in tydct.items() if k in commonattr])
+                tydct = dict([(k, v) for k, v in tydct.items()
+                              if k in commonattr])
                 enzlst.append(name)
                 typedict[typename] = (bases, enzlst)
             for letter in cls.__dict__['suppl']:
@@ -479,15 +456,18 @@ class DictionaryBuilder(object):
         # update = config.updatefolder
 
         update = os.getcwd()
-        with open(os.path.join(update, 'Restriction_Dictionary.py'), 'w') as results:
-            print('Writing the dictionary containing the new Restriction classes...')
+        with open(os.path.join(update, 'Restriction_Dictionary.py'),
+                  'w') as results:
+            print('Writing the dictionary containing the new Restriction ' +
+                  'classes...')
             results.write(start)
             results.write('rest_dict = {}\n')
             for name in sorted(classdict):
                 results.write("def _temp():\n")
                 results.write("    return {\n")
                 for key, value in classdict[name].items():
-                    results.write("        %s: %s,\n" % (repr(key), repr(value)))
+                    results.write("        %s: %s,\n" %
+                                  (repr(key), repr(value)))
                 results.write("    }\n")
                 results.write("rest_dict[%s] = _temp()\n" % repr(name))
                 results.write("\n")
@@ -513,9 +493,9 @@ class DictionaryBuilder(object):
                 results.write("    )\n")
                 results.write("typedict[%s] = _temp()\n" % repr(name))
                 results.write("\n")
-            # I had wanted to do "del _temp" at each stage (just for clarity), but
-            # that pushed the code size just over the Jython JVM limit. We include
-            # one the final "del _temp" to clean up the namespace.
+            # I had wanted to do "del _temp" at each stage (just for clarity),
+            # but that pushed the code size just over the Jython JVM limit. We
+            # include one the final "del _temp" to clean up the namespace.
             results.write("del _temp\n")
             results.write("\n")
             print('OK.\n')
@@ -526,8 +506,9 @@ class DictionaryBuilder(object):
 
         Install the newly created dictionary in the site-packages folder.
 
-        May need super user privilege on some architectures."""
-        print('\n ' +'*'*78 + ' \n')
+        May need super user privilege on some architectures.
+        """
+        print('\n ' + '*' * 78 + ' \n')
         print('\n\t\tInstalling Restriction_Dictionary.py')
         try:
             import Bio.Restriction.Restriction_Dictionary as rd
@@ -554,17 +535,17 @@ class DictionaryBuilder(object):
             print('\
             \n\tThe new file seems ok. Proceeding with the installation.')
         except SyntaxError:
-            print('\
-            \n The new dictionary file is corrupted. Aborting the installation.')
+            print('\n The new dictionary file is corrupted. Aborting the ' +
+                  'installation.')
             return
         try:
             shutil.copyfile(new, old)
             print('\n\t Everything ok. If you need it a version of the old\
             \n\t dictionary have been saved in the Updates folder under\
             \n\t the name Restriction_Dictionary.old.')
-            print('\n ' +'*'*78 + ' \n')
+            print('\n ' + '*' * 78 + ' \n')
         except IOError:
-            print('\n ' +'*'*78 + ' \n')
+            print('\n ' + '*' * 78 + ' \n')
             print('\
             \n\t WARNING : Impossible to install the new dictionary.\
             \n\t Are you sure you have write permission to the folder :\n\
@@ -575,8 +556,9 @@ class DictionaryBuilder(object):
     def no_install(self):
         """BD.no_install() -> None.
 
-        build the new dictionary but do not install the dictionary."""
-        print('\n ' +'*'*78 + '\n')
+        build the new dictionary but do not install the dictionary.
+        """
+        print('\n ' + '*' * 78 + '\n')
         # update = config.updatefolder
         try:
             import Bio.Restriction.Restriction_Dictionary as rd
@@ -591,7 +573,8 @@ class DictionaryBuilder(object):
         old = os.path.join(os.path.split(rd.__file__)[0],
                            'Restriction_Dictionary.py')
         update = os.getcwd()
-        shutil.copyfile(old, os.path.join(update, 'Restriction_Dictionary.old'))
+        shutil.copyfile(old, os.path.join(update,
+                                          'Restriction_Dictionary.old'))
         places = update, os.path.split(Bio.Restriction.Restriction.__file__)[0]
         print("\t\tCompilation of the new dictionary : OK.\
         \n\t\tInstallation : No.\n\
@@ -603,13 +586,13 @@ class DictionaryBuilder(object):
         \n note : \
         \n This folder should be :\n\
         \n\t%s\n" % places)
-        print('\n ' +'*'*78 + '\n')
+        print('\n ' + '*' * 78 + '\n')
         return
 
     def lastrebasefile(self):
         """BD.lastrebasefile() -> None.
 
-        Check the emboss files are up to date and download them if they are not.
+        Check the emboss files are up to date and download them if not.
         """
         embossnames = ('emboss_e', 'emboss_r', 'emboss_s')
         #
@@ -619,7 +602,7 @@ class DictionaryBuilder(object):
         update_needed = False
         # dircontent = os.listdir(config.Rebase) #    local database content
         dircontent = os.listdir(os.getcwd())
-        base = os.getcwd() # added for biopython current directory
+        base = os.getcwd()  # added for biopython current directory
         for name in emboss_now:
             if name in dircontent:
                 pass
@@ -630,7 +613,7 @@ class DictionaryBuilder(object):
             #
             #   nothing to be done
             #
-            print('\n Using the files : %s'% ', '.join(emboss_now))
+            print('\n Using the files : %s' % ', '.join(emboss_now))
             return tuple(open(os.path.join(base, n)) for n in emboss_now)
         else:
             #
@@ -640,12 +623,12 @@ class DictionaryBuilder(object):
             \n Would you like to update them before proceeding?(y/n)')
             r = _input(' update [n] >>> ')
             if r in ['y', 'yes', 'Y', 'Yes']:
-                updt = RebaseUpdate(self.rebase_pass, self.proxy)
+                updt = RebaseUpdate(self.proxy)
                 updt.openRebase()
                 updt.getfiles()
                 updt.close()
                 print('\n Update complete. Creating the dictionaries.\n')
-                print('\n Using the files : %s'% ', '.join(emboss_now))
+                print('\n Using the files : %s' % ', '.join(emboss_now))
                 return tuple(open(os.path.join(base, n)) for n in emboss_now)
             else:
                 #
@@ -663,7 +646,8 @@ class DictionaryBuilder(object):
                             pass
                         raise NotFoundError
                     except NotFoundError:
-                        print("\nNo %s file found. Upgrade is impossible.\n"%name)
+                        print("\nNo %s file found. Upgrade is impossible.\n" %
+                              name)
                         sys.exit()
                     continue
                 pass
@@ -689,24 +673,28 @@ class DictionaryBuilder(object):
             last[0], last[-1] = last[-1], last[0]
 
         for number in last:
-            files = [(name, name+'.%s'%number) for name in embossnames]
+            files = [(name, name + '.%s' % number) for name in embossnames]
             strmess = '\nLast EMBOSS files found are :\n'
             try:
                 for name, file in files:
                     if os.path.isfile(os.path.join(base, file)):
-                        strmess += '\t%s.\n'%file
+                        strmess += '\t%s.\n' % file
                     else:
                         raise ValueError
                 print(strmess)
-                emboss_e = open(os.path.join(base, 'emboss_e.%s'%number), 'r')
-                emboss_r = open(os.path.join(base, 'emboss_r.%s'%number), 'r')
-                emboss_s = open(os.path.join(base, 'emboss_s.%s'%number), 'r')
+                emboss_e = open(os.path.join(base, 'emboss_e.%s' % number),
+                                'r')
+                emboss_r = open(os.path.join(base, 'emboss_r.%s' % number),
+                                'r')
+                emboss_s = open(os.path.join(base, 'emboss_s.%s' % number),
+                                'r')
                 return emboss_e, emboss_r, emboss_s
             except ValueError:
                 continue
 
     def parseline(self, line):
-        line = [line[0]]+[line[1].upper()]+[int(i) for i in line[2:9]]+line[9:]
+        line = [line[0]] + [line[1].upper()] + [int(i) for i in line[2:9]] + \
+               line[9:]
         name = line[0].replace("-", "_").replace(".", "_")
         site = line[1]  # sequence of the recognition site
         dna = Seq(site, generic_dna)
@@ -753,7 +741,8 @@ class DictionaryBuilder(object):
             print('\
             \nWARNING : %s cut twice with different overhang length each time.\
             \n\tUnable to deal with this behaviour. \
-            \n\tThis enzyme will not be included in the database. Sorry.' %name)
+            \n\tThis enzyme will not be included in the database. Sorry.' %
+                  name)
             print('\tChecking...')
             raise OverhangError
         if 0 <= fst5 <= size and 0 <= fst3 <= size:
@@ -830,12 +819,12 @@ class DictionaryBuilder(object):
             #
             #   3' overhang. site is included.
             #
-            ovhgseq = abs(fst3)*'N' + site + (fst5-size)*'N'
-        elif fst5 < 0 and size <fst3:
+            ovhgseq = abs(fst3) * 'N' + site + (fst5 - size) * 'N'
+        elif fst5 < 0 and size < fst3:
             #
             #   5' overhang. site is included.
             #
-            ovhgseq = abs(fst5)*'N' + site + (fst3-size)*'N'
+            ovhgseq = abs(fst5) * 'N' + site + (fst3 - size) * 'N'
         else:
             #
             #   5' and  3' outside of the site
@@ -888,8 +877,8 @@ class DictionaryBuilder(object):
         #
         #   exact frequency of the site. (ie freq(N) == 1, ...)
         #
-        f = [4/len(dna_alphabet[l]) for l in site.upper()]
-        freq = reduce(lambda x, y: x*y, f)
+        f = [4 / len(amb_dna[l]) for l in site.upper()]
+        freq = reduce(lambda x, y: x * y, f)
         line.append(freq)
         #
         #   append regex and ovhg1, they have not been appended before not to
@@ -904,15 +893,17 @@ class DictionaryBuilder(object):
         #
         #   remove the heading of the file.
         #
-        return [l for l in itertools.dropwhile(lambda l:l.startswith('#'), file)]
+        return [l for l in itertools.dropwhile(lambda l:l.startswith('#'),
+                                               file)]
 
     def getblock(self, file, index):
         #
         #   emboss_r.txt, separation between blocks is //
         #
         take = itertools.takewhile
-        block = [l for l in take(lambda l: not l.startswith('//'), file[index:])]
-        index += len(block)+1
+        block = [l for l in take(lambda l: not l.startswith('//'),
+                                 file[index:])]
+        index += len(block) + 1
         return block, index
 
     def get(self, block):
@@ -937,7 +928,7 @@ class DictionaryBuilder(object):
         sitefile = self.removestart(file2)
         supplier = self.removestart(file3)
 
-        i1, i2= 0, 0
+        i1, i2 = 0, 0
         try:
             while True:
                 block, i1 = self.getblock(methfile, i1)
@@ -962,13 +953,16 @@ class DictionaryBuilder(object):
                 except OverhangError:   # overhang error
                     n = name            # do not include the enzyme
                     if not bl[2]:
-                        print('Anyway, %s is not commercially available.\n' %n)
+                        print('Anyway, %s is not commercially available.\n' %
+                              n)
                     else:
-                        print('Unfortunately, %s is commercially available.\n'%n)
+                        print('Unfortunately, %s is commercially ' % n +
+                              ' available.\n')
 
                     continue
                 # Hyphens and dots can't be used as a Python name, nor as a
-                # group name in a regular expression. e.g. 'CviKI-1', 'R2.BceSIV'
+                # group name in a regular expression. e.g. 'CviKI-1',
+                # 'R2.BceSIV'
                 name = name.replace("-", "_").replace(".", "_")
                 if name in enzymedict:
                     #
@@ -982,8 +976,9 @@ class DictionaryBuilder(object):
                     dna = Seq(enzymedict[other][0], generic_dna)
                     sense2 = regex(dna)
                     antisense2 = regex(dna.reverse_complement())
-                    sense = '(?P<'+other+'>'+sense1+'|'+sense2+')'
-                    antisense = '(?P<'+other+'_as>'+antisense1+'|'+antisense2 + ')'
+                    sense = '(?P<' + other + '>' + sense1 + '|' + sense2 + ')'
+                    antisense = '(?P<' + other + '_as>' + antisense1 + '|' + \
+                                antisense2 + ')'
                     reg = sense + '|' + antisense
                     line[1] = line[1] + '|' + enzymedict[other][0]
                     line[-1] = reg
@@ -1014,12 +1009,6 @@ def standalone():
         help="compile and install the newly created file. "
         "default behaviour (without switch): "
         "Compile the enzymes and store them in the Updates folder")
-    add('-m', '--e-mail',
-        action="store",
-        dest='rebase_password',
-        default='',
-        help="set the e-mail address to be used as password for the"
-        "anonymous ftp connection to Rebase.")
     add('-p', '--proxy',
         action="store",
         dest='ftp_proxy',
@@ -1030,7 +1019,7 @@ def standalone():
 
 if __name__ == '__main__':
     options, args = standalone()
-    Builder = DictionaryBuilder(options.rebase_password, options.ftp_proxy)
+    Builder = DictionaryBuilder(options.ftp_proxy)
     Builder.build_dict()
     if options.i:
         Builder.install_dict()
diff --git a/Scripts/Restriction/rebase_update.py b/Scripts/Restriction/rebase_update.py
index e147f35..510315d 100644
--- a/Scripts/Restriction/rebase_update.py
+++ b/Scripts/Restriction/rebase_update.py
@@ -18,6 +18,7 @@ import sys
 import time
 import optparse
 
+
 try:
     from urllib import FancyURLopener
 except ImportError:
@@ -29,26 +30,21 @@ from Bio.Restriction.RanaConfig import *
 
 class RebaseUpdate(FancyURLopener):
 
-    def __init__(self, e_mail='', ftpproxy=''):
-        """RebaseUpdate([e_mail[, ftpproxy]]) -> new RebaseUpdate instance.
+    def __init__(self, ftpproxy=''):
+        """RebaseUpdate([ftpproxy]]) -> new RebaseUpdate instance.
 
-        if e_mail and ftpproxy are not given RebaseUpdate uses the corresponding
+        if ftpproxy is not given RebaseUpdate uses the corresponding
         variable from RanaConfig.
 
-        e_mail is the password for the anonymous ftp connection to Rebase.
-        ftpproxy is the proxy to use if any."""
+        ftpproxy is the proxy to use if any.
+        """
         proxy = {'ftp': ftpproxy or ftp_proxy}
-        global Rebase_password
-        Rebase_password = e_mail or Rebase_password
-        if not Rebase_password:
-            raise FtpPasswordError('Rebase')
         if not Rebase_name:
             raise FtpNameError('Rebase')
+        if not proxy['ftp']:
+            proxy = {}
         FancyURLopener.__init__(self, proxy)
 
-    def prompt_user_passwd(self, host, realm):
-        return (Rebase_name, Rebase_password)
-
     def openRebase(self, name=ftp_Rebase):
         print('\n Please wait, trying to connect to Rebase\n')
         try:
@@ -74,7 +70,7 @@ class RebaseUpdate(FancyURLopener):
         month = str(t.tm_mon)
         if len(month) == 1:
             month = '0' + month
-        return year+month
+        return year + month
 
     def update(self, *files):
         if not files:
@@ -98,16 +94,6 @@ class FtpNameError(ValueError):
         sys.exit()
 
 
-class FtpPasswordError(ValueError):
-
-    def __init__(self, which_server):
-        print("\n\
-        \n In order to connect to %s ftp server, you must provide a password.\
-        \n Use the --e-mail switch to enter your e-mail address.\
-        \n\n" % which_server)
-        sys.exit()
-
-
 class ConnectionError(IOError):
 
     def __init__(self, which_server):
@@ -124,12 +110,6 @@ if __name__ == '__main__':
     parser = optparse.OptionParser()
     add = parser.add_option
 
-    add('-m', '--e-mail',
-        action="store",
-        dest='rebase_password',
-        default='',
-        help="set the e-mail address to be used as password for the"
-        "anonymous ftp connection to Rebase.")
     add('-p', '--proxy',
         action="store",
         dest='ftp_proxy',
@@ -138,7 +118,7 @@ if __name__ == '__main__':
 
     (option, args) = parser.parse_args()
 
-    Getfiles = RebaseUpdate(option.rebase_password, option.ftp_proxy)
+    Getfiles = RebaseUpdate(option.ftp_proxy)
     Getfiles.openRebase()
     Getfiles.getfiles()
     Getfiles.close()
diff --git a/Scripts/SeqGui/SeqGui.py b/Scripts/SeqGui/SeqGui.py
index 35718bd..a054971 100644
--- a/Scripts/SeqGui/SeqGui.py
+++ b/Scripts/SeqGui/SeqGui.py
@@ -8,6 +8,7 @@ from __future__ import print_function
 from Bio.Seq import translate, transcribe, back_transcribe
 import wx
 
+
 ID_APPLY = 101
 ID_CLEAR = 102
 ID_EXIT = 103
diff --git a/Scripts/Structure/hsexpo b/Scripts/Structure/hsexpo
index 6ac693c..5a1e378 100755
--- a/Scripts/Structure/hsexpo
+++ b/Scripts/Structure/hsexpo
@@ -10,6 +10,9 @@ from optparse import OptionParser
 from Bio.PDB import *
 import sys
 
+__docformat__ = "restructuredtext en"
+
+
 __doc__ = """
 This program calculates solvent exposure for all amino
 acids in a PDB file using 5 different methods:
@@ -25,7 +28,7 @@ See --help for all options.
 """
 
 if len(sys.argv) == 1:
-    print __doc__
+    print(__doc__)
     sys.exit()
 
 # Get the user's options
@@ -102,7 +105,7 @@ elif options.exp == "RDA":
     k = 'EXP_RD_CA'
     format = "%4.1f"
 else:
-    print "ERROR: Unknown option."
+    print("ERROR: Unknown option.")
     sys.exit()
 
 residue_list = Selection.unfold_entities(m, 'R')
@@ -125,7 +128,7 @@ for r in residue_list:
 
         resname = r.get_resname()
 
-        print (("%s %4i %c\t" + format) % (resname, resseq, icode, exposure))
+        print(("%s %4i %c\t" + format) % (resname, resseq, icode, exposure))
     else:
         exposure = 0.0
 
diff --git a/Scripts/debug/debug_blast_parser.py b/Scripts/debug/debug_blast_parser.py
index 87bb8f9..c2d4a8e 100755
--- a/Scripts/debug/debug_blast_parser.py
+++ b/Scripts/debug/debug_blast_parser.py
@@ -16,6 +16,7 @@ import traceback
 from Bio import ParserSupport
 from Bio.Blast import NCBIStandalone
 
+
 CONTEXT = 5   # show 5 lines of context around the error in the format file
 
 USAGE = """%s [-h] [-v] [-p] [-n] [-o] <testfile>
@@ -37,7 +38,7 @@ OPTIONS:
 """ % sys.argv[0]
 
 
-class DebuggingConsumer:
+class DebuggingConsumer(object):
     def __init__(self, decorated=None):
         self.linenum = 0
         if decorated is None:
diff --git a/Scripts/xbbtools/nextorf.py b/Scripts/xbbtools/nextorf.py
index 4d9e868..b2faebb 100755
--- a/Scripts/xbbtools/nextorf.py
+++ b/Scripts/xbbtools/nextorf.py
@@ -30,7 +30,7 @@ class ProteinX(Alphabet.ProteinAlphabet):
 proteinX = ProteinX()
 
 
-class MissingTable:
+class MissingTable(object):
     def __init__(self, table):
         self._table = table
 
@@ -50,7 +50,7 @@ def makeTableX(table):
                                 table.stop_codons)
 
 
-class NextOrf:
+class NextOrf(object):
     def __init__(self, file, options):
         self.options = options
         self.file = file
@@ -104,9 +104,9 @@ class NextOrf:
             d[nt] = [0, 0, 0]
 
         for i in range(0, l, 3):
-            codon = seq[i:i+3]
+            codon = seq[i:i + 3]
             if len(codon) < 3:
-                codon = codon + '  '
+                codon += '  '
             for pos in range(0, 3):
                 for nt in ['A', 'T', 'G', 'C']:
                     if codon[pos] == nt:
@@ -123,7 +123,7 @@ class NextOrf:
                 gc[i] = 0
 
             gcall = gcall + d['G'][i] + d['C'][i]
-            nall = nall + n
+            nall += n
 
         gcall = 100.0 * gcall / nall
         res = '%.1f%%, %.1f%%, %.1f%%, %.1f%%' % (gcall, gc[0], gc[1], gc[2])
@@ -143,7 +143,7 @@ class NextOrf:
         for frame in range(0, 3):
             coordinates = []
             for i in range(0 + frame, n - n % 3, 3):
-                codon = s[i:i+3]
+                codon = s[i:i + 3]
                 if codon in start_codons:
                     coordinates.append((i + 1, 1, codon))
                 elif codon in stop_codons:
@@ -160,7 +160,7 @@ class NextOrf:
         CDS = []
         f = 0
         for frame in frame_coordinates:
-            f+=1
+            f += 1
             start_site = 0
             if nostart == '1':
                 start_site = 1
@@ -180,9 +180,9 @@ class NextOrf:
                         if nostart == '1' and start_site == 1:
                             start_site = start_site + f - 1
                         if codon == 'XXX':
-                            stop = start_site + 3*((int((stop-1)-start_site)/3))
-                        s = seq[start_site-1:stop]
-                        CDS.append((start_site, stop, length, s, strand*f))
+                            stop = start_site + 3 * ((int((stop - 1) - start_site) // 3))
+                        s = seq[start_site - 1:stop]
+                        CDS.append((start_site, stop, length, s, strand * f))
                         start_site = 0
                         if nostart == '1':
                             start_site = stop + 1
@@ -202,7 +202,7 @@ class NextOrf:
             if strand > 0:
                 head = 'orf_%s:%s:%d:%d:%d' % (self.counter, self.header, strand, start, stop)
             if strand < 0:
-                head = 'orf_%s:%s:%d:%d:%d' % (self.counter, self.header, strand, n-stop+1, n-start+1)
+                head = 'orf_%s:%s:%d:%d:%d' % (self.counter, self.header, strand, n - stop + 1, n - start + 1)
             if self.options['gc']:
                 head = '%s:%s' % (head, self.Gc2(subs.data))
 
diff --git a/Scripts/xbbtools/xbb_blast.py b/Scripts/xbbtools/xbb_blast.py
index cace2b9..de8d36c 100644
--- a/Scripts/xbbtools/xbb_blast.py
+++ b/Scripts/xbbtools/xbb_blast.py
@@ -12,9 +12,9 @@ import sys
 from threading import *
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 import Pmw
 sys.path.insert(0, '.')
@@ -23,7 +23,7 @@ from xbb_utils import NotePad
 import xbb_blastbg
 
 
-class BlastIt:
+class BlastIt(object):
     def __init__(self, seq, parent=None):
         self.seq = seq
         self.parent = parent
@@ -115,7 +115,7 @@ class BlastIt:
         self.Update()
 
         print(self.command)
-        self.pipe = posix.popen(self.command)
+        self.pipe = os.popen(self.command)
         while True:
             try:
                 char = self.pipe.read(1)
@@ -150,7 +150,7 @@ class BlastIt:
 
         try:
             self.pipe.close()
-            del(pipe)
+            del(self.pipe)
         except:
             pass
         self.notepad.destroy()
diff --git a/Scripts/xbbtools/xbb_blastbg.py b/Scripts/xbbtools/xbb_blastbg.py
index d49b72b..35041c8 100644
--- a/Scripts/xbbtools/xbb_blastbg.py
+++ b/Scripts/xbbtools/xbb_blastbg.py
@@ -6,29 +6,27 @@
 
 from __future__ import print_function
 
-import posix
-import posixpath
 import os
 import sys
 sys.path.insert(0, '.')
 
 try:
-    import Queue as queue # Python 2
+    import Queue as queue  # Python 2
 except ImportError:
-    import queue # Python 3
+    import queue  # Python 3
 
 import tempfile
 import threading
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 from xbb_utils import NotePad
 
 
-class BlastDisplayer:
+class BlastDisplayer(object):
     def __init__(self, command, text_id=None):
         self.command = command
         self.tid = text_id
@@ -37,8 +35,8 @@ class BlastDisplayer:
         self.outfile = tempfile.mktemp()
 
         # make sure outfile exists and is empty
-        fid = open(self.outfile, 'w+')
-        fid.close()
+        with open(self.outfile, 'w+') as fid:
+            pass
 
         com = '%s > %s' % (self.command, self.outfile)
 
@@ -48,23 +46,22 @@ class BlastDisplayer:
 
     def UpdateResults(self):
         # open the oufile and displays new appended text
-        fid = open(self.outfile)
-        size = 0
-        while True:
-            if self.worker.finished:
-                break
-            fid.seek(size)
-            txt = fid.read()
-            size = os.stat(self.outfile)[6]
-            try:
-                self.tid.insert(END, txt)
-                self.tid.update()
-            except:
-                # text widget is detroyed, we assume the search
-                # has been cancelled
-                break
-
-        fid.close()
+        with open(self.outfile) as fid:
+            size = 0
+            while True:
+                if self.worker.finished:
+                    break
+                fid.seek(size)
+                txt = fid.read()
+                size = os.stat(self.outfile)[6]
+                try:
+                    self.tid.insert(END, txt)
+                    self.tid.update()
+                except:
+                    # text widget is detroyed, we assume the search
+                    # has been cancelled
+                    break
+
         self.Exit()
 
     def Exit(self):
diff --git a/Scripts/xbbtools/xbb_help.py b/Scripts/xbbtools/xbb_help.py
index a5516bd..4949e62 100644
--- a/Scripts/xbbtools/xbb_help.py
+++ b/Scripts/xbbtools/xbb_help.py
@@ -5,14 +5,14 @@
 # File: xbb_help.py
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 try:
-    import ScrolledText as scrolledtext # Python 2
+    import ScrolledText as scrolledtext  # Python 2
 except ImportError:
-    from tkinter import scrolledtext # Python 3
+    from tkinter import scrolledtext  # Python 3
 
 
 class xbbtools_help(Toplevel):
diff --git a/Scripts/xbbtools/xbb_search.py b/Scripts/xbbtools/xbb_search.py
index d7f6f41..40d4d5e 100644
--- a/Scripts/xbbtools/xbb_search.py
+++ b/Scripts/xbbtools/xbb_search.py
@@ -7,23 +7,24 @@
 import os
 import re
 import sys
+
 sys.path.insert(0, '.')
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 try:
-    import tkColorChooser as colorchooser # Python 2
+    import tkColorChooser as colorchooser  # Python 2
 except ImportError:
-    from tkinter import colorchooser # Python 3
+    from tkinter import colorchooser  # Python 3
 
 from Bio.Data.IUPACData import ambiguous_dna_values
 from Bio.Seq import reverse_complement
 
 
-class DNAsearch:
+class DNAsearch(object):
     def __init__(self):
         self.init_alphabet()
         self.sequence = ''
@@ -123,7 +124,7 @@ class XDNAsearch(Toplevel, DNAsearch):
         if not color:
             try:
                 color = colorchooser.askcolor()[1]
-            except:
+            except Exception:  # TODO - Which exceptions?
                 color = 'cyan'
         self.current_color = color
         self.current_tag = 'searched_%s' % self.current_color
diff --git a/Scripts/xbbtools/xbb_translations.py b/Scripts/xbbtools/xbb_translations.py
index 332fe35..705de05 100644
--- a/Scripts/xbbtools/xbb_translations.py
+++ b/Scripts/xbbtools/xbb_translations.py
@@ -12,16 +12,16 @@ import time
 sys.path.insert(0, '.')
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 
 from Bio.Seq import reverse_complement, translate
 from Bio.SeqUtils import GC
 
 
-class xbb_translations:
+class xbb_translations(object):
     def __init__(self):
         ""
 
@@ -71,10 +71,10 @@ class xbb_translations:
         protein = self.frame(seq, frame, translation_table)
         res = self.header_nice('Plus one frame translation', seq)
         for i in range(0, length, 60):
-            subseq = seq[i:i+60]
-            p = i/3
-            res += '%d/%d\n' % (i+1, i/3+1)
-            res += '  '.join(protein[p:p+20]) + '\n'
+            subseq = seq[i:i + 60]
+            p = i // 3
+            res += '%d/%d\n' % (i + 1, i // 3 + 1)
+            res += '  '.join(protein[p:p + 20]) + '\n'
             # seq
             res += subseq.lower() + '%5d %%\n' % int(self.gc(subseq))
 
@@ -91,27 +91,27 @@ class xbb_translations:
         length = len(seq)
         frames = {}
         for i in range(0, 3):
-            frames[i+1] = self.frame1(seq[i:], translation_table)
-            frames[-(i+1)] = self.reverse(self.frame1(anti[i:], translation_table))
+            frames[i + 1] = self.frame1(seq[i:], translation_table)
+            frames[-(i + 1)] = self.reverse(self.frame1(anti[i:], translation_table))
 
         res = self.header_nice('GCFrame', seq)
 
         for i in range(0, length, 60):
-            subseq = seq[i:i+60]
-            csubseq = comp[i:i+60]
-            p = i/3
+            subseq = seq[i:i + 60]
+            csubseq = comp[i:i + 60]
+            p = i // 3
             # + frames
-            res += '%d/%d\n' % (i+1, i/3+1)
-            res += '  ' + '  '.join(frames[3][p:p+20]) + '\n'
-            res += ' ' + '  '.join(frames[2][p:p+20]) + '\n'
-            res += '  '.join(frames[1][p:p+20]) + '\n'
+            res += '%d/%d\n' % (i + 1, i // 3 + 1)
+            res += '  ' + '  '.join(frames[3][p:p + 20]) + '\n'
+            res += ' ' + '  '.join(frames[2][p:p + 20]) + '\n'
+            res += '  '.join(frames[1][p:p + 20]) + '\n'
             # seq
             res += subseq.lower() + '%5d %%\n' % int(self.gc(subseq))
             res += csubseq.lower() + '\n'
             # - frames
-            res += '  '.join(frames[-2][p:p+20]) + ' \n'
-            res += ' ' + '  '.join(frames[-1][p:p+20]) + '\n'
-            res += '  ' + '  '.join(frames[-3][p:p+20]) + '\n\n'
+            res += '  '.join(frames[-2][p:p + 20]) + ' \n'
+            res += ' ' + '  '.join(frames[-1][p:p + 20]) + '\n'
+            res += '  ' + '  '.join(frames[-3][p:p + 20]) + '\n\n'
 
         return res
 
diff --git a/Scripts/xbbtools/xbb_utils.py b/Scripts/xbbtools/xbb_utils.py
index 04419e7..34834c5 100644
--- a/Scripts/xbbtools/xbb_utils.py
+++ b/Scripts/xbbtools/xbb_utils.py
@@ -5,17 +5,18 @@
 # File: xbb_utils.py
 
 import sys
+
 sys.path.insert(0, '.')
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 try:
-    import tkFileDialog as filedialog # Python 2
+    import tkFileDialog as filedialog  # Python 2
 except ImportError:
-    from tkinter import filedialog # Python 3
+    from tkinter import filedialog  # Python 3
 
 
 class NotePad(Toplevel):
@@ -45,6 +46,5 @@ class NotePad(Toplevel):
         fd = filedialog.SaveFileDialog(self)
         file = fd.go(key="test")
         if file:
-            fid = open(file, 'w')
-            fid.write(self.tid.get(0.0, END))
-            fid.close()
+            with open(file, 'w') as fid:
+                fid.write(self.tid.get(0.0, END))
diff --git a/Scripts/xbbtools/xbb_widget.py b/Scripts/xbbtools/xbb_widget.py
index 7fcd9f3..200f867 100644
--- a/Scripts/xbbtools/xbb_widget.py
+++ b/Scripts/xbbtools/xbb_widget.py
@@ -16,14 +16,14 @@ import sys
 import time
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 try:
-    import tkFileDialog as filedialog # Python 2
+    import tkFileDialog as filedialog  # Python 2
 except ImportError:
-    from tkinter import filedialog # Python 3
+    from tkinter import filedialog  # Python 3
 
 sys.path.insert(0, '.')
 from xbb_utils import *
@@ -35,7 +35,7 @@ from Bio.Data import CodonTable
 from Bio.SeqIO.FastaIO import SimpleFastaParser
 
 
-class xbb_widget:
+class xbb_widget(object):
     def __init__(self, parent=None):
         self.is_a_master = (parent is None)
         self.parent = parent
@@ -296,7 +296,7 @@ class xbb_widget:
         try:
             return w.selection_get()
             # return string.upper(w.get(sel.first, sel.last))
-        except:
+        except Exception:  # TODO - Which exceptions?
             return ''
 
     def get_self_selection(self):
@@ -306,7 +306,7 @@ class xbb_widget:
             return w.selection_get()
             # return string.upper(w.get(sel.first, sel.last))
             # return string.upper(w.selection_own_get())
-        except:
+        except Exception:  # TODO - Which exceptions?
             return ''
 
     def count_selection(self, event):
@@ -326,7 +326,7 @@ class xbb_widget:
             for nt in ['A', 'C', 'G', 'T']:
                 n = seq.count(nt)
                 self.statistics_ids[nt].configure(text='%s=%d' % (nt, n))
-        except:
+        except Exception:  # TODO - Which exceptions?
             pass
 
     def position(self, event):
@@ -429,7 +429,7 @@ GC=%f
         w.selection_own()
         try:
             start, stop = w.tag_ranges(SEL)
-        except:
+        except Exception:  # TODO - Which exceptions?
             start, stop = 1.0, self.sequence_id.index(END)
 
         seq = w.get(start, stop)
@@ -448,7 +448,7 @@ GC=%f
         w.selection_own()
         try:
             start, stop = w.tag_ranges(SEL)
-        except:
+        except Exception:  # Which exceptions?
             start, stop = 1.0, self.sequence_id.index(END)
 
         seq = w.get(start, stop)
@@ -467,7 +467,7 @@ GC=%f
         w.selection_own()
         try:
             start, stop = w.tag_ranges(SEL)
-        except:
+        except Exception:  # TODO - Which exceptions?
             start, stop = 1.0, self.sequence_id.index(END)
 
         seq = w.get(start, stop)
@@ -488,14 +488,14 @@ GC=%f
         pos = self.goto_entry.get()
         try:
             pos = int(pos) - 1
-        except:
+        except ValueError:
             try:
                 start, stop = pos.split(':')
                 start = int(start) - 1
                 stop = int(stop)
                 self.mark(start, stop)
                 return
-            except:
+            except Exception:  # TODO - which exceptions?
                 import traceback
                 traceback.print_exc()
 
diff --git a/Scripts/xbbtools/xbbtools.py b/Scripts/xbbtools/xbbtools.py
index 9e192e5..3409559 100755
--- a/Scripts/xbbtools/xbbtools.py
+++ b/Scripts/xbbtools/xbbtools.py
@@ -10,12 +10,13 @@
 # as part of this package.
 
 import sys
+
 sys.path.insert(0, '.')
 
 try:
-    from Tkinter import * # Python 2
+    from Tkinter import *  # Python 2
 except ImportError:
-    from tkinter import * # Python 3
+    from tkinter import *  # Python 3
 
 from xbb_widget import xbb_widget
 
@@ -25,7 +26,7 @@ xbbtools.main_frame.option_add('*frame.background', 'dimgrey')
 
 try:
     xbbtools.open(sys.argv[1])
-except:
+except Exception:  # TODO - Which exceptions?
     pass
 
 win.mainloop()
diff --git a/Tests/.cvsignore b/Tests/.cvsignore
new file mode 100644
index 0000000..532bb5b
--- /dev/null
+++ b/Tests/.cvsignore
@@ -0,0 +1,2 @@
+dump
+*.pyc
diff --git a/Tests/BioSQL/biosqldb-pg.sql b/Tests/BioSQL/biosqldb-pg.sql
index 9e9468a..0ec220c 100644
--- a/Tests/BioSQL/biosqldb-pg.sql
+++ b/Tests/BioSQL/biosqldb-pg.sql
@@ -768,6 +768,12 @@ ALTER TABLE location_qualifier_value ADD CONSTRAINT FKterm_locqual
 -- code that you use and hence add unnecessary overhead.
 --
 
+
+/* Commenting out these rules intended for BioPerl only, in order
+to silence the Biopython test warning from open_database function.
+See: https://redmine.open-bio.org/issues/2839
+aka https://github.com/biosql/biosql/issues/4
+
 CREATE RULE rule_bioentry_i1
        AS ON INSERT TO bioentry
        WHERE (
@@ -1127,6 +1133,9 @@ CREATE RULE rule_term_synonym_i
        DO INSTEAD NOTHING
 ;
 
+-- End of commented out rules
+*/
+
 --
 -- Functions that may be used as an API by applications, e.g. load scripts etc.
 -- 
diff --git a/Tests/BioSQL/biosqldb-sqlite.sql b/Tests/BioSQL/biosqldb-sqlite.sql
index 1620e78..f570b6d 100644
--- a/Tests/BioSQL/biosqldb-sqlite.sql
+++ b/Tests/BioSQL/biosqldb-sqlite.sql
@@ -61,7 +61,8 @@ CREATE TABLE taxon_name (
        taxon_id		INTEGER,
        name		VARCHAR(255)  NOT NULL,
        name_class	VARCHAR(32)  NOT NULL,
-       UNIQUE (taxon_id,name,name_class)
+    UNIQUE (taxon_id,name,name_class),
+    FOREIGN KEY ( taxon_id ) REFERENCES taxon ( taxon_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX taxnametaxonid ON taxon_name(taxon_id);
@@ -82,7 +83,8 @@ CREATE TABLE term (
 	is_obsolete	   CHAR(1),
 	ontology_id	   INTEGER,
 	UNIQUE (identifier),
-        UNIQUE (name,ontology_id,is_obsolete)
+    UNIQUE (name,ontology_id,is_obsolete),
+    FOREIGN KEY ( ontology_id ) REFERENCES ontology ( ontology_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX term_ont ON term(ontology_id);
@@ -90,14 +92,19 @@ CREATE INDEX term_ont ON term(ontology_id);
 CREATE TABLE term_synonym (
        synonym		  VARCHAR(255)  NOT NULL,
        term_id		  INTEGER,
-       PRIMARY KEY (term_id,synonym)
+    PRIMARY KEY (term_id,synonym),
+
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE
 );
 
 CREATE TABLE term_dbxref (
        	term_id	          INTEGER,
        	dbxref_id         INTEGER,
 	rank		  SMALLINT,
-	PRIMARY KEY (term_id, dbxref_id)
+    PRIMARY KEY (term_id, dbxref_id),
+
+    FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX trmdbxref_dbxrefid ON term_dbxref(dbxref_id);
@@ -108,7 +115,13 @@ CREATE TABLE term_relationship (
        	predicate_term_id    INTEGER,
        	object_term_id       INTEGER,
 	ontology_id	INTEGER,
-	UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id)
+    UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id),
+
+    FOREIGN KEY ( subject_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+    FOREIGN KEY ( predicate_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+    FOREIGN KEY ( object_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+    FOREIGN KEY ( ontology_id ) REFERENCES ontology ( ontology_id ) ON DELETE CASCADE
+
 );
 
 CREATE INDEX trmrel_predicateid ON term_relationship(predicate_term_id);
@@ -118,7 +131,10 @@ CREATE INDEX trmrel_ontid ON term_relationship(ontology_id);
 CREATE TABLE term_relationship_term (
         term_relationship_id INTEGER PRIMARY KEY,
         term_id              INTEGER,
-        UNIQUE ( term_id ) 
+    UNIQUE ( term_id ),
+
+    FOREIGN KEY (term_relationship_id) REFERENCES term_relationship(term_relationship_id) ON DELETE CASCADE,
+    FOREIGN KEY (term_id) REFERENCES term(term_id) ON DELETE CASCADE
 );
 
 CREATE TABLE term_path (
@@ -128,7 +144,11 @@ CREATE TABLE term_path (
        	object_term_id       INTEGER,
 	ontology_id          INTEGER,
 	distance	     INT(10) ,
-	UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id,distance)
+    UNIQUE (subject_term_id,predicate_term_id,object_term_id,ontology_id,distance),
+    FOREIGN KEY ( subject_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+    FOREIGN KEY ( predicate_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+    FOREIGN KEY ( object_term_id ) REFERENCES term ( term_id ) ON DELETE CASCADE ,
+    FOREIGN KEY ( ontology_id ) REFERENCES ontology ( ontology_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX trmpath_predicateid ON term_path(predicate_term_id);
@@ -146,7 +166,10 @@ CREATE TABLE bioentry (
   	description  	TEXT,
   	version 	SMALLINT  NOT NULL, 
   	UNIQUE (accession,biodatabase_id,version),
- 	UNIQUE (identifier, biodatabase_id)
+    UNIQUE (identifier, biodatabase_id),
+
+    FOREIGN KEY ( taxon_id ) REFERENCES taxon ( taxon_id ),
+    FOREIGN KEY ( biodatabase_id ) REFERENCES biodatabase ( biodatabase_id )
 );
 
 CREATE INDEX bioentry_name ON bioentry(name);
@@ -159,7 +182,10 @@ CREATE TABLE bioentry_relationship (
    	subject_bioentry_id 	 INTEGER,
    	term_id 		 INTEGER,
    	rank 			 INT(5),
-	UNIQUE (object_bioentry_id,subject_bioentry_id,term_id)
+    UNIQUE (object_bioentry_id,subject_bioentry_id,term_id),
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( object_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( subject_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX bioentryrel_trm   ON bioentry_relationship(term_id);
@@ -170,7 +196,11 @@ CREATE TABLE bioentry_path (
    	subject_bioentry_id 	INTEGER,
    	term_id 		INTEGER,
 	distance	     	INT(10) ,
-	UNIQUE (object_bioentry_id,subject_bioentry_id,term_id,distance)
+    UNIQUE (object_bioentry_id,subject_bioentry_id,term_id,distance),
+
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
+    FOREIGN KEY ( object_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( subject_bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX bioentrypath_trm   ON bioentry_path(term_id);
@@ -181,7 +211,8 @@ CREATE TABLE biosequence (
   	version     	SMALLINT, 
   	length      	INT(10),
   	alphabet        VARCHAR(10),
-  	seq 		LONGTEXT
+    seq 		LONGTEXT,
+    FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
 );
 
 CREATE TABLE dbxref (
@@ -199,7 +230,10 @@ CREATE TABLE dbxref_qualifier_value (
        	term_id 		INTEGER,
   	rank  		   	SMALLINT NOT NULL DEFAULT 0,
        	value			TEXT,
-	PRIMARY KEY (dbxref_id,term_id,rank)
+    PRIMARY KEY (dbxref_id,term_id,rank),
+
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX dbxrefqual_dbx ON dbxref_qualifier_value(dbxref_id);
@@ -209,7 +243,10 @@ CREATE TABLE bioentry_dbxref (
        	bioentry_id        INTEGER,
        	dbxref_id          INTEGER,
   	rank  		   SMALLINT,
-	PRIMARY KEY (bioentry_id,dbxref_id)
+    PRIMARY KEY (bioentry_id,dbxref_id),
+    FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE
+
 );
 
 CREATE INDEX dblink_dbx  ON bioentry_dbxref(dbxref_id);
@@ -222,7 +259,8 @@ CREATE TABLE reference (
   	authors  	   TEXT,
   	crc	   	   VARCHAR(32),
 	UNIQUE (dbxref_id),
-	UNIQUE (crc)
+    UNIQUE (crc),
+    FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id )
 );
 
 CREATE TABLE bioentry_reference (
@@ -231,7 +269,9 @@ CREATE TABLE bioentry_reference (
   	start_pos	INT(10),
   	end_pos	  	INT(10),
   	rank  		SMALLINT NOT NULL DEFAULT 0,
-  	PRIMARY KEY(bioentry_id,reference_id,rank)
+    PRIMARY KEY(bioentry_id,reference_id,rank),
+    FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
+    FOREIGN KEY ( reference_id ) REFERENCES reference ( reference_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX bioentryref_ref ON bioentry_reference(reference_id);
@@ -241,7 +281,8 @@ CREATE TABLE comment (
   	bioentry_id    	INTEGER,
   	comment_text   	TEXT NOT NULL,
   	rank   		SMALLINT NOT NULL DEFAULT 0,
-  	UNIQUE(bioentry_id, rank)
+    UNIQUE(bioentry_id, rank),
+    FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
 );
 
 CREATE TABLE bioentry_qualifier_value (
@@ -249,7 +290,9 @@ CREATE TABLE bioentry_qualifier_value (
    	term_id  		INTEGER,
    	value         		TEXT,
 	rank			INT(5) NOT NULL DEFAULT 0,
-	UNIQUE (bioentry_id,term_id,rank)
+    UNIQUE (bioentry_id,term_id,rank),
+    FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
 );
 
 CREATE INDEX bioentryqual_trm ON bioentry_qualifier_value(term_id);
@@ -261,7 +304,10 @@ CREATE TABLE seqfeature (
    	source_term_id  	INTEGER,
 	display_name		VARCHAR(64),
    	rank 			SMALLINT  NOT NULL DEFAULT 0,
-	UNIQUE (bioentry_id,type_term_id,source_term_id,rank)
+    UNIQUE (bioentry_id,type_term_id,source_term_id,rank),
+    FOREIGN KEY ( type_term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( source_term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( bioentry_id ) REFERENCES bioentry ( bioentry_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX seqfeature_trm  ON seqfeature(type_term_id);
@@ -273,7 +319,10 @@ CREATE TABLE seqfeature_relationship (
    	subject_seqfeature_id 	INTEGER,
    	term_id 	        INTEGER,
    	rank 			INT(5),
-	UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id)
+    UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id),
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( object_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( subject_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX seqfeaturerel_trm   ON seqfeature_relationship(term_id);
@@ -284,7 +333,10 @@ CREATE TABLE seqfeature_path (
    	subject_seqfeature_id 	INTEGER,
    	term_id 		INTEGER,
 	distance	     	INT(10) ,
-	UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id,distance)
+    UNIQUE (object_seqfeature_id,subject_seqfeature_id,term_id,distance),
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( object_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( subject_seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX seqfeaturepath_trm   ON seqfeature_path(term_id);
@@ -295,7 +347,9 @@ CREATE TABLE seqfeature_qualifier_value (
    	term_id 		INTEGER,
    	rank 			SMALLINT NOT NULL DEFAULT 0,
    	value  			TEXT NOT NULL,
-   	PRIMARY KEY (seqfeature_id,term_id,rank)
+    PRIMARY KEY (seqfeature_id,term_id,rank),
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id ),
+    FOREIGN KEY ( seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX seqfeaturequal_trm ON seqfeature_qualifier_value(term_id);
@@ -304,7 +358,9 @@ CREATE TABLE seqfeature_dbxref (
        	seqfeature_id      INTEGER,
        	dbxref_id          INTEGER,
   	rank  		   SMALLINT,
-	PRIMARY KEY (seqfeature_id,dbxref_id)
+    PRIMARY KEY (seqfeature_id,dbxref_id),
+    FOREIGN KEY ( seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ) ON DELETE CASCADE
 );
 
 CREATE INDEX feadblink_dbx  ON seqfeature_dbxref(dbxref_id);
@@ -318,7 +374,10 @@ CREATE TABLE location (
    	end_pos                	INT(10),
    	strand             	TINYINT NOT NULL DEFAULT 0,
    	rank          		SMALLINT NOT NULL DEFAULT 0,
-   	UNIQUE (seqfeature_id, rank)
+    UNIQUE (seqfeature_id, rank),
+    FOREIGN KEY ( seqfeature_id ) REFERENCES seqfeature ( seqfeature_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( dbxref_id ) REFERENCES dbxref ( dbxref_id ),
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
 );
 
 CREATE INDEX seqfeatureloc_start ON location(start_pos, end_pos);
@@ -330,14 +389,9 @@ CREATE TABLE location_qualifier_value (
    	term_id 		INTEGER,
    	value  			VARCHAR(255) NOT NULL,
    	int_value 		INT(10),
-	PRIMARY KEY (location_id,term_id)
+    PRIMARY KEY (location_id,term_id),
+    FOREIGN KEY ( location_id ) REFERENCES location ( location_id ) ON DELETE CASCADE,
+    FOREIGN KEY ( term_id ) REFERENCES term ( term_id )
 );
 
 CREATE INDEX locationqual_trm ON location_qualifier_value(term_id);
-
--- SQLite does not enforce foreign key constraints. There are some trigger
--- based ways to replicate this:
---
--- http://www.sqlite.org/cvstrac/wiki?p=ForeignKeyTriggers
---
--- Currently no foreign key constraints are added.
diff --git a/Tests/EMBL/DS830848.embl b/Tests/EMBL/DS830848.embl
new file mode 100644
index 0000000..d0d7b59
--- /dev/null
+++ b/Tests/EMBL/DS830848.embl
@@ -0,0 +1,86 @@
+ID   DS830848; SV 1; linear; genomic DNA; CON; INV; 1311 BP.
+XX
+AC   DS830848;
+XX
+PR   Project:PRJNA16232;
+XX
+DT   21-APR-2008 (Rel. 95, Created)
+DT   18-NOV-2008 (Rel. 97, Last updated, Version 3)
+XX
+DE   Ixodes scapularis 1108462666300 genomic scaffold, whole genome shotgun
+DE   sequence.
+XX
+KW   .
+XX
+OS   Ixodes scapularis (black-legged tick)
+OC   Eukaryota; Metazoa; Ecdysozoa; Arthropoda; Chelicerata; Arachnida; Acari;
+OC   Parasitiformes; Ixodida; Ixodoidea; Ixodidae; Ixodinae; Ixodes.
+XX
+RN   [1]
+RP   1-1311
+RG   Ixodes scapularis Genome Project Consortium
+RA   Caler E., Hannick L.I., Bidwell S., Joardar V., Thiagarajan M., Amedeo P.,
+RA   Galinsky K.J., Schobel S., Inman J., Hostetler J., Miller J., Hammond M.,
+RA   Megy K., Lawson D., Kodira C., Sutton G., Meyer J., Hill C.A., Birren B.,
+RA   Nene V., Collins F., Alarcon-Chaidez F., Wikel S., Strausberg R.;
+RT   "Annotation of Ixodes scapularis";
+RL   Unpublished.
+XX
+RN   [2]
+RP   1-1311
+RG   Ixodes scapularis Genome Project Consortium
+RA   Nene V.;
+RT   ;
+RL   Submitted (18-MAR-2008) to the INSDC.
+RL   J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850,
+RL   USA
+XX
+DR   MD5; 387e72e4f7ae804780d06f875ab3bc41.
+DR   ENA; ABJB010000000; SET.
+DR   ENA; ABJB000000000; SET.
+DR   BioSample; SAMN03004382.
+XX
+CC   This Wikel tick colony was established in 1996 using field
+CC   collected ticks from New York, Oklahoma and a Lyme Disease endemic
+CC   area of Connecticut.  This colony has been continuously in-bred
+CC   since establishment and has not been supplemented with field
+CC   collected material.  The colony is known to be a competent vector
+CC   of various Borrelia and Babesia isolates. Genomic DNA was prepared
+CC   from embryonated eggs.
+CC   The Ixodes scapularis Genome Project (IGP) is a community-based
+CC   effort initiated in early 2004 to sequence the I. scapularis
+CC   genome.  The IGP is funded by the National Institute of Allergy and
+CC   Infectious Diseases (NIAID)/National Institute of Health as part of
+CC   the NIAID-funded Microbial Sequencing Centers (MSCs) at The J.
+CC   Craig Venter Institute (http://www.jcvi.org) and The Broad
+CC   Institute (http://www.broad.mit.edu/).  The genome sequencing was
+CC   performed at both MSCs and the assembly at JCVI. The sum of all
+CC   reads equals 6X based on an estimated genome size of 2.1 Gbp, while
+CC   the coverage on assembled contigs is 3.8X.  The scaffolds are
+CC   comprised of 570637 contigs; the other 570958 contigs in the 01
+CC   version of this project represent degenerate contigs that were not
+CC   incorporated into scaffolds.  The annotation for this assembly will
+CC   be produced jointly by the MSCs and VectorBase
+CC   (http://www.vectorbase.org).  Data release and use follow NIAID
+CC   guidelines
+CC   (http://www.niaid.nih.gov/dmid/genomes/mscs/data_release.htm).
+CC   VectorBase (www.vectorbase.org), a NIAID Bioinformatics Resource
+CC   Center, is responsible for the long-term curation of the assembled
+CC   genome sequence  and its annotation.  For more information, contact
+CC   info at vectorbase.org.
+CC   Assembly name: JCVI_ISG_i3_1.0
+CC   Coverage: 3.8x on assembled contigs; 6x based on an estimated
+CC   genome size of 2.1 Gbp
+CC   Annotation was added to the scaffolds in December 2008.
+XX
+FH   Key             Location/Qualifiers
+FH
+FT   source          1..1311
+FT                   /organism="Ixodes scapularis"
+FT                   /strain="Wikel colony"
+FT                   /mol_type="genomic DNA"
+FT                   /dev_stage="embryonated eggs"
+FT                   /db_xref="taxon:6945"
+XX
+CO   join(ABJB010667125.1:1..1311)
+//
diff --git a/Tests/GenBank/.cvsignore b/Tests/GenBank/.cvsignore
new file mode 100644
index 0000000..61debbd
--- /dev/null
+++ b/Tests/GenBank/.cvsignore
@@ -0,0 +1 @@
+cor6_6.idx
diff --git a/Tests/GenBank/DS830848.gb b/Tests/GenBank/DS830848.gb
new file mode 100644
index 0000000..e300dac
--- /dev/null
+++ b/Tests/GenBank/DS830848.gb
@@ -0,0 +1,73 @@
+LOCUS       DS830848                1311 bp    DNA     linear   CON 13-NOV-2008
+DEFINITION  Ixodes scapularis 1108462666300 genomic scaffold, whole genome
+            shotgun sequence.
+ACCESSION   DS830848 ABJB010000000
+VERSION     DS830848.1  GI:183757675
+DBLINK      BioProject: PRJNA16232
+            BioSample: SAMN03004382
+KEYWORDS    WGS.
+SOURCE      Ixodes scapularis (black-legged tick)
+  ORGANISM  Ixodes scapularis
+            Eukaryota; Metazoa; Ecdysozoa; Arthropoda; Chelicerata; Arachnida;
+            Acari; Parasitiformes; Ixodida; Ixodoidea; Ixodidae; Ixodinae;
+            Ixodes.
+REFERENCE   1  (bases 1 to 1311)
+  AUTHORS   Caler,E., Hannick,L.I., Bidwell,S., Joardar,V., Thiagarajan,M.,
+            Amedeo,P., Galinsky,K.J., Schobel,S., Inman,J., Hostetler,J.,
+            Miller,J., Hammond,M., Megy,K., Lawson,D., Kodira,C., Sutton,G.,
+            Meyer,J., Hill,C.A., Birren,B., Nene,V., Collins,F.,
+            Alarcon-Chaidez,F., Wikel,S. and Strausberg,R.
+  CONSRTM   Ixodes scapularis Genome Project Consortium
+  TITLE     Annotation of Ixodes scapularis
+  JOURNAL   Unpublished
+REFERENCE   2  (bases 1 to 1311)
+  AUTHORS   Nene,V.
+  CONSRTM   Ixodes scapularis Genome Project Consortium
+  TITLE     Direct Submission
+  JOURNAL   Submitted (18-MAR-2008) J. Craig Venter Institute, 9704 Medical
+            Center Drive, Rockville, MD 20850, USA
+COMMENT     
+            This Wikel tick colony was established in 1996 using field
+            collected ticks from New York, Oklahoma and a Lyme Disease endemic
+            area of Connecticut.  This colony has been continuously in-bred
+            since establishment and has not been supplemented with field
+            collected material.  The colony is known to be a competent vector
+            of various Borrelia and Babesia isolates. Genomic DNA was prepared
+            from embryonated eggs.
+            
+            The Ixodes scapularis Genome Project (IGP) is a community-based
+            effort initiated in early 2004 to sequence the I. scapularis
+            genome.  The IGP is funded by the National Institute of Allergy and
+            Infectious Diseases (NIAID)/National Institute of Health as part of
+            the NIAID-funded Microbial Sequencing Centers (MSCs) at The J.
+            Craig Venter Institute (http://www.jcvi.org) and The Broad
+            Institute (http://www.broad.mit.edu/).  The genome sequencing was
+            performed at both MSCs and the assembly at JCVI. The sum of all
+            reads equals 6X based on an estimated genome size of 2.1 Gbp, while
+            the coverage on assembled contigs is 3.8X.  The scaffolds are
+            comprised of 570637 contigs; the other 570958 contigs in the 01
+            version of this project represent degenerate contigs that were not
+            incorporated into scaffolds.  The annotation for this assembly will
+            be produced jointly by the MSCs and VectorBase
+            (http://www.vectorbase.org).  Data release and use follow NIAID
+            guidelines
+            (http://www.niaid.nih.gov/dmid/genomes/mscs/data_release.htm).
+            
+            VectorBase (www.vectorbase.org), a NIAID Bioinformatics Resource
+            Center, is responsible for the long-term curation of the assembled
+            genome sequence  and its annotation.  For more information, contact
+            info at vectorbase.org.
+            Assembly name: JCVI_ISG_i3_1.0
+            Coverage: 3.8x on assembled contigs; 6x based on an estimated
+            genome size of 2.1 Gbp
+            Annotation was added to the scaffolds in December 2008.
+FEATURES             Location/Qualifiers
+     source          1..1311
+                     /organism="Ixodes scapularis"
+                     /mol_type="genomic DNA"
+                     /strain="Wikel colony"
+                     /db_xref="taxon:6945"
+                     /dev_stage="embryonated eggs"
+CONTIG      join(ABJB010667125.1:1..1311)
+//
+
diff --git a/Tests/GenBank/EU851978.gbk b/Tests/GenBank/EU851978.gbk
new file mode 100644
index 0000000..c5b6bb5
--- /dev/null
+++ b/Tests/GenBank/EU851978.gbk
@@ -0,0 +1,87 @@
+LOCUS       EU851978                1698 bp    cRNA    linear   VRL 24-OCT-2008
+DEFINITION  Influenza A virus (A/Wisconsin/36/2007(H1N1)) segment 4
+            hemagglutinin (HA) gene, complete cds.
+ACCESSION   EU851978
+VERSION     EU851978.1  GI:193084886
+KEYWORDS    .
+SOURCE      Influenza A virus (A/Wisconsin/36/2007(H1N1))
+  ORGANISM  Influenza A virus (A/Wisconsin/36/2007(H1N1))
+            Viruses; ssRNA viruses; ssRNA negative-strand viruses;
+            Orthomyxoviridae; Influenzavirus A.
+REFERENCE   1  (bases 1 to 1698)
+  AUTHORS   Garten,R., Barnes,J., Myrick,A., Smith,C., Xu,X. and Klimov,A.
+  TITLE     Direct Submission
+  JOURNAL   Submitted (25-JUN-2008) NCIRD/Influenza Division/VSDB, Centers for
+            Disease Control and Prevention, 1600 Clifton Road, NE, MS-G16,
+            Atlanta, GA 30333, USA
+COMMENT     ##FluData-START##
+            LabID             :: 2008704957
+            Antigen_Character :: A/Solomon Islands/03/2006-like (H1N1)
+            Passage_Details   :: SPFCK3E1
+            Drug Sensitivity  :: adamantane-sensitive, oseltamivir-sensitive
+            LANL Accession    :: ISDN306605
+            ##FluData-END##
+FEATURES             Location/Qualifiers
+     source          1..1698
+                     /organism="Influenza A virus (A/Wisconsin/36/2007(H1N1))"
+                     /mol_type="viral cRNA"
+                     /strain="A/Wisconsin/36/2007"
+                     /serotype="H1N1"
+                     /isolate="2008704957"
+                     /host="Homo sapiens"
+                     /db_xref="taxon:535875"
+                     /segment="4"
+                     /country="USA"
+                     /collection_date="30-Dec-2007"
+     gene            1..1698
+                     /gene="HA"
+     CDS             1..1698
+                     /gene="HA"
+                     /codon_start=1
+                     /product="hemagglutinin"
+                     /protein_id="ACF10333.1"
+                     /db_xref="GI:193084887"
+                     /translation="MKVKLLVLLCTFTATYADTICIGYHANNSTDTVDTVLEKNVTVT
+                     HSVNLLENSHNGKLCLLKGIAPLQLGNCSVAGWILGNPECELLISKESWSYIVEKPNP
+                     ENGTCYPGHFADYEELREQLSSVSSFERFEIFPKESSWPNHTVTGVSASCSHNGESSF
+                     YRNLLWLTGKNGLYPNLSKSYANNKEKEVLVLWGVHHPPNIGGQKALYHTENAYVSVV
+                     SSHYSRKFTPEIAKRPKVRDQEGRINYYWTLLEPGDTIIFEANGNLIAPRYAFALSRG
+                     FGSGIINSNAPMDKCDAKCQTPQGAINSSLPFQNVHPVTIGECPKYVRSAKLRMVTGL
+                     RNIPSIQSRGLFGAIAGFIEGGWTGMVDGWYGYHHQNEQGSGYAADQKSTQNAINGIT
+                     NKVNSVIEKMNTQFTAVGKEFNKLERRMENLNKKVDDGFIDIWTYNAELLVLLENERT
+                     LDFHDSNVKNLYEKVKSQLKNNAKEIGNGCFEFYHKCNDECMESVKNGTYDYPKYSEE
+                     SKLNREKIDGVKLESMGVYQILAIYSTVASSLVLLVSLGAISFWMCSNGSLQCRICI"
+     misc_feature    1..1698
+                     /gene="HA"
+                     /note="ISDN306605"
+ORIGIN      
+        1 atgaaagtaa aactactggt cctgttatgc acattcacag ctacatatgc agacacaata
+       61 tgtataggct accatgctaa caactcgacc gacactgttg acacagtact tgaaaagaat
+      121 gtgacagtga cacactctgt caacctgctt gagaacagtc acaatggaaa actatgtcta
+      181 ttaaaaggaa tagccccact acaattgggt aactgcagcg ttgccgggtg gatcttagga
+      241 aacccagaat gcgaattact gatttccaag gagtcatggt cctacattgt agaaaaacca
+      301 aatcctgaga atggaacatg ttacccaggg catttcgctg actatgagga actgagggag
+      361 caattgagtt cagtatcttc atttgagagg ttcgaaatat tccccaaaga aagctcatgg
+      421 cccaaccaca ccgtaaccgg agtgtcagca tcatgctccc ataatgggga aagtagtttt
+      481 tacagaaatt tgctatggct gacggggaag aatggtttgt acccaaacct gagcaagtcc
+      541 tatgcaaaca acaaagaaaa agaagtcctt gtactatggg gtgttcatca cccgccaaac
+      601 ataggtggcc aaaaggccct ctatcataca gaaaatgctt atgtttctgt agtgtcttca
+      661 cattatagca gaaaattcac cccagaaata gccaaaagac ccaaagtaag agatcaagaa
+      721 ggaagaatca actactactg gactctgctt gaacccgggg atacaataat atttgaggca
+      781 aatggaaatc taatagcgcc aagatatgct ttcgcactga gtagaggctt tggatcagga
+      841 atcatcaact caaatgcacc aatggataaa tgtgatgcga agtgccaaac acctcaggga
+      901 gctataaaca gcagtcttcc tttccagaac gtacacccag tcacaatagg agagtgtcca
+      961 aagtatgtca ggagtgcaaa attaaggatg gttacaggac taaggaacat cccatccatt
+     1021 caatccagag gtttgtttgg agccattgcc ggtttcattg aaggggggtg gactggaatg
+     1081 gtagatggtt ggtatggtta tcatcatcag aatgagcaag gatctggcta tgctgcagat
+     1141 caaaaaagca cacaaaatgc cattaatgga attacaaaca aggtgaattc tgtaattgag
+     1201 aaaatgaaca ctcaattcac agccgtgggc aaagaattca acaaattgga aagaaggatg
+     1261 gaaaacttga ataaaaaagt tgatgatggg tttatagaca tctggacata taatgcagaa
+     1321 ctgttggttc tactggaaaa tgaaaggact ttggatttcc atgactccaa tgtgaagaat
+     1381 ctgtatgaga aagtaaaaag ccagttaaag aataatgcta aagaaatagg aaatgggtgt
+     1441 tttgaattct atcacaagtg taacgatgaa tgcatggaga gtgtaaagaa tggaacttat
+     1501 gactatccaa aatattccga agaatcaaag ttaaacaggg agaaaattga tggagtgaaa
+     1561 ttggaatcaa tgggagtcta tcagattctg gcgatctact caacagtcgc cagttctctg
+     1621 gttcttttgg tctccctggg ggcaatcagc ttctggatgt gttccaatgg gtctttgcag
+     1681 tgtagaatat gcatctaa
+//
diff --git a/Tests/GenBank/HM138502.gbk b/Tests/GenBank/HM138502.gbk
new file mode 100644
index 0000000..3a1a8bf
--- /dev/null
+++ b/Tests/GenBank/HM138502.gbk
@@ -0,0 +1,79 @@
+LOCUS       HM138502                1410 bp    cRNA    linear   VRL 22-APR-2010
+DEFINITION  Influenza A virus (A/California/07/2009(H1N1)) segment 6
+            neuraminidase (NA) gene, complete cds.
+ACCESSION   HM138502
+VERSION     HM138502.1  GI:295002854
+DBLINK      BioProject: PRJNA37813
+KEYWORDS    .
+SOURCE      Influenza A virus (A/California/07/2009(H1N1))
+  ORGANISM  Influenza A virus (A/California/07/2009(H1N1))
+            Viruses; ssRNA negative-strand viruses; Orthomyxoviridae;
+            Influenzavirus A.
+REFERENCE   1  (bases 1 to 1410)
+  AUTHORS   Starick,E.
+  TITLE     Direct Submission
+  JOURNAL   Submitted (21-APR-2010) Friedrich-Loeffler-Institut, Suedufer 10,
+            Greifswald-Insel Riems 17493, Germany
+COMMENT     Swine influenza A (H1N1) virus isolated during human swine flu
+            outbreak of 2009.
+            
+            ##GISAID_EpiFlu(TM)Data-START##
+            Isolate         :: A/California/07/09
+            Subtype         :: H1N1
+            Lineage         :: swl
+            ##GISAID_EpiFlu(TM)Data-END##
+FEATURES             Location/Qualifiers
+     source          1..1410
+                     /organism="Influenza A virus (A/California/07/2009(H1N1))"
+                     /mol_type="viral cRNA"
+                     /strain="A/California/07/2009"
+                     /serotype="H1N1"
+                     /host="Homo sapiens"
+                     /db_xref="taxon:641809"
+                     /segment="6"
+                     /country="USA"
+                     /collection_date="09-Apr-2009"
+                     /note="lineage: swl"
+     gene            1..1410
+                     /gene="NA"
+     CDS             1..1410
+                     /gene="NA"
+                     /codon_start=1
+                     /product="neuraminidase"
+                     /protein_id="ADF58339.1"
+                     /db_xref="GI:295002855"
+                     /translation="MNPNQKIITIGSVCMTIGMANLILQIGNIISIWISHSIQLGNQN
+                     QIETCNQSVITYENNTWVNQTYVNISNTNFAAGQSVVSVKLAGNSSLCPVSGWAIYSK
+                     DNSVRIGSKGDVFVIREPFISCSPLECRTFFLTQGALLNDKHSNGTIKDRSPYRTLMS
+                     CPIGEVPSPYNSRFESVAWSASACHDGINWLTIGISGPDNGAVAVLKYNGIITDTIKS
+                     WRNNILRTQESECACVNGSCFTVMTDGPSNGQASYKIFRIEKGKIVKSVEMNAPNYHY
+                     EECSCYPDSSEITCVCRDNWHGSNRPWVSFNQNLEYQIGYICSGIFGDNPRPNDKTGS
+                     CGPVSSNGANGVKGFSFKYGNGVWIGRTKSISSRNGFEMIWDPNGWTGTDNNFSIKQD
+                     IVGINEWSGYSGSFVQHPELTGLDCIRPCFWVELIRGRPKENTIWTSGSSISFCGVNS
+                     DTVGWSWPDGAELPFTIDK"
+ORIGIN      
+        1 atgaatccaa accaaaagat aataaccatt ggttcggtct gtatgacaat tggaatggct
+       61 aacttaatat tacaaattgg aaacataatc tcaatatgga ttagccactc aattcaactt
+      121 gggaatcaaa atcagattga aacatgcaat caaagcgtca ttacttatga aaacaacact
+      181 tgggtaaatc agacatatgt taacatcagc aacaccaact ttgctgctgg acagtcagtg
+      241 gtttccgtga aattagcagg caattcctct ctctgccctg ttagtggatg ggctatatac
+      301 agtaaagaca acagtgtaag aatcggttcc aagggggatg tgtttgtcat aagggaacca
+      361 ttcatatcat gctccccctt ggaatgcaga accttcttct tgactcaagg ggccttgcta
+      421 aatgacaaac attccaatgg aaccattaaa gacaggagcc catatcgaac cctaatgagc
+      481 tgtcctattg gtgaagttcc ctctccatac aactcaagat ttgagtcagt cgcttggtca
+      541 gcaagtgctt gtcatgatgg catcaattgg ctaacaattg gaatttctgg cccagacaat
+      601 ggggcagtgg ctgtgttaaa gtacaacggc ataataacag acactatcaa gagttggaga
+      661 aacaatatat tgagaacaca agagtctgaa tgtgcatgtg taaatggttc ttgctttact
+      721 gtaatgaccg atggaccaag taatggacag gcctcataca agatcttcag aatagaaaag
+      781 ggaaagatag tcaaatcagt cgaaatgaat gcccctaatt atcactatga ggaatgctcc
+      841 tgttatcctg attctagtga aatcacatgt gtgtgcaggg ataactggca tggctcgaat
+      901 cgaccgtggg tgtctttcaa ccagaatctg gaatatcaga taggatacat atgcagtggg
+      961 attttcggag acaatccacg ccctaatgat aagacaggca gttgtggtcc agtatcgtct
+     1021 aatggagcaa atggagtaaa agggttttca ttcaaatacg gcaatggtgt ttggataggg
+     1081 agaactaaaa gcattagttc aagaaacggt tttgagatga tttgggatcc gaacggatgg
+     1141 actgggacag acaataactt ctcaataaag caagatatcg taggaataaa tgagtggtca
+     1201 ggatatagcg ggagttttgt tcagcatcca gaactaacag ggctggattg tataagacct
+     1261 tgcttctggg ttgaactaat cagagggcga cccaaagaga acacaatctg gactagcggg
+     1321 agcagcatat ccttttgtgg tgtaaacagt gacactgtgg gttggtcttg gccagacggt
+     1381 gctgagttgc catttaccat tgacaagtaa
+//
diff --git a/Tests/GenBank/KF527485.gbk b/Tests/GenBank/KF527485.gbk
new file mode 100644
index 0000000..c64eb23
--- /dev/null
+++ b/Tests/GenBank/KF527485.gbk
@@ -0,0 +1,82 @@
+LOCUS       KF527485                1444 bp    cRNA    linear   VRL 02-OCT-2013
+DEFINITION  Influenza A virus (A/California/07/2009(H1N1)) segment 5
+            nucleocapsid protein (NP) gene, partial cds.
+ACCESSION   KF527485
+VERSION     KF527485.1  GI:537446968
+KEYWORDS    .
+SOURCE      Influenza A virus (A/California/07/2009(H1N1))
+  ORGANISM  Influenza A virus (A/California/07/2009(H1N1))
+            Viruses; ssRNA negative-strand viruses; Orthomyxoviridae;
+            Influenzavirus A.
+REFERENCE   1  (bases 1 to 1444)
+  AUTHORS   Le,V.L., Courtney,C.L., Steel,J. and Compans,R.W.
+  TITLE     Closely Related Influenza Viruses Induce Contrasting Respiratory
+            Tract Immunopathology
+  JOURNAL   PLoS ONE 8 (9), E76708 (2013)
+   PUBMED   24086762
+  REMARK    Publication Status: Online-Only
+REFERENCE   2  (bases 1 to 1444)
+  AUTHORS   Le,V.L., Courtney,C.L., Steel,J. and Compans,R.W.
+  TITLE     Direct Submission
+  JOURNAL   Submitted (07-AUG-2013) Microbiology and Immunology, Emory
+            University, 1518 Clifton Rd Suite 5000, Atlanta, GA 30322, USA
+COMMENT     ##Assembly-Data-START##
+            Assembly Method       :: Lasergene v. 10
+            Sequencing Technology :: ABI37XL; Sanger dideoxy sequencing
+            ##Assembly-Data-END##
+FEATURES             Location/Qualifiers
+     source          1..1444
+                     /organism="Influenza A virus (A/California/07/2009(H1N1))"
+                     /mol_type="viral cRNA"
+                     /strain="A/California/07/2009"
+                     /serotype="H1N1"
+                     /host="Homo sapiens"
+                     /db_xref="taxon:641809"
+                     /segment="5"
+                     /country="USA"
+                     /collection_date="09-Apr-2009"
+                     /note="passage details: MDCK 6"
+     gene            <1..>1444
+                     /gene="NP"
+     CDS             <1..>1444
+                     /gene="NP"
+                     /codon_start=2
+                     /product="nucleocapsid protein"
+                     /protein_id="AGU69828.1"
+                     /db_xref="GI:537446969"
+                     /translation="ASQGTKRSYEQMETGGERQDATEIRASVGRMIGGIGRFYIQMCT
+                     ELKLSDYDGRLIQNSITIERMVLSAFDERRNKYLEEHPSAGKDPKKTGGPIYRRVGGK
+                     WMRELILYDKEEIRRVWRQANNGEDATAGLTHIMIWHSNLNDATYQRTRALVRTGMDP
+                     RMCSLMQGSTLPRRSGAAGAAVKGVGTIAMELIRMIKRGINDRNFWRGENGRRTRVAY
+                     ERMCNILKGKFQTAAQRAMMDQVRESRNPGNAEIEDLIFLARSALILRGSVAHKSCLP
+                     ACVYGLAVASGHDFEREGYSLVGIDPFKLLQNSQVVSLMRPNENPAHKSQLVWMACHS
+                     AAFEDLRVSSFIRGKKVIPRGKLSTRGVQIASNENVETMDSNTLELRSRYWAIRTRSG
+                     GNTNQQKASAGQISVQPTFSVQRNLPFERATVMAAFSGNNEGRTSDMRTEVIRMMESA
+                     KPEDLSFQGRGVFELSDEKATNPIVPSFDMS"
+ORIGIN      
+        1 ggcgtctcaa ggcaccaaac gatcatatga acaaatggag actggtgggg agcgccagga
+       61 tgccacagaa atcagagcat ctgtcggaag aatgattggt ggaatcggga gattctacat
+      121 ccaaatgtgc actgaactca aactcagtga ttatgatgga cgactaatcc agaatagcat
+      181 aacaatagag aggatggtgc tttctgcttt tgatgagaga agaaataaat acctagaaga
+      241 gcatcccagt gctgggaagg accctaagaa aacaggagga cccatatata gaagagtagg
+      301 cggaaagtgg atgagagaac tcatccttta tgacaaagaa gaaataagga gagtttggcg
+      361 ccaagcaaac aatggcgaag atgcaacagc aggtcttact catatcatga tttggcattc
+      421 caacctgaat gatgccacat atcagagaac aagagcgctt gttcgcaccg gaatggatcc
+      481 cagaatgtgc tctctaatgc aaggttcaac acttcccaga aggtctggtg ccgcaggtgc
+      541 tgcggtgaaa ggagttggaa caatagcaat ggagttaatc agaatgatca aacgtggaat
+      601 caatgaccga aatttctgga ggggtgaaaa tggacgaagg acaagggttg cttatgaaag
+      661 aatgtgcaat atcctcaaag gaaaatttca aacagctgcc cagagggcaa tgatggatca
+      721 agtaagagaa agtcgaaacc caggaaacgc tgagattgaa gacctcattt tcctggcacg
+      781 gtcagcactc attctgaggg gatcagttgc acataaatcc tgcctgcctg cttgtgtgta
+      841 tgggcttgca gtagcaagtg ggcatgactt tgaaagggaa gggtactcac tggtcgggat
+      901 agacccattc aaattactcc aaaacagcca agtggtcagc ctgatgagac caaatgaaaa
+      961 cccagctcac aagagtcaat tggtgtggat ggcatgccac tctgctgcat ttgaagattt
+     1021 aagagtatca agtttcataa gaggaaagaa agtgattcca agaggaaagc tttccacaag
+     1081 aggggtccag attgcttcaa atgagaatgt ggaaaccatg gactccaata ccctggaact
+     1141 gagaagcaga tactgggcca taaggaccag gagtggagga aataccaatc aacaaaaggc
+     1201 atccgcaggc cagatcagtg tgcagcctac attctcagtg cagcggaatc tcccttttga
+     1261 aagagcaacc gttatggcag cattcagcgg gaacaatgaa ggacggacat ccgacatgcg
+     1321 aacagaagtt ataagaatga tggaaagtgc aaagccagaa gatttgtcct tccaggggcg
+     1381 gggagtcttc gagctctcgg acgaaaaggc aacgaacccg atcgtgcctt cctttgacat
+     1441 gagt
+//
\ No newline at end of file
diff --git a/Tests/Graphics/.cvsignore b/Tests/Graphics/.cvsignore
new file mode 100644
index 0000000..e17b15c
--- /dev/null
+++ b/Tests/Graphics/.cvsignore
@@ -0,0 +1,6 @@
+multi_bar.pdf
+organism.eps
+organism.pdf
+random_organism.pdf
+scatter_test.pdf
+simple_bar.pdf
diff --git a/Tests/NeuralNetwork/.cvsignore b/Tests/NeuralNetwork/.cvsignore
new file mode 100644
index 0000000..77ebef4
--- /dev/null
+++ b/Tests/NeuralNetwork/.cvsignore
@@ -0,0 +1 @@
+patternio.txt
diff --git a/Tests/Nexus/vSysLab_Ganaspidium_multistate.nex b/Tests/Nexus/vSysLab_Ganaspidium_multistate.nex
new file mode 100644
index 0000000..cc26391
--- /dev/null
+++ b/Tests/Nexus/vSysLab_Ganaspidium_multistate.nex
@@ -0,0 +1,48 @@
+#NEXUS
+
+BEGIN TAXA;
+	DIMENSIONS NTAX=6;
+	TAXLABELS
+		'Ganaspidium didionae'
+		'Ganaspidium eldiablo'
+		'Ganaspidium flemingi'
+		'Ganaspidium kolmaci'
+		'Ganaspidium konzaense'
+		'Ganaspidium pusillae'
+	;
+END;
+
+BEGIN CHARACTERS;
+	DIMENSIONS NCHAR=14;
+	FORMAT DATATYPE=STANDARD MISSING=? GAP=-
+		SYMBOLS="012345"
+	;
+	CHARSET 'Head' = 1 2 3 4;
+	CHARSET 'Mesosoma' = 5 6 7 8 9 10 11 12;
+	CHARSET 'Forewing' = 13;
+	CHARSET 'Metasoma' = 14;
+	CHARSTATELABELS
+		1 'Malar sulcus'/'simple' 'compound',
+		2 'Malar space'/'smooth' 'partially striate, striations extending 1/2 to 2/3 distance from ventral margin of malar space to base of compound eye' 'completely striate from ventral margin of malar space to base of compound eye' 'partially striate, striations extending to 1/4 distance from ventral margin of malar space to base of compound eye',
+		3 'Malar protuberance'/'smooth, elongate, extending beyond length of ventral margin of malar space' 'striate, elongate, extending beyond length of ventral margin of malar space' 'smooth, short, not extending beyond length of ventral margin of malar space' 'striate, short, not extending beyond length of ventral margin of malar space',
+		4 'Clypeal protuberance'/'elongate, overhanging anterior margin of clypeus' 'short, not overhanging anterior margin of clypeus',
+		5 'Tubercles of scutellar plate'/'present, extremely reduced, hardly distinguishable' 'present, distinct, small, length of tuber less than 1/2 width of tubercle base' 'present, distinct, large, length of tubercle equal to or greater than width of tubercle base' 'absent',
+		6 'Dorsal surface of scutellar plate'/'flat, smooth, setal bearing pits present surrounding midpit' 'flat, horizontally striate, setal bearing pits present surrounding midpit' 'gently convex, smooth, setal bearing pits surrounding midpit' 'concave, smooth, setal bearing pits surrounding midpit' 'concave around midpit, two setal bearing pits at base of each tubercle' 'concave, radially striate, setal bearing pits surrounding midpit',
+		7 'Carina along posterior margin of scutellum'/'absent' 'present, delicate, defining transition from dorsal surface of scutellum from posterior surface' 'present, distinctly cleft, defining transition from dorsal surface of scutellum from posterior surface',
+		8 'Dorsal surface of scutellum'/'entirely smooth' 'smooth except for faint wrinkles along posterior carina' 'smooth anteriorly, reticular/strigate posteriorly' 'smooth anteriorly, longitudinally striate posteriorly' 'entirely rugulose/wrinkled' 'smooth anteriorly, gently striate posteriorly',
+		9 'Midpit of scutellar plate'/'in center of plate; plate large, obscuring dorsal surface of scutellum when viewed dorsally' 'in center of plate; plate small, revealing dorsal surface of scutellum when viewed dorsally' 'in posterior half of plate; plate small, revealing dorsal surface of scutellum when viewed dorsally',
+		10 'Mesopopleura setal patch'/'present' 'absent',
+		11 'Mesopleuron'/'entirely smooth' 'smooth anteriorly, striate along the postero-dorsal margin' 'distinctly striate along the anterior, posterior margins; often entire sclerite striate' 'striate within confines of mesopleural triangle, remainder of sclerite smooth',
+		12 'Lateral aspect of pronotum'/'covered in long, thin, white setae' 'anteriorly with some short setae, remainder glabrous',
+		13 'Marginal cell of forewing'/'as deep as long' 'distinctly deeper than long',
+		14 'Size of metasoma (in lateral view)'/'sub-equal in size to mesosoma' 'distinctly larger than mesosoma (longer and deeper)'
+	;
+	MATRIX
+		'Ganaspidium didionae' 1100242421(01)110
+		'Ganaspidium eldiablo' 10000405210110
+		'Ganaspidium flemingi' 10(02)(01)1411210110
+		'Ganaspidium kolmaci' 010000(01)221(01)100
+		'Ganaspidium konzaense' 12002424212100
+		'Ganaspidium pusillae' 01(02)(01)141221(01)110
+	;
+END;
diff --git a/Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex b/Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex
new file mode 100644
index 0000000..a79283e
--- /dev/null
+++ b/Tests/Nexus/vSysLab_Heptascelio_no-states_10+chars.nex
@@ -0,0 +1,105 @@
+#NEXUS
+
+BEGIN TAXA;
+	DIMENSIONS NTAX=19;
+	TAXLABELS
+		'Heptascelio albipes'
+		'Heptascelio anthonyi'
+		'Heptascelio aquilinus'
+		'Heptascelio bivius'
+		'Heptascelio castor'
+		'Heptascelio dayi'
+		'Heptascelio dispar'
+		'Heptascelio hamatus'
+		'Heptascelio lateralis'
+		'Heptascelio noyesi'
+		'Heptascelio orarius'
+		'Heptascelio paralugens'
+		'Heptascelio strigatus'
+		'Heptascelio teres'
+		'Heptascelio watshami'
+		'Heptascelio lugens'
+		'Heptascelio sicarius'
+		'Heptascelio striatosternus'
+		'A'
+	;
+END;
+
+BEGIN CHARACTERS;
+	DIMENSIONS NCHAR=43;
+	FORMAT DATATYPE=STANDARD MISSING=? GAP=-
+		SYMBOLS="0123456789A"
+	;
+	CHARSET 'Head' = 1 2 3 4 5 6 7 8 9 10;
+	CHARSET 'Antenna' = 11 12;
+	CHARSET 'Mesosoma' = 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31;
+	CHARSET 'Legs' = 32 33;
+	CHARSET 'Wings' = 34 35 36;
+	CHARSET 'Metasoma' = 37 38 39 40 41 42 43;
+	CHARSTATELABELS
+		1 'body color of female'/'entirely dark, dark brown to black' 'bicolored: head and mesosoma dark brown, metasoma yellow',
+		2 'body color of male'/'entirely black' 'entirely dark brown' 'head and metasoma black, mesosoma reddish brown' 'head and mesosoma black, metasoma dark brown',
+		3 'sculpture of occiput and posterior vertex'/'areolate-rugose' 'with strong transverse rugae' 'coarsely punctate' 'irregularly rugulose' 'areolate rugose, with distinct transverse tendency' 'with strong arched rugae',
+		4 'sculpture of frons below ocellus in female'/'transversely striate' 'areolate rugose' 'coarsely punctate' 'dorsoventrally striate',
+		5 'shape of dorsal margin of frontal scrobe'/'evenly arcuate, weakly produced' 'distinctly incised medially, strongly produced anteriorly' 'ecarinate, rounded onto dorsal portion of frons',
+		6 'sculpture of frontal depression in female'/'transversely striate ventrally, with smooth field dorsomedially' 'entirely smooth' 'with short transverse striae on sides, medially entirely smooth' 'irregularly areolate ventrally, smooth dorsally and medially' 'coarsely punctate ventrally and laterally, otherwise smooth' 'transversely rugose throughout' 'diagonally striate laterally, smooth medially' 'coarsely punctate' 'punctate, with broad smooth spaces between punctures',
+		7 'sculpture of gena'/'with irregular dorsoventral rugae' 'with irregular, nearly longitudinal rugulae' 'coarsely punctate' 'areolate rugose, with distinct dorsoventral tendency',
+		8 'setation of gena'/'with short, uniform setae, with few short bristles interspersed' 'with numerous strong, erect, dark bristles amid shorter setation' 'with short, uniform setae, without bristles',
+		9 'shape of mandibles'/'normal length, crossed transversely below head when closed, tips overlapping' 'elongate, extending ventrally below head, tips meeting apically',
+		10 'sculpture of male mesoscutellum'/'coarsely reticulate' 'coarsely reticulate, with distinct longitudinal elements' 'areolate rugose' 'coarsely longitudinally areolate rugose' 'longitudinally strigose',
+		11 'color of female antenna'/'A1-A2 pale brown, A3-A6 brown, A7-A12 dark brown' 'A1 brown, A2-A5 light brown, A6-A12 dark brown' 'dark brown to black throughout' 'dark brown to black throughout, extremities of A1, A2 lighter in color' 'brown to dark brown throughout' 'A1 brownish yellow, A2-A5 yellow, A6-A12 dark brown to black' 'A1 dark brown, A2-A5 off-white, A6-A12 dark brown to black' 'A1 dark brown, A2-A4 brownish yellow, A5-A12 dark brown to black' 'A1 brown, A2-A5 yellow, A6-A12 [...]
+		12 'color of male antenna'/'light brown throughout' 'brown to dark brown throughout' 'brown' 'A1-A5 brown, A6-A12 pale brown',
+		13 'sculpture of dorsal pronotum'/'longitudinally aciculate, with smooth interspaces' 'areolate rugose' 'coarsely reticulate' 'coarsely punctate' 'areolate rugose, with distinct longitudinal tendency' 'longitudinally strigose',
+		14 'notaulus',
+		15 'sculpture of mesoscutum in female'/'coarsely areolate-rugose' 'finely longitudinally aciculate with smooth interspaces' 'coarsely longitudinally strigose with smooth interspaces' 'longitudinally strigose, with strong transverse sculpture in interspaces' 'sparsely punctate, with broad smooth areas between punctures' 'longitudinally strigose with fine sculpture in interspaces' 'sparsely punctate, with broad smooth areas between punctures, with weak indications of longitudinal rugulae [...]
+		16 'sculpture of mesoscutum in male'/'with dense umbilicate punctures' 'finely longitudinally aciculate' 'coarsely areolate rugose' 'sparsely punctate, largely smooth' 'longitudinally strigose, with strong transverse sculpture in interspaces' 'sparsely punctate, with broad smooth areas between punctures, with weak indications of longitudinal rugulae' 'coarsely areolate rugose with distinct longitudinal tendency' 'coarsely longitudinally areolate rugose',
+		17 'parapsidal line'/'present, clearly impressed' 'absent' 'present, evanescent',
+		18 'mesoscutellum shape'/'distinctly narrowed apically, with median longitudinal depression' 'broad, weakly convex, apex broadly rounded' 'nearly parallel-sided, apex weakly excavate, with weak median longitudinal impression' 'semicircular, posteriorly distinctly sloping ventrad, without distinct median longitudinal furrow' 'roughly trapezoidal, sides weakly converging apically, apex excavate, with distinct median longitudinal furrow' 'roughly semicircular, with distinct median longitu [...]
+		19 'sculpture of female mesoscutellum'/'coarsely reticulate, with distinct longitudinal elements' 'areolate rugose' 'longitudinally strigate' 'coarsely punctate',
+		20 'mesoscutellar points of female'/'absent, scutellum with rounded lobes laterally' 'broad, short, acute' 'narrow, elongate, acute' 'forming short, acute teeth' 'forming short, sharp angles, not protruding' 'flattened, broadly rounded apically' 'moderately elongate, narrow, acute, curving medially',
+		21 'mesoscutellar points of male'/'apex of scutellum transverse, posterolateral corners very weakly produced' 'apex of scutellum rounded, points entirely absent' 'narrow, short, acute' 'flattened, broadly rounded apically' 'broad, short, acute',
+		22 'posterior surface of propodeum'/'with distinct straight longitudinal paramedian keel arising from apex of inner propodeal projection' 'with medial area covered by elongate areolae, bounded laterally by irregular carinae' 'with central row of quadrate areolae above large smooth central area, bounded laterally by carinae' 'irregularly areolate, without distinct paramedian keel' 'with medial area with elongate areolae, bounded laterally by distinct paramedial carinae' 'with sinuous pa [...]
+		23 'length of outer propodeal projection in female'/'extending beyond apex of T1' 'extending at most to midlength of T1' 'rather short, extending at most to midlength of T1, distinctly longer than inner propodeal projection' 'moderately elongate, not reaching midpoint of length of T1, subequal in length to inner propodeal projection' 'elongate, extending near or beyond apex of T1, distinctly longer than inner propodeal projection' 'moderately elongate, extending to midpoint of length o [...]
+		24 'sculpture of propodeum between inner and outer propodeal projections'/'areolate rugose' 'coarsely punctate' 'largely smooth, with variably developed diagonal carinae' 'longitudinally carinate with punctures in interspaces' 'areolate rugose, with distinct longitudinal tendency',
+		25 'netrion shape'/'moderately wide, parallel-sided in ventral half, open' 'strongly narrowed, nearly linear, foveae on surface nearly as high as wide' 'narrow, gradually widened ventrally, foveae on surface distinctly wider than high' 'moderately wide, nearly parallel-sided, with two columns of large punctures' 'moderately wide, nearly parallel-sided, weakly widened ventrally, with single column of large foveae' 'moderately wide, weakly fusiform, with column of elongate foveae' 'moder [...]
+		26 'netrion setation'/'glabrous' 'densely setose',
+		27 'sculpture of lateral pronotum posterior to epomial carina'/'smooth' 'coarsely sculptured' 'with large smooth field dorsally, elsewhere finely rugulose' 'with smooth field dorsally, elsewhere areolate rugose' 'with small smooth field near posterior margin, elsewhere finely rugulose' 'coarsely punctate' 'coarsely rugulose throughout' 'coarsely foveolate immediately behind epomial carina, elsewhere sparsely punctate',
+		28 'setation of posterior half of lateral pronotum'/'largely glabrous, setae limited to small patch near spiracle' 'with numerous scattered setae, densest near spiracle',
+		29 'sculpture of mesopleural scrobe'/'smooth' 'coarsely areolate to rugose' 'finely microreticulate to smooth' 'coarsely punctate' 'with narrow smooth field medially, elsewhere diagonally striate' 'smooth medially, elsewhere rugose',
+		30 'sculpture of lower mesepisternum'/'smooth' 'rugulose to punctate' 'finely microreticulate, nearly smooth, sparsely punctate' 'with distinctly separated, coarse punctures' 'largely smooth, with 2-3 longitudinal lines of punctures',
+		31 'sculpture of metapleuron'/'areolate rugose' 'irregularly longitudinally rugose, with smooth field medially' 'largely smooth' 'areolate rugose above, with smooth or finely sculptured field ventrally' 'coarsely punctate' 'with smooth field in posteroventral quadrant, elsewhere areolate rugose' 'coarsely areolate to rugose throughout' 'with smooth field medially, elsewhere coarsely punctate',
+		32 'color of legs'/'coxae and femora dark brown, tibiae and tarsi yellow' 'coxae dark brown, femora largely brown, legs otherwise brownish yellow' 'tarsi light brown, otherwise brown to dark brown throughout' 'coxae dark brown to black, legs otherwise brown to dark brown' 'coxae, femora dark brown, tarsi brown, tibiae pale, contrasting with femora and tibiae' 'coxae and femora dark brown, otherwise brown' 'coxae dark brown, otherwise yellowish brown throughout' 'coxae dark brown to bla [...]
+		33 'long dark bristles on legs'/'present on all femora, tibiae' 'absent' 'present on hind femur only',
+		34 'color of wing membrane'/'generally hyaline throughout' 'with faint brown infuscation throughout, with strong dark streak below submarginal vein' 'hyaline basally, infuscate in apical two-thirds, streak below submarginal vein weak' 'with weak general brown infuscation, longitudinal dark streak below submarginal vein moderately developed' 'with weak general brown infuscation, longitudinal streak below submarginal vein weak' 'moderately infuscate throughout, streak below submarginal v [...]
+		35 'fore wing venation'/'well-developed, with R, r-rs clearly visible' 'reduced to basal stub of R, with pale pseudostigma in wing disk',
+		36 'submarginal vein bristles'/'with 1-2 dark bristles near base of fore wing' 'with dark bristles extending length of submarginal vein' 'with dark bristles in basal half of submarginal vein' 'absent',
+		37 'T1 depression'/'glabrous or sparsely setose' 'moderately to densely setose',
+		38 'sublateral lamella on T1'/'distinctly raised more or less perpendicular to T1' 'indicated as low carina' 'absent',
+		39 'sculpture of T2-T4'/'longitudinally striate, with fine cross striae, punctures, smooth or finely punctulate transverse band apically' 'longitudinally striate with cross striae laterally, sculpture effaced medially',
+		40 'setation of laterotergites'/'glabrous' 'setose' 'very sparsely setose',
+		41 'sculpture of S2, S3 of female'/'strongly longitudinally rugose' 'nearly smooth, with scattered small punctures' 'irregularly longitudinally rugulose, apex of S3 nearly smooth' 'longitudinally rugulose laterally, nearly smooth medially, with scattered small punctures',
+		42 'sculpture of S2, S3 of male'/'longitudinal rugulae largely effaced, strongest laterally, otherwise smooth with scattered setigerous punctures' 'strongly longitudinally rugose' 'nearly smooth, with scattered setigerous punctures',
+		43 'distribution of felt fields'/'present on S2-S3' 'present on S3-S4' 'present on S3 only'
+	;
+	MATRIX
+		'Heptascelio albipes' 001(01)0(06)0004(46)15?5413224(04)4(23)(12)030(45)1(16)(47)1101010(12)320
+		'Heptascelio anthonyi' 0?1103000?1?1?3?1012?02010310111120101020?0
+		'Heptascelio aquilinus' ?130161110?12??301??11?020402202001001002?2
+		'Heptascelio bivius' 0?2221200?4?3?7?1134?23051713049130310013?0
+		'Heptascelio castor' 0321053002324?341213203241610151130110010?0
+		'Heptascelio dayi' 0?2224220?2?3?4?1234?33131510343110210010?0
+		'Heptascelio dispar' 1113100101000?111?0500201020213000100200121
+		'Heptascelio hamatus' 0(03)010(0137)0002311?02141(13)(24)33(01)41(56)1(01)1(03)(26)(12)30110010(01)0
+		'Heptascelio lateralis' 0?0102300?5?4?0?1510?(15)5040312101120102013?0
+		'Heptascelio noyesi' 0(03)(05)3010002225?26060320441000002115010111120
+		'Heptascelio orarius' 0201010002431?0?163333404000002803010111?20
+		'Heptascelio paralugens' 0022282002224?43121113516151036614011001310
+		'Heptascelio strigatus' 0011000003725?2(27)132230402021116916010202120
+		'Heptascelio teres' 0?4301000?8?3?4?0630?6645000002A150201010?0
+		'Heptascelio watshami' 0001000002901?32161330401020016922010101020
+		'Heptascelio lugens' 0022043002315?6502162350(05)1(16)1013114011001010
+		'Heptascelio sicarius' 0?2301220?9?3?4?2230?7403030047A160101022?0
+		'Heptascelio striatosternus' 0041000102434?34141330312121017503011001000
+		'A' ???????????????????????????????????????????
+	;
+END;
diff --git a/Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex b/Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex
new file mode 100644
index 0000000..ed45ef8
--- /dev/null
+++ b/Tests/Nexus/vSysLab_Oreiscelio_discrete+continuous.nex
@@ -0,0 +1,162 @@
+#NEXUS
+
+BEGIN TAXA;
+	DIMENSIONS NTAX=19;
+	TAXLABELS
+		'Oreiscelio zulu'
+		'Oreiscelio aequalis'
+		'Oreiscelio alluaudi'
+		'Oreiscelio badius'
+		'Oreiscelio coracinus'
+		'Oreiscelio cultrarius'
+		'Oreiscelio gryphus'
+		'Oreiscelio iommii'
+		'Oreiscelio magnipennis'
+		'Oreiscelio majikununuensis'
+		'Oreiscelio megadontus'
+		'Oreiscelio naevus'
+		'Oreiscelio paradoxus'
+		'Oreiscelio rostratus'
+		'Oreiscelio rugosus'
+		'Oreiscelio scapularis'
+		'Oreiscelio sechellensis'
+		'Oreiscelio turneri'
+		'Oreiscelio zuzkae'
+	;
+END;
+
+BEGIN CHARACTERS;
+	DIMENSIONS NCHAR=68;
+	FORMAT DATATYPE=STANDARD MISSING=? GAP=-
+		SYMBOLS="0123456789"
+	;
+	CHARSET 'General' = 1;
+	CHARSET 'Antenna' = 2 3 4;
+	CHARSET 'Head' = 5 6 7 8 9 10 11 12 13 14 15 16 17 18;
+	CHARSET 'Legs' = 19;
+	CHARSET 'Mesosoma' = 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42;
+	CHARSET 'Metasoma' = 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68;
+	CHARSTATELABELS
+		1 'body color'/'dark brown to black' 'black' 'head and mesosoma black; metasoma dark brown to black' 'metasoma often lighter than head and mesosoma',
+		2 'color of antennae in female'/'dark brown to black throughout' 'A1-A6 yellow; A7-A12 dark brown to black' 'A1-A6 pale brown; A7-A12 dark brown' 'pale brown throughout' 'A1, A3-A6 dark brown; A2 yellow; A7-A12 black' 'A1 black, A2-A6 pale brown, A7-A12 black' 'A1 dark brown, A2-A6 yellow, A7-A12 dark brown' 'A1-A6 brown; A7-A12 dark brown to black' 'A1 brown, A2-A6 yellow, A7-A12 brown' 'A1 brown, A2 pale brown, A3-A12 brown',
+		3 'color of antennae in male'/'A1-A2 brown; A3-A12 variably becoming yellow apically, antennomeres often yellow ventrally' 'A1 brown; A2-A12 yellow' 'brown' 'A1-A2 brown; A3-A12 yellow',
+		4 'seta on pedicel of antenna in female'/'present, reaching past apex of A3' 'present, not reaching apex of A3' 'present, reaching to apex of A3',
+		5 'color of setae on the head'/'white' 'brown' 'yellow',
+		6 'sculpture of dorsal frons between frontal carina and median ocellus'/'coarsely foveate' 'areolate' 'areolate, effaced medially' 'foveate with contiguous cells' 'transversely rugose',
+		7 'preocellar pit in females'/'absent' 'indicated by a small round depression' 'present as a distinct pit',
+		8 'transverse rugae at vertex'/'absent' 'present',
+		9 'sculpture of posterior vertex'/'areolate' 'coarsely foveate' 'transversely strigose',
+		10 'sculpture of occipital rim'/'areolate' 'coarsely foveate, cells often contiguous.',
+		11 'interantennal process'/'extended dorsally into medial flange, flange reaching from near apex of interantennal process to frons' 'diverging into two carinae that extend laterally along surface of frons' 'simple' 'with semiparallel carinae dorsally that do not extend laterally onto frons',
+		12 'number of anteriorly projecting setae on anteclypeus'/'6' '8',
+		13 'color of mandibles in male'/'yellow throughout, teeth brown' 'basal half black; apical half pale brown' 'black throughout' 'black throughout with brown teeth' 'basal half black, apical half brown' 'basal half black, apical half brown, teeth dark brown to black',
+		14 'color of mandibles in female'/'brown throughout with teeth black' 'basal half black; apical half dark brown' 'yellow throughout with teeth brown' 'dark brown throughout' 'black throughout' 'basal half black; apical half pale brown' 'brown throughout' 'basal half black; apical half yellow' 'pale brown with teeth dark brown',
+		15 'mandibular teeth in female'/'dorsal tooth larger' 'teeth of equal size' 'ventral tooth larger, but less than 2x as long as dorsal tooth' 'ventral tooth at least twice as long and wide at base than dorsal tooth',
+		16 'sculpture of ventral gena anterior to genal carina'/'row of areolae' 'row of shallow areolae with reduced ridges between cells' 'row of contiguous punctures' 'row of well separated punctures' 'dorsoventrally rugose',
+		17 'sculpture of ventral gena posterior to genal carina'/'areolate' 'smooth' 'mostly smooth, with sparse large punctures' 'mostly smooth, with sparse small punctures' 'coarsely and irregularly punctate' 'irregularly rugose',
+		18 'shape of anteclypeus'/'striplike, straight to slightly convex' 'concave medially, with points laterally',
+		19 'color of legs'/'entirely yellow' 'dark brown, tarsi and distal portion of tibiae light brown' 'coxae brown, legs otherwise yellow' 'coxae dark brown, femora light brown, tibiae and tarsi yellow' 'brown, tarsi and distal portion of tibiae yellow' 'brown, distal portion of tibiae and tarsomeres 1-4 yellow, apical tarsomere brown' 'coxae and femora dark brown, trochanters, tibia and tarsi yellowish-brown' 'coxae black; femora brown; trochanters, tibia and tarsomeres 1-4 yellowish-brow [...]
+		20 'sculpture of dorsal pronotum'/'areolate' 'areolate with expanded ridges' 'shallowly areolate',
+		21 'color of setae on the dorsal mesosoma'/'white' 'brown' 'yellow',
+		22 'sculpture of medial mesoscutum'/'coarsely foveate' 'areolate' 'areolate, with pronounced longitudinal ridges' 'shallowly areolate, becoming smooth posteriorly' 'longitudinally strigose throughout' 'partially to mostly effaced',
+		23 'sculpture of lateral mesoscutum'/'same as medial mesoscutum with a subtle confusion of sculptural elements' 'smooth throughout' 'with an elevated patch of smoother sculpture, bounded by mesoscutal humeral sulcus laterally, extending to scuto-scutellar sulcus posteriorly' 'with small patch of smoother sculpture, formed by fusion of sculptural ridges, smooth patch not extending to scuto-scutellar sulcus' 'areolate',
+		24 'notaulus in female'/'present' 'absent',
+		25 'parapsidal line'/'delimited' 'indistinguishable',
+		26 'sculpture of scutellum'/'coarsely foveate' 'areolate',
+		27 'median furrow on scutellum'/'absent' 'present as a weak indentation' 'present as a distinct furrow reaching from posterior to anterior margin',
+		28 'posterior margin of scutellum'/'emarginate' 'deeply notched' 'convex',
+		29 'outer projection of the propodeum'/'present as distinct corner or posteriorly projecting spine' 'rounded',
+		30 'posterior margin of propodeal shelf between inner and outer propodeal projections in female'/'nearly straight, perpendicular to lateral margin of propodeal shelf' 'concave' 'nearly straight, strongly oblique',
+		31 'posterior margin of propodeal shelf between inner and outer propodeal projections in male'/'nearly straight' 'concave',
+		32 'sculpture of ventral pronotum'/'foveate reticulate, becoming smooth at anterolateral corner' 'obliterated' 'mostly smooth with longitudinal rugulae ventrally' 'mostly smooth with small foveolae around antespiracular patch',
+		33 'anterior pronotal pit'/'absent' 'small' 'large',
+		34 'pronotal cervical sulcus'/'poorly defined' 'well defined by contiguous punctures',
+		35 'mesepimeral sulcus'/'indicated by dorsoventral line of foveae' 'crenulate' 'indicated by a row of faint and shallow depressions',
+		36 'sculpture of femoral depression in female'/'smooth throughout' 'transversely striate throughout' 'anterior half smooth, posterior half finely foveate' 'finely foveate throughout' 'anterior half smooth, posterior half longitudinally striate',
+		37 'sculpture of femoral depression in male'/'smooth throughout' 'smooth medially, bounded by small foveae' 'finely foveate throughout' 'with weak transverse striae throughout' 'anterior half smooth, posterior half finely foveate' 'anterior half smooth, posterior half longitudinally striate',
+		38 'mesopleural carina'/'indicated by one or two rows of punctures' 'present as one or two carinae' 'absent' 'weakly defined by fine ridges',
+		39 'sculpture of ventral mesepisternum'/'smooth' 'foveate' 'with smooth area surrounded by small foveae' 'irregularly areolate',
+		40 'pilosity of metapleural triangle in female'/'present as many short fine white setae' 'glabrous or with at most 3 small setae',
+		41 'pilosity of metapleural triangle in male'/'absent' 'present as many short fine white setae' 'sparsely setose',
+		42 'sculpture of ventral area of metapleuron'/'smooth' 'foveate reticulate' 'foveate reticulate, with slightly appressed ridges',
+		43 'submarginal ridge on T1'/'percurrent, well-defined' 'indicated by an irregular ridge' 'absent' 'partially fused with marginal carina',
+		44 'sublateral carina on T2'/'percurrent' 'absent' 'present in anterior half',
+		45 'sculpture of T2-T3'/'reticulate rugose' 'longitudinally striate' 'longitudinally strigose with rugose interstices' 'weakly rugulose',
+		46 'sculpture of T4'/'reticulate with finely rugulose interstices' 'longitudinally strigose' 'weakly rugulose' 'longitudinally striate' 'reticulate rugose',
+		47 'sculpure of T5'/'longitudinally strigose' 'weakly rugulose' 'reticulate rugose',
+		48 'shape of subapical T6 in female in dorsal view'/'semicircular, width along anterior margin twice the length.' 'short, width along anterior margin at least three times the length' 'trapezoidal, basal width twice the apical width',
+		49 'apex of T7 in male'/'slightly convex' 'bispinose' 'straight to slightly concave, with small points laterally',
+		50 'longitudinal medial keel on S1'/'absent' 'present',
+		51 'sculpture of medial S2 in female'/'punctate reticulate' 'reticulate rugose' 'foveolate' 'punctate',
+		52 'sculpture of medial S3 in female'/'longitudinally strigose' 'foveate' 'effaced to smooth',
+		53 'sculpture of medial S4 in female'/'longitudinally strigose' 'effaced to smooth',
+		54 'sculpture of medial S5 in female'/'reticulate rugose' 'longitudinally strigose' 'effaced to smooth',
+		55 'sculpture of lateral S3 in female'/'reticulate rugose' 'foveolate' 'longitudinally strigose' 'smooth' 'punctate crenulate' 'rugulose',
+		56 'sculpture of lateral S4 in female'/'reticulate rugose' 'longitudinally strigose' 'foveolate' 'smooth' 'rugulose' 'punctate crenulate',
+		57 'sculpture of lateral S5 in female'/'reticulate rugose' 'foveate' 'longitudinally strigose' 'punctate crenulate',
+		58 'sculpture of marginal depression of S2 in female'/'mostly smooth, with scattered punctures or shallow rugulae' 'rugulose' 'finely punctate throughout' 'smooth',
+		59 'sculpture of marginal depression of S3 in female'/'mostly smooth, with scattered punctures or shallow rugulae' 'smooth' 'finely punctate throughout' 'rugulose',
+		60 'sculpture of marginal depression of S4 in female'/'mostly smooth with scattered punctures or shallow rugulae' 'finely punctate throughout' 'rugulose',
+		61 'sculpture of marginal depression of S5 in female'/'rugulose' 'mostly smooth with scattered punctures' 'finely punctate throughout',
+		62 'apical spine on S6 in female'/'not extending beyond apex of T6 and not visible in dorsal view' 'extending beyond apex of T6 and visible in dorsal view',
+		63 'sculpture of medial S2 in male'/'punctate reticulate' 'longitudinally strigose' 'foveolate' 'punctate' 'reticulate rugose',
+		64 'sculpture of medial S3 in male'/'smooth' 'foveate' 'longitudinally reticulate' 'weakly longitudinally strigose',
+		65 'sculpture of lateral S3 in male'/'smooth' 'reticulate rugose' 'longitudinally strigose' 'foveolate' 'rugulose',
+		66 'sculpture of lateral S4 in male'/'smooth' 'rugulose' 'longitudinally strigose' 'foveolate' 'reticulate rugose',
+		67 'sculpture of medial S5 in male'/'smooth' 'foveate' 'longitudinally strigose',
+		68 'sculpture of lateral S5 in male'/'foveolate' 'longitudinally strigose' 'smooth' 'reticulate rugose' 'rugulose'
+	;
+	MATRIX
+		'Oreiscelio zulu' 1000010100003511(05)06001(04)111110110111(13)312022200421100(12)114531320(01)031221
+		'Oreiscelio aequalis' 25?021210010?12000402101011001?021(01)(13)?110?1002102?0021245310011??????
+		'Oreiscelio alluaudi' 1(17)1020(12)11020020(02)(24)0012020010(02)01102102(23)01102210421010212121(02)(02)200003300
+		'Oreiscelio badius' (03)3000(12)(01)1002012(12)(04)(02)0(45)00(12)0111(01)(02)010(02)(01)(01)(01)4(03)(13)2010(12)10101201212(02)(01)013201002101
+		'Oreiscelio coracinus' 0000(012)1(012)10010(23)(145)(12)0(04)0(15)0(012)(12)01(01)1(01)00110(12)1(01)(0123)(023)1(12)01(012)0(01)0(14)01100(012)11(015)(01)(02)(012)(02)(02)01(02)1(034)(013)(01)(013)
+		'Oreiscelio cultrarius' 2(01)?000(12)11030?(23)(12)140(23)0010101(01)001?0210(03)?(13)10?20(12)0101?00212(14)(15)(02)00(02)(01)1??????
+		'Oreiscelio gryphus' 10?011210010?1(12)04010110111(01)001?01103?130?1010101?002120001(03)201??????
+		'Oreiscelio iommii' 10?021110000?11130122311010101?10020?200?0023211?0021134222201??????
+		'Oreiscelio magnipennis' 16?011(12)11010?61200701(12)01011001?01111?110?2022101?002120(01)(02)13201??????
+		'Oreiscelio majikununuensis' 04?22(01)000021?031(04)160210101(01)000?0(12)002?0(13)0?0000401?00(12)121(02)0(03)0201??????
+		'Oreiscelio megadontus' 11?020211021?23130202231010001?01100?020?0300101?0021012031201??????
+		'Oreiscelio naevus' 10000(01)01(01)02002(012)04030012(01)01(01)(02)011(02)110(03)(34)10010210(04)21003212(01)(02)(01)(12)(23)(12)(02)0003(134)(01)(03)
+		'Oreiscelio paradoxus' 10?101211010?5(12)150(14)00(12)01010212?0100(013)?3(02)0?1000101?00(02)(01)(12)4120(01)001??????
+		'Oreiscelio rostratus' 1(01)002(01)01(01)000020(23)(24)00(01)20200002011321000021(01)(02)201121010212(01)2(01)(13)(13)200(04)(02)(13)(34)0(03)
+		'Oreiscelio rugosus' 20202(14)(12)1(02)0201(1567)1110502(1245)(013)1(01)1(01)(02)011(012)(01)(01)(01)4(35)(13)201(02)(0123)(12)0(14)0100221(12)(03)(03)0(13)(13)20100(04)1(02)(14)
+		'Oreiscelio scapularis' 11002(03)011120020(23)(24)001202000020110210010210020130000?(02)(01)(12)21231(02)001(03)22(02)1
+		'Oreiscelio sechellensis' 12312011102046112020213101(12)1011001004(01)201(12)010(14)(02)120321233(03)302(01)0300002
+		'Oreiscelio turneri' (03)(05689)(02)221(01)100105(168)3(12)(04)0402(12)0111(01)00110(01)0(01)(012)(0234)1(12)01(012)(012)(12)0(14)(02)11(01)1212(45)(145)011001(02)(01)(23)(123)(01)(0134)
+		'Oreiscelio zuzkae' 11?101(012)10020?221(45)020(02)100012101?021(01)(123)?1(12)0?(012)(01)2(02)(13)01?01(12)11(01)(02)(02)(01)00(01)0??????
+	;
+END;
+
+BEGIN CHARACTERS;
+	DIMENSIONS NCHAR=2;
+	FORMAT DATATYPE=CONTINUOUS MISSING=?
+		ITEMS=(MIN MAX MEDIAN AVERAGE VARIANCE SAMPLESIZE)
+	;
+	CHARSTATELABELS
+		1 'body length of female'
+		2 'body length of male'
+	;
+	MATRIX
+		'Oreiscelio zulu' (2.40 2.50 ? 2.45 0.0025 3)?
+		'Oreiscelio aequalis' (3.30 3.30 ? 3.30 ? 1)?
+		'Oreiscelio alluaudi' (3.20 3.80 ? 3.50 ? 10)(3.01 3.01 ? 3.01 ? 1)
+		'Oreiscelio badius' (2.20 2.40 ? 2.30 0.0036 20)(2.10 2.10 ? 2.10 ? 1)
+		'Oreiscelio coracinus' (2.30 3.10 ? 2.60 0.0400 20)(2.30 2.90 ? 2.70 0.0400 20)
+		'Oreiscelio cultrarius' (3.00 3.20 ? 3.10 0.0025 5)?
+		'Oreiscelio gryphus' (2.90 3.30 2.90 3.00 0.0289 5)?
+		'Oreiscelio iommii' (2.42 2.42 2.42 2.42 ? 1)?
+		'Oreiscelio magnipennis' (3.10 3.20 ? 3.20 0.0100 2)?
+		'Oreiscelio majikununuensis' ??
+		'Oreiscelio megadontus' (4.10 4.10 4.10 4.10 ? 4.1)?
+		'Oreiscelio naevus' (2.20 3.00 ? 2.70 0.0900 20)(2.20 3.00 ? 2.70 0.0900 20)
+		'Oreiscelio paradoxus' (3.00 3.00 3.00 3.00 0.0000 2)?
+		'Oreiscelio rostratus' (3.30 3.70 ? 3.50 0.0100 20)(3.40 3.90 ? 3.60 0.0400 5)
+		'Oreiscelio rugosus' (2.10 2.70 ? 2.40 0.0100 20)(2.30 2.50 ? 2.50 ? 4)
+		'Oreiscelio scapularis' (3.10 4.10 ? 3.70 0.0400 15)(3.60 3.80 ? 3.70 0.0100 6)
+		'Oreiscelio sechellensis' (3.10 3.40 ? 3.20 0.0081 9)(? ? ? 3.00 ? 1)
+		'Oreiscelio turneri' (2.30 2.90 ? 2.60 0.0400 20)(2.20 2.70 ? 2.40 0.0100 20)
+		'Oreiscelio zuzkae' (3.30 3.90 ? 3.70 0.0225 14)?
+	;
+END;
diff --git a/Tests/PAML/Control_files/codeml/m2a_rel.ctl b/Tests/PAML/Control_files/codeml/m2a_rel.ctl
new file mode 100644
index 0000000..129ba49
--- /dev/null
+++ b/Tests/PAML/Control_files/codeml/m2a_rel.ctl
@@ -0,0 +1,25 @@
+seqfile = ../alignment.phylip
+outfile = temp.out
+treefile = ../species.tree
+verbose = 0
+CodonFreq = 2
+cleandata = 1
+fix_blength = 1
+NSsites = 22
+fix_omega = 0
+clock = 0
+runmode = 0
+fix_kappa = 0
+fix_alpha = 1
+alpha = 0
+method = 0
+Malpha = 0
+RateAncestor = 0
+icode = 0
+seqtype = 1
+omega = 1
+getSE = 0
+noisy = 0
+Mgene = 0
+kappa = 4.54006
+model = 0
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out
new file mode 100644
index 0000000..afab33d
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_6.out
@@ -0,0 +1,145 @@
+CODONML (in paml version 4.6, August 2012)  ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches, 
+Codon frequency model: F3x4
+Site-class models:  M2a_rel (3 categories)
+ns =   5  ls =  74
+
+Codon usage in sequences
+--------------------------------------------------------------------------------------------------
+Phe TTT  0  0  0  0  0 | Ser TCT  1  1  1  1  1 | Tyr TAT  0  0  1  0  0 | Cys TGT  3  2  3  3  3
+    TTC  1  1  1  1  1 |     TCC  1  1  1  1  1 |     TAC  1  1  0  1  1 |     TGC  0  1  0  0  0
+Leu TTA  0  0  0  0  0 |     TCA  1  1  1  1  1 | *** TAA  0  0  0  0  0 | *** TGA  0  0  0  0  0
+    TTG  1  1  1  1  1 |     TCG  0  0  0  0  0 |     TAG  0  0  0  0  0 | Trp TGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+Leu CTT  0  0  0  0  0 | Pro CCT  0  0  0  0  0 | His CAT  0  0  0  0  0 | Arg CGT  0  0  0  0  0
+    CTC  2  2  2  2  2 |     CCC  1  1  1  1  1 |     CAC  0  0  0  0  0 |     CGC  0  0  0  0  0
+    CTA  1  1  1  1  1 |     CCA  3  3  3  3  3 | Gln CAA  0  0  0  0  0 |     CGA  1  1  1  1  1
+    CTG  3  3  3  3  3 |     CCG  0  0  0  0  0 |     CAG  1  1  1  1  1 |     CGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+Ile ATT  2  2  2  2  2 | Thr ACT  0  0  0  0  0 | Asn AAT  2  2  2  2  2 | Ser AGT  0  0  0  0  0
+    ATC  2  2  2  2  2 |     ACC  0  0  0  0  0 |     AAC  0  0  0  0  0 |     AGC  0  0  0  0  0
+    ATA  0  0  0  0  0 |     ACA  2  2  2  2  2 | Lys AAA  5  5  5  5  5 | Arg AGA  2  2  2  2  2
+Met ATG  3  3  3  3  3 |     ACG  0  0  0  0  0 |     AAG  5  5  5  5  5 |     AGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+Val GTT  3  3  3  3  1 | Ala GCT  0  0  0  0  0 | Asp GAT  2  2  2  2  2 | Gly GGT  0  0  0  0  0
+    GTC  0  0  0  0  1 |     GCC  0  0  0  0  0 |     GAC  4  4  4  4  4 |     GGC  3  3  3  3  3
+    GTA  3  3  3  3  4 |     GCA  0  0  0  0  0 | Glu GAA  8  8  8  8  8 |     GGA  1  1  1  1  1
+    GTG  2  2  2  2  2 |     GCG  0  0  0  0  0 |     GAG  4  4  4  4  4 |     GGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#2: Pan_troglo     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.16216    C:0.21622    A:0.36486    G:0.25676
+Average         T:0.19820    C:0.16667    A:0.36937    G:0.26577
+
+#3: Gorilla_go     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.18919    C:0.18919    A:0.36486    G:0.25676
+Average         T:0.20721    C:0.15766    A:0.36937    G:0.26577
+
+#4: Pongo_pygm     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#5: Macaca_mul     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.14865    C:0.21622    A:0.37838    G:0.25676
+Average         T:0.19369    C:0.16667    A:0.37387    G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT       0 | Ser S TCT       5 | Tyr Y TAT       1 | Cys C TGT      14
+      TTC       5 |       TCC       5 |       TAC       4 |       TGC       1
+Leu L TTA       0 |       TCA       5 | *** * TAA       0 | *** * TGA       0
+      TTG       5 |       TCG       0 |       TAG       0 | Trp W TGG       0
+------------------------------------------------------------------------------
+Leu L CTT       0 | Pro P CCT       0 | His H CAT       0 | Arg R CGT       0
+      CTC      10 |       CCC       5 |       CAC       0 |       CGC       0
+      CTA       5 |       CCA      15 | Gln Q CAA       0 |       CGA       5
+      CTG      15 |       CCG       0 |       CAG       5 |       CGG       0
+------------------------------------------------------------------------------
+Ile I ATT      10 | Thr T ACT       0 | Asn N AAT      10 | Ser S AGT       0
+      ATC      10 |       ACC       0 |       AAC       0 |       AGC       0
+      ATA       0 |       ACA      10 | Lys K AAA      25 | Arg R AGA      10
+Met M ATG      15 |       ACG       0 |       AAG      25 |       AGG       0
+------------------------------------------------------------------------------
+Val V GTT      13 | Ala A GCT       0 | Asp D GAT      10 | Gly G GGT       0
+      GTC       1 |       GCC       0 |       GAC      20 |       GGC      15
+      GTA      16 |       GCA       0 | Glu E GAA      40 |       GGA       5
+      GTG      10 |       GCG       0 |       GAG      20 |       GGG       0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17027    C:0.20541    A:0.36757    G:0.25676
+Average         T:0.20090    C:0.16306    A:0.37027    G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie          
+Pan_troglo          -1.0000 (0.0000 0.0207)
+Gorilla_go          -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm          -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul          -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE #  1:  (((1, 2), 3), 4, 5);   MP score: 4
+check convergence..
+lnL(ntime:  7  np: 12):   -308.031579      +0.000000
+   6..7     7..8     8..1     8..2     7..3     6..4     6..5  
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518248 0.999999 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length =   0.05472
+
+(((1: 0.000004, 2: 0.013438): 0.000004, 3: 0.013431): 0.000004, 4: 0.000004, 5: 0.027839);
+
+(((Homo_sapie: 0.000004, Pan_troglo: 0.013438): 0.000004, Gorilla_go: 0.013431): 0.000004, Pongo_pygm: 0.000004, Macaca_mul: 0.027839);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) =  1.51825
+
+Parameters in M22 (M2a_rel):
+ 
+
+dN/dS (w) for site classes (K=3)
+
+p:   1.00000  0.00000  0.00000
+w:   0.00000  1.00000  0.00000
+
+dN & dS for each branch
+
+ branch          t       N       S   dN/dS      dN      dS  N*dN  S*dS
+
+   6..7       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   7..8       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..1       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..2       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   7..3       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   6..4       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   6..5       0.028    167.7     54.3   0.0000   0.0000   0.0379    0.0    2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used:  0:05
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out
new file mode 100644
index 0000000..94df814
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_7.out
@@ -0,0 +1,145 @@
+CODONML (in paml version 4.7, January 2013)  ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches, 
+Codon frequency model: F3x4
+Site-class models:  M2a_rel (3 categories)
+ns =   5  ls =  74
+
+Codon usage in sequences
+--------------------------------------------------------------------------------------------------
+Phe TTT  0  0  0  0  0 | Ser TCT  1  1  1  1  1 | Tyr TAT  0  0  1  0  0 | Cys TGT  3  2  3  3  3
+    TTC  1  1  1  1  1 |     TCC  1  1  1  1  1 |     TAC  1  1  0  1  1 |     TGC  0  1  0  0  0
+Leu TTA  0  0  0  0  0 |     TCA  1  1  1  1  1 | *** TAA  0  0  0  0  0 | *** TGA  0  0  0  0  0
+    TTG  1  1  1  1  1 |     TCG  0  0  0  0  0 |     TAG  0  0  0  0  0 | Trp TGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+Leu CTT  0  0  0  0  0 | Pro CCT  0  0  0  0  0 | His CAT  0  0  0  0  0 | Arg CGT  0  0  0  0  0
+    CTC  2  2  2  2  2 |     CCC  1  1  1  1  1 |     CAC  0  0  0  0  0 |     CGC  0  0  0  0  0
+    CTA  1  1  1  1  1 |     CCA  3  3  3  3  3 | Gln CAA  0  0  0  0  0 |     CGA  1  1  1  1  1
+    CTG  3  3  3  3  3 |     CCG  0  0  0  0  0 |     CAG  1  1  1  1  1 |     CGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+Ile ATT  2  2  2  2  2 | Thr ACT  0  0  0  0  0 | Asn AAT  2  2  2  2  2 | Ser AGT  0  0  0  0  0
+    ATC  2  2  2  2  2 |     ACC  0  0  0  0  0 |     AAC  0  0  0  0  0 |     AGC  0  0  0  0  0
+    ATA  0  0  0  0  0 |     ACA  2  2  2  2  2 | Lys AAA  5  5  5  5  5 | Arg AGA  2  2  2  2  2
+Met ATG  3  3  3  3  3 |     ACG  0  0  0  0  0 |     AAG  5  5  5  5  5 |     AGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+Val GTT  3  3  3  3  1 | Ala GCT  0  0  0  0  0 | Asp GAT  2  2  2  2  2 | Gly GGT  0  0  0  0  0
+    GTC  0  0  0  0  1 |     GCC  0  0  0  0  0 |     GAC  4  4  4  4  4 |     GGC  3  3  3  3  3
+    GTA  3  3  3  3  4 |     GCA  0  0  0  0  0 | Glu GAA  8  8  8  8  8 |     GGA  1  1  1  1  1
+    GTG  2  2  2  2  2 |     GCG  0  0  0  0  0 |     GAG  4  4  4  4  4 |     GGG  0  0  0  0  0
+--------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#2: Pan_troglo     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.16216    C:0.21622    A:0.36486    G:0.25676
+Average         T:0.19820    C:0.16667    A:0.36937    G:0.26577
+
+#3: Gorilla_go     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.18919    C:0.18919    A:0.36486    G:0.25676
+Average         T:0.20721    C:0.15766    A:0.36937    G:0.26577
+
+#4: Pongo_pygm     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#5: Macaca_mul     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.14865    C:0.21622    A:0.37838    G:0.25676
+Average         T:0.19369    C:0.16667    A:0.37387    G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT       0 | Ser S TCT       5 | Tyr Y TAT       1 | Cys C TGT      14
+      TTC       5 |       TCC       5 |       TAC       4 |       TGC       1
+Leu L TTA       0 |       TCA       5 | *** * TAA       0 | *** * TGA       0
+      TTG       5 |       TCG       0 |       TAG       0 | Trp W TGG       0
+------------------------------------------------------------------------------
+Leu L CTT       0 | Pro P CCT       0 | His H CAT       0 | Arg R CGT       0
+      CTC      10 |       CCC       5 |       CAC       0 |       CGC       0
+      CTA       5 |       CCA      15 | Gln Q CAA       0 |       CGA       5
+      CTG      15 |       CCG       0 |       CAG       5 |       CGG       0
+------------------------------------------------------------------------------
+Ile I ATT      10 | Thr T ACT       0 | Asn N AAT      10 | Ser S AGT       0
+      ATC      10 |       ACC       0 |       AAC       0 |       AGC       0
+      ATA       0 |       ACA      10 | Lys K AAA      25 | Arg R AGA      10
+Met M ATG      15 |       ACG       0 |       AAG      25 |       AGG       0
+------------------------------------------------------------------------------
+Val V GTT      13 | Ala A GCT       0 | Asp D GAT      10 | Gly G GGT       0
+      GTC       1 |       GCC       0 |       GAC      20 |       GGC      15
+      GTA      16 |       GCA       0 | Glu E GAA      40 |       GGA       5
+      GTG      10 |       GCG       0 |       GAG      20 |       GGG       0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17027    C:0.20541    A:0.36757    G:0.25676
+Average         T:0.20090    C:0.16306    A:0.37027    G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie          
+Pan_troglo          -1.0000 (0.0000 0.0207)
+Gorilla_go          -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm          -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul          -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE #  1:  (((1, 2), 3), 4, 5);   MP score: 4
+check convergence..
+lnL(ntime:  7  np: 12):   -308.031579      +0.000000
+   6..7     7..8     8..1     8..2     7..3     6..4     6..5  
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518244 1.000000 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length =   0.05472
+
+(((1: 0.00000, 2: 0.01344): 0.00000, 3: 0.01343): 0.00000, 4: 0.00000, 5: 0.02784);
+
+(((Homo_sapie: 0.00000, Pan_troglo: 0.01344): 0.00000, Gorilla_go: 0.01343): 0.00000, Pongo_pygm: 0.00000, Macaca_mul: 0.02784);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) =  1.51824
+
+Parameters in M22 (M2a_rel):
+ 
+
+dN/dS (w) for site classes (K=3)
+
+p:   1.00000  0.00000  0.00000
+w:   0.00000  1.00000  0.00000
+
+dN & dS for each branch
+
+ branch          t       N       S   dN/dS      dN      dS  N*dN  S*dS
+
+   6..7       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   7..8       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..1       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..2       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   7..3       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   6..4       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   6..5       0.028    167.7     54.3   0.0000   0.0000   0.0379    0.0    2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used:  0:05
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out
new file mode 100644
index 0000000..f3957e5
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_8.out
@@ -0,0 +1,145 @@
+CODONML (in paml version 4.8, March 2014)  ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches, 
+Codon frequency model: F3x4
+Site-class models:  M2a_rel (3 categories)
+ns =   5  ls =  74
+
+Codon usage in sequences
+----------------------------------------------------------------------------------------------------------------------
+Phe TTT   0   0   0   0   0 | Ser TCT   1   1   1   1   1 | Tyr TAT   0   0   1   0   0 | Cys TGT   3   2   3   3   3
+    TTC   1   1   1   1   1 |     TCC   1   1   1   1   1 |     TAC   1   1   0   1   1 |     TGC   0   1   0   0   0
+Leu TTA   0   0   0   0   0 |     TCA   1   1   1   1   1 | *** TAA   0   0   0   0   0 | *** TGA   0   0   0   0   0
+    TTG   1   1   1   1   1 |     TCG   0   0   0   0   0 |     TAG   0   0   0   0   0 | Trp TGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+Leu CTT   0   0   0   0   0 | Pro CCT   0   0   0   0   0 | His CAT   0   0   0   0   0 | Arg CGT   0   0   0   0   0
+    CTC   2   2   2   2   2 |     CCC   1   1   1   1   1 |     CAC   0   0   0   0   0 |     CGC   0   0   0   0   0
+    CTA   1   1   1   1   1 |     CCA   3   3   3   3   3 | Gln CAA   0   0   0   0   0 |     CGA   1   1   1   1   1
+    CTG   3   3   3   3   3 |     CCG   0   0   0   0   0 |     CAG   1   1   1   1   1 |     CGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+Ile ATT   2   2   2   2   2 | Thr ACT   0   0   0   0   0 | Asn AAT   2   2   2   2   2 | Ser AGT   0   0   0   0   0
+    ATC   2   2   2   2   2 |     ACC   0   0   0   0   0 |     AAC   0   0   0   0   0 |     AGC   0   0   0   0   0
+    ATA   0   0   0   0   0 |     ACA   2   2   2   2   2 | Lys AAA   5   5   5   5   5 | Arg AGA   2   2   2   2   2
+Met ATG   3   3   3   3   3 |     ACG   0   0   0   0   0 |     AAG   5   5   5   5   5 |     AGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+Val GTT   3   3   3   3   1 | Ala GCT   0   0   0   0   0 | Asp GAT   2   2   2   2   2 | Gly GGT   0   0   0   0   0
+    GTC   0   0   0   0   1 |     GCC   0   0   0   0   0 |     GAC   4   4   4   4   4 |     GGC   3   3   3   3   3
+    GTA   3   3   3   3   4 |     GCA   0   0   0   0   0 | Glu GAA   8   8   8   8   8 |     GGA   1   1   1   1   1
+    GTG   2   2   2   2   2 |     GCG   0   0   0   0   0 |     GAG   4   4   4   4   4 |     GGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#2: Pan_troglo     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.16216    C:0.21622    A:0.36486    G:0.25676
+Average         T:0.19820    C:0.16667    A:0.36937    G:0.26577
+
+#3: Gorilla_go     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.18919    C:0.18919    A:0.36486    G:0.25676
+Average         T:0.20721    C:0.15766    A:0.36937    G:0.26577
+
+#4: Pongo_pygm     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#5: Macaca_mul     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.14865    C:0.21622    A:0.37838    G:0.25676
+Average         T:0.19369    C:0.16667    A:0.37387    G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT       0 | Ser S TCT       5 | Tyr Y TAT       1 | Cys C TGT      14
+      TTC       5 |       TCC       5 |       TAC       4 |       TGC       1
+Leu L TTA       0 |       TCA       5 | *** * TAA       0 | *** * TGA       0
+      TTG       5 |       TCG       0 |       TAG       0 | Trp W TGG       0
+------------------------------------------------------------------------------
+Leu L CTT       0 | Pro P CCT       0 | His H CAT       0 | Arg R CGT       0
+      CTC      10 |       CCC       5 |       CAC       0 |       CGC       0
+      CTA       5 |       CCA      15 | Gln Q CAA       0 |       CGA       5
+      CTG      15 |       CCG       0 |       CAG       5 |       CGG       0
+------------------------------------------------------------------------------
+Ile I ATT      10 | Thr T ACT       0 | Asn N AAT      10 | Ser S AGT       0
+      ATC      10 |       ACC       0 |       AAC       0 |       AGC       0
+      ATA       0 |       ACA      10 | Lys K AAA      25 | Arg R AGA      10
+Met M ATG      15 |       ACG       0 |       AAG      25 |       AGG       0
+------------------------------------------------------------------------------
+Val V GTT      13 | Ala A GCT       0 | Asp D GAT      10 | Gly G GGT       0
+      GTC       1 |       GCC       0 |       GAC      20 |       GGC      15
+      GTA      16 |       GCA       0 | Glu E GAA      40 |       GGA       5
+      GTG      10 |       GCG       0 |       GAG      20 |       GGG       0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17027    C:0.20541    A:0.36757    G:0.25676
+Average         T:0.20090    C:0.16306    A:0.37027    G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie          
+Pan_troglo          -1.0000 (0.0000 0.0207)
+Gorilla_go          -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm          -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul          -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE #  1:  (((1, 2), 3), 4, 5);   MP score: 4
+check convergence..
+lnL(ntime:  7  np: 12):   -308.031579      +0.000000
+   6..7     7..8     8..1     8..2     7..3     6..4     6..5  
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518244 1.000000 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length =   0.05472
+
+(((1: 0.000004, 2: 0.013438): 0.000004, 3: 0.013431): 0.000004, 4: 0.000004, 5: 0.027839);
+
+(((Homo_sapie: 0.000004, Pan_troglo: 0.013438): 0.000004, Gorilla_go: 0.013431): 0.000004, Pongo_pygm: 0.000004, Macaca_mul: 0.027839);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) =  1.51824
+
+Parameters in M22 (M2a_rel):
+ 
+
+dN/dS (w) for site classes (K=3)
+
+p:   1.00000  0.00000  0.00000
+w:   0.00000  1.00000  0.00000
+
+dN & dS for each branch
+
+ branch          t       N       S   dN/dS      dN      dS  N*dN  S*dS
+
+   6..7       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   7..8       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..1       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..2       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   7..3       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   6..4       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   6..5       0.028    167.7     54.3   0.0000   0.0000   0.0379    0.0    2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used:  0:04
diff --git a/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out
new file mode 100644
index 0000000..dd19dfa
--- /dev/null
+++ b/Tests/PAML/Results/codeml/m2a_rel/m2a_rel-4_9a.out
@@ -0,0 +1,144 @@
+CODONML (in paml version 4.9, March 2015)  ../Alignments/alignment.phylip
+Model: One dN/dS ratio for branches, 
+Codon frequency model: F3x4
+Site-class models:  M2a_rel (3 categories)
+ns =   5  ls =  74
+
+Codon usage in sequences
+----------------------------------------------------------------------------------------------------------------------
+Phe TTT   0   0   0   0   0 | Ser TCT   1   1   1   1   1 | Tyr TAT   0   0   1   0   0 | Cys TGT   3   2   3   3   3
+    TTC   1   1   1   1   1 |     TCC   1   1   1   1   1 |     TAC   1   1   0   1   1 |     TGC   0   1   0   0   0
+Leu TTA   0   0   0   0   0 |     TCA   1   1   1   1   1 | *** TAA   0   0   0   0   0 | *** TGA   0   0   0   0   0
+    TTG   1   1   1   1   1 |     TCG   0   0   0   0   0 |     TAG   0   0   0   0   0 | Trp TGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+Leu CTT   0   0   0   0   0 | Pro CCT   0   0   0   0   0 | His CAT   0   0   0   0   0 | Arg CGT   0   0   0   0   0
+    CTC   2   2   2   2   2 |     CCC   1   1   1   1   1 |     CAC   0   0   0   0   0 |     CGC   0   0   0   0   0
+    CTA   1   1   1   1   1 |     CCA   3   3   3   3   3 | Gln CAA   0   0   0   0   0 |     CGA   1   1   1   1   1
+    CTG   3   3   3   3   3 |     CCG   0   0   0   0   0 |     CAG   1   1   1   1   1 |     CGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+Ile ATT   2   2   2   2   2 | Thr ACT   0   0   0   0   0 | Asn AAT   2   2   2   2   2 | Ser AGT   0   0   0   0   0
+    ATC   2   2   2   2   2 |     ACC   0   0   0   0   0 |     AAC   0   0   0   0   0 |     AGC   0   0   0   0   0
+    ATA   0   0   0   0   0 |     ACA   2   2   2   2   2 | Lys AAA   5   5   5   5   5 | Arg AGA   2   2   2   2   2
+Met ATG   3   3   3   3   3 |     ACG   0   0   0   0   0 |     AAG   5   5   5   5   5 |     AGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+Val GTT   3   3   3   3   1 | Ala GCT   0   0   0   0   0 | Asp GAT   2   2   2   2   2 | Gly GGT   0   0   0   0   0
+    GTC   0   0   0   0   1 |     GCC   0   0   0   0   0 |     GAC   4   4   4   4   4 |     GGC   3   3   3   3   3
+    GTA   3   3   3   3   4 |     GCA   0   0   0   0   0 | Glu GAA   8   8   8   8   8 |     GGA   1   1   1   1   1
+    GTG   2   2   2   2   2 |     GCG   0   0   0   0   0 |     GAG   4   4   4   4   4 |     GGG   0   0   0   0   0
+----------------------------------------------------------------------------------------------------------------------
+
+Codon position x base (3x4) table for each sequence.
+
+#1: Homo_sapie     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#2: Pan_troglo     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.16216    C:0.21622    A:0.36486    G:0.25676
+Average         T:0.19820    C:0.16667    A:0.36937    G:0.26577
+
+#3: Gorilla_go     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.18919    C:0.18919    A:0.36486    G:0.25676
+Average         T:0.20721    C:0.15766    A:0.36937    G:0.26577
+
+#4: Pongo_pygm     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17568    C:0.20270    A:0.36486    G:0.25676
+Average         T:0.20270    C:0.16216    A:0.36937    G:0.26577
+
+#5: Macaca_mul     
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.14865    C:0.21622    A:0.37838    G:0.25676
+Average         T:0.19369    C:0.16667    A:0.37387    G:0.26577
+
+Sums of codon usage counts
+------------------------------------------------------------------------------
+Phe F TTT       0 | Ser S TCT       5 | Tyr Y TAT       1 | Cys C TGT      14
+      TTC       5 |       TCC       5 |       TAC       4 |       TGC       1
+Leu L TTA       0 |       TCA       5 | *** * TAA       0 | *** * TGA       0
+      TTG       5 |       TCG       0 |       TAG       0 | Trp W TGG       0
+------------------------------------------------------------------------------
+Leu L CTT       0 | Pro P CCT       0 | His H CAT       0 | Arg R CGT       0
+      CTC      10 |       CCC       5 |       CAC       0 |       CGC       0
+      CTA       5 |       CCA      15 | Gln Q CAA       0 |       CGA       5
+      CTG      15 |       CCG       0 |       CAG       5 |       CGG       0
+------------------------------------------------------------------------------
+Ile I ATT      10 | Thr T ACT       0 | Asn N AAT      10 | Ser S AGT       0
+      ATC      10 |       ACC       0 |       AAC       0 |       AGC       0
+      ATA       0 |       ACA      10 | Lys K AAA      25 | Arg R AGA      10
+Met M ATG      15 |       ACG       0 |       AAG      25 |       AGG       0
+------------------------------------------------------------------------------
+Val V GTT      13 | Ala A GCT       0 | Asp D GAT      10 | Gly G GGT       0
+      GTC       1 |       GCC       0 |       GAC      20 |       GGC      15
+      GTA      16 |       GCA       0 | Glu E GAA      40 |       GGA       5
+      GTG      10 |       GCG       0 |       GAG      20 |       GGG       0
+------------------------------------------------------------------------------
+
+
+Codon position x base (3x4) table, overall
+
+position  1:    T:0.12162    C:0.16216    A:0.31081    G:0.40541
+position  2:    T:0.31081    C:0.12162    A:0.43243    G:0.13514
+position  3:    T:0.17027    C:0.20541    A:0.36757    G:0.25676
+Average         T:0.20090    C:0.16306    A:0.37027    G:0.26577
+
+
+Nei & Gojobori 1986. dN/dS (dN, dS)
+(Note: This matrix is not used in later ML. analysis.
+Use runmode = -2 for ML pairwise comparison.)
+
+Homo_sapie          
+Pan_troglo          -1.0000 (0.0000 0.0207)
+Gorilla_go          -1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0421)
+Pongo_pygm          -1.0000 (0.0000 0.0000)-1.0000 (0.0000 0.0207)-1.0000 (0.0000 0.0207)
+Macaca_mul          -1.0000 (0.0000 0.0421)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0640)-1.0000 (0.0000 0.0421)
+
+
+TREE #  1:  (((1, 2), 3), 4, 5);   MP score: 4
+lnL(ntime:  7  np: 12):   -308.031579      +0.000000
+   6..7     7..8     8..1     8..2     7..3     6..4     6..5  
+ 0.000004 0.000004 0.000004 0.013438 0.013431 0.000004 0.027839 1.518250 0.999999 0.000000 0.000001 0.000001
+
+Note: Branch length is defined as number of nucleotide substitutions per codon (not per neucleotide site).
+
+tree length =   0.05472
+
+(((1: 0.000004, 2: 0.013438): 0.000004, 3: 0.013431): 0.000004, 4: 0.000004, 5: 0.027839);
+
+(((Homo_sapie: 0.000004, Pan_troglo: 0.013438): 0.000004, Gorilla_go: 0.013431): 0.000004, Pongo_pygm: 0.000004, Macaca_mul: 0.027839);
+
+Detailed output identifying parameters
+
+kappa (ts/tv) =  1.51825
+
+Parameters in M22 (M2a_rel):
+ 
+
+dN/dS (w) for site classes (K=3)
+
+p:   1.00000  0.00000  0.00000
+w:   0.00000  1.00000  0.00000
+
+dN & dS for each branch
+
+ branch          t       N       S   dN/dS      dN      dS  N*dN  S*dS
+
+   6..7       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   7..8       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..1       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   8..2       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   7..3       0.013    167.7     54.3   0.0000   0.0000   0.0183    0.0    1.0
+   6..4       0.000    167.7     54.3   0.0000   0.0000   0.0000    0.0    0.0
+   6..5       0.028    167.7     54.3   0.0000   0.0000   0.0379    0.0    2.1
+
+
+Naive Empirical Bayes (NEB) analysis
+Time used:  0:06
diff --git a/Tests/PAML/gen_results.py b/Tests/PAML/gen_results.py
index 88af40d..9872980 100644
--- a/Tests/PAML/gen_results.py
+++ b/Tests/PAML/gen_results.py
@@ -1,4 +1,4 @@
-# Copyright (C) 2012, 2013 by Brandon Invergo (b.invergo at gmail.com)
+# Copyright (C) 2012, 2013, 2016 by Brandon Invergo (b.invergo at gmail.com)
 # This code is part of the Biopython distribution and governed by its
 # license. Please see the LICENSE file that should have been included
 # as part of this package.
@@ -11,7 +11,7 @@ import sys
 from Bio._py3k import range
 
 
-VERSIONS = ["4_1", "4_3", "4_4", "4_4c", "4_5", "4_6", "4_7"]
+VERSIONS = ["4_1", "4_3", "4_4", "4_4c", "4_5", "4_6", "4_7", "4_8", "4_9a"]
 
 
 def codeml(vers=None, verbose=False):
@@ -29,7 +29,8 @@ def codeml(vers=None, verbose=False):
             ("ngene2_mgene02", "lysinYangSwanson2002.nuc", "lysin.trees"),
             ("ngene2_mgene34", "lysinYangSwanson2002.nuc", "lysin.trees"),
             ("pairwise", "alignment.phylip", "species.tree"),
-            ("SE", "alignment.phylip", "species.tree")]
+            ("SE", "alignment.phylip", "species.tree"),
+            ("m2a_rel", "alignment.phylip", "species.tree")]
 
     for test in tests:
         print(test[0])
@@ -44,6 +45,9 @@ def codeml(vers=None, verbose=False):
         cml.alignment = alignment
         cml.tree = tree
         for version in versions:
+            # M2a_rel (NSsites 22) was introduced in PAML 4.6
+            if test[0] == "m2a_rel" and int(version.split("_")[1][0]) < 6:
+                continue
             print("\t{0}".format(version.replace('_', '.')))
             if test[0] in ["ngene2_mgene02", "ngene2_mgene34"] and \
                version == "4_6":
diff --git a/Tests/PDB/1LCD.pdb b/Tests/PDB/1LCD.pdb
new file mode 100644
index 0000000..4c0d82a
--- /dev/null
+++ b/Tests/PDB/1LCD.pdb
@@ -0,0 +1,3884 @@
+TITLE     STRUCTURE OF THE COMPLEX OF LAC REPRESSOR HEADPIECE AND AN
+TITLE    2 11 BASE-PAIR HALF-OPERATOR DETERMINED BY NUCLEAR MAGNETIC
+TITLE    3 RESONANCE SPECTROSCOPY AND RESTRAINED MOLECULAR DYNAMICS
+COMPND    MOL_ID: 1;
+COMPND   2 MOLECULE: DNA (5'-D(*AP*AP*TP*TP*GP*TP*GP*AP*GP*CP*G)-3');
+COMPND   3 CHAIN: B;
+COMPND   4 ENGINEERED: YES;
+COMPND   5 MOL_ID: 2;
+COMPND   6 MOLECULE: DNA (5'-D(*CP*GP*CP*TP*CP*AP*CP*AP*AP*TP*T)-3');
+COMPND   7 CHAIN: C;
+COMPND   8 ENGINEERED: YES;
+COMPND   9 MOL_ID: 3;
+COMPND  10 MOLECULE: LAC REPRESSOR;
+COMPND  11 CHAIN: A;
+COMPND  12 ENGINEERED: YES
+SOURCE    MOL_ID: 1;
+SOURCE   2 SYNTHETIC: YES;
+SOURCE   3 MOL_ID: 2;
+SOURCE   4 SYNTHETIC: YES;
+SOURCE   5 MOL_ID: 3;
+SOURCE   6 ORGANISM_SCIENTIFIC: ESCHERICHIA COLI;
+SOURCE   7 ORGANISM_TAXID: 562;
+SOURCE   8 EXPRESSION_SYSTEM_VECTOR_TYPE: LAC
+KEYWDS    GENE REGULATION/DNA
+EXPDTA    SOLUTION NMR
+NUMMDL    3
+AUTHOR    V.P.CHUPRINA,J.A.C.RULLMANN,R.M.J.N.LAMERICHS,J.H.VAN BOOM,
+AUTHOR   2 R.BOELENS,R.KAPTEIN
+REVDAT   3   24-FEB-09 1LCD    1       VERSN
+REVDAT   2   01-APR-03 1LCD    1       JRNL
+REVDAT   1   31-JAN-94 1LCD    0
+JRNL        AUTH   V.P.CHUPRINA,J.A.RULLMANN,R.M.LAMERICHS,
+JRNL        AUTH 2 J.H.VAN BOOM,R.BOELENS,R.KAPTEIN
+JRNL        TITL   STRUCTURE OF THE COMPLEX OF LAC REPRESSOR
+JRNL        TITL 2 HEADPIECE AND AN 11 BASE-PAIR HALF-OPERATOR
+JRNL        TITL 3 DETERMINED BY NUCLEAR MAGNETIC RESONANCE
+JRNL        TITL 4 SPECTROSCOPY AND RESTRAINED MOLECULAR DYNAMICS.
+JRNL        REF    J.MOL.BIOL.                   V. 234   446 1993
+JRNL        REFN                   ISSN 0022-2836
+JRNL        PMID   8230225
+JRNL        DOI    10.1006/JMBI.1993.1598
+REMARK   1
+REMARK   1 REFERENCE 1
+REMARK   1  AUTH   R.M.J.N.LAMERICHS,R.BOELENS,G.A.VAN DER MAREL,
+REMARK   1  AUTH 2 J.H.VAN BOOM,R.KAPTEIN
+REMARK   1  TITL   ASSIGNMENT OF THE 1H-NMR SPECTRUM OF A LAC
+REMARK   1  TITL 2 REPRESSOR HEADPIECE-OPERATOR COMPLEX IN H2O AND
+REMARK   1  TITL 3 IDENTIFICATION OF NOES. CONSEQUENCES FOR
+REMARK   1  TITL 4 PROTEIN-DNA INTERACTION
+REMARK   1  REF    EUR.J.BIOCHEM.                V. 194   629 1990
+REMARK   1  REFN                   ISSN 0014-2956
+REMARK   1 REFERENCE 2
+REMARK   1  AUTH   R.KAPTEIN,R.M.J.N.LAMERICHS,R.BOELENS,
+REMARK   1  AUTH 2 J.A.C.RULLMANN
+REMARK   1  TITL   TWO-DIMENSIONAL NMR STUDY OF A PROTEIN-DNA
+REMARK   1  TITL 2 COMPLEX. LAC REPRESSOR HEADPIECE-OPERATOR
+REMARK   1  TITL 3 INTERACTION
+REMARK   1  REF    BIOCHEM.PHARM.                V.  40    89 1990
+REMARK   1  REFN                   ISSN 0006-2952
+REMARK   1 REFERENCE 3
+REMARK   1  AUTH   R.M.J.N.LAMERICHS,R.BOELENS,G.A.VAN DER MAREL,
+REMARK   1  AUTH 2 J.H.VAN BOOM,R.KAPTEIN,F.BUCK,B.FERA,H.RUETERJANS
+REMARK   1  TITL   1H NMR STUDY OF A COMPLEX BETWEEN THE LAC
+REMARK   1  TITL 2 REPRESSOR HEADPIECE AND A 22 BASE PAIR SYMMETRIC
+REMARK   1  TITL 3 LAC OPERATOR
+REMARK   1  REF    BIOCHEMISTRY                  V.  28  2985 1989
+REMARK   1  REFN                   ISSN 0006-2960
+REMARK   1 REFERENCE 4
+REMARK   1  AUTH   J.A.C.RULLMANN,R.BOELENS,R.KAPTEIN
+REMARK   1  TITL   NMR BASED DOCKING STUDIES OF LAC REPRESSOR
+REMARK   1  TITL 2 HEADPIECE ON A LAC OPERATOR FRAGMENT
+REMARK   1  REF    UCLA SYMP.MOL.CELL.BIOL.,     V.  95    11 1989
+REMARK   1  REF  2 NEW SER.
+REMARK   1  REFN                   ISSN 0735-9543
+REMARK   1 REFERENCE 5
+REMARK   1  AUTH   R.BOELENS,R.M.J.N.LAMERICHS,J.A.C.RULLMANN,
+REMARK   1  AUTH 2 J.H.VAN BOOM,R.KAPTEIN
+REMARK   1  TITL   THE INTERACTION OF LAC REPRESSOR HEADPIECE WITH
+REMARK   1  TITL 2 ITS OPERATOR: AN NMR VIEW
+REMARK   1  REF    PROTEIN SEQ.DATA ANAL.        V.   1   487 1988
+REMARK   1  REFN                   ISSN 0931-9506
+REMARK   1 REFERENCE 6
+REMARK   1  AUTH   J.DE VLIEG,R.M.SCHEEK,W.F.VAN GUNSTEREN,
+REMARK   1  AUTH 2 H.J.C.BERENDSEN,R.KAPTEIN,J.THOMASON
+REMARK   1  TITL   COMBINED PROCEDURE OF DISTANCE GEOMETRY AND
+REMARK   1  TITL 2 RESTRAINED MOLECULAR DYNAMICS TECHNIQUES FOR
+REMARK   1  TITL 3 PROTEIN STRUCTURE DETERMINATION FROM NUCLEAR
+REMARK   1  TITL 4 MAGNETIC RESONANCE DATA: APPLICATION TO THE DNA
+REMARK   1  TITL 5 BINDING DOMAIN OF LAC REPRESSOR FROM ESCHERICHIA
+REMARK   1  TITL 6 COLI
+REMARK   1  REF    PROTEINS                      V.   3   209 1988
+REMARK   1  REFN                   ISSN 0887-3585
+REMARK   1 REFERENCE 7
+REMARK   1  AUTH   R.BOELENS,R.M.SCHEEK,R.M.J.N.LAMERICHS,J.DE VLIEG,
+REMARK   1  AUTH 2 J.H.VAN BOOM,R.KAPTEIN
+REMARK   1  TITL   A TWO-DIMENSIONAL NMR STUDY OF THE COMPLEX OF LAC
+REMARK   1  TITL 2 REPRESSOR HEADPIECE WITH A 14 BASE PAIR LAC
+REMARK   1  TITL 3 OPERATOR FRAGMENT
+REMARK   1  REF    NATO ASI SER.,SER.A           V. 137   191 1987
+REMARK   1  REFN                   ISSN 0161-0449
+REMARK   1 REFERENCE 8
+REMARK   1  AUTH   R.BOELENS,R.M.SCHEEK,J.H.VAN BOOM,R.KAPTEIN
+REMARK   1  TITL   COMPLEX OF LAC REPRESSOR HEADPIECE WITH A 14 BASE
+REMARK   1  TITL 2 PAIR LAC OPERATOR FRAGMENT STUDIED BY
+REMARK   1  TITL 3 TWO-DIMENSIONAL NUCLEAR MAGNETIC RESONANCE
+REMARK   1  REF    J.MOL.BIOL.                   V. 193   213 1987
+REMARK   1  REFN                   ISSN 0022-2836
+REMARK   1 REFERENCE 9
+REMARK   1  AUTH   J.DE VLIEG,R.BOELENS,R.M.SCHEEK,R.KAPTEIN,
+REMARK   1  AUTH 2 W.F.VAN GUNSTEREN
+REMARK   1  TITL   RESTRAINED MOLECULAR DYNAMICS PROCEDURE FOR
+REMARK   1  TITL 2 PROTEIN TERTIARY STRUCTURE DETERMINATION FROM NMR
+REMARK   1  TITL 3 DATA: A LAC REPRESSOR HEADPIECE STRUCTURE BASED ON
+REMARK   1  TITL 4 INFORMATION ON J-COUPLING AND FROM PRESENCE AND
+REMARK   1  TITL 5 ABSENCE OF NOES
+REMARK   1  REF    ISR.J.CHEM.                   V.  27   181 1986
+REMARK   1  REFN                   ISSN 0021-2148
+REMARK   2
+REMARK   2 RESOLUTION. NOT APPLICABLE.
+REMARK   3
+REMARK   3 REFINEMENT.
+REMARK   3   PROGRAM     : NULL
+REMARK   3   AUTHORS     : NULL
+REMARK   3
+REMARK   3  OTHER REFINEMENT REMARKS: NULL
+REMARK   4
+REMARK   4 1LCD COMPLIES WITH FORMAT V. 3.15, 01-DEC-08
+REMARK 100
+REMARK 100 THIS ENTRY HAS BEEN PROCESSED BY BNL.
+REMARK 210
+REMARK 210 EXPERIMENTAL DETAILS
+REMARK 210  EXPERIMENT TYPE                : NMR
+REMARK 210  TEMPERATURE           (KELVIN) : NULL
+REMARK 210  PH                             : NULL
+REMARK 210  IONIC STRENGTH                 : NULL
+REMARK 210  PRESSURE                       : NULL
+REMARK 210  SAMPLE CONTENTS                : NULL
+REMARK 210
+REMARK 210  NMR EXPERIMENTS CONDUCTED      : NULL
+REMARK 210  SPECTROMETER FIELD STRENGTH    : NULL
+REMARK 210  SPECTROMETER MODEL             : NULL
+REMARK 210  SPECTROMETER MANUFACTURER      : NULL
+REMARK 210
+REMARK 210  STRUCTURE DETERMINATION.
+REMARK 210   SOFTWARE USED                 : NULL
+REMARK 210   METHOD USED                   : NULL
+REMARK 210
+REMARK 210 CONFORMERS, NUMBER CALCULATED   : NULL
+REMARK 210 CONFORMERS, NUMBER SUBMITTED    : 3
+REMARK 210 CONFORMERS, SELECTION CRITERIA  : NULL
+REMARK 210
+REMARK 210 BEST REPRESENTATIVE CONFORMER IN THIS ENSEMBLE : NULL
+REMARK 210
+REMARK 210 REMARK: NULL
+REMARK 215
+REMARK 215 NMR STUDY
+REMARK 215 THE COORDINATES IN THIS ENTRY WERE GENERATED FROM SOLUTION
+REMARK 215 NMR DATA.  PROTEIN DATA BANK CONVENTIONS REQUIRE THAT
+REMARK 215 CRYST1 AND SCALE RECORDS BE INCLUDED, BUT THE VALUES ON
+REMARK 215 THESE RECORDS ARE MEANINGLESS.
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: COVALENT BOND LENGTHS
+REMARK 500
+REMARK 500 THE STEREOCHEMICAL PARAMETERS OF THE FOLLOWING RESIDUES
+REMARK 500 HAVE VALUES WHICH DEVIATE FROM EXPECTED VALUES BY MORE
+REMARK 500 THAN 6*RMSD (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN
+REMARK 500 IDENTIFIER; SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 STANDARD TABLE:
+REMARK 500 FORMAT: (10X,I3,1X,2(A3,1X,A1,I4,A1,1X,A4,3X),1X,F6.3)
+REMARK 500
+REMARK 500 EXPECTED VALUES PROTEIN: ENGH AND HUBER, 1999
+REMARK 500 EXPECTED VALUES NUCLEIC ACID: CLOWNEY ET AL 1996
+REMARK 500
+REMARK 500  M RES CSSEQI ATM1   RES CSSEQI ATM2   DEVIATION
+REMARK 500  1  DA B   1   C5     DA B   1   N7     -0.052
+REMARK 500  1  DA B   1   N9     DA B   1   C4     -0.041
+REMARK 500  1  DA B   2   C5     DA B   2   N7     -0.060
+REMARK 500  1  DA B   2   N9     DA B   2   C4     -0.051
+REMARK 500  1  DT B   3   C5     DT B   3   C6      0.055
+REMARK 500  1  DT B   3   C6     DT B   3   N1      0.042
+REMARK 500  1  DT B   4   C5     DT B   4   C6      0.062
+REMARK 500  1  DG B   5   C5     DG B   5   N7     -0.051
+REMARK 500  1  DG B   5   N7     DG B   5   C8      0.040
+REMARK 500  1  DG B   5   C8     DG B   5   N9     -0.053
+REMARK 500  1  DG B   5   N9     DG B   5   C4     -0.056
+REMARK 500  1  DG B   7   C5     DG B   7   N7     -0.050
+REMARK 500  1  DG B   7   C8     DG B   7   N9     -0.050
+REMARK 500  1  DG B   7   N9     DG B   7   C4     -0.055
+REMARK 500  1  DA B   8   C5     DA B   8   N7     -0.070
+REMARK 500  1  DA B   8   N9     DA B   8   C4     -0.042
+REMARK 500  1  DG B   9   C5     DG B   9   N7     -0.063
+REMARK 500  1  DG B   9   C8     DG B   9   N9     -0.052
+REMARK 500  1  DC B  10   C5     DC B  10   C6      0.064
+REMARK 500  1  DG B  11   C5     DG B  11   N7     -0.052
+REMARK 500  1  DG B  11   N9     DG B  11   C4     -0.051
+REMARK 500  1  DC C   1   N1     DC C   1   C6      0.041
+REMARK 500  1  DC C   1   C5     DC C   1   C6      0.052
+REMARK 500  1  DG C   2   C5     DG C   2   N7     -0.043
+REMARK 500  1  DC C   3   N1     DC C   3   C6      0.043
+REMARK 500  1  DT C   4   C5     DT C   4   C6      0.064
+REMARK 500  1  DC C   5   N1     DC C   5   C6      0.048
+REMARK 500  1  DC C   5   C5     DC C   5   C6      0.057
+REMARK 500  1  DA C   6   C5     DA C   6   N7     -0.058
+REMARK 500  1  DA C   6   C8     DA C   6   N9     -0.054
+REMARK 500  1  DA C   6   N9     DA C   6   C4     -0.045
+REMARK 500  1  DC C   7   N1     DC C   7   C6      0.043
+REMARK 500  1  DC C   7   C5     DC C   7   C6      0.061
+REMARK 500  1  DA C   8   C5     DA C   8   N7     -0.048
+REMARK 500  1  DA C   8   C8     DA C   8   N9     -0.051
+REMARK 500  1  DA C   8   N9     DA C   8   C4     -0.040
+REMARK 500  1  DA C   9   C5     DA C   9   N7     -0.059
+REMARK 500  1  DA C   9   C8     DA C   9   N9     -0.050
+REMARK 500  1  DA C   9   N9     DA C   9   C4     -0.046
+REMARK 500  1  DT C  10   C5     DT C  10   C6      0.049
+REMARK 500  1  DT C  10   C5     DT C  10   C7      0.036
+REMARK 500  1  DT C  11   C5     DT C  11   C6      0.051
+REMARK 500  2  DA B   1   C5     DA B   1   N7     -0.051
+REMARK 500  2  DA B   1   C8     DA B   1   N9     -0.060
+REMARK 500  2  DA B   1   N9     DA B   1   C4     -0.048
+REMARK 500  2  DA B   2   C5     DA B   2   N7     -0.049
+REMARK 500  2  DA B   2   C8     DA B   2   N9     -0.053
+REMARK 500  2  DA B   2   N9     DA B   2   C4     -0.054
+REMARK 500  2  DT B   3   C5     DT B   3   C6      0.058
+REMARK 500  2  DT B   3   C5     DT B   3   C7      0.038
+REMARK 500
+REMARK 500 THIS ENTRY HAS     125 BOND DEVIATIONS.
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: COVALENT BOND ANGLES
+REMARK 500
+REMARK 500 THE STEREOCHEMICAL PARAMETERS OF THE FOLLOWING RESIDUES
+REMARK 500 HAVE VALUES WHICH DEVIATE FROM EXPECTED VALUES BY MORE
+REMARK 500 THAN 6*RMSD (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN
+REMARK 500 IDENTIFIER; SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 STANDARD TABLE:
+REMARK 500 FORMAT: (10X,I3,1X,A3,1X,A1,I4,A1,3(1X,A4,2X),12X,F5.1)
+REMARK 500
+REMARK 500 EXPECTED VALUES PROTEIN: ENGH AND HUBER, 1999
+REMARK 500 EXPECTED VALUES NUCLEIC ACID: CLOWNEY ET AL 1996
+REMARK 500
+REMARK 500  M RES CSSEQI ATM1   ATM2   ATM3
+REMARK 500  1  DA B   1   C3' -  C2' -  C1' ANGL. DEV. =  -4.9 DEGREES
+REMARK 500  1  DA B   1   N1  -  C2  -  N3  ANGL. DEV. =  -9.7 DEGREES
+REMARK 500  1  DA B   1   C2  -  N3  -  C4  ANGL. DEV. =  11.8 DEGREES
+REMARK 500  1  DA B   1   N3  -  C4  -  C5  ANGL. DEV. =  -7.9 DEGREES
+REMARK 500  1  DA B   1   C5  -  C6  -  N1  ANGL. DEV. =   4.0 DEGREES
+REMARK 500  1  DA B   1   N3  -  C4  -  N9  ANGL. DEV. =   7.4 DEGREES
+REMARK 500  1  DA B   2   N1  -  C2  -  N3  ANGL. DEV. =  -9.7 DEGREES
+REMARK 500  1  DA B   2   C2  -  N3  -  C4  ANGL. DEV. =  10.4 DEGREES
+REMARK 500  1  DA B   2   N3  -  C4  -  C5  ANGL. DEV. =  -5.5 DEGREES
+REMARK 500  1  DA B   2   C5  -  C6  -  N1  ANGL. DEV. =   4.3 DEGREES
+REMARK 500  1  DA B   2   C5  -  N7  -  C8  ANGL. DEV. =   3.2 DEGREES
+REMARK 500  1  DA B   2   N7  -  C8  -  N9  ANGL. DEV. =  -4.1 DEGREES
+REMARK 500  1  DA B   2   C8  -  N9  -  C4  ANGL. DEV. =   2.7 DEGREES
+REMARK 500  1  DA B   2   N3  -  C4  -  N9  ANGL. DEV. =   4.8 DEGREES
+REMARK 500  1  DA B   2   C6  -  C5  -  N7  ANGL. DEV. =   4.2 DEGREES
+REMARK 500  1  DT B   3   C5' -  C4' -  O4' ANGL. DEV. =   7.7 DEGREES
+REMARK 500  1  DT B   3   C1' -  O4' -  C4' ANGL. DEV. = -10.6 DEGREES
+REMARK 500  1  DT B   3   O4' -  C1' -  N1  ANGL. DEV. =   3.9 DEGREES
+REMARK 500  1  DT B   3   N1  -  C2  -  N3  ANGL. DEV. =   4.8 DEGREES
+REMARK 500  1  DT B   3   C2  -  N3  -  C4  ANGL. DEV. =  -6.7 DEGREES
+REMARK 500  1  DT B   3   N3  -  C4  -  C5  ANGL. DEV. =   6.0 DEGREES
+REMARK 500  1  DT B   3   N3  -  C2  -  O2  ANGL. DEV. =  -7.0 DEGREES
+REMARK 500  1  DT B   3   N3  -  C4  -  O4  ANGL. DEV. =  -4.8 DEGREES
+REMARK 500  1  DT B   4   O4' -  C1' -  C2' ANGL. DEV. =   3.0 DEGREES
+REMARK 500  1  DT B   4   N1  -  C2  -  N3  ANGL. DEV. =   5.7 DEGREES
+REMARK 500  1  DT B   4   C2  -  N3  -  C4  ANGL. DEV. =  -6.7 DEGREES
+REMARK 500  1  DT B   4   N3  -  C4  -  C5  ANGL. DEV. =   5.0 DEGREES
+REMARK 500  1  DT B   4   N3  -  C2  -  O2  ANGL. DEV. =  -5.7 DEGREES
+REMARK 500  1  DT B   4   N3  -  C4  -  O4  ANGL. DEV. =  -4.8 DEGREES
+REMARK 500  1  DT B   4   C5  -  C4  -  O4  ANGL. DEV. =  -4.5 DEGREES
+REMARK 500  1  DG B   5   C6  -  N1  -  C2  ANGL. DEV. =  -6.1 DEGREES
+REMARK 500  1  DG B   5   C2  -  N3  -  C4  ANGL. DEV. =   9.4 DEGREES
+REMARK 500  1  DG B   5   N3  -  C4  -  C5  ANGL. DEV. =  -7.2 DEGREES
+REMARK 500  1  DG B   5   C5  -  C6  -  N1  ANGL. DEV. =   8.0 DEGREES
+REMARK 500  1  DG B   5   C4  -  C5  -  N7  ANGL. DEV. =  -4.5 DEGREES
+REMARK 500  1  DG B   5   C5  -  N7  -  C8  ANGL. DEV. =   3.3 DEGREES
+REMARK 500  1  DG B   5   N7  -  C8  -  N9  ANGL. DEV. =  -3.2 DEGREES
+REMARK 500  1  DG B   5   N9  -  C4  -  C5  ANGL. DEV. =   2.5 DEGREES
+REMARK 500  1  DG B   5   N3  -  C4  -  N9  ANGL. DEV. =   4.6 DEGREES
+REMARK 500  1  DG B   5   C6  -  C5  -  N7  ANGL. DEV. =   5.6 DEGREES
+REMARK 500  1  DG B   5   C5  -  C6  -  O6  ANGL. DEV. =  -6.3 DEGREES
+REMARK 500  1  DT B   6   C2  -  N3  -  C4  ANGL. DEV. =  -7.2 DEGREES
+REMARK 500  1  DT B   6   N3  -  C4  -  C5  ANGL. DEV. =   6.9 DEGREES
+REMARK 500  1  DT B   6   N3  -  C2  -  O2  ANGL. DEV. =  -4.8 DEGREES
+REMARK 500  1  DT B   6   C6  -  C5  -  C7  ANGL. DEV. =  -6.7 DEGREES
+REMARK 500  1  DG B   7   O4' -  C1' -  C2' ANGL. DEV. =  -5.5 DEGREES
+REMARK 500  1  DG B   7   C6  -  N1  -  C2  ANGL. DEV. =  -6.1 DEGREES
+REMARK 500  1  DG B   7   C2  -  N3  -  C4  ANGL. DEV. =   9.5 DEGREES
+REMARK 500  1  DG B   7   N3  -  C4  -  C5  ANGL. DEV. =  -7.2 DEGREES
+REMARK 500  1  DG B   7   C5  -  C6  -  N1  ANGL. DEV. =   8.1 DEGREES
+REMARK 500
+REMARK 500 THIS ENTRY HAS     465 ANGLE DEVIATIONS.
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: TORSION ANGLES
+REMARK 500
+REMARK 500 TORSION ANGLES OUTSIDE THE EXPECTED RAMACHANDRAN REGIONS:
+REMARK 500 (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER;
+REMARK 500 SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500 STANDARD TABLE:
+REMARK 500 FORMAT:(10X,I3,1X,A3,1X,A1,I4,A1,4X,F7.2,3X,F7.2)
+REMARK 500
+REMARK 500 EXPECTED VALUES: GJ KLEYWEGT AND TA JONES (1996). PHI/PSI-
+REMARK 500 CHOLOGY: RAMACHANDRAN REVISITED. STRUCTURE 4, 1395 - 1400
+REMARK 500
+REMARK 500  M RES CSSEQI        PSI       PHI
+REMARK 500  1 LYS A   2      109.85    -39.18
+REMARK 500  1 SER A  28      144.36   -175.28
+REMARK 500  1 MET A  42      -31.64    -39.56
+REMARK 500  1 ASN A  50      -15.91     91.14
+REMARK 500  2 HIS A  29       69.75     66.50
+REMARK 500  2 ASN A  46       38.34    -82.06
+REMARK 500  3 TYR A  17      -50.84    -22.99
+REMARK 500  3 SER A  28      147.83   -173.11
+REMARK 500  3 HIS A  29       72.40     59.78
+REMARK 500  3 ASN A  46      -36.46    -19.68
+REMARK 500  3 TYR A  47      124.90     53.23
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: PLANAR GROUPS
+REMARK 500
+REMARK 500 PLANAR GROUPS IN THE FOLLOWING RESIDUES HAVE A TOTAL
+REMARK 500 RMS DISTANCE OF ALL ATOMS FROM THE BEST-FIT PLANE
+REMARK 500 BY MORE THAN AN EXPECTED VALUE OF 6*RMSD, WITH AN
+REMARK 500 RMSD 0.02 ANGSTROMS, OR AT LEAST ONE ATOM HAS
+REMARK 500 AN RMSD GREATER THAN THIS VALUE
+REMARK 500 (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER;
+REMARK 500 SSEQ=SEQUENCE NUMBER; I=INSERTION CODE).
+REMARK 500
+REMARK 500  M RES CSSEQI        RMS     TYPE
+REMARK 500  1  DA B   1         0.08    SIDE_CHAIN
+REMARK 500  1  DA B   2         0.07    SIDE_CHAIN
+REMARK 500  1  DT B   3         0.24    SIDE_CHAIN
+REMARK 500  1  DT B   4         0.23    SIDE_CHAIN
+REMARK 500  1  DG B   5         0.15    SIDE_CHAIN
+REMARK 500  1  DT B   6         0.11    SIDE_CHAIN
+REMARK 500  1  DG B   7         0.16    SIDE_CHAIN
+REMARK 500  1  DA B   8         0.07    SIDE_CHAIN
+REMARK 500  1  DG B  11         0.12    SIDE_CHAIN
+REMARK 500  1  DC C   1         0.11    SIDE_CHAIN
+REMARK 500  1  DG C   2         0.11    SIDE_CHAIN
+REMARK 500  1  DC C   3         0.08    SIDE_CHAIN
+REMARK 500  1  DC C   5         0.14    SIDE_CHAIN
+REMARK 500  1  DA C   6         0.17    SIDE_CHAIN
+REMARK 500  1  DC C   7         0.08    SIDE_CHAIN
+REMARK 500  1  DA C   9         0.13    SIDE_CHAIN
+REMARK 500  1  DT C  10         0.15    SIDE_CHAIN
+REMARK 500  1  DT C  11         0.20    SIDE_CHAIN
+REMARK 500  1 TYR A   7         0.14    SIDE_CHAIN
+REMARK 500  1 TYR A  12         0.07    SIDE_CHAIN
+REMARK 500  1 TYR A  17         0.08    SIDE_CHAIN
+REMARK 500  1 TYR A  47         0.09    SIDE_CHAIN
+REMARK 500  2  DA B   1         0.07    SIDE_CHAIN
+REMARK 500  2  DA B   2         0.09    SIDE_CHAIN
+REMARK 500  2  DT B   3         0.09    SIDE_CHAIN
+REMARK 500  2  DT B   4         0.10    SIDE_CHAIN
+REMARK 500  2  DG B   5         0.10    SIDE_CHAIN
+REMARK 500  2  DT B   6         0.09    SIDE_CHAIN
+REMARK 500  2  DG B   7         0.12    SIDE_CHAIN
+REMARK 500  2  DA B   8         0.07    SIDE_CHAIN
+REMARK 500  2  DG B   9         0.09    SIDE_CHAIN
+REMARK 500  2  DC B  10         0.07    SIDE_CHAIN
+REMARK 500  2  DG B  11         0.12    SIDE_CHAIN
+REMARK 500  2  DC C   1         0.13    SIDE_CHAIN
+REMARK 500  2  DC C   3         0.11    SIDE_CHAIN
+REMARK 500  2  DT C   4         0.09    SIDE_CHAIN
+REMARK 500  2  DC C   5         0.07    SIDE_CHAIN
+REMARK 500  2  DA C   6         0.08    SIDE_CHAIN
+REMARK 500  2  DC C   7         0.08    SIDE_CHAIN
+REMARK 500  2  DA C   8         0.08    SIDE_CHAIN
+REMARK 500  2  DA C   9         0.07    SIDE_CHAIN
+REMARK 500  2  DT C  10         0.09    SIDE_CHAIN
+REMARK 500  2  DT C  11         0.11    SIDE_CHAIN
+REMARK 500  2 TYR A   7         0.09    SIDE_CHAIN
+REMARK 500  2 TYR A  12         0.11    SIDE_CHAIN
+REMARK 500  2 TYR A  47         0.14    SIDE_CHAIN
+REMARK 500  3  DT B   3         0.12    SIDE_CHAIN
+REMARK 500  3  DT B   4         0.10    SIDE_CHAIN
+REMARK 500  3  DG B   5         0.12    SIDE_CHAIN
+REMARK 500  3  DT B   6         0.17    SIDE_CHAIN
+REMARK 500  3  DG B   7         0.11    SIDE_CHAIN
+REMARK 500  3  DG B   9         0.07    SIDE_CHAIN
+REMARK 500  3  DC B  10         0.10    SIDE_CHAIN
+REMARK 500  3  DG B  11         0.08    SIDE_CHAIN
+REMARK 500  3  DC C   1         0.14    SIDE_CHAIN
+REMARK 500  3  DG C   2         0.12    SIDE_CHAIN
+REMARK 500  3  DT C   4         0.10    SIDE_CHAIN
+REMARK 500  3  DC C   5         0.13    SIDE_CHAIN
+REMARK 500  3  DA C   6         0.11    SIDE_CHAIN
+REMARK 500  3  DA C   8         0.10    SIDE_CHAIN
+REMARK 500  3  DA C   9         0.10    SIDE_CHAIN
+REMARK 500  3  DT C  10         0.09    SIDE_CHAIN
+REMARK 500  3  DT C  11         0.11    SIDE_CHAIN
+REMARK 500  3 TYR A   7         0.12    SIDE_CHAIN
+REMARK 500  3 TYR A  12         0.12    SIDE_CHAIN
+REMARK 500  3 TYR A  17         0.15    SIDE_CHAIN
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 500
+REMARK 500 GEOMETRY AND STEREOCHEMISTRY
+REMARK 500 SUBTOPIC: MAIN CHAIN PLANARITY
+REMARK 500
+REMARK 500 THE FOLLOWING RESIDUES HAVE A PSEUDO PLANARITY
+REMARK 500 TORSION, C(I) - CA(I) - N(I+1) - O(I), GREATER
+REMARK 500 10.0 DEGREES. (M=MODEL NUMBER; RES=RESIDUE NAME;
+REMARK 500 C=CHAIN IDENTIFIER; SSEQ=SEQUENCE NUMBER;
+REMARK 500 I=INSERTION CODE).
+REMARK 500
+REMARK 500  M RES CSSEQI        ANGLE
+REMARK 500  1 TYR A   7        -10.45
+REMARK 500  2 GLU A  11        -10.29
+REMARK 500  2 ALA A  27         12.13
+REMARK 500  2 HIS A  29        -12.13
+REMARK 500  2 PRO A  49         12.22
+REMARK 500  3 THR A  19        -10.04
+REMARK 500
+REMARK 500 REMARK: NULL
+REMARK 620
+REMARK 620 METAL COORDINATION
+REMARK 620  (M=MODEL NUMBER; RES=RESIDUE NAME; C=CHAIN IDENTIFIER;
+REMARK 620  SSEQ=SEQUENCE NUMBER; I=INSERTION CODE):
+REMARK 620
+REMARK 620 COORDINATION ANGLES FOR:  M RES CSSEQI METAL
+REMARK 620                           1  NA C  12  NA
+REMARK 620 N RES CSSEQI ATOM
+REMARK 620 1  DT C   4   OP1
+REMARK 620 2 HOH A  53   O   100.4
+REMARK 620 3 HOH A  57   O   102.6  84.0
+REMARK 620 4 HOH C 923   O    87.5 144.8 128.0
+REMARK 620 N                    1     2     3
+REMARK 800
+REMARK 800 SITE
+REMARK 800 SITE_IDENTIFIER: AC1
+REMARK 800 EVIDENCE_CODE: SOFTWARE
+REMARK 800 SITE_DESCRIPTION: BINDING SITE FOR RESIDUE NA C 12
+REMARK 900
+REMARK 900 RELATED ENTRIES
+REMARK 900 RELATED ID: 1LCC   RELATED DB: PDB
+DBREF  1LCD A    1    51  UNP    P03023   LACI_ECOLI       1     51
+DBREF  1LCD B    1    11  PDB    1LCD     1LCD             1     11
+DBREF  1LCD C    1    11  PDB    1LCD     1LCD             1     11
+SEQRES   1 B   11   DA  DA  DT  DT  DG  DT  DG  DA  DG  DC  DG
+SEQRES   1 C   11   DC  DG  DC  DT  DC  DA  DC  DA  DA  DT  DT
+SEQRES   1 A   51  MET LYS PRO VAL THR LEU TYR ASP VAL ALA GLU TYR ALA
+SEQRES   2 A   51  GLY VAL SER TYR GLN THR VAL SER ARG VAL VAL ASN GLN
+SEQRES   3 A   51  ALA SER HIS VAL SER ALA LYS THR ARG GLU LYS VAL GLU
+SEQRES   4 A   51  ALA ALA MET ALA GLU LEU ASN TYR ILE PRO ASN ARG
+HET     NA  C  12       1
+HETNAM      NA SODIUM ION
+FORMUL   4   NA    NA 1+
+FORMUL   5  HOH   *49(H2 O)
+HELIX    1   1 THR A    5  GLY A   14  1                                  10
+HELIX    2   2 SER A   16  ASN A   25  1                                  10
+HELIX    3   3 SER A   31  LEU A   45  1                                  15
+LINK        NA    NA C  12                 OP1  DT C   4     1555   1555  2.52
+LINK        NA    NA C  12                 O   HOH A  53     1555   1555  2.10
+LINK        NA    NA C  12                 O   HOH A  57     1555   1555  2.22
+LINK        NA    NA C  12                 O   HOH C 923     1555   1555  2.13
+SITE     1 AC1  6 VAL A  24  HOH A  53  HOH A  57   DC C   3
+SITE     2 AC1  6  DT C   4  HOH C 923
+CRYST1    1.000    1.000    1.000  90.00  90.00  90.00 P 1           1
+ORIGX1      1.000000  0.000000  0.000000        0.00000
+ORIGX2      0.000000  1.000000  0.000000        0.00000
+ORIGX3      0.000000  0.000000  1.000000        0.00000
+SCALE1      1.000000  0.000000  0.000000        0.00000
+SCALE2      0.000000  1.000000  0.000000        0.00000
+SCALE3      0.000000  0.000000  1.000000        0.00000
+MODEL        1
+ATOM      1  O5'  DA B   1       8.090  29.550  48.440  1.00  0.00           O
+ATOM      2  C5'  DA B   1       8.340  29.590  47.030  1.00  0.00           C
+ATOM      3  C4'  DA B   1       9.490  30.500  46.610  1.00  0.00           C
+ATOM      4  O4'  DA B   1      10.740  30.100  47.230  1.00  0.00           O
+ATOM      5  C3'  DA B   1       9.700  30.280  45.130  1.00  0.00           C
+ATOM      6  O3'  DA B   1       9.870  31.540  44.460  1.00  0.00           O
+ATOM      7  C2'  DA B   1      10.890  29.370  45.060  1.00  0.00           C
+ATOM      8  C1'  DA B   1      11.710  30.030  46.160  1.00  0.00           C
+ATOM      9  N9   DA B   1      12.880  29.240  46.620  1.00  0.00           N
+ATOM     10  C8   DA B   1      12.750  27.970  46.990  1.00  0.00           C
+ATOM     11  N7   DA B   1      13.820  27.550  47.660  1.00  0.00           N
+ATOM     12  C5   DA B   1      14.670  28.580  47.680  1.00  0.00           C
+ATOM     13  C6   DA B   1      15.950  28.780  48.190  1.00  0.00           C
+ATOM     14  N6   DA B   1      16.660  27.750  48.610  1.00  0.00           N
+ATOM     15  N1   DA B   1      16.580  29.960  48.070  1.00  0.00           N
+ATOM     16  C2   DA B   1      16.010  30.970  47.410  1.00  0.00           C
+ATOM     17  N3   DA B   1      14.790  30.810  46.880  1.00  0.00           N
+ATOM     18  C4   DA B   1      14.090  29.650  47.000  1.00  0.00           C
+ATOM     19  H61  DA B   1      17.580  27.900  48.940  1.00  0.00           H
+ATOM     20  H62  DA B   1      16.240  26.840  48.580  1.00  0.00           H
+ATOM     21 HO5'  DA B   1       7.710  30.440  48.770  1.00  0.00           H
+ATOM     22  P    DA B   2       9.650  31.670  42.870  1.00  0.00           P
+ATOM     23  OP1  DA B   2       8.790  32.860  42.710  1.00  0.00           O
+ATOM     24  OP2  DA B   2       9.140  30.390  42.310  1.00  0.00           O
+ATOM     25  O5'  DA B   2      11.160  31.910  42.400  1.00  0.00           O
+ATOM     26  C5'  DA B   2      11.610  33.140  41.820  1.00  0.00           C
+ATOM     27  C4'  DA B   2      13.070  33.060  41.360  1.00  0.00           C
+ATOM     28  O4'  DA B   2      13.910  32.250  42.210  1.00  0.00           O
+ATOM     29  C3'  DA B   2      13.170  32.460  39.980  1.00  0.00           C
+ATOM     30  O3'  DA B   2      14.220  33.040  39.190  1.00  0.00           O
+ATOM     31  C2'  DA B   2      13.470  31.010  40.230  1.00  0.00           C
+ATOM     32  C1'  DA B   2      14.430  31.170  41.390  1.00  0.00           C
+ATOM     33  N9   DA B   2      14.490  29.960  42.220  1.00  0.00           N
+ATOM     34  C8   DA B   2      13.430  29.300  42.690  1.00  0.00           C
+ATOM     35  N7   DA B   2      13.830  28.360  43.550  1.00  0.00           N
+ATOM     36  C5   DA B   2      15.150  28.460  43.650  1.00  0.00           C
+ATOM     37  C6   DA B   2      16.170  27.780  44.320  1.00  0.00           C
+ATOM     38  N6   DA B   2      15.860  26.880  45.230  1.00  0.00           N
+ATOM     39  N1   DA B   2      17.470  28.040  44.090  1.00  0.00           N
+ATOM     40  C2   DA B   2      17.840  28.970  43.210  1.00  0.00           C
+ATOM     41  N3   DA B   2      16.900  29.680  42.560  1.00  0.00           N
+ATOM     42  C4   DA B   2      15.580  29.450  42.770  1.00  0.00           C
+ATOM     43  H61  DA B   2      16.600  26.370  45.680  1.00  0.00           H
+ATOM     44  H62  DA B   2      14.910  26.770  45.540  1.00  0.00           H
+ATOM     45  P    DT B   3      13.840  33.560  37.730  1.00  0.00           P
+ATOM     46  OP1  DT B   3      13.260  34.900  37.950  1.00  0.00           O
+ATOM     47  OP2  DT B   3      13.020  32.550  37.010  1.00  0.00           O
+ATOM     48  O5'  DT B   3      15.250  33.720  36.970  1.00  0.00           O
+ATOM     49  C5'  DT B   3      15.730  32.690  36.110  1.00  0.00           C
+ATOM     50  C4'  DT B   3      16.570  31.730  36.930  1.00  0.00           C
+ATOM     51  O4'  DT B   3      15.880  30.830  37.810  1.00  0.00           O
+ATOM     52  C3'  DT B   3      17.360  30.810  36.060  1.00  0.00           C
+ATOM     53  O3'  DT B   3      18.400  31.530  35.350  1.00  0.00           O
+ATOM     54  C2'  DT B   3      17.950  29.920  37.130  1.00  0.00           C
+ATOM     55  C1'  DT B   3      17.000  30.070  38.300  1.00  0.00           C
+ATOM     56  N1   DT B   3      16.580  28.780  38.910  1.00  0.00           N
+ATOM     57  C2   DT B   3      17.500  28.110  39.760  1.00  0.00           C
+ATOM     58  O2   DT B   3      18.680  28.010  39.470  1.00  0.00           O
+ATOM     59  N3   DT B   3      17.030  27.170  40.670  1.00  0.00           N
+ATOM     60  C4   DT B   3      15.670  26.860  40.730  1.00  0.00           C
+ATOM     61  O4   DT B   3      15.300  26.340  41.780  1.00  0.00           O
+ATOM     62  C5   DT B   3      14.760  27.410  39.820  1.00  0.00           C
+ATOM     63  C7   DT B   3      13.320  26.900  39.770  1.00  0.00           C
+ATOM     64  C6   DT B   3      15.220  28.370  38.920  1.00  0.00           C
+ATOM     65  H3   DT B   3      17.670  26.650  41.230  1.00  0.00           H
+ATOM     66  P    DT B   4      19.180  30.910  34.100  1.00  0.00           P
+ATOM     67  OP1  DT B   4      19.750  32.080  33.400  1.00  0.00           O
+ATOM     68  OP2  DT B   4      18.370  29.930  33.350  1.00  0.00           O
+ATOM     69  O5'  DT B   4      20.360  30.080  34.770  1.00  0.00           O
+ATOM     70  C5'  DT B   4      21.250  30.730  35.690  1.00  0.00           C
+ATOM     71  C4'  DT B   4      22.180  29.780  36.430  1.00  0.00           C
+ATOM     72  O4'  DT B   4      21.470  28.850  37.270  1.00  0.00           O
+ATOM     73  C3'  DT B   4      23.050  28.950  35.520  1.00  0.00           C
+ATOM     74  O3'  DT B   4      24.330  28.890  36.150  1.00  0.00           O
+ATOM     75  C2'  DT B   4      22.440  27.580  35.450  1.00  0.00           C
+ATOM     76  C1'  DT B   4      21.850  27.520  36.840  1.00  0.00           C
+ATOM     77  N1   DT B   4      20.620  26.700  36.820  1.00  0.00           N
+ATOM     78  C2   DT B   4      20.420  25.770  37.840  1.00  0.00           C
+ATOM     79  O2   DT B   4      21.330  25.330  38.530  1.00  0.00           O
+ATOM     80  N3   DT B   4      19.150  25.280  38.100  1.00  0.00           N
+ATOM     81  C4   DT B   4      18.050  25.720  37.350  1.00  0.00           C
+ATOM     82  O4   DT B   4      16.970  25.710  37.950  1.00  0.00           O
+ATOM     83  C5   DT B   4      18.240  26.560  36.250  1.00  0.00           C
+ATOM     84  C7   DT B   4      17.100  26.890  35.290  1.00  0.00           C
+ATOM     85  C6   DT B   4      19.530  27.040  35.990  1.00  0.00           C
+ATOM     86  H3   DT B   4      19.030  24.650  38.860  1.00  0.00           H
+ATOM     87  P    DG B   5      25.670  28.660  35.310  1.00  0.00           P
+ATOM     88  OP1  DG B   5      26.720  29.390  36.060  1.00  0.00           O
+ATOM     89  OP2  DG B   5      25.490  29.100  33.910  1.00  0.00           O
+ATOM     90  O5'  DG B   5      25.890  27.070  35.350  1.00  0.00           O
+ATOM     91  C5'  DG B   5      26.310  26.520  36.610  1.00  0.00           C
+ATOM     92  C4'  DG B   5      25.780  25.140  36.960  1.00  0.00           C
+ATOM     93  O4'  DG B   5      24.370  25.060  36.690  1.00  0.00           O
+ATOM     94  C3'  DG B   5      26.460  24.060  36.140  1.00  0.00           C
+ATOM     95  O3'  DG B   5      27.350  23.330  37.020  1.00  0.00           O
+ATOM     96  C2'  DG B   5      25.330  23.250  35.550  1.00  0.00           C
+ATOM     97  C1'  DG B   5      24.110  23.690  36.370  1.00  0.00           C
+ATOM     98  N9   DG B   5      22.830  23.580  35.630  1.00  0.00           N
+ATOM     99  C8   DG B   5      22.500  24.250  34.540  1.00  0.00           C
+ATOM    100  N7   DG B   5      21.170  24.190  34.350  1.00  0.00           N
+ATOM    101  C5   DG B   5      20.650  23.470  35.350  1.00  0.00           C
+ATOM    102  C6   DG B   5      19.370  23.000  35.700  1.00  0.00           C
+ATOM    103  O6   DG B   5      18.340  23.450  35.190  1.00  0.00           O
+ATOM    104  N1   DG B   5      19.230  22.120  36.780  1.00  0.00           N
+ATOM    105  C2   DG B   5      20.360  21.740  37.490  1.00  0.00           C
+ATOM    106  N2   DG B   5      20.290  20.690  38.240  1.00  0.00           N
+ATOM    107  N3   DG B   5      21.560  22.240  37.190  1.00  0.00           N
+ATOM    108  C4   DG B   5      21.730  23.070  36.150  1.00  0.00           C
+ATOM    109  H1   DG B   5      18.350  21.740  37.040  1.00  0.00           H
+ATOM    110  H21  DG B   5      19.430  20.240  38.490  1.00  0.00           H
+ATOM    111  H22  DG B   5      21.150  20.370  38.710  1.00  0.00           H
+ATOM    112  P    DT B   6      28.440  22.240  36.580  1.00  0.00           P
+ATOM    113  OP1  DT B   6      29.640  22.540  37.390  1.00  0.00           O
+ATOM    114  OP2  DT B   6      28.600  22.170  35.110  1.00  0.00           O
+ATOM    115  O5'  DT B   6      27.770  20.840  36.990  1.00  0.00           O
+ATOM    116  C5'  DT B   6      27.150  20.610  38.270  1.00  0.00           C
+ATOM    117  C4'  DT B   6      26.100  19.500  38.220  1.00  0.00           C
+ATOM    118  O4'  DT B   6      24.820  19.860  37.670  1.00  0.00           O
+ATOM    119  C3'  DT B   6      26.630  18.340  37.420  1.00  0.00           C
+ATOM    120  O3'  DT B   6      26.850  17.200  38.260  1.00  0.00           O
+ATOM    121  C2'  DT B   6      25.580  18.060  36.390  1.00  0.00           C
+ATOM    122  C1'  DT B   6      24.330  18.670  37.030  1.00  0.00           C
+ATOM    123  N1   DT B   6      23.310  19.090  36.040  1.00  0.00           N
+ATOM    124  C2   DT B   6      21.970  18.630  36.090  1.00  0.00           C
+ATOM    125  O2   DT B   6      21.630  17.600  36.660  1.00  0.00           O
+ATOM    126  N3   DT B   6      21.030  19.250  35.260  1.00  0.00           N
+ATOM    127  C4   DT B   6      21.440  20.230  34.350  1.00  0.00           C
+ATOM    128  O4   DT B   6      20.560  20.760  33.680  1.00  0.00           O
+ATOM    129  C5   DT B   6      22.780  20.610  34.220  1.00  0.00           C
+ATOM    130  C7   DT B   6      23.350  21.050  32.880  1.00  0.00           C
+ATOM    131  C6   DT B   6      23.680  20.080  35.120  1.00  0.00           C
+ATOM    132  H3   DT B   6      20.050  19.070  35.400  1.00  0.00           H
+ATOM    133  P    DG B   7      27.660  15.900  37.780  1.00  0.00           P
+ATOM    134  OP1  DG B   7      28.440  15.370  38.930  1.00  0.00           O
+ATOM    135  OP2  DG B   7      28.390  16.180  36.510  1.00  0.00           O
+ATOM    136  O5'  DG B   7      26.470  14.880  37.420  1.00  0.00           O
+ATOM    137  C5'  DG B   7      25.230  14.820  38.170  1.00  0.00           C
+ATOM    138  C4'  DG B   7      23.990  14.520  37.310  1.00  0.00           C
+ATOM    139  O4'  DG B   7      23.700  15.530  36.320  1.00  0.00           O
+ATOM    140  C3'  DG B   7      24.080  13.240  36.500  1.00  0.00           C
+ATOM    141  O3'  DG B   7      23.060  12.310  36.920  1.00  0.00           O
+ATOM    142  C2'  DG B   7      23.820  13.640  35.070  1.00  0.00           C
+ATOM    143  C1'  DG B   7      22.950  14.870  35.300  1.00  0.00           C
+ATOM    144  N9   DG B   7      22.780  15.770  34.140  1.00  0.00           N
+ATOM    145  C8   DG B   7      23.740  16.420  33.500  1.00  0.00           C
+ATOM    146  N7   DG B   7      23.210  17.320  32.660  1.00  0.00           N
+ATOM    147  C5   DG B   7      21.880  17.240  32.780  1.00  0.00           C
+ATOM    148  C6   DG B   7      20.780  17.890  32.200  1.00  0.00           C
+ATOM    149  O6   DG B   7      20.910  18.890  31.490  1.00  0.00           O
+ATOM    150  N1   DG B   7      19.480  17.490  32.540  1.00  0.00           N
+ATOM    151  C2   DG B   7      19.310  16.470  33.460  1.00  0.00           C
+ATOM    152  N2   DG B   7      18.240  15.750  33.330  1.00  0.00           N
+ATOM    153  N3   DG B   7      20.360  15.890  34.040  1.00  0.00           N
+ATOM    154  C4   DG B   7      21.620  16.240  33.720  1.00  0.00           C
+ATOM    155  H1   DG B   7      18.690  17.770  32.010  1.00  0.00           H
+ATOM    156  H21  DG B   7      17.410  16.060  32.860  1.00  0.00           H
+ATOM    157  H22  DG B   7      18.300  14.810  33.650  1.00  0.00           H
+ATOM    158  P    DA B   8      23.280  10.730  36.760  1.00  0.00           P
+ATOM    159  OP1  DA B   8      22.960  10.160  38.090  1.00  0.00           O
+ATOM    160  OP2  DA B   8      24.620  10.480  36.190  1.00  0.00           O
+ATOM    161  O5'  DA B   8      22.240  10.280  35.630  1.00  0.00           O
+ATOM    162  C5'  DA B   8      20.800  10.330  35.740  1.00  0.00           C
+ATOM    163  C4'  DA B   8      20.150  10.660  34.400  1.00  0.00           C
+ATOM    164  O4'  DA B   8      20.330  12.020  33.900  1.00  0.00           O
+ATOM    165  C3'  DA B   8      20.620   9.740  33.280  1.00  0.00           C
+ATOM    166  O3'  DA B   8      19.570   9.230  32.450  1.00  0.00           O
+ATOM    167  C2'  DA B   8      21.400  10.660  32.390  1.00  0.00           C
+ATOM    168  C1'  DA B   8      20.520  11.890  32.470  1.00  0.00           C
+ATOM    169  N9   DA B   8      21.090  13.090  31.800  1.00  0.00           N
+ATOM    170  C8   DA B   8      22.390  13.340  31.660  1.00  0.00           C
+ATOM    171  N7   DA B   8      22.590  14.370  30.850  1.00  0.00           N
+ATOM    172  C5   DA B   8      21.400  14.790  30.470  1.00  0.00           C
+ATOM    173  C6   DA B   8      20.930  15.870  29.720  1.00  0.00           C
+ATOM    174  N6   DA B   8      21.770  16.580  28.990  1.00  0.00           N
+ATOM    175  N1   DA B   8      19.610  16.080  29.570  1.00  0.00           N
+ATOM    176  C2   DA B   8      18.710  15.260  30.110  1.00  0.00           C
+ATOM    177  N3   DA B   8      19.120  14.230  30.850  1.00  0.00           N
+ATOM    178  C4   DA B   8      20.430  13.980  31.060  1.00  0.00           C
+ATOM    179  H61  DA B   8      21.530  17.480  28.620  1.00  0.00           H
+ATOM    180  H62  DA B   8      22.640  16.150  28.730  1.00  0.00           H
+ATOM    181  P    DG B   9      18.370   8.320  33.000  1.00  0.00           P
+ATOM    182  OP1  DG B   9      17.340   9.180  33.650  1.00  0.00           O
+ATOM    183  OP2  DG B   9      18.890   7.140  33.720  1.00  0.00           O
+ATOM    184  O5'  DG B   9      17.780   7.940  31.560  1.00  0.00           O
+ATOM    185  C5'  DG B   9      16.960   8.920  30.890  1.00  0.00           C
+ATOM    186  C4'  DG B   9      17.520   9.480  29.610  1.00  0.00           C
+ATOM    187  O4'  DG B   9      18.570  10.450  29.690  1.00  0.00           O
+ATOM    188  C3'  DG B   9      18.030   8.360  28.740  1.00  0.00           C
+ATOM    189  O3'  DG B   9      17.220   8.330  27.560  1.00  0.00           O
+ATOM    190  C2'  DG B   9      19.410   8.790  28.390  1.00  0.00           C
+ATOM    191  C1'  DG B   9      19.150  10.290  28.390  1.00  0.00           C
+ATOM    192  N9   DG B   9      20.330  11.140  28.190  1.00  0.00           N
+ATOM    193  C8   DG B   9      21.500  10.910  28.760  1.00  0.00           C
+ATOM    194  N7   DG B   9      22.340  11.900  28.480  1.00  0.00           N
+ATOM    195  C5   DG B   9      21.680  12.770  27.730  1.00  0.00           C
+ATOM    196  C6   DG B   9      22.030  13.970  27.090  1.00  0.00           C
+ATOM    197  O6   DG B   9      23.130  14.490  27.270  1.00  0.00           O
+ATOM    198  N1   DG B   9      21.100  14.630  26.280  1.00  0.00           N
+ATOM    199  C2   DG B   9      19.820  14.100  26.120  1.00  0.00           C
+ATOM    200  N2   DG B   9      18.930  14.710  25.370  1.00  0.00           N
+ATOM    201  N3   DG B   9      19.490  12.960  26.760  1.00  0.00           N
+ATOM    202  C4   DG B   9      20.380  12.300  27.530  1.00  0.00           C
+ATOM    203  H1   DG B   9      21.350  15.470  25.810  1.00  0.00           H
+ATOM    204  H21  DG B   9      19.140  15.560  24.870  1.00  0.00           H
+ATOM    205  H22  DG B   9      17.990  14.360  25.280  1.00  0.00           H
+ATOM    206  P    DC B  10      17.220   7.100  26.550  1.00  0.00           P
+ATOM    207  OP1  DC B  10      16.070   6.260  26.960  1.00  0.00           O
+ATOM    208  OP2  DC B  10      18.580   6.520  26.410  1.00  0.00           O
+ATOM    209  O5'  DC B  10      16.910   7.900  25.210  1.00  0.00           O
+ATOM    210  C5'  DC B  10      15.770   8.770  25.150  1.00  0.00           C
+ATOM    211  C4'  DC B  10      15.960   9.890  24.130  1.00  0.00           C
+ATOM    212  O4'  DC B  10      17.160  10.640  24.440  1.00  0.00           O
+ATOM    213  C3'  DC B  10      16.180   9.370  22.720  1.00  0.00           C
+ATOM    214  O3'  DC B  10      15.420  10.200  21.780  1.00  0.00           O
+ATOM    215  C2'  DC B  10      17.680   9.360  22.560  1.00  0.00           C
+ATOM    216  C1'  DC B  10      17.940  10.680  23.230  1.00  0.00           C
+ATOM    217  N1   DC B  10      19.380  10.880  23.540  1.00  0.00           N
+ATOM    218  C2   DC B  10      20.060  12.050  23.150  1.00  0.00           C
+ATOM    219  O2   DC B  10      19.590  12.820  22.310  1.00  0.00           O
+ATOM    220  N3   DC B  10      21.310  12.260  23.590  1.00  0.00           N
+ATOM    221  C4   DC B  10      21.930  11.370  24.390  1.00  0.00           C
+ATOM    222  N4   DC B  10      23.190  11.660  24.600  1.00  0.00           N
+ATOM    223  C5   DC B  10      21.320  10.170  24.790  1.00  0.00           C
+ATOM    224  C6   DC B  10      20.020   9.930  24.320  1.00  0.00           C
+ATOM    225  H41  DC B  10      23.560  12.450  24.110  1.00  0.00           H
+ATOM    226  H42  DC B  10      23.760  11.110  25.220  1.00  0.00           H
+ATOM    227  P    DG B  11      15.250   9.800  20.220  1.00  0.00           P
+ATOM    228  OP1  DG B  11      13.950  10.240  19.670  1.00  0.00           O
+ATOM    229  OP2  DG B  11      15.530   8.360  20.050  1.00  0.00           O
+ATOM    230  O5'  DG B  11      16.490  10.570  19.540  1.00  0.00           O
+ATOM    231  C5'  DG B  11      16.380  11.850  18.890  1.00  0.00           C
+ATOM    232  C4'  DG B  11      17.750  12.330  18.410  1.00  0.00           C
+ATOM    233  O4'  DG B  11      18.730  12.430  19.470  1.00  0.00           O
+ATOM    234  C3'  DG B  11      18.350  11.430  17.340  1.00  0.00           C
+ATOM    235  O3'  DG B  11      18.000  11.770  15.980  1.00  0.00           O
+ATOM    236  C2'  DG B  11      19.840  11.660  17.530  1.00  0.00           C
+ATOM    237  C1'  DG B  11      19.940  12.600  18.720  1.00  0.00           C
+ATOM    238  N9   DG B  11      21.120  12.260  19.550  1.00  0.00           N
+ATOM    239  C8   DG B  11      21.240  11.120  20.230  1.00  0.00           C
+ATOM    240  N7   DG B  11      22.430  11.070  20.840  1.00  0.00           N
+ATOM    241  C5   DG B  11      23.080  12.190  20.510  1.00  0.00           C
+ATOM    242  C6   DG B  11      24.360  12.670  20.770  1.00  0.00           C
+ATOM    243  O6   DG B  11      24.960  12.380  21.810  1.00  0.00           O
+ATOM    244  N1   DG B  11      24.790  13.820  20.110  1.00  0.00           N
+ATOM    245  C2   DG B  11      23.950  14.500  19.260  1.00  0.00           C
+ATOM    246  N2   DG B  11      24.340  15.690  18.900  1.00  0.00           N
+ATOM    247  N3   DG B  11      22.690  14.080  19.080  1.00  0.00           N
+ATOM    248  C4   DG B  11      22.250  12.940  19.670  1.00  0.00           C
+ATOM    249 HO3'  DG B  11      17.020  11.980  15.720  1.00  0.00           H
+ATOM    250  H1   DG B  11      25.650  14.240  20.390  1.00  0.00           H
+ATOM    251  H21  DG B  11      25.300  15.940  18.950  1.00  0.00           H
+ATOM    252  H22  DG B  11      23.660  16.430  18.850  1.00  0.00           H
+TER     253       DG B  11
+ATOM    254  O5'  DC C   1      33.280  17.670  18.830  1.00  0.00           O
+ATOM    255  C5'  DC C   1      32.290  18.610  19.290  1.00  0.00           C
+ATOM    256  C4'  DC C   1      30.880  18.030  19.290  1.00  0.00           C
+ATOM    257  O4'  DC C   1      30.650  16.820  20.030  1.00  0.00           O
+ATOM    258  C3'  DC C   1      29.780  18.990  19.780  1.00  0.00           C
+ATOM    259  O3'  DC C   1      29.280  19.700  18.650  1.00  0.00           O
+ATOM    260  C2'  DC C   1      28.700  18.090  20.320  1.00  0.00           C
+ATOM    261  C1'  DC C   1      29.210  16.720  20.000  1.00  0.00           C
+ATOM    262  N1   DC C   1      28.720  15.740  20.990  1.00  0.00           N
+ATOM    263  C2   DC C   1      27.870  14.690  20.590  1.00  0.00           C
+ATOM    264  O2   DC C   1      27.350  14.690  19.480  1.00  0.00           O
+ATOM    265  N3   DC C   1      27.350  13.880  21.540  1.00  0.00           N
+ATOM    266  C4   DC C   1      27.590  14.080  22.850  1.00  0.00           C
+ATOM    267  N4   DC C   1      27.250  13.050  23.590  1.00  0.00           N
+ATOM    268  C5   DC C   1      28.350  15.160  23.320  1.00  0.00           C
+ATOM    269  C6   DC C   1      28.960  15.960  22.360  1.00  0.00           C
+ATOM    270  H41  DC C   1      26.800  12.320  23.090  1.00  0.00           H
+ATOM    271  H42  DC C   1      27.400  13.020  24.590  1.00  0.00           H
+ATOM    272 HO5'  DC C   1      34.030  17.610  19.560  1.00  0.00           H
+ATOM    273  P    DG C   2      28.590  21.140  18.770  1.00  0.00           P
+ATOM    274  OP1  DG C   2      28.990  21.790  17.500  1.00  0.00           O
+ATOM    275  OP2  DG C   2      28.960  21.760  20.070  1.00  0.00           O
+ATOM    276  O5'  DG C   2      27.050  20.730  18.870  1.00  0.00           O
+ATOM    277  C5'  DG C   2      26.230  20.390  17.740  1.00  0.00           C
+ATOM    278  C4'  DG C   2      24.850  19.980  18.240  1.00  0.00           C
+ATOM    279  O4'  DG C   2      24.820  18.730  18.970  1.00  0.00           O
+ATOM    280  C3'  DG C   2      24.300  21.080  19.140  1.00  0.00           C
+ATOM    281  O3'  DG C   2      23.290  21.870  18.460  1.00  0.00           O
+ATOM    282  C2'  DG C   2      23.880  20.350  20.400  1.00  0.00           C
+ATOM    283  C1'  DG C   2      23.780  18.900  19.940  1.00  0.00           C
+ATOM    284  N9   DG C   2      24.020  17.920  21.030  1.00  0.00           N
+ATOM    285  C8   DG C   2      25.110  17.840  21.800  1.00  0.00           C
+ATOM    286  N7   DG C   2      24.990  16.830  22.660  1.00  0.00           N
+ATOM    287  C5   DG C   2      23.800  16.240  22.450  1.00  0.00           C
+ATOM    288  C6   DG C   2      23.120  15.140  22.980  1.00  0.00           C
+ATOM    289  O6   DG C   2      23.400  14.630  24.060  1.00  0.00           O
+ATOM    290  N1   DG C   2      21.900  14.760  22.410  1.00  0.00           N
+ATOM    291  C2   DG C   2      21.360  15.480  21.360  1.00  0.00           C
+ATOM    292  N2   DG C   2      20.090  15.460  21.080  1.00  0.00           N
+ATOM    293  N3   DG C   2      22.010  16.550  20.890  1.00  0.00           N
+ATOM    294  C4   DG C   2      23.200  16.940  21.400  1.00  0.00           C
+ATOM    295  H1   DG C   2      21.380  14.010  22.830  1.00  0.00           H
+ATOM    296  H21  DG C   2      19.490  14.680  21.210  1.00  0.00           H
+ATOM    297  H22  DG C   2      19.730  16.340  20.770  1.00  0.00           H
+ATOM    298  P    DC C   3      22.460  23.100  19.110  1.00  0.00           P
+ATOM    299  OP1  DC C   3      21.830  23.940  18.060  1.00  0.00           O
+ATOM    300  OP2  DC C   3      23.220  23.800  20.160  1.00  0.00           O
+ATOM    301  O5'  DC C   3      21.290  22.350  19.920  1.00  0.00           O
+ATOM    302  C5'  DC C   3      20.300  21.520  19.290  1.00  0.00           C
+ATOM    303  C4'  DC C   3      19.570  20.630  20.280  1.00  0.00           C
+ATOM    304  O4'  DC C   3      20.380  19.640  20.960  1.00  0.00           O
+ATOM    305  C3'  DC C   3      18.850  21.440  21.320  1.00  0.00           C
+ATOM    306  O3'  DC C   3      17.440  21.410  21.060  1.00  0.00           O
+ATOM    307  C2'  DC C   3      19.310  20.840  22.640  1.00  0.00           C
+ATOM    308  C1'  DC C   3      19.730  19.430  22.230  1.00  0.00           C
+ATOM    309  N1   DC C   3      20.670  18.760  23.150  1.00  0.00           N
+ATOM    310  C2   DC C   3      20.300  17.590  23.840  1.00  0.00           C
+ATOM    311  O2   DC C   3      19.140  17.180  23.850  1.00  0.00           O
+ATOM    312  N3   DC C   3      21.140  17.090  24.780  1.00  0.00           N
+ATOM    313  C4   DC C   3      22.270  17.730  25.110  1.00  0.00           C
+ATOM    314  N4   DC C   3      23.070  17.160  25.980  1.00  0.00           N
+ATOM    315  C5   DC C   3      22.690  18.890  24.460  1.00  0.00           C
+ATOM    316  C6   DC C   3      21.890  19.400  23.450  1.00  0.00           C
+ATOM    317  H41  DC C   3      23.180  16.170  25.990  1.00  0.00           H
+ATOM    318  H42  DC C   3      23.830  17.710  26.330  1.00  0.00           H
+ATOM    319  P    DT C   4      16.390  22.240  21.940  1.00  0.00           P
+ATOM    320  OP1  DT C   4      15.330  22.670  21.000  1.00  0.00           O
+ATOM    321  OP2  DT C   4      17.130  23.300  22.650  1.00  0.00           O
+ATOM    322  O5'  DT C   4      15.920  21.110  22.960  1.00  0.00           O
+ATOM    323  C5'  DT C   4      15.210  19.950  22.490  1.00  0.00           C
+ATOM    324  C4'  DT C   4      14.910  18.940  23.590  1.00  0.00           C
+ATOM    325  O4'  DT C   4      16.100  18.580  24.310  1.00  0.00           O
+ATOM    326  C3'  DT C   4      13.940  19.420  24.650  1.00  0.00           C
+ATOM    327  O3'  DT C   4      12.880  18.440  24.650  1.00  0.00           O
+ATOM    328  C2'  DT C   4      14.720  19.600  25.910  1.00  0.00           C
+ATOM    329  C1'  DT C   4      15.700  18.460  25.690  1.00  0.00           C
+ATOM    330  N1   DT C   4      16.980  18.620  26.400  1.00  0.00           N
+ATOM    331  C2   DT C   4      17.640  17.540  27.010  1.00  0.00           C
+ATOM    332  O2   DT C   4      17.080  16.470  27.260  1.00  0.00           O
+ATOM    333  N3   DT C   4      18.940  17.730  27.480  1.00  0.00           N
+ATOM    334  C4   DT C   4      19.580  18.960  27.330  1.00  0.00           C
+ATOM    335  O4   DT C   4      20.640  19.100  27.950  1.00  0.00           O
+ATOM    336  C5   DT C   4      18.940  20.040  26.720  1.00  0.00           C
+ATOM    337  C7   DT C   4      19.610  21.390  26.510  1.00  0.00           C
+ATOM    338  C6   DT C   4      17.620  19.860  26.280  1.00  0.00           C
+ATOM    339  H3   DT C   4      19.310  17.060  28.120  1.00  0.00           H
+ATOM    340  P    DC C   5      11.390  18.780  25.110  1.00  0.00           P
+ATOM    341  OP1  DC C   5      10.500  17.910  24.310  1.00  0.00           O
+ATOM    342  OP2  DC C   5      11.150  20.240  25.020  1.00  0.00           O
+ATOM    343  O5'  DC C   5      11.450  18.330  26.650  1.00  0.00           O
+ATOM    344  C5'  DC C   5      11.530  16.940  27.010  1.00  0.00           C
+ATOM    345  C4'  DC C   5      12.200  16.730  28.370  1.00  0.00           C
+ATOM    346  O4'  DC C   5      13.580  17.100  28.380  1.00  0.00           O
+ATOM    347  C3'  DC C   5      11.540  17.480  29.520  1.00  0.00           C
+ATOM    348  O3'  DC C   5      10.530  16.640  30.140  1.00  0.00           O
+ATOM    349  C2'  DC C   5      12.680  17.700  30.470  1.00  0.00           C
+ATOM    350  C1'  DC C   5      13.900  17.110  29.780  1.00  0.00           C
+ATOM    351  N1   DC C   5      15.070  17.970  29.990  1.00  0.00           N
+ATOM    352  C2   DC C   5      16.200  17.470  30.670  1.00  0.00           C
+ATOM    353  O2   DC C   5      16.090  16.630  31.550  1.00  0.00           O
+ATOM    354  N3   DC C   5      17.310  18.220  30.700  1.00  0.00           N
+ATOM    355  C4   DC C   5      17.320  19.450  30.160  1.00  0.00           C
+ATOM    356  N4   DC C   5      18.490  20.040  30.250  1.00  0.00           N
+ATOM    357  C5   DC C   5      16.220  20.050  29.560  1.00  0.00           C
+ATOM    358  C6   DC C   5      15.070  19.270  29.430  1.00  0.00           C
+ATOM    359  H41  DC C   5      19.260  19.610  30.730  1.00  0.00           H
+ATOM    360  H42  DC C   5      18.640  20.930  29.790  1.00  0.00           H
+ATOM    361  P    DA C   6       9.400  17.170  31.160  1.00  0.00           P
+ATOM    362  OP1  DA C   6       8.370  16.100  31.200  1.00  0.00           O
+ATOM    363  OP2  DA C   6       8.980  18.530  30.770  1.00  0.00           O
+ATOM    364  O5'  DA C   6      10.120  17.210  32.590  1.00  0.00           O
+ATOM    365  C5'  DA C   6      10.380  15.930  33.190  1.00  0.00           C
+ATOM    366  C4'  DA C   6      11.470  15.890  34.240  1.00  0.00           C
+ATOM    367  O4'  DA C   6      12.650  16.580  33.790  1.00  0.00           O
+ATOM    368  C3'  DA C   6      11.030  16.660  35.470  1.00  0.00           C
+ATOM    369  O3'  DA C   6      11.220  15.800  36.620  1.00  0.00           O
+ATOM    370  C2'  DA C   6      11.780  17.950  35.430  1.00  0.00           C
+ATOM    371  C1'  DA C   6      13.080  17.340  34.940  1.00  0.00           C
+ATOM    372  N9   DA C   6      14.050  18.370  34.560  1.00  0.00           N
+ATOM    373  C8   DA C   6      13.800  19.240  33.600  1.00  0.00           C
+ATOM    374  N7   DA C   6      14.930  19.770  33.120  1.00  0.00           N
+ATOM    375  C5   DA C   6      15.900  19.300  33.900  1.00  0.00           C
+ATOM    376  C6   DA C   6      17.230  19.670  34.120  1.00  0.00           C
+ATOM    377  N6   DA C   6      17.980  20.160  33.160  1.00  0.00           N
+ATOM    378  N1   DA C   6      17.910  19.260  35.200  1.00  0.00           N
+ATOM    379  C2   DA C   6      17.360  18.410  36.070  1.00  0.00           C
+ATOM    380  N3   DA C   6      16.100  18.000  35.880  1.00  0.00           N
+ATOM    381  C4   DA C   6      15.350  18.420  34.830  1.00  0.00           C
+ATOM    382  H61  DA C   6      18.920  20.410  33.450  1.00  0.00           H
+ATOM    383  H62  DA C   6      17.780  20.030  32.190  1.00  0.00           H
+ATOM    384  P    DC C   7      10.910  16.270  38.120  1.00  0.00           P
+ATOM    385  OP1  DC C   7      10.370  15.100  38.830  1.00  0.00           O
+ATOM    386  OP2  DC C   7      10.110  17.530  38.140  1.00  0.00           O
+ATOM    387  O5'  DC C   7      12.390  16.590  38.640  1.00  0.00           O
+ATOM    388  C5'  DC C   7      13.430  15.590  38.730  1.00  0.00           C
+ATOM    389  C4'  DC C   7      14.710  16.090  39.400  1.00  0.00           C
+ATOM    390  O4'  DC C   7      15.320  17.220  38.740  1.00  0.00           O
+ATOM    391  C3'  DC C   7      14.330  16.540  40.780  1.00  0.00           C
+ATOM    392  O3'  DC C   7      15.200  15.970  41.760  1.00  0.00           O
+ATOM    393  C2'  DC C   7      14.230  18.050  40.660  1.00  0.00           C
+ATOM    394  C1'  DC C   7      15.380  18.310  39.700  1.00  0.00           C
+ATOM    395  N1   DC C   7      15.290  19.540  38.870  1.00  0.00           N
+ATOM    396  C2   DC C   7      16.460  20.280  38.590  1.00  0.00           C
+ATOM    397  O2   DC C   7      17.520  20.080  39.180  1.00  0.00           O
+ATOM    398  N3   DC C   7      16.440  21.240  37.640  1.00  0.00           N
+ATOM    399  C4   DC C   7      15.300  21.530  36.980  1.00  0.00           C
+ATOM    400  N4   DC C   7      15.430  22.440  36.040  1.00  0.00           N
+ATOM    401  C5   DC C   7      14.090  20.860  37.210  1.00  0.00           C
+ATOM    402  C6   DC C   7      14.100  19.850  38.180  1.00  0.00           C
+ATOM    403  H41  DC C   7      16.320  22.760  35.740  1.00  0.00           H
+ATOM    404  H42  DC C   7      14.610  22.770  35.520  1.00  0.00           H
+ATOM    405  P    DA C   8      14.710  15.840  43.280  1.00  0.00           P
+ATOM    406  OP1  DA C   8      14.800  14.410  43.660  1.00  0.00           O
+ATOM    407  OP2  DA C   8      13.420  16.550  43.430  1.00  0.00           O
+ATOM    408  O5'  DA C   8      15.830  16.650  44.070  1.00  0.00           O
+ATOM    409  C5'  DA C   8      17.200  16.260  43.870  1.00  0.00           C
+ATOM    410  C4'  DA C   8      18.150  17.450  43.910  1.00  0.00           C
+ATOM    411  O4'  DA C   8      17.720  18.440  42.970  1.00  0.00           O
+ATOM    412  C3'  DA C   8      18.090  18.090  45.280  1.00  0.00           C
+ATOM    413  O3'  DA C   8      19.410  18.330  45.850  1.00  0.00           O
+ATOM    414  C2'  DA C   8      17.350  19.380  45.030  1.00  0.00           C
+ATOM    415  C1'  DA C   8      17.960  19.680  43.670  1.00  0.00           C
+ATOM    416  N9   DA C   8      17.250  20.680  42.850  1.00  0.00           N
+ATOM    417  C8   DA C   8      15.930  20.730  42.800  1.00  0.00           C
+ATOM    418  N7   DA C   8      15.510  21.640  41.920  1.00  0.00           N
+ATOM    419  C5   DA C   8      16.620  22.190  41.410  1.00  0.00           C
+ATOM    420  C6   DA C   8      16.870  23.060  40.340  1.00  0.00           C
+ATOM    421  N6   DA C   8      15.910  23.710  39.710  1.00  0.00           N
+ATOM    422  N1   DA C   8      18.120  23.270  39.890  1.00  0.00           N
+ATOM    423  C2   DA C   8      19.160  22.650  40.460  1.00  0.00           C
+ATOM    424  N3   DA C   8      18.970  21.830  41.490  1.00  0.00           N
+ATOM    425  C4   DA C   8      17.730  21.570  41.980  1.00  0.00           C
+ATOM    426  H61  DA C   8      16.160  24.270  38.920  1.00  0.00           H
+ATOM    427  H62  DA C   8      14.950  23.630  40.000  1.00  0.00           H
+ATOM    428  P    DA C   9      20.360  17.180  46.460  1.00  0.00           P
+ATOM    429  OP1  DA C   9      20.670  16.150  45.440  1.00  0.00           O
+ATOM    430  OP2  DA C   9      19.790  16.750  47.760  1.00  0.00           O
+ATOM    431  O5'  DA C   9      21.720  17.920  46.880  1.00  0.00           O
+ATOM    432  C5'  DA C   9      22.600  18.420  45.860  1.00  0.00           C
+ATOM    433  C4'  DA C   9      22.560  19.940  45.740  1.00  0.00           C
+ATOM    434  O4'  DA C   9      21.390  20.640  45.260  1.00  0.00           O
+ATOM    435  C3'  DA C   9      22.970  20.600  47.040  1.00  0.00           C
+ATOM    436  O3'  DA C   9      24.390  20.790  46.990  1.00  0.00           O
+ATOM    437  C2'  DA C   9      22.150  21.850  47.110  1.00  0.00           C
+ATOM    438  C1'  DA C   9      21.630  22.000  45.680  1.00  0.00           C
+ATOM    439  N9   DA C   9      20.350  22.740  45.660  1.00  0.00           N
+ATOM    440  C8   DA C   9      19.280  22.400  46.360  1.00  0.00           C
+ATOM    441  N7   DA C   9      18.180  22.970  45.850  1.00  0.00           N
+ATOM    442  C5   DA C   9      18.570  23.670  44.790  1.00  0.00           C
+ATOM    443  C6   DA C   9      17.970  24.430  43.780  1.00  0.00           C
+ATOM    444  N6   DA C   9      16.670  24.580  43.650  1.00  0.00           N
+ATOM    445  N1   DA C   9      18.680  25.030  42.800  1.00  0.00           N
+ATOM    446  C2   DA C   9      20.010  24.930  42.790  1.00  0.00           C
+ATOM    447  N3   DA C   9      20.640  24.210  43.720  1.00  0.00           N
+ATOM    448  C4   DA C   9      19.960  23.570  44.700  1.00  0.00           C
+ATOM    449  H61  DA C   9      16.330  25.200  42.940  1.00  0.00           H
+ATOM    450  H62  DA C   9      16.040  24.190  44.340  1.00  0.00           H
+ATOM    451  P    DT C  10      25.260  21.500  48.140  1.00  0.00           P
+ATOM    452  OP1  DT C  10      26.600  20.870  48.060  1.00  0.00           O
+ATOM    453  OP2  DT C  10      24.530  21.440  49.430  1.00  0.00           O
+ATOM    454  O5'  DT C  10      25.350  23.040  47.690  1.00  0.00           O
+ATOM    455  C5'  DT C  10      25.880  23.400  46.400  1.00  0.00           C
+ATOM    456  C4'  DT C  10      25.410  24.710  45.790  1.00  0.00           C
+ATOM    457  O4'  DT C  10      23.980  24.760  45.630  1.00  0.00           O
+ATOM    458  C3'  DT C  10      25.810  25.930  46.610  1.00  0.00           C
+ATOM    459  O3'  DT C  10      26.740  26.780  45.900  1.00  0.00           O
+ATOM    460  C2'  DT C  10      24.510  26.650  46.890  1.00  0.00           C
+ATOM    461  C1'  DT C  10      23.640  26.140  45.770  1.00  0.00           C
+ATOM    462  N1   DT C  10      22.190  26.230  46.090  1.00  0.00           N
+ATOM    463  C2   DT C  10      21.300  27.000  45.310  1.00  0.00           C
+ATOM    464  O2   DT C  10      21.610  27.350  44.180  1.00  0.00           O
+ATOM    465  N3   DT C  10      19.940  27.040  45.660  1.00  0.00           N
+ATOM    466  C4   DT C  10      19.460  26.270  46.720  1.00  0.00           C
+ATOM    467  O4   DT C  10      18.260  25.990  46.660  1.00  0.00           O
+ATOM    468  C5   DT C  10      20.340  25.540  47.520  1.00  0.00           C
+ATOM    469  C7   DT C  10      19.930  24.920  48.860  1.00  0.00           C
+ATOM    470  C6   DT C  10      21.690  25.510  47.200  1.00  0.00           C
+ATOM    471  H3   DT C  10      19.340  27.740  45.270  1.00  0.00           H
+ATOM    472  P    DT C  11      27.460  28.020  46.650  1.00  0.00           P
+ATOM    473  OP1  DT C  11      28.720  28.370  45.940  1.00  0.00           O
+ATOM    474  OP2  DT C  11      27.610  27.680  48.080  1.00  0.00           O
+ATOM    475  O5'  DT C  11      26.350  29.190  46.650  1.00  0.00           O
+ATOM    476  C5'  DT C  11      25.900  29.910  45.480  1.00  0.00           C
+ATOM    477  C4'  DT C  11      24.540  30.590  45.670  1.00  0.00           C
+ATOM    478  O4'  DT C  11      23.540  29.650  46.090  1.00  0.00           O
+ATOM    479  C3'  DT C  11      24.460  31.700  46.710  1.00  0.00           C
+ATOM    480  O3'  DT C  11      24.740  33.020  46.220  1.00  0.00           O
+ATOM    481  C2'  DT C  11      22.990  31.740  47.080  1.00  0.00           C
+ATOM    482  C1'  DT C  11      22.430  30.480  46.430  1.00  0.00           C
+ATOM    483  N1   DT C  11      21.560  29.760  47.390  1.00  0.00           N
+ATOM    484  C2   DT C  11      20.170  29.920  47.310  1.00  0.00           C
+ATOM    485  O2   DT C  11      19.620  29.940  46.210  1.00  0.00           O
+ATOM    486  N3   DT C  11      19.380  29.510  48.380  1.00  0.00           N
+ATOM    487  C4   DT C  11      19.940  28.820  49.460  1.00  0.00           C
+ATOM    488  O4   DT C  11      19.170  28.020  49.990  1.00  0.00           O
+ATOM    489  C5   DT C  11      21.320  28.650  49.550  1.00  0.00           C
+ATOM    490  C7   DT C  11      21.990  28.060  50.790  1.00  0.00           C
+ATOM    491  C6   DT C  11      22.120  29.110  48.510  1.00  0.00           C
+ATOM    492 HO3'  DT C  11      25.720  33.030  45.940  1.00  0.00           H
+ATOM    493  H3   DT C  11      18.440  29.840  48.450  1.00  0.00           H
+TER     494       DT C  11
+ATOM    495  N   MET A   1      27.960  27.500   6.070  1.00  0.00           N
+ATOM    496  CA  MET A   1      27.910  28.670   6.970  1.00  0.00           C
+ATOM    497  C   MET A   1      28.300  28.280   8.410  1.00  0.00           C
+ATOM    498  O   MET A   1      28.040  27.180   8.890  1.00  0.00           O
+ATOM    499  CB  MET A   1      26.500  29.280   6.980  1.00  0.00           C
+ATOM    500  CG  MET A   1      26.140  29.930   5.650  1.00  0.00           C
+ATOM    501  SD  MET A   1      26.900  31.580   5.430  1.00  0.00           S
+ATOM    502  CE  MET A   1      26.360  31.940   3.770  1.00  0.00           C
+ATOM    503  H1  MET A   1      27.340  26.800   6.430  1.00  0.00           H
+ATOM    504  H2  MET A   1      27.690  27.730   5.140  1.00  0.00           H
+ATOM    505  H3  MET A   1      28.910  27.170   6.080  1.00  0.00           H
+ATOM    506  N   LYS A   2      28.880  29.280   9.060  1.00  0.00           N
+ATOM    507  CA  LYS A   2      29.320  29.300  10.470  1.00  0.00           C
+ATOM    508  C   LYS A   2      28.280  28.560  11.340  1.00  0.00           C
+ATOM    509  O   LYS A   2      27.140  29.030  11.440  1.00  0.00           O
+ATOM    510  CB  LYS A   2      29.500  30.750  10.940  1.00  0.00           C
+ATOM    511  CG  LYS A   2      29.910  30.840  12.410  1.00  0.00           C
+ATOM    512  CD  LYS A   2      30.340  32.240  12.840  1.00  0.00           C
+ATOM    513  CE  LYS A   2      31.460  32.180  13.890  1.00  0.00           C
+ATOM    514  NZ  LYS A   2      31.090  31.610  15.200  1.00  0.00           N
+ATOM    515  H   LYS A   2      28.960  30.170   8.570  1.00  0.00           H
+ATOM    516  HZ1 LYS A   2      30.240  32.000  15.540  1.00  0.00           H
+ATOM    517  HZ2 LYS A   2      30.940  30.620  15.150  1.00  0.00           H
+ATOM    518  HZ3 LYS A   2      31.820  31.770  15.890  1.00  0.00           H
+ATOM    519  N   PRO A   3      28.630  27.380  11.860  1.00  0.00           N
+ATOM    520  CA  PRO A   3      27.650  26.540  12.560  1.00  0.00           C
+ATOM    521  C   PRO A   3      27.460  27.020  14.000  1.00  0.00           C
+ATOM    522  O   PRO A   3      28.120  26.570  14.930  1.00  0.00           O
+ATOM    523  CB  PRO A   3      28.160  25.110  12.370  1.00  0.00           C
+ATOM    524  CG  PRO A   3      29.670  25.240  12.200  1.00  0.00           C
+ATOM    525  CD  PRO A   3      29.900  26.650  11.650  1.00  0.00           C
+ATOM    526  N   VAL A   4      26.560  27.990  14.090  1.00  0.00           N
+ATOM    527  CA  VAL A   4      26.220  28.700  15.340  1.00  0.00           C
+ATOM    528  C   VAL A   4      25.270  27.810  16.160  1.00  0.00           C
+ATOM    529  O   VAL A   4      24.060  27.820  15.940  1.00  0.00           O
+ATOM    530  CB  VAL A   4      25.540  30.060  15.050  1.00  0.00           C
+ATOM    531  CG1 VAL A   4      25.300  30.850  16.350  1.00  0.00           C
+ATOM    532  CG2 VAL A   4      26.340  30.940  14.090  1.00  0.00           C
+ATOM    533  H   VAL A   4      26.040  28.300  13.290  1.00  0.00           H
+ATOM    534  N   THR A   5      25.870  27.230  17.190  1.00  0.00           N
+ATOM    535  CA  THR A   5      25.170  26.410  18.210  1.00  0.00           C
+ATOM    536  C   THR A   5      24.430  27.300  19.210  1.00  0.00           C
+ATOM    537  O   THR A   5      24.770  28.470  19.370  1.00  0.00           O
+ATOM    538  CB  THR A   5      26.120  25.530  19.050  1.00  0.00           C
+ATOM    539  OG1 THR A   5      27.230  26.240  19.620  1.00  0.00           O
+ATOM    540  CG2 THR A   5      26.530  24.250  18.320  1.00  0.00           C
+ATOM    541  H   THR A   5      26.800  27.540  17.440  1.00  0.00           H
+ATOM    542  HG1 THR A   5      27.650  26.900  18.950  1.00  0.00           H
+ATOM    543  N   LEU A   6      23.560  26.690  20.020  1.00  0.00           N
+ATOM    544  CA  LEU A   6      23.050  27.360  21.240  1.00  0.00           C
+ATOM    545  C   LEU A   6      24.160  27.970  22.110  1.00  0.00           C
+ATOM    546  O   LEU A   6      24.040  29.130  22.510  1.00  0.00           O
+ATOM    547  CB  LEU A   6      22.240  26.420  22.130  1.00  0.00           C
+ATOM    548  CG  LEU A   6      20.920  25.970  21.480  1.00  0.00           C
+ATOM    549  CD1 LEU A   6      20.340  24.850  22.340  1.00  0.00           C
+ATOM    550  CD2 LEU A   6      19.910  27.100  21.290  1.00  0.00           C
+ATOM    551  H   LEU A   6      23.120  25.810  19.790  1.00  0.00           H
+ATOM    552  N   TYR A   7      25.260  27.230  22.280  1.00  0.00           N
+ATOM    553  CA  TYR A   7      26.380  27.660  23.160  1.00  0.00           C
+ATOM    554  C   TYR A   7      26.920  29.050  22.800  1.00  0.00           C
+ATOM    555  O   TYR A   7      26.920  29.930  23.660  1.00  0.00           O
+ATOM    556  CB  TYR A   7      27.560  26.680  23.180  1.00  0.00           C
+ATOM    557  CG  TYR A   7      27.260  25.290  23.760  1.00  0.00           C
+ATOM    558  CD1 TYR A   7      26.860  24.290  22.850  1.00  0.00           C
+ATOM    559  CD2 TYR A   7      27.750  24.960  25.050  1.00  0.00           C
+ATOM    560  CE1 TYR A   7      26.990  22.940  23.200  1.00  0.00           C
+ATOM    561  CE2 TYR A   7      27.890  23.600  25.410  1.00  0.00           C
+ATOM    562  CZ  TYR A   7      27.520  22.610  24.470  1.00  0.00           C
+ATOM    563  OH  TYR A   7      27.760  21.300  24.760  1.00  0.00           O
+ATOM    564  H   TYR A   7      25.380  26.360  21.810  1.00  0.00           H
+ATOM    565  HH  TYR A   7      26.910  20.920  25.220  1.00  0.00           H
+ATOM    566  N   ASP A   8      26.960  29.290  21.490  1.00  0.00           N
+ATOM    567  CA  ASP A   8      27.420  30.510  20.800  1.00  0.00           C
+ATOM    568  C   ASP A   8      26.540  31.740  21.040  1.00  0.00           C
+ATOM    569  O   ASP A   8      27.060  32.820  21.320  1.00  0.00           O
+ATOM    570  CB  ASP A   8      27.420  30.230  19.310  1.00  0.00           C
+ATOM    571  CG  ASP A   8      28.580  29.330  18.890  1.00  0.00           C
+ATOM    572  OD1 ASP A   8      29.730  29.710  19.180  1.00  0.00           O
+ATOM    573  OD2 ASP A   8      28.330  28.340  18.180  1.00  0.00           O
+ATOM    574  H   ASP A   8      26.650  28.600  20.840  1.00  0.00           H
+ATOM    575  N   VAL A   9      25.240  31.570  20.800  1.00  0.00           N
+ATOM    576  CA  VAL A   9      24.270  32.640  21.130  1.00  0.00           C
+ATOM    577  C   VAL A   9      24.240  32.930  22.630  1.00  0.00           C
+ATOM    578  O   VAL A   9      24.470  34.060  23.040  1.00  0.00           O
+ATOM    579  CB  VAL A   9      22.840  32.430  20.570  1.00  0.00           C
+ATOM    580  CG1 VAL A   9      22.700  33.140  19.230  1.00  0.00           C
+ATOM    581  CG2 VAL A   9      22.380  30.970  20.490  1.00  0.00           C
+ATOM    582  H   VAL A   9      24.860  30.690  20.500  1.00  0.00           H
+ATOM    583  N   ALA A  10      24.300  31.850  23.400  1.00  0.00           N
+ATOM    584  CA  ALA A  10      24.270  31.910  24.870  1.00  0.00           C
+ATOM    585  C   ALA A  10      25.460  32.720  25.450  1.00  0.00           C
+ATOM    586  O   ALA A  10      25.250  33.730  26.140  1.00  0.00           O
+ATOM    587  CB  ALA A  10      24.260  30.500  25.460  1.00  0.00           C
+ATOM    588  H   ALA A  10      24.410  30.940  23.000  1.00  0.00           H
+ATOM    589  N   GLU A  11      26.660  32.350  25.010  1.00  0.00           N
+ATOM    590  CA  GLU A  11      27.930  33.050  25.340  1.00  0.00           C
+ATOM    591  C   GLU A  11      27.930  34.560  25.060  1.00  0.00           C
+ATOM    592  O   GLU A  11      28.080  35.350  26.000  1.00  0.00           O
+ATOM    593  CB  GLU A  11      29.130  32.410  24.630  1.00  0.00           C
+ATOM    594  CG  GLU A  11      29.550  31.070  25.250  1.00  0.00           C
+ATOM    595  CD  GLU A  11      30.460  30.260  24.320  1.00  0.00           C
+ATOM    596  OE1 GLU A  11      31.520  30.790  23.920  1.00  0.00           O
+ATOM    597  OE2 GLU A  11      30.140  29.070  24.110  1.00  0.00           O
+ATOM    598  H   GLU A  11      26.770  31.430  24.620  1.00  0.00           H
+ATOM    599  N   TYR A  12      27.480  34.910  23.850  1.00  0.00           N
+ATOM    600  CA  TYR A  12      27.310  36.280  23.350  1.00  0.00           C
+ATOM    601  C   TYR A  12      26.210  37.070  24.090  1.00  0.00           C
+ATOM    602  O   TYR A  12      26.340  38.270  24.310  1.00  0.00           O
+ATOM    603  CB  TYR A  12      26.900  36.090  21.880  1.00  0.00           C
+ATOM    604  CG  TYR A  12      26.840  37.380  21.060  1.00  0.00           C
+ATOM    605  CD1 TYR A  12      28.050  37.900  20.570  1.00  0.00           C
+ATOM    606  CD2 TYR A  12      25.600  38.020  20.840  1.00  0.00           C
+ATOM    607  CE1 TYR A  12      28.040  39.100  19.850  1.00  0.00           C
+ATOM    608  CE2 TYR A  12      25.590  39.220  20.120  1.00  0.00           C
+ATOM    609  CZ  TYR A  12      26.800  39.740  19.620  1.00  0.00           C
+ATOM    610  OH  TYR A  12      26.750  40.740  18.710  1.00  0.00           O
+ATOM    611  H   TYR A  12      27.240  34.220  23.170  1.00  0.00           H
+ATOM    612  HH  TYR A  12      27.690  40.830  18.270  1.00  0.00           H
+ATOM    613  N   ALA A  13      25.120  36.380  24.390  1.00  0.00           N
+ATOM    614  CA  ALA A  13      23.940  37.010  25.030  1.00  0.00           C
+ATOM    615  C   ALA A  13      24.160  37.330  26.510  1.00  0.00           C
+ATOM    616  O   ALA A  13      23.950  38.470  26.910  1.00  0.00           O
+ATOM    617  CB  ALA A  13      22.690  36.150  24.870  1.00  0.00           C
+ATOM    618  H   ALA A  13      24.990  35.430  24.080  1.00  0.00           H
+ATOM    619  N   GLY A  14      24.790  36.410  27.240  1.00  0.00           N
+ATOM    620  CA  GLY A  14      24.990  36.520  28.700  1.00  0.00           C
+ATOM    621  C   GLY A  14      24.240  35.450  29.520  1.00  0.00           C
+ATOM    622  O   GLY A  14      23.980  35.620  30.720  1.00  0.00           O
+ATOM    623  H   GLY A  14      25.280  35.650  26.790  1.00  0.00           H
+ATOM    624  N   VAL A  15      24.090  34.300  28.890  1.00  0.00           N
+ATOM    625  CA  VAL A  15      23.310  33.140  29.380  1.00  0.00           C
+ATOM    626  C   VAL A  15      24.000  31.790  29.080  1.00  0.00           C
+ATOM    627  O   VAL A  15      24.890  31.670  28.240  1.00  0.00           O
+ATOM    628  CB  VAL A  15      21.840  33.090  28.950  1.00  0.00           C
+ATOM    629  CG1 VAL A  15      20.980  33.770  30.020  1.00  0.00           C
+ATOM    630  CG2 VAL A  15      21.520  33.690  27.590  1.00  0.00           C
+ATOM    631  H   VAL A  15      24.640  34.100  28.070  1.00  0.00           H
+ATOM    632  N   SER A  16      23.540  30.790  29.820  1.00  0.00           N
+ATOM    633  CA  SER A  16      23.980  29.400  29.670  1.00  0.00           C
+ATOM    634  C   SER A  16      23.200  28.720  28.530  1.00  0.00           C
+ATOM    635  O   SER A  16      22.030  29.050  28.290  1.00  0.00           O
+ATOM    636  CB  SER A  16      23.770  28.680  31.010  1.00  0.00           C
+ATOM    637  OG  SER A  16      22.420  28.260  31.230  1.00  0.00           O
+ATOM    638  H   SER A  16      22.890  30.940  30.580  1.00  0.00           H
+ATOM    639  HG  SER A  16      21.820  29.050  31.450  1.00  0.00           H
+ATOM    640  N   TYR A  17      23.860  27.800  27.840  1.00  0.00           N
+ATOM    641  CA  TYR A  17      23.270  27.060  26.700  1.00  0.00           C
+ATOM    642  C   TYR A  17      21.930  26.390  27.050  1.00  0.00           C
+ATOM    643  O   TYR A  17      21.060  26.250  26.190  1.00  0.00           O
+ATOM    644  CB  TYR A  17      24.330  26.080  26.170  1.00  0.00           C
+ATOM    645  CG  TYR A  17      24.160  24.590  26.480  1.00  0.00           C
+ATOM    646  CD1 TYR A  17      23.380  23.870  25.560  1.00  0.00           C
+ATOM    647  CD2 TYR A  17      24.730  23.960  27.620  1.00  0.00           C
+ATOM    648  CE1 TYR A  17      23.130  22.510  25.790  1.00  0.00           C
+ATOM    649  CE2 TYR A  17      24.480  22.590  27.850  1.00  0.00           C
+ATOM    650  CZ  TYR A  17      23.660  21.890  26.930  1.00  0.00           C
+ATOM    651  OH  TYR A  17      23.140  20.660  27.200  1.00  0.00           O
+ATOM    652  H   TYR A  17      24.770  27.470  28.110  1.00  0.00           H
+ATOM    653  HH  TYR A  17      23.290  20.370  28.170  1.00  0.00           H
+ATOM    654  N   GLN A  18      21.760  26.020  28.320  1.00  0.00           N
+ATOM    655  CA  GLN A  18      20.500  25.480  28.870  1.00  0.00           C
+ATOM    656  C   GLN A  18      19.280  26.410  28.700  1.00  0.00           C
+ATOM    657  O   GLN A  18      18.160  25.960  28.440  1.00  0.00           O
+ATOM    658  CB  GLN A  18      20.760  25.220  30.350  1.00  0.00           C
+ATOM    659  CG  GLN A  18      19.990  24.020  30.910  1.00  0.00           C
+ATOM    660  CD  GLN A  18      20.040  22.790  30.010  1.00  0.00           C
+ATOM    661  OE1 GLN A  18      19.080  22.500  29.320  1.00  0.00           O
+ATOM    662  NE2 GLN A  18      21.060  21.960  30.090  1.00  0.00           N
+ATOM    663  H   GLN A  18      22.550  25.950  28.940  1.00  0.00           H
+ATOM    664 HE21 GLN A  18      21.950  22.290  30.490  1.00  0.00           H
+ATOM    665 HE22 GLN A  18      21.100  21.150  29.510  1.00  0.00           H
+ATOM    666  N   THR A  19      19.530  27.640  29.120  1.00  0.00           N
+ATOM    667  CA  THR A  19      18.600  28.790  29.200  1.00  0.00           C
+ATOM    668  C   THR A  19      18.180  29.210  27.780  1.00  0.00           C
+ATOM    669  O   THR A  19      17.000  29.220  27.460  1.00  0.00           O
+ATOM    670  CB  THR A  19      19.350  29.910  29.950  1.00  0.00           C
+ATOM    671  OG1 THR A  19      19.920  29.400  31.160  1.00  0.00           O
+ATOM    672  CG2 THR A  19      18.510  31.130  30.310  1.00  0.00           C
+ATOM    673  H   THR A  19      20.430  27.830  29.490  1.00  0.00           H
+ATOM    674  HG1 THR A  19      19.250  29.530  31.930  1.00  0.00           H
+ATOM    675  N   VAL A  20      19.160  29.340  26.890  1.00  0.00           N
+ATOM    676  CA  VAL A  20      18.940  29.560  25.440  1.00  0.00           C
+ATOM    677  C   VAL A  20      18.220  28.370  24.760  1.00  0.00           C
+ATOM    678  O   VAL A  20      17.330  28.580  23.940  1.00  0.00           O
+ATOM    679  CB  VAL A  20      20.290  30.030  24.870  1.00  0.00           C
+ATOM    680  CG1 VAL A  20      20.470  30.050  23.360  1.00  0.00           C
+ATOM    681  CG2 VAL A  20      20.440  31.470  25.320  1.00  0.00           C
+ATOM    682  H   VAL A  20      20.120  29.440  27.170  1.00  0.00           H
+ATOM    683  N   SER A  21      18.480  27.170  25.260  1.00  0.00           N
+ATOM    684  CA  SER A  21      17.640  25.970  24.980  1.00  0.00           C
+ATOM    685  C   SER A  21      16.190  26.120  25.450  1.00  0.00           C
+ATOM    686  O   SER A  21      15.300  26.040  24.620  1.00  0.00           O
+ATOM    687  CB  SER A  21      18.250  24.690  25.560  1.00  0.00           C
+ATOM    688  OG  SER A  21      17.380  23.580  25.340  1.00  0.00           O
+ATOM    689  H   SER A  21      19.360  26.990  25.730  1.00  0.00           H
+ATOM    690  HG  SER A  21      17.280  23.470  24.320  1.00  0.00           H
+ATOM    691  N   ARG A  22      15.980  26.470  26.720  1.00  0.00           N
+ATOM    692  CA  ARG A  22      14.660  26.790  27.310  1.00  0.00           C
+ATOM    693  C   ARG A  22      14.040  28.170  26.960  1.00  0.00           C
+ATOM    694  O   ARG A  22      13.220  28.730  27.680  1.00  0.00           O
+ATOM    695  CB  ARG A  22      14.840  26.590  28.810  1.00  0.00           C
+ATOM    696  CG  ARG A  22      15.160  25.140  29.160  1.00  0.00           C
+ATOM    697  CD  ARG A  22      14.710  24.980  30.600  1.00  0.00           C
+ATOM    698  NE  ARG A  22      15.140  23.680  31.120  1.00  0.00           N
+ATOM    699  CZ  ARG A  22      16.170  23.490  31.960  1.00  0.00           C
+ATOM    700  NH1 ARG A  22      16.900  24.520  32.400  1.00  0.00           N
+ATOM    701  NH2 ARG A  22      16.430  22.240  32.340  1.00  0.00           N
+ATOM    702  H   ARG A  22      16.680  26.290  27.420  1.00  0.00           H
+ATOM    703  HE  ARG A  22      14.490  22.940  30.980  1.00  0.00           H
+ATOM    704 HH11 ARG A  22      16.800  25.410  31.940  1.00  0.00           H
+ATOM    705 HH12 ARG A  22      17.570  24.420  33.130  1.00  0.00           H
+ATOM    706 HH21 ARG A  22      15.770  21.530  32.100  1.00  0.00           H
+ATOM    707 HH22 ARG A  22      17.200  21.980  32.930  1.00  0.00           H
+ATOM    708  N   VAL A  23      14.620  28.800  25.950  1.00  0.00           N
+ATOM    709  CA  VAL A  23      13.950  29.820  25.110  1.00  0.00           C
+ATOM    710  C   VAL A  23      13.140  29.160  23.970  1.00  0.00           C
+ATOM    711  O   VAL A  23      11.990  29.500  23.700  1.00  0.00           O
+ATOM    712  CB  VAL A  23      15.070  30.780  24.640  1.00  0.00           C
+ATOM    713  CG1 VAL A  23      14.740  31.620  23.400  1.00  0.00           C
+ATOM    714  CG2 VAL A  23      15.450  31.700  25.790  1.00  0.00           C
+ATOM    715  H   VAL A  23      15.610  28.870  25.930  1.00  0.00           H
+ATOM    716  N   VAL A  24      13.750  28.110  23.430  1.00  0.00           N
+ATOM    717  CA  VAL A  24      13.250  27.320  22.290  1.00  0.00           C
+ATOM    718  C   VAL A  24      12.370  26.160  22.780  1.00  0.00           C
+ATOM    719  O   VAL A  24      11.160  26.140  22.550  1.00  0.00           O
+ATOM    720  CB  VAL A  24      14.540  26.920  21.540  1.00  0.00           C
+ATOM    721  CG1 VAL A  24      14.380  25.780  20.550  1.00  0.00           C
+ATOM    722  CG2 VAL A  24      15.180  28.150  20.870  1.00  0.00           C
+ATOM    723  H   VAL A  24      14.620  27.760  23.790  1.00  0.00           H
+ATOM    724  N   ASN A  25      12.970  25.240  23.540  1.00  0.00           N
+ATOM    725  CA  ASN A  25      12.330  24.020  24.060  1.00  0.00           C
+ATOM    726  C   ASN A  25      12.630  23.770  25.540  1.00  0.00           C
+ATOM    727  O   ASN A  25      13.700  24.100  26.060  1.00  0.00           O
+ATOM    728  CB  ASN A  25      12.780  22.830  23.210  1.00  0.00           C
+ATOM    729  CG  ASN A  25      11.850  22.640  22.020  1.00  0.00           C
+ATOM    730  OD1 ASN A  25      10.640  22.470  22.140  1.00  0.00           O
+ATOM    731  ND2 ASN A  25      12.400  22.510  20.830  1.00  0.00           N
+ATOM    732  H   ASN A  25      13.940  25.330  23.800  1.00  0.00           H
+ATOM    733 HD21 ASN A  25      13.410  22.470  20.780  1.00  0.00           H
+ATOM    734 HD22 ASN A  25      11.840  22.490  20.010  1.00  0.00           H
+ATOM    735  N   GLN A  26      11.600  23.240  26.200  1.00  0.00           N
+ATOM    736  CA  GLN A  26      11.420  23.150  27.660  1.00  0.00           C
+ATOM    737  C   GLN A  26      11.400  24.560  28.290  1.00  0.00           C
+ATOM    738  O   GLN A  26      11.720  24.750  29.470  1.00  0.00           O
+ATOM    739  CB  GLN A  26      12.440  22.210  28.330  1.00  0.00           C
+ATOM    740  CG  GLN A  26      11.770  21.520  29.510  1.00  0.00           C
+ATOM    741  CD  GLN A  26      12.480  21.800  30.840  1.00  0.00           C
+ATOM    742  OE1 GLN A  26      13.500  21.210  31.160  1.00  0.00           O
+ATOM    743  NE2 GLN A  26      12.110  22.820  31.580  1.00  0.00           N
+ATOM    744  H   GLN A  26      10.810  22.890  25.690  1.00  0.00           H
+ATOM    745 HE21 GLN A  26      11.500  23.540  31.230  1.00  0.00           H
+ATOM    746 HE22 GLN A  26      12.540  22.920  32.480  1.00  0.00           H
+ATOM    747  N   ALA A  27      10.780  25.490  27.570  1.00  0.00           N
+ATOM    748  CA  ALA A  27      10.810  26.920  27.900  1.00  0.00           C
+ATOM    749  C   ALA A  27      10.290  27.300  29.280  1.00  0.00           C
+ATOM    750  O   ALA A  27       9.120  27.120  29.630  1.00  0.00           O
+ATOM    751  CB  ALA A  27      10.130  27.710  26.780  1.00  0.00           C
+ATOM    752  H   ALA A  27      10.130  25.260  26.840  1.00  0.00           H
+ATOM    753  N   SER A  28      11.260  27.760  30.060  1.00  0.00           N
+ATOM    754  CA  SER A  28      11.120  28.100  31.480  1.00  0.00           C
+ATOM    755  C   SER A  28      12.450  28.700  31.950  1.00  0.00           C
+ATOM    756  O   SER A  28      13.530  28.250  31.570  1.00  0.00           O
+ATOM    757  CB  SER A  28      10.870  26.820  32.270  1.00  0.00           C
+ATOM    758  OG  SER A  28      10.560  27.170  33.610  1.00  0.00           O
+ATOM    759  H   SER A  28      12.180  27.940  29.690  1.00  0.00           H
+ATOM    760  HG  SER A  28       9.550  27.300  33.660  1.00  0.00           H
+ATOM    761  N   HIS A  29      12.340  29.670  32.840  1.00  0.00           N
+ATOM    762  CA  HIS A  29      13.460  30.330  33.540  1.00  0.00           C
+ATOM    763  C   HIS A  29      14.480  31.030  32.620  1.00  0.00           C
+ATOM    764  O   HIS A  29      15.660  30.710  32.560  1.00  0.00           O
+ATOM    765  CB  HIS A  29      14.110  29.370  34.550  1.00  0.00           C
+ATOM    766  CG  HIS A  29      13.130  29.030  35.670  1.00  0.00           C
+ATOM    767  ND1 HIS A  29      12.560  29.900  36.510  1.00  0.00           N
+ATOM    768  CD2 HIS A  29      12.530  27.860  35.800  1.00  0.00           C
+ATOM    769  CE1 HIS A  29      11.600  29.240  37.150  1.00  0.00           C
+ATOM    770  NE2 HIS A  29      11.570  27.990  36.700  1.00  0.00           N
+ATOM    771  H   HIS A  29      11.430  29.970  33.170  1.00  0.00           H
+ATOM    772  HD1 HIS A  29      12.850  30.860  36.670  1.00  0.00           H
+ATOM    773  HE2 HIS A  29      10.920  27.250  36.990  1.00  0.00           H
+ATOM    774  N   VAL A  30      13.920  31.970  31.870  1.00  0.00           N
+ATOM    775  CA  VAL A  30      14.670  32.930  31.040  1.00  0.00           C
+ATOM    776  C   VAL A  30      14.070  34.320  31.210  1.00  0.00           C
+ATOM    777  O   VAL A  30      13.060  34.730  30.620  1.00  0.00           O
+ATOM    778  CB  VAL A  30      14.830  32.400  29.590  1.00  0.00           C
+ATOM    779  CG1 VAL A  30      13.550  32.440  28.740  1.00  0.00           C
+ATOM    780  CG2 VAL A  30      15.960  33.210  28.960  1.00  0.00           C
+ATOM    781  H   VAL A  30      12.930  32.160  31.940  1.00  0.00           H
+ATOM    782  N   SER A  31      14.800  35.030  32.050  1.00  0.00           N
+ATOM    783  CA  SER A  31      14.280  36.190  32.770  1.00  0.00           C
+ATOM    784  C   SER A  31      14.460  37.440  31.910  1.00  0.00           C
+ATOM    785  O   SER A  31      15.030  37.370  30.830  1.00  0.00           O
+ATOM    786  CB  SER A  31      15.010  36.310  34.110  1.00  0.00           C
+ATOM    787  OG  SER A  31      14.150  35.780  35.110  1.00  0.00           O
+ATOM    788  H   SER A  31      15.800  35.060  31.930  1.00  0.00           H
+ATOM    789  HG  SER A  31      13.780  34.880  34.770  1.00  0.00           H
+ATOM    790  N   ALA A  32      13.930  38.550  32.370  1.00  0.00           N
+ATOM    791  CA  ALA A  32      13.670  39.730  31.520  1.00  0.00           C
+ATOM    792  C   ALA A  32      14.830  40.190  30.620  1.00  0.00           C
+ATOM    793  O   ALA A  32      14.680  40.200  29.390  1.00  0.00           O
+ATOM    794  CB  ALA A  32      13.160  40.910  32.340  1.00  0.00           C
+ATOM    795  H   ALA A  32      13.600  38.620  33.340  1.00  0.00           H
+ATOM    796  N   LYS A  33      15.920  40.640  31.220  1.00  0.00           N
+ATOM    797  CA  LYS A  33      17.060  41.170  30.450  1.00  0.00           C
+ATOM    798  C   LYS A  33      17.850  40.050  29.760  1.00  0.00           C
+ATOM    799  O   LYS A  33      18.200  40.170  28.600  1.00  0.00           O
+ATOM    800  CB  LYS A  33      17.920  42.050  31.350  1.00  0.00           C
+ATOM    801  CG  LYS A  33      19.010  42.820  30.600  1.00  0.00           C
+ATOM    802  CD  LYS A  33      19.470  43.990  31.460  1.00  0.00           C
+ATOM    803  CE  LYS A  33      20.500  44.840  30.720  1.00  0.00           C
+ATOM    804  NZ  LYS A  33      20.830  46.030  31.500  1.00  0.00           N
+ATOM    805  H   LYS A  33      16.080  40.470  32.200  1.00  0.00           H
+ATOM    806  HZ1 LYS A  33      21.100  45.810  32.450  1.00  0.00           H
+ATOM    807  HZ2 LYS A  33      21.560  46.540  31.060  1.00  0.00           H
+ATOM    808  HZ3 LYS A  33      20.030  46.630  31.550  1.00  0.00           H
+ATOM    809  N   THR A  34      17.920  38.880  30.400  1.00  0.00           N
+ATOM    810  CA  THR A  34      18.630  37.710  29.850  1.00  0.00           C
+ATOM    811  C   THR A  34      17.950  37.180  28.570  1.00  0.00           C
+ATOM    812  O   THR A  34      18.620  36.970  27.560  1.00  0.00           O
+ATOM    813  CB  THR A  34      18.800  36.600  30.890  1.00  0.00           C
+ATOM    814  OG1 THR A  34      17.550  36.200  31.440  1.00  0.00           O
+ATOM    815  CG2 THR A  34      19.770  37.040  31.990  1.00  0.00           C
+ATOM    816  H   THR A  34      17.450  38.710  31.270  1.00  0.00           H
+ATOM    817  HG1 THR A  34      17.750  35.500  32.170  1.00  0.00           H
+ATOM    818  N   ARG A  35      16.630  37.100  28.610  1.00  0.00           N
+ATOM    819  CA  ARG A  35      15.740  36.850  27.460  1.00  0.00           C
+ATOM    820  C   ARG A  35      15.770  37.970  26.410  1.00  0.00           C
+ATOM    821  O   ARG A  35      15.990  37.670  25.240  1.00  0.00           O
+ATOM    822  CB  ARG A  35      14.330  36.580  27.990  1.00  0.00           C
+ATOM    823  CG  ARG A  35      13.380  35.960  26.960  1.00  0.00           C
+ATOM    824  CD  ARG A  35      11.990  35.740  27.560  1.00  0.00           C
+ATOM    825  NE  ARG A  35      11.350  37.010  27.920  1.00  0.00           N
+ATOM    826  CZ  ARG A  35      10.930  37.400  29.130  1.00  0.00           C
+ATOM    827  NH1 ARG A  35      11.110  36.700  30.250  1.00  0.00           N
+ATOM    828  NH2 ARG A  35      10.330  38.570  29.220  1.00  0.00           N
+ATOM    829  H   ARG A  35      16.170  36.970  29.500  1.00  0.00           H
+ATOM    830  HE  ARG A  35      11.110  37.600  27.130  1.00  0.00           H
+ATOM    831 HH11 ARG A  35      11.760  35.940  30.320  1.00  0.00           H
+ATOM    832 HH12 ARG A  35      10.680  37.060  31.090  1.00  0.00           H
+ATOM    833 HH21 ARG A  35      10.050  39.080  28.400  1.00  0.00           H
+ATOM    834 HH22 ARG A  35      10.020  38.890  30.120  1.00  0.00           H
+ATOM    835  N   GLU A  36      15.790  39.240  26.840  1.00  0.00           N
+ATOM    836  CA  GLU A  36      16.070  40.360  25.910  1.00  0.00           C
+ATOM    837  C   GLU A  36      17.410  40.210  25.190  1.00  0.00           C
+ATOM    838  O   GLU A  36      17.470  40.370  23.970  1.00  0.00           O
+ATOM    839  CB  GLU A  36      16.030  41.740  26.550  1.00  0.00           C
+ATOM    840  CG  GLU A  36      14.600  42.290  26.600  1.00  0.00           C
+ATOM    841  CD  GLU A  36      14.630  43.810  26.530  1.00  0.00           C
+ATOM    842  OE1 GLU A  36      15.010  44.330  25.450  1.00  0.00           O
+ATOM    843  OE2 GLU A  36      14.360  44.440  27.570  1.00  0.00           O
+ATOM    844  H   GLU A  36      15.630  39.480  27.810  1.00  0.00           H
+ATOM    845  N   LYS A  37      18.440  39.740  25.900  1.00  0.00           N
+ATOM    846  CA  LYS A  37      19.760  39.430  25.320  1.00  0.00           C
+ATOM    847  C   LYS A  37      19.700  38.300  24.280  1.00  0.00           C
+ATOM    848  O   LYS A  37      20.490  38.330  23.340  1.00  0.00           O
+ATOM    849  CB  LYS A  37      20.740  39.020  26.420  1.00  0.00           C
+ATOM    850  CG  LYS A  37      21.100  40.090  27.460  1.00  0.00           C
+ATOM    851  CD  LYS A  37      22.020  41.130  26.840  1.00  0.00           C
+ATOM    852  CE  LYS A  37      22.390  42.220  27.840  1.00  0.00           C
+ATOM    853  NZ  LYS A  37      23.200  43.190  27.080  1.00  0.00           N
+ATOM    854  H   LYS A  37      18.390  39.650  26.910  1.00  0.00           H
+ATOM    855  HZ1 LYS A  37      22.830  43.250  26.150  1.00  0.00           H
+ATOM    856  HZ2 LYS A  37      23.190  44.090  27.520  1.00  0.00           H
+ATOM    857  HZ3 LYS A  37      24.140  42.850  27.010  1.00  0.00           H
+ATOM    858  N   VAL A  38      18.860  37.280  24.500  1.00  0.00           N
+ATOM    859  CA  VAL A  38      18.670  36.150  23.560  1.00  0.00           C
+ATOM    860  C   VAL A  38      17.890  36.590  22.300  1.00  0.00           C
+ATOM    861  O   VAL A  38      18.290  36.230  21.190  1.00  0.00           O
+ATOM    862  CB  VAL A  38      17.980  34.920  24.210  1.00  0.00           C
+ATOM    863  CG1 VAL A  38      18.160  33.690  23.320  1.00  0.00           C
+ATOM    864  CG2 VAL A  38      18.590  34.570  25.570  1.00  0.00           C
+ATOM    865  H   VAL A  38      18.390  37.180  25.380  1.00  0.00           H
+ATOM    866  N   GLU A  39      16.920  37.480  22.510  1.00  0.00           N
+ATOM    867  CA  GLU A  39      16.140  38.140  21.440  1.00  0.00           C
+ATOM    868  C   GLU A  39      17.010  39.070  20.580  1.00  0.00           C
+ATOM    869  O   GLU A  39      16.880  39.080  19.360  1.00  0.00           O
+ATOM    870  CB  GLU A  39      14.960  38.930  22.000  1.00  0.00           C
+ATOM    871  CG  GLU A  39      13.860  38.000  22.520  1.00  0.00           C
+ATOM    872  CD  GLU A  39      12.600  38.760  22.950  1.00  0.00           C
+ATOM    873  OE1 GLU A  39      12.590  39.360  24.040  1.00  0.00           O
+ATOM    874  OE2 GLU A  39      11.580  38.610  22.250  1.00  0.00           O
+ATOM    875  H   GLU A  39      16.660  37.750  23.430  1.00  0.00           H
+ATOM    876  N   ALA A  40      17.990  39.710  21.230  1.00  0.00           N
+ATOM    877  CA  ALA A  40      19.080  40.440  20.580  1.00  0.00           C
+ATOM    878  C   ALA A  40      19.950  39.480  19.740  1.00  0.00           C
+ATOM    879  O   ALA A  40      19.930  39.520  18.510  1.00  0.00           O
+ATOM    880  CB  ALA A  40      19.840  41.120  21.720  1.00  0.00           C
+ATOM    881  H   ALA A  40      18.030  39.750  22.240  1.00  0.00           H
+ATOM    882  N   ALA A  41      20.640  38.580  20.440  1.00  0.00           N
+ATOM    883  CA  ALA A  41      21.480  37.480  19.900  1.00  0.00           C
+ATOM    884  C   ALA A  41      20.930  36.720  18.690  1.00  0.00           C
+ATOM    885  O   ALA A  41      21.650  36.590  17.710  1.00  0.00           O
+ATOM    886  CB  ALA A  41      21.820  36.470  21.000  1.00  0.00           C
+ATOM    887  H   ALA A  41      20.720  38.730  21.420  1.00  0.00           H
+ATOM    888  N   MET A  42      19.720  36.160  18.790  1.00  0.00           N
+ATOM    889  CA  MET A  42      19.010  35.560  17.630  1.00  0.00           C
+ATOM    890  C   MET A  42      19.150  36.340  16.310  1.00  0.00           C
+ATOM    891  O   MET A  42      19.170  35.720  15.250  1.00  0.00           O
+ATOM    892  CB  MET A  42      17.540  35.330  17.980  1.00  0.00           C
+ATOM    893  CG  MET A  42      17.410  34.130  18.920  1.00  0.00           C
+ATOM    894  SD  MET A  42      18.490  32.710  18.500  1.00  0.00           S
+ATOM    895  CE  MET A  42      18.800  32.080  20.130  1.00  0.00           C
+ATOM    896  H   MET A  42      19.280  35.990  19.670  1.00  0.00           H
+ATOM    897  N   ALA A  43      19.270  37.660  16.410  1.00  0.00           N
+ATOM    898  CA  ALA A  43      19.540  38.540  15.260  1.00  0.00           C
+ATOM    899  C   ALA A  43      21.040  38.850  15.050  1.00  0.00           C
+ATOM    900  O   ALA A  43      21.590  38.580  13.980  1.00  0.00           O
+ATOM    901  CB  ALA A  43      18.690  39.820  15.360  1.00  0.00           C
+ATOM    902  H   ALA A  43      19.260  38.110  17.310  1.00  0.00           H
+ATOM    903  N   GLU A  44      21.710  39.300  16.110  1.00  0.00           N
+ATOM    904  CA  GLU A  44      23.170  39.550  16.110  1.00  0.00           C
+ATOM    905  C   GLU A  44      24.120  38.400  15.720  1.00  0.00           C
+ATOM    906  O   GLU A  44      25.210  38.670  15.220  1.00  0.00           O
+ATOM    907  CB  GLU A  44      23.600  40.190  17.430  1.00  0.00           C
+ATOM    908  CG  GLU A  44      23.720  41.710  17.230  1.00  0.00           C
+ATOM    909  CD  GLU A  44      22.390  42.460  17.270  1.00  0.00           C
+ATOM    910  OE1 GLU A  44      21.650  42.290  18.260  1.00  0.00           O
+ATOM    911  OE2 GLU A  44      22.190  43.330  16.390  1.00  0.00           O
+ATOM    912  H   GLU A  44      21.230  39.670  16.910  1.00  0.00           H
+ATOM    913  N   LEU A  45      23.720  37.170  16.020  1.00  0.00           N
+ATOM    914  CA  LEU A  45      24.250  35.940  15.390  1.00  0.00           C
+ATOM    915  C   LEU A  45      23.070  35.230  14.690  1.00  0.00           C
+ATOM    916  O   LEU A  45      21.930  35.250  15.150  1.00  0.00           O
+ATOM    917  CB  LEU A  45      24.890  34.970  16.400  1.00  0.00           C
+ATOM    918  CG  LEU A  45      25.900  35.590  17.380  1.00  0.00           C
+ATOM    919  CD1 LEU A  45      26.410  34.540  18.370  1.00  0.00           C
+ATOM    920  CD2 LEU A  45      27.110  36.240  16.700  1.00  0.00           C
+ATOM    921  H   LEU A  45      23.000  37.000  16.700  1.00  0.00           H
+ATOM    922  N   ASN A  46      23.360  34.600  13.560  1.00  0.00           N
+ATOM    923  CA  ASN A  46      22.330  33.850  12.810  1.00  0.00           C
+ATOM    924  C   ASN A  46      22.370  32.340  13.120  1.00  0.00           C
+ATOM    925  O   ASN A  46      23.110  31.550  12.540  1.00  0.00           O
+ATOM    926  CB  ASN A  46      22.310  34.200  11.310  1.00  0.00           C
+ATOM    927  CG  ASN A  46      23.640  34.000  10.600  1.00  0.00           C
+ATOM    928  OD1 ASN A  46      23.930  32.960  10.020  1.00  0.00           O
+ATOM    929  ND2 ASN A  46      24.500  34.970  10.720  1.00  0.00           N
+ATOM    930  H   ASN A  46      24.320  34.540  13.250  1.00  0.00           H
+ATOM    931 HD21 ASN A  46      24.250  35.780  11.280  1.00  0.00           H
+ATOM    932 HD22 ASN A  46      25.370  34.900  10.220  1.00  0.00           H
+ATOM    933  N   TYR A  47      21.760  32.080  14.270  1.00  0.00           N
+ATOM    934  CA  TYR A  47      21.600  30.740  14.850  1.00  0.00           C
+ATOM    935  C   TYR A  47      21.060  29.670  13.890  1.00  0.00           C
+ATOM    936  O   TYR A  47      19.910  29.760  13.460  1.00  0.00           O
+ATOM    937  CB  TYR A  47      20.750  30.930  16.110  1.00  0.00           C
+ATOM    938  CG  TYR A  47      20.450  29.650  16.890  1.00  0.00           C
+ATOM    939  CD1 TYR A  47      21.490  28.830  17.370  1.00  0.00           C
+ATOM    940  CD2 TYR A  47      19.120  29.200  16.820  1.00  0.00           C
+ATOM    941  CE1 TYR A  47      21.200  27.500  17.720  1.00  0.00           C
+ATOM    942  CE2 TYR A  47      18.810  27.880  17.200  1.00  0.00           C
+ATOM    943  CZ  TYR A  47      19.860  27.050  17.640  1.00  0.00           C
+ATOM    944  OH  TYR A  47      19.550  25.830  18.160  1.00  0.00           O
+ATOM    945  H   TYR A  47      21.640  32.820  14.930  1.00  0.00           H
+ATOM    946  HH  TYR A  47      20.430  25.360  18.360  1.00  0.00           H
+ATOM    947  N   ILE A  48      21.780  28.540  13.950  1.00  0.00           N
+ATOM    948  CA  ILE A  48      21.520  27.310  13.180  1.00  0.00           C
+ATOM    949  C   ILE A  48      21.020  26.220  14.160  1.00  0.00           C
+ATOM    950  O   ILE A  48      21.830  25.610  14.860  1.00  0.00           O
+ATOM    951  CB  ILE A  48      22.690  26.740  12.360  1.00  0.00           C
+ATOM    952  CG1 ILE A  48      23.290  27.810  11.440  1.00  0.00           C
+ATOM    953  CG2 ILE A  48      22.190  25.530  11.530  1.00  0.00           C
+ATOM    954  CD1 ILE A  48      24.590  27.340  10.790  1.00  0.00           C
+ATOM    955  H   ILE A  48      22.490  28.450  14.670  1.00  0.00           H
+ATOM    956  N   PRO A  49      19.710  25.990  14.170  1.00  0.00           N
+ATOM    957  CA  PRO A  49      19.060  24.890  14.910  1.00  0.00           C
+ATOM    958  C   PRO A  49      19.640  23.510  14.600  1.00  0.00           C
+ATOM    959  O   PRO A  49      19.760  23.140  13.430  1.00  0.00           O
+ATOM    960  CB  PRO A  49      17.610  24.990  14.460  1.00  0.00           C
+ATOM    961  CG  PRO A  49      17.390  26.490  14.310  1.00  0.00           C
+ATOM    962  CD  PRO A  49      18.690  26.910  13.630  1.00  0.00           C
+ATOM    963  N   ASN A  50      20.220  22.920  15.650  1.00  0.00           N
+ATOM    964  CA  ASN A  50      20.850  21.580  15.700  1.00  0.00           C
+ATOM    965  C   ASN A  50      22.360  21.540  15.390  1.00  0.00           C
+ATOM    966  O   ASN A  50      22.980  20.500  15.630  1.00  0.00           O
+ATOM    967  CB  ASN A  50      20.150  20.440  14.940  1.00  0.00           C
+ATOM    968  CG  ASN A  50      18.800  20.030  15.540  1.00  0.00           C
+ATOM    969  OD1 ASN A  50      17.740  20.340  15.010  1.00  0.00           O
+ATOM    970  ND2 ASN A  50      18.790  19.260  16.610  1.00  0.00           N
+ATOM    971  H   ASN A  50      20.430  23.470  16.470  1.00  0.00           H
+ATOM    972 HD21 ASN A  50      19.610  19.040  17.130  1.00  0.00           H
+ATOM    973 HD22 ASN A  50      17.880  18.990  16.950  1.00  0.00           H
+ATOM    974  N   ARG A  51      22.930  22.590  14.800  1.00  0.00           N
+ATOM    975  CA  ARG A  51      24.390  22.580  14.560  1.00  0.00           C
+ATOM    976  C   ARG A  51      25.100  23.850  15.040  1.00  0.00           C
+ATOM    977  O   ARG A  51      26.320  23.720  15.290  1.00  0.00           O
+ATOM    978  CB  ARG A  51      24.730  22.340  13.080  1.00  0.00           C
+ATOM    979  CG  ARG A  51      24.750  20.870  12.630  1.00  0.00           C
+ATOM    980  CD  ARG A  51      25.740  19.980  13.390  1.00  0.00           C
+ATOM    981  NE  ARG A  51      27.140  20.390  13.220  1.00  0.00           N
+ATOM    982  CZ  ARG A  51      28.150  19.870  13.950  1.00  0.00           C
+ATOM    983  NH1 ARG A  51      27.950  18.830  14.750  1.00  0.00           N
+ATOM    984  NH2 ARG A  51      29.390  20.340  13.830  1.00  0.00           N
+ATOM    985  OXT ARG A  51      24.530  24.930  14.790  1.00  0.00           O
+ATOM    986  H   ARG A  51      22.470  23.450  14.570  1.00  0.00           H
+ATOM    987  HE  ARG A  51      27.330  21.010  12.450  1.00  0.00           H
+ATOM    988 HH11 ARG A  51      27.040  18.440  14.940  1.00  0.00           H
+ATOM    989 HH12 ARG A  51      28.740  18.400  15.210  1.00  0.00           H
+ATOM    990 HH21 ARG A  51      29.650  21.100  13.230  1.00  0.00           H
+ATOM    991 HH22 ARG A  51      30.100  19.980  14.450  1.00  0.00           H
+TER     992      ARG A  51
+HETATM  993 NA    NA C  12      16.260  23.720  18.910  1.00  0.00          NA
+HETATM  994  O   HOH B 835      19.850  26.680  33.020  1.00  0.00           O
+HETATM  995  H1  HOH B 835      20.310  27.550  32.820  1.00  0.00           H
+HETATM  996  H2  HOH B 835      20.450  26.110  33.580  1.00  0.00           H
+HETATM  997  O   HOH B1026       8.460  29.650  39.060  1.00  0.00           O
+HETATM  998  H1  HOH B1026       7.740  30.120  38.550  1.00  0.00           H
+HETATM  999  H2  HOH B1026       8.660  30.160  39.900  1.00  0.00           H
+HETATM 1000  O   HOH B1417      11.340  36.480  36.160  1.00  0.00           O
+HETATM 1001  H1  HOH B1417      11.830  35.700  36.560  1.00  0.00           H
+HETATM 1002  H2  HOH B1417      11.000  37.060  36.900  1.00  0.00           H
+HETATM 1003  O   HOH B1632       9.240  32.250  36.410  1.00  0.00           O
+HETATM 1004  H1  HOH B1632       8.260  32.430  36.340  1.00  0.00           H
+HETATM 1005  H2  HOH B1632       9.630  32.830  37.120  1.00  0.00           H
+HETATM 1006  O   HOH B1924      17.300  15.020  16.580  1.00  0.00           O
+HETATM 1007  H1  HOH B1924      17.310  15.610  15.770  1.00  0.00           H
+HETATM 1008  H2  HOH B1924      18.220  14.670  16.760  1.00  0.00           H
+HETATM 1009  O   HOH B2488      25.040  25.940  32.620  1.00  0.00           O
+HETATM 1010  H1  HOH B2488      25.500  26.460  33.340  1.00  0.00           H
+HETATM 1011  H2  HOH B2488      25.510  25.070  32.490  1.00  0.00           H
+HETATM 1012  O   HOH B2504      10.230  33.610  38.760  1.00  0.00           O
+HETATM 1013  H1  HOH B2504      11.090  33.110  38.900  1.00  0.00           H
+HETATM 1014  H2  HOH B2504      10.230  34.420  39.350  1.00  0.00           H
+HETATM 1015  O   HOH B2561      24.170  31.850  33.870  1.00  0.00           O
+HETATM 1016  H1  HOH B2561      24.070  30.860  33.800  1.00  0.00           H
+HETATM 1017  H2  HOH B2561      25.120  32.080  34.090  1.00  0.00           H
+HETATM 1018  O   HOH B2639      20.330  34.690  34.970  1.00  0.00           O
+HETATM 1019  H1  HOH B2639      19.610  35.290  34.620  1.00  0.00           H
+HETATM 1020  H2  HOH B2639      20.310  33.820  34.480  1.00  0.00           H
+HETATM 1021  O   HOH B2869      20.450  15.490  17.620  1.00  0.00           O
+HETATM 1022  H1  HOH B2869      19.830  16.270  17.740  1.00  0.00           H
+HETATM 1023  H2  HOH B2869      21.050  15.420  18.410  1.00  0.00           H
+HETATM 1024  O   HOH B3222      27.210  30.220  31.760  1.00  0.00           O
+HETATM 1025  H1  HOH B3222      26.570  29.800  32.400  1.00  0.00           H
+HETATM 1026  H2  HOH B3222      27.970  30.630  32.260  1.00  0.00           H
+HETATM 1027  O   HOH B3293      17.620  35.240  36.380  1.00  0.00           O
+HETATM 1028  H1  HOH B3293      16.710  34.810  36.390  1.00  0.00           H
+HETATM 1029  H2  HOH B3293      18.310  34.550  36.540  1.00  0.00           H
+HETATM 1030  O   HOH C 327      19.190  17.630  19.270  1.00  0.00           O
+HETATM 1031  H1  HOH C 327      18.230  17.890  19.180  1.00  0.00           H
+HETATM 1032  H2  HOH C 327      19.660  18.260  19.890  1.00  0.00           H
+HETATM 1033  O   HOH C 827      10.030  19.550  21.880  1.00  0.00           O
+HETATM 1034  H1  HOH C 827       9.960  18.970  22.690  1.00  0.00           H
+HETATM 1035  H2  HOH C 827      10.360  20.450  22.140  1.00  0.00           H
+HETATM 1036  O   HOH C 923      17.630  24.770  20.160  1.00  0.00           O
+HETATM 1037  H1  HOH C 923      18.510  25.250  20.070  1.00  0.00           H
+HETATM 1038  H2  HOH C 923      17.540  24.430  21.090  1.00  0.00           H
+HETATM 1039  O   HOH C1041       9.720  20.420  33.120  1.00  0.00           O
+HETATM 1040  H1  HOH C1041      10.090  19.830  32.400  1.00  0.00           H
+HETATM 1041  H2  HOH C1041       9.200  19.860  33.760  1.00  0.00           H
+HETATM 1042  O   HOH C1046      13.770  23.410  34.090  1.00  0.00           O
+HETATM 1043  H1  HOH C1046      13.830  24.400  34.050  1.00  0.00           H
+HETATM 1044  H2  HOH C1046      12.840  23.150  34.380  1.00  0.00           H
+HETATM 1045  O   HOH C1830      29.220  23.780  15.410  1.00  0.00           O
+HETATM 1046  H1  HOH C1830      29.420  23.320  16.270  1.00  0.00           H
+HETATM 1047  H2  HOH C1830      28.230  23.840  15.280  1.00  0.00           H
+HETATM 1048  O   HOH C1959      26.820  17.410  25.340  1.00  0.00           O
+HETATM 1049  H1  HOH C1959      26.870  16.650  24.690  1.00  0.00           H
+HETATM 1050  H2  HOH C1959      26.640  17.060  26.250  1.00  0.00           H
+HETATM 1051  O   HOH C1993      30.970  23.530  22.110  1.00  0.00           O
+HETATM 1052  H1  HOH C1993      30.730  24.140  21.360  1.00  0.00           H
+HETATM 1053  H2  HOH C1993      30.670  22.600  21.890  1.00  0.00           H
+HETATM 1054  O   HOH C2471      22.920  22.570  22.780  1.00  0.00           O
+HETATM 1055  H1  HOH C2471      23.070  23.190  22.010  1.00  0.00           H
+HETATM 1056  H2  HOH C2471      22.310  21.830  22.500  1.00  0.00           H
+HETATM 1057  O   HOH C2518       8.990  20.940  27.510  1.00  0.00           O
+HETATM 1058  H1  HOH C2518       8.800  19.970  27.630  1.00  0.00           H
+HETATM 1059  H2  HOH C2518       9.700  21.060  26.820  1.00  0.00           H
+HETATM 1060  O   HOH C2570      26.760  20.210  21.950  1.00  0.00           O
+HETATM 1061  H1  HOH C2570      27.690  20.310  22.290  1.00  0.00           H
+HETATM 1062  H2  HOH C2570      26.710  20.540  21.000  1.00  0.00           H
+HETATM 1063  O   HOH A  52      21.650  18.570  17.310  1.00  0.00           O
+HETATM 1064  H1  HOH A  52      21.560  18.800  18.280  1.00  0.00           H
+HETATM 1065  H2  HOH A  52      22.420  19.070  16.930  1.00  0.00           H
+HETATM 1066  O   HOH A  53      14.510  23.890  17.760  1.00  0.00           O
+HETATM 1067  H1  HOH A  53      14.790  24.370  16.920  1.00  0.00           H
+HETATM 1068  H2  HOH A  53      13.570  24.160  17.990  1.00  0.00           H
+HETATM 1069  O   HOH A  54      10.250  25.780  40.110  1.00  0.00           O
+HETATM 1070  H1  HOH A  54      10.970  25.150  40.390  1.00  0.00           H
+HETATM 1071  H2  HOH A  54      10.190  26.530  40.770  1.00  0.00           H
+HETATM 1072  O   HOH A  55      16.680  22.390  27.940  1.00  0.00           O
+HETATM 1073  H1  HOH A  55      17.560  22.710  28.300  1.00  0.00           H
+HETATM 1074  H2  HOH A  55      16.640  22.580  26.960  1.00  0.00           H
+HETATM 1075  O   HOH A  56       9.310  22.490  24.900  1.00  0.00           O
+HETATM 1076  H1  HOH A  56       8.430  22.400  24.440  1.00  0.00           H
+HETATM 1077  H2  HOH A  56       9.960  21.840  24.510  1.00  0.00           H
+HETATM 1078  O   HOH A  57      16.660  21.960  17.620  1.00  0.00           O
+HETATM 1079  H1  HOH A  57      17.390  21.990  16.940  1.00  0.00           H
+HETATM 1080  H2  HOH A  57      16.500  21.010  17.900  1.00  0.00           H
+HETATM 1081  O   HOH A  58      16.590  18.980  18.620  1.00  0.00           O
+HETATM 1082  H1  HOH A  58      15.840  18.530  18.130  1.00  0.00           H
+HETATM 1083  H2  HOH A  58      16.290  19.200  19.550  1.00  0.00           H
+HETATM 1084  O   HOH A  59      17.560  33.900  32.820  1.00  0.00           O
+HETATM 1085  H1  HOH A  59      16.850  33.260  33.120  1.00  0.00           H
+HETATM 1086  H2  HOH A  59      18.430  33.410  32.720  1.00  0.00           H
+HETATM 1087  O   HOH A  60      16.400  27.600  32.270  1.00  0.00           O
+HETATM 1088  H1  HOH A  60      15.860  28.370  31.940  1.00  0.00           H
+HETATM 1089  H2  HOH A  60      17.170  27.950  32.820  1.00  0.00           H
+HETATM 1090  O   HOH A  61      11.430  22.110  34.630  1.00  0.00           O
+HETATM 1091  H1  HOH A  61      11.250  21.740  35.540  1.00  0.00           H
+HETATM 1092  H2  HOH A  61      10.950  21.550  33.950  1.00  0.00           H
+HETATM 1093  O   HOH A  62      13.340  25.120  37.320  1.00  0.00           O
+HETATM 1094  H1  HOH A  62      14.120  25.130  36.690  1.00  0.00           H
+HETATM 1095  H2  HOH A  62      12.500  25.170  36.790  1.00  0.00           H
+HETATM 1096  O   HOH A  63      12.950  33.600  34.310  1.00  0.00           O
+HETATM 1097  H1  HOH A  63      12.010  33.640  33.970  1.00  0.00           H
+HETATM 1098  H2  HOH A  63      12.950  33.200  35.230  1.00  0.00           H
+HETATM 1099  O   HOH A  64      30.670  19.500  16.170  1.00  0.00           O
+HETATM 1100  H1  HOH A  64      30.080  20.050  16.760  1.00  0.00           H
+HETATM 1101  H2  HOH A  64      30.480  18.530  16.320  1.00  0.00           H
+HETATM 1102  O   HOH A  65      33.140  20.790  16.330  1.00  0.00           O
+HETATM 1103  H1  HOH A  65      32.310  20.230  16.410  1.00  0.00           H
+HETATM 1104  H2  HOH A  65      32.960  21.700  16.700  1.00  0.00           H
+HETATM 1105  O   HOH A  66      25.770  17.510  15.790  1.00  0.00           O
+HETATM 1106  H1  HOH A  66      25.390  18.010  16.580  1.00  0.00           H
+HETATM 1107  H2  HOH A  66      25.370  16.590  15.770  1.00  0.00           H
+HETATM 1108  O   HOH A  67      25.870  20.080  26.020  1.00  0.00           O
+HETATM 1109  H1  HOH A  67      25.250  20.440  25.330  1.00  0.00           H
+HETATM 1110  H2  HOH A  67      26.210  19.180  25.720  1.00  0.00           H
+HETATM 1111  O   HOH A  68      23.280  19.710  29.590  1.00  0.00           O
+HETATM 1112  H1  HOH A  68      24.260  19.560  29.690  1.00  0.00           H
+HETATM 1113  H2  HOH A  68      22.850  19.660  30.500  1.00  0.00           H
+HETATM 1114  O   HOH A  69      13.660  26.110  33.530  1.00  0.00           O
+HETATM 1115  H1  HOH A  69      12.660  26.140  33.590  1.00  0.00           H
+HETATM 1116  H2  HOH A  69      13.990  26.950  33.090  1.00  0.00           H
+HETATM 1117  O   HOH A  70      23.390  23.170  30.810  1.00  0.00           O
+HETATM 1118  H1  HOH A  70      23.570  24.040  30.350  1.00  0.00           H
+HETATM 1119  H2  HOH A  70      24.080  23.030  31.520  1.00  0.00           H
+HETATM 1120  O   HOH A  71      22.150  31.070  32.160  1.00  0.00           O
+HETATM 1121  H1  HOH A  71      21.270  31.180  32.640  1.00  0.00           H
+HETATM 1122  H2  HOH A  71      22.860  31.560  32.660  1.00  0.00           H
+HETATM 1123  O   HOH A  72      30.140  25.470  20.070  1.00  0.00           O
+HETATM 1124  H1  HOH A  72      30.270  26.220  20.720  1.00  0.00           H
+HETATM 1125  H2  HOH A  72      29.180  25.430  19.800  1.00  0.00           H
+HETATM 1126  O   HOH A  73      29.660  20.790  22.690  1.00  0.00           O
+HETATM 1127  H1  HOH A  73      29.290  21.250  21.880  1.00  0.00           H
+HETATM 1128  H2  HOH A  73      29.030  20.930  23.460  1.00  0.00           H
+HETATM 1129  O   HOH A  74      24.720  20.740  23.650  1.00  0.00           O
+HETATM 1130  H1  HOH A  74      25.450  20.660  22.980  1.00  0.00           H
+HETATM 1131  H2  HOH A  74      24.130  21.510  23.410  1.00  0.00           H
+HETATM 1132  O   HOH A  75      11.330  24.730  35.270  1.00  0.00           O
+HETATM 1133  H1  HOH A  75      11.210  23.740  35.300  1.00  0.00           H
+HETATM 1134  H2  HOH A  75      10.450  25.170  35.090  1.00  0.00           H
+HETATM 1135  O   HOH A  76      16.550  37.270  38.760  1.00  0.00           O
+HETATM 1136  H1  HOH A  76      15.770  37.560  38.210  1.00  0.00           H
+HETATM 1137  H2  HOH A  76      16.690  36.290  38.640  1.00  0.00           H
+HETATM 1138  O   HOH A  77      14.290  37.710  37.220  1.00  0.00           O
+HETATM 1139  H1  HOH A  77      13.730  37.200  37.870  1.00  0.00           H
+HETATM 1140  H2  HOH A  77      14.340  37.220  36.350  1.00  0.00           H
+ENDMDL
+MODEL        2
+ATOM      1  O5'  DA B   1       7.900  34.300  47.200  1.00  0.00           O
+ATOM      2  C5'  DA B   1       8.090  33.680  45.920  1.00  0.00           C
+ATOM      3  C4'  DA B   1       9.560  33.570  45.550  1.00  0.00           C
+ATOM      4  O4'  DA B   1      10.240  32.680  46.480  1.00  0.00           O
+ATOM      5  C3'  DA B   1       9.700  32.900  44.190  1.00  0.00           C
+ATOM      6  O3'  DA B   1      10.560  33.600  43.250  1.00  0.00           O
+ATOM      7  C2'  DA B   1      10.150  31.500  44.510  1.00  0.00           C
+ATOM      8  C1'  DA B   1      11.070  31.870  45.640  1.00  0.00           C
+ATOM      9  N9   DA B   1      11.670  30.720  46.360  1.00  0.00           N
+ATOM     10  C8   DA B   1      10.990  29.700  46.830  1.00  0.00           C
+ATOM     11  N7   DA B   1      11.810  28.780  47.370  1.00  0.00           N
+ATOM     12  C5   DA B   1      13.050  29.250  47.200  1.00  0.00           C
+ATOM     13  C6   DA B   1      14.350  28.810  47.490  1.00  0.00           C
+ATOM     14  N6   DA B   1      14.560  27.820  48.320  1.00  0.00           N
+ATOM     15  N1   DA B   1      15.430  29.500  47.080  1.00  0.00           N
+ATOM     16  C2   DA B   1      15.300  30.660  46.440  1.00  0.00           C
+ATOM     17  N3   DA B   1      14.080  31.150  46.200  1.00  0.00           N
+ATOM     18  C4   DA B   1      12.960  30.480  46.550  1.00  0.00           C
+ATOM     19  H61  DA B   1      15.510  27.610  48.570  1.00  0.00           H
+ATOM     20  H62  DA B   1      13.810  27.230  48.630  1.00  0.00           H
+ATOM     21 HO5'  DA B   1       8.540  35.110  47.220  1.00  0.00           H
+ATOM     22  P    DA B   2       9.940  34.790  42.370  1.00  0.00           P
+ATOM     23  OP1  DA B   2       9.750  35.990  43.220  1.00  0.00           O
+ATOM     24  OP2  DA B   2       8.760  34.230  41.670  1.00  0.00           O
+ATOM     25  O5'  DA B   2      10.990  35.100  41.200  1.00  0.00           O
+ATOM     26  C5'  DA B   2      12.310  35.610  41.490  1.00  0.00           C
+ATOM     27  C4'  DA B   2      13.370  34.550  41.300  1.00  0.00           C
+ATOM     28  O4'  DA B   2      13.240  33.470  42.230  1.00  0.00           O
+ATOM     29  C3'  DA B   2      13.320  33.940  39.900  1.00  0.00           C
+ATOM     30  O3'  DA B   2      14.290  34.540  38.990  1.00  0.00           O
+ATOM     31  C2'  DA B   2      13.530  32.470  40.140  1.00  0.00           C
+ATOM     32  C1'  DA B   2      14.020  32.470  41.580  1.00  0.00           C
+ATOM     33  N9   DA B   2      13.910  31.210  42.320  1.00  0.00           N
+ATOM     34  C8   DA B   2      12.820  30.480  42.470  1.00  0.00           C
+ATOM     35  N7   DA B   2      13.150  29.270  42.920  1.00  0.00           N
+ATOM     36  C5   DA B   2      14.480  29.250  43.070  1.00  0.00           C
+ATOM     37  C6   DA B   2      15.470  28.330  43.460  1.00  0.00           C
+ATOM     38  N6   DA B   2      15.220  27.080  43.710  1.00  0.00           N
+ATOM     39  N1   DA B   2      16.780  28.670  43.520  1.00  0.00           N
+ATOM     40  C2   DA B   2      17.180  29.880  43.150  1.00  0.00           C
+ATOM     41  N3   DA B   2      16.280  30.780  42.730  1.00  0.00           N
+ATOM     42  C4   DA B   2      14.960  30.500  42.690  1.00  0.00           C
+ATOM     43  H61  DA B   2      15.980  26.440  43.790  1.00  0.00           H
+ATOM     44  H62  DA B   2      14.300  26.840  44.050  1.00  0.00           H
+ATOM     45  P    DT B   3      13.810  35.070  37.550  1.00  0.00           P
+ATOM     46  OP1  DT B   3      13.210  36.410  37.690  1.00  0.00           O
+ATOM     47  OP2  DT B   3      13.000  33.990  36.940  1.00  0.00           O
+ATOM     48  O5'  DT B   3      15.170  35.260  36.720  1.00  0.00           O
+ATOM     49  C5'  DT B   3      15.760  34.190  35.960  1.00  0.00           C
+ATOM     50  C4'  DT B   3      16.530  33.120  36.740  1.00  0.00           C
+ATOM     51  O4'  DT B   3      15.910  32.240  37.690  1.00  0.00           O
+ATOM     52  C3'  DT B   3      17.220  32.220  35.750  1.00  0.00           C
+ATOM     53  O3'  DT B   3      18.440  32.850  35.280  1.00  0.00           O
+ATOM     54  C2'  DT B   3      17.520  30.980  36.550  1.00  0.00           C
+ATOM     55  C1'  DT B   3      17.040  31.360  37.930  1.00  0.00           C
+ATOM     56  N1   DT B   3      16.710  30.160  38.750  1.00  0.00           N
+ATOM     57  C2   DT B   3      17.730  29.450  39.420  1.00  0.00           C
+ATOM     58  O2   DT B   3      18.910  29.610  39.150  1.00  0.00           O
+ATOM     59  N3   DT B   3      17.410  28.320  40.170  1.00  0.00           N
+ATOM     60  C4   DT B   3      16.090  27.860  40.200  1.00  0.00           C
+ATOM     61  O4   DT B   3      15.910  26.770  40.740  1.00  0.00           O
+ATOM     62  C5   DT B   3      15.060  28.540  39.540  1.00  0.00           C
+ATOM     63  C7   DT B   3      13.590  28.120  39.660  1.00  0.00           C
+ATOM     64  C6   DT B   3      15.380  29.700  38.830  1.00  0.00           C
+ATOM     65  H3   DT B   3      18.080  27.970  40.840  1.00  0.00           H
+ATOM     66  P    DT B   4      19.340  32.370  34.040  1.00  0.00           P
+ATOM     67  OP1  DT B   4      20.000  33.580  33.490  1.00  0.00           O
+ATOM     68  OP2  DT B   4      18.630  31.460  33.110  1.00  0.00           O
+ATOM     69  O5'  DT B   4      20.470  31.440  34.690  1.00  0.00           O
+ATOM     70  C5'  DT B   4      21.550  32.010  35.460  1.00  0.00           C
+ATOM     71  C4'  DT B   4      22.290  30.950  36.270  1.00  0.00           C
+ATOM     72  O4'  DT B   4      21.350  30.090  36.940  1.00  0.00           O
+ATOM     73  C3'  DT B   4      23.170  29.980  35.480  1.00  0.00           C
+ATOM     74  O3'  DT B   4      24.410  29.890  36.230  1.00  0.00           O
+ATOM     75  C2'  DT B   4      22.440  28.670  35.400  1.00  0.00           C
+ATOM     76  C1'  DT B   4      21.760  28.720  36.750  1.00  0.00           C
+ATOM     77  N1   DT B   4      20.540  27.900  36.770  1.00  0.00           N
+ATOM     78  C2   DT B   4      20.340  26.960  37.810  1.00  0.00           C
+ATOM     79  O2   DT B   4      21.210  26.640  38.620  1.00  0.00           O
+ATOM     80  N3   DT B   4      19.110  26.330  37.920  1.00  0.00           N
+ATOM     81  C4   DT B   4      18.070  26.630  37.030  1.00  0.00           C
+ATOM     82  O4   DT B   4      16.970  26.210  37.340  1.00  0.00           O
+ATOM     83  C5   DT B   4      18.260  27.540  35.980  1.00  0.00           C
+ATOM     84  C7   DT B   4      17.150  27.860  34.980  1.00  0.00           C
+ATOM     85  C6   DT B   4      19.500  28.160  35.850  1.00  0.00           C
+ATOM     86  H3   DT B   4      19.010  25.610  38.610  1.00  0.00           H
+ATOM     87  P    DG B   5      25.700  29.160  35.640  1.00  0.00           P
+ATOM     88  OP1  DG B   5      26.910  29.640  36.350  1.00  0.00           O
+ATOM     89  OP2  DG B   5      25.710  29.280  34.170  1.00  0.00           O
+ATOM     90  O5'  DG B   5      25.450  27.610  35.940  1.00  0.00           O
+ATOM     91  C5'  DG B   5      25.510  27.120  37.290  1.00  0.00           C
+ATOM     92  C4'  DG B   5      24.950  25.710  37.370  1.00  0.00           C
+ATOM     93  O4'  DG B   5      23.540  25.570  37.200  1.00  0.00           O
+ATOM     94  C3'  DG B   5      25.640  24.800  36.380  1.00  0.00           C
+ATOM     95  O3'  DG B   5      26.510  23.920  37.090  1.00  0.00           O
+ATOM     96  C2'  DG B   5      24.530  24.010  35.740  1.00  0.00           C
+ATOM     97  C1'  DG B   5      23.370  24.230  36.700  1.00  0.00           C
+ATOM     98  N9   DG B   5      22.110  24.240  35.950  1.00  0.00           N
+ATOM     99  C8   DG B   5      21.820  25.160  35.050  1.00  0.00           C
+ATOM    100  N7   DG B   5      20.510  25.190  34.800  1.00  0.00           N
+ATOM    101  C5   DG B   5      19.960  24.220  35.530  1.00  0.00           C
+ATOM    102  C6   DG B   5      18.680  23.650  35.550  1.00  0.00           C
+ATOM    103  O6   DG B   5      17.730  24.150  34.950  1.00  0.00           O
+ATOM    104  N1   DG B   5      18.480  22.470  36.270  1.00  0.00           N
+ATOM    105  C2   DG B   5      19.550  21.900  36.940  1.00  0.00           C
+ATOM    106  N2   DG B   5      19.400  20.810  37.640  1.00  0.00           N
+ATOM    107  N3   DG B   5      20.770  22.460  36.920  1.00  0.00           N
+ATOM    108  C4   DG B   5      20.990  23.600  36.250  1.00  0.00           C
+ATOM    109  H1   DG B   5      17.570  22.080  36.400  1.00  0.00           H
+ATOM    110  H21  DG B   5      18.500  20.400  37.820  1.00  0.00           H
+ATOM    111  H22  DG B   5      20.220  20.410  38.070  1.00  0.00           H
+ATOM    112  P    DT B   6      27.570  22.980  36.320  1.00  0.00           P
+ATOM    113  OP1  DT B   6      28.840  23.090  37.070  1.00  0.00           O
+ATOM    114  OP2  DT B   6      27.550  23.320  34.880  1.00  0.00           O
+ATOM    115  O5'  DT B   6      26.970  21.500  36.460  1.00  0.00           O
+ATOM    116  C5'  DT B   6      26.660  21.010  37.780  1.00  0.00           C
+ATOM    117  C4'  DT B   6      25.480  20.060  37.830  1.00  0.00           C
+ATOM    118  O4'  DT B   6      24.270  20.590  37.250  1.00  0.00           O
+ATOM    119  C3'  DT B   6      25.870  18.830  37.050  1.00  0.00           C
+ATOM    120  O3'  DT B   6      26.180  17.750  37.970  1.00  0.00           O
+ATOM    121  C2'  DT B   6      24.730  18.570  36.110  1.00  0.00           C
+ATOM    122  C1'  DT B   6      23.620  19.470  36.650  1.00  0.00           C
+ATOM    123  N1   DT B   6      22.770  19.970  35.540  1.00  0.00           N
+ATOM    124  C2   DT B   6      21.450  19.520  35.410  1.00  0.00           C
+ATOM    125  O2   DT B   6      21.040  18.520  35.990  1.00  0.00           O
+ATOM    126  N3   DT B   6      20.620  20.080  34.450  1.00  0.00           N
+ATOM    127  C4   DT B   6      21.110  21.080  33.610  1.00  0.00           C
+ATOM    128  O4   DT B   6      20.250  21.610  32.900  1.00  0.00           O
+ATOM    129  C5   DT B   6      22.430  21.530  33.690  1.00  0.00           C
+ATOM    130  C7   DT B   6      23.010  22.560  32.720  1.00  0.00           C
+ATOM    131  C6   DT B   6      23.260  20.950  34.660  1.00  0.00           C
+ATOM    132  H3   DT B   6      19.690  19.760  34.330  1.00  0.00           H
+ATOM    133  P    DG B   7      26.940  16.410  37.520  1.00  0.00           P
+ATOM    134  OP1  DG B   7      27.300  15.670  38.750  1.00  0.00           O
+ATOM    135  OP2  DG B   7      27.980  16.660  36.490  1.00  0.00           O
+ATOM    136  O5'  DG B   7      25.760  15.610  36.810  1.00  0.00           O
+ATOM    137  C5'  DG B   7      24.810  14.940  37.670  1.00  0.00           C
+ATOM    138  C4'  DG B   7      23.600  14.400  36.950  1.00  0.00           C
+ATOM    139  O4'  DG B   7      23.010  15.470  36.210  1.00  0.00           O
+ATOM    140  C3'  DG B   7      24.030  13.350  35.960  1.00  0.00           C
+ATOM    141  O3'  DG B   7      23.420  12.100  36.330  1.00  0.00           O
+ATOM    142  C2'  DG B   7      23.660  13.880  34.610  1.00  0.00           C
+ATOM    143  C1'  DG B   7      22.540  14.850  35.010  1.00  0.00           C
+ATOM    144  N9   DG B   7      22.230  15.850  33.980  1.00  0.00           N
+ATOM    145  C8   DG B   7      23.070  16.740  33.480  1.00  0.00           C
+ATOM    146  N7   DG B   7      22.410  17.670  32.800  1.00  0.00           N
+ATOM    147  C5   DG B   7      21.130  17.310  32.800  1.00  0.00           C
+ATOM    148  C6   DG B   7      20.000  17.790  32.140  1.00  0.00           C
+ATOM    149  O6   DG B   7      19.960  18.950  31.720  1.00  0.00           O
+ATOM    150  N1   DG B   7      18.810  17.070  32.210  1.00  0.00           N
+ATOM    151  C2   DG B   7      18.740  15.960  33.030  1.00  0.00           C
+ATOM    152  N2   DG B   7      17.560  15.540  33.390  1.00  0.00           N
+ATOM    153  N3   DG B   7      19.820  15.510  33.670  1.00  0.00           N
+ATOM    154  C4   DG B   7      21.010  16.150  33.560  1.00  0.00           C
+ATOM    155  H1   DG B   7      18.110  17.200  31.510  1.00  0.00           H
+ATOM    156  H21  DG B   7      16.720  15.890  32.970  1.00  0.00           H
+ATOM    157  H22  DG B   7      17.510  14.780  34.050  1.00  0.00           H
+ATOM    158  P    DA B   8      23.780  10.720  35.610  1.00  0.00           P
+ATOM    159  OP1  DA B   8      23.750   9.670  36.660  1.00  0.00           O
+ATOM    160  OP2  DA B   8      25.000  10.920  34.800  1.00  0.00           O
+ATOM    161  O5'  DA B   8      22.510  10.570  34.640  1.00  0.00           O
+ATOM    162  C5'  DA B   8      21.190  10.840  35.170  1.00  0.00           C
+ATOM    163  C4'  DA B   8      20.100  11.090  34.130  1.00  0.00           C
+ATOM    164  O4'  DA B   8      20.340  12.330  33.460  1.00  0.00           O
+ATOM    165  C3'  DA B   8      20.100  10.000  33.080  1.00  0.00           C
+ATOM    166  O3'  DA B   8      18.760   9.510  32.870  1.00  0.00           O
+ATOM    167  C2'  DA B   8      20.780  10.680  31.900  1.00  0.00           C
+ATOM    168  C1'  DA B   8      20.130  12.040  32.070  1.00  0.00           C
+ATOM    169  N9   DA B   8      20.790  13.120  31.320  1.00  0.00           N
+ATOM    170  C8   DA B   8      22.080  13.410  31.300  1.00  0.00           C
+ATOM    171  N7   DA B   8      22.300  14.550  30.630  1.00  0.00           N
+ATOM    172  C5   DA B   8      21.110  14.940  30.180  1.00  0.00           C
+ATOM    173  C6   DA B   8      20.640  15.950  29.340  1.00  0.00           C
+ATOM    174  N6   DA B   8      21.420  16.930  28.940  1.00  0.00           N
+ATOM    175  N1   DA B   8      19.370  15.960  28.910  1.00  0.00           N
+ATOM    176  C2   DA B   8      18.490  15.020  29.280  1.00  0.00           C
+ATOM    177  N3   DA B   8      18.880  14.080  30.130  1.00  0.00           N
+ATOM    178  C4   DA B   8      20.160  14.020  30.580  1.00  0.00           C
+ATOM    179  H61  DA B   8      21.040  17.710  28.440  1.00  0.00           H
+ATOM    180  H62  DA B   8      22.400  16.850  29.120  1.00  0.00           H
+ATOM    181  P    DG B   9      18.500   8.090  32.190  1.00  0.00           P
+ATOM    182  OP1  DG B   9      17.370   7.460  32.890  1.00  0.00           O
+ATOM    183  OP2  DG B   9      19.770   7.340  32.050  1.00  0.00           O
+ATOM    184  O5'  DG B   9      17.940   8.490  30.750  1.00  0.00           O
+ATOM    185  C5'  DG B   9      16.660   9.140  30.600  1.00  0.00           C
+ATOM    186  C4'  DG B   9      16.600   9.790  29.230  1.00  0.00           C
+ATOM    187  O4'  DG B   9      17.530  10.880  29.010  1.00  0.00           O
+ATOM    188  C3'  DG B   9      16.920   8.750  28.180  1.00  0.00           C
+ATOM    189  O3'  DG B   9      15.740   8.540  27.370  1.00  0.00           O
+ATOM    190  C2'  DG B   9      18.230   9.180  27.600  1.00  0.00           C
+ATOM    191  C1'  DG B   9      18.080  10.690  27.680  1.00  0.00           C
+ATOM    192  N9   DG B   9      19.340  11.420  27.560  1.00  0.00           N
+ATOM    193  C8   DG B   9      20.420  11.200  28.300  1.00  0.00           C
+ATOM    194  N7   DG B   9      21.340  12.130  28.060  1.00  0.00           N
+ATOM    195  C5   DG B   9      20.830  12.960  27.150  1.00  0.00           C
+ATOM    196  C6   DG B   9      21.240  14.180  26.620  1.00  0.00           C
+ATOM    197  O6   DG B   9      22.390  14.590  26.760  1.00  0.00           O
+ATOM    198  N1   DG B   9      20.360  14.940  25.860  1.00  0.00           N
+ATOM    199  C2   DG B   9      19.090  14.440  25.580  1.00  0.00           C
+ATOM    200  N2   DG B   9      18.250  15.210  24.940  1.00  0.00           N
+ATOM    201  N3   DG B   9      18.710  13.240  26.040  1.00  0.00           N
+ATOM    202  C4   DG B   9      19.540  12.510  26.820  1.00  0.00           C
+ATOM    203  H1   DG B   9      20.570  15.910  25.730  1.00  0.00           H
+ATOM    204  H21  DG B   9      18.610  16.090  24.620  1.00  0.00           H
+ATOM    205  H22  DG B   9      17.280  14.980  24.850  1.00  0.00           H
+ATOM    206  P    DC B  10      15.730   7.600  26.080  1.00  0.00           P
+ATOM    207  OP1  DC B  10      14.330   7.280  25.720  1.00  0.00           O
+ATOM    208  OP2  DC B  10      16.670   6.470  26.320  1.00  0.00           O
+ATOM    209  O5'  DC B  10      16.290   8.620  24.970  1.00  0.00           O
+ATOM    210  C5'  DC B  10      15.340   9.470  24.320  1.00  0.00           C
+ATOM    211  C4'  DC B  10      15.820  10.070  23.010  1.00  0.00           C
+ATOM    212  O4'  DC B  10      16.890  11.010  23.240  1.00  0.00           O
+ATOM    213  C3'  DC B  10      16.310   8.970  22.090  1.00  0.00           C
+ATOM    214  O3'  DC B  10      15.690   9.130  20.800  1.00  0.00           O
+ATOM    215  C2'  DC B  10      17.810   9.110  22.140  1.00  0.00           C
+ATOM    216  C1'  DC B  10      17.940  10.620  22.340  1.00  0.00           C
+ATOM    217  N1   DC B  10      19.210  10.960  23.030  1.00  0.00           N
+ATOM    218  C2   DC B  10      19.840  12.200  22.840  1.00  0.00           C
+ATOM    219  O2   DC B  10      19.430  12.980  21.980  1.00  0.00           O
+ATOM    220  N3   DC B  10      20.970  12.490  23.530  1.00  0.00           N
+ATOM    221  C4   DC B  10      21.520  11.600  24.370  1.00  0.00           C
+ATOM    222  N4   DC B  10      22.740  11.850  24.800  1.00  0.00           N
+ATOM    223  C5   DC B  10      20.940  10.340  24.620  1.00  0.00           C
+ATOM    224  C6   DC B  10      19.790  10.030  23.910  1.00  0.00           C
+ATOM    225  H41  DC B  10      23.290  12.590  24.420  1.00  0.00           H
+ATOM    226  H42  DC B  10      23.190  11.230  25.480  1.00  0.00           H
+ATOM    227  P    DG B  11      15.990   8.210  19.520  1.00  0.00           P
+ATOM    228  OP1  DG B  11      14.760   8.240  18.690  1.00  0.00           O
+ATOM    229  OP2  DG B  11      16.470   6.880  19.990  1.00  0.00           O
+ATOM    230  O5'  DG B  11      17.250   8.920  18.800  1.00  0.00           O
+ATOM    231  C5'  DG B  11      17.210   9.990  17.830  1.00  0.00           C
+ATOM    232  C4'  DG B  11      18.630  10.490  17.530  1.00  0.00           C
+ATOM    233  O4'  DG B  11      19.330  10.890  18.730  1.00  0.00           O
+ATOM    234  C3'  DG B  11      19.540   9.440  16.910  1.00  0.00           C
+ATOM    235  O3'  DG B  11      19.610   9.540  15.490  1.00  0.00           O
+ATOM    236  C2'  DG B  11      20.930   9.760  17.400  1.00  0.00           C
+ATOM    237  C1'  DG B  11      20.690  10.950  18.310  1.00  0.00           C
+ATOM    238  N9   DG B  11      21.650  10.960  19.440  1.00  0.00           N
+ATOM    239  C8   DG B  11      21.820  10.040  20.380  1.00  0.00           C
+ATOM    240  N7   DG B  11      22.680  10.500  21.300  1.00  0.00           N
+ATOM    241  C5   DG B  11      23.100  11.700  20.890  1.00  0.00           C
+ATOM    242  C6   DG B  11      24.120  12.570  21.280  1.00  0.00           C
+ATOM    243  O6   DG B  11      24.870  12.330  22.220  1.00  0.00           O
+ATOM    244  N1   DG B  11      24.460  13.650  20.470  1.00  0.00           N
+ATOM    245  C2   DG B  11      23.760  13.860  19.280  1.00  0.00           C
+ATOM    246  N2   DG B  11      24.220  14.690  18.390  1.00  0.00           N
+ATOM    247  N3   DG B  11      22.760  13.050  18.930  1.00  0.00           N
+ATOM    248  C4   DG B  11      22.430  11.990  19.700  1.00  0.00           C
+ATOM    249 HO3'  DG B  11      20.280   8.910  15.000  1.00  0.00           H
+ATOM    250  H1   DG B  11      25.240  14.220  20.700  1.00  0.00           H
+ATOM    251  H21  DG B  11      25.030  15.270  18.540  1.00  0.00           H
+ATOM    252  H22  DG B  11      23.670  14.920  17.570  1.00  0.00           H
+TER     253       DG B  11
+ATOM    254  O5'  DC C   1      33.320  17.310  20.690  1.00  0.00           O
+ATOM    255  C5'  DC C   1      32.290  18.300  20.720  1.00  0.00           C
+ATOM    256  C4'  DC C   1      31.090  17.790  19.950  1.00  0.00           C
+ATOM    257  O4'  DC C   1      30.630  16.570  20.570  1.00  0.00           O
+ATOM    258  C3'  DC C   1      29.960  18.800  20.020  1.00  0.00           C
+ATOM    259  O3'  DC C   1      29.530  19.110  18.680  1.00  0.00           O
+ATOM    260  C2'  DC C   1      28.910  18.110  20.820  1.00  0.00           C
+ATOM    261  C1'  DC C   1      29.200  16.680  20.500  1.00  0.00           C
+ATOM    262  N1   DC C   1      28.600  15.780  21.490  1.00  0.00           N
+ATOM    263  C2   DC C   1      27.590  14.850  21.150  1.00  0.00           C
+ATOM    264  O2   DC C   1      26.910  15.000  20.140  1.00  0.00           O
+ATOM    265  N3   DC C   1      27.060  14.100  22.140  1.00  0.00           N
+ATOM    266  C4   DC C   1      27.430  14.280  23.410  1.00  0.00           C
+ATOM    267  N4   DC C   1      26.640  13.680  24.250  1.00  0.00           N
+ATOM    268  C5   DC C   1      28.380  15.230  23.830  1.00  0.00           C
+ATOM    269  C6   DC C   1      28.990  15.960  22.830  1.00  0.00           C
+ATOM    270  H41  DC C   1      25.710  13.520  23.930  1.00  0.00           H
+ATOM    271  H42  DC C   1      26.780  13.740  25.260  1.00  0.00           H
+ATOM    272 HO5'  DC C   1      34.100  17.690  21.200  1.00  0.00           H
+ATOM    273  P    DG C   2      28.950  20.560  18.340  1.00  0.00           P
+ATOM    274  OP1  DG C   2      29.030  20.690  16.870  1.00  0.00           O
+ATOM    275  OP2  DG C   2      29.690  21.550  19.160  1.00  0.00           O
+ATOM    276  O5'  DG C   2      27.440  20.410  18.850  1.00  0.00           O
+ATOM    277  C5'  DG C   2      26.370  20.150  17.910  1.00  0.00           C
+ATOM    278  C4'  DG C   2      25.070  19.960  18.660  1.00  0.00           C
+ATOM    279  O4'  DG C   2      25.120  18.750  19.420  1.00  0.00           O
+ATOM    280  C3'  DG C   2      24.780  21.120  19.620  1.00  0.00           C
+ATOM    281  O3'  DG C   2      23.740  21.980  19.110  1.00  0.00           O
+ATOM    282  C2'  DG C   2      24.370  20.440  20.910  1.00  0.00           C
+ATOM    283  C1'  DG C   2      24.180  19.010  20.460  1.00  0.00           C
+ATOM    284  N9   DG C   2      24.460  18.080  21.560  1.00  0.00           N
+ATOM    285  C8   DG C   2      25.530  18.060  22.360  1.00  0.00           C
+ATOM    286  N7   DG C   2      25.390  17.080  23.260  1.00  0.00           N
+ATOM    287  C5   DG C   2      24.210  16.500  23.040  1.00  0.00           C
+ATOM    288  C6   DG C   2      23.490  15.490  23.660  1.00  0.00           C
+ATOM    289  O6   DG C   2      23.980  14.750  24.510  1.00  0.00           O
+ATOM    290  N1   DG C   2      22.210  15.160  23.210  1.00  0.00           N
+ATOM    291  C2   DG C   2      21.690  15.840  22.110  1.00  0.00           C
+ATOM    292  N2   DG C   2      20.600  15.390  21.560  1.00  0.00           N
+ATOM    293  N3   DG C   2      22.360  16.830  21.520  1.00  0.00           N
+ATOM    294  C4   DG C   2      23.610  17.150  21.960  1.00  0.00           C
+ATOM    295  H1   DG C   2      21.710  14.430  23.660  1.00  0.00           H
+ATOM    296  H21  DG C   2      20.220  14.510  21.850  1.00  0.00           H
+ATOM    297  H22  DG C   2      20.070  15.980  20.940  1.00  0.00           H
+ATOM    298  P    DC C   3      23.090  23.250  19.860  1.00  0.00           P
+ATOM    299  OP1  DC C   3      22.510  24.150  18.830  1.00  0.00           O
+ATOM    300  OP2  DC C   3      24.020  23.820  20.840  1.00  0.00           O
+ATOM    301  O5'  DC C   3      21.920  22.640  20.770  1.00  0.00           O
+ATOM    302  C5'  DC C   3      20.840  21.960  20.140  1.00  0.00           C
+ATOM    303  C4'  DC C   3      20.060  21.050  21.060  1.00  0.00           C
+ATOM    304  O4'  DC C   3      20.890  20.160  21.830  1.00  0.00           O
+ATOM    305  C3'  DC C   3      19.230  21.870  22.030  1.00  0.00           C
+ATOM    306  O3'  DC C   3      17.830  21.700  21.770  1.00  0.00           O
+ATOM    307  C2'  DC C   3      19.690  21.370  23.390  1.00  0.00           C
+ATOM    308  C1'  DC C   3      20.150  19.960  23.050  1.00  0.00           C
+ATOM    309  N1   DC C   3      20.980  19.350  24.090  1.00  0.00           N
+ATOM    310  C2   DC C   3      20.530  18.250  24.870  1.00  0.00           C
+ATOM    311  O2   DC C   3      19.390  17.790  24.760  1.00  0.00           O
+ATOM    312  N3   DC C   3      21.310  17.780  25.860  1.00  0.00           N
+ATOM    313  C4   DC C   3      22.510  18.330  26.120  1.00  0.00           C
+ATOM    314  N4   DC C   3      23.390  17.580  26.740  1.00  0.00           N
+ATOM    315  C5   DC C   3      22.990  19.440  25.410  1.00  0.00           C
+ATOM    316  C6   DC C   3      22.220  19.930  24.380  1.00  0.00           C
+ATOM    317  H41  DC C   3      23.210  16.610  26.920  1.00  0.00           H
+ATOM    318  H42  DC C   3      24.280  17.960  27.030  1.00  0.00           H
+ATOM    319  P    DT C   4      16.730  22.600  22.510  1.00  0.00           P
+ATOM    320  OP1  DT C   4      15.940  23.190  21.420  1.00  0.00           O
+ATOM    321  OP2  DT C   4      17.390  23.530  23.450  1.00  0.00           O
+ATOM    322  O5'  DT C   4      15.940  21.460  23.310  1.00  0.00           O
+ATOM    323  C5'  DT C   4      15.200  20.450  22.600  1.00  0.00           C
+ATOM    324  C4'  DT C   4      14.530  19.450  23.540  1.00  0.00           C
+ATOM    325  O4'  DT C   4      15.580  18.840  24.320  1.00  0.00           O
+ATOM    326  C3'  DT C   4      13.590  20.020  24.600  1.00  0.00           C
+ATOM    327  O3'  DT C   4      12.530  19.040  24.730  1.00  0.00           O
+ATOM    328  C2'  DT C   4      14.410  20.280  25.820  1.00  0.00           C
+ATOM    329  C1'  DT C   4      15.270  19.030  25.700  1.00  0.00           C
+ATOM    330  N1   DT C   4      16.570  19.160  26.390  1.00  0.00           N
+ATOM    331  C2   DT C   4      17.090  18.020  27.020  1.00  0.00           C
+ATOM    332  O2   DT C   4      16.400  17.030  27.240  1.00  0.00           O
+ATOM    333  N3   DT C   4      18.340  18.120  27.610  1.00  0.00           N
+ATOM    334  C4   DT C   4      19.070  19.310  27.600  1.00  0.00           C
+ATOM    335  O4   DT C   4      19.980  19.380  28.420  1.00  0.00           O
+ATOM    336  C5   DT C   4      18.570  20.450  26.950  1.00  0.00           C
+ATOM    337  C7   DT C   4      19.320  21.780  26.980  1.00  0.00           C
+ATOM    338  C6   DT C   4      17.300  20.360  26.350  1.00  0.00           C
+ATOM    339  H3   DT C   4      18.710  17.320  28.090  1.00  0.00           H
+ATOM    340  P    DC C   5      11.090  19.350  25.360  1.00  0.00           P
+ATOM    341  OP1  DC C   5      10.040  18.610  24.610  1.00  0.00           O
+ATOM    342  OP2  DC C   5      10.920  20.800  25.570  1.00  0.00           O
+ATOM    343  O5'  DC C   5      11.160  18.700  26.830  1.00  0.00           O
+ATOM    344  C5'  DC C   5      11.450  17.300  27.010  1.00  0.00           C
+ATOM    345  C4'  DC C   5      12.190  17.020  28.310  1.00  0.00           C
+ATOM    346  O4'  DC C   5      13.490  17.630  28.380  1.00  0.00           O
+ATOM    347  C3'  DC C   5      11.430  17.570  29.490  1.00  0.00           C
+ATOM    348  O3'  DC C   5      10.670  16.480  30.030  1.00  0.00           O
+ATOM    349  C2'  DC C   5      12.460  18.170  30.410  1.00  0.00           C
+ATOM    350  C1'  DC C   5      13.780  17.670  29.790  1.00  0.00           C
+ATOM    351  N1   DC C   5      14.900  18.610  29.950  1.00  0.00           N
+ATOM    352  C2   DC C   5      16.100  18.220  30.570  1.00  0.00           C
+ATOM    353  O2   DC C   5      16.230  17.100  31.070  1.00  0.00           O
+ATOM    354  N3   DC C   5      17.120  19.100  30.670  1.00  0.00           N
+ATOM    355  C4   DC C   5      16.970  20.360  30.230  1.00  0.00           C
+ATOM    356  N4   DC C   5      18.070  21.070  30.130  1.00  0.00           N
+ATOM    357  C5   DC C   5      15.810  20.840  29.610  1.00  0.00           C
+ATOM    358  C6   DC C   5      14.780  19.920  29.440  1.00  0.00           C
+ATOM    359  H41  DC C   5      18.940  20.560  30.090  1.00  0.00           H
+ATOM    360  H42  DC C   5      17.980  22.060  29.950  1.00  0.00           H
+ATOM    361  P    DA C   6       9.320  16.700  30.860  1.00  0.00           P
+ATOM    362  OP1  DA C   6       8.590  15.430  30.730  1.00  0.00           O
+ATOM    363  OP2  DA C   6       8.630  17.980  30.570  1.00  0.00           O
+ATOM    364  O5'  DA C   6       9.900  16.830  32.340  1.00  0.00           O
+ATOM    365  C5'  DA C   6      10.480  15.640  32.900  1.00  0.00           C
+ATOM    366  C4'  DA C   6      11.440  15.990  34.010  1.00  0.00           C
+ATOM    367  O4'  DA C   6      12.550  16.780  33.560  1.00  0.00           O
+ATOM    368  C3'  DA C   6      10.750  16.800  35.070  1.00  0.00           C
+ATOM    369  O3'  DA C   6      10.150  15.830  35.940  1.00  0.00           O
+ATOM    370  C2'  DA C   6      11.790  17.780  35.560  1.00  0.00           C
+ATOM    371  C1'  DA C   6      13.030  17.410  34.760  1.00  0.00           C
+ATOM    372  N9   DA C   6      13.800  18.580  34.290  1.00  0.00           N
+ATOM    373  C8   DA C   6      13.320  19.620  33.620  1.00  0.00           C
+ATOM    374  N7   DA C   6      14.310  20.410  33.210  1.00  0.00           N
+ATOM    375  C5   DA C   6      15.450  19.880  33.660  1.00  0.00           C
+ATOM    376  C6   DA C   6      16.810  20.230  33.710  1.00  0.00           C
+ATOM    377  N6   DA C   6      17.380  21.210  33.020  1.00  0.00           N
+ATOM    378  N1   DA C   6      17.700  19.520  34.430  1.00  0.00           N
+ATOM    379  C2   DA C   6      17.320  18.460  35.130  1.00  0.00           C
+ATOM    380  N3   DA C   6      16.040  18.060  35.100  1.00  0.00           N
+ATOM    381  C4   DA C   6      15.110  18.730  34.380  1.00  0.00           C
+ATOM    382  H61  DA C   6      18.360  21.360  33.170  1.00  0.00           H
+ATOM    383  H62  DA C   6      17.010  21.550  32.170  1.00  0.00           H
+ATOM    384  P    DC C   7       9.770  16.120  37.460  1.00  0.00           P
+ATOM    385  OP1  DC C   7       9.000  14.970  37.960  1.00  0.00           O
+ATOM    386  OP2  DC C   7       9.160  17.460  37.550  1.00  0.00           O
+ATOM    387  O5'  DC C   7      11.200  16.200  38.190  1.00  0.00           O
+ATOM    388  C5'  DC C   7      12.190  15.160  38.260  1.00  0.00           C
+ATOM    389  C4'  DC C   7      13.530  15.800  38.650  1.00  0.00           C
+ATOM    390  O4'  DC C   7      14.030  16.930  37.890  1.00  0.00           O
+ATOM    391  C3'  DC C   7      13.490  16.250  40.080  1.00  0.00           C
+ATOM    392  O3'  DC C   7      14.470  15.440  40.750  1.00  0.00           O
+ATOM    393  C2'  DC C   7      13.730  17.750  40.060  1.00  0.00           C
+ATOM    394  C1'  DC C   7      14.630  17.850  38.850  1.00  0.00           C
+ATOM    395  N1   DC C   7      14.790  19.210  38.280  1.00  0.00           N
+ATOM    396  C2   DC C   7      16.080  19.660  37.950  1.00  0.00           C
+ATOM    397  O2   DC C   7      17.070  19.030  38.300  1.00  0.00           O
+ATOM    398  N3   DC C   7      16.250  20.780  37.200  1.00  0.00           N
+ATOM    399  C4   DC C   7      15.190  21.500  36.780  1.00  0.00           C
+ATOM    400  N4   DC C   7      15.430  22.550  36.010  1.00  0.00           N
+ATOM    401  C5   DC C   7      13.870  21.130  37.100  1.00  0.00           C
+ATOM    402  C6   DC C   7      13.680  19.970  37.870  1.00  0.00           C
+ATOM    403  H41  DC C   7      16.340  22.900  35.780  1.00  0.00           H
+ATOM    404  H42  DC C   7      14.630  23.080  35.670  1.00  0.00           H
+ATOM    405  P    DA C   8      14.530  15.310  42.350  1.00  0.00           P
+ATOM    406  OP1  DA C   8      15.100  13.980  42.700  1.00  0.00           O
+ATOM    407  OP2  DA C   8      13.200  15.660  42.910  1.00  0.00           O
+ATOM    408  O5'  DA C   8      15.620  16.420  42.710  1.00  0.00           O
+ATOM    409  C5'  DA C   8      16.960  16.100  42.280  1.00  0.00           C
+ATOM    410  C4'  DA C   8      18.070  17.130  42.440  1.00  0.00           C
+ATOM    411  O4'  DA C   8      17.960  18.250  41.550  1.00  0.00           O
+ATOM    412  C3'  DA C   8      18.180  17.680  43.840  1.00  0.00           C
+ATOM    413  O3'  DA C   8      19.530  17.380  44.280  1.00  0.00           O
+ATOM    414  C2'  DA C   8      17.950  19.160  43.670  1.00  0.00           C
+ATOM    415  C1'  DA C   8      18.490  19.400  42.270  1.00  0.00           C
+ATOM    416  N9   DA C   8      17.880  20.580  41.590  1.00  0.00           N
+ATOM    417  C8   DA C   8      16.560  20.710  41.530  1.00  0.00           C
+ATOM    418  N7   DA C   8      16.210  21.700  40.710  1.00  0.00           N
+ATOM    419  C5   DA C   8      17.340  22.210  40.250  1.00  0.00           C
+ATOM    420  C6   DA C   8      17.630  23.370  39.530  1.00  0.00           C
+ATOM    421  N6   DA C   8      16.630  24.080  39.070  1.00  0.00           N
+ATOM    422  N1   DA C   8      18.890  23.840  39.400  1.00  0.00           N
+ATOM    423  C2   DA C   8      19.900  23.190  39.990  1.00  0.00           C
+ATOM    424  N3   DA C   8      19.670  22.060  40.680  1.00  0.00           N
+ATOM    425  C4   DA C   8      18.420  21.540  40.830  1.00  0.00           C
+ATOM    426  H61  DA C   8      16.760  24.970  38.610  1.00  0.00           H
+ATOM    427  H62  DA C   8      15.700  23.730  39.150  1.00  0.00           H
+ATOM    428  P    DA C   9      20.060  17.430  45.800  1.00  0.00           P
+ATOM    429  OP1  DA C   9      20.990  16.280  45.900  1.00  0.00           O
+ATOM    430  OP2  DA C   9      18.920  17.430  46.730  1.00  0.00           O
+ATOM    431  O5'  DA C   9      20.840  18.830  45.970  1.00  0.00           O
+ATOM    432  C5'  DA C   9      22.080  19.110  45.300  1.00  0.00           C
+ATOM    433  C4'  DA C   9      22.540  20.570  45.260  1.00  0.00           C
+ATOM    434  O4'  DA C   9      21.590  21.340  44.490  1.00  0.00           O
+ATOM    435  C3'  DA C   9      22.680  21.250  46.620  1.00  0.00           C
+ATOM    436  O3'  DA C   9      24.020  21.810  46.760  1.00  0.00           O
+ATOM    437  C2'  DA C   9      21.470  22.140  46.660  1.00  0.00           C
+ATOM    438  C1'  DA C   9      21.360  22.570  45.190  1.00  0.00           C
+ATOM    439  N9   DA C   9      20.000  23.020  44.810  1.00  0.00           N
+ATOM    440  C8   DA C   9      18.900  22.330  45.060  1.00  0.00           C
+ATOM    441  N7   DA C   9      17.840  22.900  44.470  1.00  0.00           N
+ATOM    442  C5   DA C   9      18.300  23.970  43.840  1.00  0.00           C
+ATOM    443  C6   DA C   9      17.750  24.930  42.980  1.00  0.00           C
+ATOM    444  N6   DA C   9      16.510  24.780  42.580  1.00  0.00           N
+ATOM    445  N1   DA C   9      18.500  25.860  42.350  1.00  0.00           N
+ATOM    446  C2   DA C   9      19.820  25.870  42.530  1.00  0.00           C
+ATOM    447  N3   DA C   9      20.400  24.990  43.360  1.00  0.00           N
+ATOM    448  C4   DA C   9      19.680  24.040  44.020  1.00  0.00           C
+ATOM    449  H61  DA C   9      16.130  25.440  41.930  1.00  0.00           H
+ATOM    450  H62  DA C   9      16.030  23.950  42.850  1.00  0.00           H
+ATOM    451  P    DT C  10      24.700  22.330  48.140  1.00  0.00           P
+ATOM    452  OP1  DT C  10      26.170  22.130  48.190  1.00  0.00           O
+ATOM    453  OP2  DT C  10      23.930  21.860  49.310  1.00  0.00           O
+ATOM    454  O5'  DT C  10      24.490  23.900  47.970  1.00  0.00           O
+ATOM    455  C5'  DT C  10      25.050  24.590  46.850  1.00  0.00           C
+ATOM    456  C4'  DT C  10      24.180  25.810  46.560  1.00  0.00           C
+ATOM    457  O4'  DT C  10      22.790  25.540  46.310  1.00  0.00           O
+ATOM    458  C3'  DT C  10      24.220  26.890  47.630  1.00  0.00           C
+ATOM    459  O3'  DT C  10      25.030  27.970  47.110  1.00  0.00           O
+ATOM    460  C2'  DT C  10      22.790  27.360  47.730  1.00  0.00           C
+ATOM    461  C1'  DT C  10      22.240  26.870  46.430  1.00  0.00           C
+ATOM    462  N1   DT C  10      20.750  26.810  46.490  1.00  0.00           N
+ATOM    463  C2   DT C  10      19.920  27.750  45.860  1.00  0.00           C
+ATOM    464  O2   DT C  10      20.360  28.690  45.200  1.00  0.00           O
+ATOM    465  N3   DT C  10      18.530  27.580  45.900  1.00  0.00           N
+ATOM    466  C4   DT C  10      17.980  26.480  46.580  1.00  0.00           C
+ATOM    467  O4   DT C  10      16.810  26.210  46.330  1.00  0.00           O
+ATOM    468  C5   DT C  10      18.800  25.570  47.260  1.00  0.00           C
+ATOM    469  C7   DT C  10      18.260  24.440  48.130  1.00  0.00           C
+ATOM    470  C6   DT C  10      20.180  25.750  47.190  1.00  0.00           C
+ATOM    471  H3   DT C  10      17.940  28.160  45.340  1.00  0.00           H
+ATOM    472  P    DT C  11      25.820  29.030  48.030  1.00  0.00           P
+ATOM    473  OP1  DT C  11      26.890  29.650  47.210  1.00  0.00           O
+ATOM    474  OP2  DT C  11      26.200  28.380  49.300  1.00  0.00           O
+ATOM    475  O5'  DT C  11      24.720  30.140  48.340  1.00  0.00           O
+ATOM    476  C5'  DT C  11      24.140  30.860  47.240  1.00  0.00           C
+ATOM    477  C4'  DT C  11      22.750  31.380  47.550  1.00  0.00           C
+ATOM    478  O4'  DT C  11      21.760  30.360  47.680  1.00  0.00           O
+ATOM    479  C3'  DT C  11      22.740  32.100  48.870  1.00  0.00           C
+ATOM    480  O3'  DT C  11      22.750  33.520  48.640  1.00  0.00           O
+ATOM    481  C2'  DT C  11      21.450  31.730  49.550  1.00  0.00           C
+ATOM    482  C1'  DT C  11      20.740  31.060  48.390  1.00  0.00           C
+ATOM    483  N1   DT C  11      19.750  30.060  48.830  1.00  0.00           N
+ATOM    484  C2   DT C  11      18.380  30.370  48.750  1.00  0.00           C
+ATOM    485  O2   DT C  11      17.980  31.340  48.110  1.00  0.00           O
+ATOM    486  N3   DT C  11      17.470  29.460  49.270  1.00  0.00           N
+ATOM    487  C4   DT C  11      17.890  28.240  49.790  1.00  0.00           C
+ATOM    488  O4   DT C  11      17.040  27.350  49.810  1.00  0.00           O
+ATOM    489  C5   DT C  11      19.250  27.940  49.910  1.00  0.00           C
+ATOM    490  C7   DT C  11      19.740  26.800  50.820  1.00  0.00           C
+ATOM    491  C6   DT C  11      20.170  28.870  49.430  1.00  0.00           C
+ATOM    492 HO3'  DT C  11      22.760  33.950  49.590  1.00  0.00           H
+ATOM    493  H3   DT C  11      16.540  29.780  49.510  1.00  0.00           H
+TER     494       DT C  11
+ATOM    495  N   MET A   1      32.840  26.300   6.980  1.00  0.00           N
+ATOM    496  CA  MET A   1      32.290  27.380   7.830  1.00  0.00           C
+ATOM    497  C   MET A   1      32.050  26.990   9.300  1.00  0.00           C
+ATOM    498  O   MET A   1      31.550  25.910   9.630  1.00  0.00           O
+ATOM    499  CB  MET A   1      30.980  27.820   7.190  1.00  0.00           C
+ATOM    500  CG  MET A   1      31.140  28.320   5.740  1.00  0.00           C
+ATOM    501  SD  MET A   1      32.060  29.890   5.590  1.00  0.00           S
+ATOM    502  CE  MET A   1      31.010  30.940   6.560  1.00  0.00           C
+ATOM    503  H1  MET A   1      33.660  25.890   7.390  1.00  0.00           H
+ATOM    504  H2  MET A   1      32.130  25.600   6.880  1.00  0.00           H
+ATOM    505  H3  MET A   1      33.070  26.700   6.090  1.00  0.00           H
+ATOM    506  N   LYS A   2      32.520  27.880  10.170  1.00  0.00           N
+ATOM    507  CA  LYS A   2      32.550  27.760  11.640  1.00  0.00           C
+ATOM    508  C   LYS A   2      31.120  27.820  12.230  1.00  0.00           C
+ATOM    509  O   LYS A   2      30.510  28.900  12.200  1.00  0.00           O
+ATOM    510  CB  LYS A   2      33.400  28.950  12.100  1.00  0.00           C
+ATOM    511  CG  LYS A   2      34.030  28.730  13.460  1.00  0.00           C
+ATOM    512  CD  LYS A   2      33.750  29.870  14.440  1.00  0.00           C
+ATOM    513  CE  LYS A   2      34.470  29.480  15.720  1.00  0.00           C
+ATOM    514  NZ  LYS A   2      33.960  30.250  16.850  1.00  0.00           N
+ATOM    515  H   LYS A   2      32.940  28.740   9.830  1.00  0.00           H
+ATOM    516  HZ1 LYS A   2      32.960  30.190  16.880  1.00  0.00           H
+ATOM    517  HZ2 LYS A   2      34.320  29.900  17.710  1.00  0.00           H
+ATOM    518  HZ3 LYS A   2      34.220  31.230  16.770  1.00  0.00           H
+ATOM    519  N   PRO A   3      30.580  26.720  12.790  1.00  0.00           N
+ATOM    520  CA  PRO A   3      29.180  26.690  13.240  1.00  0.00           C
+ATOM    521  C   PRO A   3      28.980  27.400  14.580  1.00  0.00           C
+ATOM    522  O   PRO A   3      29.920  27.540  15.370  1.00  0.00           O
+ATOM    523  CB  PRO A   3      28.830  25.210  13.360  1.00  0.00           C
+ATOM    524  CG  PRO A   3      30.170  24.570  13.740  1.00  0.00           C
+ATOM    525  CD  PRO A   3      31.220  25.390  12.980  1.00  0.00           C
+ATOM    526  N   VAL A   4      27.750  27.800  14.830  1.00  0.00           N
+ATOM    527  CA  VAL A   4      27.430  28.430  16.110  1.00  0.00           C
+ATOM    528  C   VAL A   4      26.360  27.620  16.860  1.00  0.00           C
+ATOM    529  O   VAL A   4      25.190  27.640  16.500  1.00  0.00           O
+ATOM    530  CB  VAL A   4      27.060  29.900  15.840  1.00  0.00           C
+ATOM    531  CG1 VAL A   4      26.430  30.610  17.050  1.00  0.00           C
+ATOM    532  CG2 VAL A   4      28.280  30.690  15.360  1.00  0.00           C
+ATOM    533  H   VAL A   4      27.040  27.890  14.130  1.00  0.00           H
+ATOM    534  N   THR A   5      26.890  27.040  17.940  1.00  0.00           N
+ATOM    535  CA  THR A   5      26.190  26.210  18.950  1.00  0.00           C
+ATOM    536  C   THR A   5      25.400  27.100  19.920  1.00  0.00           C
+ATOM    537  O   THR A   5      25.630  28.310  19.970  1.00  0.00           O
+ATOM    538  CB  THR A   5      27.220  25.450  19.820  1.00  0.00           C
+ATOM    539  OG1 THR A   5      28.110  26.350  20.480  1.00  0.00           O
+ATOM    540  CG2 THR A   5      27.990  24.370  19.050  1.00  0.00           C
+ATOM    541  H   THR A   5      27.850  27.230  18.150  1.00  0.00           H
+ATOM    542  HG1 THR A   5      28.900  26.570  19.860  1.00  0.00           H
+ATOM    543  N   LEU A   6      24.590  26.490  20.780  1.00  0.00           N
+ATOM    544  CA  LEU A   6      24.010  27.210  21.940  1.00  0.00           C
+ATOM    545  C   LEU A   6      25.100  27.830  22.840  1.00  0.00           C
+ATOM    546  O   LEU A   6      25.030  29.020  23.120  1.00  0.00           O
+ATOM    547  CB  LEU A   6      23.150  26.370  22.880  1.00  0.00           C
+ATOM    548  CG  LEU A   6      21.800  25.900  22.330  1.00  0.00           C
+ATOM    549  CD1 LEU A   6      21.280  24.920  23.370  1.00  0.00           C
+ATOM    550  CD2 LEU A   6      20.780  27.020  22.090  1.00  0.00           C
+ATOM    551  H   LEU A   6      24.320  25.520  20.670  1.00  0.00           H
+ATOM    552  N   TYR A   7      26.180  27.080  23.090  1.00  0.00           N
+ATOM    553  CA  TYR A   7      27.320  27.520  23.910  1.00  0.00           C
+ATOM    554  C   TYR A   7      27.890  28.830  23.370  1.00  0.00           C
+ATOM    555  O   TYR A   7      27.820  29.850  24.050  1.00  0.00           O
+ATOM    556  CB  TYR A   7      28.420  26.460  23.950  1.00  0.00           C
+ATOM    557  CG  TYR A   7      27.930  25.140  24.560  1.00  0.00           C
+ATOM    558  CD1 TYR A   7      27.170  24.280  23.740  1.00  0.00           C
+ATOM    559  CD2 TYR A   7      28.070  24.930  25.940  1.00  0.00           C
+ATOM    560  CE1 TYR A   7      26.510  23.200  24.330  1.00  0.00           C
+ATOM    561  CE2 TYR A   7      27.410  23.830  26.530  1.00  0.00           C
+ATOM    562  CZ  TYR A   7      26.650  22.960  25.710  1.00  0.00           C
+ATOM    563  OH  TYR A   7      26.370  21.720  26.200  1.00  0.00           O
+ATOM    564  H   TYR A   7      26.350  26.230  22.580  1.00  0.00           H
+ATOM    565  HH  TYR A   7      26.040  21.150  25.390  1.00  0.00           H
+ATOM    566  N   ASP A   8      28.140  28.840  22.060  1.00  0.00           N
+ATOM    567  CA  ASP A   8      28.650  30.020  21.340  1.00  0.00           C
+ATOM    568  C   ASP A   8      27.800  31.280  21.570  1.00  0.00           C
+ATOM    569  O   ASP A   8      28.260  32.210  22.230  1.00  0.00           O
+ATOM    570  CB  ASP A   8      28.730  29.720  19.840  1.00  0.00           C
+ATOM    571  CG  ASP A   8      29.900  28.830  19.410  1.00  0.00           C
+ATOM    572  OD1 ASP A   8      31.060  29.240  19.590  1.00  0.00           O
+ATOM    573  OD2 ASP A   8      29.640  27.820  18.730  1.00  0.00           O
+ATOM    574  H   ASP A   8      28.000  28.020  21.480  1.00  0.00           H
+ATOM    575  N   VAL A   9      26.520  31.220  21.210  1.00  0.00           N
+ATOM    576  CA  VAL A   9      25.620  32.410  21.350  1.00  0.00           C
+ATOM    577  C   VAL A   9      25.410  32.860  22.800  1.00  0.00           C
+ATOM    578  O   VAL A   9      25.400  34.050  23.120  1.00  0.00           O
+ATOM    579  CB  VAL A   9      24.250  32.260  20.670  1.00  0.00           C
+ATOM    580  CG1 VAL A   9      24.370  32.660  19.200  1.00  0.00           C
+ATOM    581  CG2 VAL A   9      23.580  30.890  20.860  1.00  0.00           C
+ATOM    582  H   VAL A   9      26.080  30.350  20.970  1.00  0.00           H
+ATOM    583  N   ALA A  10      25.370  31.840  23.650  1.00  0.00           N
+ATOM    584  CA  ALA A  10      25.290  31.940  25.120  1.00  0.00           C
+ATOM    585  C   ALA A  10      26.490  32.680  25.750  1.00  0.00           C
+ATOM    586  O   ALA A  10      26.320  33.600  26.540  1.00  0.00           O
+ATOM    587  CB  ALA A  10      25.170  30.570  25.790  1.00  0.00           C
+ATOM    588  H   ALA A  10      25.310  30.910  23.280  1.00  0.00           H
+ATOM    589  N   GLU A  11      27.690  32.310  25.310  1.00  0.00           N
+ATOM    590  CA  GLU A  11      28.940  33.020  25.670  1.00  0.00           C
+ATOM    591  C   GLU A  11      28.910  34.480  25.200  1.00  0.00           C
+ATOM    592  O   GLU A  11      28.840  35.370  26.050  1.00  0.00           O
+ATOM    593  CB  GLU A  11      30.190  32.330  25.130  1.00  0.00           C
+ATOM    594  CG  GLU A  11      30.450  30.980  25.810  1.00  0.00           C
+ATOM    595  CD  GLU A  11      31.550  30.180  25.090  1.00  0.00           C
+ATOM    596  OE1 GLU A  11      32.740  30.480  25.290  1.00  0.00           O
+ATOM    597  OE2 GLU A  11      31.200  29.230  24.360  1.00  0.00           O
+ATOM    598  H   GLU A  11      27.800  31.450  24.800  1.00  0.00           H
+ATOM    599  N   TYR A  12      28.570  34.680  23.920  1.00  0.00           N
+ATOM    600  CA  TYR A  12      28.560  36.020  23.290  1.00  0.00           C
+ATOM    601  C   TYR A  12      27.530  36.980  23.920  1.00  0.00           C
+ATOM    602  O   TYR A  12      27.690  38.200  23.860  1.00  0.00           O
+ATOM    603  CB  TYR A  12      28.340  35.890  21.770  1.00  0.00           C
+ATOM    604  CG  TYR A  12      28.800  37.120  20.950  1.00  0.00           C
+ATOM    605  CD1 TYR A  12      28.010  38.290  20.930  1.00  0.00           C
+ATOM    606  CD2 TYR A  12      29.930  37.000  20.120  1.00  0.00           C
+ATOM    607  CE1 TYR A  12      28.340  39.350  20.070  1.00  0.00           C
+ATOM    608  CE2 TYR A  12      30.270  38.050  19.240  1.00  0.00           C
+ATOM    609  CZ  TYR A  12      29.450  39.200  19.220  1.00  0.00           C
+ATOM    610  OH  TYR A  12      29.580  40.070  18.190  1.00  0.00           O
+ATOM    611  H   TYR A  12      28.260  33.910  23.350  1.00  0.00           H
+ATOM    612  HH  TYR A  12      30.200  39.590  17.490  1.00  0.00           H
+ATOM    613  N   ALA A  13      26.400  36.450  24.370  1.00  0.00           N
+ATOM    614  CA  ALA A  13      25.420  37.240  25.150  1.00  0.00           C
+ATOM    615  C   ALA A  13      25.800  37.490  26.620  1.00  0.00           C
+ATOM    616  O   ALA A  13      25.430  38.520  27.200  1.00  0.00           O
+ATOM    617  CB  ALA A  13      24.060  36.560  25.090  1.00  0.00           C
+ATOM    618  H   ALA A  13      26.050  35.580  24.000  1.00  0.00           H
+ATOM    619  N   GLY A  14      26.590  36.590  27.200  1.00  0.00           N
+ATOM    620  CA  GLY A  14      26.840  36.560  28.660  1.00  0.00           C
+ATOM    621  C   GLY A  14      25.650  35.910  29.360  1.00  0.00           C
+ATOM    622  O   GLY A  14      24.830  36.590  29.970  1.00  0.00           O
+ATOM    623  H   GLY A  14      27.200  35.990  26.660  1.00  0.00           H
+ATOM    624  N   VAL A  15      25.570  34.610  29.090  1.00  0.00           N
+ATOM    625  CA  VAL A  15      24.550  33.650  29.560  1.00  0.00           C
+ATOM    626  C   VAL A  15      25.070  32.200  29.510  1.00  0.00           C
+ATOM    627  O   VAL A  15      26.070  31.880  28.880  1.00  0.00           O
+ATOM    628  CB  VAL A  15      23.210  33.730  28.790  1.00  0.00           C
+ATOM    629  CG1 VAL A  15      22.240  34.640  29.540  1.00  0.00           C
+ATOM    630  CG2 VAL A  15      23.270  34.170  27.330  1.00  0.00           C
+ATOM    631  H   VAL A  15      26.210  34.210  28.430  1.00  0.00           H
+ATOM    632  N   SER A  16      24.320  31.340  30.180  1.00  0.00           N
+ATOM    633  CA  SER A  16      24.430  29.880  30.100  1.00  0.00           C
+ATOM    634  C   SER A  16      23.660  29.380  28.870  1.00  0.00           C
+ATOM    635  O   SER A  16      22.730  30.010  28.370  1.00  0.00           O
+ATOM    636  CB  SER A  16      23.870  29.220  31.360  1.00  0.00           C
+ATOM    637  OG  SER A  16      23.780  27.810  31.130  1.00  0.00           O
+ATOM    638  H   SER A  16      23.570  31.680  30.780  1.00  0.00           H
+ATOM    639  HG  SER A  16      23.790  27.330  32.060  1.00  0.00           H
+ATOM    640  N   TYR A  17      24.070  28.200  28.420  1.00  0.00           N
+ATOM    641  CA  TYR A  17      23.430  27.400  27.380  1.00  0.00           C
+ATOM    642  C   TYR A  17      22.050  26.900  27.830  1.00  0.00           C
+ATOM    643  O   TYR A  17      21.180  26.690  26.980  1.00  0.00           O
+ATOM    644  CB  TYR A  17      24.410  26.280  26.960  1.00  0.00           C
+ATOM    645  CG  TYR A  17      24.100  24.840  27.390  1.00  0.00           C
+ATOM    646  CD1 TYR A  17      24.440  24.360  28.680  1.00  0.00           C
+ATOM    647  CD2 TYR A  17      23.350  24.050  26.490  1.00  0.00           C
+ATOM    648  CE1 TYR A  17      24.000  23.080  29.070  1.00  0.00           C
+ATOM    649  CE2 TYR A  17      22.900  22.780  26.880  1.00  0.00           C
+ATOM    650  CZ  TYR A  17      23.220  22.310  28.170  1.00  0.00           C
+ATOM    651  OH  TYR A  17      22.790  21.070  28.480  1.00  0.00           O
+ATOM    652  H   TYR A  17      24.850  27.740  28.900  1.00  0.00           H
+ATOM    653  HH  TYR A  17      23.270  20.760  29.350  1.00  0.00           H
+ATOM    654  N   GLN A  18      21.830  26.780  29.140  1.00  0.00           N
+ATOM    655  CA  GLN A  18      20.490  26.450  29.660  1.00  0.00           C
+ATOM    656  C   GLN A  18      19.540  27.630  29.420  1.00  0.00           C
+ATOM    657  O   GLN A  18      18.470  27.460  28.830  1.00  0.00           O
+ATOM    658  CB  GLN A  18      20.570  26.050  31.140  1.00  0.00           C
+ATOM    659  CG  GLN A  18      19.270  25.460  31.720  1.00  0.00           C
+ATOM    660  CD  GLN A  18      18.770  24.160  31.090  1.00  0.00           C
+ATOM    661  OE1 GLN A  18      17.590  23.830  31.120  1.00  0.00           O
+ATOM    662  NE2 GLN A  18      19.690  23.320  30.650  1.00  0.00           N
+ATOM    663  H   GLN A  18      22.490  27.100  29.820  1.00  0.00           H
+ATOM    664 HE21 GLN A  18      20.640  23.490  30.920  1.00  0.00           H
+ATOM    665 HE22 GLN A  18      19.470  22.460  30.210  1.00  0.00           H
+ATOM    666  N   THR A  19      19.930  28.820  29.880  1.00  0.00           N
+ATOM    667  CA  THR A  19      19.270  30.110  29.590  1.00  0.00           C
+ATOM    668  C   THR A  19      18.930  30.310  28.090  1.00  0.00           C
+ATOM    669  O   THR A  19      17.770  30.560  27.750  1.00  0.00           O
+ATOM    670  CB  THR A  19      20.210  31.200  30.110  1.00  0.00           C
+ATOM    671  OG1 THR A  19      20.740  30.790  31.380  1.00  0.00           O
+ATOM    672  CG2 THR A  19      19.510  32.550  30.250  1.00  0.00           C
+ATOM    673  H   THR A  19      20.510  28.900  30.690  1.00  0.00           H
+ATOM    674  HG1 THR A  19      20.030  30.960  32.100  1.00  0.00           H
+ATOM    675  N   VAL A  20      19.890  30.000  27.220  1.00  0.00           N
+ATOM    676  CA  VAL A  20      19.740  30.060  25.750  1.00  0.00           C
+ATOM    677  C   VAL A  20      18.840  28.940  25.150  1.00  0.00           C
+ATOM    678  O   VAL A  20      17.940  29.240  24.380  1.00  0.00           O
+ATOM    679  CB  VAL A  20      21.170  30.240  25.180  1.00  0.00           C
+ATOM    680  CG1 VAL A  20      21.350  30.030  23.690  1.00  0.00           C
+ATOM    681  CG2 VAL A  20      21.640  31.660  25.470  1.00  0.00           C
+ATOM    682  H   VAL A  20      20.820  29.830  27.550  1.00  0.00           H
+ATOM    683  N   SER A  21      19.000  27.690  25.600  1.00  0.00           N
+ATOM    684  CA  SER A  21      18.010  26.600  25.390  1.00  0.00           C
+ATOM    685  C   SER A  21      16.580  27.040  25.720  1.00  0.00           C
+ATOM    686  O   SER A  21      15.740  27.080  24.830  1.00  0.00           O
+ATOM    687  CB  SER A  21      18.350  25.360  26.240  1.00  0.00           C
+ATOM    688  OG  SER A  21      17.410  24.310  26.020  1.00  0.00           O
+ATOM    689  H   SER A  21      19.890  27.370  25.960  1.00  0.00           H
+ATOM    690  HG  SER A  21      17.470  24.020  25.020  1.00  0.00           H
+ATOM    691  N   ARG A  22      16.390  27.600  26.920  1.00  0.00           N
+ATOM    692  CA  ARG A  22      15.110  28.220  27.340  1.00  0.00           C
+ATOM    693  C   ARG A  22      14.810  29.650  26.860  1.00  0.00           C
+ATOM    694  O   ARG A  22      13.950  30.370  27.380  1.00  0.00           O
+ATOM    695  CB  ARG A  22      14.910  28.030  28.850  1.00  0.00           C
+ATOM    696  CG  ARG A  22      14.530  26.580  29.140  1.00  0.00           C
+ATOM    697  CD  ARG A  22      14.340  26.450  30.640  1.00  0.00           C
+ATOM    698  NE  ARG A  22      14.270  25.050  31.100  1.00  0.00           N
+ATOM    699  CZ  ARG A  22      14.760  24.610  32.260  1.00  0.00           C
+ATOM    700  NH1 ARG A  22      15.680  25.320  32.930  1.00  0.00           N
+ATOM    701  NH2 ARG A  22      14.600  23.340  32.620  1.00  0.00           N
+ATOM    702  H   ARG A  22      17.100  27.480  27.630  1.00  0.00           H
+ATOM    703  HE  ARG A  22      13.780  24.410  30.490  1.00  0.00           H
+ATOM    704 HH11 ARG A  22      16.030  26.170  32.510  1.00  0.00           H
+ATOM    705 HH12 ARG A  22      16.090  24.990  33.770  1.00  0.00           H
+ATOM    706 HH21 ARG A  22      14.110  22.710  32.030  1.00  0.00           H
+ATOM    707 HH22 ARG A  22      14.900  23.040  33.520  1.00  0.00           H
+ATOM    708  N   VAL A  23      15.400  29.990  25.720  1.00  0.00           N
+ATOM    709  CA  VAL A  23      15.020  31.120  24.860  1.00  0.00           C
+ATOM    710  C   VAL A  23      14.470  30.480  23.570  1.00  0.00           C
+ATOM    711  O   VAL A  23      13.490  30.990  23.010  1.00  0.00           O
+ATOM    712  CB  VAL A  23      16.210  32.090  24.660  1.00  0.00           C
+ATOM    713  CG1 VAL A  23      16.270  32.800  23.310  1.00  0.00           C
+ATOM    714  CG2 VAL A  23      16.180  33.120  25.790  1.00  0.00           C
+ATOM    715  H   VAL A  23      16.110  29.400  25.340  1.00  0.00           H
+ATOM    716  N   VAL A  24      15.180  29.460  23.090  1.00  0.00           N
+ATOM    717  CA  VAL A  24      14.790  28.610  21.950  1.00  0.00           C
+ATOM    718  C   VAL A  24      13.510  27.800  22.260  1.00  0.00           C
+ATOM    719  O   VAL A  24      12.450  28.100  21.710  1.00  0.00           O
+ATOM    720  CB  VAL A  24      16.000  27.750  21.490  1.00  0.00           C
+ATOM    721  CG1 VAL A  24      15.590  26.760  20.400  1.00  0.00           C
+ATOM    722  CG2 VAL A  24      17.170  28.560  20.920  1.00  0.00           C
+ATOM    723  H   VAL A  24      16.090  29.250  23.460  1.00  0.00           H
+ATOM    724  N   ASN A  25      13.620  26.840  23.180  1.00  0.00           N
+ATOM    725  CA  ASN A  25      12.570  25.880  23.570  1.00  0.00           C
+ATOM    726  C   ASN A  25      12.320  25.730  25.070  1.00  0.00           C
+ATOM    727  O   ASN A  25      13.260  25.790  25.870  1.00  0.00           O
+ATOM    728  CB  ASN A  25      13.010  24.500  23.070  1.00  0.00           C
+ATOM    729  CG  ASN A  25      12.490  24.320  21.650  1.00  0.00           C
+ATOM    730  OD1 ASN A  25      11.330  24.560  21.360  1.00  0.00           O
+ATOM    731  ND2 ASN A  25      13.240  23.720  20.770  1.00  0.00           N
+ATOM    732  H   ASN A  25      14.490  26.700  23.660  1.00  0.00           H
+ATOM    733 HD21 ASN A  25      14.140  23.320  21.000  1.00  0.00           H
+ATOM    734 HD22 ASN A  25      12.900  23.690  19.820  1.00  0.00           H
+ATOM    735  N   GLN A  26      11.090  25.340  25.420  1.00  0.00           N
+ATOM    736  CA  GLN A  26      10.650  25.130  26.820  1.00  0.00           C
+ATOM    737  C   GLN A  26      10.990  26.360  27.700  1.00  0.00           C
+ATOM    738  O   GLN A  26      11.370  26.290  28.870  1.00  0.00           O
+ATOM    739  CB  GLN A  26      11.350  23.860  27.300  1.00  0.00           C
+ATOM    740  CG  GLN A  26      11.040  23.330  28.700  1.00  0.00           C
+ATOM    741  CD  GLN A  26      12.100  22.300  29.060  1.00  0.00           C
+ATOM    742  OE1 GLN A  26      12.890  22.480  29.990  1.00  0.00           O
+ATOM    743  NE2 GLN A  26      12.280  21.330  28.180  1.00  0.00           N
+ATOM    744  H   GLN A  26      10.340  25.370  24.750  1.00  0.00           H
+ATOM    745 HE21 GLN A  26      11.750  21.340  27.320  1.00  0.00           H
+ATOM    746 HE22 GLN A  26      12.960  20.630  28.340  1.00  0.00           H
+ATOM    747  N   ALA A  27      10.840  27.520  27.070  1.00  0.00           N
+ATOM    748  CA  ALA A  27      11.220  28.840  27.590  1.00  0.00           C
+ATOM    749  C   ALA A  27      10.520  29.220  28.890  1.00  0.00           C
+ATOM    750  O   ALA A  27       9.400  28.800  29.150  1.00  0.00           O
+ATOM    751  CB  ALA A  27      11.000  29.880  26.500  1.00  0.00           C
+ATOM    752  H   ALA A  27      10.520  27.540  26.100  1.00  0.00           H
+ATOM    753  N   SER A  28      11.420  29.560  29.800  1.00  0.00           N
+ATOM    754  CA  SER A  28      11.100  30.120  31.120  1.00  0.00           C
+ATOM    755  C   SER A  28      12.340  30.790  31.720  1.00  0.00           C
+ATOM    756  O   SER A  28      13.470  30.440  31.360  1.00  0.00           O
+ATOM    757  CB  SER A  28      10.630  29.000  32.060  1.00  0.00           C
+ATOM    758  OG  SER A  28      11.730  28.200  32.530  1.00  0.00           O
+ATOM    759  H   SER A  28      12.380  29.290  29.700  1.00  0.00           H
+ATOM    760  HG  SER A  28      11.660  27.260  32.130  1.00  0.00           H
+ATOM    761  N   HIS A  29      12.110  31.760  32.610  1.00  0.00           N
+ATOM    762  CA  HIS A  29      13.140  32.420  33.430  1.00  0.00           C
+ATOM    763  C   HIS A  29      14.150  33.270  32.650  1.00  0.00           C
+ATOM    764  O   HIS A  29      15.360  33.220  32.850  1.00  0.00           O
+ATOM    765  CB  HIS A  29      13.860  31.390  34.320  1.00  0.00           C
+ATOM    766  CG  HIS A  29      12.960  30.880  35.430  1.00  0.00           C
+ATOM    767  ND1 HIS A  29      12.970  31.330  36.680  1.00  0.00           N
+ATOM    768  CD2 HIS A  29      12.210  29.780  35.390  1.00  0.00           C
+ATOM    769  CE1 HIS A  29      12.270  30.460  37.410  1.00  0.00           C
+ATOM    770  NE2 HIS A  29      11.780  29.520  36.610  1.00  0.00           N
+ATOM    771  H   HIS A  29      11.180  32.070  32.790  1.00  0.00           H
+ATOM    772  HD1 HIS A  29      13.230  32.280  36.940  1.00  0.00           H
+ATOM    773  HE2 HIS A  29      11.070  28.840  36.860  1.00  0.00           H
+ATOM    774  N   VAL A  30      13.580  34.360  32.160  1.00  0.00           N
+ATOM    775  CA  VAL A  30      14.300  35.300  31.270  1.00  0.00           C
+ATOM    776  C   VAL A  30      13.720  36.710  31.340  1.00  0.00           C
+ATOM    777  O   VAL A  30      12.590  37.010  30.930  1.00  0.00           O
+ATOM    778  CB  VAL A  30      14.440  34.680  29.850  1.00  0.00           C
+ATOM    779  CG1 VAL A  30      13.090  34.470  29.150  1.00  0.00           C
+ATOM    780  CG2 VAL A  30      15.370  35.530  29.000  1.00  0.00           C
+ATOM    781  H   VAL A  30      12.770  34.760  32.610  1.00  0.00           H
+ATOM    782  N   SER A  31      14.600  37.600  31.780  1.00  0.00           N
+ATOM    783  CA  SER A  31      14.250  38.980  32.170  1.00  0.00           C
+ATOM    784  C   SER A  31      14.760  39.970  31.120  1.00  0.00           C
+ATOM    785  O   SER A  31      15.750  39.700  30.430  1.00  0.00           O
+ATOM    786  CB  SER A  31      14.960  39.380  33.460  1.00  0.00           C
+ATOM    787  OG  SER A  31      14.920  38.370  34.470  1.00  0.00           O
+ATOM    788  H   SER A  31      15.580  37.430  31.680  1.00  0.00           H
+ATOM    789  HG  SER A  31      13.940  38.220  34.760  1.00  0.00           H
+ATOM    790  N   ALA A  32      14.290  41.200  31.300  1.00  0.00           N
+ATOM    791  CA  ALA A  32      14.600  42.380  30.470  1.00  0.00           C
+ATOM    792  C   ALA A  32      16.070  42.510  29.990  1.00  0.00           C
+ATOM    793  O   ALA A  32      16.300  42.570  28.790  1.00  0.00           O
+ATOM    794  CB  ALA A  32      14.170  43.600  31.280  1.00  0.00           C
+ATOM    795  H   ALA A  32      13.730  41.400  32.100  1.00  0.00           H
+ATOM    796  N   LYS A  33      17.050  42.410  30.890  1.00  0.00           N
+ATOM    797  CA  LYS A  33      18.480  42.630  30.570  1.00  0.00           C
+ATOM    798  C   LYS A  33      19.050  41.520  29.670  1.00  0.00           C
+ATOM    799  O   LYS A  33      19.440  41.780  28.530  1.00  0.00           O
+ATOM    800  CB  LYS A  33      19.340  42.810  31.820  1.00  0.00           C
+ATOM    801  CG  LYS A  33      20.770  43.260  31.480  1.00  0.00           C
+ATOM    802  CD  LYS A  33      20.760  44.750  31.120  1.00  0.00           C
+ATOM    803  CE  LYS A  33      21.980  45.230  30.350  1.00  0.00           C
+ATOM    804  NZ  LYS A  33      21.650  46.580  29.880  1.00  0.00           N
+ATOM    805  H   LYS A  33      16.840  42.210  31.860  1.00  0.00           H
+ATOM    806  HZ1 LYS A  33      20.790  46.540  29.340  1.00  0.00           H
+ATOM    807  HZ2 LYS A  33      21.480  47.180  30.660  1.00  0.00           H
+ATOM    808  HZ3 LYS A  33      22.380  46.980  29.310  1.00  0.00           H
+ATOM    809  N   THR A  34      19.010  40.290  30.190  1.00  0.00           N
+ATOM    810  CA  THR A  34      19.380  39.060  29.470  1.00  0.00           C
+ATOM    811  C   THR A  34      18.680  38.860  28.120  1.00  0.00           C
+ATOM    812  O   THR A  34      19.360  38.650  27.110  1.00  0.00           O
+ATOM    813  CB  THR A  34      19.130  37.830  30.340  1.00  0.00           C
+ATOM    814  OG1 THR A  34      17.850  37.920  30.990  1.00  0.00           O
+ATOM    815  CG2 THR A  34      20.320  37.640  31.280  1.00  0.00           C
+ATOM    816  H   THR A  34      18.740  40.150  31.150  1.00  0.00           H
+ATOM    817  HG1 THR A  34      17.870  37.600  31.970  1.00  0.00           H
+ATOM    818  N   ARG A  35      17.390  39.200  28.050  1.00  0.00           N
+ATOM    819  CA  ARG A  35      16.590  39.100  26.810  1.00  0.00           C
+ATOM    820  C   ARG A  35      17.210  39.920  25.660  1.00  0.00           C
+ATOM    821  O   ARG A  35      17.510  39.360  24.600  1.00  0.00           O
+ATOM    822  CB  ARG A  35      15.140  39.510  27.080  1.00  0.00           C
+ATOM    823  CG  ARG A  35      14.270  39.330  25.830  1.00  0.00           C
+ATOM    824  CD  ARG A  35      12.800  39.100  26.170  1.00  0.00           C
+ATOM    825  NE  ARG A  35      12.280  40.320  26.800  1.00  0.00           N
+ATOM    826  CZ  ARG A  35      11.550  40.410  27.920  1.00  0.00           C
+ATOM    827  NH1 ARG A  35      10.920  39.380  28.490  1.00  0.00           N
+ATOM    828  NH2 ARG A  35      11.300  41.620  28.420  1.00  0.00           N
+ATOM    829  H   ARG A  35      16.900  39.530  28.870  1.00  0.00           H
+ATOM    830  HE  ARG A  35      12.550  41.180  26.350  1.00  0.00           H
+ATOM    831 HH11 ARG A  35      10.830  38.480  28.040  1.00  0.00           H
+ATOM    832 HH12 ARG A  35      10.460  39.550  29.350  1.00  0.00           H
+ATOM    833 HH21 ARG A  35      11.670  42.440  27.970  1.00  0.00           H
+ATOM    834 HH22 ARG A  35      10.700  41.730  29.210  1.00  0.00           H
+ATOM    835  N   GLU A  36      17.520  41.180  25.950  1.00  0.00           N
+ATOM    836  CA  GLU A  36      18.250  42.060  25.010  1.00  0.00           C
+ATOM    837  C   GLU A  36      19.590  41.440  24.540  1.00  0.00           C
+ATOM    838  O   GLU A  36      19.830  41.320  23.340  1.00  0.00           O
+ATOM    839  CB  GLU A  36      18.490  43.410  25.700  1.00  0.00           C
+ATOM    840  CG  GLU A  36      18.920  44.500  24.720  1.00  0.00           C
+ATOM    841  CD  GLU A  36      19.500  45.730  25.410  1.00  0.00           C
+ATOM    842  OE1 GLU A  36      18.760  46.690  25.720  1.00  0.00           O
+ATOM    843  OE2 GLU A  36      20.740  45.770  25.530  1.00  0.00           O
+ATOM    844  H   GLU A  36      17.210  41.630  26.790  1.00  0.00           H
+ATOM    845  N   LYS A  37      20.380  40.940  25.500  1.00  0.00           N
+ATOM    846  CA  LYS A  37      21.690  40.280  25.280  1.00  0.00           C
+ATOM    847  C   LYS A  37      21.650  39.080  24.320  1.00  0.00           C
+ATOM    848  O   LYS A  37      22.460  38.980  23.400  1.00  0.00           O
+ATOM    849  CB  LYS A  37      22.270  39.770  26.600  1.00  0.00           C
+ATOM    850  CG  LYS A  37      22.670  40.870  27.570  1.00  0.00           C
+ATOM    851  CD  LYS A  37      23.780  41.690  26.960  1.00  0.00           C
+ATOM    852  CE  LYS A  37      24.220  42.680  28.010  1.00  0.00           C
+ATOM    853  NZ  LYS A  37      25.240  43.500  27.380  1.00  0.00           N
+ATOM    854  H   LYS A  37      20.090  41.000  26.460  1.00  0.00           H
+ATOM    855  HZ1 LYS A  37      25.960  42.900  27.020  1.00  0.00           H
+ATOM    856  HZ2 LYS A  37      24.850  44.050  26.640  1.00  0.00           H
+ATOM    857  HZ3 LYS A  37      25.670  44.110  28.060  1.00  0.00           H
+ATOM    858  N   VAL A  38      20.740  38.160  24.620  1.00  0.00           N
+ATOM    859  CA  VAL A  38      20.480  36.970  23.790  1.00  0.00           C
+ATOM    860  C   VAL A  38      20.100  37.380  22.350  1.00  0.00           C
+ATOM    861  O   VAL A  38      20.750  36.910  21.420  1.00  0.00           O
+ATOM    862  CB  VAL A  38      19.390  36.040  24.390  1.00  0.00           C
+ATOM    863  CG1 VAL A  38      19.290  34.740  23.580  1.00  0.00           C
+ATOM    864  CG2 VAL A  38      19.680  35.680  25.850  1.00  0.00           C
+ATOM    865  H   VAL A  38      20.300  38.160  25.530  1.00  0.00           H
+ATOM    866  N   GLU A  39      19.200  38.350  22.200  1.00  0.00           N
+ATOM    867  CA  GLU A  39      18.860  38.870  20.860  1.00  0.00           C
+ATOM    868  C   GLU A  39      20.040  39.500  20.130  1.00  0.00           C
+ATOM    869  O   GLU A  39      20.280  39.210  18.950  1.00  0.00           O
+ATOM    870  CB  GLU A  39      17.630  39.790  20.860  1.00  0.00           C
+ATOM    871  CG  GLU A  39      16.380  38.920  20.860  1.00  0.00           C
+ATOM    872  CD  GLU A  39      15.080  39.680  20.600  1.00  0.00           C
+ATOM    873  OE1 GLU A  39      14.840  40.100  19.450  1.00  0.00           O
+ATOM    874  OE2 GLU A  39      14.340  39.880  21.580  1.00  0.00           O
+ATOM    875  H   GLU A  39      18.690  38.740  22.970  1.00  0.00           H
+ATOM    876  N   ALA A  40      20.820  40.320  20.850  1.00  0.00           N
+ATOM    877  CA  ALA A  40      22.090  40.880  20.360  1.00  0.00           C
+ATOM    878  C   ALA A  40      23.010  39.770  19.790  1.00  0.00           C
+ATOM    879  O   ALA A  40      23.350  39.820  18.610  1.00  0.00           O
+ATOM    880  CB  ALA A  40      22.760  41.620  21.520  1.00  0.00           C
+ATOM    881  H   ALA A  40      20.570  40.640  21.760  1.00  0.00           H
+ATOM    882  N   ALA A  41      23.210  38.690  20.560  1.00  0.00           N
+ATOM    883  CA  ALA A  41      24.020  37.530  20.130  1.00  0.00           C
+ATOM    884  C   ALA A  41      23.420  36.630  19.040  1.00  0.00           C
+ATOM    885  O   ALA A  41      24.130  36.240  18.110  1.00  0.00           O
+ATOM    886  CB  ALA A  41      24.440  36.670  21.320  1.00  0.00           C
+ATOM    887  H   ALA A  41      22.940  38.700  21.530  1.00  0.00           H
+ATOM    888  N   MET A  42      22.110  36.400  19.100  1.00  0.00           N
+ATOM    889  CA  MET A  42      21.380  35.640  18.060  1.00  0.00           C
+ATOM    890  C   MET A  42      21.590  36.220  16.650  1.00  0.00           C
+ATOM    891  O   MET A  42      21.820  35.460  15.700  1.00  0.00           O
+ATOM    892  CB  MET A  42      19.890  35.550  18.360  1.00  0.00           C
+ATOM    893  CG  MET A  42      19.560  34.620  19.530  1.00  0.00           C
+ATOM    894  SD  MET A  42      20.160  32.910  19.300  1.00  0.00           S
+ATOM    895  CE  MET A  42      19.560  32.180  20.800  1.00  0.00           C
+ATOM    896  H   MET A  42      21.550  36.670  19.900  1.00  0.00           H
+ATOM    897  N   ALA A  43      21.390  37.530  16.500  1.00  0.00           N
+ATOM    898  CA  ALA A  43      21.580  38.230  15.210  1.00  0.00           C
+ATOM    899  C   ALA A  43      23.050  38.400  14.750  1.00  0.00           C
+ATOM    900  O   ALA A  43      23.310  38.360  13.550  1.00  0.00           O
+ATOM    901  CB  ALA A  43      20.830  39.560  15.190  1.00  0.00           C
+ATOM    902  H   ALA A  43      20.920  38.060  17.200  1.00  0.00           H
+ATOM    903  N   GLU A  44      23.950  38.710  15.680  1.00  0.00           N
+ATOM    904  CA  GLU A  44      25.400  38.790  15.360  1.00  0.00           C
+ATOM    905  C   GLU A  44      26.070  37.480  14.910  1.00  0.00           C
+ATOM    906  O   GLU A  44      26.910  37.540  14.020  1.00  0.00           O
+ATOM    907  CB  GLU A  44      26.250  39.480  16.440  1.00  0.00           C
+ATOM    908  CG  GLU A  44      26.760  40.880  16.040  1.00  0.00           C
+ATOM    909  CD  GLU A  44      25.780  42.030  16.330  1.00  0.00           C
+ATOM    910  OE1 GLU A  44      24.970  42.330  15.440  1.00  0.00           O
+ATOM    911  OE2 GLU A  44      25.930  42.700  17.380  1.00  0.00           O
+ATOM    912  H   GLU A  44      23.680  39.210  16.510  1.00  0.00           H
+ATOM    913  N   LEU A  45      25.660  36.310  15.410  1.00  0.00           N
+ATOM    914  CA  LEU A  45      26.270  35.030  15.020  1.00  0.00           C
+ATOM    915  C   LEU A  45      25.290  34.150  14.250  1.00  0.00           C
+ATOM    916  O   LEU A  45      24.110  34.060  14.590  1.00  0.00           O
+ATOM    917  CB  LEU A  45      26.760  34.320  16.280  1.00  0.00           C
+ATOM    918  CG  LEU A  45      27.990  35.000  16.910  1.00  0.00           C
+ATOM    919  CD1 LEU A  45      28.300  34.340  18.250  1.00  0.00           C
+ATOM    920  CD2 LEU A  45      29.220  34.860  16.010  1.00  0.00           C
+ATOM    921  H   LEU A  45      25.020  36.210  16.180  1.00  0.00           H
+ATOM    922  N   ASN A  46      25.860  33.310  13.400  1.00  0.00           N
+ATOM    923  CA  ASN A  46      25.120  32.510  12.410  1.00  0.00           C
+ATOM    924  C   ASN A  46      24.500  31.210  12.970  1.00  0.00           C
+ATOM    925  O   ASN A  46      24.530  30.160  12.330  1.00  0.00           O
+ATOM    926  CB  ASN A  46      26.070  32.230  11.230  1.00  0.00           C
+ATOM    927  CG  ASN A  46      27.380  31.550  11.650  1.00  0.00           C
+ATOM    928  OD1 ASN A  46      27.440  30.380  11.990  1.00  0.00           O
+ATOM    929  ND2 ASN A  46      28.440  32.320  11.750  1.00  0.00           N
+ATOM    930  H   ASN A  46      26.850  33.150  13.400  1.00  0.00           H
+ATOM    931 HD21 ASN A  46      28.370  33.290  11.440  1.00  0.00           H
+ATOM    932 HD22 ASN A  46      29.300  31.940  12.080  1.00  0.00           H
+ATOM    933  N   TYR A  47      24.000  31.310  14.190  1.00  0.00           N
+ATOM    934  CA  TYR A  47      23.300  30.230  14.920  1.00  0.00           C
+ATOM    935  C   TYR A  47      22.340  29.440  14.030  1.00  0.00           C
+ATOM    936  O   TYR A  47      21.390  29.980  13.480  1.00  0.00           O
+ATOM    937  CB  TYR A  47      22.530  30.820  16.100  1.00  0.00           C
+ATOM    938  CG  TYR A  47      21.870  29.750  16.960  1.00  0.00           C
+ATOM    939  CD1 TYR A  47      22.750  28.990  17.760  1.00  0.00           C
+ATOM    940  CD2 TYR A  47      20.530  29.340  16.750  1.00  0.00           C
+ATOM    941  CE1 TYR A  47      22.320  27.770  18.300  1.00  0.00           C
+ATOM    942  CE2 TYR A  47      20.090  28.120  17.300  1.00  0.00           C
+ATOM    943  CZ  TYR A  47      21.010  27.340  18.040  1.00  0.00           C
+ATOM    944  OH  TYR A  47      20.670  26.070  18.410  1.00  0.00           O
+ATOM    945  H   TYR A  47      24.110  32.170  14.700  1.00  0.00           H
+ATOM    946  HH  TYR A  47      21.500  25.560  18.740  1.00  0.00           H
+ATOM    947  N   ILE A  48      22.460  28.130  14.190  1.00  0.00           N
+ATOM    948  CA  ILE A  48      21.710  27.130  13.410  1.00  0.00           C
+ATOM    949  C   ILE A  48      21.100  26.210  14.480  1.00  0.00           C
+ATOM    950  O   ILE A  48      21.860  25.650  15.280  1.00  0.00           O
+ATOM    951  CB  ILE A  48      22.670  26.240  12.600  1.00  0.00           C
+ATOM    952  CG1 ILE A  48      23.790  27.000  11.900  1.00  0.00           C
+ATOM    953  CG2 ILE A  48      21.910  25.360  11.590  1.00  0.00           C
+ATOM    954  CD1 ILE A  48      25.000  27.130  12.840  1.00  0.00           C
+ATOM    955  H   ILE A  48      23.140  27.720  14.800  1.00  0.00           H
+ATOM    956  N   PRO A  49      19.780  25.980  14.420  1.00  0.00           N
+ATOM    957  CA  PRO A  49      19.140  25.030  15.340  1.00  0.00           C
+ATOM    958  C   PRO A  49      19.820  23.670  15.140  1.00  0.00           C
+ATOM    959  O   PRO A  49      20.240  23.350  14.030  1.00  0.00           O
+ATOM    960  CB  PRO A  49      17.670  25.050  14.920  1.00  0.00           C
+ATOM    961  CG  PRO A  49      17.490  26.410  14.260  1.00  0.00           C
+ATOM    962  CD  PRO A  49      18.800  26.600  13.510  1.00  0.00           C
+ATOM    963  N   ASN A  50      20.330  23.150  16.260  1.00  0.00           N
+ATOM    964  CA  ASN A  50      20.960  21.810  16.320  1.00  0.00           C
+ATOM    965  C   ASN A  50      22.370  21.680  15.710  1.00  0.00           C
+ATOM    966  O   ASN A  50      22.830  20.580  15.410  1.00  0.00           O
+ATOM    967  CB  ASN A  50      20.060  20.740  15.690  1.00  0.00           C
+ATOM    968  CG  ASN A  50      18.850  20.510  16.570  1.00  0.00           C
+ATOM    969  OD1 ASN A  50      17.810  21.150  16.470  1.00  0.00           O
+ATOM    970  ND2 ASN A  50      19.020  19.610  17.510  1.00  0.00           N
+ATOM    971  H   ASN A  50      20.450  23.710  17.080  1.00  0.00           H
+ATOM    972 HD21 ASN A  50      19.930  19.190  17.630  1.00  0.00           H
+ATOM    973 HD22 ASN A  50      18.280  19.400  18.150  1.00  0.00           H
+ATOM    974  N   ARG A  51      23.070  22.800  15.610  1.00  0.00           N
+ATOM    975  CA  ARG A  51      24.480  22.760  15.180  1.00  0.00           C
+ATOM    976  C   ARG A  51      25.400  23.530  16.120  1.00  0.00           C
+ATOM    977  O   ARG A  51      25.050  24.700  16.380  1.00  0.00           O
+ATOM    978  CB  ARG A  51      24.660  23.260  13.750  1.00  0.00           C
+ATOM    979  CG  ARG A  51      24.250  22.200  12.710  1.00  0.00           C
+ATOM    980  CD  ARG A  51      25.170  20.970  12.740  1.00  0.00           C
+ATOM    981  NE  ARG A  51      26.580  21.360  12.540  1.00  0.00           N
+ATOM    982  CZ  ARG A  51      27.660  20.860  13.150  1.00  0.00           C
+ATOM    983  NH1 ARG A  51      27.580  19.940  14.110  1.00  0.00           N
+ATOM    984  NH2 ARG A  51      28.840  21.320  12.750  1.00  0.00           N
+ATOM    985  OXT ARG A  51      26.530  23.030  16.340  1.00  0.00           O
+ATOM    986  H   ARG A  51      22.730  23.700  15.910  1.00  0.00           H
+ATOM    987  HE  ARG A  51      26.750  22.050  11.820  1.00  0.00           H
+ATOM    988 HH11 ARG A  51      26.710  19.690  14.530  1.00  0.00           H
+ATOM    989 HH12 ARG A  51      28.420  19.520  14.450  1.00  0.00           H
+ATOM    990 HH21 ARG A  51      28.890  21.900  11.940  1.00  0.00           H
+ATOM    991 HH22 ARG A  51      29.700  21.010  13.170  1.00  0.00           H
+TER     992      ARG A  51
+HETATM  993 NA    NA C  12      16.870  24.560  19.270  1.00  0.00          NA
+HETATM  994  O   HOH B1032      10.310  28.620  40.780  1.00  0.00           O
+HETATM  995  H1  HOH B1032      10.500  28.310  41.720  1.00  0.00           H
+HETATM  996  H2  HOH B1032      10.350  29.620  40.750  1.00  0.00           H
+HETATM  997  O   HOH B1584      20.350  36.610  34.330  1.00  0.00           O
+HETATM  998  H1  HOH B1584      20.260  35.680  33.950  1.00  0.00           H
+HETATM  999  H2  HOH B1584      21.170  37.040  33.970  1.00  0.00           H
+HETATM 1000  O   HOH B1619      17.340  37.240  36.200  1.00  0.00           O
+HETATM 1001  H1  HOH B1619      17.030  37.910  36.870  1.00  0.00           H
+HETATM 1002  H2  HOH B1619      16.640  36.540  36.090  1.00  0.00           H
+HETATM 1003  O   HOH B1904      24.200  17.380  30.380  1.00  0.00           O
+HETATM 1004  H1  HOH B1904      24.750  17.450  29.550  1.00  0.00           H
+HETATM 1005  H2  HOH B1904      23.620  18.190  30.460  1.00  0.00           H
+HETATM 1006  O   HOH B2484      25.960  22.390  31.380  1.00  0.00           O
+HETATM 1007  H1  HOH B2484      26.820  22.410  31.890  1.00  0.00           H
+HETATM 1008  H2  HOH B2484      26.150  22.240  30.410  1.00  0.00           H
+HETATM 1009  O   HOH B2496      26.950  27.600  32.130  1.00  0.00           O
+HETATM 1010  H1  HOH B2496      26.510  28.190  32.810  1.00  0.00           H
+HETATM 1011  H2  HOH B2496      26.560  26.680  32.180  1.00  0.00           H
+HETATM 1012  O   HOH B2514      25.640  24.890  32.750  1.00  0.00           O
+HETATM 1013  H1  HOH B2514      26.270  24.580  33.470  1.00  0.00           H
+HETATM 1014  H2  HOH B2514      25.480  24.140  32.110  1.00  0.00           H
+HETATM 1015  O   HOH B2963      10.310  33.430  38.870  1.00  0.00           O
+HETATM 1016  H1  HOH B2963      10.550  34.070  39.600  1.00  0.00           H
+HETATM 1017  H2  HOH B2963       9.320  33.440  38.740  1.00  0.00           H
+HETATM 1018  O   HOH B3222      25.390  32.260  33.890  1.00  0.00           O
+HETATM 1019  H1  HOH B3222      24.600  32.300  33.280  1.00  0.00           H
+HETATM 1020  H2  HOH B3222      25.560  31.310  34.150  1.00  0.00           H
+HETATM 1021  O   HOH B3303      10.450  31.430  41.030  1.00  0.00           O
+HETATM 1022  H1  HOH B3303      10.580  32.020  41.830  1.00  0.00           H
+HETATM 1023  H2  HOH B3303      10.360  32.000  40.210  1.00  0.00           H
+HETATM 1024  O   HOH C 317       9.180  20.100  28.640  1.00  0.00           O
+HETATM 1025  H1  HOH C 317      10.000  19.740  28.200  1.00  0.00           H
+HETATM 1026  H2  HOH C 317       8.780  19.390  29.230  1.00  0.00           H
+HETATM 1027  O   HOH C 327      18.700  15.530  18.720  1.00  0.00           O
+HETATM 1028  H1  HOH C 327      18.930  16.340  19.260  1.00  0.00           H
+HETATM 1029  H2  HOH C 327      18.290  15.810  17.860  1.00  0.00           H
+HETATM 1030  O   HOH C 328      19.370  17.630  20.250  1.00  0.00           O
+HETATM 1031  H1  HOH C 328      18.660  17.720  20.950  1.00  0.00           H
+HETATM 1032  H2  HOH C 328      20.180  18.150  20.540  1.00  0.00           H
+HETATM 1033  O   HOH C 827       7.540  20.510  25.950  1.00  0.00           O
+HETATM 1034  H1  HOH C 827       8.410  20.890  26.280  1.00  0.00           H
+HETATM 1035  H2  HOH C 827       7.680  19.540  25.710  1.00  0.00           H
+HETATM 1036  O   HOH C 845      28.930  16.400  17.250  1.00  0.00           O
+HETATM 1037  H1  HOH C 845      29.030  17.390  17.300  1.00  0.00           H
+HETATM 1038  H2  HOH C 845      29.790  15.970  17.510  1.00  0.00           H
+HETATM 1039  O   HOH C 923      18.220  25.060  20.920  1.00  0.00           O
+HETATM 1040  H1  HOH C 923      17.680  24.720  21.690  1.00  0.00           H
+HETATM 1041  H2  HOH C 923      19.190  24.890  21.090  1.00  0.00           H
+HETATM 1042  O   HOH C 952      14.110  25.370  38.310  1.00  0.00           O
+HETATM 1043  H1  HOH C 952      13.490  24.600  38.150  1.00  0.00           H
+HETATM 1044  H2  HOH C 952      13.870  26.120  37.690  1.00  0.00           H
+HETATM 1045  O   HOH C1002      10.170  22.880  23.630  1.00  0.00           O
+HETATM 1046  H1  HOH C1002      10.500  22.380  22.820  1.00  0.00           H
+HETATM 1047  H2  HOH C1002      10.420  22.370  24.450  1.00  0.00           H
+HETATM 1048  O   HOH C1419      15.760  19.980  19.340  1.00  0.00           O
+HETATM 1049  H1  HOH C1419      15.850  20.300  20.290  1.00  0.00           H
+HETATM 1050  H2  HOH C1419      14.920  20.360  18.950  1.00  0.00           H
+HETATM 1051  O   HOH C1771      31.010  19.980  14.940  1.00  0.00           O
+HETATM 1052  H1  HOH C1771      30.230  20.380  15.430  1.00  0.00           H
+HETATM 1053  H2  HOH C1771      31.470  19.330  15.540  1.00  0.00           H
+HETATM 1054  O   HOH C1817      29.700  22.480  21.810  1.00  0.00           O
+HETATM 1055  H1  HOH C1817      30.090  23.400  21.780  1.00  0.00           H
+HETATM 1056  H2  HOH C1817      29.600  22.130  20.880  1.00  0.00           H
+HETATM 1057  O   HOH C1958      29.040  19.060  23.620  1.00  0.00           O
+HETATM 1058  H1  HOH C1958      28.510  19.640  23.000  1.00  0.00           H
+HETATM 1059  H2  HOH C1958      28.590  19.020  24.510  1.00  0.00           H
+HETATM 1060  O   HOH C1961      25.720  18.250  28.220  1.00  0.00           O
+HETATM 1061  H1  HOH C1961      26.120  19.150  28.410  1.00  0.00           H
+HETATM 1062  H2  HOH C1961      26.430  17.660  27.830  1.00  0.00           H
+HETATM 1063  O   HOH C2011      30.890  23.360  17.030  1.00  0.00           O
+HETATM 1064  H1  HOH C2011      30.500  22.880  17.830  1.00  0.00           H
+HETATM 1065  H2  HOH C2011      30.150  23.640  16.430  1.00  0.00           H
+HETATM 1066  O   HOH C2471      23.550  22.330  23.470  1.00  0.00           O
+HETATM 1067  H1  HOH C2471      22.610  22.570  23.220  1.00  0.00           H
+HETATM 1068  H2  HOH C2471      24.180  22.730  22.810  1.00  0.00           H
+HETATM 1069  O   HOH C2570      27.480  20.700  21.920  1.00  0.00           O
+HETATM 1070  H1  HOH C2570      27.300  20.490  20.960  1.00  0.00           H
+HETATM 1071  H2  HOH C2570      28.120  21.470  21.970  1.00  0.00           H
+HETATM 1072  O   HOH A  52      21.460  18.730  18.280  1.00  0.00           O
+HETATM 1073  H1  HOH A  52      22.360  19.090  18.550  1.00  0.00           H
+HETATM 1074  H2  HOH A  52      21.140  18.090  18.970  1.00  0.00           H
+HETATM 1075  O   HOH A  53      17.180  27.410  31.630  1.00  0.00           O
+HETATM 1076  H1  HOH A  53      16.940  28.270  32.080  1.00  0.00           H
+HETATM 1077  H2  HOH A  53      16.760  27.390  30.720  1.00  0.00           H
+HETATM 1078  O   HOH A  54      15.310  23.600  27.950  1.00  0.00           O
+HETATM 1079  H1  HOH A  54      16.070  23.800  27.330  1.00  0.00           H
+HETATM 1080  H2  HOH A  54      14.460  23.920  27.530  1.00  0.00           H
+HETATM 1081  O   HOH A  55      18.030  22.820  18.820  1.00  0.00           O
+HETATM 1082  H1  HOH A  55      18.390  22.540  19.710  1.00  0.00           H
+HETATM 1083  H2  HOH A  55      18.080  22.050  18.190  1.00  0.00           H
+HETATM 1084  O   HOH A  56      17.510  34.670  31.650  1.00  0.00           O
+HETATM 1085  H1  HOH A  56      16.780  34.430  32.300  1.00  0.00           H
+HETATM 1086  H2  HOH A  56      18.280  34.030  31.770  1.00  0.00           H
+HETATM 1087  O   HOH A  57      12.480  37.730  35.190  1.00  0.00           O
+HETATM 1088  H1  HOH A  57      11.660  38.290  35.330  1.00  0.00           H
+HETATM 1089  H2  HOH A  57      12.660  37.180  36.010  1.00  0.00           H
+HETATM 1090  O   HOH A  58      16.160  29.790  32.320  1.00  0.00           O
+HETATM 1091  H1  HOH A  58      15.800  30.370  31.600  1.00  0.00           H
+HETATM 1092  H2  HOH A  58      16.960  30.230  32.740  1.00  0.00           H
+HETATM 1093  O   HOH A  59      10.310  32.330  35.810  1.00  0.00           O
+HETATM 1094  H1  HOH A  59      10.750  33.150  36.170  1.00  0.00           H
+HETATM 1095  H2  HOH A  59       9.390  32.250  36.210  1.00  0.00           H
+HETATM 1096  O   HOH A  60      13.440  27.020  35.920  1.00  0.00           O
+HETATM 1097  H1  HOH A  60      12.570  26.630  35.610  1.00  0.00           H
+HETATM 1098  H2  HOH A  60      13.880  27.490  35.150  1.00  0.00           H
+HETATM 1099  O   HOH A  61      11.610  22.860  32.990  1.00  0.00           O
+HETATM 1100  H1  HOH A  61      11.460  23.770  32.600  1.00  0.00           H
+HETATM 1101  H2  HOH A  61      11.240  22.170  32.370  1.00  0.00           H
+HETATM 1102  O   HOH A  62      25.560  17.840  14.880  1.00  0.00           O
+HETATM 1103  H1  HOH A  62      25.370  17.600  15.830  1.00  0.00           H
+HETATM 1104  H2  HOH A  62      25.980  17.060  14.410  1.00  0.00           H
+HETATM 1105  O   HOH A  63      13.250  24.000  34.990  1.00  0.00           O
+HETATM 1106  H1  HOH A  63      12.720  24.250  35.790  1.00  0.00           H
+HETATM 1107  H2  HOH A  63      12.620  23.810  34.230  1.00  0.00           H
+HETATM 1108  O   HOH A  64      24.050  20.340  30.530  1.00  0.00           O
+HETATM 1109  H1  HOH A  64      24.730  19.610  30.440  1.00  0.00           H
+HETATM 1110  H2  HOH A  64      24.450  21.110  31.030  1.00  0.00           H
+HETATM 1111  O   HOH A  65      14.320  28.110  33.490  1.00  0.00           O
+HETATM 1112  H1  HOH A  65      15.000  28.780  33.200  1.00  0.00           H
+HETATM 1113  H2  HOH A  65      13.460  28.280  33.010  1.00  0.00           H
+HETATM 1114  O   HOH A  66      23.030  32.690  32.190  1.00  0.00           O
+HETATM 1115  H1  HOH A  66      22.060  32.740  32.400  1.00  0.00           H
+HETATM 1116  H2  HOH A  66      23.430  33.610  32.220  1.00  0.00           H
+HETATM 1117  O   HOH A  67      23.740  26.650  33.310  1.00  0.00           O
+HETATM 1118  H1  HOH A  67      24.190  27.140  34.060  1.00  0.00           H
+HETATM 1119  H2  HOH A  67      24.280  25.840  33.070  1.00  0.00           H
+HETATM 1120  O   HOH A  68      25.600  20.490  24.090  1.00  0.00           O
+HETATM 1121  H1  HOH A  68      26.180  20.550  23.270  1.00  0.00           H
+HETATM 1122  H2  HOH A  68      24.750  21.000  23.930  1.00  0.00           H
+HETATM 1123  O   HOH A  69      15.450  38.970  37.560  1.00  0.00           O
+HETATM 1124  H1  HOH A  69      15.090  39.190  38.470  1.00  0.00           H
+HETATM 1125  H2  HOH A  69      14.710  38.630  36.980  1.00  0.00           H
+HETATM 1126  O   HOH A  70      10.170  21.260  31.180  1.00  0.00           O
+HETATM 1127  H1  HOH A  70       9.830  20.890  30.320  1.00  0.00           H
+HETATM 1128  H2  HOH A  70      10.190  20.530  31.870  1.00  0.00           H
+ENDMDL
+MODEL        3
+ATOM      1  O5'  DA B   1       7.850  31.870  48.800  1.00  0.00           O
+ATOM      2  C5'  DA B   1       7.900  31.760  47.370  1.00  0.00           C
+ATOM      3  C4'  DA B   1       9.300  31.550  46.800  1.00  0.00           C
+ATOM      4  O4'  DA B   1      10.200  30.670  47.500  1.00  0.00           O
+ATOM      5  C3'  DA B   1       9.200  31.020  45.380  1.00  0.00           C
+ATOM      6  O3'  DA B   1       9.670  32.030  44.480  1.00  0.00           O
+ATOM      7  C2'  DA B   1      10.030  29.770  45.340  1.00  0.00           C
+ATOM      8  C1'  DA B   1      11.000  30.210  46.410  1.00  0.00           C
+ATOM      9  N9   DA B   1      11.900  29.180  46.870  1.00  0.00           N
+ATOM     10  C8   DA B   1      11.620  27.890  47.020  1.00  0.00           C
+ATOM     11  N7   DA B   1      12.680  27.270  47.540  1.00  0.00           N
+ATOM     12  C5   DA B   1      13.620  28.200  47.710  1.00  0.00           C
+ATOM     13  C6   DA B   1      14.900  28.250  48.260  1.00  0.00           C
+ATOM     14  N6   DA B   1      15.490  27.150  48.660  1.00  0.00           N
+ATOM     15  N1   DA B   1      15.610  29.400  48.300  1.00  0.00           N
+ATOM     16  C2   DA B   1      15.110  30.540  47.820  1.00  0.00           C
+ATOM     17  N3   DA B   1      13.880  30.540  47.310  1.00  0.00           N
+ATOM     18  C4   DA B   1      13.130  29.410  47.260  1.00  0.00           C
+ATOM     19  H61  DA B   1      16.330  27.200  49.200  1.00  0.00           H
+ATOM     20  H62  DA B   1      15.260  26.290  48.200  1.00  0.00           H
+ATOM     21 HO5'  DA B   1       7.700  32.850  49.090  1.00  0.00           H
+ATOM     22  P    DA B   2       9.150  32.050  42.970  1.00  0.00           P
+ATOM     23  OP1  DA B   2       7.990  32.960  42.850  1.00  0.00           O
+ATOM     24  OP2  DA B   2       8.900  30.660  42.510  1.00  0.00           O
+ATOM     25  O5'  DA B   2      10.470  32.550  42.230  1.00  0.00           O
+ATOM     26  C5'  DA B   2      10.870  33.930  42.340  1.00  0.00           C
+ATOM     27  C4'  DA B   2      12.350  34.160  42.010  1.00  0.00           C
+ATOM     28  O4'  DA B   2      13.130  33.300  42.870  1.00  0.00           O
+ATOM     29  C3'  DA B   2      12.670  33.760  40.570  1.00  0.00           C
+ATOM     30  O3'  DA B   2      13.520  34.620  39.790  1.00  0.00           O
+ATOM     31  C2'  DA B   2      13.250  32.370  40.700  1.00  0.00           C
+ATOM     32  C1'  DA B   2      13.920  32.390  42.070  1.00  0.00           C
+ATOM     33  N9   DA B   2      13.900  31.060  42.730  1.00  0.00           N
+ATOM     34  C8   DA B   2      12.800  30.340  42.990  1.00  0.00           C
+ATOM     35  N7   DA B   2      13.130  29.220  43.650  1.00  0.00           N
+ATOM     36  C5   DA B   2      14.450  29.210  43.810  1.00  0.00           C
+ATOM     37  C6   DA B   2      15.420  28.420  44.460  1.00  0.00           C
+ATOM     38  N6   DA B   2      15.240  27.200  44.920  1.00  0.00           N
+ATOM     39  N1   DA B   2      16.710  28.800  44.550  1.00  0.00           N
+ATOM     40  C2   DA B   2      17.120  29.950  44.010  1.00  0.00           C
+ATOM     41  N3   DA B   2      16.250  30.710  43.350  1.00  0.00           N
+ATOM     42  C4   DA B   2      14.930  30.390  43.240  1.00  0.00           C
+ATOM     43  H61  DA B   2      16.000  26.710  45.370  1.00  0.00           H
+ATOM     44  H62  DA B   2      14.350  26.740  44.860  1.00  0.00           H
+ATOM     45  P    DT B   3      13.250  34.690  38.200  1.00  0.00           P
+ATOM     46  OP1  DT B   3      12.620  36.010  37.990  1.00  0.00           O
+ATOM     47  OP2  DT B   3      12.510  33.480  37.750  1.00  0.00           O
+ATOM     48  O5'  DT B   3      14.700  34.750  37.510  1.00  0.00           O
+ATOM     49  C5'  DT B   3      15.270  33.610  36.840  1.00  0.00           C
+ATOM     50  C4'  DT B   3      16.480  32.990  37.540  1.00  0.00           C
+ATOM     51  O4'  DT B   3      16.200  32.300  38.780  1.00  0.00           O
+ATOM     52  C3'  DT B   3      17.140  31.960  36.660  1.00  0.00           C
+ATOM     53  O3'  DT B   3      17.930  32.620  35.660  1.00  0.00           O
+ATOM     54  C2'  DT B   3      18.050  31.310  37.690  1.00  0.00           C
+ATOM     55  C1'  DT B   3      17.330  31.440  39.020  1.00  0.00           C
+ATOM     56  N1   DT B   3      16.880  30.140  39.610  1.00  0.00           N
+ATOM     57  C2   DT B   3      17.790  29.340  40.350  1.00  0.00           C
+ATOM     58  O2   DT B   3      19.000  29.370  40.150  1.00  0.00           O
+ATOM     59  N3   DT B   3      17.330  28.240  41.060  1.00  0.00           N
+ATOM     60  C4   DT B   3      15.990  27.880  40.970  1.00  0.00           C
+ATOM     61  O4   DT B   3      15.610  26.960  41.690  1.00  0.00           O
+ATOM     62  C5   DT B   3      15.070  28.610  40.210  1.00  0.00           C
+ATOM     63  C7   DT B   3      13.700  28.010  39.860  1.00  0.00           C
+ATOM     64  C6   DT B   3      15.510  29.770  39.560  1.00  0.00           C
+ATOM     65  H3   DT B   3      17.920  27.790  41.730  1.00  0.00           H
+ATOM     66  P    DT B   4      18.620  31.900  34.410  1.00  0.00           P
+ATOM     67  OP1  DT B   4      19.280  33.010  33.700  1.00  0.00           O
+ATOM     68  OP2  DT B   4      17.640  31.110  33.640  1.00  0.00           O
+ATOM     69  O5'  DT B   4      19.770  30.920  34.990  1.00  0.00           O
+ATOM     70  C5'  DT B   4      20.960  31.410  35.650  1.00  0.00           C
+ATOM     71  C4'  DT B   4      21.880  30.340  36.230  1.00  0.00           C
+ATOM     72  O4'  DT B   4      21.240  29.420  37.140  1.00  0.00           O
+ATOM     73  C3'  DT B   4      22.590  29.450  35.230  1.00  0.00           C
+ATOM     74  O3'  DT B   4      23.970  29.340  35.680  1.00  0.00           O
+ATOM     75  C2'  DT B   4      21.790  28.180  35.160  1.00  0.00           C
+ATOM     76  C1'  DT B   4      21.220  28.110  36.560  1.00  0.00           C
+ATOM     77  N1   DT B   4      19.800  27.680  36.640  1.00  0.00           N
+ATOM     78  C2   DT B   4      19.450  26.970  37.790  1.00  0.00           C
+ATOM     79  O2   DT B   4      20.270  26.460  38.540  1.00  0.00           O
+ATOM     80  N3   DT B   4      18.110  26.850  38.130  1.00  0.00           N
+ATOM     81  C4   DT B   4      17.130  27.410  37.330  1.00  0.00           C
+ATOM     82  O4   DT B   4      15.980  27.200  37.710  1.00  0.00           O
+ATOM     83  C5   DT B   4      17.440  28.110  36.160  1.00  0.00           C
+ATOM     84  C7   DT B   4      16.330  28.690  35.280  1.00  0.00           C
+ATOM     85  C6   DT B   4      18.790  28.230  35.810  1.00  0.00           C
+ATOM     86  H3   DT B   4      17.860  26.190  38.840  1.00  0.00           H
+ATOM     87  P    DG B   5      25.160  28.750  34.770  1.00  0.00           P
+ATOM     88  OP1  DG B   5      26.460  29.280  35.250  1.00  0.00           O
+ATOM     89  OP2  DG B   5      24.810  28.960  33.350  1.00  0.00           O
+ATOM     90  O5'  DG B   5      25.080  27.200  35.150  1.00  0.00           O
+ATOM     91  C5'  DG B   5      25.230  26.820  36.530  1.00  0.00           C
+ATOM     92  C4'  DG B   5      24.650  25.440  36.790  1.00  0.00           C
+ATOM     93  O4'  DG B   5      23.220  25.260  36.700  1.00  0.00           O
+ATOM     94  C3'  DG B   5      25.250  24.380  35.910  1.00  0.00           C
+ATOM     95  O3'  DG B   5      26.070  23.620  36.830  1.00  0.00           O
+ATOM     96  C2'  DG B   5      24.100  23.700  35.210  1.00  0.00           C
+ATOM     97  C1'  DG B   5      22.990  23.900  36.250  1.00  0.00           C
+ATOM     98  N9   DG B   5      21.610  23.780  35.750  1.00  0.00           N
+ATOM     99  C8   DG B   5      21.120  24.570  34.820  1.00  0.00           C
+ATOM    100  N7   DG B   5      19.790  24.440  34.750  1.00  0.00           N
+ATOM    101  C5   DG B   5      19.440  23.520  35.650  1.00  0.00           C
+ATOM    102  C6   DG B   5      18.240  22.870  35.960  1.00  0.00           C
+ATOM    103  O6   DG B   5      17.200  23.150  35.380  1.00  0.00           O
+ATOM    104  N1   DG B   5      18.240  21.820  36.870  1.00  0.00           N
+ATOM    105  C2   DG B   5      19.430  21.420  37.460  1.00  0.00           C
+ATOM    106  N2   DG B   5      19.410  20.230  38.000  1.00  0.00           N
+ATOM    107  N3   DG B   5      20.570  22.060  37.180  1.00  0.00           N
+ATOM    108  C4   DG B   5      20.610  23.090  36.290  1.00  0.00           C
+ATOM    109  H1   DG B   5      17.380  21.380  37.140  1.00  0.00           H
+ATOM    110  H21  DG B   5      18.520  19.800  38.230  1.00  0.00           H
+ATOM    111  H22  DG B   5      20.250  19.730  38.160  1.00  0.00           H
+ATOM    112  P    DT B   6      27.020  22.390  36.430  1.00  0.00           P
+ATOM    113  OP1  DT B   6      28.060  22.220  37.470  1.00  0.00           O
+ATOM    114  OP2  DT B   6      27.370  22.520  35.010  1.00  0.00           O
+ATOM    115  O5'  DT B   6      26.040  21.150  36.590  1.00  0.00           O
+ATOM    116  C5'  DT B   6      25.450  20.770  37.840  1.00  0.00           C
+ATOM    117  C4'  DT B   6      24.400  19.730  37.510  1.00  0.00           C
+ATOM    118  O4'  DT B   6      23.340  20.260  36.700  1.00  0.00           O
+ATOM    119  C3'  DT B   6      25.000  18.610  36.690  1.00  0.00           C
+ATOM    120  O3'  DT B   6      25.280  17.510  37.570  1.00  0.00           O
+ATOM    121  C2'  DT B   6      24.060  18.330  35.550  1.00  0.00           C
+ATOM    122  C1'  DT B   6      22.840  19.110  36.010  1.00  0.00           C
+ATOM    123  N1   DT B   6      21.990  19.580  34.910  1.00  0.00           N
+ATOM    124  C2   DT B   6      20.620  19.290  35.020  1.00  0.00           C
+ATOM    125  O2   DT B   6      20.230  18.240  35.520  1.00  0.00           O
+ATOM    126  N3   DT B   6      19.700  20.060  34.320  1.00  0.00           N
+ATOM    127  C4   DT B   6      20.130  21.060  33.460  1.00  0.00           C
+ATOM    128  O4   DT B   6      19.250  21.750  32.960  1.00  0.00           O
+ATOM    129  C5   DT B   6      21.500  21.310  33.260  1.00  0.00           C
+ATOM    130  C7   DT B   6      21.980  22.170  32.080  1.00  0.00           C
+ATOM    131  C6   DT B   6      22.420  20.600  34.030  1.00  0.00           C
+ATOM    132  H3   DT B   6      18.730  19.870  34.420  1.00  0.00           H
+ATOM    133  P    DG B   7      26.020  16.170  37.090  1.00  0.00           P
+ATOM    134  OP1  DG B   7      26.480  15.600  38.380  1.00  0.00           O
+ATOM    135  OP2  DG B   7      27.020  16.510  36.050  1.00  0.00           O
+ATOM    136  O5'  DG B   7      24.810  15.370  36.420  1.00  0.00           O
+ATOM    137  C5'  DG B   7      23.610  15.000  37.140  1.00  0.00           C
+ATOM    138  C4'  DG B   7      22.560  14.450  36.190  1.00  0.00           C
+ATOM    139  O4'  DG B   7      22.010  15.510  35.390  1.00  0.00           O
+ATOM    140  C3'  DG B   7      23.160  13.480  35.180  1.00  0.00           C
+ATOM    141  O3'  DG B   7      22.310  12.320  35.050  1.00  0.00           O
+ATOM    142  C2'  DG B   7      23.300  14.280  33.920  1.00  0.00           C
+ATOM    143  C1'  DG B   7      21.900  14.920  34.090  1.00  0.00           C
+ATOM    144  N9   DG B   7      21.510  15.970  33.140  1.00  0.00           N
+ATOM    145  C8   DG B   7      22.350  16.810  32.550  1.00  0.00           C
+ATOM    146  N7   DG B   7      21.660  17.780  31.950  1.00  0.00           N
+ATOM    147  C5   DG B   7      20.380  17.500  32.110  1.00  0.00           C
+ATOM    148  C6   DG B   7      19.210  18.060  31.590  1.00  0.00           C
+ATOM    149  O6   DG B   7      19.250  19.070  30.890  1.00  0.00           O
+ATOM    150  N1   DG B   7      17.980  17.450  31.860  1.00  0.00           N
+ATOM    151  C2   DG B   7      17.940  16.300  32.640  1.00  0.00           C
+ATOM    152  N2   DG B   7      16.920  15.490  32.620  1.00  0.00           N
+ATOM    153  N3   DG B   7      19.070  15.780  33.140  1.00  0.00           N
+ATOM    154  C4   DG B   7      20.270  16.350  32.880  1.00  0.00           C
+ATOM    155  H1   DG B   7      17.150  17.800  31.420  1.00  0.00           H
+ATOM    156  H21  DG B   7      16.020  15.750  32.260  1.00  0.00           H
+ATOM    157  H22  DG B   7      17.130  14.540  32.900  1.00  0.00           H
+ATOM    158  P    DA B   8      23.020  10.950  34.640  1.00  0.00           P
+ATOM    159  OP1  DA B   8      24.010  10.640  35.700  1.00  0.00           O
+ATOM    160  OP2  DA B   8      23.480  11.010  33.240  1.00  0.00           O
+ATOM    161  O5'  DA B   8      21.910   9.780  34.740  1.00  0.00           O
+ATOM    162  C5'  DA B   8      20.490  10.020  34.890  1.00  0.00           C
+ATOM    163  C4'  DA B   8      19.700  10.400  33.640  1.00  0.00           C
+ATOM    164  O4'  DA B   8      19.970  11.710  33.080  1.00  0.00           O
+ATOM    165  C3'  DA B   8      19.780   9.400  32.480  1.00  0.00           C
+ATOM    166  O3'  DA B   8      18.520   8.720  32.270  1.00  0.00           O
+ATOM    167  C2'  DA B   8      20.250  10.250  31.320  1.00  0.00           C
+ATOM    168  C1'  DA B   8      19.640  11.580  31.690  1.00  0.00           C
+ATOM    169  N9   DA B   8      20.230  12.720  30.950  1.00  0.00           N
+ATOM    170  C8   DA B   8      21.530  12.940  30.770  1.00  0.00           C
+ATOM    171  N7   DA B   8      21.730  14.080  30.110  1.00  0.00           N
+ATOM    172  C5   DA B   8      20.520  14.590  29.840  1.00  0.00           C
+ATOM    173  C6   DA B   8      20.030  15.720  29.190  1.00  0.00           C
+ATOM    174  N6   DA B   8      20.820  16.630  28.660  1.00  0.00           N
+ATOM    175  N1   DA B   8      18.700  15.900  29.030  1.00  0.00           N
+ATOM    176  C2   DA B   8      17.820  15.020  29.500  1.00  0.00           C
+ATOM    177  N3   DA B   8      18.250  13.940  30.160  1.00  0.00           N
+ATOM    178  C4   DA B   8      19.570  13.710  30.360  1.00  0.00           C
+ATOM    179  H61  DA B   8      20.490  17.490  28.260  1.00  0.00           H
+ATOM    180  H62  DA B   8      21.820  16.420  28.620  1.00  0.00           H
+ATOM    181  P    DG B   9      18.310   7.590  31.130  1.00  0.00           P
+ATOM    182  OP1  DG B   9      17.390   6.530  31.600  1.00  0.00           O
+ATOM    183  OP2  DG B   9      19.630   7.150  30.640  1.00  0.00           O
+ATOM    184  O5'  DG B   9      17.550   8.360  29.930  1.00  0.00           O
+ATOM    185  C5'  DG B   9      16.230   8.930  30.070  1.00  0.00           C
+ATOM    186  C4'  DG B   9      15.680   9.660  28.830  1.00  0.00           C
+ATOM    187  O4'  DG B   9      16.430  10.860  28.510  1.00  0.00           O
+ATOM    188  C3'  DG B   9      15.770   8.800  27.580  1.00  0.00           C
+ATOM    189  O3'  DG B   9      14.640   8.970  26.690  1.00  0.00           O
+ATOM    190  C2'  DG B   9      17.090   9.170  27.000  1.00  0.00           C
+ATOM    191  C1'  DG B   9      16.960  10.680  27.180  1.00  0.00           C
+ATOM    192  N9   DG B   9      18.250  11.390  27.130  1.00  0.00           N
+ATOM    193  C8   DG B   9      19.360  11.020  27.750  1.00  0.00           C
+ATOM    194  N7   DG B   9      20.310  11.940  27.560  1.00  0.00           N
+ATOM    195  C5   DG B   9      19.770  12.920  26.820  1.00  0.00           C
+ATOM    196  C6   DG B   9      20.230  14.150  26.340  1.00  0.00           C
+ATOM    197  O6   DG B   9      21.420  14.420  26.270  1.00  0.00           O
+ATOM    198  N1   DG B   9      19.350  15.010  25.670  1.00  0.00           N
+ATOM    199  C2   DG B   9      18.040  14.610  25.490  1.00  0.00           C
+ATOM    200  N2   DG B   9      17.290  15.310  24.700  1.00  0.00           N
+ATOM    201  N3   DG B   9      17.610  13.400  25.890  1.00  0.00           N
+ATOM    202  C4   DG B   9      18.450  12.570  26.560  1.00  0.00           C
+ATOM    203  H1   DG B   9      19.710  15.710  25.070  1.00  0.00           H
+ATOM    204  H21  DG B   9      17.490  16.270  24.440  1.00  0.00           H
+ATOM    205  H22  DG B   9      16.500  14.850  24.290  1.00  0.00           H
+ATOM    206  P    DC B  10      14.360   7.930  25.490  1.00  0.00           P
+ATOM    207  OP1  DC B  10      12.950   7.480  25.480  1.00  0.00           O
+ATOM    208  OP2  DC B  10      15.390   6.870  25.420  1.00  0.00           O
+ATOM    209  O5'  DC B  10      14.640   8.880  24.240  1.00  0.00           O
+ATOM    210  C5'  DC B  10      13.620   9.790  23.810  1.00  0.00           C
+ATOM    211  C4'  DC B  10      14.050  10.610  22.610  1.00  0.00           C
+ATOM    212  O4'  DC B  10      15.140  11.500  22.950  1.00  0.00           O
+ATOM    213  C3'  DC B  10      14.530   9.640  21.550  1.00  0.00           C
+ATOM    214  O3'  DC B  10      13.940   9.880  20.270  1.00  0.00           O
+ATOM    215  C2'  DC B  10      16.010   9.830  21.550  1.00  0.00           C
+ATOM    216  C1'  DC B  10      16.120  11.300  21.920  1.00  0.00           C
+ATOM    217  N1   DC B  10      17.390  11.610  22.610  1.00  0.00           N
+ATOM    218  C2   DC B  10      18.030  12.860  22.480  1.00  0.00           C
+ATOM    219  O2   DC B  10      17.680  13.660  21.620  1.00  0.00           O
+ATOM    220  N3   DC B  10      19.260  13.030  23.030  1.00  0.00           N
+ATOM    221  C4   DC B  10      19.890  12.020  23.650  1.00  0.00           C
+ATOM    222  N4   DC B  10      21.130  12.210  24.020  1.00  0.00           N
+ATOM    223  C5   DC B  10      19.320  10.750  23.810  1.00  0.00           C
+ATOM    224  C6   DC B  10      18.060  10.570  23.280  1.00  0.00           C
+ATOM    225  H41  DC B  10      21.570  13.100  23.930  1.00  0.00           H
+ATOM    226  H42  DC B  10      21.670  11.460  24.420  1.00  0.00           H
+ATOM    227  P    DG B  11      14.040   8.750  19.120  1.00  0.00           P
+ATOM    228  OP1  DG B  11      12.850   8.930  18.260  1.00  0.00           O
+ATOM    229  OP2  DG B  11      14.430   7.440  19.690  1.00  0.00           O
+ATOM    230  O5'  DG B  11      15.380   9.170  18.360  1.00  0.00           O
+ATOM    231  C5'  DG B  11      15.320  10.350  17.570  1.00  0.00           C
+ATOM    232  C4'  DG B  11      16.690  10.940  17.350  1.00  0.00           C
+ATOM    233  O4'  DG B  11      17.450  11.130  18.560  1.00  0.00           O
+ATOM    234  C3'  DG B  11      17.560  10.110  16.420  1.00  0.00           C
+ATOM    235  O3'  DG B  11      17.340  10.500  15.050  1.00  0.00           O
+ATOM    236  C2'  DG B  11      18.970  10.410  16.910  1.00  0.00           C
+ATOM    237  C1'  DG B  11      18.760  11.420  18.040  1.00  0.00           C
+ATOM    238  N9   DG B  11      19.800  11.300  19.090  1.00  0.00           N
+ATOM    239  C8   DG B  11      19.950  10.230  19.860  1.00  0.00           C
+ATOM    240  N7   DG B  11      21.030  10.350  20.640  1.00  0.00           N
+ATOM    241  C5   DG B  11      21.620  11.500  20.300  1.00  0.00           C
+ATOM    242  C6   DG B  11      22.840  12.100  20.630  1.00  0.00           C
+ATOM    243  O6   DG B  11      23.510  11.720  21.590  1.00  0.00           O
+ATOM    244  N1   DG B  11      23.250  13.250  19.940  1.00  0.00           N
+ATOM    245  C2   DG B  11      22.420  13.800  18.980  1.00  0.00           C
+ATOM    246  N2   DG B  11      22.830  14.820  18.260  1.00  0.00           N
+ATOM    247  N3   DG B  11      21.240  13.240  18.700  1.00  0.00           N
+ATOM    248  C4   DG B  11      20.830  12.110  19.320  1.00  0.00           C
+ATOM    249 HO3'  DG B  11      17.930  10.160  14.300  1.00  0.00           H
+ATOM    250  H1   DG B  11      24.150  13.640  20.140  1.00  0.00           H
+ATOM    251  H21  DG B  11      23.720  15.230  18.440  1.00  0.00           H
+ATOM    252  H22  DG B  11      22.290  15.120  17.470  1.00  0.00           H
+TER     253       DG B  11
+ATOM    254  O5'  DC C   1      31.690  17.510  19.400  1.00  0.00           O
+ATOM    255  C5'  DC C   1      30.410  18.150  19.490  1.00  0.00           C
+ATOM    256  C4'  DC C   1      29.450  17.860  18.330  1.00  0.00           C
+ATOM    257  O4'  DC C   1      28.900  16.550  18.620  1.00  0.00           O
+ATOM    258  C3'  DC C   1      28.270  18.840  18.350  1.00  0.00           C
+ATOM    259  O3'  DC C   1      27.840  19.190  17.010  1.00  0.00           O
+ATOM    260  C2'  DC C   1      27.230  18.170  19.180  1.00  0.00           C
+ATOM    261  C1'  DC C   1      27.490  16.720  18.840  1.00  0.00           C
+ATOM    262  N1   DC C   1      27.110  15.780  19.910  1.00  0.00           N
+ATOM    263  C2   DC C   1      26.380  14.630  19.550  1.00  0.00           C
+ATOM    264  O2   DC C   1      25.650  14.710  18.560  1.00  0.00           O
+ATOM    265  N3   DC C   1      26.070  13.730  20.500  1.00  0.00           N
+ATOM    266  C4   DC C   1      26.370  13.960  21.790  1.00  0.00           C
+ATOM    267  N4   DC C   1      25.790  13.160  22.670  1.00  0.00           N
+ATOM    268  C5   DC C   1      27.060  15.110  22.220  1.00  0.00           C
+ATOM    269  C6   DC C   1      27.460  16.010  21.250  1.00  0.00           C
+ATOM    270  H41  DC C   1      25.110  12.480  22.370  1.00  0.00           H
+ATOM    271  H42  DC C   1      25.950  13.310  23.650  1.00  0.00           H
+ATOM    272 HO5'  DC C   1      31.600  16.480  19.370  1.00  0.00           H
+ATOM    273  P    DG C   2      27.280  20.660  16.670  1.00  0.00           P
+ATOM    274  OP1  DG C   2      27.280  20.790  15.190  1.00  0.00           O
+ATOM    275  OP2  DG C   2      28.090  21.620  17.460  1.00  0.00           O
+ATOM    276  O5'  DG C   2      25.790  20.710  17.270  1.00  0.00           O
+ATOM    277  C5'  DG C   2      24.620  20.460  16.470  1.00  0.00           C
+ATOM    278  C4'  DG C   2      23.430  20.150  17.350  1.00  0.00           C
+ATOM    279  O4'  DG C   2      23.710  19.020  18.190  1.00  0.00           O
+ATOM    280  C3'  DG C   2      23.030  21.290  18.270  1.00  0.00           C
+ATOM    281  O3'  DG C   2      21.910  22.040  17.730  1.00  0.00           O
+ATOM    282  C2'  DG C   2      22.750  20.580  19.590  1.00  0.00           C
+ATOM    283  C1'  DG C   2      22.710  19.110  19.200  1.00  0.00           C
+ATOM    284  N9   DG C   2      23.020  18.110  20.240  1.00  0.00           N
+ATOM    285  C8   DG C   2      24.070  18.090  21.060  1.00  0.00           C
+ATOM    286  N7   DG C   2      23.910  17.110  21.960  1.00  0.00           N
+ATOM    287  C5   DG C   2      22.740  16.520  21.720  1.00  0.00           C
+ATOM    288  C6   DG C   2      21.950  15.580  22.400  1.00  0.00           C
+ATOM    289  O6   DG C   2      22.400  14.840  23.280  1.00  0.00           O
+ATOM    290  N1   DG C   2      20.640  15.370  21.980  1.00  0.00           N
+ATOM    291  C2   DG C   2      20.150  16.050  20.880  1.00  0.00           C
+ATOM    292  N2   DG C   2      18.870  16.050  20.650  1.00  0.00           N
+ATOM    293  N3   DG C   2      20.930  16.890  20.190  1.00  0.00           N
+ATOM    294  C4   DG C   2      22.180  17.150  20.610  1.00  0.00           C
+ATOM    295  H1   DG C   2      20.030  14.750  22.480  1.00  0.00           H
+ATOM    296  H21  DG C   2      18.260  15.460  21.180  1.00  0.00           H
+ATOM    297  H22  DG C   2      18.500  16.610  19.890  1.00  0.00           H
+ATOM    298  P    DC C   3      21.270  23.340  18.420  1.00  0.00           P
+ATOM    299  OP1  DC C   3      20.460  24.060  17.410  1.00  0.00           O
+ATOM    300  OP2  DC C   3      22.300  24.060  19.200  1.00  0.00           O
+ATOM    301  O5'  DC C   3      20.280  22.650  19.480  1.00  0.00           O
+ATOM    302  C5'  DC C   3      19.160  21.860  19.040  1.00  0.00           C
+ATOM    303  C4'  DC C   3      18.510  21.060  20.160  1.00  0.00           C
+ATOM    304  O4'  DC C   3      19.370  20.130  20.820  1.00  0.00           O
+ATOM    305  C3'  DC C   3      17.900  21.930  21.240  1.00  0.00           C
+ATOM    306  O3'  DC C   3      16.480  21.840  21.140  1.00  0.00           O
+ATOM    307  C2'  DC C   3      18.450  21.340  22.530  1.00  0.00           C
+ATOM    308  C1'  DC C   3      18.740  19.910  22.090  1.00  0.00           C
+ATOM    309  N1   DC C   3      19.640  19.180  22.980  1.00  0.00           N
+ATOM    310  C2   DC C   3      19.130  18.240  23.900  1.00  0.00           C
+ATOM    311  O2   DC C   3      17.970  17.870  23.890  1.00  0.00           O
+ATOM    312  N3   DC C   3      19.930  17.770  24.880  1.00  0.00           N
+ATOM    313  C4   DC C   3      21.210  18.160  24.970  1.00  0.00           C
+ATOM    314  N4   DC C   3      21.930  17.380  25.750  1.00  0.00           N
+ATOM    315  C5   DC C   3      21.780  19.090  24.100  1.00  0.00           C
+ATOM    316  C6   DC C   3      20.980  19.610  23.080  1.00  0.00           C
+ATOM    317  H41  DC C   3      21.560  16.540  26.160  1.00  0.00           H
+ATOM    318  H42  DC C   3      22.890  17.590  25.930  1.00  0.00           H
+ATOM    319  P    DT C   4      15.430  22.610  22.080  1.00  0.00           P
+ATOM    320  OP1  DT C   4      14.400  23.180  21.180  1.00  0.00           O
+ATOM    321  OP2  DT C   4      16.090  23.500  23.070  1.00  0.00           O
+ATOM    322  O5'  DT C   4      14.770  21.410  22.920  1.00  0.00           O
+ATOM    323  C5'  DT C   4      13.940  20.410  22.290  1.00  0.00           C
+ATOM    324  C4'  DT C   4      13.570  19.260  23.210  1.00  0.00           C
+ATOM    325  O4'  DT C   4      14.720  18.590  23.800  1.00  0.00           O
+ATOM    326  C3'  DT C   4      12.700  19.740  24.360  1.00  0.00           C
+ATOM    327  O3'  DT C   4      11.480  18.980  24.310  1.00  0.00           O
+ATOM    328  C2'  DT C   4      13.570  19.670  25.580  1.00  0.00           C
+ATOM    329  C1'  DT C   4      14.540  18.550  25.240  1.00  0.00           C
+ATOM    330  N1   DT C   4      15.850  18.750  25.910  1.00  0.00           N
+ATOM    331  C2   DT C   4      16.380  17.750  26.740  1.00  0.00           C
+ATOM    332  O2   DT C   4      15.650  16.890  27.240  1.00  0.00           O
+ATOM    333  N3   DT C   4      17.610  17.960  27.350  1.00  0.00           N
+ATOM    334  C4   DT C   4      18.290  19.170  27.200  1.00  0.00           C
+ATOM    335  O4   DT C   4      19.400  19.240  27.720  1.00  0.00           O
+ATOM    336  C5   DT C   4      17.760  20.200  26.400  1.00  0.00           C
+ATOM    337  C7   DT C   4      18.490  21.530  26.190  1.00  0.00           C
+ATOM    338  C6   DT C   4      16.520  19.970  25.770  1.00  0.00           C
+ATOM    339  H3   DT C   4      18.100  17.170  27.730  1.00  0.00           H
+ATOM    340  P    DC C   5      10.100  19.520  24.960  1.00  0.00           P
+ATOM    341  OP1  DC C   5       9.010  18.860  24.200  1.00  0.00           O
+ATOM    342  OP2  DC C   5      10.120  21.000  25.060  1.00  0.00           O
+ATOM    343  O5'  DC C   5      10.250  18.890  26.430  1.00  0.00           O
+ATOM    344  C5'  DC C   5      10.100  17.490  26.680  1.00  0.00           C
+ATOM    345  C4'  DC C   5      10.860  17.030  27.920  1.00  0.00           C
+ATOM    346  O4'  DC C   5      12.230  17.460  27.940  1.00  0.00           O
+ATOM    347  C3'  DC C   5      10.270  17.510  29.240  1.00  0.00           C
+ATOM    348  O3'  DC C   5       9.620  16.380  29.860  1.00  0.00           O
+ATOM    349  C2'  DC C   5      11.430  17.860  30.110  1.00  0.00           C
+ATOM    350  C1'  DC C   5      12.630  17.360  29.320  1.00  0.00           C
+ATOM    351  N1   DC C   5      13.760  18.270  29.580  1.00  0.00           N
+ATOM    352  C2   DC C   5      14.900  17.850  30.290  1.00  0.00           C
+ATOM    353  O2   DC C   5      15.010  16.710  30.750  1.00  0.00           O
+ATOM    354  N3   DC C   5      15.920  18.710  30.460  1.00  0.00           N
+ATOM    355  C4   DC C   5      15.870  19.950  29.950  1.00  0.00           C
+ATOM    356  N4   DC C   5      17.050  20.470  29.770  1.00  0.00           N
+ATOM    357  C5   DC C   5      14.760  20.470  29.270  1.00  0.00           C
+ATOM    358  C6   DC C   5      13.700  19.580  29.070  1.00  0.00           C
+ATOM    359  H41  DC C   5      17.850  19.950  30.110  1.00  0.00           H
+ATOM    360  H42  DC C   5      17.170  21.390  29.370  1.00  0.00           H
+ATOM    361  P    DA C   6       8.520  16.510  31.020  1.00  0.00           P
+ATOM    362  OP1  DA C   6       7.410  15.610  30.650  1.00  0.00           O
+ATOM    363  OP2  DA C   6       8.150  17.920  31.270  1.00  0.00           O
+ATOM    364  O5'  DA C   6       9.220  16.040  32.380  1.00  0.00           O
+ATOM    365  C5'  DA C   6       9.910  14.790  32.480  1.00  0.00           C
+ATOM    366  C4'  DA C   6      11.110  14.960  33.390  1.00  0.00           C
+ATOM    367  O4'  DA C   6      12.020  15.980  32.930  1.00  0.00           O
+ATOM    368  C3'  DA C   6      10.670  15.340  34.790  1.00  0.00           C
+ATOM    369  O3'  DA C   6      10.710  14.140  35.610  1.00  0.00           O
+ATOM    370  C2'  DA C   6      11.560  16.490  35.170  1.00  0.00           C
+ATOM    371  C1'  DA C   6      12.660  16.480  34.130  1.00  0.00           C
+ATOM    372  N9   DA C   6      13.200  17.830  33.880  1.00  0.00           N
+ATOM    373  C8   DA C   6      12.530  18.880  33.440  1.00  0.00           C
+ATOM    374  N7   DA C   6      13.350  19.920  33.240  1.00  0.00           N
+ATOM    375  C5   DA C   6      14.560  19.510  33.600  1.00  0.00           C
+ATOM    376  C6   DA C   6      15.820  20.100  33.720  1.00  0.00           C
+ATOM    377  N6   DA C   6      16.080  21.240  33.080  1.00  0.00           N
+ATOM    378  N1   DA C   6      16.830  19.450  34.340  1.00  0.00           N
+ATOM    379  C2   DA C   6      16.670  18.210  34.820  1.00  0.00           C
+ATOM    380  N3   DA C   6      15.500  17.580  34.660  1.00  0.00           N
+ATOM    381  C4   DA C   6      14.460  18.190  34.050  1.00  0.00           C
+ATOM    382  H61  DA C   6      16.980  21.660  33.120  1.00  0.00           H
+ATOM    383  H62  DA C   6      15.330  21.730  32.630  1.00  0.00           H
+ATOM    384  P    DC C   7      10.390  14.130  37.180  1.00  0.00           P
+ATOM    385  OP1  DC C   7      10.450  12.740  37.700  1.00  0.00           O
+ATOM    386  OP2  DC C   7       9.190  14.940  37.450  1.00  0.00           O
+ATOM    387  O5'  DC C   7      11.670  14.870  37.800  1.00  0.00           O
+ATOM    388  C5'  DC C   7      12.960  14.210  37.790  1.00  0.00           C
+ATOM    389  C4'  DC C   7      14.060  15.070  38.400  1.00  0.00           C
+ATOM    390  O4'  DC C   7      14.390  16.260  37.670  1.00  0.00           O
+ATOM    391  C3'  DC C   7      13.730  15.520  39.810  1.00  0.00           C
+ATOM    392  O3'  DC C   7      14.840  15.130  40.660  1.00  0.00           O
+ATOM    393  C2'  DC C   7      13.570  17.020  39.690  1.00  0.00           C
+ATOM    394  C1'  DC C   7      14.660  17.280  38.670  1.00  0.00           C
+ATOM    395  N1   DC C   7      14.610  18.600  38.030  1.00  0.00           N
+ATOM    396  C2   DC C   7      15.760  19.430  37.940  1.00  0.00           C
+ATOM    397  O2   DC C   7      16.800  19.200  38.540  1.00  0.00           O
+ATOM    398  N3   DC C   7      15.670  20.600  37.300  1.00  0.00           N
+ATOM    399  C4   DC C   7      14.510  21.010  36.760  1.00  0.00           C
+ATOM    400  N4   DC C   7      14.520  22.250  36.360  1.00  0.00           N
+ATOM    401  C5   DC C   7      13.330  20.270  36.810  1.00  0.00           C
+ATOM    402  C6   DC C   7      13.390  19.040  37.470  1.00  0.00           C
+ATOM    403  H41  DC C   7      15.290  22.840  36.570  1.00  0.00           H
+ATOM    404  H42  DC C   7      13.690  22.640  35.900  1.00  0.00           H
+ATOM    405  P    DA C   8      14.940  15.420  42.240  1.00  0.00           P
+ATOM    406  OP1  DA C   8      15.710  14.320  42.860  1.00  0.00           O
+ATOM    407  OP2  DA C   8      13.650  15.780  42.860  1.00  0.00           O
+ATOM    408  O5'  DA C   8      15.800  16.760  42.230  1.00  0.00           O
+ATOM    409  C5'  DA C   8      17.180  16.700  41.850  1.00  0.00           C
+ATOM    410  C4'  DA C   8      17.930  17.940  42.290  1.00  0.00           C
+ATOM    411  O4'  DA C   8      17.620  18.990  41.360  1.00  0.00           O
+ATOM    412  C3'  DA C   8      17.500  18.410  43.680  1.00  0.00           C
+ATOM    413  O3'  DA C   8      18.590  18.780  44.570  1.00  0.00           O
+ATOM    414  C2'  DA C   8      16.730  19.660  43.340  1.00  0.00           C
+ATOM    415  C1'  DA C   8      17.530  20.180  42.160  1.00  0.00           C
+ATOM    416  N9   DA C   8      16.800  21.080  41.240  1.00  0.00           N
+ATOM    417  C8   DA C   8      15.520  20.960  40.940  1.00  0.00           C
+ATOM    418  N7   DA C   8      15.150  21.910  40.080  1.00  0.00           N
+ATOM    419  C5   DA C   8      16.240  22.640  39.830  1.00  0.00           C
+ATOM    420  C6   DA C   8      16.530  23.820  39.130  1.00  0.00           C
+ATOM    421  N6   DA C   8      15.590  24.660  38.750  1.00  0.00           N
+ATOM    422  N1   DA C   8      17.760  24.350  39.160  1.00  0.00           N
+ATOM    423  C2   DA C   8      18.750  23.820  39.870  1.00  0.00           C
+ATOM    424  N3   DA C   8      18.510  22.700  40.560  1.00  0.00           N
+ATOM    425  C4   DA C   8      17.290  22.100  40.560  1.00  0.00           C
+ATOM    426  H61  DA C   8      15.810  25.500  38.240  1.00  0.00           H
+ATOM    427  H62  DA C   8      14.630  24.500  39.030  1.00  0.00           H
+ATOM    428  P    DA C   9      18.980  18.030  45.940  1.00  0.00           P
+ATOM    429  OP1  DA C   9      19.160  16.580  45.690  1.00  0.00           O
+ATOM    430  OP2  DA C   9      18.040  18.390  47.030  1.00  0.00           O
+ATOM    431  O5'  DA C   9      20.370  18.690  46.380  1.00  0.00           O
+ATOM    432  C5'  DA C   9      21.570  18.480  45.610  1.00  0.00           C
+ATOM    433  C4'  DA C   9      22.280  19.750  45.150  1.00  0.00           C
+ATOM    434  O4'  DA C   9      21.370  20.590  44.390  1.00  0.00           O
+ATOM    435  C3'  DA C   9      22.840  20.620  46.280  1.00  0.00           C
+ATOM    436  O3'  DA C   9      24.220  20.950  45.980  1.00  0.00           O
+ATOM    437  C2'  DA C   9      21.890  21.770  46.330  1.00  0.00           C
+ATOM    438  C1'  DA C   9      21.500  21.960  44.870  1.00  0.00           C
+ATOM    439  N9   DA C   9      20.210  22.660  44.800  1.00  0.00           N
+ATOM    440  C8   DA C   9      19.080  22.180  45.320  1.00  0.00           C
+ATOM    441  N7   DA C   9      18.040  22.870  44.860  1.00  0.00           N
+ATOM    442  C5   DA C   9      18.520  23.820  44.070  1.00  0.00           C
+ATOM    443  C6   DA C   9      17.970  24.910  43.380  1.00  0.00           C
+ATOM    444  N6   DA C   9      16.670  24.960  43.240  1.00  0.00           N
+ATOM    445  N1   DA C   9      18.750  25.810  42.750  1.00  0.00           N
+ATOM    446  C2   DA C   9      20.090  25.670  42.750  1.00  0.00           C
+ATOM    447  N3   DA C   9      20.650  24.640  43.380  1.00  0.00           N
+ATOM    448  C4   DA C   9      19.910  23.710  44.040  1.00  0.00           C
+ATOM    449  H61  DA C   9      16.200  25.730  42.790  1.00  0.00           H
+ATOM    450  H62  DA C   9      16.150  24.150  43.570  1.00  0.00           H
+ATOM    451  P    DT C  10      25.190  21.850  46.910  1.00  0.00           P
+ATOM    452  OP1  DT C  10      26.550  21.700  46.350  1.00  0.00           O
+ATOM    453  OP2  DT C  10      25.010  21.450  48.320  1.00  0.00           O
+ATOM    454  O5'  DT C  10      24.680  23.350  46.620  1.00  0.00           O
+ATOM    455  C5'  DT C  10      25.190  24.110  45.510  1.00  0.00           C
+ATOM    456  C4'  DT C  10      24.580  25.490  45.370  1.00  0.00           C
+ATOM    457  O4'  DT C  10      23.150  25.400  45.280  1.00  0.00           O
+ATOM    458  C3'  DT C  10      24.870  26.450  46.520  1.00  0.00           C
+ATOM    459  O3'  DT C  10      25.520  27.610  45.970  1.00  0.00           O
+ATOM    460  C2'  DT C  10      23.530  26.880  47.090  1.00  0.00           C
+ATOM    461  C1'  DT C  10      22.700  26.620  45.860  1.00  0.00           C
+ATOM    462  N1   DT C  10      21.270  26.460  46.210  1.00  0.00           N
+ATOM    463  C2   DT C  10      20.320  27.320  45.650  1.00  0.00           C
+ATOM    464  O2   DT C  10      20.640  28.400  45.160  1.00  0.00           O
+ATOM    465  N3   DT C  10      18.970  27.060  45.840  1.00  0.00           N
+ATOM    466  C4   DT C  10      18.560  25.980  46.630  1.00  0.00           C
+ATOM    467  O4   DT C  10      17.350  25.790  46.710  1.00  0.00           O
+ATOM    468  C5   DT C  10      19.510  25.150  47.260  1.00  0.00           C
+ATOM    469  C7   DT C  10      19.150  24.250  48.440  1.00  0.00           C
+ATOM    470  C6   DT C  10      20.860  25.370  47.010  1.00  0.00           C
+ATOM    471  H3   DT C  10      18.290  27.680  45.470  1.00  0.00           H
+ATOM    472  P    DT C  11      26.140  28.760  46.900  1.00  0.00           P
+ATOM    473  OP1  DT C  11      27.220  29.420  46.130  1.00  0.00           O
+ATOM    474  OP2  DT C  11      26.450  28.240  48.250  1.00  0.00           O
+ATOM    475  O5'  DT C  11      24.880  29.730  47.090  1.00  0.00           O
+ATOM    476  C5'  DT C  11      24.470  30.660  46.070  1.00  0.00           C
+ATOM    477  C4'  DT C  11      23.280  31.490  46.500  1.00  0.00           C
+ATOM    478  O4'  DT C  11      22.080  30.720  46.640  1.00  0.00           O
+ATOM    479  C3'  DT C  11      23.510  32.230  47.810  1.00  0.00           C
+ATOM    480  O3'  DT C  11      24.130  33.520  47.640  1.00  0.00           O
+ATOM    481  C2'  DT C  11      22.110  32.380  48.390  1.00  0.00           C
+ATOM    482  C1'  DT C  11      21.240  31.500  47.500  1.00  0.00           C
+ATOM    483  N1   DT C  11      20.470  30.500  48.280  1.00  0.00           N
+ATOM    484  C2   DT C  11      19.070  30.510  48.180  1.00  0.00           C
+ATOM    485  O2   DT C  11      18.400  31.470  47.780  1.00  0.00           O
+ATOM    486  N3   DT C  11      18.360  29.420  48.660  1.00  0.00           N
+ATOM    487  C4   DT C  11      19.000  28.350  49.280  1.00  0.00           C
+ATOM    488  O4   DT C  11      18.300  27.370  49.530  1.00  0.00           O
+ATOM    489  C5   DT C  11      20.390  28.330  49.420  1.00  0.00           C
+ATOM    490  C7   DT C  11      21.140  27.220  50.160  1.00  0.00           C
+ATOM    491  C6   DT C  11      21.110  29.400  48.900  1.00  0.00           C
+ATOM    492 HO3'  DT C  11      25.060  33.370  47.220  1.00  0.00           H
+ATOM    493  H3   DT C  11      17.360  29.430  48.580  1.00  0.00           H
+TER     494       DT C  11
+ATOM    495  N   MET A   1      34.170  31.500  11.380  1.00  0.00           N
+ATOM    496  CA  MET A   1      33.550  30.380  10.640  1.00  0.00           C
+ATOM    497  C   MET A   1      33.130  29.230  11.590  1.00  0.00           C
+ATOM    498  O   MET A   1      33.730  28.160  11.640  1.00  0.00           O
+ATOM    499  CB  MET A   1      34.600  29.880   9.650  1.00  0.00           C
+ATOM    500  CG  MET A   1      34.840  30.790   8.450  1.00  0.00           C
+ATOM    501  SD  MET A   1      33.500  30.720   7.210  1.00  0.00           S
+ATOM    502  CE  MET A   1      33.700  29.030   6.670  1.00  0.00           C
+ATOM    503  H1  MET A   1      34.930  31.130  11.920  1.00  0.00           H
+ATOM    504  H2  MET A   1      34.510  32.180  10.730  1.00  0.00           H
+ATOM    505  H3  MET A   1      33.520  31.940  12.010  1.00  0.00           H
+ATOM    506  N   LYS A   2      32.100  29.510  12.380  1.00  0.00           N
+ATOM    507  CA  LYS A   2      31.700  28.620  13.480  1.00  0.00           C
+ATOM    508  C   LYS A   2      30.170  28.440  13.530  1.00  0.00           C
+ATOM    509  O   LYS A   2      29.450  29.430  13.670  1.00  0.00           O
+ATOM    510  CB  LYS A   2      32.260  29.170  14.800  1.00  0.00           C
+ATOM    511  CG  LYS A   2      33.170  28.150  15.500  1.00  0.00           C
+ATOM    512  CD  LYS A   2      33.810  28.700  16.790  1.00  0.00           C
+ATOM    513  CE  LYS A   2      32.830  28.960  17.940  1.00  0.00           C
+ATOM    514  NZ  LYS A   2      33.470  29.840  18.940  1.00  0.00           N
+ATOM    515  H   LYS A   2      31.490  30.280  12.190  1.00  0.00           H
+ATOM    516  HZ1 LYS A   2      34.390  29.480  19.180  1.00  0.00           H
+ATOM    517  HZ2 LYS A   2      33.590  30.760  18.560  1.00  0.00           H
+ATOM    518  HZ3 LYS A   2      32.950  29.910  19.800  1.00  0.00           H
+ATOM    519  N   PRO A   3      29.680  27.230  13.210  1.00  0.00           N
+ATOM    520  CA  PRO A   3      28.260  26.870  13.400  1.00  0.00           C
+ATOM    521  C   PRO A   3      27.770  27.260  14.800  1.00  0.00           C
+ATOM    522  O   PRO A   3      28.400  26.930  15.810  1.00  0.00           O
+ATOM    523  CB  PRO A   3      28.220  25.370  13.150  1.00  0.00           C
+ATOM    524  CG  PRO A   3      29.320  25.130  12.130  1.00  0.00           C
+ATOM    525  CD  PRO A   3      30.380  26.180  12.450  1.00  0.00           C
+ATOM    526  N   VAL A   4      26.860  28.230  14.770  1.00  0.00           N
+ATOM    527  CA  VAL A   4      26.260  28.810  15.990  1.00  0.00           C
+ATOM    528  C   VAL A   4      25.230  27.860  16.620  1.00  0.00           C
+ATOM    529  O   VAL A   4      24.160  27.610  16.070  1.00  0.00           O
+ATOM    530  CB  VAL A   4      25.690  30.230  15.750  1.00  0.00           C
+ATOM    531  CG1 VAL A   4      25.380  30.890  17.100  1.00  0.00           C
+ATOM    532  CG2 VAL A   4      26.670  31.160  15.030  1.00  0.00           C
+ATOM    533  H   VAL A   4      26.690  28.720  13.930  1.00  0.00           H
+ATOM    534  N   THR A   5      25.730  27.220  17.680  1.00  0.00           N
+ATOM    535  CA  THR A   5      24.990  26.290  18.580  1.00  0.00           C
+ATOM    536  C   THR A   5      24.260  27.050  19.700  1.00  0.00           C
+ATOM    537  O   THR A   5      24.670  28.170  20.030  1.00  0.00           O
+ATOM    538  CB  THR A   5      25.930  25.300  19.300  1.00  0.00           C
+ATOM    539  OG1 THR A   5      26.930  25.980  20.060  1.00  0.00           O
+ATOM    540  CG2 THR A   5      26.530  24.250  18.360  1.00  0.00           C
+ATOM    541  H   THR A   5      26.690  27.390  17.930  1.00  0.00           H
+ATOM    542  HG1 THR A   5      27.440  26.640  19.440  1.00  0.00           H
+ATOM    543  N   LEU A   6      23.390  26.360  20.450  1.00  0.00           N
+ATOM    544  CA  LEU A   6      22.740  26.930  21.650  1.00  0.00           C
+ATOM    545  C   LEU A   6      23.770  27.430  22.680  1.00  0.00           C
+ATOM    546  O   LEU A   6      23.640  28.530  23.220  1.00  0.00           O
+ATOM    547  CB  LEU A   6      21.780  25.930  22.310  1.00  0.00           C
+ATOM    548  CG  LEU A   6      20.400  25.890  21.640  1.00  0.00           C
+ATOM    549  CD1 LEU A   6      19.520  24.900  22.380  1.00  0.00           C
+ATOM    550  CD2 LEU A   6      19.700  27.250  21.660  1.00  0.00           C
+ATOM    551  H   LEU A   6      23.120  25.410  20.250  1.00  0.00           H
+ATOM    552  N   TYR A   7      24.830  26.650  22.840  1.00  0.00           N
+ATOM    553  CA  TYR A   7      26.040  27.020  23.600  1.00  0.00           C
+ATOM    554  C   TYR A   7      26.670  28.370  23.220  1.00  0.00           C
+ATOM    555  O   TYR A   7      26.960  29.160  24.110  1.00  0.00           O
+ATOM    556  CB  TYR A   7      27.150  25.990  23.420  1.00  0.00           C
+ATOM    557  CG  TYR A   7      26.810  24.620  23.980  1.00  0.00           C
+ATOM    558  CD1 TYR A   7      26.860  24.460  25.380  1.00  0.00           C
+ATOM    559  CD2 TYR A   7      26.780  23.530  23.090  1.00  0.00           C
+ATOM    560  CE1 TYR A   7      26.930  23.160  25.910  1.00  0.00           C
+ATOM    561  CE2 TYR A   7      26.840  22.230  23.620  1.00  0.00           C
+ATOM    562  CZ  TYR A   7      26.930  22.070  25.010  1.00  0.00           C
+ATOM    563  OH  TYR A   7      27.110  20.820  25.510  1.00  0.00           O
+ATOM    564  H   TYR A   7      24.770  25.680  22.580  1.00  0.00           H
+ATOM    565  HH  TYR A   7      26.980  20.190  24.730  1.00  0.00           H
+ATOM    566  N   ASP A   8      26.910  28.580  21.930  1.00  0.00           N
+ATOM    567  CA  ASP A   8      27.590  29.780  21.400  1.00  0.00           C
+ATOM    568  C   ASP A   8      26.820  31.050  21.790  1.00  0.00           C
+ATOM    569  O   ASP A   8      27.310  31.820  22.620  1.00  0.00           O
+ATOM    570  CB  ASP A   8      27.800  29.640  19.880  1.00  0.00           C
+ATOM    571  CG  ASP A   8      28.730  28.480  19.480  1.00  0.00           C
+ATOM    572  OD1 ASP A   8      29.860  28.390  19.990  1.00  0.00           O
+ATOM    573  OD2 ASP A   8      28.330  27.680  18.620  1.00  0.00           O
+ATOM    574  H   ASP A   8      26.610  27.930  21.230  1.00  0.00           H
+ATOM    575  N   VAL A   9      25.530  31.060  21.480  1.00  0.00           N
+ATOM    576  CA  VAL A   9      24.590  32.150  21.880  1.00  0.00           C
+ATOM    577  C   VAL A   9      24.400  32.310  23.400  1.00  0.00           C
+ATOM    578  O   VAL A   9      24.280  33.420  23.910  1.00  0.00           O
+ATOM    579  CB  VAL A   9      23.230  32.110  21.150  1.00  0.00           C
+ATOM    580  CG1 VAL A   9      23.330  32.920  19.870  1.00  0.00           C
+ATOM    581  CG2 VAL A   9      22.640  30.710  20.900  1.00  0.00           C
+ATOM    582  H   VAL A   9      25.110  30.300  20.980  1.00  0.00           H
+ATOM    583  N   ALA A  10      24.390  31.170  24.090  1.00  0.00           N
+ATOM    584  CA  ALA A  10      24.450  31.120  25.560  1.00  0.00           C
+ATOM    585  C   ALA A  10      25.710  31.810  26.130  1.00  0.00           C
+ATOM    586  O   ALA A  10      25.610  32.890  26.720  1.00  0.00           O
+ATOM    587  CB  ALA A  10      24.360  29.650  25.970  1.00  0.00           C
+ATOM    588  H   ALA A  10      24.210  30.280  23.650  1.00  0.00           H
+ATOM    589  N   GLU A  11      26.890  31.250  25.850  1.00  0.00           N
+ATOM    590  CA  GLU A  11      28.230  31.820  26.120  1.00  0.00           C
+ATOM    591  C   GLU A  11      28.300  33.360  25.970  1.00  0.00           C
+ATOM    592  O   GLU A  11      28.470  34.040  26.980  1.00  0.00           O
+ATOM    593  CB  GLU A  11      29.260  31.180  25.180  1.00  0.00           C
+ATOM    594  CG  GLU A  11      30.120  30.090  25.820  1.00  0.00           C
+ATOM    595  CD  GLU A  11      31.300  29.720  24.910  1.00  0.00           C
+ATOM    596  OE1 GLU A  11      31.070  29.060  23.880  1.00  0.00           O
+ATOM    597  OE2 GLU A  11      32.440  30.130  25.220  1.00  0.00           O
+ATOM    598  H   GLU A  11      26.940  30.320  25.450  1.00  0.00           H
+ATOM    599  N   TYR A  12      27.950  33.850  24.780  1.00  0.00           N
+ATOM    600  CA  TYR A  12      28.020  35.270  24.390  1.00  0.00           C
+ATOM    601  C   TYR A  12      27.170  36.210  25.230  1.00  0.00           C
+ATOM    602  O   TYR A  12      27.600  37.300  25.610  1.00  0.00           O
+ATOM    603  CB  TYR A  12      27.560  35.390  22.940  1.00  0.00           C
+ATOM    604  CG  TYR A  12      27.920  36.750  22.360  1.00  0.00           C
+ATOM    605  CD1 TYR A  12      29.250  36.890  21.940  1.00  0.00           C
+ATOM    606  CD2 TYR A  12      26.920  37.660  21.950  1.00  0.00           C
+ATOM    607  CE1 TYR A  12      29.600  37.880  21.020  1.00  0.00           C
+ATOM    608  CE2 TYR A  12      27.260  38.670  21.030  1.00  0.00           C
+ATOM    609  CZ  TYR A  12      28.590  38.750  20.570  1.00  0.00           C
+ATOM    610  OH  TYR A  12      28.950  39.760  19.750  1.00  0.00           O
+ATOM    611  H   TYR A  12      27.670  33.250  24.030  1.00  0.00           H
+ATOM    612  HH  TYR A  12      30.010  39.790  19.700  1.00  0.00           H
+ATOM    613  N   ALA A  13      25.920  35.810  25.400  1.00  0.00           N
+ATOM    614  CA  ALA A  13      24.980  36.610  26.210  1.00  0.00           C
+ATOM    615  C   ALA A  13      25.210  36.430  27.720  1.00  0.00           C
+ATOM    616  O   ALA A  13      24.610  37.140  28.540  1.00  0.00           O
+ATOM    617  CB  ALA A  13      23.550  36.280  25.820  1.00  0.00           C
+ATOM    618  H   ALA A  13      25.590  34.940  25.040  1.00  0.00           H
+ATOM    619  N   GLY A  14      26.110  35.510  28.060  1.00  0.00           N
+ATOM    620  CA  GLY A  14      26.550  35.210  29.430  1.00  0.00           C
+ATOM    621  C   GLY A  14      25.400  34.630  30.260  1.00  0.00           C
+ATOM    622  O   GLY A  14      25.090  35.130  31.340  1.00  0.00           O
+ATOM    623  H   GLY A  14      26.460  34.880  27.360  1.00  0.00           H
+ATOM    624  N   VAL A  15      24.770  33.630  29.640  1.00  0.00           N
+ATOM    625  CA  VAL A  15      23.490  33.010  30.070  1.00  0.00           C
+ATOM    626  C   VAL A  15      23.530  31.510  29.720  1.00  0.00           C
+ATOM    627  O   VAL A  15      24.340  31.050  28.910  1.00  0.00           O
+ATOM    628  CB  VAL A  15      22.260  33.760  29.560  1.00  0.00           C
+ATOM    629  CG1 VAL A  15      22.030  35.050  30.350  1.00  0.00           C
+ATOM    630  CG2 VAL A  15      22.280  34.000  28.060  1.00  0.00           C
+ATOM    631  H   VAL A  15      25.180  33.200  28.830  1.00  0.00           H
+ATOM    632  N   SER A  16      22.760  30.760  30.490  1.00  0.00           N
+ATOM    633  CA  SER A  16      22.730  29.300  30.460  1.00  0.00           C
+ATOM    634  C   SER A  16      22.080  28.750  29.200  1.00  0.00           C
+ATOM    635  O   SER A  16      21.190  29.340  28.600  1.00  0.00           O
+ATOM    636  CB  SER A  16      21.960  28.770  31.650  1.00  0.00           C
+ATOM    637  OG  SER A  16      21.790  27.350  31.580  1.00  0.00           O
+ATOM    638  H   SER A  16      22.100  31.180  31.120  1.00  0.00           H
+ATOM    639  HG  SER A  16      22.340  26.920  32.360  1.00  0.00           H
+ATOM    640  N   TYR A  17      22.750  27.700  28.750  1.00  0.00           N
+ATOM    641  CA  TYR A  17      22.240  26.640  27.870  1.00  0.00           C
+ATOM    642  C   TYR A  17      20.720  26.480  27.900  1.00  0.00           C
+ATOM    643  O   TYR A  17      20.080  26.610  26.850  1.00  0.00           O
+ATOM    644  CB  TYR A  17      22.960  25.390  28.370  1.00  0.00           C
+ATOM    645  CG  TYR A  17      22.900  24.220  27.400  1.00  0.00           C
+ATOM    646  CD1 TYR A  17      23.100  24.430  26.020  1.00  0.00           C
+ATOM    647  CD2 TYR A  17      22.890  22.940  27.980  1.00  0.00           C
+ATOM    648  CE1 TYR A  17      23.340  23.320  25.190  1.00  0.00           C
+ATOM    649  CE2 TYR A  17      23.140  21.830  27.170  1.00  0.00           C
+ATOM    650  CZ  TYR A  17      23.380  22.040  25.790  1.00  0.00           C
+ATOM    651  OH  TYR A  17      23.940  21.010  25.110  1.00  0.00           O
+ATOM    652  H   TYR A  17      23.750  27.690  28.870  1.00  0.00           H
+ATOM    653  HH  TYR A  17      24.280  20.320  25.810  1.00  0.00           H
+ATOM    654  N   GLN A  18      20.190  26.370  29.120  1.00  0.00           N
+ATOM    655  CA  GLN A  18      18.760  26.290  29.480  1.00  0.00           C
+ATOM    656  C   GLN A  18      17.950  27.600  29.390  1.00  0.00           C
+ATOM    657  O   GLN A  18      16.940  27.590  28.700  1.00  0.00           O
+ATOM    658  CB  GLN A  18      18.740  25.660  30.870  1.00  0.00           C
+ATOM    659  CG  GLN A  18      18.020  24.300  30.910  1.00  0.00           C
+ATOM    660  CD  GLN A  18      18.360  23.320  29.780  1.00  0.00           C
+ATOM    661  OE1 GLN A  18      17.640  23.250  28.790  1.00  0.00           O
+ATOM    662  NE2 GLN A  18      19.180  22.320  30.020  1.00  0.00           N
+ATOM    663  H   GLN A  18      20.790  26.220  29.900  1.00  0.00           H
+ATOM    664 HE21 GLN A  18      19.470  22.110  30.960  1.00  0.00           H
+ATOM    665 HE22 GLN A  18      19.440  21.710  29.280  1.00  0.00           H
+ATOM    666  N   THR A  19      18.500  28.720  29.860  1.00  0.00           N
+ATOM    667  CA  THR A  19      17.900  30.070  29.700  1.00  0.00           C
+ATOM    668  C   THR A  19      17.700  30.380  28.200  1.00  0.00           C
+ATOM    669  O   THR A  19      16.590  30.710  27.800  1.00  0.00           O
+ATOM    670  CB  THR A  19      18.800  31.150  30.340  1.00  0.00           C
+ATOM    671  OG1 THR A  19      19.260  30.630  31.590  1.00  0.00           O
+ATOM    672  CG2 THR A  19      18.070  32.470  30.580  1.00  0.00           C
+ATOM    673  H   THR A  19      19.350  28.740  30.390  1.00  0.00           H
+ATOM    674  HG1 THR A  19      18.570  30.840  32.320  1.00  0.00           H
+ATOM    675  N   VAL A  20      18.660  29.880  27.410  1.00  0.00           N
+ATOM    676  CA  VAL A  20      18.710  30.020  25.940  1.00  0.00           C
+ATOM    677  C   VAL A  20      17.840  28.950  25.230  1.00  0.00           C
+ATOM    678  O   VAL A  20      16.990  29.300  24.430  1.00  0.00           O
+ATOM    679  CB  VAL A  20      20.180  30.140  25.510  1.00  0.00           C
+ATOM    680  CG1 VAL A  20      20.450  30.090  24.000  1.00  0.00           C
+ATOM    681  CG2 VAL A  20      20.700  31.490  26.000  1.00  0.00           C
+ATOM    682  H   VAL A  20      19.410  29.340  27.820  1.00  0.00           H
+ATOM    683  N   SER A  21      17.910  27.690  25.670  1.00  0.00           N
+ATOM    684  CA  SER A  21      16.900  26.650  25.330  1.00  0.00           C
+ATOM    685  C   SER A  21      15.430  27.040  25.610  1.00  0.00           C
+ATOM    686  O   SER A  21      14.580  26.780  24.770  1.00  0.00           O
+ATOM    687  CB  SER A  21      17.260  25.350  26.060  1.00  0.00           C
+ATOM    688  OG  SER A  21      16.270  24.350  25.790  1.00  0.00           O
+ATOM    689  H   SER A  21      18.760  27.310  26.060  1.00  0.00           H
+ATOM    690  HG  SER A  21      16.100  24.340  24.780  1.00  0.00           H
+ATOM    691  N   ARG A  22      15.140  27.790  26.670  1.00  0.00           N
+ATOM    692  CA  ARG A  22      13.760  28.280  26.950  1.00  0.00           C
+ATOM    693  C   ARG A  22      13.280  29.270  25.890  1.00  0.00           C
+ATOM    694  O   ARG A  22      12.190  29.150  25.320  1.00  0.00           O
+ATOM    695  CB  ARG A  22      13.710  28.980  28.320  1.00  0.00           C
+ATOM    696  CG  ARG A  22      14.140  28.210  29.570  1.00  0.00           C
+ATOM    697  CD  ARG A  22      13.130  27.220  30.150  1.00  0.00           C
+ATOM    698  NE  ARG A  22      12.970  26.100  29.230  1.00  0.00           N
+ATOM    699  CZ  ARG A  22      13.820  25.100  29.000  1.00  0.00           C
+ATOM    700  NH1 ARG A  22      15.030  25.000  29.570  1.00  0.00           N
+ATOM    701  NH2 ARG A  22      13.470  24.230  28.070  1.00  0.00           N
+ATOM    702  H   ARG A  22      15.840  28.010  27.360  1.00  0.00           H
+ATOM    703  HE  ARG A  22      12.190  26.200  28.600  1.00  0.00           H
+ATOM    704 HH11 ARG A  22      15.260  25.470  30.430  1.00  0.00           H
+ATOM    705 HH12 ARG A  22      15.670  24.330  29.200  1.00  0.00           H
+ATOM    706 HH21 ARG A  22      12.740  24.480  27.430  1.00  0.00           H
+ATOM    707 HH22 ARG A  22      13.980  23.380  27.890  1.00  0.00           H
+ATOM    708  N   VAL A  23      14.190  30.180  25.560  1.00  0.00           N
+ATOM    709  CA  VAL A  23      14.050  31.110  24.410  1.00  0.00           C
+ATOM    710  C   VAL A  23      13.650  30.360  23.110  1.00  0.00           C
+ATOM    711  O   VAL A  23      12.830  30.840  22.320  1.00  0.00           O
+ATOM    712  CB  VAL A  23      15.360  31.920  24.320  1.00  0.00           C
+ATOM    713  CG1 VAL A  23      15.530  32.640  22.980  1.00  0.00           C
+ATOM    714  CG2 VAL A  23      15.490  32.890  25.490  1.00  0.00           C
+ATOM    715  H   VAL A  23      15.000  30.310  26.130  1.00  0.00           H
+ATOM    716  N   VAL A  24      14.140  29.130  22.970  1.00  0.00           N
+ATOM    717  CA  VAL A  24      13.780  28.270  21.830  1.00  0.00           C
+ATOM    718  C   VAL A  24      12.460  27.510  22.080  1.00  0.00           C
+ATOM    719  O   VAL A  24      11.530  27.630  21.270  1.00  0.00           O
+ATOM    720  CB  VAL A  24      15.000  27.400  21.460  1.00  0.00           C
+ATOM    721  CG1 VAL A  24      14.700  26.430  20.320  1.00  0.00           C
+ATOM    722  CG2 VAL A  24      16.180  28.290  21.030  1.00  0.00           C
+ATOM    723  H   VAL A  24      14.800  28.740  23.610  1.00  0.00           H
+ATOM    724  N   ASN A  25      12.320  26.870  23.240  1.00  0.00           N
+ATOM    725  CA  ASN A  25      11.140  26.090  23.630  1.00  0.00           C
+ATOM    726  C   ASN A  25      10.990  25.860  25.140  1.00  0.00           C
+ATOM    727  O   ASN A  25      11.960  25.840  25.900  1.00  0.00           O
+ATOM    728  CB  ASN A  25      11.220  24.700  22.980  1.00  0.00           C
+ATOM    729  CG  ASN A  25      10.670  24.730  21.550  1.00  0.00           C
+ATOM    730  OD1 ASN A  25       9.520  25.050  21.270  1.00  0.00           O
+ATOM    731  ND2 ASN A  25      11.520  24.370  20.630  1.00  0.00           N
+ATOM    732  H   ASN A  25      13.090  26.800  23.890  1.00  0.00           H
+ATOM    733 HD21 ASN A  25      12.460  24.140  20.890  1.00  0.00           H
+ATOM    734 HD22 ASN A  25      11.210  24.350  19.680  1.00  0.00           H
+ATOM    735  N   GLN A  26       9.720  25.700  25.530  1.00  0.00           N
+ATOM    736  CA  GLN A  26       9.280  25.380  26.910  1.00  0.00           C
+ATOM    737  C   GLN A  26       9.680  26.530  27.860  1.00  0.00           C
+ATOM    738  O   GLN A  26      10.110  26.330  28.990  1.00  0.00           O
+ATOM    739  CB  GLN A  26       9.880  24.020  27.280  1.00  0.00           C
+ATOM    740  CG  GLN A  26       9.290  23.300  28.490  1.00  0.00           C
+ATOM    741  CD  GLN A  26      10.290  22.230  28.930  1.00  0.00           C
+ATOM    742  OE1 GLN A  26      11.200  22.450  29.720  1.00  0.00           O
+ATOM    743  NE2 GLN A  26      10.440  21.200  28.140  1.00  0.00           N
+ATOM    744  H   GLN A  26       8.980  25.790  24.870  1.00  0.00           H
+ATOM    745 HE21 GLN A  26      10.150  21.260  27.180  1.00  0.00           H
+ATOM    746 HE22 GLN A  26      11.020  20.450  28.420  1.00  0.00           H
+ATOM    747  N   ALA A  27       9.530  27.760  27.360  1.00  0.00           N
+ATOM    748  CA  ALA A  27       9.930  28.980  28.090  1.00  0.00           C
+ATOM    749  C   ALA A  27       9.310  29.130  29.490  1.00  0.00           C
+ATOM    750  O   ALA A  27       8.110  28.910  29.670  1.00  0.00           O
+ATOM    751  CB  ALA A  27       9.720  30.200  27.180  1.00  0.00           C
+ATOM    752  H   ALA A  27       9.110  27.900  26.460  1.00  0.00           H
+ATOM    753  N   SER A  28      10.170  29.410  30.460  1.00  0.00           N
+ATOM    754  CA  SER A  28       9.900  29.590  31.910  1.00  0.00           C
+ATOM    755  C   SER A  28      11.170  30.070  32.620  1.00  0.00           C
+ATOM    756  O   SER A  28      12.290  29.670  32.280  1.00  0.00           O
+ATOM    757  CB  SER A  28       9.470  28.290  32.590  1.00  0.00           C
+ATOM    758  OG  SER A  28      10.510  27.320  32.430  1.00  0.00           O
+ATOM    759  H   SER A  28      11.090  29.730  30.210  1.00  0.00           H
+ATOM    760  HG  SER A  28      10.040  26.400  32.340  1.00  0.00           H
+ATOM    761  N   HIS A  29      10.980  30.870  33.660  1.00  0.00           N
+ATOM    762  CA  HIS A  29      12.070  31.430  34.500  1.00  0.00           C
+ATOM    763  C   HIS A  29      13.080  32.300  33.730  1.00  0.00           C
+ATOM    764  O   HIS A  29      14.280  32.050  33.700  1.00  0.00           O
+ATOM    765  CB  HIS A  29      12.770  30.340  35.330  1.00  0.00           C
+ATOM    766  CG  HIS A  29      11.980  30.000  36.590  1.00  0.00           C
+ATOM    767  ND1 HIS A  29      11.850  30.810  37.640  1.00  0.00           N
+ATOM    768  CD2 HIS A  29      11.310  28.870  36.820  1.00  0.00           C
+ATOM    769  CE1 HIS A  29      11.130  30.130  38.540  1.00  0.00           C
+ATOM    770  NE2 HIS A  29      10.770  28.950  38.030  1.00  0.00           N
+ATOM    771  H   HIS A  29      10.060  31.200  33.880  1.00  0.00           H
+ATOM    772  HD1 HIS A  29      12.030  31.810  37.640  1.00  0.00           H
+ATOM    773  HE2 HIS A  29      10.240  28.230  38.520  1.00  0.00           H
+ATOM    774  N   VAL A  30      12.570  33.470  33.380  1.00  0.00           N
+ATOM    775  CA  VAL A  30      13.260  34.430  32.500  1.00  0.00           C
+ATOM    776  C   VAL A  30      12.940  35.880  32.880  1.00  0.00           C
+ATOM    777  O   VAL A  30      11.800  36.340  32.910  1.00  0.00           O
+ATOM    778  CB  VAL A  30      12.980  34.060  31.020  1.00  0.00           C
+ATOM    779  CG1 VAL A  30      11.540  34.300  30.560  1.00  0.00           C
+ATOM    780  CG2 VAL A  30      13.990  34.790  30.130  1.00  0.00           C
+ATOM    781  H   VAL A  30      11.770  33.860  33.860  1.00  0.00           H
+ATOM    782  N   SER A  31      14.040  36.510  33.260  1.00  0.00           N
+ATOM    783  CA  SER A  31      14.110  37.930  33.670  1.00  0.00           C
+ATOM    784  C   SER A  31      14.310  38.840  32.450  1.00  0.00           C
+ATOM    785  O   SER A  31      14.840  38.440  31.420  1.00  0.00           O
+ATOM    786  CB  SER A  31      15.280  38.160  34.630  1.00  0.00           C
+ATOM    787  OG  SER A  31      14.980  37.670  35.940  1.00  0.00           O
+ATOM    788  H   SER A  31      14.920  36.060  33.110  1.00  0.00           H
+ATOM    789  HG  SER A  31      14.950  36.660  35.930  1.00  0.00           H
+ATOM    790  N   ALA A  32      13.900  40.080  32.640  1.00  0.00           N
+ATOM    791  CA  ALA A  32      13.980  41.150  31.620  1.00  0.00           C
+ATOM    792  C   ALA A  32      15.360  41.330  30.990  1.00  0.00           C
+ATOM    793  O   ALA A  32      15.500  41.110  29.790  1.00  0.00           O
+ATOM    794  CB  ALA A  32      13.430  42.460  32.180  1.00  0.00           C
+ATOM    795  H   ALA A  32      13.640  40.410  33.560  1.00  0.00           H
+ATOM    796  N   LYS A  33      16.360  41.570  31.840  1.00  0.00           N
+ATOM    797  CA  LYS A  33      17.740  41.830  31.390  1.00  0.00           C
+ATOM    798  C   LYS A  33      18.240  40.650  30.540  1.00  0.00           C
+ATOM    799  O   LYS A  33      18.690  40.850  29.420  1.00  0.00           O
+ATOM    800  CB  LYS A  33      18.680  42.030  32.580  1.00  0.00           C
+ATOM    801  CG  LYS A  33      19.930  42.900  32.340  1.00  0.00           C
+ATOM    802  CD  LYS A  33      19.650  44.400  32.300  1.00  0.00           C
+ATOM    803  CE  LYS A  33      20.970  45.180  32.160  1.00  0.00           C
+ATOM    804  NZ  LYS A  33      21.600  44.930  30.850  1.00  0.00           N
+ATOM    805  H   LYS A  33      16.220  41.630  32.830  1.00  0.00           H
+ATOM    806  HZ1 LYS A  33      21.570  43.960  30.600  1.00  0.00           H
+ATOM    807  HZ2 LYS A  33      21.120  45.450  30.140  1.00  0.00           H
+ATOM    808  HZ3 LYS A  33      22.560  45.230  30.890  1.00  0.00           H
+ATOM    809  N   THR A  34      18.160  39.440  31.080  1.00  0.00           N
+ATOM    810  CA  THR A  34      18.670  38.250  30.370  1.00  0.00           C
+ATOM    811  C   THR A  34      18.000  37.930  29.040  1.00  0.00           C
+ATOM    812  O   THR A  34      18.690  37.680  28.060  1.00  0.00           O
+ATOM    813  CB  THR A  34      18.570  36.990  31.220  1.00  0.00           C
+ATOM    814  OG1 THR A  34      17.220  36.870  31.640  1.00  0.00           O
+ATOM    815  CG2 THR A  34      19.580  37.020  32.370  1.00  0.00           C
+ATOM    816  H   THR A  34      18.010  39.300  32.070  1.00  0.00           H
+ATOM    817  HG1 THR A  34      17.110  36.060  32.280  1.00  0.00           H
+ATOM    818  N   ARG A  35      16.670  38.010  29.000  1.00  0.00           N
+ATOM    819  CA  ARG A  35      15.890  37.850  27.760  1.00  0.00           C
+ATOM    820  C   ARG A  35      16.290  38.870  26.670  1.00  0.00           C
+ATOM    821  O   ARG A  35      16.510  38.510  25.510  1.00  0.00           O
+ATOM    822  CB  ARG A  35      14.420  37.910  28.190  1.00  0.00           C
+ATOM    823  CG  ARG A  35      13.440  37.800  27.020  1.00  0.00           C
+ATOM    824  CD  ARG A  35      11.980  38.020  27.440  1.00  0.00           C
+ATOM    825  NE  ARG A  35      11.870  39.220  28.300  1.00  0.00           N
+ATOM    826  CZ  ARG A  35      11.390  39.260  29.540  1.00  0.00           C
+ATOM    827  NH1 ARG A  35      10.770  38.220  30.130  1.00  0.00           N
+ATOM    828  NH2 ARG A  35      11.390  40.420  30.170  1.00  0.00           N
+ATOM    829  H   ARG A  35      16.140  38.100  29.850  1.00  0.00           H
+ATOM    830  HE  ARG A  35      12.370  40.040  27.990  1.00  0.00           H
+ATOM    831 HH11 ARG A  35      10.370  37.500  29.560  1.00  0.00           H
+ATOM    832 HH12 ARG A  35      10.520  38.240  31.100  1.00  0.00           H
+ATOM    833 HH21 ARG A  35      11.770  41.260  29.760  1.00  0.00           H
+ATOM    834 HH22 ARG A  35      11.040  40.450  31.120  1.00  0.00           H
+ATOM    835  N   GLU A  36      16.580  40.110  27.080  1.00  0.00           N
+ATOM    836  CA  GLU A  36      17.270  41.110  26.240  1.00  0.00           C
+ATOM    837  C   GLU A  36      18.640  40.630  25.710  1.00  0.00           C
+ATOM    838  O   GLU A  36      18.920  40.660  24.510  1.00  0.00           O
+ATOM    839  CB  GLU A  36      17.360  42.340  27.150  1.00  0.00           C
+ATOM    840  CG  GLU A  36      18.130  43.550  26.610  1.00  0.00           C
+ATOM    841  CD  GLU A  36      17.190  44.750  26.460  1.00  0.00           C
+ATOM    842  OE1 GLU A  36      16.120  44.570  25.840  1.00  0.00           O
+ATOM    843  OE2 GLU A  36      17.450  45.810  27.080  1.00  0.00           O
+ATOM    844  H   GLU A  36      16.190  40.490  27.930  1.00  0.00           H
+ATOM    845  N   LYS A  37      19.470  40.090  26.610  1.00  0.00           N
+ATOM    846  CA  LYS A  37      20.810  39.580  26.280  1.00  0.00           C
+ATOM    847  C   LYS A  37      20.790  38.520  25.170  1.00  0.00           C
+ATOM    848  O   LYS A  37      21.460  38.660  24.150  1.00  0.00           O
+ATOM    849  CB  LYS A  37      21.410  38.970  27.550  1.00  0.00           C
+ATOM    850  CG  LYS A  37      22.010  40.020  28.470  1.00  0.00           C
+ATOM    851  CD  LYS A  37      23.430  40.350  28.010  1.00  0.00           C
+ATOM    852  CE  LYS A  37      23.910  41.630  28.700  1.00  0.00           C
+ATOM    853  NZ  LYS A  37      25.190  42.140  28.170  1.00  0.00           N
+ATOM    854  H   LYS A  37      19.200  39.980  27.570  1.00  0.00           H
+ATOM    855  HZ1 LYS A  37      25.250  42.080  27.170  1.00  0.00           H
+ATOM    856  HZ2 LYS A  37      25.270  43.110  28.430  1.00  0.00           H
+ATOM    857  HZ3 LYS A  37      25.980  41.660  28.570  1.00  0.00           H
+ATOM    858  N   VAL A  38      19.950  37.520  25.400  1.00  0.00           N
+ATOM    859  CA  VAL A  38      19.790  36.350  24.490  1.00  0.00           C
+ATOM    860  C   VAL A  38      19.200  36.750  23.140  1.00  0.00           C
+ATOM    861  O   VAL A  38      19.700  36.290  22.120  1.00  0.00           O
+ATOM    862  CB  VAL A  38      18.970  35.170  25.060  1.00  0.00           C
+ATOM    863  CG1 VAL A  38      19.170  33.930  24.180  1.00  0.00           C
+ATOM    864  CG2 VAL A  38      19.360  34.860  26.500  1.00  0.00           C
+ATOM    865  H   VAL A  38      19.420  37.510  26.250  1.00  0.00           H
+ATOM    866  N   GLU A  39      18.280  37.710  23.140  1.00  0.00           N
+ATOM    867  CA  GLU A  39      17.770  38.270  21.870  1.00  0.00           C
+ATOM    868  C   GLU A  39      18.870  38.970  21.050  1.00  0.00           C
+ATOM    869  O   GLU A  39      19.130  38.580  19.910  1.00  0.00           O
+ATOM    870  CB  GLU A  39      16.560  39.170  22.140  1.00  0.00           C
+ATOM    871  CG  GLU A  39      15.360  38.290  22.510  1.00  0.00           C
+ATOM    872  CD  GLU A  39      14.070  39.070  22.780  1.00  0.00           C
+ATOM    873  OE1 GLU A  39      13.720  39.940  21.960  1.00  0.00           O
+ATOM    874  OE2 GLU A  39      13.380  38.730  23.770  1.00  0.00           O
+ATOM    875  H   GLU A  39      17.810  38.030  23.970  1.00  0.00           H
+ATOM    876  N   ALA A  40      19.680  39.780  21.740  1.00  0.00           N
+ATOM    877  CA  ALA A  40      20.870  40.440  21.160  1.00  0.00           C
+ATOM    878  C   ALA A  40      21.940  39.420  20.730  1.00  0.00           C
+ATOM    879  O   ALA A  40      22.630  39.640  19.740  1.00  0.00           O
+ATOM    880  CB  ALA A  40      21.450  41.450  22.150  1.00  0.00           C
+ATOM    881  H   ALA A  40      19.500  40.020  22.700  1.00  0.00           H
+ATOM    882  N   ALA A  41      22.080  38.340  21.500  1.00  0.00           N
+ATOM    883  CA  ALA A  41      22.950  37.180  21.190  1.00  0.00           C
+ATOM    884  C   ALA A  41      22.540  36.460  19.900  1.00  0.00           C
+ATOM    885  O   ALA A  41      23.330  36.490  18.960  1.00  0.00           O
+ATOM    886  CB  ALA A  41      22.950  36.190  22.360  1.00  0.00           C
+ATOM    887  H   ALA A  41      21.740  38.330  22.450  1.00  0.00           H
+ATOM    888  N   MET A  42      21.260  36.080  19.820  1.00  0.00           N
+ATOM    889  CA  MET A  42      20.600  35.540  18.610  1.00  0.00           C
+ATOM    890  C   MET A  42      20.790  36.400  17.350  1.00  0.00           C
+ATOM    891  O   MET A  42      20.680  35.910  16.230  1.00  0.00           O
+ATOM    892  CB  MET A  42      19.090  35.360  18.780  1.00  0.00           C
+ATOM    893  CG  MET A  42      18.770  34.160  19.660  1.00  0.00           C
+ATOM    894  SD  MET A  42      19.640  32.630  19.180  1.00  0.00           S
+ATOM    895  CE  MET A  42      19.010  31.680  20.550  1.00  0.00           C
+ATOM    896  H   MET A  42      20.670  36.080  20.640  1.00  0.00           H
+ATOM    897  N   ALA A  43      20.960  37.700  17.570  1.00  0.00           N
+ATOM    898  CA  ALA A  43      21.120  38.680  16.480  1.00  0.00           C
+ATOM    899  C   ALA A  43      22.580  38.780  16.000  1.00  0.00           C
+ATOM    900  O   ALA A  43      22.910  38.270  14.930  1.00  0.00           O
+ATOM    901  CB  ALA A  43      20.520  40.010  16.940  1.00  0.00           C
+ATOM    902  H   ALA A  43      21.080  38.040  18.500  1.00  0.00           H
+ATOM    903  N   GLU A  44      23.490  39.250  16.860  1.00  0.00           N
+ATOM    904  CA  GLU A  44      24.940  39.280  16.560  1.00  0.00           C
+ATOM    905  C   GLU A  44      25.530  37.940  16.130  1.00  0.00           C
+ATOM    906  O   GLU A  44      26.290  37.870  15.160  1.00  0.00           O
+ATOM    907  CB  GLU A  44      25.690  39.900  17.750  1.00  0.00           C
+ATOM    908  CG  GLU A  44      25.740  41.430  17.590  1.00  0.00           C
+ATOM    909  CD  GLU A  44      24.340  42.050  17.410  1.00  0.00           C
+ATOM    910  OE1 GLU A  44      23.550  41.990  18.360  1.00  0.00           O
+ATOM    911  OE2 GLU A  44      24.010  42.460  16.270  1.00  0.00           O
+ATOM    912  H   GLU A  44      23.230  39.780  17.670  1.00  0.00           H
+ATOM    913  N   LEU A  45      25.000  36.880  16.730  1.00  0.00           N
+ATOM    914  CA  LEU A  45      25.190  35.500  16.280  1.00  0.00           C
+ATOM    915  C   LEU A  45      23.860  34.990  15.710  1.00  0.00           C
+ATOM    916  O   LEU A  45      22.970  34.550  16.430  1.00  0.00           O
+ATOM    917  CB  LEU A  45      25.610  34.630  17.460  1.00  0.00           C
+ATOM    918  CG  LEU A  45      26.930  35.050  18.120  1.00  0.00           C
+ATOM    919  CD1 LEU A  45      27.140  34.200  19.370  1.00  0.00           C
+ATOM    920  CD2 LEU A  45      28.130  34.860  17.180  1.00  0.00           C
+ATOM    921  H   LEU A  45      24.490  36.950  17.600  1.00  0.00           H
+ATOM    922  N   ASN A  46      23.730  35.180  14.400  1.00  0.00           N
+ATOM    923  CA  ASN A  46      22.700  34.550  13.550  1.00  0.00           C
+ATOM    924  C   ASN A  46      21.970  33.270  14.040  1.00  0.00           C
+ATOM    925  O   ASN A  46      20.770  33.180  13.810  1.00  0.00           O
+ATOM    926  CB  ASN A  46      23.330  34.310  12.180  1.00  0.00           C
+ATOM    927  CG  ASN A  46      24.330  33.150  12.170  1.00  0.00           C
+ATOM    928  OD1 ASN A  46      23.980  31.990  11.990  1.00  0.00           O
+ATOM    929  ND2 ASN A  46      25.540  33.380  12.590  1.00  0.00           N
+ATOM    930  H   ASN A  46      24.210  35.940  13.950  1.00  0.00           H
+ATOM    931 HD21 ASN A  46      25.820  34.230  13.080  1.00  0.00           H
+ATOM    932 HD22 ASN A  46      26.250  32.690  12.410  1.00  0.00           H
+ATOM    933  N   TYR A  47      22.640  32.350  14.740  1.00  0.00           N
+ATOM    934  CA  TYR A  47      22.080  31.050  15.200  1.00  0.00           C
+ATOM    935  C   TYR A  47      21.430  30.160  14.140  1.00  0.00           C
+ATOM    936  O   TYR A  47      20.420  30.490  13.510  1.00  0.00           O
+ATOM    937  CB  TYR A  47      21.170  31.230  16.420  1.00  0.00           C
+ATOM    938  CG  TYR A  47      20.650  29.910  16.990  1.00  0.00           C
+ATOM    939  CD1 TYR A  47      21.540  28.900  17.430  1.00  0.00           C
+ATOM    940  CD2 TYR A  47      19.260  29.690  16.960  1.00  0.00           C
+ATOM    941  CE1 TYR A  47      21.030  27.630  17.800  1.00  0.00           C
+ATOM    942  CE2 TYR A  47      18.750  28.450  17.350  1.00  0.00           C
+ATOM    943  CZ  TYR A  47      19.630  27.420  17.750  1.00  0.00           C
+ATOM    944  OH  TYR A  47      19.010  26.260  18.120  1.00  0.00           O
+ATOM    945  H   TYR A  47      23.500  32.610  15.200  1.00  0.00           H
+ATOM    946  HH  TYR A  47      19.530  25.440  17.810  1.00  0.00           H
+ATOM    947  N   ILE A  48      21.950  28.940  14.100  1.00  0.00           N
+ATOM    948  CA  ILE A  48      21.440  27.940  13.150  1.00  0.00           C
+ATOM    949  C   ILE A  48      20.990  26.690  13.920  1.00  0.00           C
+ATOM    950  O   ILE A  48      21.840  26.010  14.490  1.00  0.00           O
+ATOM    951  CB  ILE A  48      22.420  27.570  12.020  1.00  0.00           C
+ATOM    952  CG1 ILE A  48      23.240  28.750  11.490  1.00  0.00           C
+ATOM    953  CG2 ILE A  48      21.640  26.940  10.850  1.00  0.00           C
+ATOM    954  CD1 ILE A  48      24.520  28.880  12.330  1.00  0.00           C
+ATOM    955  H   ILE A  48      22.690  28.630  14.710  1.00  0.00           H
+ATOM    956  N   PRO A  49      19.670  26.420  13.950  1.00  0.00           N
+ATOM    957  CA  PRO A  49      19.120  25.200  14.570  1.00  0.00           C
+ATOM    958  C   PRO A  49      19.730  23.960  13.910  1.00  0.00           C
+ATOM    959  O   PRO A  49      19.940  23.910  12.700  1.00  0.00           O
+ATOM    960  CB  PRO A  49      17.620  25.250  14.300  1.00  0.00           C
+ATOM    961  CG  PRO A  49      17.350  26.740  14.130  1.00  0.00           C
+ATOM    962  CD  PRO A  49      18.590  27.260  13.410  1.00  0.00           C
+ATOM    963  N   ASN A  50      20.230  23.100  14.800  1.00  0.00           N
+ATOM    964  CA  ASN A  50      20.870  21.790  14.540  1.00  0.00           C
+ATOM    965  C   ASN A  50      22.250  21.900  13.900  1.00  0.00           C
+ATOM    966  O   ASN A  50      22.870  20.930  13.470  1.00  0.00           O
+ATOM    967  CB  ASN A  50      19.970  20.810  13.760  1.00  0.00           C
+ATOM    968  CG  ASN A  50      18.660  20.550  14.490  1.00  0.00           C
+ATOM    969  OD1 ASN A  50      18.250  21.200  15.450  1.00  0.00           O
+ATOM    970  ND2 ASN A  50      17.980  19.500  14.120  1.00  0.00           N
+ATOM    971  H   ASN A  50      20.320  23.380  15.770  1.00  0.00           H
+ATOM    972 HD21 ASN A  50      18.410  18.670  13.730  1.00  0.00           H
+ATOM    973 HD22 ASN A  50      17.050  19.450  14.490  1.00  0.00           H
+ATOM    974  N   ARG A  51      22.720  23.140  13.920  1.00  0.00           N
+ATOM    975  CA  ARG A  51      24.150  23.480  13.800  1.00  0.00           C
+ATOM    976  C   ARG A  51      24.730  24.080  15.100  1.00  0.00           C
+ATOM    977  O   ARG A  51      25.970  24.100  15.180  1.00  0.00           O
+ATOM    978  CB  ARG A  51      24.410  24.400  12.610  1.00  0.00           C
+ATOM    979  CG  ARG A  51      24.100  23.730  11.260  1.00  0.00           C
+ATOM    980  CD  ARG A  51      24.620  22.300  11.070  1.00  0.00           C
+ATOM    981  NE  ARG A  51      26.080  22.300  11.080  1.00  0.00           N
+ATOM    982  CZ  ARG A  51      26.870  22.150  10.010  1.00  0.00           C
+ATOM    983  NH1 ARG A  51      26.470  21.770   8.800  1.00  0.00           N
+ATOM    984  NH2 ARG A  51      28.170  22.230  10.200  1.00  0.00           N
+ATOM    985  OXT ARG A  51      23.920  24.460  15.970  1.00  0.00           O
+ATOM    986  H   ARG A  51      22.090  23.920  14.030  1.00  0.00           H
+ATOM    987  HE  ARG A  51      26.510  22.440  11.980  1.00  0.00           H
+ATOM    988 HH11 ARG A  51      25.550  21.440   8.590  1.00  0.00           H
+ATOM    989 HH12 ARG A  51      27.140  21.830   8.050  1.00  0.00           H
+ATOM    990 HH21 ARG A  51      28.480  22.470  11.120  1.00  0.00           H
+ATOM    991 HH22 ARG A  51      28.800  22.020   9.460  1.00  0.00           H
+TER     992      ARG A  51
+HETATM  993 NA    NA C  52      14.830  25.040  17.790  1.00  0.00          NA
+HETATM  994  O   HOH B 853      11.350  37.460  35.810  1.00  0.00           O
+HETATM  995  H1  HOH B 853      11.630  37.130  36.710  1.00  0.00           H
+HETATM  996  H2  HOH B 853      12.070  37.250  35.150  1.00  0.00           H
+HETATM  997  O   HOH B 902      21.100  16.580  16.780  1.00  0.00           O
+HETATM  998  H1  HOH B 902      20.760  17.510  16.610  1.00  0.00           H
+HETATM  999  H2  HOH B 902      21.120  16.410  17.760  1.00  0.00           H
+HETATM 1000  O   HOH B 965      14.600  37.540  39.970  1.00  0.00           O
+HETATM 1001  H1  HOH B 965      13.990  36.990  39.400  1.00  0.00           H
+HETATM 1002  H2  HOH B 965      14.720  37.090  40.850  1.00  0.00           H
+HETATM 1003  O   HOH B1584      20.250  35.110  35.540  1.00  0.00           O
+HETATM 1004  H1  HOH B1584      21.230  34.940  35.520  1.00  0.00           H
+HETATM 1005  H2  HOH B1584      19.770  34.400  35.030  1.00  0.00           H
+HETATM 1006  O   HOH B2184      17.420  25.760  33.560  1.00  0.00           O
+HETATM 1007  H1  HOH B2184      16.610  25.460  34.070  1.00  0.00           H
+HETATM 1008  H2  HOH B2184      18.250  25.440  34.020  1.00  0.00           H
+HETATM 1009  O   HOH B2490      17.170  36.180  37.910  1.00  0.00           O
+HETATM 1010  H1  HOH B2490      16.250  35.770  37.920  1.00  0.00           H
+HETATM 1011  H2  HOH B2490      17.220  36.900  38.610  1.00  0.00           H
+HETATM 1012  O   HOH B2582      25.550  27.840  30.820  1.00  0.00           O
+HETATM 1013  H1  HOH B2582      25.050  28.210  31.610  1.00  0.00           H
+HETATM 1014  H2  HOH B2582      25.550  26.840  30.880  1.00  0.00           H
+HETATM 1015  O   HOH B2691       8.400  30.640  39.650  1.00  0.00           O
+HETATM 1016  H1  HOH B2691       8.700  30.650  40.610  1.00  0.00           H
+HETATM 1017  H2  HOH B2691       8.420  31.570  39.290  1.00  0.00           H
+HETATM 1018  O   HOH B3222      24.800  31.820  32.830  1.00  0.00           O
+HETATM 1019  H1  HOH B3222      24.810  30.820  32.840  1.00  0.00           H
+HETATM 1020  H2  HOH B3222      23.850  32.140  32.840  1.00  0.00           H
+HETATM 1021  O   HOH B3303       9.650  27.990  42.380  1.00  0.00           O
+HETATM 1022  H1  HOH B3303       9.440  28.940  42.580  1.00  0.00           H
+HETATM 1023  H2  HOH B3303       9.760  27.870  41.390  1.00  0.00           H
+HETATM 1024  O   HOH C 223      10.800  20.840  21.340  1.00  0.00           O
+HETATM 1025  H1  HOH C 223      11.080  21.420  22.110  1.00  0.00           H
+HETATM 1026  H2  HOH C 223       9.910  21.150  21.000  1.00  0.00           H
+HETATM 1027  O   HOH C 703      12.240  20.830  18.770  1.00  0.00           O
+HETATM 1028  H1  HOH C 703      11.940  20.590  19.690  1.00  0.00           H
+HETATM 1029  H2  HOH C 703      12.070  20.060  18.160  1.00  0.00           H
+HETATM 1030  O   HOH C 895      13.700  23.370  18.470  1.00  0.00           O
+HETATM 1031  H1  HOH C 895      14.020  23.310  19.420  1.00  0.00           H
+HETATM 1032  H2  HOH C 895      12.970  22.690  18.320  1.00  0.00           H
+HETATM 1033  O   HOH C1046      14.110  22.870  31.590  1.00  0.00           O
+HETATM 1034  H1  HOH C1046      13.340  22.260  31.420  1.00  0.00           H
+HETATM 1035  H2  HOH C1046      13.800  23.680  32.090  1.00  0.00           H
+HETATM 1036  O   HOH C1817      28.090  19.320  21.810  1.00  0.00           O
+HETATM 1037  H1  HOH C1817      28.700  20.080  22.020  1.00  0.00           H
+HETATM 1038  H2  HOH C1817      27.370  19.640  21.190  1.00  0.00           H
+HETATM 1039  O   HOH C2471      21.860  22.600  22.100  1.00  0.00           O
+HETATM 1040  H1  HOH C2471      22.240  22.840  21.210  1.00  0.00           H
+HETATM 1041  H2  HOH C2471      20.860  22.610  22.050  1.00  0.00           H
+HETATM 1042  O   HOH C2520      28.710  21.930  20.330  1.00  0.00           O
+HETATM 1043  H1  HOH C2520      28.560  21.890  19.340  1.00  0.00           H
+HETATM 1044  H2  HOH C2520      27.830  22.030  20.800  1.00  0.00           H
+HETATM 1045  O   HOH C2570      26.030  20.710  20.620  1.00  0.00           O
+HETATM 1046  H1  HOH C2570      25.380  20.910  21.350  1.00  0.00           H
+HETATM 1047  H2  HOH C2570      25.610  20.900  19.740  1.00  0.00           H
+HETATM 1048  O   HOH A  53      20.010  19.420  17.130  1.00  0.00           O
+HETATM 1049  H1  HOH A  53      19.310  19.970  16.670  1.00  0.00           H
+HETATM 1050  H2  HOH A  53      20.560  20.010  17.710  1.00  0.00           H
+HETATM 1051  O   HOH A  54      25.390  19.890  13.150  1.00  0.00           O
+HETATM 1052  H1  HOH A  54      24.510  20.310  13.390  1.00  0.00           H
+HETATM 1053  H2  HOH A  54      26.040  20.030  13.900  1.00  0.00           H
+HETATM 1054  O   HOH A  55      17.420  17.010  16.310  1.00  0.00           O
+HETATM 1055  H1  HOH A  55      17.200  16.050  16.450  1.00  0.00           H
+HETATM 1056  H2  HOH A  55      18.130  17.080  15.610  1.00  0.00           H
+HETATM 1057  O   HOH A  56      10.520  34.570  35.210  1.00  0.00           O
+HETATM 1058  H1  HOH A  56      10.930  35.380  35.630  1.00  0.00           H
+HETATM 1059  H2  HOH A  56       9.770  34.850  34.610  1.00  0.00           H
+HETATM 1060  O   HOH A  57      28.810  17.600  23.950  1.00  0.00           O
+HETATM 1061  H1  HOH A  57      28.350  18.310  23.410  1.00  0.00           H
+HETATM 1062  H2  HOH A  57      29.220  16.940  23.330  1.00  0.00           H
+HETATM 1063  O   HOH A  58      15.950  27.370  31.920  1.00  0.00           O
+HETATM 1064  H1  HOH A  58      16.640  27.080  32.580  1.00  0.00           H
+HETATM 1065  H2  HOH A  58      15.980  28.370  31.840  1.00  0.00           H
+HETATM 1066  O   HOH A  59      27.850  18.090  12.160  1.00  0.00           O
+HETATM 1067  H1  HOH A  59      27.430  17.450  11.520  1.00  0.00           H
+HETATM 1068  H2  HOH A  59      27.160  18.700  12.530  1.00  0.00           H
+HETATM 1069  O   HOH A  60      14.640  22.250  26.360  1.00  0.00           O
+HETATM 1070  H1  HOH A  60      15.280  22.990  26.190  1.00  0.00           H
+HETATM 1071  H2  HOH A  60      14.390  21.830  25.490  1.00  0.00           H
+HETATM 1072  O   HOH A  61      16.780  34.950  33.190  1.00  0.00           O
+HETATM 1073  H1  HOH A  61      17.380  34.160  33.070  1.00  0.00           H
+HETATM 1074  H2  HOH A  61      17.030  35.430  34.040  1.00  0.00           H
+HETATM 1075  O   HOH A  62      16.580  24.210  18.810  1.00  0.00           O
+HETATM 1076  H1  HOH A  62      16.670  23.230  18.990  1.00  0.00           H
+HETATM 1077  H2  HOH A  62      17.440  24.670  19.050  1.00  0.00           H
+HETATM 1078  O   HOH A  63      10.010  26.900  39.580  1.00  0.00           O
+HETATM 1079  H1  HOH A  63      10.550  26.570  40.360  1.00  0.00           H
+HETATM 1080  H2  HOH A  63       9.500  26.130  39.180  1.00  0.00           H
+HETATM 1081  O   HOH A  64      15.400  30.150  31.860  1.00  0.00           O
+HETATM 1082  H1  HOH A  64      14.610  30.670  32.180  1.00  0.00           H
+HETATM 1083  H2  HOH A  64      16.220  30.470  32.350  1.00  0.00           H
+HETATM 1084  O   HOH A  65       8.220  23.410  24.280  1.00  0.00           O
+HETATM 1085  H1  HOH A  65       8.680  22.570  24.560  1.00  0.00           H
+HETATM 1086  H2  HOH A  65       7.430  23.190  23.710  1.00  0.00           H
+HETATM 1087  O   HOH A  66       9.720  32.220  36.490  1.00  0.00           O
+HETATM 1088  H1  HOH A  66      10.130  33.070  36.160  1.00  0.00           H
+HETATM 1089  H2  HOH A  66       9.210  32.410  37.330  1.00  0.00           H
+HETATM 1090  O   HOH A  67      12.620  24.920  32.910  1.00  0.00           O
+HETATM 1091  H1  HOH A  67      11.670  25.170  32.700  1.00  0.00           H
+HETATM 1092  H2  HOH A  67      13.160  25.760  33.030  1.00  0.00           H
+HETATM 1093  O   HOH A  68      28.570  21.930  12.850  1.00  0.00           O
+HETATM 1094  H1  HOH A  68      29.330  22.500  13.170  1.00  0.00           H
+HETATM 1095  H2  HOH A  68      28.070  21.580  13.640  1.00  0.00           H
+HETATM 1096  O   HOH A  69      25.970  18.840  23.510  1.00  0.00           O
+HETATM 1097  H1  HOH A  69      24.990  18.960  23.340  1.00  0.00           H
+HETATM 1098  H2  HOH A  69      26.490  19.110  22.710  1.00  0.00           H
+HETATM 1099  O   HOH A  70      25.880  16.840  25.510  1.00  0.00           O
+HETATM 1100  H1  HOH A  70      25.380  15.980  25.480  1.00  0.00           H
+HETATM 1101  H2  HOH A  70      25.970  17.200  24.580  1.00  0.00           H
+HETATM 1102  O   HOH A  71      29.420  24.050  15.940  1.00  0.00           O
+HETATM 1103  H1  HOH A  71      29.400  23.390  16.690  1.00  0.00           H
+HETATM 1104  H2  HOH A  71      28.640  24.670  16.030  1.00  0.00           H
+HETATM 1105  O   HOH A  72      24.720  19.130  26.650  1.00  0.00           O
+HETATM 1106  H1  HOH A  72      25.330  19.390  27.410  1.00  0.00           H
+HETATM 1107  H2  HOH A  72      25.030  18.260  26.260  1.00  0.00           H
+HETATM 1108  O   HOH A  73      13.710  27.300  33.780  1.00  0.00           O
+HETATM 1109  H1  HOH A  73      14.570  27.330  33.270  1.00  0.00           H
+HETATM 1110  H2  HOH A  73      13.270  28.200  33.740  1.00  0.00           H
+HETATM 1111  O   HOH A  74      22.010  32.300  32.770  1.00  0.00           O
+HETATM 1112  H1  HOH A  74      21.130  31.860  32.940  1.00  0.00           H
+HETATM 1113  H2  HOH A  74      21.940  33.270  32.990  1.00  0.00           H
+HETATM 1114  O   HOH A  75      23.180  26.070  33.300  1.00  0.00           O
+HETATM 1115  H1  HOH A  75      23.810  26.310  34.040  1.00  0.00           H
+HETATM 1116  H2  HOH A  75      23.530  25.270  32.820  1.00  0.00           H
+HETATM 1117  O   HOH A  76      24.200  21.220  22.450  1.00  0.00           O
+HETATM 1118  H1  HOH A  76      24.270  21.120  23.440  1.00  0.00           H
+HETATM 1119  H2  HOH A  76      23.410  21.800  22.230  1.00  0.00           H
+HETATM 1120  O   HOH A  77      17.820  36.560  35.230  1.00  0.00           O
+HETATM 1121  H1  HOH A  77      17.450  36.580  36.160  1.00  0.00           H
+HETATM 1122  H2  HOH A  77      18.690  36.060  35.240  1.00  0.00           H
+HETATM 1123  O   HOH A  78      24.880  21.920  30.570  1.00  0.00           O
+HETATM 1124  H1  HOH A  78      24.540  21.580  31.450  1.00  0.00           H
+HETATM 1125  H2  HOH A  78      25.870  22.040  30.610  1.00  0.00           H
+ENDMDL
+CONECT  320  993
+CONECT  993  320 1036 1066 1078
+CONECT 1036  993
+CONECT 1066  993
+CONECT 1078  993
+MASTER      408    0    1    3    0    0    2    6 3384    9    5    6
+END
diff --git a/Tests/PDB/1MOT.pdb b/Tests/PDB/1MOT.pdb
deleted file mode 100644
index fe8dc7a..0000000
--- a/Tests/PDB/1MOT.pdb
+++ /dev/null
@@ -1,8220 +0,0 @@
-MODEL      1
-ATOM      1  N   ALA A 249       3.679  -9.708 -12.388  1.00  3.15           N  
-ATOM      2  CA  ALA A 249       3.984  -8.784 -11.262  1.00  2.35           C  
-ATOM      3  C   ALA A 249       2.849  -8.759 -10.242  1.00  1.70           C  
-ATOM      4  O   ALA A 249       1.696  -9.031 -10.576  1.00  2.08           O  
-ATOM      5  CB  ALA A 249       4.257  -7.381 -11.774  1.00  2.86           C  
-ATOM      6  H1  ALA A 249       4.412 -10.440 -12.454  1.00  3.49           H  
-ATOM      7  H2  ALA A 249       3.649  -9.185 -13.286  1.00  3.56           H  
-ATOM      8  H3  ALA A 249       2.756 -10.168 -12.235  1.00  3.34           H  
-ATOM      9  HA  ALA A 249       4.878  -9.138 -10.771  1.00  2.13           H  
-ATOM     10  HB1 ALA A 249       3.739  -6.669 -11.146  1.00  3.20           H  
-ATOM     11  HB2 ALA A 249       3.906  -7.289 -12.790  1.00  3.15           H  
-ATOM     12  HB3 ALA A 249       5.318  -7.185 -11.737  1.00  3.20           H  
-ATOM     13  N   PRO A 250       3.167  -8.435  -8.976  1.00  1.01           N  
-ATOM     14  CA  PRO A 250       2.181  -8.373  -7.892  1.00  0.87           C  
-ATOM     15  C   PRO A 250       1.189  -7.218  -8.067  1.00  0.66           C  
-ATOM     16  O   PRO A 250       1.533  -6.049  -7.868  1.00  0.58           O  
-ATOM     17  CB  PRO A 250       3.050  -8.158  -6.639  1.00  1.33           C  
-ATOM     18  CG  PRO A 250       4.432  -8.514  -7.058  1.00  1.35           C  
-ATOM     19  CD  PRO A 250       4.509  -8.097  -8.487  1.00  1.22           C  
-ATOM     20  HA  PRO A 250       1.638  -9.302  -7.799  1.00  1.37           H  
-ATOM     21  HB2 PRO A 250       2.995  -7.124  -6.335  1.00  1.56           H  
-ATOM     22  HB3 PRO A 250       2.707  -8.796  -5.844  1.00  1.83           H  
-ATOM     23  HG2 PRO A 250       5.153  -7.972  -6.462  1.00  1.90           H  
-ATOM     24  HG3 PRO A 250       4.585  -9.579  -6.967  1.00  1.40           H  
-ATOM     25  HD2 PRO A 250       4.688  -7.034  -8.559  1.00  1.66           H  
-ATOM     26  HD3 PRO A 250       5.266  -8.655  -9.006  1.00  1.40           H  
-ATOM     27  N   ALA A 251      -0.045  -7.554  -8.441  1.00  0.72           N  
-ATOM     28  CA  ALA A 251      -1.089  -6.550  -8.651  1.00  0.67           C  
-ATOM     29  C   ALA A 251      -1.385  -5.764  -7.372  1.00  0.51           C  
-ATOM     30  O   ALA A 251      -1.542  -4.544  -7.408  1.00  0.51           O  
-ATOM     31  CB  ALA A 251      -2.356  -7.213  -9.166  1.00  0.89           C  
-ATOM     32  H   ALA A 251      -0.256  -8.500  -8.586  1.00  0.89           H  
-ATOM     33  HA  ALA A 251      -0.740  -5.862  -9.407  1.00  0.69           H  
-ATOM     34  HB1 ALA A 251      -2.832  -7.753  -8.363  1.00  1.31           H  
-ATOM     35  HB2 ALA A 251      -2.104  -7.897  -9.963  1.00  1.26           H  
-ATOM     36  HB3 ALA A 251      -3.033  -6.457  -9.541  1.00  1.54           H  
-ATOM     37  N   ARG A 252      -1.451  -6.472  -6.248  1.00  0.47           N  
-ATOM     38  CA  ARG A 252      -1.723  -5.848  -4.954  1.00  0.42           C  
-ATOM     39  C   ARG A 252      -0.639  -4.820  -4.608  1.00  0.34           C  
-ATOM     40  O   ARG A 252      -0.940  -3.706  -4.171  1.00  0.36           O  
-ATOM     41  CB  ARG A 252      -1.801  -6.927  -3.869  1.00  0.54           C  
-ATOM     42  CG  ARG A 252      -2.814  -6.638  -2.769  1.00  0.71           C  
-ATOM     43  CD  ARG A 252      -2.473  -5.375  -1.993  1.00  0.83           C  
-ATOM     44  NE  ARG A 252      -3.440  -5.099  -0.935  1.00  1.18           N  
-ATOM     45  CZ  ARG A 252      -3.429  -4.005  -0.207  1.00  1.47           C  
-ATOM     46  NH1 ARG A 252      -2.547  -3.067  -0.440  1.00  1.50           N  
-ATOM     47  NH2 ARG A 252      -4.311  -3.844   0.745  1.00  1.81           N  
-ATOM     48  H   ARG A 252      -1.314  -7.439  -6.289  1.00  0.57           H  
-ATOM     49  HA  ARG A 252      -2.673  -5.344  -5.019  1.00  0.51           H  
-ATOM     50  HB2 ARG A 252      -2.069  -7.864  -4.335  1.00  0.66           H  
-ATOM     51  HB3 ARG A 252      -0.827  -7.031  -3.413  1.00  0.60           H  
-ATOM     52  HG2 ARG A 252      -3.789  -6.516  -3.216  1.00  0.86           H  
-ATOM     53  HG3 ARG A 252      -2.834  -7.475  -2.085  1.00  0.84           H  
-ATOM     54  HD2 ARG A 252      -1.495  -5.495  -1.550  1.00  0.87           H  
-ATOM     55  HD3 ARG A 252      -2.455  -4.541  -2.680  1.00  0.81           H  
-ATOM     56  HE  ARG A 252      -4.122  -5.774  -0.757  1.00  1.28           H  
-ATOM     57 HH11 ARG A 252      -1.882  -3.182  -1.174  1.00  1.30           H  
-ATOM     58 HH12 ARG A 252      -2.541  -2.240   0.115  1.00  1.77           H  
-ATOM     59 HH21 ARG A 252      -4.992  -4.552   0.919  1.00  1.87           H  
-ATOM     60 HH22 ARG A 252      -4.302  -3.015   1.298  1.00  2.07           H  
-ATOM     61  N   VAL A 253       0.619  -5.196  -4.821  1.00  0.36           N  
-ATOM     62  CA  VAL A 253       1.744  -4.305  -4.547  1.00  0.40           C  
-ATOM     63  C   VAL A 253       1.707  -3.089  -5.470  1.00  0.36           C  
-ATOM     64  O   VAL A 253       1.822  -1.952  -5.014  1.00  0.37           O  
-ATOM     65  CB  VAL A 253       3.093  -5.038  -4.703  1.00  0.54           C  
-ATOM     66  CG1 VAL A 253       4.263  -4.080  -4.519  1.00  0.68           C  
-ATOM     67  CG2 VAL A 253       3.181  -6.188  -3.712  1.00  0.67           C  
-ATOM     68  H   VAL A 253       0.792  -6.087  -5.181  1.00  0.42           H  
-ATOM     69  HA  VAL A 253       1.658  -3.968  -3.527  1.00  0.44           H  
-ATOM     70  HB  VAL A 253       3.144  -5.447  -5.701  1.00  0.59           H  
-ATOM     71 HG11 VAL A 253       3.895  -3.127  -4.169  1.00  1.28           H  
-ATOM     72 HG12 VAL A 253       4.769  -3.946  -5.464  1.00  1.24           H  
-ATOM     73 HG13 VAL A 253       4.952  -4.488  -3.796  1.00  1.23           H  
-ATOM     74 HG21 VAL A 253       4.013  -6.025  -3.044  1.00  0.96           H  
-ATOM     75 HG22 VAL A 253       3.324  -7.115  -4.248  1.00  1.04           H  
-ATOM     76 HG23 VAL A 253       2.265  -6.240  -3.141  1.00  0.95           H  
-ATOM     77  N   GLY A 254       1.518  -3.343  -6.767  1.00  0.39           N  
-ATOM     78  CA  GLY A 254       1.438  -2.263  -7.742  1.00  0.43           C  
-ATOM     79  C   GLY A 254       0.325  -1.285  -7.410  1.00  0.38           C  
-ATOM     80  O   GLY A 254       0.535  -0.068  -7.382  1.00  0.38           O  
-ATOM     81  H   GLY A 254       1.416  -4.276  -7.061  1.00  0.44           H  
-ATOM     82  HA2 GLY A 254       2.380  -1.733  -7.759  1.00  0.47           H  
-ATOM     83  HA3 GLY A 254       1.254  -2.686  -8.718  1.00  0.51           H  
-ATOM     84  N   LEU A 255      -0.858  -1.824  -7.122  1.00  0.39           N  
-ATOM     85  CA  LEU A 255      -2.008  -1.009  -6.749  1.00  0.41           C  
-ATOM     86  C   LEU A 255      -1.726  -0.229  -5.474  1.00  0.35           C  
-ATOM     87  O   LEU A 255      -2.018   0.964  -5.380  1.00  0.39           O  
-ATOM     88  CB  LEU A 255      -3.236  -1.887  -6.536  1.00  0.49           C  
-ATOM     89  CG  LEU A 255      -4.175  -1.965  -7.729  1.00  0.68           C  
-ATOM     90  CD1 LEU A 255      -5.352  -2.865  -7.404  1.00  0.79           C  
-ATOM     91  CD2 LEU A 255      -4.645  -0.569  -8.111  1.00  0.84           C  
-ATOM     92  H   LEU A 255      -0.953  -2.805  -7.140  1.00  0.41           H  
-ATOM     93  HA  LEU A 255      -2.208  -0.318  -7.554  1.00  0.48           H  
-ATOM     94  HB2 LEU A 255      -2.902  -2.886  -6.298  1.00  0.54           H  
-ATOM     95  HB3 LEU A 255      -3.789  -1.498  -5.695  1.00  0.53           H  
-ATOM     96  HG  LEU A 255      -3.648  -2.387  -8.571  1.00  0.76           H  
-ATOM     97 HD11 LEU A 255      -6.272  -2.315  -7.535  1.00  1.31           H  
-ATOM     98 HD12 LEU A 255      -5.275  -3.201  -6.379  1.00  1.26           H  
-ATOM     99 HD13 LEU A 255      -5.345  -3.719  -8.064  1.00  1.35           H  
-ATOM    100 HD21 LEU A 255      -4.894  -0.017  -7.216  1.00  1.29           H  
-ATOM    101 HD22 LEU A 255      -5.514  -0.640  -8.745  1.00  1.36           H  
-ATOM    102 HD23 LEU A 255      -3.853  -0.056  -8.639  1.00  1.41           H  
-ATOM    103  N   GLY A 256      -1.156  -0.919  -4.492  1.00  0.29           N  
-ATOM    104  CA  GLY A 256      -0.839  -0.295  -3.227  1.00  0.29           C  
-ATOM    105  C   GLY A 256       0.156   0.850  -3.358  1.00  0.28           C  
-ATOM    106  O   GLY A 256      -0.111   1.954  -2.886  1.00  0.33           O  
-ATOM    107  H   GLY A 256      -0.951  -1.871  -4.631  1.00  0.31           H  
-ATOM    108  HA2 GLY A 256      -1.752   0.087  -2.796  1.00  0.35           H  
-ATOM    109  HA3 GLY A 256      -0.428  -1.042  -2.568  1.00  0.31           H  
-ATOM    110  N   ILE A 257       1.299   0.602  -4.010  1.00  0.28           N  
-ATOM    111  CA  ILE A 257       2.304   1.652  -4.188  1.00  0.34           C  
-ATOM    112  C   ILE A 257       1.745   2.807  -5.023  1.00  0.35           C  
-ATOM    113  O   ILE A 257       2.004   3.973  -4.725  1.00  0.40           O  
-ATOM    114  CB  ILE A 257       3.631   1.124  -4.801  1.00  0.45           C  
-ATOM    115  CG1 ILE A 257       3.423   0.497  -6.184  1.00  0.48           C  
-ATOM    116  CG2 ILE A 257       4.280   0.121  -3.861  1.00  0.54           C  
-ATOM    117  CD1 ILE A 257       3.744   1.429  -7.336  1.00  0.58           C  
-ATOM    118  H   ILE A 257       1.463  -0.299  -4.380  1.00  0.29           H  
-ATOM    119  HA  ILE A 257       2.531   2.037  -3.203  1.00  0.37           H  
-ATOM    120  HB  ILE A 257       4.305   1.964  -4.898  1.00  0.52           H  
-ATOM    121 HG12 ILE A 257       4.056  -0.372  -6.277  1.00  0.55           H  
-ATOM    122 HG13 ILE A 257       2.389   0.193  -6.281  1.00  0.48           H  
-ATOM    123 HG21 ILE A 257       4.372   0.556  -2.876  1.00  1.15           H  
-ATOM    124 HG22 ILE A 257       5.261  -0.139  -4.233  1.00  1.10           H  
-ATOM    125 HG23 ILE A 257       3.669  -0.768  -3.805  1.00  1.25           H  
-ATOM    126 HD11 ILE A 257       4.644   1.981  -7.111  1.00  1.22           H  
-ATOM    127 HD12 ILE A 257       2.925   2.118  -7.480  1.00  1.17           H  
-ATOM    128 HD13 ILE A 257       3.891   0.851  -8.236  1.00  1.18           H  
-ATOM    129  N   THR A 258       0.948   2.482  -6.046  1.00  0.37           N  
-ATOM    130  CA  THR A 258       0.330   3.507  -6.888  1.00  0.44           C  
-ATOM    131  C   THR A 258      -0.622   4.364  -6.054  1.00  0.42           C  
-ATOM    132  O   THR A 258      -0.635   5.590  -6.164  1.00  0.47           O  
-ATOM    133  CB  THR A 258      -0.451   2.887  -8.069  1.00  0.53           C  
-ATOM    134  OG1 THR A 258       0.422   2.078  -8.867  1.00  0.58           O  
-ATOM    135  CG2 THR A 258      -1.076   3.968  -8.942  1.00  0.66           C  
-ATOM    136  H   THR A 258       0.758   1.530  -6.226  1.00  0.38           H  
-ATOM    137  HA  THR A 258       1.114   4.135  -7.285  1.00  0.49           H  
-ATOM    138  HB  THR A 258      -1.241   2.266  -7.672  1.00  0.53           H  
-ATOM    139  HG1 THR A 258       0.540   1.216  -8.440  1.00  0.54           H  
-ATOM    140 HG21 THR A 258      -1.991   3.594  -9.378  1.00  0.96           H  
-ATOM    141 HG22 THR A 258      -0.387   4.239  -9.729  1.00  1.06           H  
-ATOM    142 HG23 THR A 258      -1.294   4.837  -8.340  1.00  1.16           H  
-ATOM    143  N   THR A 259      -1.400   3.704  -5.197  1.00  0.40           N  
-ATOM    144  CA  THR A 259      -2.340   4.395  -4.317  1.00  0.43           C  
-ATOM    145  C   THR A 259      -1.586   5.314  -3.360  1.00  0.37           C  
-ATOM    146  O   THR A 259      -1.917   6.493  -3.226  1.00  0.42           O  
-ATOM    147  CB  THR A 259      -3.193   3.398  -3.497  1.00  0.47           C  
-ATOM    148  OG1 THR A 259      -3.872   2.488  -4.374  1.00  0.53           O  
-ATOM    149  CG2 THR A 259      -4.217   4.129  -2.640  1.00  0.59           C  
-ATOM    150  H   THR A 259      -1.326   2.725  -5.143  1.00  0.39           H  
-ATOM    151  HA  THR A 259      -3.002   4.989  -4.931  1.00  0.50           H  
-ATOM    152  HB  THR A 259      -2.538   2.837  -2.847  1.00  0.44           H  
-ATOM    153  HG1 THR A 259      -3.230   1.876  -4.766  1.00  0.51           H  
-ATOM    154 HG21 THR A 259      -5.196   3.707  -2.810  1.00  1.12           H  
-ATOM    155 HG22 THR A 259      -4.226   5.177  -2.904  1.00  1.21           H  
-ATOM    156 HG23 THR A 259      -3.956   4.023  -1.597  1.00  1.24           H  
-ATOM    157  N   VAL A 260      -0.550   4.771  -2.718  1.00  0.33           N  
-ATOM    158  CA  VAL A 260       0.275   5.545  -1.793  1.00  0.33           C  
-ATOM    159  C   VAL A 260       0.906   6.740  -2.511  1.00  0.33           C  
-ATOM    160  O   VAL A 260       0.813   7.874  -2.038  1.00  0.34           O  
-ATOM    161  CB  VAL A 260       1.384   4.676  -1.159  1.00  0.38           C  
-ATOM    162  CG1 VAL A 260       2.288   5.515  -0.267  1.00  0.51           C  
-ATOM    163  CG2 VAL A 260       0.776   3.528  -0.369  1.00  0.44           C  
-ATOM    164  H   VAL A 260      -0.325   3.825  -2.884  1.00  0.32           H  
-ATOM    165  HA  VAL A 260      -0.365   5.910  -1.003  1.00  0.38           H  
-ATOM    166  HB  VAL A 260       1.987   4.259  -1.953  1.00  0.41           H  
-ATOM    167 HG11 VAL A 260       1.890   6.515  -0.191  1.00  1.14           H  
-ATOM    168 HG12 VAL A 260       3.279   5.553  -0.695  1.00  1.20           H  
-ATOM    169 HG13 VAL A 260       2.337   5.069   0.715  1.00  1.14           H  
-ATOM    170 HG21 VAL A 260       0.221   2.886  -1.038  1.00  1.10           H  
-ATOM    171 HG22 VAL A 260       0.111   3.921   0.385  1.00  1.12           H  
-ATOM    172 HG23 VAL A 260       1.563   2.959   0.104  1.00  1.14           H  
-ATOM    173  N   LEU A 261       1.517   6.479  -3.671  1.00  0.36           N  
-ATOM    174  CA  LEU A 261       2.133   7.536  -4.473  1.00  0.41           C  
-ATOM    175  C   LEU A 261       1.098   8.606  -4.827  1.00  0.41           C  
-ATOM    176  O   LEU A 261       1.384   9.801  -4.782  1.00  0.45           O  
-ATOM    177  CB  LEU A 261       2.740   6.955  -5.747  1.00  0.50           C  
-ATOM    178  CG  LEU A 261       3.623   7.924  -6.529  1.00  0.61           C  
-ATOM    179  CD1 LEU A 261       4.730   8.466  -5.636  1.00  0.67           C  
-ATOM    180  CD2 LEU A 261       4.204   7.236  -7.750  1.00  0.77           C  
-ATOM    181  H   LEU A 261       1.537   5.551  -4.005  1.00  0.37           H  
-ATOM    182  HA  LEU A 261       2.921   7.988  -3.890  1.00  0.44           H  
-ATOM    183  HB2 LEU A 261       3.333   6.091  -5.479  1.00  0.52           H  
-ATOM    184  HB3 LEU A 261       1.936   6.634  -6.393  1.00  0.53           H  
-ATOM    185  HG  LEU A 261       3.024   8.758  -6.864  1.00  0.66           H  
-ATOM    186 HD11 LEU A 261       4.294   9.047  -4.836  1.00  1.13           H  
-ATOM    187 HD12 LEU A 261       5.389   9.093  -6.217  1.00  1.19           H  
-ATOM    188 HD13 LEU A 261       5.290   7.643  -5.216  1.00  1.36           H  
-ATOM    189 HD21 LEU A 261       4.051   6.169  -7.667  1.00  1.33           H  
-ATOM    190 HD22 LEU A 261       5.262   7.445  -7.810  1.00  1.31           H  
-ATOM    191 HD23 LEU A 261       3.712   7.603  -8.637  1.00  1.28           H  
-ATOM    192  N   THR A 262      -0.112   8.159  -5.150  1.00  0.43           N  
-ATOM    193  CA  THR A 262      -1.210   9.064  -5.481  1.00  0.49           C  
-ATOM    194  C   THR A 262      -1.583   9.921  -4.269  1.00  0.45           C  
-ATOM    195  O   THR A 262      -1.714  11.140  -4.371  1.00  0.50           O  
-ATOM    196  CB  THR A 262      -2.461   8.279  -5.938  1.00  0.57           C  
-ATOM    197  OG1 THR A 262      -2.158   7.492  -7.098  1.00  0.66           O  
-ATOM    198  CG2 THR A 262      -3.616   9.219  -6.245  1.00  0.69           C  
-ATOM    199  H   THR A 262      -0.276   7.188  -5.149  1.00  0.43           H  
-ATOM    200  HA  THR A 262      -0.891   9.708  -6.289  1.00  0.54           H  
-ATOM    201  HB  THR A 262      -2.761   7.618  -5.137  1.00  0.54           H  
-ATOM    202  HG1 THR A 262      -1.575   6.758  -6.847  1.00  0.62           H  
-ATOM    203 HG21 THR A 262      -3.416   9.746  -7.164  1.00  1.24           H  
-ATOM    204 HG22 THR A 262      -3.722   9.930  -5.438  1.00  1.28           H  
-ATOM    205 HG23 THR A 262      -4.526   8.648  -6.345  1.00  1.23           H  
-ATOM    206  N   MET A 263      -1.752   9.264  -3.124  1.00  0.40           N  
-ATOM    207  CA  MET A 263      -2.110   9.951  -1.880  1.00  0.43           C  
-ATOM    208  C   MET A 263      -1.038  10.955  -1.438  1.00  0.38           C  
-ATOM    209  O   MET A 263      -1.358  12.087  -1.071  1.00  0.43           O  
-ATOM    210  CB  MET A 263      -2.354   8.934  -0.761  1.00  0.48           C  
-ATOM    211  CG  MET A 263      -3.543   8.021  -1.016  1.00  0.59           C  
-ATOM    212  SD  MET A 263      -5.082   8.932  -1.243  1.00  0.75           S  
-ATOM    213  CE  MET A 263      -5.255   9.719   0.356  1.00  0.86           C  
-ATOM    214  H   MET A 263      -1.636   8.287  -3.117  1.00  0.38           H  
-ATOM    215  HA  MET A 263      -3.027  10.489  -2.060  1.00  0.49           H  
-ATOM    216  HB2 MET A 263      -1.473   8.320  -0.651  1.00  0.48           H  
-ATOM    217  HB3 MET A 263      -2.528   9.467   0.162  1.00  0.53           H  
-ATOM    218  HG2 MET A 263      -3.350   7.442  -1.906  1.00  0.61           H  
-ATOM    219  HG3 MET A 263      -3.656   7.357  -0.173  1.00  0.65           H  
-ATOM    220  HE1 MET A 263      -4.967  10.758   0.280  1.00  1.29           H  
-ATOM    221  HE2 MET A 263      -4.619   9.222   1.073  1.00  1.42           H  
-ATOM    222  HE3 MET A 263      -6.283   9.655   0.681  1.00  1.38           H  
-ATOM    223  N   THR A 264       0.234  10.551  -1.467  1.00  0.35           N  
-ATOM    224  CA  THR A 264       1.325  11.445  -1.055  1.00  0.38           C  
-ATOM    225  C   THR A 264       1.683  12.475  -2.145  1.00  0.34           C  
-ATOM    226  O   THR A 264       2.842  12.602  -2.546  1.00  0.40           O  
-ATOM    227  CB  THR A 264       2.596  10.663  -0.629  1.00  0.46           C  
-ATOM    228  OG1 THR A 264       3.620  11.580  -0.224  1.00  0.53           O  
-ATOM    229  CG2 THR A 264       3.125   9.777  -1.750  1.00  0.48           C  
-ATOM    230  H   THR A 264       0.442   9.633  -1.765  1.00  0.35           H  
-ATOM    231  HA  THR A 264       0.974  11.991  -0.190  1.00  0.45           H  
-ATOM    232  HB  THR A 264       2.340  10.035   0.214  1.00  0.53           H  
-ATOM    233  HG1 THR A 264       3.834  12.165  -0.964  1.00  0.52           H  
-ATOM    234 HG21 THR A 264       2.349   9.093  -2.063  1.00  1.03           H  
-ATOM    235 HG22 THR A 264       3.976   9.216  -1.394  1.00  0.95           H  
-ATOM    236 HG23 THR A 264       3.421  10.392  -2.586  1.00  0.98           H  
-ATOM    237  N   THR A 265       0.678  13.211  -2.616  1.00  0.32           N  
-ATOM    238  CA  THR A 265       0.884  14.229  -3.653  1.00  0.34           C  
-ATOM    239  C   THR A 265       1.861  15.322  -3.208  1.00  0.39           C  
-ATOM    240  O   THR A 265       2.772  15.680  -3.957  1.00  0.48           O  
-ATOM    241  CB  THR A 265      -0.445  14.891  -4.097  1.00  0.37           C  
-ATOM    242  OG1 THR A 265      -0.180  16.084  -4.855  1.00  0.39           O  
-ATOM    243  CG2 THR A 265      -1.326  15.230  -2.902  1.00  0.44           C  
-ATOM    244  H   THR A 265      -0.227  13.066  -2.254  1.00  0.35           H  
-ATOM    245  HA  THR A 265       1.306  13.729  -4.514  1.00  0.42           H  
-ATOM    246  HB  THR A 265      -0.978  14.191  -4.727  1.00  0.46           H  
-ATOM    247  HG1 THR A 265       0.117  15.843  -5.751  1.00  0.40           H  
-ATOM    248 HG21 THR A 265      -2.083  15.939  -3.201  1.00  1.08           H  
-ATOM    249 HG22 THR A 265      -0.719  15.659  -2.118  1.00  1.12           H  
-ATOM    250 HG23 THR A 265      -1.799  14.329  -2.537  1.00  1.15           H  
-ATOM    251  N   GLN A 266       1.666  15.842  -1.987  1.00  0.45           N  
-ATOM    252  CA  GLN A 266       2.519  16.901  -1.412  1.00  0.59           C  
-ATOM    253  C   GLN A 266       2.346  18.244  -2.136  1.00  0.59           C  
-ATOM    254  O   GLN A 266       2.179  19.284  -1.500  1.00  0.71           O  
-ATOM    255  CB  GLN A 266       3.994  16.483  -1.422  1.00  0.74           C  
-ATOM    256  CG  GLN A 266       4.281  15.231  -0.606  1.00  0.86           C  
-ATOM    257  CD  GLN A 266       5.736  14.798  -0.671  1.00  1.12           C  
-ATOM    258  OE1 GLN A 266       6.114  13.771  -0.119  1.00  1.33           O  
-ATOM    259  NE2 GLN A 266       6.566  15.582  -1.340  1.00  1.25           N  
-ATOM    260  H   GLN A 266       0.919  15.503  -1.452  1.00  0.46           H  
-ATOM    261  HA  GLN A 266       2.210  17.034  -0.389  1.00  0.68           H  
-ATOM    262  HB2 GLN A 266       4.297  16.298  -2.442  1.00  0.75           H  
-ATOM    263  HB3 GLN A 266       4.588  17.291  -1.021  1.00  0.88           H  
-ATOM    264  HG2 GLN A 266       4.028  15.424   0.426  1.00  0.95           H  
-ATOM    265  HG3 GLN A 266       3.665  14.426  -0.980  1.00  0.82           H  
-ATOM    266 HE21 GLN A 266       6.206  16.390  -1.756  1.00  1.19           H  
-ATOM    267 HE22 GLN A 266       7.507  15.318  -1.389  1.00  1.48           H  
-ATOM    268  N   SER A 267       2.381  18.211  -3.465  1.00  0.53           N  
-ATOM    269  CA  SER A 267       2.223  19.415  -4.282  1.00  0.59           C  
-ATOM    270  C   SER A 267       0.792  19.953  -4.221  1.00  0.61           C  
-ATOM    271  O   SER A 267       0.559  21.130  -4.495  1.00  0.74           O  
-ATOM    272  CB  SER A 267       2.603  19.137  -5.738  1.00  0.62           C  
-ATOM    273  OG  SER A 267       2.423  20.294  -6.540  1.00  0.75           O  
-ATOM    274  H   SER A 267       2.515  17.341  -3.912  1.00  0.49           H  
-ATOM    275  HA  SER A 267       2.889  20.168  -3.888  1.00  0.72           H  
-ATOM    276  HB2 SER A 267       3.639  18.838  -5.787  1.00  0.70           H  
-ATOM    277  HB3 SER A 267       1.981  18.344  -6.126  1.00  0.58           H  
-ATOM    278  HG  SER A 267       1.920  20.954  -6.047  1.00  0.76           H  
-ATOM    279  N   SER A 268      -0.157  19.076  -3.869  1.00  0.58           N  
-ATOM    280  CA  SER A 268      -1.578  19.439  -3.766  1.00  0.73           C  
-ATOM    281  C   SER A 268      -2.171  19.815  -5.130  1.00  0.74           C  
-ATOM    282  O   SER A 268      -2.856  20.831  -5.272  1.00  0.97           O  
-ATOM    283  CB  SER A 268      -1.780  20.587  -2.764  1.00  0.94           C  
-ATOM    284  OG  SER A 268      -3.146  20.957  -2.677  1.00  1.15           O  
-ATOM    285  H   SER A 268       0.109  18.152  -3.673  1.00  0.55           H  
-ATOM    286  HA  SER A 268      -2.104  18.570  -3.400  1.00  0.76           H  
-ATOM    287  HB2 SER A 268      -1.443  20.272  -1.788  1.00  0.99           H  
-ATOM    288  HB3 SER A 268      -1.206  21.444  -3.084  1.00  0.98           H  
-ATOM    289  HG  SER A 268      -3.473  21.168  -3.563  1.00  1.14           H  
-ATOM    290  N   GLY A 269      -1.905  18.981  -6.130  1.00  0.62           N  
-ATOM    291  CA  GLY A 269      -2.416  19.235  -7.469  1.00  0.70           C  
-ATOM    292  C   GLY A 269      -2.719  17.956  -8.235  1.00  0.61           C  
-ATOM    293  O   GLY A 269      -2.291  17.799  -9.384  1.00  0.59           O  
-ATOM    294  H   GLY A 269      -1.354  18.187  -5.959  1.00  0.60           H  
-ATOM    295  HA2 GLY A 269      -3.322  19.818  -7.391  1.00  0.86           H  
-ATOM    296  HA3 GLY A 269      -1.682  19.805  -8.020  1.00  0.73           H  
-ATOM    297  N   SER A 270      -3.461  17.045  -7.586  1.00  0.63           N  
-ATOM    298  CA  SER A 270      -3.855  15.744  -8.168  1.00  0.63           C  
-ATOM    299  C   SER A 270      -2.663  14.791  -8.325  1.00  0.53           C  
-ATOM    300  O   SER A 270      -2.699  13.656  -7.851  1.00  0.61           O  
-ATOM    301  CB  SER A 270      -4.556  15.932  -9.522  1.00  0.72           C  
-ATOM    302  OG  SER A 270      -5.740  16.700  -9.387  1.00  0.91           O  
-ATOM    303  H   SER A 270      -3.759  17.254  -6.676  1.00  0.69           H  
-ATOM    304  HA  SER A 270      -4.558  15.291  -7.483  1.00  0.72           H  
-ATOM    305  HB2 SER A 270      -3.888  16.441 -10.201  1.00  0.71           H  
-ATOM    306  HB3 SER A 270      -4.812  14.964  -9.927  1.00  0.78           H  
-ATOM    307  HG  SER A 270      -5.942  17.129 -10.223  1.00  1.09           H  
-ATOM    308  N   ARG A 271      -1.611  15.264  -8.980  1.00  0.48           N  
-ATOM    309  CA  ARG A 271      -0.404  14.468  -9.187  1.00  0.47           C  
-ATOM    310  C   ARG A 271       0.541  14.598  -7.995  1.00  0.40           C  
-ATOM    311  O   ARG A 271       0.549  15.621  -7.308  1.00  0.40           O  
-ATOM    312  CB  ARG A 271       0.343  14.903 -10.457  1.00  0.52           C  
-ATOM    313  CG  ARG A 271      -0.417  14.685 -11.761  1.00  0.70           C  
-ATOM    314  CD  ARG A 271      -1.423  15.798 -12.035  1.00  0.79           C  
-ATOM    315  NE  ARG A 271      -0.818  17.134 -11.994  1.00  0.73           N  
-ATOM    316  CZ  ARG A 271       0.090  17.579 -12.849  1.00  0.71           C  
-ATOM    317  NH1 ARG A 271       0.512  16.841 -13.840  1.00  0.74           N  
-ATOM    318  NH2 ARG A 271       0.579  18.781 -12.721  1.00  0.80           N  
-ATOM    319  H   ARG A 271      -1.642  16.187  -9.326  1.00  0.54           H  
-ATOM    320  HA  ARG A 271      -0.701  13.435  -9.289  1.00  0.59           H  
-ATOM    321  HB2 ARG A 271       0.571  15.955 -10.379  1.00  0.47           H  
-ATOM    322  HB3 ARG A 271       1.271  14.351 -10.515  1.00  0.61           H  
-ATOM    323  HG2 ARG A 271       0.291  14.650 -12.575  1.00  0.72           H  
-ATOM    324  HG3 ARG A 271      -0.945  13.744 -11.701  1.00  0.85           H  
-ATOM    325  HD2 ARG A 271      -1.852  15.641 -13.012  1.00  0.94           H  
-ATOM    326  HD3 ARG A 271      -2.205  15.746 -11.291  1.00  0.86           H  
-ATOM    327  HE  ARG A 271      -1.112  17.734 -11.276  1.00  0.79           H  
-ATOM    328 HH11 ARG A 271       0.152  15.924 -13.973  1.00  0.83           H  
-ATOM    329 HH12 ARG A 271       1.213  17.215 -14.474  1.00  0.77           H  
-ATOM    330 HH21 ARG A 271       0.271  19.374 -11.983  1.00  0.91           H  
-ATOM    331 HH22 ARG A 271       1.267  19.105 -13.378  1.00  0.83           H  
-ATOM    332  N   ALA A 272       1.348  13.568  -7.764  1.00  0.45           N  
-ATOM    333  CA  ALA A 272       2.318  13.584  -6.671  1.00  0.47           C  
-ATOM    334  C   ALA A 272       3.512  14.477  -7.036  1.00  0.41           C  
-ATOM    335  O   ALA A 272       3.344  15.655  -7.355  1.00  0.45           O  
-ATOM    336  CB  ALA A 272       2.777  12.167  -6.356  1.00  0.61           C  
-ATOM    337  H   ALA A 272       1.304  12.785  -8.352  1.00  0.54           H  
-ATOM    338  HA  ALA A 272       1.830  13.989  -5.795  1.00  0.51           H  
-ATOM    339  HB1 ALA A 272       1.914  11.544  -6.169  1.00  1.25           H  
-ATOM    340  HB2 ALA A 272       3.408  12.180  -5.479  1.00  1.20           H  
-ATOM    341  HB3 ALA A 272       3.331  11.774  -7.194  1.00  1.15           H  
-ATOM    342  N   SER A 273       4.716  13.917  -7.022  1.00  0.44           N  
-ATOM    343  CA  SER A 273       5.910  14.677  -7.387  1.00  0.46           C  
-ATOM    344  C   SER A 273       6.065  14.769  -8.891  1.00  0.42           C  
-ATOM    345  O   SER A 273       7.133  14.513  -9.447  1.00  0.53           O  
-ATOM    346  CB  SER A 273       7.162  14.074  -6.770  1.00  0.62           C  
-ATOM    347  OG  SER A 273       7.239  12.680  -7.023  1.00  0.72           O  
-ATOM    348  H   SER A 273       4.807  12.970  -6.784  1.00  0.52           H  
-ATOM    349  HA  SER A 273       5.790  15.688  -7.017  1.00  0.46           H  
-ATOM    350  HB2 SER A 273       8.028  14.556  -7.201  1.00  0.68           H  
-ATOM    351  HB3 SER A 273       7.147  14.242  -5.707  1.00  0.72           H  
-ATOM    352  HG  SER A 273       7.764  12.529  -7.816  1.00  1.02           H  
-ATOM    353  N   LEU A 274       4.998  15.201  -9.536  1.00  0.36           N  
-ATOM    354  CA  LEU A 274       5.004  15.407 -10.966  1.00  0.40           C  
-ATOM    355  C   LEU A 274       4.471  16.813 -11.293  1.00  0.36           C  
-ATOM    356  O   LEU A 274       3.582  16.954 -12.133  1.00  0.46           O  
-ATOM    357  CB  LEU A 274       4.164  14.331 -11.665  1.00  0.53           C  
-ATOM    358  CG  LEU A 274       4.303  14.279 -13.190  1.00  0.65           C  
-ATOM    359  CD1 LEU A 274       5.735  13.949 -13.585  1.00  0.72           C  
-ATOM    360  CD2 LEU A 274       3.336  13.263 -13.778  1.00  0.84           C  
-ATOM    361  H   LEU A 274       4.192  15.432  -9.018  1.00  0.35           H  
-ATOM    362  HA  LEU A 274       6.026  15.334 -11.307  1.00  0.45           H  
-ATOM    363  HB2 LEU A 274       4.447  13.368 -11.265  1.00  0.63           H  
-ATOM    364  HB3 LEU A 274       3.125  14.507 -11.428  1.00  0.57           H  
-ATOM    365  HG  LEU A 274       4.061  15.249 -13.600  1.00  0.67           H  
-ATOM    366 HD11 LEU A 274       6.401  14.700 -13.186  1.00  1.33           H  
-ATOM    367 HD12 LEU A 274       5.816  13.931 -14.662  1.00  1.24           H  
-ATOM    368 HD13 LEU A 274       6.005  12.981 -13.188  1.00  1.23           H  
-ATOM    369 HD21 LEU A 274       3.020  12.578 -13.006  1.00  1.45           H  
-ATOM    370 HD22 LEU A 274       3.827  12.714 -14.568  1.00  1.38           H  
-ATOM    371 HD23 LEU A 274       2.475  13.776 -14.180  1.00  1.24           H  
-ATOM    372  N   PRO A 275       4.978  17.879 -10.599  1.00  0.34           N  
-ATOM    373  CA  PRO A 275       4.523  19.280 -10.811  1.00  0.45           C  
-ATOM    374  C   PRO A 275       4.757  19.828 -12.238  1.00  0.51           C  
-ATOM    375  O   PRO A 275       5.410  20.857 -12.417  1.00  0.60           O  
-ATOM    376  CB  PRO A 275       5.363  20.078  -9.806  1.00  0.56           C  
-ATOM    377  CG  PRO A 275       5.710  19.087  -8.763  1.00  0.52           C  
-ATOM    378  CD  PRO A 275       5.993  17.824  -9.514  1.00  0.40           C  
-ATOM    379  HA  PRO A 275       3.481  19.388 -10.554  1.00  0.51           H  
-ATOM    380  HB2 PRO A 275       6.245  20.467 -10.296  1.00  0.62           H  
-ATOM    381  HB3 PRO A 275       4.777  20.890  -9.403  1.00  0.68           H  
-ATOM    382  HG2 PRO A 275       6.580  19.410  -8.211  1.00  0.65           H  
-ATOM    383  HG3 PRO A 275       4.866  18.948  -8.106  1.00  0.54           H  
-ATOM    384  HD2 PRO A 275       6.998  17.831  -9.906  1.00  0.49           H  
-ATOM    385  HD3 PRO A 275       5.840  16.952  -8.871  1.00  0.39           H  
-ATOM    386  N   LYS A 276       4.216  19.140 -13.240  1.00  0.57           N  
-ATOM    387  CA  LYS A 276       4.349  19.542 -14.642  1.00  0.68           C  
-ATOM    388  C   LYS A 276       3.326  18.812 -15.522  1.00  0.80           C  
-ATOM    389  O   LYS A 276       3.585  18.668 -16.742  1.00  0.94           O  
-ATOM    390  CB  LYS A 276       5.762  19.252 -15.158  1.00  0.70           C  
-ATOM    391  CG  LYS A 276       6.176  17.793 -15.024  1.00  0.73           C  
-ATOM    392  CD  LYS A 276       7.182  17.392 -16.092  1.00  0.87           C  
-ATOM    393  CE  LYS A 276       6.496  16.938 -17.378  1.00  1.01           C  
-ATOM    394  NZ  LYS A 276       5.706  18.025 -18.024  1.00  1.07           N  
-ATOM    395  OXT LYS A 276       2.283  18.374 -14.981  1.00  0.87           O  
-ATOM    396  H   LYS A 276       3.702  18.323 -13.028  1.00  0.60           H  
-ATOM    397  HA  LYS A 276       4.164  20.605 -14.701  1.00  0.75           H  
-ATOM    398  HB2 LYS A 276       5.814  19.523 -16.202  1.00  0.79           H  
-ATOM    399  HB3 LYS A 276       6.466  19.855 -14.604  1.00  0.71           H  
-ATOM    400  HG2 LYS A 276       6.621  17.643 -14.052  1.00  0.74           H  
-ATOM    401  HG3 LYS A 276       5.297  17.171 -15.118  1.00  0.79           H  
-ATOM    402  HD2 LYS A 276       7.810  18.241 -16.316  1.00  0.91           H  
-ATOM    403  HD3 LYS A 276       7.790  16.583 -15.714  1.00  0.95           H  
-ATOM    404  HE2 LYS A 276       7.252  16.599 -18.071  1.00  1.14           H  
-ATOM    405  HE3 LYS A 276       5.835  16.116 -17.143  1.00  1.10           H  
-ATOM    406  HZ1 LYS A 276       6.266  18.899 -18.068  1.00  1.34           H  
-ATOM    407  HZ2 LYS A 276       4.827  18.215 -17.474  1.00  1.52           H  
-ATOM    408  HZ3 LYS A 276       5.440  17.746 -18.989  1.00  1.34           H  
-TER
-ENDMDL
-MODEL      2
-ATOM      1  N   ALA A 249       2.985 -12.247   3.088  1.00  3.58           N  
-ATOM      2  CA  ALA A 249       3.192 -11.415   1.867  1.00  3.44           C  
-ATOM      3  C   ALA A 249       1.900 -10.700   1.455  1.00  2.78           C  
-ATOM      4  O   ALA A 249       0.807 -11.156   1.783  1.00  2.62           O  
-ATOM      5  CB  ALA A 249       3.715 -12.274   0.725  1.00  3.93           C  
-ATOM      6  H1  ALA A 249       3.513 -11.846   3.890  1.00  3.69           H  
-ATOM      7  H2  ALA A 249       3.321 -13.217   2.919  1.00  4.00           H  
-ATOM      8  H3  ALA A 249       1.972 -12.281   3.329  1.00  3.31           H  
-ATOM      9  HA  ALA A 249       3.942 -10.672   2.096  1.00  3.65           H  
-ATOM     10  HB1 ALA A 249       4.795 -12.266   0.739  1.00  4.14           H  
-ATOM     11  HB2 ALA A 249       3.366 -11.874  -0.215  1.00  4.21           H  
-ATOM     12  HB3 ALA A 249       3.356 -13.283   0.843  1.00  4.24           H  
-ATOM     13  N   PRO A 250       2.021  -9.559   0.744  1.00  2.62           N  
-ATOM     14  CA  PRO A 250       0.870  -8.765   0.292  1.00  2.15           C  
-ATOM     15  C   PRO A 250       0.142  -9.379  -0.923  1.00  1.89           C  
-ATOM     16  O   PRO A 250       0.069 -10.601  -1.062  1.00  2.59           O  
-ATOM     17  CB  PRO A 250       1.514  -7.415  -0.054  1.00  2.42           C  
-ATOM     18  CG  PRO A 250       2.919  -7.721  -0.427  1.00  2.96           C  
-ATOM     19  CD  PRO A 250       3.302  -8.957   0.333  1.00  3.13           C  
-ATOM     20  HA  PRO A 250       0.158  -8.622   1.091  1.00  2.16           H  
-ATOM     21  HB2 PRO A 250       0.988  -6.960  -0.872  1.00  2.41           H  
-ATOM     22  HB3 PRO A 250       1.479  -6.770   0.811  1.00  2.56           H  
-ATOM     23  HG2 PRO A 250       2.988  -7.896  -1.489  1.00  3.35           H  
-ATOM     24  HG3 PRO A 250       3.560  -6.896  -0.141  1.00  3.12           H  
-ATOM     25  HD2 PRO A 250       3.840  -9.629  -0.308  1.00  3.40           H  
-ATOM     26  HD3 PRO A 250       3.895  -8.695   1.194  1.00  3.54           H  
-ATOM     27  N   ALA A 251      -0.403  -8.526  -1.790  1.00  1.31           N  
-ATOM     28  CA  ALA A 251      -1.132  -8.991  -2.979  1.00  1.53           C  
-ATOM     29  C   ALA A 251      -1.037  -7.981  -4.124  1.00  1.32           C  
-ATOM     30  O   ALA A 251      -0.369  -8.220  -5.129  1.00  1.33           O  
-ATOM     31  CB  ALA A 251      -2.600  -9.242  -2.640  1.00  2.06           C  
-ATOM     32  H   ALA A 251      -0.321  -7.566  -1.623  1.00  1.26           H  
-ATOM     33  HA  ALA A 251      -0.697  -9.928  -3.299  1.00  1.86           H  
-ATOM     34  HB1 ALA A 251      -2.891 -10.221  -2.990  1.00  2.55           H  
-ATOM     35  HB2 ALA A 251      -3.212  -8.493  -3.121  1.00  2.27           H  
-ATOM     36  HB3 ALA A 251      -2.738  -9.185  -1.571  1.00  2.39           H  
-ATOM     37  N   ARG A 252      -1.704  -6.845  -3.966  1.00  1.19           N  
-ATOM     38  CA  ARG A 252      -1.683  -5.805  -4.988  1.00  1.05           C  
-ATOM     39  C   ARG A 252      -0.619  -4.759  -4.710  1.00  0.92           C  
-ATOM     40  O   ARG A 252      -0.918  -3.602  -4.412  1.00  0.85           O  
-ATOM     41  CB  ARG A 252      -3.054  -5.163  -5.146  1.00  1.10           C  
-ATOM     42  CG  ARG A 252      -3.997  -6.015  -5.976  1.00  1.27           C  
-ATOM     43  CD  ARG A 252      -3.261  -6.654  -7.155  1.00  1.27           C  
-ATOM     44  NE  ARG A 252      -4.128  -7.532  -7.948  1.00  1.46           N  
-ATOM     45  CZ  ARG A 252      -3.711  -8.266  -8.965  1.00  1.53           C  
-ATOM     46  NH1 ARG A 252      -2.458  -8.222  -9.342  1.00  1.47           N  
-ATOM     47  NH2 ARG A 252      -4.551  -9.046  -9.604  1.00  1.74           N  
-ATOM     48  H   ARG A 252      -2.215  -6.700  -3.148  1.00  1.26           H  
-ATOM     49  HA  ARG A 252      -1.431  -6.290  -5.919  1.00  1.09           H  
-ATOM     50  HB2 ARG A 252      -3.488  -5.020  -4.166  1.00  1.15           H  
-ATOM     51  HB3 ARG A 252      -2.942  -4.203  -5.629  1.00  1.03           H  
-ATOM     52  HG2 ARG A 252      -4.409  -6.793  -5.351  1.00  1.42           H  
-ATOM     53  HG3 ARG A 252      -4.793  -5.391  -6.353  1.00  1.32           H  
-ATOM     54  HD2 ARG A 252      -2.874  -5.869  -7.789  1.00  1.17           H  
-ATOM     55  HD3 ARG A 252      -2.431  -7.234  -6.766  1.00  1.28           H  
-ATOM     56  HE  ARG A 252      -5.072  -7.580  -7.698  1.00  1.59           H  
-ATOM     57 HH11 ARG A 252      -1.815  -7.632  -8.864  1.00  1.37           H  
-ATOM     58 HH12 ARG A 252      -2.149  -8.781 -10.108  1.00  1.58           H  
-ATOM     59 HH21 ARG A 252      -5.508  -9.086  -9.326  1.00  1.86           H  
-ATOM     60 HH22 ARG A 252      -4.232  -9.602 -10.370  1.00  1.83           H  
-ATOM     61  N   VAL A 253       0.623  -5.192  -4.819  1.00  0.97           N  
-ATOM     62  CA  VAL A 253       1.778  -4.331  -4.596  1.00  0.96           C  
-ATOM     63  C   VAL A 253       1.731  -3.087  -5.487  1.00  0.80           C  
-ATOM     64  O   VAL A 253       1.861  -1.963  -5.005  1.00  0.73           O  
-ATOM     65  CB  VAL A 253       3.076  -5.099  -4.881  1.00  1.16           C  
-ATOM     66  CG1 VAL A 253       4.278  -4.324  -4.377  1.00  1.33           C  
-ATOM     67  CG2 VAL A 253       3.014  -6.492  -4.267  1.00  1.34           C  
-ATOM     68  H   VAL A 253       0.769  -6.132  -5.058  1.00  1.06           H  
-ATOM     69  HA  VAL A 253       1.780  -4.028  -3.562  1.00  1.00           H  
-ATOM     70  HB  VAL A 253       3.173  -5.210  -5.950  1.00  1.16           H  
-ATOM     71 HG11 VAL A 253       4.092  -3.266  -4.488  1.00  1.70           H  
-ATOM     72 HG12 VAL A 253       5.149  -4.600  -4.949  1.00  1.74           H  
-ATOM     73 HG13 VAL A 253       4.442  -4.553  -3.334  1.00  1.70           H  
-ATOM     74 HG21 VAL A 253       3.630  -6.525  -3.382  1.00  1.76           H  
-ATOM     75 HG22 VAL A 253       3.368  -7.218  -4.983  1.00  1.73           H  
-ATOM     76 HG23 VAL A 253       1.990  -6.721  -4.001  1.00  1.69           H  
-ATOM     77  N   GLY A 254       1.527  -3.308  -6.787  1.00  0.81           N  
-ATOM     78  CA  GLY A 254       1.447  -2.208  -7.738  1.00  0.77           C  
-ATOM     79  C   GLY A 254       0.328  -1.237  -7.399  1.00  0.64           C  
-ATOM     80  O   GLY A 254       0.530  -0.020  -7.386  1.00  0.61           O  
-ATOM     81  H   GLY A 254       1.422  -4.228  -7.098  1.00  0.89           H  
-ATOM     82  HA2 GLY A 254       2.386  -1.673  -7.736  1.00  0.86           H  
-ATOM     83  HA3 GLY A 254       1.275  -2.610  -8.724  1.00  0.84           H  
-ATOM     84  N   LEU A 255      -0.849  -1.778  -7.095  1.00  0.64           N  
-ATOM     85  CA  LEU A 255      -2.001  -0.960  -6.721  1.00  0.64           C  
-ATOM     86  C   LEU A 255      -1.719  -0.182  -5.442  1.00  0.56           C  
-ATOM     87  O   LEU A 255      -2.011   1.009  -5.350  1.00  0.58           O  
-ATOM     88  CB  LEU A 255      -3.235  -1.829  -6.509  1.00  0.79           C  
-ATOM     89  CG  LEU A 255      -4.223  -1.846  -7.671  1.00  0.97           C  
-ATOM     90  CD1 LEU A 255      -5.417  -2.723  -7.333  1.00  1.15           C  
-ATOM     91  CD2 LEU A 255      -4.670  -0.430  -8.001  1.00  1.12           C  
-ATOM     92  H   LEU A 255      -0.940  -2.752  -7.102  1.00  0.71           H  
-ATOM     93  HA  LEU A 255      -2.200  -0.266  -7.523  1.00  0.71           H  
-ATOM     94  HB2 LEU A 255      -2.908  -2.842  -6.326  1.00  0.86           H  
-ATOM     95  HB3 LEU A 255      -3.753  -1.471  -5.632  1.00  0.87           H  
-ATOM     96  HG  LEU A 255      -3.737  -2.259  -8.542  1.00  1.02           H  
-ATOM     97 HD11 LEU A 255      -6.117  -2.704  -8.153  1.00  1.51           H  
-ATOM     98 HD12 LEU A 255      -5.895  -2.351  -6.439  1.00  1.58           H  
-ATOM     99 HD13 LEU A 255      -5.083  -3.737  -7.167  1.00  1.62           H  
-ATOM    100 HD21 LEU A 255      -4.707  -0.306  -9.072  1.00  1.29           H  
-ATOM    101 HD22 LEU A 255      -3.967   0.275  -7.581  1.00  1.11           H  
-ATOM    102 HD23 LEU A 255      -5.648  -0.254  -7.582  1.00  1.28           H  
-ATOM    103  N   GLY A 256      -1.147  -0.868  -4.457  1.00  0.57           N  
-ATOM    104  CA  GLY A 256      -0.831  -0.238  -3.197  1.00  0.58           C  
-ATOM    105  C   GLY A 256       0.171   0.891  -3.339  1.00  0.50           C  
-ATOM    106  O   GLY A 256      -0.075   1.996  -2.861  1.00  0.51           O  
-ATOM    107  H   GLY A 256      -0.939  -1.820  -4.590  1.00  0.64           H  
-ATOM    108  HA2 GLY A 256      -1.741   0.158  -2.770  1.00  0.64           H  
-ATOM    109  HA3 GLY A 256      -0.427  -0.982  -2.532  1.00  0.69           H  
-ATOM    110  N   ILE A 257       1.296   0.631  -4.010  1.00  0.55           N  
-ATOM    111  CA  ILE A 257       2.309   1.669  -4.200  1.00  0.62           C  
-ATOM    112  C   ILE A 257       1.755   2.817  -5.051  1.00  0.58           C  
-ATOM    113  O   ILE A 257       2.047   3.984  -4.796  1.00  0.66           O  
-ATOM    114  CB  ILE A 257       3.633   1.124  -4.797  1.00  0.81           C  
-ATOM    115  CG1 ILE A 257       3.435   0.517  -6.191  1.00  0.84           C  
-ATOM    116  CG2 ILE A 257       4.244   0.105  -3.849  1.00  0.94           C  
-ATOM    117  CD1 ILE A 257       3.737   1.478  -7.319  1.00  0.96           C  
-ATOM    118  H   ILE A 257       1.442  -0.270  -4.384  1.00  0.60           H  
-ATOM    119  HA  ILE A 257       2.533   2.067  -3.217  1.00  0.66           H  
-ATOM    120  HB  ILE A 257       4.324   1.948  -4.874  1.00  0.89           H  
-ATOM    121 HG12 ILE A 257       4.082  -0.339  -6.306  1.00  0.98           H  
-ATOM    122 HG13 ILE A 257       2.408   0.201  -6.296  1.00  0.75           H  
-ATOM    123 HG21 ILE A 257       4.423  -0.820  -4.377  1.00  1.44           H  
-ATOM    124 HG22 ILE A 257       3.565  -0.075  -3.029  1.00  1.38           H  
-ATOM    125 HG23 ILE A 257       5.179   0.487  -3.463  1.00  1.44           H  
-ATOM    126 HD11 ILE A 257       3.038   2.303  -7.287  1.00  1.42           H  
-ATOM    127 HD12 ILE A 257       3.647   0.962  -8.262  1.00  1.45           H  
-ATOM    128 HD13 ILE A 257       4.740   1.854  -7.212  1.00  1.41           H  
-ATOM    129  N   THR A 258       0.923   2.481  -6.040  1.00  0.56           N  
-ATOM    130  CA  THR A 258       0.302   3.491  -6.894  1.00  0.68           C  
-ATOM    131  C   THR A 258      -0.662   4.346  -6.066  1.00  0.67           C  
-ATOM    132  O   THR A 258      -0.694   5.569  -6.195  1.00  0.78           O  
-ATOM    133  CB  THR A 258      -0.475   2.859  -8.072  1.00  0.78           C  
-ATOM    134  OG1 THR A 258       0.404   2.062  -8.876  1.00  0.84           O  
-ATOM    135  CG2 THR A 258      -1.126   3.928  -8.939  1.00  1.01           C  
-ATOM    136  H   THR A 258       0.704   1.531  -6.182  1.00  0.53           H  
-ATOM    137  HA  THR A 258       1.084   4.121  -7.294  1.00  0.80           H  
-ATOM    138  HB  THR A 258      -1.253   2.226  -7.667  1.00  0.74           H  
-ATOM    139  HG1 THR A 258       0.519   1.193  -8.464  1.00  0.76           H  
-ATOM    140 HG21 THR A 258      -0.876   3.753  -9.974  1.00  1.46           H  
-ATOM    141 HG22 THR A 258      -0.765   4.904  -8.640  1.00  1.48           H  
-ATOM    142 HG23 THR A 258      -2.198   3.890  -8.817  1.00  1.50           H  
-ATOM    143  N   THR A 259      -1.428   3.683  -5.191  1.00  0.61           N  
-ATOM    144  CA  THR A 259      -2.374   4.371  -4.311  1.00  0.72           C  
-ATOM    145  C   THR A 259      -1.622   5.288  -3.351  1.00  0.69           C  
-ATOM    146  O   THR A 259      -1.947   6.469  -3.224  1.00  0.82           O  
-ATOM    147  CB  THR A 259      -3.231   3.376  -3.493  1.00  0.79           C  
-ATOM    148  OG1 THR A 259      -3.925   2.474  -4.369  1.00  0.86           O  
-ATOM    149  CG2 THR A 259      -4.243   4.108  -2.623  1.00  0.99           C  
-ATOM    150  H   THR A 259      -1.337   2.705  -5.121  1.00  0.56           H  
-ATOM    151  HA  THR A 259      -3.033   4.968  -4.927  1.00  0.86           H  
-ATOM    152  HB  THR A 259      -2.577   2.804  -2.850  1.00  0.70           H  
-ATOM    153  HG1 THR A 259      -3.284   1.903  -4.817  1.00  0.78           H  
-ATOM    154 HG21 THR A 259      -3.751   4.907  -2.091  1.00  1.43           H  
-ATOM    155 HG22 THR A 259      -4.675   3.416  -1.912  1.00  1.42           H  
-ATOM    156 HG23 THR A 259      -5.025   4.516  -3.246  1.00  1.51           H  
-ATOM    157  N   VAL A 260      -0.591   4.743  -2.705  1.00  0.59           N  
-ATOM    158  CA  VAL A 260       0.233   5.519  -1.781  1.00  0.63           C  
-ATOM    159  C   VAL A 260       0.882   6.697  -2.507  1.00  0.67           C  
-ATOM    160  O   VAL A 260       0.800   7.833  -2.043  1.00  0.76           O  
-ATOM    161  CB  VAL A 260       1.331   4.650  -1.128  1.00  0.67           C  
-ATOM    162  CG1 VAL A 260       2.257   5.502  -0.271  1.00  0.81           C  
-ATOM    163  CG2 VAL A 260       0.714   3.539  -0.294  1.00  0.76           C  
-ATOM    164  H   VAL A 260      -0.370   3.795  -2.868  1.00  0.54           H  
-ATOM    165  HA  VAL A 260      -0.412   5.900  -1.001  1.00  0.74           H  
-ATOM    166  HB  VAL A 260       1.918   4.200  -1.914  1.00  0.68           H  
-ATOM    167 HG11 VAL A 260       1.889   5.524   0.743  1.00  1.31           H  
-ATOM    168 HG12 VAL A 260       2.288   6.508  -0.667  1.00  1.35           H  
-ATOM    169 HG13 VAL A 260       3.250   5.078  -0.288  1.00  1.33           H  
-ATOM    170 HG21 VAL A 260       1.136   2.590  -0.593  1.00  1.28           H  
-ATOM    171 HG22 VAL A 260      -0.354   3.522  -0.448  1.00  1.30           H  
-ATOM    172 HG23 VAL A 260       0.926   3.712   0.750  1.00  1.29           H  
-ATOM    173  N   LEU A 261       1.500   6.421  -3.662  1.00  0.68           N  
-ATOM    174  CA  LEU A 261       2.143   7.461  -4.471  1.00  0.83           C  
-ATOM    175  C   LEU A 261       1.126   8.550  -4.820  1.00  0.93           C  
-ATOM    176  O   LEU A 261       1.418   9.741  -4.734  1.00  1.05           O  
-ATOM    177  CB  LEU A 261       2.718   6.849  -5.757  1.00  0.92           C  
-ATOM    178  CG  LEU A 261       4.091   7.376  -6.197  1.00  0.89           C  
-ATOM    179  CD1 LEU A 261       4.026   8.853  -6.553  1.00  0.97           C  
-ATOM    180  CD2 LEU A 261       5.126   7.132  -5.109  1.00  1.30           C  
-ATOM    181  H   LEU A 261       1.514   5.490  -3.988  1.00  0.64           H  
-ATOM    182  HA  LEU A 261       2.943   7.895  -3.891  1.00  0.89           H  
-ATOM    183  HB2 LEU A 261       2.800   5.782  -5.613  1.00  1.20           H  
-ATOM    184  HB3 LEU A 261       2.018   7.032  -6.557  1.00  1.01           H  
-ATOM    185  HG  LEU A 261       4.403   6.837  -7.079  1.00  1.01           H  
-ATOM    186 HD11 LEU A 261       4.627   9.418  -5.856  1.00  1.48           H  
-ATOM    187 HD12 LEU A 261       3.002   9.191  -6.502  1.00  1.47           H  
-ATOM    188 HD13 LEU A 261       4.404   8.998  -7.554  1.00  1.45           H  
-ATOM    189 HD21 LEU A 261       5.403   6.088  -5.105  1.00  1.73           H  
-ATOM    190 HD22 LEU A 261       4.708   7.399  -4.150  1.00  1.57           H  
-ATOM    191 HD23 LEU A 261       6.000   7.736  -5.303  1.00  1.75           H  
-ATOM    192  N   THR A 262      -0.079   8.120  -5.187  1.00  0.93           N  
-ATOM    193  CA  THR A 262      -1.168   9.039  -5.522  1.00  1.10           C  
-ATOM    194  C   THR A 262      -1.529   9.912  -4.316  1.00  1.13           C  
-ATOM    195  O   THR A 262      -1.655  11.131  -4.434  1.00  1.25           O  
-ATOM    196  CB  THR A 262      -2.423   8.266  -5.984  1.00  1.16           C  
-ATOM    197  OG1 THR A 262      -2.134   7.528  -7.180  1.00  1.20           O  
-ATOM    198  CG2 THR A 262      -3.591   9.206  -6.238  1.00  1.33           C  
-ATOM    199  H   THR A 262      -0.248   7.150  -5.218  1.00  0.84           H  
-ATOM    200  HA  THR A 262      -0.836   9.673  -6.332  1.00  1.22           H  
-ATOM    201  HB  THR A 262      -2.705   7.572  -5.205  1.00  1.10           H  
-ATOM    202  HG1 THR A 262      -1.597   6.753  -6.954  1.00  1.06           H  
-ATOM    203 HG21 THR A 262      -4.128   9.369  -5.314  1.00  1.71           H  
-ATOM    204 HG22 THR A 262      -4.255   8.765  -6.967  1.00  1.72           H  
-ATOM    205 HG23 THR A 262      -3.220  10.149  -6.610  1.00  1.69           H  
-ATOM    206  N   MET A 263      -1.684   9.272  -3.157  1.00  1.04           N  
-ATOM    207  CA  MET A 263      -2.021   9.974  -1.915  1.00  1.11           C  
-ATOM    208  C   MET A 263      -0.924  10.969  -1.510  1.00  1.10           C  
-ATOM    209  O   MET A 263      -1.195  12.152  -1.315  1.00  1.23           O  
-ATOM    210  CB  MET A 263      -2.246   8.969  -0.784  1.00  1.09           C  
-ATOM    211  CG  MET A 263      -3.440   8.056  -1.004  1.00  1.19           C  
-ATOM    212  SD  MET A 263      -3.588   6.786   0.269  1.00  1.30           S  
-ATOM    213  CE  MET A 263      -3.811   7.794   1.733  1.00  1.51           C  
-ATOM    214  H   MET A 263      -1.567   8.295  -3.138  1.00  0.96           H  
-ATOM    215  HA  MET A 263      -2.937  10.519  -2.084  1.00  1.26           H  
-ATOM    216  HB2 MET A 263      -1.363   8.354  -0.684  1.00  0.99           H  
-ATOM    217  HB3 MET A 263      -2.400   9.512   0.137  1.00  1.20           H  
-ATOM    218  HG2 MET A 263      -4.339   8.654  -1.000  1.00  1.36           H  
-ATOM    219  HG3 MET A 263      -3.334   7.572  -1.964  1.00  1.15           H  
-ATOM    220  HE1 MET A 263      -4.373   8.680   1.477  1.00  1.81           H  
-ATOM    221  HE2 MET A 263      -2.845   8.081   2.122  1.00  1.94           H  
-ATOM    222  HE3 MET A 263      -4.348   7.230   2.481  1.00  1.84           H  
-ATOM    223  N   THR A 264       0.318  10.493  -1.397  1.00  0.99           N  
-ATOM    224  CA  THR A 264       1.439  11.370  -1.027  1.00  1.03           C  
-ATOM    225  C   THR A 264       2.044  12.055  -2.266  1.00  1.05           C  
-ATOM    226  O   THR A 264       3.264  12.106  -2.443  1.00  1.11           O  
-ATOM    227  CB  THR A 264       2.546  10.613  -0.240  1.00  1.05           C  
-ATOM    228  OG1 THR A 264       3.602  11.513   0.118  1.00  1.15           O  
-ATOM    229  CG2 THR A 264       3.122   9.452  -1.041  1.00  1.00           C  
-ATOM    230  H   THR A 264       0.488   9.536  -1.576  1.00  0.93           H  
-ATOM    231  HA  THR A 264       1.042  12.141  -0.379  1.00  1.12           H  
-ATOM    232  HB  THR A 264       2.106  10.218   0.666  1.00  1.11           H  
-ATOM    233  HG1 THR A 264       4.007  11.864  -0.684  1.00  1.16           H  
-ATOM    234 HG21 THR A 264       3.116   8.557  -0.436  1.00  1.40           H  
-ATOM    235 HG22 THR A 264       4.136   9.683  -1.332  1.00  1.41           H  
-ATOM    236 HG23 THR A 264       2.521   9.292  -1.925  1.00  1.45           H  
-ATOM    237  N   THR A 265       1.171  12.585  -3.122  1.00  1.11           N  
-ATOM    238  CA  THR A 265       1.586  13.265  -4.356  1.00  1.22           C  
-ATOM    239  C   THR A 265       2.187  14.660  -4.090  1.00  1.09           C  
-ATOM    240  O   THR A 265       1.897  15.626  -4.796  1.00  1.22           O  
-ATOM    241  CB  THR A 265       0.394  13.385  -5.337  1.00  1.39           C  
-ATOM    242  OG1 THR A 265       0.808  13.987  -6.573  1.00  1.55           O  
-ATOM    243  CG2 THR A 265      -0.748  14.192  -4.726  1.00  1.38           C  
-ATOM    244  H   THR A 265       0.210  12.511  -2.920  1.00  1.13           H  
-ATOM    245  HA  THR A 265       2.342  12.652  -4.827  1.00  1.33           H  
-ATOM    246  HB  THR A 265       0.028  12.389  -5.544  1.00  1.47           H  
-ATOM    247  HG1 THR A 265       1.308  14.798  -6.386  1.00  1.53           H  
-ATOM    248 HG21 THR A 265      -0.353  15.089  -4.273  1.00  1.68           H  
-ATOM    249 HG22 THR A 265      -1.246  13.598  -3.972  1.00  1.70           H  
-ATOM    250 HG23 THR A 265      -1.454  14.459  -5.498  1.00  1.78           H  
-ATOM    251  N   GLN A 266       3.041  14.755  -3.080  1.00  0.93           N  
-ATOM    252  CA  GLN A 266       3.690  16.019  -2.732  1.00  0.87           C  
-ATOM    253  C   GLN A 266       5.093  16.112  -3.349  1.00  0.94           C  
-ATOM    254  O   GLN A 266       6.093  16.152  -2.635  1.00  1.06           O  
-ATOM    255  CB  GLN A 266       3.767  16.172  -1.206  1.00  0.95           C  
-ATOM    256  CG  GLN A 266       2.419  16.422  -0.544  1.00  1.03           C  
-ATOM    257  CD  GLN A 266       1.827  17.771  -0.909  1.00  1.03           C  
-ATOM    258  OE1 GLN A 266       2.395  18.815  -0.610  1.00  1.10           O  
-ATOM    259  NE2 GLN A 266       0.682  17.758  -1.571  1.00  1.24           N  
-ATOM    260  H   GLN A 266       3.252  13.946  -2.559  1.00  0.93           H  
-ATOM    261  HA  GLN A 266       3.085  16.818  -3.130  1.00  0.80           H  
-ATOM    262  HB2 GLN A 266       4.185  15.269  -0.786  1.00  1.16           H  
-ATOM    263  HB3 GLN A 266       4.417  17.001  -0.971  1.00  0.94           H  
-ATOM    264  HG2 GLN A 266       1.730  15.649  -0.851  1.00  1.20           H  
-ATOM    265  HG3 GLN A 266       2.546  16.382   0.528  1.00  1.16           H  
-ATOM    266 HE21 GLN A 266       0.278  16.894  -1.787  1.00  1.34           H  
-ATOM    267 HE22 GLN A 266       0.284  18.620  -1.808  1.00  1.41           H  
-ATOM    268  N   SER A 267       5.165  16.140  -4.678  1.00  1.19           N  
-ATOM    269  CA  SER A 267       6.461  16.223  -5.366  1.00  1.43           C  
-ATOM    270  C   SER A 267       6.314  16.773  -6.785  1.00  1.43           C  
-ATOM    271  O   SER A 267       5.480  16.308  -7.564  1.00  1.44           O  
-ATOM    272  CB  SER A 267       7.135  14.844  -5.417  1.00  1.68           C  
-ATOM    273  OG  SER A 267       8.420  14.924  -6.017  1.00  1.93           O  
-ATOM    274  H   SER A 267       4.338  16.102  -5.207  1.00  1.35           H  
-ATOM    275  HA  SER A 267       7.089  16.895  -4.800  1.00  1.52           H  
-ATOM    276  HB2 SER A 267       7.244  14.460  -4.415  1.00  1.75           H  
-ATOM    277  HB3 SER A 267       6.523  14.168  -5.997  1.00  1.65           H  
-ATOM    278  HG  SER A 267       9.094  14.709  -5.365  1.00  2.14           H  
-ATOM    279  N   SER A 268       7.139  17.763  -7.118  1.00  1.60           N  
-ATOM    280  CA  SER A 268       7.115  18.371  -8.448  1.00  1.69           C  
-ATOM    281  C   SER A 268       8.107  17.668  -9.380  1.00  1.99           C  
-ATOM    282  O   SER A 268       9.307  17.631  -9.108  1.00  2.31           O  
-ATOM    283  CB  SER A 268       7.446  19.869  -8.367  1.00  1.93           C  
-ATOM    284  OG  SER A 268       6.452  20.582  -7.644  1.00  1.79           O  
-ATOM    285  H   SER A 268       7.786  18.085  -6.457  1.00  1.77           H  
-ATOM    286  HA  SER A 268       6.119  18.252  -8.848  1.00  1.45           H  
-ATOM    287  HB2 SER A 268       8.393  19.999  -7.866  1.00  2.18           H  
-ATOM    288  HB3 SER A 268       7.509  20.276  -9.365  1.00  2.07           H  
-ATOM    289  HG  SER A 268       6.732  21.495  -7.525  1.00  2.04           H  
-ATOM    290  N   GLY A 269       7.601  17.110 -10.479  1.00  1.92           N  
-ATOM    291  CA  GLY A 269       8.464  16.414 -11.425  1.00  2.23           C  
-ATOM    292  C   GLY A 269       7.693  15.782 -12.574  1.00  2.14           C  
-ATOM    293  O   GLY A 269       6.829  16.423 -13.170  1.00  2.00           O  
-ATOM    294  H   GLY A 269       6.637  17.166 -10.646  1.00  1.69           H  
-ATOM    295  HA2 GLY A 269       9.175  17.118 -11.831  1.00  2.45           H  
-ATOM    296  HA3 GLY A 269       9.002  15.639 -10.899  1.00  2.35           H  
-ATOM    297  N   SER A 270       8.008  14.523 -12.890  1.00  2.28           N  
-ATOM    298  CA  SER A 270       7.333  13.809 -13.983  1.00  2.29           C  
-ATOM    299  C   SER A 270       5.832  13.660 -13.711  1.00  1.96           C  
-ATOM    300  O   SER A 270       5.012  13.825 -14.612  1.00  1.89           O  
-ATOM    301  CB  SER A 270       7.965  12.428 -14.199  1.00  2.61           C  
-ATOM    302  OG  SER A 270       9.359  12.534 -14.439  1.00  2.93           O  
-ATOM    303  H   SER A 270       8.710  14.065 -12.383  1.00  2.44           H  
-ATOM    304  HA  SER A 270       7.462  14.394 -14.881  1.00  2.35           H  
-ATOM    305  HB2 SER A 270       7.810  11.821 -13.320  1.00  2.58           H  
-ATOM    306  HB3 SER A 270       7.501  11.954 -15.051  1.00  2.67           H  
-ATOM    307  HG  SER A 270       9.557  12.227 -15.328  1.00  3.17           H  
-ATOM    308  N   ARG A 271       5.475  13.365 -12.462  1.00  1.81           N  
-ATOM    309  CA  ARG A 271       4.069  13.224 -12.085  1.00  1.54           C  
-ATOM    310  C   ARG A 271       3.413  14.597 -11.915  1.00  1.20           C  
-ATOM    311  O   ARG A 271       3.916  15.451 -11.183  1.00  1.14           O  
-ATOM    312  CB  ARG A 271       3.930  12.428 -10.781  1.00  1.60           C  
-ATOM    313  CG  ARG A 271       2.496  12.356 -10.269  1.00  1.45           C  
-ATOM    314  CD  ARG A 271       2.421  11.761  -8.870  1.00  1.56           C  
-ATOM    315  NE  ARG A 271       1.105  11.948  -8.260  1.00  1.51           N  
-ATOM    316  CZ  ARG A 271       0.028  11.256  -8.567  1.00  1.63           C  
-ATOM    317  NH1 ARG A 271       0.079  10.304  -9.465  1.00  1.80           N  
-ATOM    318  NH2 ARG A 271      -1.103  11.523  -7.972  1.00  1.65           N  
-ATOM    319  H   ARG A 271       6.168  13.257 -11.778  1.00  1.91           H  
-ATOM    320  HA  ARG A 271       3.565  12.692 -12.876  1.00  1.63           H  
-ATOM    321  HB2 ARG A 271       4.282  11.421 -10.946  1.00  1.88           H  
-ATOM    322  HB3 ARG A 271       4.539  12.894 -10.020  1.00  1.56           H  
-ATOM    323  HG2 ARG A 271       2.084  13.353 -10.244  1.00  1.21           H  
-ATOM    324  HG3 ARG A 271       1.916  11.744 -10.943  1.00  1.59           H  
-ATOM    325  HD2 ARG A 271       2.631  10.703  -8.932  1.00  1.80           H  
-ATOM    326  HD3 ARG A 271       3.166  12.238  -8.250  1.00  1.53           H  
-ATOM    327  HE  ARG A 271       1.020  12.662  -7.577  1.00  1.44           H  
-ATOM    328 HH11 ARG A 271       0.938  10.102  -9.926  1.00  1.86           H  
-ATOM    329 HH12 ARG A 271      -0.742   9.777  -9.679  1.00  1.92           H  
-ATOM    330 HH21 ARG A 271      -1.143  12.249  -7.290  1.00  1.60           H  
-ATOM    331 HH22 ARG A 271      -1.922  11.000  -8.197  1.00  1.77           H  
-ATOM    332  N   ALA A 272       2.288  14.800 -12.589  1.00  1.09           N  
-ATOM    333  CA  ALA A 272       1.562  16.063 -12.508  1.00  0.89           C  
-ATOM    334  C   ALA A 272       0.851  16.210 -11.162  1.00  0.66           C  
-ATOM    335  O   ALA A 272      -0.326  15.864 -11.030  1.00  0.79           O  
-ATOM    336  CB  ALA A 272       0.561  16.169 -13.649  1.00  1.02           C  
-ATOM    337  H   ALA A 272       1.934  14.082 -13.152  1.00  1.24           H  
-ATOM    338  HA  ALA A 272       2.277  16.866 -12.613  1.00  0.99           H  
-ATOM    339  HB1 ALA A 272      -0.351  16.619 -13.287  1.00  1.44           H  
-ATOM    340  HB2 ALA A 272       0.348  15.183 -14.033  1.00  1.54           H  
-ATOM    341  HB3 ALA A 272       0.977  16.781 -14.436  1.00  1.44           H  
-ATOM    342  N   SER A 273       1.569  16.719 -10.160  1.00  0.69           N  
-ATOM    343  CA  SER A 273       0.996  16.907  -8.820  1.00  0.54           C  
-ATOM    344  C   SER A 273       0.031  18.082  -8.769  1.00  0.77           C  
-ATOM    345  O   SER A 273       0.212  19.022  -7.995  1.00  1.09           O  
-ATOM    346  CB  SER A 273       2.078  17.093  -7.758  1.00  0.73           C  
-ATOM    347  OG  SER A 273       2.882  15.931  -7.624  1.00  1.41           O  
-ATOM    348  H   SER A 273       2.504  16.970 -10.322  1.00  0.99           H  
-ATOM    349  HA  SER A 273       0.430  16.018  -8.579  1.00  0.58           H  
-ATOM    350  HB2 SER A 273       2.707  17.925  -8.031  1.00  1.13           H  
-ATOM    351  HB3 SER A 273       1.603  17.297  -6.808  1.00  0.97           H  
-ATOM    352  HG  SER A 273       3.821  16.175  -7.677  1.00  1.52           H  
-ATOM    353  N   LEU A 274      -1.024  17.991  -9.561  1.00  0.89           N  
-ATOM    354  CA  LEU A 274      -2.056  19.011  -9.566  1.00  1.35           C  
-ATOM    355  C   LEU A 274      -3.448  18.393  -9.290  1.00  1.63           C  
-ATOM    356  O   LEU A 274      -4.427  18.760  -9.939  1.00  1.96           O  
-ATOM    357  CB  LEU A 274      -2.052  19.756 -10.907  1.00  1.52           C  
-ATOM    358  CG  LEU A 274      -2.865  21.054 -10.936  1.00  2.05           C  
-ATOM    359  CD1 LEU A 274      -2.301  22.065  -9.950  1.00  2.31           C  
-ATOM    360  CD2 LEU A 274      -2.886  21.636 -12.340  1.00  2.24           C  
-ATOM    361  H   LEU A 274      -1.129  17.191 -10.128  1.00  0.80           H  
-ATOM    362  HA  LEU A 274      -1.827  19.713  -8.778  1.00  1.53           H  
-ATOM    363  HB2 LEU A 274      -1.029  19.990 -11.163  1.00  1.46           H  
-ATOM    364  HB3 LEU A 274      -2.450  19.094 -11.663  1.00  1.44           H  
-ATOM    365  HG  LEU A 274      -3.883  20.839 -10.647  1.00  2.24           H  
-ATOM    366 HD11 LEU A 274      -2.187  23.021 -10.442  1.00  2.56           H  
-ATOM    367 HD12 LEU A 274      -1.339  21.725  -9.597  1.00  2.61           H  
-ATOM    368 HD13 LEU A 274      -2.977  22.168  -9.114  1.00  2.61           H  
-ATOM    369 HD21 LEU A 274      -3.166  22.679 -12.291  1.00  2.49           H  
-ATOM    370 HD22 LEU A 274      -3.603  21.098 -12.941  1.00  2.55           H  
-ATOM    371 HD23 LEU A 274      -1.905  21.546 -12.782  1.00  2.49           H  
-ATOM    372  N   PRO A 275      -3.572  17.431  -8.323  1.00  1.64           N  
-ATOM    373  CA  PRO A 275      -4.869  16.792  -7.999  1.00  2.09           C  
-ATOM    374  C   PRO A 275      -5.915  17.797  -7.511  1.00  2.53           C  
-ATOM    375  O   PRO A 275      -7.116  17.622  -7.729  1.00  2.89           O  
-ATOM    376  CB  PRO A 275      -4.512  15.811  -6.877  1.00  2.10           C  
-ATOM    377  CG  PRO A 275      -3.074  15.540  -7.097  1.00  1.63           C  
-ATOM    378  CD  PRO A 275      -2.498  16.870  -7.474  1.00  1.31           C  
-ATOM    379  HA  PRO A 275      -5.255  16.244  -8.842  1.00  2.17           H  
-ATOM    380  HB2 PRO A 275      -4.690  16.272  -5.915  1.00  2.22           H  
-ATOM    381  HB3 PRO A 275      -5.104  14.913  -6.971  1.00  2.38           H  
-ATOM    382  HG2 PRO A 275      -2.617  15.164  -6.192  1.00  1.63           H  
-ATOM    383  HG3 PRO A 275      -2.960  14.839  -7.908  1.00  1.64           H  
-ATOM    384  HD2 PRO A 275      -2.329  17.474  -6.597  1.00  1.36           H  
-ATOM    385  HD3 PRO A 275      -1.573  16.747  -8.037  1.00  0.97           H  
-ATOM    386  N   LYS A 276      -5.445  18.853  -6.850  1.00  2.58           N  
-ATOM    387  CA  LYS A 276      -6.317  19.894  -6.332  1.00  3.06           C  
-ATOM    388  C   LYS A 276      -5.544  21.198  -6.099  1.00  3.25           C  
-ATOM    389  O   LYS A 276      -4.312  21.129  -5.893  1.00  3.07           O  
-ATOM    390  CB  LYS A 276      -6.982  19.438  -5.029  1.00  3.29           C  
-ATOM    391  CG  LYS A 276      -8.100  20.354  -4.554  1.00  3.81           C  
-ATOM    392  CD  LYS A 276      -9.332  20.252  -5.444  1.00  4.19           C  
-ATOM    393  CE  LYS A 276      -9.160  20.974  -6.776  1.00  4.29           C  
-ATOM    394  NZ  LYS A 276      -8.703  22.380  -6.596  1.00  4.42           N  
-ATOM    395  OXT LYS A 276      -6.185  22.279  -6.133  1.00  3.77           O  
-ATOM    396  H   LYS A 276      -4.481  18.938  -6.714  1.00  2.34           H  
-ATOM    397  HA  LYS A 276      -7.083  20.076  -7.070  1.00  3.34           H  
-ATOM    398  HB2 LYS A 276      -7.398  18.453  -5.180  1.00  3.36           H  
-ATOM    399  HB3 LYS A 276      -6.233  19.386  -4.253  1.00  3.19           H  
-ATOM    400  HG2 LYS A 276      -8.373  20.079  -3.546  1.00  4.00           H  
-ATOM    401  HG3 LYS A 276      -7.743  21.375  -4.564  1.00  3.88           H  
-ATOM    402  HD2 LYS A 276      -9.518  19.208  -5.646  1.00  4.20           H  
-ATOM    403  HD3 LYS A 276     -10.174  20.675  -4.922  1.00  4.56           H  
-ATOM    404  HE2 LYS A 276      -8.428  20.441  -7.366  1.00  4.02           H  
-ATOM    405  HE3 LYS A 276     -10.107  20.974  -7.294  1.00  4.71           H  
-ATOM    406  HZ1 LYS A 276      -7.661  22.403  -6.431  1.00  4.15           H  
-ATOM    407  HZ2 LYS A 276      -9.180  22.809  -5.780  1.00  4.60           H  
-ATOM    408  HZ3 LYS A 276      -8.919  22.938  -7.445  1.00  4.75           H  
-TER
-ENDMDL
-MODEL      3
-ATOM      1  N   ALA A 249       1.538 -11.872 -11.252  1.00  1.98           N  
-ATOM      2  CA  ALA A 249       2.154 -10.524 -11.138  1.00  1.52           C  
-ATOM      3  C   ALA A 249       1.771  -9.857  -9.816  1.00  1.10           C  
-ATOM      4  O   ALA A 249       0.736 -10.173  -9.230  1.00  1.28           O  
-ATOM      5  CB  ALA A 249       1.749  -9.647 -12.314  1.00  1.84           C  
-ATOM      6  H1  ALA A 249       0.866 -12.025 -10.470  1.00  2.21           H  
-ATOM      7  H2  ALA A 249       2.274 -12.605 -11.209  1.00  2.24           H  
-ATOM      8  H3  ALA A 249       1.029 -11.958 -12.154  1.00  2.27           H  
-ATOM      9  HA  ALA A 249       3.228 -10.642 -11.165  1.00  1.44           H  
-ATOM     10  HB1 ALA A 249       2.065  -8.629 -12.126  1.00  2.26           H  
-ATOM     11  HB2 ALA A 249       0.678  -9.675 -12.433  1.00  2.12           H  
-ATOM     12  HB3 ALA A 249       2.225 -10.010 -13.213  1.00  2.20           H  
-ATOM     13  N   PRO A 250       2.611  -8.931  -9.325  1.00  0.77           N  
-ATOM     14  CA  PRO A 250       2.379  -8.214  -8.064  1.00  0.67           C  
-ATOM     15  C   PRO A 250       1.294  -7.129  -8.171  1.00  0.54           C  
-ATOM     16  O   PRO A 250       1.555  -5.946  -7.938  1.00  0.61           O  
-ATOM     17  CB  PRO A 250       3.751  -7.594  -7.791  1.00  1.01           C  
-ATOM     18  CG  PRO A 250       4.307  -7.336  -9.138  1.00  1.24           C  
-ATOM     19  CD  PRO A 250       3.869  -8.504  -9.967  1.00  1.02           C  
-ATOM     20  HA  PRO A 250       2.126  -8.895  -7.264  1.00  0.90           H  
-ATOM     21  HB2 PRO A 250       3.637  -6.683  -7.231  1.00  1.30           H  
-ATOM     22  HB3 PRO A 250       4.364  -8.295  -7.246  1.00  1.26           H  
-ATOM     23  HG2 PRO A 250       3.902  -6.416  -9.535  1.00  1.68           H  
-ATOM     24  HG3 PRO A 250       5.384  -7.289  -9.091  1.00  1.55           H  
-ATOM     25  HD2 PRO A 250       3.694  -8.196 -10.985  1.00  1.31           H  
-ATOM     26  HD3 PRO A 250       4.604  -9.290  -9.926  1.00  1.11           H  
-ATOM     27  N   ALA A 251       0.074  -7.539  -8.529  1.00  0.51           N  
-ATOM     28  CA  ALA A 251      -1.049  -6.606  -8.676  1.00  0.55           C  
-ATOM     29  C   ALA A 251      -1.336  -5.848  -7.377  1.00  0.48           C  
-ATOM     30  O   ALA A 251      -1.529  -4.631  -7.388  1.00  0.50           O  
-ATOM     31  CB  ALA A 251      -2.293  -7.346  -9.145  1.00  0.73           C  
-ATOM     32  H   ALA A 251      -0.073  -8.497  -8.706  1.00  0.59           H  
-ATOM     33  HA  ALA A 251      -0.781  -5.890  -9.440  1.00  0.63           H  
-ATOM     34  HB1 ALA A 251      -2.397  -8.266  -8.587  1.00  1.40           H  
-ATOM     35  HB2 ALA A 251      -2.203  -7.572 -10.197  1.00  1.25           H  
-ATOM     36  HB3 ALA A 251      -3.163  -6.727  -8.983  1.00  1.14           H  
-ATOM     37  N   ARG A 252      -1.347  -6.572  -6.256  1.00  0.52           N  
-ATOM     38  CA  ARG A 252      -1.594  -5.965  -4.946  1.00  0.59           C  
-ATOM     39  C   ARG A 252      -0.523  -4.912  -4.644  1.00  0.52           C  
-ATOM     40  O   ARG A 252      -0.825  -3.806  -4.188  1.00  0.56           O  
-ATOM     41  CB  ARG A 252      -1.586  -7.047  -3.856  1.00  0.74           C  
-ATOM     42  CG  ARG A 252      -2.633  -6.846  -2.765  1.00  0.88           C  
-ATOM     43  CD  ARG A 252      -2.566  -5.453  -2.159  1.00  0.92           C  
-ATOM     44  NE  ARG A 252      -3.531  -5.272  -1.081  1.00  1.14           N  
-ATOM     45  CZ  ARG A 252      -3.834  -4.103  -0.560  1.00  1.38           C  
-ATOM     46  NH1 ARG A 252      -3.307  -3.006  -1.044  1.00  1.84           N  
-ATOM     47  NH2 ARG A 252      -4.677  -4.031   0.434  1.00  1.57           N  
-ATOM     48  H   ARG A 252      -1.178  -7.534  -6.312  1.00  0.58           H  
-ATOM     49  HA  ARG A 252      -2.561  -5.490  -4.971  1.00  0.64           H  
-ATOM     50  HB2 ARG A 252      -1.762  -8.007  -4.318  1.00  0.86           H  
-ATOM     51  HB3 ARG A 252      -0.612  -7.059  -3.388  1.00  0.78           H  
-ATOM     52  HG2 ARG A 252      -3.613  -6.993  -3.192  1.00  0.99           H  
-ATOM     53  HG3 ARG A 252      -2.466  -7.576  -1.986  1.00  1.01           H  
-ATOM     54  HD2 ARG A 252      -1.572  -5.293  -1.769  1.00  0.98           H  
-ATOM     55  HD3 ARG A 252      -2.766  -4.729  -2.934  1.00  0.91           H  
-ATOM     56  HE  ARG A 252      -3.962  -6.071  -0.718  1.00  1.46           H  
-ATOM     57 HH11 ARG A 252      -2.672  -3.056  -1.812  1.00  1.81           H  
-ATOM     58 HH12 ARG A 252      -3.541  -2.122  -0.647  1.00  2.36           H  
-ATOM     59 HH21 ARG A 252      -5.090  -4.861   0.797  1.00  1.60           H  
-ATOM     60 HH22 ARG A 252      -4.909  -3.147   0.827  1.00  1.93           H  
-ATOM     61  N   VAL A 253       0.729  -5.268  -4.923  1.00  0.51           N  
-ATOM     62  CA  VAL A 253       1.860  -4.370  -4.709  1.00  0.56           C  
-ATOM     63  C   VAL A 253       1.732  -3.123  -5.581  1.00  0.47           C  
-ATOM     64  O   VAL A 253       1.851  -1.998  -5.091  1.00  0.52           O  
-ATOM     65  CB  VAL A 253       3.198  -5.075  -5.018  1.00  0.67           C  
-ATOM     66  CG1 VAL A 253       4.378  -4.147  -4.761  1.00  0.86           C  
-ATOM     67  CG2 VAL A 253       3.327  -6.351  -4.199  1.00  0.80           C  
-ATOM     68  H   VAL A 253       0.892  -6.156  -5.297  1.00  0.52           H  
-ATOM     69  HA  VAL A 253       1.860  -4.075  -3.672  1.00  0.67           H  
-ATOM     70  HB  VAL A 253       3.203  -5.344  -6.064  1.00  0.65           H  
-ATOM     71 HG11 VAL A 253       4.559  -3.546  -5.639  1.00  1.36           H  
-ATOM     72 HG12 VAL A 253       5.257  -4.736  -4.540  1.00  1.41           H  
-ATOM     73 HG13 VAL A 253       4.154  -3.504  -3.924  1.00  1.35           H  
-ATOM     74 HG21 VAL A 253       3.718  -6.113  -3.221  1.00  1.36           H  
-ATOM     75 HG22 VAL A 253       3.998  -7.033  -4.700  1.00  1.38           H  
-ATOM     76 HG23 VAL A 253       2.355  -6.811  -4.096  1.00  1.22           H  
-ATOM     77  N   GLY A 254       1.462  -3.338  -6.870  1.00  0.40           N  
-ATOM     78  CA  GLY A 254       1.294  -2.229  -7.799  1.00  0.40           C  
-ATOM     79  C   GLY A 254       0.190  -1.281  -7.360  1.00  0.31           C  
-ATOM     80  O   GLY A 254       0.379  -0.063  -7.325  1.00  0.33           O  
-ATOM     81  H   GLY A 254       1.363  -4.264  -7.190  1.00  0.43           H  
-ATOM     82  HA2 GLY A 254       2.223  -1.681  -7.865  1.00  0.49           H  
-ATOM     83  HA3 GLY A 254       1.049  -2.622  -8.775  1.00  0.45           H  
-ATOM     84  N   LEU A 255      -0.956  -1.845  -6.988  1.00  0.30           N  
-ATOM     85  CA  LEU A 255      -2.087  -1.052  -6.512  1.00  0.34           C  
-ATOM     86  C   LEU A 255      -1.722  -0.263  -5.260  1.00  0.35           C  
-ATOM     87  O   LEU A 255      -2.023   0.926  -5.143  1.00  0.37           O  
-ATOM     88  CB  LEU A 255      -3.271  -1.953  -6.203  1.00  0.46           C  
-ATOM     89  CG  LEU A 255      -4.239  -2.139  -7.358  1.00  0.62           C  
-ATOM     90  CD1 LEU A 255      -5.358  -3.074  -6.945  1.00  0.78           C  
-ATOM     91  CD2 LEU A 255      -4.787  -0.788  -7.789  1.00  0.80           C  
-ATOM     92  H   LEU A 255      -1.037  -2.827  -7.017  1.00  0.33           H  
-ATOM     93  HA  LEU A 255      -2.371  -0.365  -7.294  1.00  0.37           H  
-ATOM     94  HB2 LEU A 255      -2.895  -2.923  -5.911  1.00  0.51           H  
-ATOM     95  HB3 LEU A 255      -3.815  -1.531  -5.373  1.00  0.55           H  
-ATOM     96  HG  LEU A 255      -3.719  -2.577  -8.196  1.00  0.66           H  
-ATOM     97 HD11 LEU A 255      -5.015  -4.097  -7.018  1.00  1.30           H  
-ATOM     98 HD12 LEU A 255      -6.206  -2.931  -7.594  1.00  1.36           H  
-ATOM     99 HD13 LEU A 255      -5.643  -2.862  -5.924  1.00  1.27           H  
-ATOM    100 HD21 LEU A 255      -4.927  -0.164  -6.919  1.00  1.36           H  
-ATOM    101 HD22 LEU A 255      -5.730  -0.923  -8.292  1.00  1.34           H  
-ATOM    102 HD23 LEU A 255      -4.084  -0.314  -8.457  1.00  1.30           H  
-ATOM    103  N   GLY A 256      -1.073  -0.940  -4.322  1.00  0.42           N  
-ATOM    104  CA  GLY A 256      -0.679  -0.299  -3.089  1.00  0.51           C  
-ATOM    105  C   GLY A 256       0.303   0.844  -3.298  1.00  0.48           C  
-ATOM    106  O   GLY A 256       0.079   1.951  -2.811  1.00  0.51           O  
-ATOM    107  H   GLY A 256      -0.862  -1.892  -4.471  1.00  0.44           H  
-ATOM    108  HA2 GLY A 256      -1.565   0.088  -2.609  1.00  0.57           H  
-ATOM    109  HA3 GLY A 256      -0.229  -1.037  -2.447  1.00  0.61           H  
-ATOM    110  N   ILE A 257       1.394   0.591  -4.024  1.00  0.49           N  
-ATOM    111  CA  ILE A 257       2.390   1.638  -4.266  1.00  0.54           C  
-ATOM    112  C   ILE A 257       1.809   2.806  -5.077  1.00  0.44           C  
-ATOM    113  O   ILE A 257       2.086   3.967  -4.775  1.00  0.50           O  
-ATOM    114  CB  ILE A 257       3.678   1.095  -4.941  1.00  0.66           C  
-ATOM    115  CG1 ILE A 257       3.388   0.451  -6.300  1.00  0.60           C  
-ATOM    116  CG2 ILE A 257       4.370   0.098  -4.024  1.00  0.82           C  
-ATOM    117  CD1 ILE A 257       3.751   1.325  -7.481  1.00  0.69           C  
-ATOM    118  H   ILE A 257       1.531  -0.311  -4.399  1.00  0.49           H  
-ATOM    119  HA  ILE A 257       2.673   2.025  -3.296  1.00  0.62           H  
-ATOM    120  HB  ILE A 257       4.350   1.929  -5.087  1.00  0.75           H  
-ATOM    121 HG12 ILE A 257       3.951  -0.467  -6.382  1.00  0.69           H  
-ATOM    122 HG13 ILE A 257       2.333   0.226  -6.365  1.00  0.54           H  
-ATOM    123 HG21 ILE A 257       3.973   0.192  -3.024  1.00  1.36           H  
-ATOM    124 HG22 ILE A 257       5.433   0.299  -4.009  1.00  1.36           H  
-ATOM    125 HG23 ILE A 257       4.198  -0.904  -4.387  1.00  1.28           H  
-ATOM    126 HD11 ILE A 257       4.781   1.148  -7.757  1.00  1.32           H  
-ATOM    127 HD12 ILE A 257       3.623   2.364  -7.214  1.00  1.17           H  
-ATOM    128 HD13 ILE A 257       3.110   1.086  -8.317  1.00  1.25           H  
-ATOM    129  N   THR A 258       0.995   2.505  -6.093  1.00  0.35           N  
-ATOM    130  CA  THR A 258       0.386   3.556  -6.918  1.00  0.33           C  
-ATOM    131  C   THR A 258      -0.599   4.408  -6.116  1.00  0.28           C  
-ATOM    132  O   THR A 258      -0.594   5.635  -6.226  1.00  0.33           O  
-ATOM    133  CB  THR A 258      -0.343   2.994  -8.156  1.00  0.37           C  
-ATOM    134  OG1 THR A 258      -1.240   1.943  -7.776  1.00  0.34           O  
-ATOM    135  CG2 THR A 258       0.649   2.484  -9.191  1.00  0.52           C  
-ATOM    136  H   THR A 258       0.796   1.559  -6.290  1.00  0.33           H  
-ATOM    137  HA  THR A 258       1.184   4.197  -7.265  1.00  0.43           H  
-ATOM    138  HB  THR A 258      -0.918   3.793  -8.602  1.00  0.45           H  
-ATOM    139  HG1 THR A 258      -0.748   1.110  -7.704  1.00  0.40           H  
-ATOM    140 HG21 THR A 258       1.028   3.315  -9.767  1.00  0.96           H  
-ATOM    141 HG22 THR A 258       0.156   1.784  -9.850  1.00  1.32           H  
-ATOM    142 HG23 THR A 258       1.469   1.989  -8.691  1.00  1.09           H  
-ATOM    143  N   THR A 259      -1.439   3.765  -5.304  1.00  0.28           N  
-ATOM    144  CA  THR A 259      -2.408   4.500  -4.490  1.00  0.33           C  
-ATOM    145  C   THR A 259      -1.696   5.354  -3.449  1.00  0.35           C  
-ATOM    146  O   THR A 259      -1.998   6.539  -3.300  1.00  0.38           O  
-ATOM    147  CB  THR A 259      -3.420   3.573  -3.786  1.00  0.46           C  
-ATOM    148  OG1 THR A 259      -2.737   2.514  -3.104  1.00  0.49           O  
-ATOM    149  CG2 THR A 259      -4.408   2.993  -4.786  1.00  0.54           C  
-ATOM    150  H   THR A 259      -1.399   2.781  -5.243  1.00  0.31           H  
-ATOM    151  HA  THR A 259      -2.958   5.155  -5.152  1.00  0.36           H  
-ATOM    152  HB  THR A 259      -3.970   4.157  -3.062  1.00  0.55           H  
-ATOM    153  HG1 THR A 259      -2.500   1.822  -3.740  1.00  0.51           H  
-ATOM    154 HG21 THR A 259      -3.941   2.180  -5.323  1.00  1.17           H  
-ATOM    155 HG22 THR A 259      -4.705   3.761  -5.484  1.00  1.13           H  
-ATOM    156 HG23 THR A 259      -5.279   2.626  -4.262  1.00  1.20           H  
-ATOM    157  N   VAL A 260      -0.729   4.757  -2.750  1.00  0.43           N  
-ATOM    158  CA  VAL A 260       0.043   5.487  -1.746  1.00  0.54           C  
-ATOM    159  C   VAL A 260       0.768   6.675  -2.388  1.00  0.51           C  
-ATOM    160  O   VAL A 260       0.721   7.791  -1.870  1.00  0.54           O  
-ATOM    161  CB  VAL A 260       1.063   4.569  -1.034  1.00  0.67           C  
-ATOM    162  CG1 VAL A 260       1.993   5.375  -0.138  1.00  0.84           C  
-ATOM    163  CG2 VAL A 260       0.337   3.506  -0.221  1.00  0.75           C  
-ATOM    164  H   VAL A 260      -0.519   3.807  -2.925  1.00  0.45           H  
-ATOM    165  HA  VAL A 260      -0.649   5.862  -1.006  1.00  0.59           H  
-ATOM    166  HB  VAL A 260       1.661   4.072  -1.783  1.00  0.65           H  
-ATOM    167 HG11 VAL A 260       2.959   5.466  -0.613  1.00  1.30           H  
-ATOM    168 HG12 VAL A 260       2.104   4.874   0.811  1.00  1.28           H  
-ATOM    169 HG13 VAL A 260       1.576   6.358   0.020  1.00  1.40           H  
-ATOM    170 HG21 VAL A 260       0.951   3.210   0.616  1.00  1.25           H  
-ATOM    171 HG22 VAL A 260       0.142   2.647  -0.846  1.00  1.26           H  
-ATOM    172 HG23 VAL A 260      -0.598   3.907   0.141  1.00  1.34           H  
-ATOM    173  N   LEU A 261       1.408   6.433  -3.536  1.00  0.50           N  
-ATOM    174  CA  LEU A 261       2.113   7.489  -4.263  1.00  0.56           C  
-ATOM    175  C   LEU A 261       1.141   8.598  -4.675  1.00  0.47           C  
-ATOM    176  O   LEU A 261       1.413   9.782  -4.478  1.00  0.56           O  
-ATOM    177  CB  LEU A 261       2.804   6.921  -5.499  1.00  0.65           C  
-ATOM    178  CG  LEU A 261       3.759   7.887  -6.194  1.00  0.83           C  
-ATOM    179  CD1 LEU A 261       4.810   8.387  -5.212  1.00  0.98           C  
-ATOM    180  CD2 LEU A 261       4.416   7.213  -7.385  1.00  1.02           C  
-ATOM    181  H   LEU A 261       1.390   5.523  -3.913  1.00  0.50           H  
-ATOM    182  HA  LEU A 261       2.864   7.906  -3.610  1.00  0.66           H  
-ATOM    183  HB2 LEU A 261       3.359   6.042  -5.206  1.00  0.72           H  
-ATOM    184  HB3 LEU A 261       2.045   6.628  -6.210  1.00  0.63           H  
-ATOM    185  HG  LEU A 261       3.203   8.740  -6.552  1.00  0.83           H  
-ATOM    186 HD11 LEU A 261       5.091   7.584  -4.546  1.00  1.48           H  
-ATOM    187 HD12 LEU A 261       4.401   9.204  -4.634  1.00  1.39           H  
-ATOM    188 HD13 LEU A 261       5.678   8.729  -5.754  1.00  1.46           H  
-ATOM    189 HD21 LEU A 261       4.932   6.323  -7.055  1.00  1.54           H  
-ATOM    190 HD22 LEU A 261       5.120   7.893  -7.840  1.00  1.39           H  
-ATOM    191 HD23 LEU A 261       3.658   6.943  -8.107  1.00  1.50           H  
-ATOM    192  N   THR A 262      -0.003   8.202  -5.231  1.00  0.37           N  
-ATOM    193  CA  THR A 262      -1.031   9.156  -5.654  1.00  0.39           C  
-ATOM    194  C   THR A 262      -1.520   9.985  -4.465  1.00  0.35           C  
-ATOM    195  O   THR A 262      -1.576  11.214  -4.521  1.00  0.42           O  
-ATOM    196  CB  THR A 262      -2.243   8.433  -6.280  1.00  0.45           C  
-ATOM    197  OG1 THR A 262      -1.825   7.636  -7.395  1.00  0.59           O  
-ATOM    198  CG2 THR A 262      -3.300   9.428  -6.731  1.00  0.56           C  
-ATOM    199  H   THR A 262      -0.166   7.240  -5.351  1.00  0.36           H  
-ATOM    200  HA  THR A 262      -0.602   9.815  -6.395  1.00  0.48           H  
-ATOM    201  HB  THR A 262      -2.679   7.786  -5.531  1.00  0.46           H  
-ATOM    202  HG1 THR A 262      -1.396   6.828  -7.071  1.00  0.90           H  
-ATOM    203 HG21 THR A 262      -2.991   9.884  -7.659  1.00  1.20           H  
-ATOM    204 HG22 THR A 262      -3.419  10.191  -5.975  1.00  1.14           H  
-ATOM    205 HG23 THR A 262      -4.238   8.914  -6.875  1.00  1.19           H  
-ATOM    206  N   MET A 263      -1.865   9.294  -3.388  1.00  0.35           N  
-ATOM    207  CA  MET A 263      -2.345   9.939  -2.168  1.00  0.42           C  
-ATOM    208  C   MET A 263      -1.297  10.891  -1.572  1.00  0.43           C  
-ATOM    209  O   MET A 263      -1.617  12.028  -1.215  1.00  0.47           O  
-ATOM    210  CB  MET A 263      -2.726   8.870  -1.140  1.00  0.55           C  
-ATOM    211  CG  MET A 263      -3.402   9.418   0.105  1.00  0.74           C  
-ATOM    212  SD  MET A 263      -3.776   8.132   1.312  1.00  1.00           S  
-ATOM    213  CE  MET A 263      -4.830   7.047   0.350  1.00  1.21           C  
-ATOM    214  H   MET A 263      -1.795   8.311  -3.413  1.00  0.36           H  
-ATOM    215  HA  MET A 263      -3.227  10.508  -2.422  1.00  0.47           H  
-ATOM    216  HB2 MET A 263      -3.399   8.164  -1.605  1.00  0.62           H  
-ATOM    217  HB3 MET A 263      -1.830   8.348  -0.836  1.00  0.59           H  
-ATOM    218  HG2 MET A 263      -2.746  10.144   0.564  1.00  0.76           H  
-ATOM    219  HG3 MET A 263      -4.324   9.900  -0.185  1.00  0.86           H  
-ATOM    220  HE1 MET A 263      -4.428   6.045   0.367  1.00  1.50           H  
-ATOM    221  HE2 MET A 263      -4.875   7.401  -0.669  1.00  1.59           H  
-ATOM    222  HE3 MET A 263      -5.823   7.042   0.775  1.00  1.77           H  
-ATOM    223  N   THR A 264      -0.048  10.426  -1.454  1.00  0.46           N  
-ATOM    224  CA  THR A 264       1.015  11.258  -0.879  1.00  0.57           C  
-ATOM    225  C   THR A 264       1.347  12.479  -1.746  1.00  0.53           C  
-ATOM    226  O   THR A 264       1.609  13.555  -1.212  1.00  0.64           O  
-ATOM    227  CB  THR A 264       2.313  10.466  -0.581  1.00  0.72           C  
-ATOM    228  OG1 THR A 264       3.244  11.310   0.108  1.00  0.88           O  
-ATOM    229  CG2 THR A 264       2.969   9.939  -1.846  1.00  0.74           C  
-ATOM    230  H   THR A 264       0.157   9.505  -1.745  1.00  0.46           H  
-ATOM    231  HA  THR A 264       0.642  11.625   0.068  1.00  0.63           H  
-ATOM    232  HB  THR A 264       2.064   9.627   0.055  1.00  0.79           H  
-ATOM    233  HG1 THR A 264       2.949  12.225   0.054  1.00  1.20           H  
-ATOM    234 HG21 THR A 264       2.826   8.868  -1.907  1.00  1.32           H  
-ATOM    235 HG22 THR A 264       4.025  10.160  -1.823  1.00  1.21           H  
-ATOM    236 HG23 THR A 264       2.519  10.410  -2.708  1.00  1.28           H  
-ATOM    237  N   THR A 265       1.338  12.323  -3.074  1.00  0.49           N  
-ATOM    238  CA  THR A 265       1.640  13.455  -3.960  1.00  0.57           C  
-ATOM    239  C   THR A 265       0.481  14.452  -3.988  1.00  0.51           C  
-ATOM    240  O   THR A 265       0.696  15.658  -4.101  1.00  0.63           O  
-ATOM    241  CB  THR A 265       1.989  13.025  -5.405  1.00  0.69           C  
-ATOM    242  OG1 THR A 265       2.388  14.174  -6.163  1.00  0.85           O  
-ATOM    243  CG2 THR A 265       0.816  12.354  -6.102  1.00  0.64           C  
-ATOM    244  H   THR A 265       1.126  11.442  -3.459  1.00  0.48           H  
-ATOM    245  HA  THR A 265       2.503  13.959  -3.547  1.00  0.67           H  
-ATOM    246  HB  THR A 265       2.814  12.328  -5.366  1.00  0.79           H  
-ATOM    247  HG1 THR A 265       2.157  14.975  -5.682  1.00  0.99           H  
-ATOM    248 HG21 THR A 265       0.703  11.347  -5.727  1.00  1.29           H  
-ATOM    249 HG22 THR A 265       0.997  12.325  -7.166  1.00  1.16           H  
-ATOM    250 HG23 THR A 265      -0.087  12.915  -5.906  1.00  1.16           H  
-ATOM    251  N   GLN A 266      -0.746  13.939  -3.868  1.00  0.41           N  
-ATOM    252  CA  GLN A 266      -1.939  14.783  -3.854  1.00  0.44           C  
-ATOM    253  C   GLN A 266      -1.978  15.609  -2.566  1.00  0.45           C  
-ATOM    254  O   GLN A 266      -2.289  16.800  -2.581  1.00  0.57           O  
-ATOM    255  CB  GLN A 266      -3.197  13.915  -3.963  1.00  0.51           C  
-ATOM    256  CG  GLN A 266      -4.493  14.707  -3.999  1.00  0.68           C  
-ATOM    257  CD  GLN A 266      -5.716  13.814  -4.024  1.00  0.84           C  
-ATOM    258  OE1 GLN A 266      -5.919  13.040  -4.953  1.00  1.00           O  
-ATOM    259  NE2 GLN A 266      -6.541  13.914  -2.998  1.00  0.98           N  
-ATOM    260  H   GLN A 266      -0.849  12.966  -3.774  1.00  0.40           H  
-ATOM    261  HA  GLN A 266      -1.889  15.450  -4.701  1.00  0.53           H  
-ATOM    262  HB2 GLN A 266      -3.135  13.327  -4.868  1.00  0.57           H  
-ATOM    263  HB3 GLN A 266      -3.232  13.248  -3.115  1.00  0.54           H  
-ATOM    264  HG2 GLN A 266      -4.541  15.335  -3.121  1.00  0.74           H  
-ATOM    265  HG3 GLN A 266      -4.497  15.326  -4.884  1.00  0.81           H  
-ATOM    266 HE21 GLN A 266      -6.322  14.547  -2.287  1.00  1.01           H  
-ATOM    267 HE22 GLN A 266      -7.341  13.349  -2.997  1.00  1.13           H  
-ATOM    268  N   SER A 267      -1.640  14.957  -1.457  1.00  0.44           N  
-ATOM    269  CA  SER A 267      -1.605  15.614  -0.151  1.00  0.54           C  
-ATOM    270  C   SER A 267      -0.202  16.181   0.120  1.00  0.44           C  
-ATOM    271  O   SER A 267       0.539  16.499  -0.810  1.00  1.00           O  
-ATOM    272  CB  SER A 267      -2.005  14.614   0.946  1.00  0.71           C  
-ATOM    273  OG  SER A 267      -2.047  15.223   2.231  1.00  0.87           O  
-ATOM    274  H   SER A 267      -1.389  14.009  -1.521  1.00  0.47           H  
-ATOM    275  HA  SER A 267      -2.315  16.428  -0.167  1.00  0.70           H  
-ATOM    276  HB2 SER A 267      -2.984  14.216   0.724  1.00  0.84           H  
-ATOM    277  HB3 SER A 267      -1.289  13.805   0.969  1.00  0.71           H  
-ATOM    278  HG  SER A 267      -2.811  14.896   2.715  1.00  1.03           H  
-ATOM    279  N   SER A 268       0.167  16.296   1.389  1.00  0.65           N  
-ATOM    280  CA  SER A 268       1.486  16.810   1.755  1.00  0.66           C  
-ATOM    281  C   SER A 268       2.567  15.765   1.473  1.00  0.60           C  
-ATOM    282  O   SER A 268       2.486  14.637   1.965  1.00  0.61           O  
-ATOM    283  CB  SER A 268       1.529  17.200   3.239  1.00  0.72           C  
-ATOM    284  OG  SER A 268       1.568  16.050   4.076  1.00  0.72           O  
-ATOM    285  H   SER A 268      -0.462  16.020   2.099  1.00  1.15           H  
-ATOM    286  HA  SER A 268       1.681  17.686   1.154  1.00  0.79           H  
-ATOM    287  HB2 SER A 268       2.409  17.796   3.428  1.00  0.84           H  
-ATOM    288  HB3 SER A 268       0.647  17.773   3.481  1.00  0.83           H  
-ATOM    289  HG  SER A 268       1.780  15.270   3.538  1.00  0.81           H  
-ATOM    290  N   GLY A 269       3.576  16.147   0.691  1.00  0.69           N  
-ATOM    291  CA  GLY A 269       4.667  15.235   0.362  1.00  0.78           C  
-ATOM    292  C   GLY A 269       5.590  14.968   1.543  1.00  0.70           C  
-ATOM    293  O   GLY A 269       6.772  15.304   1.509  1.00  0.81           O  
-ATOM    294  H   GLY A 269       3.583  17.060   0.334  1.00  0.78           H  
-ATOM    295  HA2 GLY A 269       4.247  14.297   0.028  1.00  0.84           H  
-ATOM    296  HA3 GLY A 269       5.247  15.662  -0.444  1.00  0.94           H  
-ATOM    297  N   SER A 270       5.036  14.377   2.595  1.00  0.60           N  
-ATOM    298  CA  SER A 270       5.789  14.073   3.809  1.00  0.60           C  
-ATOM    299  C   SER A 270       5.014  13.106   4.702  1.00  0.58           C  
-ATOM    300  O   SER A 270       3.787  13.187   4.793  1.00  0.63           O  
-ATOM    301  CB  SER A 270       6.090  15.357   4.588  1.00  0.66           C  
-ATOM    302  OG  SER A 270       6.710  15.069   5.829  1.00  0.86           O  
-ATOM    303  H   SER A 270       4.077  14.147   2.560  1.00  0.61           H  
-ATOM    304  HA  SER A 270       6.720  13.611   3.518  1.00  0.70           H  
-ATOM    305  HB2 SER A 270       6.753  15.979   4.006  1.00  0.74           H  
-ATOM    306  HB3 SER A 270       5.168  15.888   4.774  1.00  0.65           H  
-ATOM    307  HG  SER A 270       7.261  15.811   6.091  1.00  1.09           H  
-ATOM    308  N   ARG A 271       5.746  12.201   5.360  1.00  0.66           N  
-ATOM    309  CA  ARG A 271       5.159  11.197   6.262  1.00  0.74           C  
-ATOM    310  C   ARG A 271       4.392  10.119   5.491  1.00  0.72           C  
-ATOM    311  O   ARG A 271       3.295   9.720   5.881  1.00  0.91           O  
-ATOM    312  CB  ARG A 271       4.242  11.857   7.295  1.00  0.81           C  
-ATOM    313  CG  ARG A 271       4.947  12.903   8.133  1.00  0.90           C  
-ATOM    314  CD  ARG A 271       3.966  13.915   8.696  1.00  0.93           C  
-ATOM    315  NE  ARG A 271       3.205  14.586   7.642  1.00  0.83           N  
-ATOM    316  CZ  ARG A 271       2.351  15.560   7.866  1.00  0.90           C  
-ATOM    317  NH1 ARG A 271       2.145  15.988   9.085  1.00  1.09           N  
-ATOM    318  NH2 ARG A 271       1.704  16.111   6.870  1.00  0.87           N  
-ATOM    319  H   ARG A 271       6.717  12.209   5.234  1.00  0.74           H  
-ATOM    320  HA  ARG A 271       5.976  10.727   6.786  1.00  0.84           H  
-ATOM    321  HB2 ARG A 271       3.419  12.330   6.780  1.00  0.75           H  
-ATOM    322  HB3 ARG A 271       3.855  11.096   7.956  1.00  0.94           H  
-ATOM    323  HG2 ARG A 271       5.454  12.413   8.953  1.00  1.04           H  
-ATOM    324  HG3 ARG A 271       5.669  13.412   7.514  1.00  0.88           H  
-ATOM    325  HD2 ARG A 271       3.277  13.404   9.352  1.00  1.02           H  
-ATOM    326  HD3 ARG A 271       4.515  14.657   9.257  1.00  1.00           H  
-ATOM    327  HE  ARG A 271       3.343  14.284   6.718  1.00  0.76           H  
-ATOM    328 HH11 ARG A 271       2.638  15.575   9.848  1.00  1.17           H  
-ATOM    329 HH12 ARG A 271       1.497  16.727   9.251  1.00  1.20           H  
-ATOM    330 HH21 ARG A 271       1.858  15.794   5.928  1.00  0.80           H  
-ATOM    331 HH22 ARG A 271       1.058  16.847   7.046  1.00  0.97           H  
-ATOM    332  N   ALA A 272       4.985   9.644   4.401  1.00  0.91           N  
-ATOM    333  CA  ALA A 272       4.367   8.605   3.585  1.00  0.96           C  
-ATOM    334  C   ALA A 272       4.773   7.211   4.065  1.00  0.90           C  
-ATOM    335  O   ALA A 272       5.956   6.935   4.272  1.00  1.32           O  
-ATOM    336  CB  ALA A 272       4.740   8.791   2.122  1.00  1.04           C  
-ATOM    337  H   ALA A 272       5.863   9.993   4.145  1.00  1.20           H  
-ATOM    338  HA  ALA A 272       3.294   8.706   3.674  1.00  1.03           H  
-ATOM    339  HB1 ALA A 272       3.841   8.888   1.531  1.00  1.45           H  
-ATOM    340  HB2 ALA A 272       5.305   7.936   1.782  1.00  1.60           H  
-ATOM    341  HB3 ALA A 272       5.339   9.683   2.014  1.00  1.31           H  
-ATOM    342  N   SER A 273       3.790   6.336   4.246  1.00  0.71           N  
-ATOM    343  CA  SER A 273       4.055   4.972   4.702  1.00  0.73           C  
-ATOM    344  C   SER A 273       4.327   4.023   3.544  1.00  0.69           C  
-ATOM    345  O   SER A 273       4.432   4.432   2.388  1.00  0.73           O  
-ATOM    346  CB  SER A 273       2.897   4.445   5.546  1.00  0.91           C  
-ATOM    347  OG  SER A 273       1.679   4.457   4.816  1.00  1.06           O  
-ATOM    348  H   SER A 273       2.866   6.610   4.068  1.00  0.90           H  
-ATOM    349  HA  SER A 273       4.949   4.994   5.316  1.00  0.70           H  
-ATOM    350  HB2 SER A 273       3.113   3.430   5.846  1.00  0.98           H  
-ATOM    351  HB3 SER A 273       2.787   5.065   6.423  1.00  0.98           H  
-ATOM    352  HG  SER A 273       1.081   3.801   5.186  1.00  1.37           H  
-ATOM    353  N   LEU A 274       4.489   2.752   3.897  1.00  0.74           N  
-ATOM    354  CA  LEU A 274       4.818   1.693   2.943  1.00  0.78           C  
-ATOM    355  C   LEU A 274       6.113   2.008   2.151  1.00  0.72           C  
-ATOM    356  O   LEU A 274       6.173   1.770   0.944  1.00  0.79           O  
-ATOM    357  CB  LEU A 274       3.645   1.446   1.981  1.00  0.88           C  
-ATOM    358  CG  LEU A 274       3.752   0.166   1.147  1.00  1.01           C  
-ATOM    359  CD1 LEU A 274       3.743  -1.060   2.048  1.00  1.23           C  
-ATOM    360  CD2 LEU A 274       2.623   0.091   0.130  1.00  1.14           C  
-ATOM    361  H   LEU A 274       4.427   2.522   4.846  1.00  0.82           H  
-ATOM    362  HA  LEU A 274       4.985   0.794   3.516  1.00  0.88           H  
-ATOM    363  HB2 LEU A 274       2.735   1.398   2.563  1.00  0.99           H  
-ATOM    364  HB3 LEU A 274       3.577   2.285   1.306  1.00  0.86           H  
-ATOM    365  HG  LEU A 274       4.689   0.175   0.607  1.00  1.01           H  
-ATOM    366 HD11 LEU A 274       4.620  -1.048   2.679  1.00  1.61           H  
-ATOM    367 HD12 LEU A 274       3.748  -1.953   1.442  1.00  1.72           H  
-ATOM    368 HD13 LEU A 274       2.856  -1.047   2.664  1.00  1.63           H  
-ATOM    369 HD21 LEU A 274       1.695   0.385   0.599  1.00  1.48           H  
-ATOM    370 HD22 LEU A 274       2.537  -0.921  -0.236  1.00  1.60           H  
-ATOM    371 HD23 LEU A 274       2.837   0.755  -0.695  1.00  1.50           H  
-ATOM    372  N   PRO A 275       7.172   2.568   2.807  1.00  0.70           N  
-ATOM    373  CA  PRO A 275       8.437   2.909   2.121  1.00  0.80           C  
-ATOM    374  C   PRO A 275       9.054   1.745   1.329  1.00  0.90           C  
-ATOM    375  O   PRO A 275       9.183   0.628   1.835  1.00  1.02           O  
-ATOM    376  CB  PRO A 275       9.358   3.302   3.275  1.00  0.92           C  
-ATOM    377  CG  PRO A 275       8.433   3.839   4.300  1.00  0.81           C  
-ATOM    378  CD  PRO A 275       7.228   2.950   4.242  1.00  0.69           C  
-ATOM    379  HA  PRO A 275       8.307   3.758   1.469  1.00  0.85           H  
-ATOM    380  HB2 PRO A 275       9.889   2.431   3.632  1.00  1.02           H  
-ATOM    381  HB3 PRO A 275      10.060   4.053   2.943  1.00  1.06           H  
-ATOM    382  HG2 PRO A 275       8.893   3.799   5.276  1.00  0.93           H  
-ATOM    383  HG3 PRO A 275       8.162   4.852   4.046  1.00  0.84           H  
-ATOM    384  HD2 PRO A 275       7.356   2.090   4.877  1.00  0.82           H  
-ATOM    385  HD3 PRO A 275       6.332   3.505   4.529  1.00  0.65           H  
-ATOM    386  N   LYS A 276       9.449   2.026   0.086  1.00  1.04           N  
-ATOM    387  CA  LYS A 276      10.070   1.021  -0.785  1.00  1.16           C  
-ATOM    388  C   LYS A 276      11.236   1.622  -1.576  1.00  1.79           C  
-ATOM    389  O   LYS A 276      11.982   0.845  -2.210  1.00  1.93           O  
-ATOM    390  CB  LYS A 276       9.045   0.432  -1.765  1.00  1.50           C  
-ATOM    391  CG  LYS A 276       8.097  -0.590  -1.147  1.00  1.66           C  
-ATOM    392  CD  LYS A 276       8.836  -1.827  -0.655  1.00  1.68           C  
-ATOM    393  CE  LYS A 276       9.682  -2.457  -1.754  1.00  1.72           C  
-ATOM    394  NZ  LYS A 276       8.876  -2.801  -2.958  1.00  2.06           N  
-ATOM    395  OXT LYS A 276      11.388   2.863  -1.554  1.00  2.43           O  
-ATOM    396  H   LYS A 276       9.334   2.939  -0.256  1.00  1.16           H  
-ATOM    397  HA  LYS A 276      10.448   0.229  -0.156  1.00  1.11           H  
-ATOM    398  HB2 LYS A 276       8.451   1.237  -2.172  1.00  2.11           H  
-ATOM    399  HB3 LYS A 276       9.577  -0.050  -2.572  1.00  1.71           H  
-ATOM    400  HG2 LYS A 276       7.590  -0.133  -0.311  1.00  1.97           H  
-ATOM    401  HG3 LYS A 276       7.372  -0.887  -1.890  1.00  2.19           H  
-ATOM    402  HD2 LYS A 276       9.480  -1.547   0.164  1.00  1.74           H  
-ATOM    403  HD3 LYS A 276       8.113  -2.552  -0.313  1.00  2.29           H  
-ATOM    404  HE2 LYS A 276      10.455  -1.760  -2.038  1.00  1.70           H  
-ATOM    405  HE3 LYS A 276      10.136  -3.357  -1.367  1.00  2.19           H  
-ATOM    406  HZ1 LYS A 276       7.994  -3.275  -2.675  1.00  2.32           H  
-ATOM    407  HZ2 LYS A 276       9.417  -3.438  -3.578  1.00  2.41           H  
-ATOM    408  HZ3 LYS A 276       8.640  -1.937  -3.486  1.00  2.48           H  
-TER
-ENDMDL
-MODEL      4
-ATOM      1  N   ALA A 249       4.167 -11.688   1.475  1.00  2.29           N  
-ATOM      2  CA  ALA A 249       4.399 -11.189   0.094  1.00  1.77           C  
-ATOM      3  C   ALA A 249       3.650  -9.880  -0.137  1.00  1.27           C  
-ATOM      4  O   ALA A 249       2.649  -9.608   0.527  1.00  1.51           O  
-ATOM      5  CB  ALA A 249       3.973 -12.231  -0.932  1.00  2.17           C  
-ATOM      6  H1  ALA A 249       4.039 -12.719   1.468  1.00  2.60           H  
-ATOM      7  H2  ALA A 249       3.312 -11.247   1.874  1.00  2.70           H  
-ATOM      8  H3  ALA A 249       4.979 -11.451   2.082  1.00  2.56           H  
-ATOM      9  HA  ALA A 249       5.457 -11.010  -0.029  1.00  1.68           H  
-ATOM     10  HB1 ALA A 249       4.845 -12.598  -1.454  1.00  2.44           H  
-ATOM     11  HB2 ALA A 249       3.295 -11.779  -1.641  1.00  2.51           H  
-ATOM     12  HB3 ALA A 249       3.481 -13.049  -0.432  1.00  2.55           H  
-ATOM     13  N   PRO A 250       4.132  -9.050  -1.074  1.00  0.93           N  
-ATOM     14  CA  PRO A 250       3.520  -7.757  -1.398  1.00  0.88           C  
-ATOM     15  C   PRO A 250       2.170  -7.899  -2.112  1.00  0.72           C  
-ATOM     16  O   PRO A 250       2.083  -7.724  -3.330  1.00  0.66           O  
-ATOM     17  CB  PRO A 250       4.552  -7.103  -2.332  1.00  1.29           C  
-ATOM     18  CG  PRO A 250       5.794  -7.903  -2.170  1.00  1.40           C  
-ATOM     19  CD  PRO A 250       5.321  -9.289  -1.897  1.00  1.24           C  
-ATOM     20  HA  PRO A 250       3.395  -7.147  -0.515  1.00  1.16           H  
-ATOM     21  HB2 PRO A 250       4.194  -7.147  -3.347  1.00  1.46           H  
-ATOM     22  HB3 PRO A 250       4.707  -6.079  -2.040  1.00  1.62           H  
-ATOM     23  HG2 PRO A 250       6.376  -7.873  -3.079  1.00  1.82           H  
-ATOM     24  HG3 PRO A 250       6.370  -7.531  -1.336  1.00  1.44           H  
-ATOM     25  HD2 PRO A 250       5.058  -9.786  -2.819  1.00  1.52           H  
-ATOM     26  HD3 PRO A 250       6.063  -9.847  -1.354  1.00  1.40           H  
-ATOM     27  N   ALA A 251       1.124  -8.227  -1.351  1.00  0.77           N  
-ATOM     28  CA  ALA A 251      -0.214  -8.396  -1.913  1.00  0.74           C  
-ATOM     29  C   ALA A 251      -0.662  -7.143  -2.660  1.00  0.62           C  
-ATOM     30  O   ALA A 251      -0.781  -6.068  -2.073  1.00  0.65           O  
-ATOM     31  CB  ALA A 251      -1.210  -8.736  -0.819  1.00  0.94           C  
-ATOM     32  H   ALA A 251       1.258  -8.363  -0.387  1.00  0.90           H  
-ATOM     33  HA  ALA A 251      -0.182  -9.223  -2.607  1.00  0.75           H  
-ATOM     34  HB1 ALA A 251      -1.946  -9.424  -1.206  1.00  1.50           H  
-ATOM     35  HB2 ALA A 251      -1.699  -7.833  -0.487  1.00  1.25           H  
-ATOM     36  HB3 ALA A 251      -0.691  -9.191   0.010  1.00  1.40           H  
-ATOM     37  N   ARG A 252      -0.904  -7.308  -3.958  1.00  0.58           N  
-ATOM     38  CA  ARG A 252      -1.335  -6.223  -4.832  1.00  0.55           C  
-ATOM     39  C   ARG A 252      -0.431  -4.986  -4.696  1.00  0.44           C  
-ATOM     40  O   ARG A 252      -0.898  -3.843  -4.609  1.00  0.47           O  
-ATOM     41  CB  ARG A 252      -2.789  -5.894  -4.546  1.00  0.69           C  
-ATOM     42  CG  ARG A 252      -3.453  -5.091  -5.648  1.00  0.73           C  
-ATOM     43  CD  ARG A 252      -3.147  -5.687  -7.017  1.00  0.78           C  
-ATOM     44  NE  ARG A 252      -3.981  -5.119  -8.071  1.00  1.02           N  
-ATOM     45  CZ  ARG A 252      -5.283  -5.284  -8.146  1.00  1.23           C  
-ATOM     46  NH1 ARG A 252      -5.910  -6.016  -7.262  1.00  1.31           N  
-ATOM     47  NH2 ARG A 252      -5.959  -4.723  -9.114  1.00  1.49           N  
-ATOM     48  H   ARG A 252      -0.795  -8.193  -4.343  1.00  0.64           H  
-ATOM     49  HA  ARG A 252      -1.259  -6.582  -5.847  1.00  0.57           H  
-ATOM     50  HB2 ARG A 252      -3.330  -6.824  -4.426  1.00  0.80           H  
-ATOM     51  HB3 ARG A 252      -2.843  -5.333  -3.628  1.00  0.72           H  
-ATOM     52  HG2 ARG A 252      -4.522  -5.097  -5.493  1.00  0.86           H  
-ATOM     53  HG3 ARG A 252      -3.084  -4.077  -5.615  1.00  0.70           H  
-ATOM     54  HD2 ARG A 252      -2.109  -5.494  -7.251  1.00  0.73           H  
-ATOM     55  HD3 ARG A 252      -3.310  -6.754  -6.975  1.00  0.84           H  
-ATOM     56  HE  ARG A 252      -3.539  -4.581  -8.757  1.00  1.11           H  
-ATOM     57 HH11 ARG A 252      -5.399  -6.454  -6.529  1.00  1.21           H  
-ATOM     58 HH12 ARG A 252      -6.897  -6.133  -7.321  1.00  1.55           H  
-ATOM     59 HH21 ARG A 252      -5.488  -4.169  -9.795  1.00  1.56           H  
-ATOM     60 HH22 ARG A 252      -6.946  -4.852  -9.172  1.00  1.69           H  
-ATOM     61  N   VAL A 253       0.873  -5.250  -4.676  1.00  0.39           N  
-ATOM     62  CA  VAL A 253       1.899  -4.215  -4.546  1.00  0.36           C  
-ATOM     63  C   VAL A 253       1.679  -3.032  -5.495  1.00  0.32           C  
-ATOM     64  O   VAL A 253       1.678  -1.882  -5.067  1.00  0.34           O  
-ATOM     65  CB  VAL A 253       3.298  -4.820  -4.793  1.00  0.46           C  
-ATOM     66  CG1 VAL A 253       3.381  -5.526  -6.142  1.00  0.53           C  
-ATOM     67  CG2 VAL A 253       4.377  -3.756  -4.665  1.00  0.60           C  
-ATOM     68  H   VAL A 253       1.158  -6.188  -4.734  1.00  0.43           H  
-ATOM     69  HA  VAL A 253       1.870  -3.851  -3.532  1.00  0.40           H  
-ATOM     70  HB  VAL A 253       3.465  -5.557  -4.039  1.00  0.50           H  
-ATOM     71 HG11 VAL A 253       4.134  -6.298  -6.100  1.00  1.16           H  
-ATOM     72 HG12 VAL A 253       3.640  -4.810  -6.908  1.00  1.15           H  
-ATOM     73 HG13 VAL A 253       2.423  -5.969  -6.375  1.00  1.19           H  
-ATOM     74 HG21 VAL A 253       4.836  -3.592  -5.628  1.00  1.16           H  
-ATOM     75 HG22 VAL A 253       5.125  -4.086  -3.960  1.00  0.98           H  
-ATOM     76 HG23 VAL A 253       3.934  -2.836  -4.315  1.00  1.16           H  
-ATOM     77  N   GLY A 254       1.506  -3.335  -6.774  1.00  0.36           N  
-ATOM     78  CA  GLY A 254       1.299  -2.300  -7.789  1.00  0.41           C  
-ATOM     79  C   GLY A 254       0.207  -1.301  -7.424  1.00  0.40           C  
-ATOM     80  O   GLY A 254       0.440  -0.087  -7.419  1.00  0.42           O  
-ATOM     81  H   GLY A 254       1.538  -4.279  -7.032  1.00  0.41           H  
-ATOM     82  HA2 GLY A 254       2.226  -1.762  -7.927  1.00  0.44           H  
-ATOM     83  HA3 GLY A 254       1.035  -2.778  -8.721  1.00  0.48           H  
-ATOM     84  N   LEU A 255      -0.979  -1.803  -7.093  1.00  0.43           N  
-ATOM     85  CA  LEU A 255      -2.084  -0.932  -6.704  1.00  0.48           C  
-ATOM     86  C   LEU A 255      -1.761  -0.214  -5.398  1.00  0.42           C  
-ATOM     87  O   LEU A 255      -2.033   0.975  -5.251  1.00  0.45           O  
-ATOM     88  CB  LEU A 255      -3.384  -1.718  -6.554  1.00  0.60           C  
-ATOM     89  CG  LEU A 255      -4.495  -1.311  -7.524  1.00  0.78           C  
-ATOM     90  CD1 LEU A 255      -5.796  -2.003  -7.160  1.00  0.92           C  
-ATOM     91  CD2 LEU A 255      -4.676   0.200  -7.530  1.00  0.89           C  
-ATOM     92  H   LEU A 255      -1.106  -2.774  -7.092  1.00  0.46           H  
-ATOM     93  HA  LEU A 255      -2.213  -0.194  -7.482  1.00  0.53           H  
-ATOM     94  HB2 LEU A 255      -3.167  -2.766  -6.705  1.00  0.65           H  
-ATOM     95  HB3 LEU A 255      -3.749  -1.583  -5.547  1.00  0.66           H  
-ATOM     96  HG  LEU A 255      -4.220  -1.620  -8.523  1.00  0.87           H  
-ATOM     97 HD11 LEU A 255      -6.515  -1.268  -6.833  1.00  1.42           H  
-ATOM     98 HD12 LEU A 255      -5.616  -2.711  -6.364  1.00  1.39           H  
-ATOM     99 HD13 LEU A 255      -6.180  -2.523  -8.025  1.00  1.40           H  
-ATOM    100 HD21 LEU A 255      -3.928   0.655  -6.897  1.00  1.42           H  
-ATOM    101 HD22 LEU A 255      -5.660   0.447  -7.161  1.00  1.37           H  
-ATOM    102 HD23 LEU A 255      -4.566   0.571  -8.538  1.00  1.35           H  
-ATOM    103  N   GLY A 256      -1.165  -0.942  -4.459  1.00  0.38           N  
-ATOM    104  CA  GLY A 256      -0.800  -0.352  -3.185  1.00  0.39           C  
-ATOM    105  C   GLY A 256       0.183   0.801  -3.332  1.00  0.34           C  
-ATOM    106  O   GLY A 256      -0.059   1.894  -2.821  1.00  0.38           O  
-ATOM    107  H   GLY A 256      -0.962  -1.891  -4.637  1.00  0.40           H  
-ATOM    108  HA2 GLY A 256      -1.695   0.014  -2.702  1.00  0.47           H  
-ATOM    109  HA3 GLY A 256      -0.354  -1.113  -2.562  1.00  0.44           H  
-ATOM    110  N   ILE A 257       1.292   0.565  -4.038  1.00  0.32           N  
-ATOM    111  CA  ILE A 257       2.298   1.610  -4.235  1.00  0.35           C  
-ATOM    112  C   ILE A 257       1.751   2.779  -5.060  1.00  0.34           C  
-ATOM    113  O   ILE A 257       2.001   3.937  -4.726  1.00  0.39           O  
-ATOM    114  CB  ILE A 257       3.614   1.071  -4.857  1.00  0.43           C  
-ATOM    115  CG1 ILE A 257       3.386   0.428  -6.229  1.00  0.45           C  
-ATOM    116  CG2 ILE A 257       4.271   0.077  -3.913  1.00  0.52           C  
-ATOM    117  CD1 ILE A 257       3.784   1.314  -7.391  1.00  0.53           C  
-ATOM    118  H   ILE A 257       1.433  -0.330  -4.433  1.00  0.33           H  
-ATOM    119  HA  ILE A 257       2.540   1.992  -3.253  1.00  0.39           H  
-ATOM    120  HB  ILE A 257       4.290   1.907  -4.972  1.00  0.50           H  
-ATOM    121 HG12 ILE A 257       3.965  -0.481  -6.294  1.00  0.53           H  
-ATOM    122 HG13 ILE A 257       2.338   0.189  -6.337  1.00  0.46           H  
-ATOM    123 HG21 ILE A 257       5.298  -0.076  -4.208  1.00  1.16           H  
-ATOM    124 HG22 ILE A 257       3.741  -0.863  -3.953  1.00  1.07           H  
-ATOM    125 HG23 ILE A 257       4.240   0.464  -2.905  1.00  1.04           H  
-ATOM    126 HD11 ILE A 257       4.279   2.198  -7.018  1.00  1.19           H  
-ATOM    127 HD12 ILE A 257       2.902   1.602  -7.944  1.00  1.15           H  
-ATOM    128 HD13 ILE A 257       4.456   0.774  -8.043  1.00  1.16           H  
-ATOM    129  N   THR A 258       0.984   2.493  -6.119  1.00  0.34           N  
-ATOM    130  CA  THR A 258       0.407   3.566  -6.936  1.00  0.41           C  
-ATOM    131  C   THR A 258      -0.589   4.390  -6.120  1.00  0.40           C  
-ATOM    132  O   THR A 258      -0.634   5.618  -6.236  1.00  0.43           O  
-ATOM    133  CB  THR A 258      -0.286   3.045  -8.213  1.00  0.50           C  
-ATOM    134  OG1 THR A 258      -1.166   1.960  -7.899  1.00  0.52           O  
-ATOM    135  CG2 THR A 258       0.739   2.597  -9.244  1.00  0.58           C  
-ATOM    136  H   THR A 258       0.792   1.550  -6.344  1.00  0.35           H  
-ATOM    137  HA  THR A 258       1.220   4.213  -7.237  1.00  0.45           H  
-ATOM    138  HB  THR A 258      -0.866   3.851  -8.638  1.00  0.56           H  
-ATOM    139  HG1 THR A 258      -0.660   1.134  -7.876  1.00  0.59           H  
-ATOM    140 HG21 THR A 258       0.919   1.537  -9.136  1.00  1.21           H  
-ATOM    141 HG22 THR A 258       1.662   3.136  -9.092  1.00  1.15           H  
-ATOM    142 HG23 THR A 258       0.363   2.799 -10.236  1.00  1.20           H  
-ATOM    143  N   THR A 259      -1.367   3.716  -5.271  1.00  0.42           N  
-ATOM    144  CA  THR A 259      -2.337   4.399  -4.415  1.00  0.46           C  
-ATOM    145  C   THR A 259      -1.612   5.292  -3.414  1.00  0.45           C  
-ATOM    146  O   THR A 259      -1.935   6.471  -3.278  1.00  0.50           O  
-ATOM    147  CB  THR A 259      -3.236   3.404  -3.646  1.00  0.52           C  
-ATOM    148  OG1 THR A 259      -3.933   2.556  -4.566  1.00  0.56           O  
-ATOM    149  CG2 THR A 259      -4.246   4.138  -2.775  1.00  0.64           C  
-ATOM    150  H   THR A 259      -1.272   2.738  -5.206  1.00  0.43           H  
-ATOM    151  HA  THR A 259      -2.965   5.014  -5.045  1.00  0.51           H  
-ATOM    152  HB  THR A 259      -2.610   2.795  -3.010  1.00  0.52           H  
-ATOM    153  HG1 THR A 259      -3.316   1.900  -4.927  1.00  0.56           H  
-ATOM    154 HG21 THR A 259      -4.023   5.194  -2.775  1.00  1.21           H  
-ATOM    155 HG22 THR A 259      -4.191   3.759  -1.764  1.00  1.24           H  
-ATOM    156 HG23 THR A 259      -5.241   3.980  -3.164  1.00  1.20           H  
-ATOM    157  N   VAL A 260      -0.608   4.731  -2.735  1.00  0.45           N  
-ATOM    158  CA  VAL A 260       0.178   5.495  -1.769  1.00  0.48           C  
-ATOM    159  C   VAL A 260       0.855   6.690  -2.450  1.00  0.44           C  
-ATOM    160  O   VAL A 260       0.809   7.811  -1.940  1.00  0.49           O  
-ATOM    161  CB  VAL A 260       1.245   4.616  -1.080  1.00  0.53           C  
-ATOM    162  CG1 VAL A 260       2.145   5.452  -0.181  1.00  0.61           C  
-ATOM    163  CG2 VAL A 260       0.581   3.508  -0.279  1.00  0.59           C  
-ATOM    164  H   VAL A 260      -0.381   3.784  -2.900  1.00  0.45           H  
-ATOM    165  HA  VAL A 260      -0.498   5.865  -1.011  1.00  0.53           H  
-ATOM    166  HB  VAL A 260       1.859   4.161  -1.844  1.00  0.54           H  
-ATOM    167 HG11 VAL A 260       1.699   5.533   0.799  1.00  0.99           H  
-ATOM    168 HG12 VAL A 260       2.263   6.438  -0.606  1.00  0.99           H  
-ATOM    169 HG13 VAL A 260       3.112   4.978  -0.098  1.00  0.82           H  
-ATOM    170 HG21 VAL A 260       0.055   2.845  -0.951  1.00  1.21           H  
-ATOM    171 HG22 VAL A 260      -0.121   3.939   0.420  1.00  1.19           H  
-ATOM    172 HG23 VAL A 260       1.332   2.951   0.261  1.00  1.17           H  
-ATOM    173  N   LEU A 261       1.457   6.445  -3.617  1.00  0.40           N  
-ATOM    174  CA  LEU A 261       2.120   7.501  -4.380  1.00  0.41           C  
-ATOM    175  C   LEU A 261       1.121   8.612  -4.721  1.00  0.40           C  
-ATOM    176  O   LEU A 261       1.406   9.796  -4.549  1.00  0.45           O  
-ATOM    177  CB  LEU A 261       2.730   6.922  -5.664  1.00  0.47           C  
-ATOM    178  CG  LEU A 261       4.094   7.497  -6.073  1.00  0.79           C  
-ATOM    179  CD1 LEU A 261       3.985   8.974  -6.425  1.00  1.35           C  
-ATOM    180  CD2 LEU A 261       5.111   7.288  -4.962  1.00  1.30           C  
-ATOM    181  H   LEU A 261       1.443   5.530  -3.981  1.00  0.40           H  
-ATOM    182  HA  LEU A 261       2.907   7.913  -3.766  1.00  0.47           H  
-ATOM    183  HB2 LEU A 261       2.840   5.856  -5.531  1.00  0.63           H  
-ATOM    184  HB3 LEU A 261       2.037   7.093  -6.473  1.00  0.77           H  
-ATOM    185  HG  LEU A 261       4.448   6.972  -6.950  1.00  0.76           H  
-ATOM    186 HD11 LEU A 261       3.578   9.515  -5.584  1.00  1.85           H  
-ATOM    187 HD12 LEU A 261       3.335   9.095  -7.279  1.00  1.65           H  
-ATOM    188 HD13 LEU A 261       4.965   9.360  -6.661  1.00  1.95           H  
-ATOM    189 HD21 LEU A 261       4.899   6.361  -4.449  1.00  1.75           H  
-ATOM    190 HD22 LEU A 261       5.051   8.107  -4.260  1.00  1.75           H  
-ATOM    191 HD23 LEU A 261       6.104   7.247  -5.383  1.00  1.86           H  
-ATOM    192  N   THR A 262      -0.063   8.210  -5.178  1.00  0.39           N  
-ATOM    193  CA  THR A 262      -1.130   9.153  -5.522  1.00  0.44           C  
-ATOM    194  C   THR A 262      -1.541   9.971  -4.300  1.00  0.45           C  
-ATOM    195  O   THR A 262      -1.615  11.202  -4.350  1.00  0.50           O  
-ATOM    196  CB  THR A 262      -2.373   8.413  -6.061  1.00  0.51           C  
-ATOM    197  OG1 THR A 262      -2.026   7.621  -7.206  1.00  0.54           O  
-ATOM    198  CG2 THR A 262      -3.469   9.397  -6.435  1.00  0.61           C  
-ATOM    199  H   THR A 262      -0.230   7.247  -5.274  1.00  0.41           H  
-ATOM    200  HA  THR A 262      -0.763   9.817  -6.288  1.00  0.47           H  
-ATOM    201  HB  THR A 262      -2.748   7.761  -5.284  1.00  0.53           H  
-ATOM    202  HG1 THR A 262      -1.578   6.811  -6.918  1.00  0.52           H  
-ATOM    203 HG21 THR A 262      -3.560  10.143  -5.659  1.00  1.18           H  
-ATOM    204 HG22 THR A 262      -4.404   8.870  -6.539  1.00  1.22           H  
-ATOM    205 HG23 THR A 262      -3.219   9.876  -7.370  1.00  1.20           H  
-ATOM    206  N   MET A 263      -1.803   9.274  -3.200  1.00  0.47           N  
-ATOM    207  CA  MET A 263      -2.203   9.915  -1.949  1.00  0.52           C  
-ATOM    208  C   MET A 263      -1.130  10.881  -1.434  1.00  0.49           C  
-ATOM    209  O   MET A 263      -1.441  12.004  -1.041  1.00  0.54           O  
-ATOM    210  CB  MET A 263      -2.503   8.861  -0.879  1.00  0.62           C  
-ATOM    211  CG  MET A 263      -3.706   7.993  -1.201  1.00  0.72           C  
-ATOM    212  SD  MET A 263      -5.223   8.952  -1.361  1.00  0.89           S  
-ATOM    213  CE  MET A 263      -6.389   7.674  -1.819  1.00  1.09           C  
-ATOM    214  H   MET A 263      -1.725   8.293  -3.232  1.00  0.47           H  
-ATOM    215  HA  MET A 263      -3.104  10.477  -2.143  1.00  0.56           H  
-ATOM    216  HB2 MET A 263      -1.642   8.218  -0.772  1.00  0.62           H  
-ATOM    217  HB3 MET A 263      -2.686   9.360   0.061  1.00  0.70           H  
-ATOM    218  HG2 MET A 263      -3.524   7.478  -2.133  1.00  0.69           H  
-ATOM    219  HG3 MET A 263      -3.836   7.271  -0.410  1.00  0.82           H  
-ATOM    220  HE1 MET A 263      -7.090   8.067  -2.542  1.00  1.65           H  
-ATOM    221  HE2 MET A 263      -6.925   7.343  -0.942  1.00  1.47           H  
-ATOM    222  HE3 MET A 263      -5.857   6.840  -2.251  1.00  1.48           H  
-ATOM    223  N   THR A 264       0.132  10.446  -1.418  1.00  0.48           N  
-ATOM    224  CA  THR A 264       1.211  11.304  -0.923  1.00  0.52           C  
-ATOM    225  C   THR A 264       1.492  12.501  -1.845  1.00  0.49           C  
-ATOM    226  O   THR A 264       1.739  13.604  -1.358  1.00  0.59           O  
-ATOM    227  CB  THR A 264       2.528  10.529  -0.666  1.00  0.60           C  
-ATOM    228  OG1 THR A 264       3.495  11.403  -0.075  1.00  0.72           O  
-ATOM    229  CG2 THR A 264       3.109   9.944  -1.944  1.00  0.58           C  
-ATOM    230  H   THR A 264       0.338   9.531  -1.731  1.00  0.51           H  
-ATOM    231  HA  THR A 264       0.881  11.697   0.026  1.00  0.58           H  
-ATOM    232  HB  THR A 264       2.321   9.720   0.020  1.00  0.67           H  
-ATOM    233  HG1 THR A 264       3.251  12.318  -0.249  1.00  1.04           H  
-ATOM    234 HG21 THR A 264       2.345   9.922  -2.707  1.00  1.19           H  
-ATOM    235 HG22 THR A 264       3.457   8.939  -1.754  1.00  1.14           H  
-ATOM    236 HG23 THR A 264       3.933  10.555  -2.277  1.00  1.20           H  
-ATOM    237  N   THR A 265       1.464  12.294  -3.166  1.00  0.43           N  
-ATOM    238  CA  THR A 265       1.731  13.392  -4.105  1.00  0.47           C  
-ATOM    239  C   THR A 265       0.617  14.443  -4.077  1.00  0.45           C  
-ATOM    240  O   THR A 265       0.884  15.638  -4.206  1.00  0.55           O  
-ATOM    241  CB  THR A 265       1.952  12.904  -5.561  1.00  0.53           C  
-ATOM    242  OG1 THR A 265       2.400  13.994  -6.377  1.00  0.67           O  
-ATOM    243  CG2 THR A 265       0.684  12.321  -6.169  1.00  0.52           C  
-ATOM    244  H   THR A 265       1.269  11.393  -3.515  1.00  0.40           H  
-ATOM    245  HA  THR A 265       2.645  13.869  -3.778  1.00  0.54           H  
-ATOM    246  HB  THR A 265       2.714  12.138  -5.552  1.00  0.60           H  
-ATOM    247  HG1 THR A 265       2.109  14.827  -5.995  1.00  0.94           H  
-ATOM    248 HG21 THR A 265       0.052  11.935  -5.383  1.00  1.15           H  
-ATOM    249 HG22 THR A 265       0.946  11.521  -6.846  1.00  1.19           H  
-ATOM    250 HG23 THR A 265       0.156  13.091  -6.710  1.00  1.10           H  
-ATOM    251  N   GLN A 266      -0.626  14.000  -3.905  1.00  0.40           N  
-ATOM    252  CA  GLN A 266      -1.760  14.919  -3.854  1.00  0.42           C  
-ATOM    253  C   GLN A 266      -1.898  15.546  -2.461  1.00  0.44           C  
-ATOM    254  O   GLN A 266      -2.279  16.712  -2.334  1.00  0.50           O  
-ATOM    255  CB  GLN A 266      -3.057  14.203  -4.244  1.00  0.47           C  
-ATOM    256  CG  GLN A 266      -4.277  15.113  -4.241  1.00  0.54           C  
-ATOM    257  CD  GLN A 266      -5.542  14.411  -4.699  1.00  0.68           C  
-ATOM    258  OE1 GLN A 266      -5.889  13.346  -4.203  1.00  0.80           O  
-ATOM    259  NE2 GLN A 266      -6.254  15.017  -5.631  1.00  0.83           N  
-ATOM    260  H   GLN A 266      -0.783  13.033  -3.808  1.00  0.41           H  
-ATOM    261  HA  GLN A 266      -1.571  15.708  -4.565  1.00  0.48           H  
-ATOM    262  HB2 GLN A 266      -2.944  13.792  -5.236  1.00  0.56           H  
-ATOM    263  HB3 GLN A 266      -3.235  13.397  -3.547  1.00  0.52           H  
-ATOM    264  HG2 GLN A 266      -4.435  15.480  -3.237  1.00  0.60           H  
-ATOM    265  HG3 GLN A 266      -4.088  15.947  -4.901  1.00  0.62           H  
-ATOM    266 HE21 GLN A 266      -5.936  15.889  -5.974  1.00  0.88           H  
-ATOM    267 HE22 GLN A 266      -7.068  14.577  -5.941  1.00  0.96           H  
-ATOM    268  N   SER A 267      -1.595  14.759  -1.423  1.00  0.50           N  
-ATOM    269  CA  SER A 267      -1.690  15.221  -0.032  1.00  0.60           C  
-ATOM    270  C   SER A 267      -3.132  15.609   0.325  1.00  0.63           C  
-ATOM    271  O   SER A 267      -4.055  14.816   0.144  1.00  0.70           O  
-ATOM    272  CB  SER A 267      -0.731  16.394   0.216  1.00  0.70           C  
-ATOM    273  OG  SER A 267       0.620  16.006   0.024  1.00  0.78           O  
-ATOM    274  H   SER A 267      -1.306  13.833  -1.595  1.00  0.52           H  
-ATOM    275  HA  SER A 267      -1.396  14.395   0.601  1.00  0.69           H  
-ATOM    276  HB2 SER A 267      -0.963  17.195  -0.469  1.00  0.76           H  
-ATOM    277  HB3 SER A 267      -0.851  16.744   1.231  1.00  0.84           H  
-ATOM    278  HG  SER A 267       0.666  15.282  -0.613  1.00  0.79           H  
-ATOM    279  N   SER A 268      -3.331  16.830   0.827  1.00  0.69           N  
-ATOM    280  CA  SER A 268      -4.671  17.306   1.201  1.00  0.78           C  
-ATOM    281  C   SER A 268      -5.502  17.716  -0.026  1.00  0.66           C  
-ATOM    282  O   SER A 268      -6.179  18.747  -0.019  1.00  0.70           O  
-ATOM    283  CB  SER A 268      -4.556  18.488   2.168  1.00  0.98           C  
-ATOM    284  OG  SER A 268      -3.736  19.514   1.630  1.00  1.02           O  
-ATOM    285  H   SER A 268      -2.565  17.429   0.949  1.00  0.74           H  
-ATOM    286  HA  SER A 268      -5.177  16.496   1.702  1.00  0.88           H  
-ATOM    287  HB2 SER A 268      -5.539  18.893   2.357  1.00  1.12           H  
-ATOM    288  HB3 SER A 268      -4.122  18.149   3.097  1.00  1.08           H  
-ATOM    289  HG  SER A 268      -4.076  20.372   1.902  1.00  1.27           H  
-ATOM    290  N   GLY A 269      -5.452  16.896  -1.076  1.00  0.62           N  
-ATOM    291  CA  GLY A 269      -6.200  17.177  -2.298  1.00  0.60           C  
-ATOM    292  C   GLY A 269      -7.688  16.873  -2.185  1.00  0.62           C  
-ATOM    293  O   GLY A 269      -8.235  16.102  -2.977  1.00  0.77           O  
-ATOM    294  H   GLY A 269      -4.899  16.082  -1.019  1.00  0.69           H  
-ATOM    295  HA2 GLY A 269      -6.081  18.222  -2.543  1.00  0.58           H  
-ATOM    296  HA3 GLY A 269      -5.786  16.584  -3.100  1.00  0.73           H  
-ATOM    297  N   SER A 270      -8.341  17.489  -1.201  1.00  0.60           N  
-ATOM    298  CA  SER A 270      -9.778  17.300  -0.966  1.00  0.75           C  
-ATOM    299  C   SER A 270     -10.615  17.645  -2.198  1.00  0.70           C  
-ATOM    300  O   SER A 270     -10.380  18.663  -2.848  1.00  0.55           O  
-ATOM    301  CB  SER A 270     -10.236  18.172   0.208  1.00  0.85           C  
-ATOM    302  OG  SER A 270     -11.653  18.205   0.305  1.00  1.02           O  
-ATOM    303  H   SER A 270      -7.836  18.097  -0.613  1.00  0.59           H  
-ATOM    304  HA  SER A 270      -9.939  16.264  -0.714  1.00  0.93           H  
-ATOM    305  HB2 SER A 270      -9.836  17.772   1.126  1.00  0.95           H  
-ATOM    306  HB3 SER A 270      -9.875  19.180   0.067  1.00  0.80           H  
-ATOM    307  HG  SER A 270     -11.931  17.720   1.089  1.00  1.20           H  
-ATOM    308  N   ARG A 271     -11.602  16.793  -2.496  1.00  0.91           N  
-ATOM    309  CA  ARG A 271     -12.503  17.002  -3.636  1.00  0.96           C  
-ATOM    310  C   ARG A 271     -11.743  17.083  -4.967  1.00  0.94           C  
-ATOM    311  O   ARG A 271     -11.962  18.003  -5.755  1.00  0.93           O  
-ATOM    312  CB  ARG A 271     -13.326  18.276  -3.413  1.00  0.88           C  
-ATOM    313  CG  ARG A 271     -14.282  18.184  -2.232  1.00  1.01           C  
-ATOM    314  CD  ARG A 271     -14.613  19.559  -1.672  1.00  0.94           C  
-ATOM    315  NE  ARG A 271     -13.512  20.109  -0.879  1.00  0.80           N  
-ATOM    316  CZ  ARG A 271     -13.470  21.350  -0.440  1.00  0.77           C  
-ATOM    317  NH1 ARG A 271     -14.424  22.194  -0.721  1.00  0.86           N  
-ATOM    318  NH2 ARG A 271     -12.469  21.761   0.283  1.00  0.80           N  
-ATOM    319  H   ARG A 271     -11.739  16.009  -1.923  1.00  1.06           H  
-ATOM    320  HA  ARG A 271     -13.177  16.160  -3.676  1.00  1.15           H  
-ATOM    321  HB2 ARG A 271     -12.652  19.102  -3.240  1.00  0.72           H  
-ATOM    322  HB3 ARG A 271     -13.905  18.477  -4.302  1.00  0.95           H  
-ATOM    323  HG2 ARG A 271     -15.196  17.710  -2.558  1.00  1.18           H  
-ATOM    324  HG3 ARG A 271     -13.822  17.589  -1.457  1.00  1.07           H  
-ATOM    325  HD2 ARG A 271     -14.821  20.229  -2.493  1.00  0.92           H  
-ATOM    326  HD3 ARG A 271     -15.489  19.476  -1.044  1.00  1.11           H  
-ATOM    327  HE  ARG A 271     -12.768  19.506  -0.654  1.00  0.82           H  
-ATOM    328 HH11 ARG A 271     -15.199  21.906  -1.274  1.00  0.94           H  
-ATOM    329 HH12 ARG A 271     -14.368  23.135  -0.376  1.00  0.91           H  
-ATOM    330 HH21 ARG A 271     -11.727  21.138   0.512  1.00  0.83           H  
-ATOM    331 HH22 ARG A 271     -12.450  22.727   0.601  1.00  0.87           H  
-ATOM    332  N   ALA A 272     -10.846  16.116  -5.200  1.00  1.01           N  
-ATOM    333  CA  ALA A 272     -10.038  16.068  -6.425  1.00  1.06           C  
-ATOM    334  C   ALA A 272      -9.157  17.316  -6.561  1.00  0.89           C  
-ATOM    335  O   ALA A 272      -9.132  17.965  -7.608  1.00  1.00           O  
-ATOM    336  CB  ALA A 272     -10.928  15.895  -7.654  1.00  1.29           C  
-ATOM    337  H   ALA A 272     -10.720  15.419  -4.523  1.00  1.07           H  
-ATOM    338  HA  ALA A 272      -9.396  15.202  -6.358  1.00  1.13           H  
-ATOM    339  HB1 ALA A 272     -11.503  14.986  -7.557  1.00  1.67           H  
-ATOM    340  HB2 ALA A 272     -10.312  15.838  -8.539  1.00  1.72           H  
-ATOM    341  HB3 ALA A 272     -11.597  16.738  -7.735  1.00  1.66           H  
-ATOM    342  N   SER A 273      -8.443  17.638  -5.477  1.00  0.70           N  
-ATOM    343  CA  SER A 273      -7.551  18.806  -5.423  1.00  0.62           C  
-ATOM    344  C   SER A 273      -8.314  20.110  -5.564  1.00  0.61           C  
-ATOM    345  O   SER A 273      -7.938  20.993  -6.335  1.00  0.76           O  
-ATOM    346  CB  SER A 273      -6.442  18.740  -6.474  1.00  0.81           C  
-ATOM    347  OG  SER A 273      -5.684  17.538  -6.372  1.00  0.87           O  
-ATOM    348  H   SER A 273      -8.524  17.070  -4.678  1.00  0.69           H  
-ATOM    349  HA  SER A 273      -7.091  18.821  -4.441  1.00  0.52           H  
-ATOM    350  HB2 SER A 273      -6.883  18.795  -7.454  1.00  0.95           H  
-ATOM    351  HB3 SER A 273      -5.778  19.580  -6.329  1.00  0.86           H  
-ATOM    352  HG  SER A 273      -4.816  17.671  -6.767  1.00  1.23           H  
-ATOM    353  N   LEU A 274      -9.349  20.248  -4.754  1.00  0.52           N  
-ATOM    354  CA  LEU A 274     -10.131  21.468  -4.717  1.00  0.55           C  
-ATOM    355  C   LEU A 274     -10.222  21.984  -3.266  1.00  0.43           C  
-ATOM    356  O   LEU A 274     -11.317  22.251  -2.774  1.00  0.49           O  
-ATOM    357  CB  LEU A 274     -11.530  21.216  -5.293  1.00  0.70           C  
-ATOM    358  CG  LEU A 274     -12.372  22.470  -5.549  1.00  0.82           C  
-ATOM    359  CD1 LEU A 274     -11.741  23.327  -6.635  1.00  1.04           C  
-ATOM    360  CD2 LEU A 274     -13.793  22.084  -5.928  1.00  1.01           C  
-ATOM    361  H   LEU A 274      -9.567  19.521  -4.123  1.00  0.50           H  
-ATOM    362  HA  LEU A 274      -9.625  22.207  -5.322  1.00  0.68           H  
-ATOM    363  HB2 LEU A 274     -11.420  20.686  -6.227  1.00  0.86           H  
-ATOM    364  HB3 LEU A 274     -12.069  20.584  -4.603  1.00  0.74           H  
-ATOM    365  HG  LEU A 274     -12.414  23.057  -4.643  1.00  0.83           H  
-ATOM    366 HD11 LEU A 274     -10.726  23.000  -6.810  1.00  1.34           H  
-ATOM    367 HD12 LEU A 274     -11.736  24.361  -6.321  1.00  1.46           H  
-ATOM    368 HD13 LEU A 274     -12.311  23.232  -7.546  1.00  1.68           H  
-ATOM    369 HD21 LEU A 274     -14.238  21.514  -5.126  1.00  1.34           H  
-ATOM    370 HD22 LEU A 274     -13.776  21.487  -6.827  1.00  1.23           H  
-ATOM    371 HD23 LEU A 274     -14.374  22.977  -6.100  1.00  1.55           H  
-ATOM    372  N   PRO A 275      -9.066  22.084  -2.538  1.00  0.43           N  
-ATOM    373  CA  PRO A 275      -9.026  22.533  -1.118  1.00  0.55           C  
-ATOM    374  C   PRO A 275      -9.566  23.957  -0.855  1.00  0.70           C  
-ATOM    375  O   PRO A 275      -8.844  24.819  -0.354  1.00  0.90           O  
-ATOM    376  CB  PRO A 275      -7.532  22.483  -0.781  1.00  0.69           C  
-ATOM    377  CG  PRO A 275      -6.989  21.468  -1.712  1.00  0.57           C  
-ATOM    378  CD  PRO A 275      -7.703  21.716  -3.003  1.00  0.50           C  
-ATOM    379  HA  PRO A 275      -9.545  21.835  -0.482  1.00  0.56           H  
-ATOM    380  HB2 PRO A 275      -7.088  23.454  -0.946  1.00  0.83           H  
-ATOM    381  HB3 PRO A 275      -7.399  22.187   0.249  1.00  0.79           H  
-ATOM    382  HG2 PRO A 275      -5.923  21.599  -1.830  1.00  0.71           H  
-ATOM    383  HG3 PRO A 275      -7.217  20.481  -1.342  1.00  0.52           H  
-ATOM    384  HD2 PRO A 275      -7.237  22.521  -3.548  1.00  0.66           H  
-ATOM    385  HD3 PRO A 275      -7.725  20.807  -3.612  1.00  0.46           H  
-ATOM    386  N   LYS A 276     -10.834  24.193  -1.175  1.00  0.72           N  
-ATOM    387  CA  LYS A 276     -11.459  25.499  -0.955  1.00  0.89           C  
-ATOM    388  C   LYS A 276     -12.606  25.406   0.061  1.00  0.98           C  
-ATOM    389  O   LYS A 276     -12.898  24.276   0.527  1.00  0.92           O  
-ATOM    390  CB  LYS A 276     -11.986  26.069  -2.274  1.00  0.96           C  
-ATOM    391  CG  LYS A 276     -10.901  26.328  -3.304  1.00  1.05           C  
-ATOM    392  CD  LYS A 276     -11.484  26.886  -4.591  1.00  1.21           C  
-ATOM    393  CE  LYS A 276     -10.398  27.191  -5.607  1.00  1.48           C  
-ATOM    394  NZ  LYS A 276     -10.962  27.723  -6.878  1.00  1.76           N  
-ATOM    395  OXT LYS A 276     -13.206  26.454   0.377  1.00  1.28           O  
-ATOM    396  H   LYS A 276     -11.371  23.463  -1.567  1.00  0.70           H  
-ATOM    397  HA  LYS A 276     -10.704  26.165  -0.565  1.00  1.02           H  
-ATOM    398  HB2 LYS A 276     -12.695  25.371  -2.697  1.00  0.90           H  
-ATOM    399  HB3 LYS A 276     -12.491  27.002  -2.072  1.00  1.13           H  
-ATOM    400  HG2 LYS A 276     -10.197  27.040  -2.902  1.00  1.23           H  
-ATOM    401  HG3 LYS A 276     -10.394  25.400  -3.522  1.00  1.01           H  
-ATOM    402  HD2 LYS A 276     -12.163  26.159  -5.012  1.00  1.17           H  
-ATOM    403  HD3 LYS A 276     -12.022  27.796  -4.367  1.00  1.36           H  
-ATOM    404  HE2 LYS A 276      -9.724  27.924  -5.187  1.00  1.64           H  
-ATOM    405  HE3 LYS A 276      -9.853  26.281  -5.817  1.00  1.46           H  
-ATOM    406  HZ1 LYS A 276     -10.471  28.599  -7.148  1.00  2.15           H  
-ATOM    407  HZ2 LYS A 276     -11.974  27.928  -6.760  1.00  2.01           H  
-ATOM    408  HZ3 LYS A 276     -10.848  27.023  -7.638  1.00  2.10           H  
-TER
-ENDMDL
-MODEL      5
-ATOM      1  N   ALA A 249       1.664 -11.791 -11.075  1.00  2.74           N  
-ATOM      2  CA  ALA A 249       2.234 -10.419 -11.017  1.00  2.53           C  
-ATOM      3  C   ALA A 249       1.865  -9.733  -9.700  1.00  2.36           C  
-ATOM      4  O   ALA A 249       0.857 -10.074  -9.082  1.00  2.39           O  
-ATOM      5  CB  ALA A 249       1.752  -9.597 -12.204  1.00  2.50           C  
-ATOM      6  H1  ALA A 249       1.157 -11.929 -11.973  1.00  3.03           H  
-ATOM      7  H2  ALA A 249       0.997 -11.935 -10.289  1.00  2.82           H  
-ATOM      8  H3  ALA A 249       2.424 -12.498 -11.006  1.00  2.82           H  
-ATOM      9  HA  ALA A 249       3.310 -10.496 -11.078  1.00  2.57           H  
-ATOM     10  HB1 ALA A 249       2.601  -9.143 -12.694  1.00  2.71           H  
-ATOM     11  HB2 ALA A 249       1.081  -8.824 -11.858  1.00  2.72           H  
-ATOM     12  HB3 ALA A 249       1.234 -10.239 -12.900  1.00  2.68           H  
-ATOM     13  N   PRO A 250       2.671  -8.751  -9.259  1.00  2.21           N  
-ATOM     14  CA  PRO A 250       2.423  -8.021  -8.022  1.00  2.08           C  
-ATOM     15  C   PRO A 250       1.305  -6.978  -8.161  1.00  1.90           C  
-ATOM     16  O   PRO A 250       1.525  -5.788  -7.939  1.00  1.76           O  
-ATOM     17  CB  PRO A 250       3.760  -7.335  -7.718  1.00  2.07           C  
-ATOM     18  CG  PRO A 250       4.671  -7.588  -8.871  1.00  2.01           C  
-ATOM     19  CD  PRO A 250       3.876  -8.271  -9.938  1.00  2.23           C  
-ATOM     20  HA  PRO A 250       2.174  -8.694  -7.215  1.00  2.18           H  
-ATOM     21  HB2 PRO A 250       3.595  -6.280  -7.592  1.00  2.18           H  
-ATOM     22  HB3 PRO A 250       4.168  -7.750  -6.812  1.00  2.11           H  
-ATOM     23  HG2 PRO A 250       5.050  -6.647  -9.241  1.00  1.74           H  
-ATOM     24  HG3 PRO A 250       5.490  -8.217  -8.555  1.00  2.40           H  
-ATOM     25  HD2 PRO A 250       3.617  -7.566 -10.710  1.00  2.09           H  
-ATOM     26  HD3 PRO A 250       4.437  -9.092 -10.344  1.00  2.65           H  
-ATOM     27  N   ALA A 251       0.108  -7.432  -8.536  1.00  1.95           N  
-ATOM     28  CA  ALA A 251      -1.042  -6.538  -8.713  1.00  1.85           C  
-ATOM     29  C   ALA A 251      -1.365  -5.757  -7.434  1.00  1.71           C  
-ATOM     30  O   ALA A 251      -1.552  -4.542  -7.473  1.00  1.56           O  
-ATOM     31  CB  ALA A 251      -2.260  -7.329  -9.170  1.00  2.00           C  
-ATOM     32  H   ALA A 251      -0.001  -8.396  -8.706  1.00  2.09           H  
-ATOM     33  HA  ALA A 251      -0.791  -5.834  -9.493  1.00  1.81           H  
-ATOM     34  HB1 ALA A 251      -2.832  -7.638  -8.308  1.00  2.26           H  
-ATOM     35  HB2 ALA A 251      -1.938  -8.200  -9.721  1.00  2.28           H  
-ATOM     36  HB3 ALA A 251      -2.873  -6.707  -9.805  1.00  2.29           H  
-ATOM     37  N   ARG A 252      -1.418  -6.461  -6.303  1.00  1.81           N  
-ATOM     38  CA  ARG A 252      -1.708  -5.828  -5.015  1.00  1.75           C  
-ATOM     39  C   ARG A 252      -0.605  -4.827  -4.654  1.00  1.62           C  
-ATOM     40  O   ARG A 252      -0.882  -3.726  -4.179  1.00  1.49           O  
-ATOM     41  CB  ARG A 252      -1.851  -6.896  -3.923  1.00  1.95           C  
-ATOM     42  CG  ARG A 252      -2.868  -6.551  -2.837  1.00  2.07           C  
-ATOM     43  CD  ARG A 252      -2.386  -5.429  -1.926  1.00  2.09           C  
-ATOM     44  NE  ARG A 252      -3.392  -5.053  -0.934  1.00  2.30           N  
-ATOM     45  CZ  ARG A 252      -4.475  -4.354  -1.207  1.00  2.40           C  
-ATOM     46  NH1 ARG A 252      -4.684  -3.903  -2.418  1.00  2.34           N  
-ATOM     47  NH2 ARG A 252      -5.345  -4.096  -0.265  1.00  2.65           N  
-ATOM     48  H   ARG A 252      -1.253  -7.425  -6.335  1.00  1.94           H  
-ATOM     49  HA  ARG A 252      -2.640  -5.293  -5.109  1.00  1.70           H  
-ATOM     50  HB2 ARG A 252      -2.156  -7.824  -4.384  1.00  2.05           H  
-ATOM     51  HB3 ARG A 252      -0.891  -7.040  -3.452  1.00  1.99           H  
-ATOM     52  HG2 ARG A 252      -3.788  -6.243  -3.308  1.00  2.09           H  
-ATOM     53  HG3 ARG A 252      -3.048  -7.434  -2.240  1.00  2.22           H  
-ATOM     54  HD2 ARG A 252      -1.494  -5.758  -1.416  1.00  2.15           H  
-ATOM     55  HD3 ARG A 252      -2.153  -4.567  -2.535  1.00  1.96           H  
-ATOM     56  HE  ARG A 252      -3.253  -5.354  -0.013  1.00  2.44           H  
-ATOM     57 HH11 ARG A 252      -4.019  -4.087  -3.138  1.00  2.19           H  
-ATOM     58 HH12 ARG A 252      -5.509  -3.382  -2.620  1.00  2.50           H  
-ATOM     59 HH21 ARG A 252      -5.188  -4.427   0.663  1.00  2.75           H  
-ATOM     60 HH22 ARG A 252      -6.162  -3.563  -0.472  1.00  2.79           H  
-ATOM     61  N   VAL A 253       0.643  -5.215  -4.909  1.00  1.69           N  
-ATOM     62  CA  VAL A 253       1.792  -4.352  -4.638  1.00  1.66           C  
-ATOM     63  C   VAL A 253       1.737  -3.103  -5.519  1.00  1.46           C  
-ATOM     64  O   VAL A 253       1.856  -1.980  -5.026  1.00  1.36           O  
-ATOM     65  CB  VAL A 253       3.126  -5.094  -4.879  1.00  1.83           C  
-ATOM     66  CG1 VAL A 253       4.315  -4.203  -4.548  1.00  1.87           C  
-ATOM     67  CG2 VAL A 253       3.175  -6.379  -4.064  1.00  2.06           C  
-ATOM     68  H   VAL A 253       0.793  -6.095  -5.306  1.00  1.81           H  
-ATOM     69  HA  VAL A 253       1.748  -4.053  -3.603  1.00  1.69           H  
-ATOM     70  HB  VAL A 253       3.183  -5.355  -5.925  1.00  1.82           H  
-ATOM     71 HG11 VAL A 253       5.051  -4.774  -4.003  1.00  2.14           H  
-ATOM     72 HG12 VAL A 253       3.986  -3.371  -3.945  1.00  2.18           H  
-ATOM     73 HG13 VAL A 253       4.753  -3.833  -5.463  1.00  2.13           H  
-ATOM     74 HG21 VAL A 253       4.131  -6.452  -3.567  1.00  2.35           H  
-ATOM     75 HG22 VAL A 253       3.043  -7.225  -4.720  1.00  2.34           H  
-ATOM     76 HG23 VAL A 253       2.386  -6.367  -3.328  1.00  2.35           H  
-ATOM     77  N   GLY A 254       1.526  -3.314  -6.818  1.00  1.45           N  
-ATOM     78  CA  GLY A 254       1.426  -2.206  -7.757  1.00  1.33           C  
-ATOM     79  C   GLY A 254       0.298  -1.256  -7.391  1.00  1.15           C  
-ATOM     80  O   GLY A 254       0.490  -0.038  -7.346  1.00  1.03           O  
-ATOM     81  H   GLY A 254       1.422  -4.239  -7.141  1.00  1.56           H  
-ATOM     82  HA2 GLY A 254       2.359  -1.661  -7.759  1.00  1.34           H  
-ATOM     83  HA3 GLY A 254       1.246  -2.599  -8.746  1.00  1.40           H  
-ATOM     84  N   LEU A 255      -0.873  -1.819  -7.100  1.00  1.18           N  
-ATOM     85  CA  LEU A 255      -2.032  -1.026  -6.698  1.00  1.08           C  
-ATOM     86  C   LEU A 255      -1.739  -0.243  -5.425  1.00  0.96           C  
-ATOM     87  O   LEU A 255      -2.039   0.948  -5.327  1.00  0.84           O  
-ATOM     88  CB  LEU A 255      -3.241  -1.924  -6.461  1.00  1.22           C  
-ATOM     89  CG  LEU A 255      -4.186  -2.046  -7.645  1.00  1.37           C  
-ATOM     90  CD1 LEU A 255      -5.345  -2.961  -7.296  1.00  1.56           C  
-ATOM     91  CD2 LEU A 255      -4.685  -0.669  -8.054  1.00  1.34           C  
-ATOM     92  H   LEU A 255      -0.956  -2.800  -7.135  1.00  1.31           H  
-ATOM     93  HA  LEU A 255      -2.263  -0.336  -7.496  1.00  1.05           H  
-ATOM     94  HB2 LEU A 255      -2.884  -2.911  -6.206  1.00  1.34           H  
-ATOM     95  HB3 LEU A 255      -3.796  -1.532  -5.623  1.00  1.20           H  
-ATOM     96  HG  LEU A 255      -3.655  -2.475  -8.482  1.00  1.45           H  
-ATOM     97 HD11 LEU A 255      -5.025  -3.990  -7.367  1.00  1.90           H  
-ATOM     98 HD12 LEU A 255      -6.161  -2.788  -7.981  1.00  1.91           H  
-ATOM     99 HD13 LEU A 255      -5.672  -2.755  -6.286  1.00  1.91           H  
-ATOM    100 HD21 LEU A 255      -3.951  -0.195  -8.691  1.00  1.71           H  
-ATOM    101 HD22 LEU A 255      -4.834  -0.065  -7.171  1.00  1.71           H  
-ATOM    102 HD23 LEU A 255      -5.617  -0.765  -8.587  1.00  1.66           H  
-ATOM    103  N   GLY A 256      -1.151  -0.930  -4.451  1.00  1.08           N  
-ATOM    104  CA  GLY A 256      -0.823  -0.306  -3.190  1.00  1.09           C  
-ATOM    105  C   GLY A 256       0.172   0.838  -3.328  1.00  1.01           C  
-ATOM    106  O   GLY A 256      -0.086   1.942  -2.846  1.00  0.93           O  
-ATOM    107  H   GLY A 256      -0.942  -1.880  -4.594  1.00  1.20           H  
-ATOM    108  HA2 GLY A 256      -1.733   0.076  -2.751  1.00  1.06           H  
-ATOM    109  HA3 GLY A 256      -0.408  -1.054  -2.534  1.00  1.25           H  
-ATOM    110  N   ILE A 257       1.306   0.592  -3.993  1.00  1.09           N  
-ATOM    111  CA  ILE A 257       2.310   1.645  -4.176  1.00  1.09           C  
-ATOM    112  C   ILE A 257       1.745   2.806  -5.000  1.00  0.90           C  
-ATOM    113  O   ILE A 257       2.012   3.970  -4.704  1.00  0.89           O  
-ATOM    114  CB  ILE A 257       3.633   1.120  -4.802  1.00  1.27           C  
-ATOM    115  CG1 ILE A 257       3.410   0.491  -6.182  1.00  1.25           C  
-ATOM    116  CG2 ILE A 257       4.298   0.121  -3.868  1.00  1.50           C  
-ATOM    117  CD1 ILE A 257       3.720   1.422  -7.338  1.00  1.22           C  
-ATOM    118  H   ILE A 257       1.467  -0.307  -4.369  1.00  1.19           H  
-ATOM    119  HA  ILE A 257       2.545   2.025  -3.190  1.00  1.17           H  
-ATOM    120  HB  ILE A 257       4.301   1.963  -4.907  1.00  1.29           H  
-ATOM    121 HG12 ILE A 257       4.045  -0.377  -6.279  1.00  1.40           H  
-ATOM    122 HG13 ILE A 257       2.378   0.187  -6.268  1.00  1.19           H  
-ATOM    123 HG21 ILE A 257       4.267   0.498  -2.857  1.00  1.86           H  
-ATOM    124 HG22 ILE A 257       5.326  -0.022  -4.168  1.00  1.85           H  
-ATOM    125 HG23 ILE A 257       3.774  -0.821  -3.917  1.00  1.88           H  
-ATOM    126 HD11 ILE A 257       4.119   2.350  -6.956  1.00  1.56           H  
-ATOM    127 HD12 ILE A 257       2.815   1.621  -7.893  1.00  1.60           H  
-ATOM    128 HD13 ILE A 257       4.446   0.959  -7.989  1.00  1.63           H  
-ATOM    129  N   THR A 258       0.937   2.485  -6.016  1.00  0.80           N  
-ATOM    130  CA  THR A 258       0.314   3.509  -6.856  1.00  0.70           C  
-ATOM    131  C   THR A 258      -0.651   4.356  -6.028  1.00  0.57           C  
-ATOM    132  O   THR A 258      -0.678   5.582  -6.150  1.00  0.54           O  
-ATOM    133  CB  THR A 258      -0.454   2.888  -8.046  1.00  0.78           C  
-ATOM    134  OG1 THR A 258       0.431   2.089  -8.841  1.00  0.96           O  
-ATOM    135  CG2 THR A 258      -1.084   3.966  -8.920  1.00  0.85           C  
-ATOM    136  H   THR A 258       0.743   1.534  -6.194  1.00  0.87           H  
-ATOM    137  HA  THR A 258       1.096   4.144  -7.247  1.00  0.75           H  
-ATOM    138  HB  THR A 258      -1.241   2.258  -7.656  1.00  0.79           H  
-ATOM    139  HG1 THR A 258       0.550   1.224  -8.418  1.00  1.03           H  
-ATOM    140 HG21 THR A 258      -2.159   3.909  -8.842  1.00  1.33           H  
-ATOM    141 HG22 THR A 258      -0.787   3.813  -9.948  1.00  1.35           H  
-ATOM    142 HG23 THR A 258      -0.750   4.938  -8.588  1.00  1.35           H  
-ATOM    143  N   THR A 259      -1.424   3.695  -5.166  1.00  0.57           N  
-ATOM    144  CA  THR A 259      -2.375   4.386  -4.295  1.00  0.53           C  
-ATOM    145  C   THR A 259      -1.632   5.309  -3.331  1.00  0.56           C  
-ATOM    146  O   THR A 259      -1.988   6.479  -3.179  1.00  0.56           O  
-ATOM    147  CB  THR A 259      -3.235   3.390  -3.480  1.00  0.64           C  
-ATOM    148  OG1 THR A 259      -3.904   2.476  -4.361  1.00  0.71           O  
-ATOM    149  CG2 THR A 259      -4.268   4.122  -2.634  1.00  0.68           C  
-ATOM    150  H   THR A 259      -1.340   2.718  -5.103  1.00  0.65           H  
-ATOM    151  HA  THR A 259      -3.031   4.977  -4.915  1.00  0.50           H  
-ATOM    152  HB  THR A 259      -2.583   2.831  -2.821  1.00  0.75           H  
-ATOM    153  HG1 THR A 259      -3.258   1.860  -4.740  1.00  0.77           H  
-ATOM    154 HG21 THR A 259      -3.791   4.526  -1.753  1.00  1.25           H  
-ATOM    155 HG22 THR A 259      -5.045   3.432  -2.338  1.00  1.23           H  
-ATOM    156 HG23 THR A 259      -4.701   4.925  -3.211  1.00  1.24           H  
-ATOM    157  N   VAL A 260      -0.582   4.778  -2.702  1.00  0.70           N  
-ATOM    158  CA  VAL A 260       0.231   5.558  -1.770  1.00  0.84           C  
-ATOM    159  C   VAL A 260       0.882   6.749  -2.479  1.00  0.83           C  
-ATOM    160  O   VAL A 260       0.800   7.880  -2.002  1.00  0.89           O  
-ATOM    161  CB  VAL A 260       1.326   4.690  -1.109  1.00  1.04           C  
-ATOM    162  CG1 VAL A 260       2.237   5.539  -0.236  1.00  1.25           C  
-ATOM    163  CG2 VAL A 260       0.699   3.570  -0.292  1.00  1.14           C  
-ATOM    164  H   VAL A 260      -0.340   3.838  -2.881  1.00  0.75           H  
-ATOM    165  HA  VAL A 260      -0.421   5.931  -0.993  1.00  0.87           H  
-ATOM    166  HB  VAL A 260       1.925   4.245  -1.892  1.00  1.04           H  
-ATOM    167 HG11 VAL A 260       3.224   5.574  -0.672  1.00  1.67           H  
-ATOM    168 HG12 VAL A 260       2.295   5.106   0.752  1.00  1.61           H  
-ATOM    169 HG13 VAL A 260       1.838   6.540  -0.167  1.00  1.67           H  
-ATOM    170 HG21 VAL A 260       0.704   2.657  -0.869  1.00  1.53           H  
-ATOM    171 HG22 VAL A 260      -0.319   3.833  -0.046  1.00  1.57           H  
-ATOM    172 HG23 VAL A 260       1.264   3.427   0.615  1.00  1.56           H  
-ATOM    173  N   LEU A 261       1.506   6.485  -3.632  1.00  0.80           N  
-ATOM    174  CA  LEU A 261       2.154   7.535  -4.424  1.00  0.83           C  
-ATOM    175  C   LEU A 261       1.137   8.612  -4.815  1.00  0.74           C  
-ATOM    176  O   LEU A 261       1.416   9.808  -4.739  1.00  0.85           O  
-ATOM    177  CB  LEU A 261       2.783   6.928  -5.684  1.00  0.86           C  
-ATOM    178  CG  LEU A 261       4.161   7.476  -6.068  1.00  1.10           C  
-ATOM    179  CD1 LEU A 261       4.087   8.954  -6.425  1.00  1.52           C  
-ATOM    180  CD2 LEU A 261       5.155   7.248  -4.939  1.00  1.54           C  
-ATOM    181  H   LEU A 261       1.522   5.558  -3.966  1.00  0.79           H  
-ATOM    182  HA  LEU A 261       2.928   7.983  -3.819  1.00  0.98           H  
-ATOM    183  HB2 LEU A 261       2.874   5.861  -5.535  1.00  1.01           H  
-ATOM    184  HB3 LEU A 261       2.111   7.099  -6.511  1.00  0.97           H  
-ATOM    185  HG  LEU A 261       4.519   6.944  -6.939  1.00  1.09           H  
-ATOM    186 HD11 LEU A 261       3.829   9.059  -7.469  1.00  1.91           H  
-ATOM    187 HD12 LEU A 261       5.045   9.417  -6.242  1.00  1.94           H  
-ATOM    188 HD13 LEU A 261       3.333   9.433  -5.818  1.00  1.97           H  
-ATOM    189 HD21 LEU A 261       6.084   7.748  -5.169  1.00  1.98           H  
-ATOM    190 HD22 LEU A 261       5.334   6.188  -4.828  1.00  1.94           H  
-ATOM    191 HD23 LEU A 261       4.751   7.643  -4.019  1.00  1.98           H  
-ATOM    192  N   THR A 262      -0.052   8.167  -5.220  1.00  0.62           N  
-ATOM    193  CA  THR A 262      -1.132   9.074  -5.607  1.00  0.61           C  
-ATOM    194  C   THR A 262      -1.576   9.936  -4.422  1.00  0.70           C  
-ATOM    195  O   THR A 262      -1.694  11.156  -4.535  1.00  0.82           O  
-ATOM    196  CB  THR A 262      -2.355   8.292  -6.136  1.00  0.56           C  
-ATOM    197  OG1 THR A 262      -1.988   7.522  -7.288  1.00  0.59           O  
-ATOM    198  CG2 THR A 262      -3.497   9.231  -6.492  1.00  0.73           C  
-ATOM    199  H   THR A 262      -0.208   7.196  -5.250  1.00  0.59           H  
-ATOM    200  HA  THR A 262      -0.767   9.717  -6.396  1.00  0.68           H  
-ATOM    201  HB  THR A 262      -2.691   7.621  -5.359  1.00  0.52           H  
-ATOM    202  HG1 THR A 262      -1.515   6.724  -7.004  1.00  0.72           H  
-ATOM    203 HG21 THR A 262      -3.725   9.139  -7.543  1.00  1.30           H  
-ATOM    204 HG22 THR A 262      -3.209  10.248  -6.272  1.00  1.27           H  
-ATOM    205 HG23 THR A 262      -4.369   8.971  -5.910  1.00  1.29           H  
-ATOM    206  N   MET A 263      -1.824   9.284  -3.287  1.00  0.71           N  
-ATOM    207  CA  MET A 263      -2.258   9.979  -2.074  1.00  0.84           C  
-ATOM    208  C   MET A 263      -1.184  10.952  -1.568  1.00  0.94           C  
-ATOM    209  O   MET A 263      -1.460  12.132  -1.347  1.00  1.06           O  
-ATOM    210  CB  MET A 263      -2.596   8.961  -0.981  1.00  0.90           C  
-ATOM    211  CG  MET A 263      -3.191   9.584   0.271  1.00  1.08           C  
-ATOM    212  SD  MET A 263      -4.737  10.449  -0.056  1.00  1.24           S  
-ATOM    213  CE  MET A 263      -5.162  11.020   1.587  1.00  1.54           C  
-ATOM    214  H   MET A 263      -1.716   8.305  -3.265  1.00  0.65           H  
-ATOM    215  HA  MET A 263      -3.148  10.539  -2.316  1.00  0.89           H  
-ATOM    216  HB2 MET A 263      -3.307   8.250  -1.375  1.00  0.87           H  
-ATOM    217  HB3 MET A 263      -1.694   8.437  -0.702  1.00  0.92           H  
-ATOM    218  HG2 MET A 263      -3.378   8.802   0.992  1.00  1.15           H  
-ATOM    219  HG3 MET A 263      -2.480  10.286   0.680  1.00  1.16           H  
-ATOM    220  HE1 MET A 263      -4.268  11.349   2.096  1.00  1.91           H  
-ATOM    221  HE2 MET A 263      -5.615  10.212   2.142  1.00  1.87           H  
-ATOM    222  HE3 MET A 263      -5.857  11.843   1.514  1.00  1.93           H  
-ATOM    223  N   THR A 264       0.037  10.458  -1.380  1.00  0.95           N  
-ATOM    224  CA  THR A 264       1.126  11.296  -0.900  1.00  1.06           C  
-ATOM    225  C   THR A 264       2.396  11.118  -1.739  1.00  1.09           C  
-ATOM    226  O   THR A 264       2.804  10.001  -2.054  1.00  1.11           O  
-ATOM    227  CB  THR A 264       1.434  11.003   0.581  1.00  1.12           C  
-ATOM    228  OG1 THR A 264       2.365  11.968   1.092  1.00  1.21           O  
-ATOM    229  CG2 THR A 264       1.995   9.598   0.777  1.00  1.13           C  
-ATOM    230  H   THR A 264       0.208   9.509  -1.559  1.00  0.92           H  
-ATOM    231  HA  THR A 264       0.805  12.325  -0.976  1.00  1.11           H  
-ATOM    232  HB  THR A 264       0.509  11.074   1.127  1.00  1.13           H  
-ATOM    233  HG1 THR A 264       2.594  11.749   2.009  1.00  1.27           H  
-ATOM    234 HG21 THR A 264       2.403   9.510   1.774  1.00  1.53           H  
-ATOM    235 HG22 THR A 264       2.774   9.417   0.051  1.00  1.54           H  
-ATOM    236 HG23 THR A 264       1.205   8.873   0.646  1.00  1.49           H  
-ATOM    237  N   THR A 265       3.020  12.233  -2.101  1.00  1.13           N  
-ATOM    238  CA  THR A 265       4.243  12.195  -2.901  1.00  1.17           C  
-ATOM    239  C   THR A 265       5.444  11.735  -2.070  1.00  1.07           C  
-ATOM    240  O   THR A 265       6.353  11.086  -2.585  1.00  1.15           O  
-ATOM    241  CB  THR A 265       4.555  13.566  -3.542  1.00  1.22           C  
-ATOM    242  OG1 THR A 265       5.824  13.524  -4.211  1.00  1.29           O  
-ATOM    243  CG2 THR A 265       4.561  14.677  -2.500  1.00  1.15           C  
-ATOM    244  H   THR A 265       2.650  13.097  -1.826  1.00  1.15           H  
-ATOM    245  HA  THR A 265       4.087  11.483  -3.699  1.00  1.31           H  
-ATOM    246  HB  THR A 265       3.785  13.785  -4.270  1.00  1.34           H  
-ATOM    247  HG1 THR A 265       6.171  12.625  -4.189  1.00  1.30           H  
-ATOM    248 HG21 THR A 265       4.757  15.623  -2.983  1.00  1.54           H  
-ATOM    249 HG22 THR A 265       5.330  14.479  -1.769  1.00  1.48           H  
-ATOM    250 HG23 THR A 265       3.599  14.715  -2.010  1.00  1.58           H  
-ATOM    251  N   GLN A 266       5.429  12.072  -0.782  1.00  0.95           N  
-ATOM    252  CA  GLN A 266       6.506  11.695   0.131  1.00  0.90           C  
-ATOM    253  C   GLN A 266       6.049  11.820   1.584  1.00  0.81           C  
-ATOM    254  O   GLN A 266       5.548  12.864   2.004  1.00  0.74           O  
-ATOM    255  CB  GLN A 266       7.742  12.570  -0.112  1.00  0.86           C  
-ATOM    256  CG  GLN A 266       8.882  12.313   0.862  1.00  0.89           C  
-ATOM    257  CD  GLN A 266      10.134  13.088   0.502  1.00  1.01           C  
-ATOM    258  OE1 GLN A 266      10.777  12.818  -0.505  1.00  1.20           O  
-ATOM    259  NE2 GLN A 266      10.477  14.075   1.310  1.00  1.00           N  
-ATOM    260  H   GLN A 266       4.669  12.584  -0.436  1.00  0.94           H  
-ATOM    261  HA  GLN A 266       6.760  10.665  -0.066  1.00  1.05           H  
-ATOM    262  HB2 GLN A 266       8.106  12.386  -1.111  1.00  0.99           H  
-ATOM    263  HB3 GLN A 266       7.455  13.608  -0.029  1.00  0.81           H  
-ATOM    264  HG2 GLN A 266       8.568  12.606   1.852  1.00  0.84           H  
-ATOM    265  HG3 GLN A 266       9.116  11.259   0.855  1.00  1.02           H  
-ATOM    266 HE21 GLN A 266       9.901  14.258   2.105  1.00  0.93           H  
-ATOM    267 HE22 GLN A 266      11.279  14.586   1.092  1.00  1.14           H  
-ATOM    268  N   SER A 267       6.226  10.746   2.346  1.00  0.90           N  
-ATOM    269  CA  SER A 267       5.838  10.717   3.762  1.00  0.88           C  
-ATOM    270  C   SER A 267       6.506   9.545   4.478  1.00  1.05           C  
-ATOM    271  O   SER A 267       7.163   9.725   5.502  1.00  1.07           O  
-ATOM    272  CB  SER A 267       4.314  10.612   3.919  1.00  0.96           C  
-ATOM    273  OG  SER A 267       3.655  11.774   3.435  1.00  0.89           O  
-ATOM    274  H   SER A 267       6.631   9.947   1.947  1.00  1.02           H  
-ATOM    275  HA  SER A 267       6.176  11.638   4.213  1.00  0.76           H  
-ATOM    276  HB2 SER A 267       3.956   9.757   3.364  1.00  1.11           H  
-ATOM    277  HB3 SER A 267       4.073  10.487   4.965  1.00  1.02           H  
-ATOM    278  HG  SER A 267       4.304  12.360   3.008  1.00  0.82           H  
-ATOM    279  N   SER A 268       6.347   8.346   3.924  1.00  1.26           N  
-ATOM    280  CA  SER A 268       6.951   7.146   4.502  1.00  1.48           C  
-ATOM    281  C   SER A 268       8.473   7.179   4.366  1.00  1.51           C  
-ATOM    282  O   SER A 268       9.004   7.528   3.311  1.00  1.58           O  
-ATOM    283  CB  SER A 268       6.394   5.893   3.822  1.00  1.83           C  
-ATOM    284  OG  SER A 268       6.327   6.063   2.413  1.00  1.91           O  
-ATOM    285  H   SER A 268       5.821   8.264   3.100  1.00  1.32           H  
-ATOM    286  HA  SER A 268       6.696   7.119   5.550  1.00  1.47           H  
-ATOM    287  HB2 SER A 268       7.038   5.054   4.040  1.00  2.01           H  
-ATOM    288  HB3 SER A 268       5.401   5.693   4.197  1.00  1.91           H  
-ATOM    289  HG  SER A 268       7.205   5.958   2.034  1.00  1.95           H  
-ATOM    290  N   GLY A 269       9.172   6.822   5.439  1.00  1.57           N  
-ATOM    291  CA  GLY A 269      10.628   6.834   5.414  1.00  1.69           C  
-ATOM    292  C   GLY A 269      11.258   5.776   6.310  1.00  1.94           C  
-ATOM    293  O   GLY A 269      10.665   5.355   7.303  1.00  2.03           O  
-ATOM    294  H   GLY A 269       8.696   6.557   6.261  1.00  1.60           H  
-ATOM    295  HA2 GLY A 269      10.956   6.668   4.399  1.00  1.80           H  
-ATOM    296  HA3 GLY A 269      10.970   7.807   5.734  1.00  1.58           H  
-ATOM    297  N   SER A 270      12.463   5.339   5.942  1.00  2.17           N  
-ATOM    298  CA  SER A 270      13.196   4.311   6.698  1.00  2.46           C  
-ATOM    299  C   SER A 270      13.547   4.774   8.117  1.00  2.44           C  
-ATOM    300  O   SER A 270      14.028   5.891   8.310  1.00  2.37           O  
-ATOM    301  CB  SER A 270      14.479   3.941   5.948  1.00  2.77           C  
-ATOM    302  OG  SER A 270      15.158   2.853   6.559  1.00  3.07           O  
-ATOM    303  H   SER A 270      12.871   5.715   5.134  1.00  2.23           H  
-ATOM    304  HA  SER A 270      12.565   3.438   6.761  1.00  2.55           H  
-ATOM    305  HB2 SER A 270      14.232   3.665   4.934  1.00  2.85           H  
-ATOM    306  HB3 SER A 270      15.139   4.796   5.933  1.00  2.74           H  
-ATOM    307  HG  SER A 270      15.915   2.613   6.013  1.00  3.46           H  
-ATOM    308  N   ARG A 271      13.295   3.895   9.101  1.00  2.58           N  
-ATOM    309  CA  ARG A 271      13.566   4.169  10.532  1.00  2.69           C  
-ATOM    310  C   ARG A 271      12.624   5.233  11.105  1.00  2.50           C  
-ATOM    311  O   ARG A 271      11.957   5.008  12.113  1.00  2.61           O  
-ATOM    312  CB  ARG A 271      15.027   4.590  10.761  1.00  2.87           C  
-ATOM    313  CG  ARG A 271      16.002   3.429  10.922  1.00  3.17           C  
-ATOM    314  CD  ARG A 271      16.115   2.601   9.652  1.00  3.18           C  
-ATOM    315  NE  ARG A 271      17.047   1.488   9.799  1.00  3.50           N  
-ATOM    316  CZ  ARG A 271      17.269   0.590   8.864  1.00  3.64           C  
-ATOM    317  NH1 ARG A 271      16.615   0.638   7.732  1.00  3.52           N  
-ATOM    318  NH2 ARG A 271      18.141  -0.363   9.068  1.00  3.97           N  
-ATOM    319  H   ARG A 271      12.910   3.029   8.858  1.00  2.68           H  
-ATOM    320  HA  ARG A 271      13.393   3.248  11.070  1.00  2.86           H  
-ATOM    321  HB2 ARG A 271      15.350   5.182   9.918  1.00  2.78           H  
-ATOM    322  HB3 ARG A 271      15.078   5.199  11.652  1.00  2.93           H  
-ATOM    323  HG2 ARG A 271      16.975   3.822  11.168  1.00  3.32           H  
-ATOM    324  HG3 ARG A 271      15.657   2.794  11.725  1.00  3.31           H  
-ATOM    325  HD2 ARG A 271      15.138   2.209   9.406  1.00  3.10           H  
-ATOM    326  HD3 ARG A 271      16.455   3.241   8.851  1.00  3.08           H  
-ATOM    327  HE  ARG A 271      17.538   1.416  10.641  1.00  3.67           H  
-ATOM    328 HH11 ARG A 271      15.941   1.364   7.575  1.00  3.30           H  
-ATOM    329 HH12 ARG A 271      16.784  -0.046   7.028  1.00  3.70           H  
-ATOM    330 HH21 ARG A 271      18.636  -0.407   9.932  1.00  4.11           H  
-ATOM    331 HH22 ARG A 271      18.307  -1.044   8.362  1.00  4.12           H  
-ATOM    332  N   ALA A 272      12.565   6.378  10.450  1.00  2.37           N  
-ATOM    333  CA  ALA A 272      11.703   7.469  10.873  1.00  2.22           C  
-ATOM    334  C   ALA A 272      11.066   8.124   9.653  1.00  1.92           C  
-ATOM    335  O   ALA A 272      11.762   8.557   8.735  1.00  1.92           O  
-ATOM    336  CB  ALA A 272      12.492   8.492  11.681  1.00  2.37           C  
-ATOM    337  H   ALA A 272      13.115   6.492   9.639  1.00  2.48           H  
-ATOM    338  HA  ALA A 272      10.923   7.062  11.503  1.00  2.28           H  
-ATOM    339  HB1 ALA A 272      12.692   9.355  11.066  1.00  2.60           H  
-ATOM    340  HB2 ALA A 272      13.429   8.057  12.002  1.00  2.61           H  
-ATOM    341  HB3 ALA A 272      11.917   8.791  12.548  1.00  2.67           H  
-ATOM    342  N   SER A 273       9.742   8.177   9.632  1.00  1.74           N  
-ATOM    343  CA  SER A 273       9.029   8.769   8.506  1.00  1.48           C  
-ATOM    344  C   SER A 273       8.778  10.249   8.714  1.00  1.32           C  
-ATOM    345  O   SER A 273       9.218  10.838   9.701  1.00  1.49           O  
-ATOM    346  CB  SER A 273       7.715   8.041   8.250  1.00  1.47           C  
-ATOM    347  OG  SER A 273       7.945   6.671   7.947  1.00  1.74           O  
-ATOM    348  H   SER A 273       9.235   7.807  10.384  1.00  1.84           H  
-ATOM    349  HA  SER A 273       9.657   8.669   7.627  1.00  1.49           H  
-ATOM    350  HB2 SER A 273       7.093   8.109   9.127  1.00  1.52           H  
-ATOM    351  HB3 SER A 273       7.212   8.503   7.411  1.00  1.32           H  
-ATOM    352  HG  SER A 273       8.773   6.383   8.354  1.00  2.24           H  
-ATOM    353  N   LEU A 274       8.113  10.846   7.732  1.00  1.09           N  
-ATOM    354  CA  LEU A 274       7.835  12.273   7.724  1.00  1.01           C  
-ATOM    355  C   LEU A 274       9.142  13.108   7.777  1.00  1.11           C  
-ATOM    356  O   LEU A 274       9.176  14.157   8.424  1.00  1.28           O  
-ATOM    357  CB  LEU A 274       6.905  12.642   8.889  1.00  1.20           C  
-ATOM    358  CG  LEU A 274       6.247  14.019   8.793  1.00  1.31           C  
-ATOM    359  CD1 LEU A 274       5.349  14.097   7.567  1.00  1.24           C  
-ATOM    360  CD2 LEU A 274       5.460  14.316  10.060  1.00  1.63           C  
-ATOM    361  H   LEU A 274       7.830  10.307   6.952  1.00  1.07           H  
-ATOM    362  HA  LEU A 274       7.330  12.496   6.796  1.00  0.88           H  
-ATOM    363  HB2 LEU A 274       6.125  11.897   8.946  1.00  1.22           H  
-ATOM    364  HB3 LEU A 274       7.479  12.606   9.803  1.00  1.37           H  
-ATOM    365  HG  LEU A 274       7.016  14.771   8.692  1.00  1.38           H  
-ATOM    366 HD11 LEU A 274       4.598  14.857   7.718  1.00  1.62           H  
-ATOM    367 HD12 LEU A 274       4.870  13.142   7.410  1.00  1.73           H  
-ATOM    368 HD13 LEU A 274       5.944  14.346   6.701  1.00  1.48           H  
-ATOM    369 HD21 LEU A 274       4.525  13.775  10.039  1.00  2.02           H  
-ATOM    370 HD22 LEU A 274       5.261  15.376  10.120  1.00  1.96           H  
-ATOM    371 HD23 LEU A 274       6.034  14.007  10.921  1.00  1.98           H  
-ATOM    372  N   PRO A 275      10.255  12.668   7.107  1.00  1.14           N  
-ATOM    373  CA  PRO A 275      11.527  13.418   7.123  1.00  1.35           C  
-ATOM    374  C   PRO A 275      11.381  14.830   6.550  1.00  1.28           C  
-ATOM    375  O   PRO A 275      11.964  15.790   7.058  1.00  1.55           O  
-ATOM    376  CB  PRO A 275      12.445  12.583   6.226  1.00  1.44           C  
-ATOM    377  CG  PRO A 275      11.875  11.219   6.309  1.00  1.44           C  
-ATOM    378  CD  PRO A 275      10.391  11.426   6.310  1.00  1.15           C  
-ATOM    379  HA  PRO A 275      11.943  13.465   8.115  1.00  1.59           H  
-ATOM    380  HB2 PRO A 275      12.413  12.967   5.216  1.00  1.34           H  
-ATOM    381  HB3 PRO A 275      13.455  12.615   6.603  1.00  1.70           H  
-ATOM    382  HG2 PRO A 275      12.182  10.629   5.458  1.00  1.54           H  
-ATOM    383  HG3 PRO A 275      12.186  10.756   7.233  1.00  1.63           H  
-ATOM    384  HD2 PRO A 275      10.023  11.557   5.304  1.00  1.05           H  
-ATOM    385  HD3 PRO A 275       9.886  10.593   6.800  1.00  1.19           H  
-ATOM    386  N   LYS A 276      10.592  14.930   5.486  1.00  1.02           N  
-ATOM    387  CA  LYS A 276      10.330  16.194   4.807  1.00  1.04           C  
-ATOM    388  C   LYS A 276       9.223  15.994   3.766  1.00  0.93           C  
-ATOM    389  O   LYS A 276       9.063  14.843   3.298  1.00  0.89           O  
-ATOM    390  CB  LYS A 276      11.619  16.720   4.147  1.00  1.21           C  
-ATOM    391  CG  LYS A 276      11.463  18.066   3.455  1.00  1.40           C  
-ATOM    392  CD  LYS A 276      11.108  17.905   1.984  1.00  1.44           C  
-ATOM    393  CE  LYS A 276      10.296  19.084   1.479  1.00  1.73           C  
-ATOM    394  NZ  LYS A 276       8.965  19.158   2.145  1.00  1.74           N  
-ATOM    395  OXT LYS A 276       8.521  16.978   3.431  1.00  1.14           O  
-ATOM    396  H   LYS A 276      10.162  14.119   5.143  1.00  0.90           H  
-ATOM    397  HA  LYS A 276       9.992  16.906   5.545  1.00  1.23           H  
-ATOM    398  HB2 LYS A 276      12.380  16.821   4.906  1.00  1.41           H  
-ATOM    399  HB3 LYS A 276      11.952  16.001   3.413  1.00  1.16           H  
-ATOM    400  HG2 LYS A 276      10.677  18.621   3.945  1.00  1.51           H  
-ATOM    401  HG3 LYS A 276      12.392  18.610   3.535  1.00  1.64           H  
-ATOM    402  HD2 LYS A 276      12.018  17.834   1.408  1.00  1.60           H  
-ATOM    403  HD3 LYS A 276      10.529  17.001   1.859  1.00  1.32           H  
-ATOM    404  HE2 LYS A 276      10.842  19.994   1.680  1.00  1.94           H  
-ATOM    405  HE3 LYS A 276      10.152  18.978   0.413  1.00  1.91           H  
-ATOM    406  HZ1 LYS A 276       8.952  19.936   2.834  1.00  1.98           H  
-ATOM    407  HZ2 LYS A 276       8.761  18.255   2.653  1.00  1.46           H  
-ATOM    408  HZ3 LYS A 276       8.220  19.318   1.439  1.00  2.00           H  
-TER
-ENDMDL
-MODEL      6
-ATOM      1  N   ALA A 249      -2.113  -2.429 -13.441  1.00  1.95           N  
-ATOM      2  CA  ALA A 249      -1.140  -2.258 -12.328  1.00  1.57           C  
-ATOM      3  C   ALA A 249      -0.568  -3.605 -11.886  1.00  1.35           C  
-ATOM      4  O   ALA A 249      -1.211  -4.641 -12.055  1.00  1.49           O  
-ATOM      5  CB  ALA A 249      -1.793  -1.548 -11.150  1.00  1.56           C  
-ATOM      6  H1  ALA A 249      -3.086  -2.410 -13.074  1.00  2.27           H  
-ATOM      7  H2  ALA A 249      -1.954  -3.342 -13.914  1.00  2.25           H  
-ATOM      8  H3  ALA A 249      -2.001  -1.665 -14.137  1.00  2.22           H  
-ATOM      9  HA  ALA A 249      -0.329  -1.638 -12.683  1.00  1.55           H  
-ATOM     10  HB1 ALA A 249      -2.170  -2.281 -10.454  1.00  1.83           H  
-ATOM     11  HB2 ALA A 249      -2.606  -0.934 -11.506  1.00  1.90           H  
-ATOM     12  HB3 ALA A 249      -1.061  -0.926 -10.657  1.00  1.90           H  
-ATOM     13  N   PRO A 250       0.659  -3.602 -11.328  1.00  1.10           N  
-ATOM     14  CA  PRO A 250       1.355  -4.816 -10.856  1.00  1.02           C  
-ATOM     15  C   PRO A 250       0.674  -5.500  -9.654  1.00  0.83           C  
-ATOM     16  O   PRO A 250       1.271  -5.635  -8.585  1.00  0.77           O  
-ATOM     17  CB  PRO A 250       2.742  -4.288 -10.454  1.00  1.02           C  
-ATOM     18  CG  PRO A 250       2.867  -2.980 -11.143  1.00  1.10           C  
-ATOM     19  CD  PRO A 250       1.489  -2.410 -11.123  1.00  1.05           C  
-ATOM     20  HA  PRO A 250       1.465  -5.536 -11.654  1.00  1.21           H  
-ATOM     21  HB2 PRO A 250       2.783  -4.167  -9.382  1.00  0.90           H  
-ATOM     22  HB3 PRO A 250       3.501  -4.975 -10.779  1.00  1.20           H  
-ATOM     23  HG2 PRO A 250       3.550  -2.341 -10.603  1.00  1.11           H  
-ATOM     24  HG3 PRO A 250       3.201  -3.125 -12.159  1.00  1.30           H  
-ATOM     25  HD2 PRO A 250       1.283  -1.956 -10.164  1.00  0.95           H  
-ATOM     26  HD3 PRO A 250       1.356  -1.705 -11.923  1.00  1.22           H  
-ATOM     27  N   ALA A 251      -0.570  -5.933  -9.850  1.00  0.88           N  
-ATOM     28  CA  ALA A 251      -1.352  -6.620  -8.815  1.00  0.86           C  
-ATOM     29  C   ALA A 251      -1.499  -5.808  -7.517  1.00  0.78           C  
-ATOM     30  O   ALA A 251      -1.667  -4.587  -7.544  1.00  0.74           O  
-ATOM     31  CB  ALA A 251      -0.745  -7.990  -8.529  1.00  0.93           C  
-ATOM     32  H   ALA A 251      -0.980  -5.794 -10.735  1.00  1.01           H  
-ATOM     33  HA  ALA A 251      -2.340  -6.785  -9.221  1.00  0.99           H  
-ATOM     34  HB1 ALA A 251      -1.454  -8.589  -7.976  1.00  1.39           H  
-ATOM     35  HB2 ALA A 251       0.156  -7.869  -7.945  1.00  1.41           H  
-ATOM     36  HB3 ALA A 251      -0.507  -8.480  -9.460  1.00  1.39           H  
-ATOM     37  N   ARG A 252      -1.459  -6.512  -6.383  1.00  0.80           N  
-ATOM     38  CA  ARG A 252      -1.615  -5.899  -5.061  1.00  0.80           C  
-ATOM     39  C   ARG A 252      -0.515  -4.871  -4.762  1.00  0.70           C  
-ATOM     40  O   ARG A 252      -0.806  -3.741  -4.365  1.00  0.68           O  
-ATOM     41  CB  ARG A 252      -1.611  -6.997  -3.989  1.00  0.93           C  
-ATOM     42  CG  ARG A 252      -2.523  -6.726  -2.796  1.00  1.08           C  
-ATOM     43  CD  ARG A 252      -2.089  -5.501  -2.004  1.00  1.05           C  
-ATOM     44  NE  ARG A 252      -2.894  -5.314  -0.800  1.00  1.29           N  
-ATOM     45  CZ  ARG A 252      -2.775  -4.282   0.007  1.00  1.42           C  
-ATOM     46  NH1 ARG A 252      -1.935  -3.319  -0.270  1.00  1.36           N  
-ATOM     47  NH2 ARG A 252      -3.508  -4.208   1.087  1.00  1.70           N  
-ATOM     48  H   ARG A 252      -1.343  -7.483  -6.438  1.00  0.85           H  
-ATOM     49  HA  ARG A 252      -2.567  -5.398  -5.040  1.00  0.85           H  
-ATOM     50  HB2 ARG A 252      -1.925  -7.924  -4.444  1.00  1.02           H  
-ATOM     51  HB3 ARG A 252      -0.603  -7.115  -3.621  1.00  0.91           H  
-ATOM     52  HG2 ARG A 252      -3.528  -6.566  -3.156  1.00  1.19           H  
-ATOM     53  HG3 ARG A 252      -2.507  -7.587  -2.145  1.00  1.21           H  
-ATOM     54  HD2 ARG A 252      -1.055  -5.622  -1.719  1.00  1.04           H  
-ATOM     55  HD3 ARG A 252      -2.189  -4.629  -2.633  1.00  0.98           H  
-ATOM     56  HE  ARG A 252      -3.546  -6.008  -0.580  1.00  1.43           H  
-ATOM     57 HH11 ARG A 252      -1.381  -3.366  -1.098  1.00  1.19           H  
-ATOM     58 HH12 ARG A 252      -1.848  -2.542   0.346  1.00  1.53           H  
-ATOM     59 HH21 ARG A 252      -4.157  -4.935   1.300  1.00  1.81           H  
-ATOM     60 HH22 ARG A 252      -3.417  -3.426   1.698  1.00  1.83           H  
-ATOM     61  N   VAL A 253       0.742  -5.270  -4.948  1.00  0.69           N  
-ATOM     62  CA  VAL A 253       1.876  -4.381  -4.690  1.00  0.69           C  
-ATOM     63  C   VAL A 253       1.791  -3.125  -5.553  1.00  0.58           C  
-ATOM     64  O   VAL A 253       1.930  -2.003  -5.060  1.00  0.55           O  
-ATOM     65  CB  VAL A 253       3.226  -5.107  -4.917  1.00  0.81           C  
-ATOM     66  CG1 VAL A 253       3.409  -5.561  -6.359  1.00  0.81           C  
-ATOM     67  CG2 VAL A 253       4.393  -4.238  -4.472  1.00  0.97           C  
-ATOM     68  H   VAL A 253       0.909  -6.181  -5.261  1.00  0.73           H  
-ATOM     69  HA  VAL A 253       1.826  -4.086  -3.653  1.00  0.76           H  
-ATOM     70  HB  VAL A 253       3.218  -5.987  -4.310  1.00  0.89           H  
-ATOM     71 HG11 VAL A 253       2.623  -6.254  -6.620  1.00  1.31           H  
-ATOM     72 HG12 VAL A 253       4.367  -6.047  -6.465  1.00  1.38           H  
-ATOM     73 HG13 VAL A 253       3.366  -4.704  -7.014  1.00  1.23           H  
-ATOM     74 HG21 VAL A 253       4.022  -3.407  -3.890  1.00  1.43           H  
-ATOM     75 HG22 VAL A 253       4.916  -3.865  -5.339  1.00  1.45           H  
-ATOM     76 HG23 VAL A 253       5.069  -4.825  -3.868  1.00  1.43           H  
-ATOM     77  N   GLY A 254       1.536  -3.330  -6.835  1.00  0.56           N  
-ATOM     78  CA  GLY A 254       1.405  -2.222  -7.765  1.00  0.53           C  
-ATOM     79  C   GLY A 254       0.285  -1.274  -7.374  1.00  0.46           C  
-ATOM     80  O   GLY A 254       0.472  -0.056  -7.352  1.00  0.45           O  
-ATOM     81  H   GLY A 254       1.428  -4.258  -7.148  1.00  0.62           H  
-ATOM     82  HA2 GLY A 254       2.335  -1.675  -7.791  1.00  0.56           H  
-ATOM     83  HA3 GLY A 254       1.201  -2.614  -8.752  1.00  0.58           H  
-ATOM     84  N   LEU A 255      -0.873  -1.832  -7.035  1.00  0.49           N  
-ATOM     85  CA  LEU A 255      -2.015  -1.027  -6.609  1.00  0.51           C  
-ATOM     86  C   LEU A 255      -1.692  -0.255  -5.337  1.00  0.47           C  
-ATOM     87  O   LEU A 255      -1.980   0.939  -5.226  1.00  0.49           O  
-ATOM     88  CB  LEU A 255      -3.232  -1.912  -6.367  1.00  0.64           C  
-ATOM     89  CG  LEU A 255      -4.222  -1.963  -7.520  1.00  0.83           C  
-ATOM     90  CD1 LEU A 255      -5.393  -2.860  -7.163  1.00  1.00           C  
-ATOM     91  CD2 LEU A 255      -4.696  -0.558  -7.860  1.00  0.95           C  
-ATOM     92  H   LEU A 255      -0.957  -2.814  -7.054  1.00  0.53           H  
-ATOM     93  HA  LEU A 255      -2.246  -0.329  -7.399  1.00  0.54           H  
-ATOM     94  HB2 LEU A 255      -2.887  -2.917  -6.168  1.00  0.73           H  
-ATOM     95  HB3 LEU A 255      -3.748  -1.546  -5.493  1.00  0.68           H  
-ATOM     96  HG  LEU A 255      -3.732  -2.374  -8.390  1.00  0.91           H  
-ATOM     97 HD11 LEU A 255      -6.296  -2.464  -7.602  1.00  1.41           H  
-ATOM     98 HD12 LEU A 255      -5.500  -2.898  -6.089  1.00  1.51           H  
-ATOM     99 HD13 LEU A 255      -5.212  -3.853  -7.544  1.00  1.45           H  
-ATOM    100 HD21 LEU A 255      -4.036   0.166  -7.400  1.00  1.39           H  
-ATOM    101 HD22 LEU A 255      -5.699  -0.416  -7.488  1.00  1.45           H  
-ATOM    102 HD23 LEU A 255      -4.686  -0.423  -8.931  1.00  1.44           H  
-ATOM    103  N   GLY A 256      -1.089  -0.948  -4.378  1.00  0.48           N  
-ATOM    104  CA  GLY A 256      -0.732  -0.325  -3.122  1.00  0.52           C  
-ATOM    105  C   GLY A 256       0.228   0.844  -3.291  1.00  0.44           C  
-ATOM    106  O   GLY A 256      -0.024   1.933  -2.783  1.00  0.45           O  
-ATOM    107  H   GLY A 256      -0.884  -1.900  -4.527  1.00  0.50           H  
-ATOM    108  HA2 GLY A 256      -1.635   0.032  -2.650  1.00  0.60           H  
-ATOM    109  HA3 GLY A 256      -0.274  -1.066  -2.486  1.00  0.60           H  
-ATOM    110  N   ILE A 257       1.323   0.634  -4.021  1.00  0.42           N  
-ATOM    111  CA  ILE A 257       2.287   1.709  -4.243  1.00  0.40           C  
-ATOM    112  C   ILE A 257       1.672   2.841  -5.076  1.00  0.33           C  
-ATOM    113  O   ILE A 257       1.925   4.015  -4.813  1.00  0.35           O  
-ATOM    114  CB  ILE A 257       3.609   1.207  -4.886  1.00  0.49           C  
-ATOM    115  CG1 ILE A 257       3.376   0.545  -6.248  1.00  0.51           C  
-ATOM    116  CG2 ILE A 257       4.315   0.240  -3.947  1.00  0.62           C  
-ATOM    117  CD1 ILE A 257       3.630   1.461  -7.428  1.00  0.58           C  
-ATOM    118  H   ILE A 257       1.480  -0.254  -4.422  1.00  0.46           H  
-ATOM    119  HA  ILE A 257       2.532   2.113  -3.269  1.00  0.46           H  
-ATOM    120  HB  ILE A 257       4.256   2.062  -5.019  1.00  0.52           H  
-ATOM    121 HG12 ILE A 257       4.034  -0.306  -6.344  1.00  0.62           H  
-ATOM    122 HG13 ILE A 257       2.351   0.208  -6.305  1.00  0.53           H  
-ATOM    123 HG21 ILE A 257       4.814   0.796  -3.166  1.00  1.31           H  
-ATOM    124 HG22 ILE A 257       5.041  -0.334  -4.501  1.00  1.21           H  
-ATOM    125 HG23 ILE A 257       3.589  -0.427  -3.506  1.00  1.12           H  
-ATOM    126 HD11 ILE A 257       2.934   1.231  -8.221  1.00  1.26           H  
-ATOM    127 HD12 ILE A 257       4.640   1.319  -7.783  1.00  1.15           H  
-ATOM    128 HD13 ILE A 257       3.499   2.488  -7.121  1.00  1.16           H  
-ATOM    129  N   THR A 258       0.843   2.488  -6.065  1.00  0.33           N  
-ATOM    130  CA  THR A 258       0.183   3.490  -6.908  1.00  0.35           C  
-ATOM    131  C   THR A 258      -0.739   4.381  -6.077  1.00  0.34           C  
-ATOM    132  O   THR A 258      -0.712   5.606  -6.202  1.00  0.37           O  
-ATOM    133  CB  THR A 258      -0.643   2.837  -8.041  1.00  0.45           C  
-ATOM    134  OG1 THR A 258       0.198   2.018  -8.860  1.00  0.52           O  
-ATOM    135  CG2 THR A 258      -1.315   3.893  -8.911  1.00  0.58           C  
-ATOM    136  H   THR A 258       0.662   1.532  -6.223  1.00  0.37           H  
-ATOM    137  HA  THR A 258       0.951   4.104  -7.357  1.00  0.39           H  
-ATOM    138  HB  THR A 258      -1.410   2.221  -7.596  1.00  0.45           H  
-ATOM    139  HG1 THR A 258       0.362   1.176  -8.411  1.00  0.49           H  
-ATOM    140 HG21 THR A 258      -0.997   4.875  -8.595  1.00  1.23           H  
-ATOM    141 HG22 THR A 258      -2.387   3.812  -8.810  1.00  1.18           H  
-ATOM    142 HG23 THR A 258      -1.037   3.739  -9.943  1.00  1.16           H  
-ATOM    143  N   THR A 259      -1.544   3.758  -5.219  1.00  0.38           N  
-ATOM    144  CA  THR A 259      -2.466   4.495  -4.359  1.00  0.46           C  
-ATOM    145  C   THR A 259      -1.702   5.354  -3.358  1.00  0.44           C  
-ATOM    146  O   THR A 259      -2.002   6.538  -3.196  1.00  0.48           O  
-ATOM    147  CB  THR A 259      -3.428   3.554  -3.605  1.00  0.57           C  
-ATOM    148  OG1 THR A 259      -2.693   2.559  -2.883  1.00  0.58           O  
-ATOM    149  CG2 THR A 259      -4.382   2.876  -4.575  1.00  0.67           C  
-ATOM    150  H   THR A 259      -1.510   2.778  -5.156  1.00  0.40           H  
-ATOM    151  HA  THR A 259      -3.055   5.144  -4.991  1.00  0.50           H  
-ATOM    152  HB  THR A 259      -4.008   4.141  -2.906  1.00  0.64           H  
-ATOM    153  HG1 THR A 259      -2.398   1.871  -3.491  1.00  1.02           H  
-ATOM    154 HG21 THR A 259      -4.581   1.869  -4.238  1.00  1.33           H  
-ATOM    155 HG22 THR A 259      -3.934   2.845  -5.557  1.00  1.23           H  
-ATOM    156 HG23 THR A 259      -5.306   3.430  -4.618  1.00  1.00           H  
-ATOM    157  N   VAL A 260      -0.698   4.763  -2.709  1.00  0.43           N  
-ATOM    158  CA  VAL A 260       0.120   5.500  -1.749  1.00  0.47           C  
-ATOM    159  C   VAL A 260       0.818   6.684  -2.430  1.00  0.41           C  
-ATOM    160  O   VAL A 260       0.730   7.817  -1.955  1.00  0.47           O  
-ATOM    161  CB  VAL A 260       1.175   4.589  -1.083  1.00  0.53           C  
-ATOM    162  CG1 VAL A 260       2.130   5.401  -0.222  1.00  0.66           C  
-ATOM    163  CG2 VAL A 260       0.498   3.512  -0.249  1.00  0.64           C  
-ATOM    164  H   VAL A 260      -0.495   3.813  -2.890  1.00  0.43           H  
-ATOM    165  HA  VAL A 260      -0.536   5.881  -0.978  1.00  0.56           H  
-ATOM    166  HB  VAL A 260       1.748   4.106  -1.861  1.00  0.49           H  
-ATOM    167 HG11 VAL A 260       1.642   5.669   0.703  1.00  1.21           H  
-ATOM    168 HG12 VAL A 260       2.416   6.298  -0.752  1.00  1.19           H  
-ATOM    169 HG13 VAL A 260       3.010   4.814  -0.008  1.00  1.29           H  
-ATOM    170 HG21 VAL A 260      -0.409   3.907   0.183  1.00  1.32           H  
-ATOM    171 HG22 VAL A 260       1.164   3.193   0.538  1.00  1.18           H  
-ATOM    172 HG23 VAL A 260       0.257   2.668  -0.880  1.00  1.16           H  
-ATOM    173  N   LEU A 261       1.487   6.418  -3.559  1.00  0.36           N  
-ATOM    174  CA  LEU A 261       2.176   7.465  -4.315  1.00  0.36           C  
-ATOM    175  C   LEU A 261       1.203   8.575  -4.720  1.00  0.36           C  
-ATOM    176  O   LEU A 261       1.486   9.755  -4.530  1.00  0.42           O  
-ATOM    177  CB  LEU A 261       2.844   6.881  -5.557  1.00  0.39           C  
-ATOM    178  CG  LEU A 261       3.782   7.839  -6.285  1.00  0.52           C  
-ATOM    179  CD1 LEU A 261       4.855   8.358  -5.335  1.00  0.68           C  
-ATOM    180  CD2 LEU A 261       4.414   7.149  -7.479  1.00  0.63           C  
-ATOM    181  H   LEU A 261       1.505   5.491  -3.900  1.00  0.36           H  
-ATOM    182  HA  LEU A 261       2.943   7.889  -3.682  1.00  0.41           H  
-ATOM    183  HB2 LEU A 261       3.407   6.009  -5.261  1.00  0.45           H  
-ATOM    184  HB3 LEU A 261       2.072   6.576  -6.247  1.00  0.42           H  
-ATOM    185  HG  LEU A 261       3.217   8.685  -6.645  1.00  0.61           H  
-ATOM    186 HD11 LEU A 261       5.737   7.741  -5.415  1.00  1.20           H  
-ATOM    187 HD12 LEU A 261       4.481   8.323  -4.321  1.00  1.36           H  
-ATOM    188 HD13 LEU A 261       5.102   9.378  -5.591  1.00  1.20           H  
-ATOM    189 HD21 LEU A 261       3.670   7.012  -8.250  1.00  1.22           H  
-ATOM    190 HD22 LEU A 261       4.799   6.186  -7.176  1.00  1.30           H  
-ATOM    191 HD23 LEU A 261       5.222   7.756  -7.860  1.00  1.11           H  
-ATOM    192  N   THR A 262       0.049   8.180  -5.254  1.00  0.34           N  
-ATOM    193  CA  THR A 262      -0.986   9.137  -5.666  1.00  0.39           C  
-ATOM    194  C   THR A 262      -1.416  10.017  -4.491  1.00  0.43           C  
-ATOM    195  O   THR A 262      -1.496  11.244  -4.607  1.00  0.49           O  
-ATOM    196  CB  THR A 262      -2.229   8.400  -6.216  1.00  0.44           C  
-ATOM    197  OG1 THR A 262      -1.877   7.626  -7.369  1.00  0.46           O  
-ATOM    198  CG2 THR A 262      -3.337   9.376  -6.577  1.00  0.54           C  
-ATOM    199  H   THR A 262      -0.121   7.217  -5.362  1.00  0.34           H  
-ATOM    200  HA  THR A 262      -0.581   9.760  -6.449  1.00  0.43           H  
-ATOM    201  HB  THR A 262      -2.597   7.733  -5.449  1.00  0.47           H  
-ATOM    202  HG1 THR A 262      -1.411   6.823  -7.085  1.00  0.54           H  
-ATOM    203 HG21 THR A 262      -3.741   9.808  -5.674  1.00  1.06           H  
-ATOM    204 HG22 THR A 262      -4.119   8.853  -7.108  1.00  1.17           H  
-ATOM    205 HG23 THR A 262      -2.936  10.160  -7.202  1.00  1.15           H  
-ATOM    206  N   MET A 263      -1.690   9.376  -3.363  1.00  0.44           N  
-ATOM    207  CA  MET A 263      -2.115  10.081  -2.155  1.00  0.53           C  
-ATOM    208  C   MET A 263      -1.005  10.978  -1.577  1.00  0.54           C  
-ATOM    209  O   MET A 263      -1.259  12.127  -1.209  1.00  0.61           O  
-ATOM    210  CB  MET A 263      -2.570   9.064  -1.105  1.00  0.60           C  
-ATOM    211  CG  MET A 263      -3.076   9.687   0.185  1.00  0.74           C  
-ATOM    212  SD  MET A 263      -3.656   8.449   1.359  1.00  0.93           S  
-ATOM    213  CE  MET A 263      -4.029   9.475   2.781  1.00  1.14           C  
-ATOM    214  H   MET A 263      -1.608   8.394  -3.342  1.00  0.43           H  
-ATOM    215  HA  MET A 263      -2.956  10.702  -2.419  1.00  0.58           H  
-ATOM    216  HB2 MET A 263      -3.366   8.465  -1.523  1.00  0.63           H  
-ATOM    217  HB3 MET A 263      -1.738   8.419  -0.865  1.00  0.62           H  
-ATOM    218  HG2 MET A 263      -2.272  10.247   0.640  1.00  0.78           H  
-ATOM    219  HG3 MET A 263      -3.892  10.355  -0.048  1.00  0.84           H  
-ATOM    220  HE1 MET A 263      -4.575   8.894   3.510  1.00  1.62           H  
-ATOM    221  HE2 MET A 263      -4.631  10.316   2.471  1.00  1.70           H  
-ATOM    222  HE3 MET A 263      -3.109   9.831   3.219  1.00  1.28           H  
-ATOM    223  N   THR A 264       0.220  10.452  -1.476  1.00  0.52           N  
-ATOM    224  CA  THR A 264       1.339  11.225  -0.912  1.00  0.57           C  
-ATOM    225  C   THR A 264       1.766  12.406  -1.793  1.00  0.57           C  
-ATOM    226  O   THR A 264       2.159  13.450  -1.274  1.00  0.64           O  
-ATOM    227  CB  THR A 264       2.580  10.352  -0.606  1.00  0.61           C  
-ATOM    228  OG1 THR A 264       3.571  11.138   0.066  1.00  0.69           O  
-ATOM    229  CG2 THR A 264       3.191   9.766  -1.871  1.00  0.59           C  
-ATOM    230  H   THR A 264       0.372   9.520  -1.769  1.00  0.49           H  
-ATOM    231  HA  THR A 264       0.993  11.627   0.030  1.00  0.63           H  
-ATOM    232  HB  THR A 264       2.280   9.539   0.040  1.00  0.67           H  
-ATOM    233  HG1 THR A 264       3.470  12.064  -0.180  1.00  0.94           H  
-ATOM    234 HG21 THR A 264       2.525   9.936  -2.704  1.00  1.24           H  
-ATOM    235 HG22 THR A 264       3.341   8.704  -1.740  1.00  1.15           H  
-ATOM    236 HG23 THR A 264       4.140  10.242  -2.065  1.00  1.15           H  
-ATOM    237  N   THR A 265       1.700  12.251  -3.117  1.00  0.53           N  
-ATOM    238  CA  THR A 265       2.095  13.334  -4.028  1.00  0.59           C  
-ATOM    239  C   THR A 265       1.023  14.421  -4.112  1.00  0.55           C  
-ATOM    240  O   THR A 265       1.196  15.414  -4.823  1.00  0.63           O  
-ATOM    241  CB  THR A 265       2.387  12.828  -5.461  1.00  0.64           C  
-ATOM    242  OG1 THR A 265       2.895  13.901  -6.261  1.00  0.77           O  
-ATOM    243  CG2 THR A 265       1.131  12.276  -6.121  1.00  0.59           C  
-ATOM    244  H   THR A 265       1.388  11.394  -3.488  1.00  0.50           H  
-ATOM    245  HA  THR A 265       3.003  13.774  -3.638  1.00  0.68           H  
-ATOM    246  HB  THR A 265       3.126  12.042  -5.410  1.00  0.69           H  
-ATOM    247  HG1 THR A 265       2.405  14.711  -6.057  1.00  0.83           H  
-ATOM    248 HG21 THR A 265       0.615  11.629  -5.426  1.00  1.17           H  
-ATOM    249 HG22 THR A 265       1.404  11.714  -7.002  1.00  1.18           H  
-ATOM    250 HG23 THR A 265       0.484  13.093  -6.401  1.00  1.19           H  
-ATOM    251  N   GLN A 266      -0.088  14.213  -3.397  1.00  0.51           N  
-ATOM    252  CA  GLN A 266      -1.208  15.155  -3.393  1.00  0.53           C  
-ATOM    253  C   GLN A 266      -1.810  15.291  -4.794  1.00  0.45           C  
-ATOM    254  O   GLN A 266      -2.055  16.396  -5.279  1.00  0.53           O  
-ATOM    255  CB  GLN A 266      -0.784  16.527  -2.852  1.00  0.70           C  
-ATOM    256  CG  GLN A 266      -0.505  16.535  -1.355  1.00  0.82           C  
-ATOM    257  CD  GLN A 266      -0.194  17.924  -0.825  1.00  0.99           C  
-ATOM    258  OE1 GLN A 266       0.790  18.545  -1.209  1.00  1.08           O  
-ATOM    259  NE2 GLN A 266      -1.037  18.425   0.065  1.00  1.12           N  
-ATOM    260  H   GLN A 266      -0.160  13.391  -2.864  1.00  0.53           H  
-ATOM    261  HA  GLN A 266      -1.965  14.747  -2.740  1.00  0.56           H  
-ATOM    262  HB2 GLN A 266       0.114  16.839  -3.364  1.00  0.74           H  
-ATOM    263  HB3 GLN A 266      -1.570  17.238  -3.054  1.00  0.78           H  
-ATOM    264  HG2 GLN A 266      -1.372  16.155  -0.837  1.00  0.86           H  
-ATOM    265  HG3 GLN A 266       0.342  15.894  -1.157  1.00  0.86           H  
-ATOM    266 HE21 GLN A 266      -1.805  17.883   0.332  1.00  1.14           H  
-ATOM    267 HE22 GLN A 266      -0.854  19.321   0.416  1.00  1.24           H  
-ATOM    268  N   SER A 267      -2.048  14.145  -5.428  1.00  0.41           N  
-ATOM    269  CA  SER A 267      -2.632  14.104  -6.769  1.00  0.42           C  
-ATOM    270  C   SER A 267      -4.094  14.549  -6.743  1.00  0.44           C  
-ATOM    271  O   SER A 267      -4.780  14.400  -5.728  1.00  0.56           O  
-ATOM    272  CB  SER A 267      -2.526  12.694  -7.356  1.00  0.61           C  
-ATOM    273  OG  SER A 267      -3.117  12.621  -8.643  1.00  0.70           O  
-ATOM    274  H   SER A 267      -1.831  13.298  -4.978  1.00  0.47           H  
-ATOM    275  HA  SER A 267      -2.073  14.785  -7.393  1.00  0.52           H  
-ATOM    276  HB2 SER A 267      -1.485  12.418  -7.436  1.00  0.77           H  
-ATOM    277  HB3 SER A 267      -3.033  11.997  -6.703  1.00  0.70           H  
-ATOM    278  HG  SER A 267      -3.152  11.703  -8.930  1.00  1.06           H  
-ATOM    279  N   SER A 268      -4.560  15.101  -7.860  1.00  0.58           N  
-ATOM    280  CA  SER A 268      -5.940  15.587  -7.985  1.00  0.83           C  
-ATOM    281  C   SER A 268      -6.967  14.442  -8.006  1.00  0.95           C  
-ATOM    282  O   SER A 268      -7.758  14.322  -8.941  1.00  1.14           O  
-ATOM    283  CB  SER A 268      -6.081  16.429  -9.258  1.00  1.03           C  
-ATOM    284  OG  SER A 268      -5.089  17.442  -9.312  1.00  1.13           O  
-ATOM    285  H   SER A 268      -3.957  15.193  -8.626  1.00  0.62           H  
-ATOM    286  HA  SER A 268      -6.144  16.216  -7.132  1.00  0.90           H  
-ATOM    287  HB2 SER A 268      -5.973  15.791 -10.122  1.00  0.99           H  
-ATOM    288  HB3 SER A 268      -7.054  16.895  -9.273  1.00  1.29           H  
-ATOM    289  HG  SER A 268      -5.368  18.132  -9.919  1.00  1.55           H  
-ATOM    290  N   GLY A 269      -6.948  13.612  -6.970  1.00  0.92           N  
-ATOM    291  CA  GLY A 269      -7.873  12.493  -6.873  1.00  1.15           C  
-ATOM    292  C   GLY A 269      -7.660  11.681  -5.604  1.00  1.06           C  
-ATOM    293  O   GLY A 269      -7.390  12.249  -4.545  1.00  0.93           O  
-ATOM    294  H   GLY A 269      -6.289  13.763  -6.250  1.00  0.79           H  
-ATOM    295  HA2 GLY A 269      -8.884  12.874  -6.879  1.00  1.34           H  
-ATOM    296  HA3 GLY A 269      -7.735  11.848  -7.729  1.00  1.27           H  
-ATOM    297  N   SER A 270      -7.770  10.353  -5.712  1.00  1.21           N  
-ATOM    298  CA  SER A 270      -7.581   9.451  -4.566  1.00  1.24           C  
-ATOM    299  C   SER A 270      -8.561   9.773  -3.421  1.00  1.27           C  
-ATOM    300  O   SER A 270      -9.720  10.107  -3.670  1.00  1.33           O  
-ATOM    301  CB  SER A 270      -6.124   9.520  -4.073  1.00  1.09           C  
-ATOM    302  OG  SER A 270      -5.841   8.497  -3.128  1.00  1.23           O  
-ATOM    303  H   SER A 270      -7.983   9.957  -6.600  1.00  1.35           H  
-ATOM    304  HA  SER A 270      -7.779   8.447  -4.911  1.00  1.44           H  
-ATOM    305  HB2 SER A 270      -5.457   9.406  -4.914  1.00  1.16           H  
-ATOM    306  HB3 SER A 270      -5.951  10.479  -3.607  1.00  0.93           H  
-ATOM    307  HG  SER A 270      -5.048   8.020  -3.397  1.00  1.74           H  
-ATOM    308  N   ARG A 271      -8.076   9.659  -2.176  1.00  1.30           N  
-ATOM    309  CA  ARG A 271      -8.878   9.922  -0.964  1.00  1.40           C  
-ATOM    310  C   ARG A 271      -9.918   8.820  -0.712  1.00  1.41           C  
-ATOM    311  O   ARG A 271     -10.778   8.557  -1.550  1.00  1.20           O  
-ATOM    312  CB  ARG A 271      -9.568  11.291  -1.048  1.00  1.46           C  
-ATOM    313  CG  ARG A 271      -8.604  12.467  -0.986  1.00  1.58           C  
-ATOM    314  CD  ARG A 271      -9.228  13.739  -1.548  1.00  1.68           C  
-ATOM    315  NE  ARG A 271     -10.450  14.130  -0.844  1.00  1.70           N  
-ATOM    316  CZ  ARG A 271     -10.485  14.712   0.341  1.00  1.78           C  
-ATOM    317  NH1 ARG A 271      -9.381  14.998   0.987  1.00  1.87           N  
-ATOM    318  NH2 ARG A 271     -11.635  15.008   0.881  1.00  1.90           N  
-ATOM    319  H   ARG A 271      -7.139   9.377  -2.067  1.00  1.30           H  
-ATOM    320  HA  ARG A 271      -8.194   9.939  -0.129  1.00  1.51           H  
-ATOM    321  HB2 ARG A 271     -10.114  11.349  -1.979  1.00  1.41           H  
-ATOM    322  HB3 ARG A 271     -10.265  11.381  -0.228  1.00  1.55           H  
-ATOM    323  HG2 ARG A 271      -8.331  12.639   0.044  1.00  1.66           H  
-ATOM    324  HG3 ARG A 271      -7.722  12.223  -1.558  1.00  1.65           H  
-ATOM    325  HD2 ARG A 271      -8.508  14.539  -1.466  1.00  1.81           H  
-ATOM    326  HD3 ARG A 271      -9.461  13.576  -2.591  1.00  1.73           H  
-ATOM    327  HE  ARG A 271     -11.299  13.940  -1.288  1.00  1.74           H  
-ATOM    328 HH11 ARG A 271      -8.497  14.777   0.581  1.00  1.86           H  
-ATOM    329 HH12 ARG A 271      -9.428  15.438   1.881  1.00  2.02           H  
-ATOM    330 HH21 ARG A 271     -12.482  14.797   0.398  1.00  1.92           H  
-ATOM    331 HH22 ARG A 271     -11.668  15.449   1.775  1.00  2.05           H  
-ATOM    332  N   ALA A 272      -9.824   8.179   0.459  1.00  1.76           N  
-ATOM    333  CA  ALA A 272     -10.746   7.098   0.840  1.00  1.90           C  
-ATOM    334  C   ALA A 272     -10.726   5.962  -0.191  1.00  1.71           C  
-ATOM    335  O   ALA A 272      -9.671   5.610  -0.716  1.00  1.72           O  
-ATOM    336  CB  ALA A 272     -12.161   7.640   1.017  1.00  1.93           C  
-ATOM    337  H   ALA A 272      -9.113   8.435   1.082  1.00  1.96           H  
-ATOM    338  HA  ALA A 272     -10.417   6.706   1.793  1.00  2.23           H  
-ATOM    339  HB1 ALA A 272     -12.133   8.718   1.060  1.00  2.18           H  
-ATOM    340  HB2 ALA A 272     -12.583   7.254   1.934  1.00  2.19           H  
-ATOM    341  HB3 ALA A 272     -12.772   7.328   0.182  1.00  2.23           H  
-ATOM    342  N   SER A 273     -11.896   5.398  -0.492  1.00  1.66           N  
-ATOM    343  CA  SER A 273     -11.986   4.319  -1.476  1.00  1.62           C  
-ATOM    344  C   SER A 273     -12.127   4.848  -2.885  1.00  1.26           C  
-ATOM    345  O   SER A 273     -12.958   4.381  -3.664  1.00  1.29           O  
-ATOM    346  CB  SER A 273     -13.135   3.368  -1.172  1.00  1.88           C  
-ATOM    347  OG  SER A 273     -14.369   4.062  -1.079  1.00  1.86           O  
-ATOM    348  H   SER A 273     -12.712   5.720  -0.057  1.00  1.74           H  
-ATOM    349  HA  SER A 273     -11.059   3.761  -1.442  1.00  1.79           H  
-ATOM    350  HB2 SER A 273     -13.203   2.642  -1.969  1.00  1.91           H  
-ATOM    351  HB3 SER A 273     -12.940   2.864  -0.240  1.00  2.16           H  
-ATOM    352  HG  SER A 273     -14.626   4.370  -1.954  1.00  1.94           H  
-ATOM    353  N   LEU A 274     -11.257   5.776  -3.230  1.00  1.06           N  
-ATOM    354  CA  LEU A 274     -11.224   6.316  -4.575  1.00  0.86           C  
-ATOM    355  C   LEU A 274      -9.808   6.210  -5.154  1.00  0.94           C  
-ATOM    356  O   LEU A 274      -9.257   7.205  -5.613  1.00  0.87           O  
-ATOM    357  CB  LEU A 274     -11.715   7.765  -4.616  1.00  0.79           C  
-ATOM    358  CG  LEU A 274     -13.237   7.951  -4.593  1.00  0.99           C  
-ATOM    359  CD1 LEU A 274     -13.915   7.008  -5.573  1.00  1.20           C  
-ATOM    360  CD2 LEU A 274     -13.798   7.773  -3.190  1.00  1.12           C  
-ATOM    361  H   LEU A 274     -10.584   6.070  -2.574  1.00  1.18           H  
-ATOM    362  HA  LEU A 274     -11.880   5.709  -5.181  1.00  0.97           H  
-ATOM    363  HB2 LEU A 274     -11.298   8.288  -3.768  1.00  0.86           H  
-ATOM    364  HB3 LEU A 274     -11.336   8.224  -5.518  1.00  0.87           H  
-ATOM    365  HG  LEU A 274     -13.457   8.952  -4.909  1.00  1.17           H  
-ATOM    366 HD11 LEU A 274     -14.590   6.357  -5.038  1.00  1.61           H  
-ATOM    367 HD12 LEU A 274     -13.168   6.415  -6.079  1.00  1.63           H  
-ATOM    368 HD13 LEU A 274     -14.470   7.584  -6.299  1.00  1.63           H  
-ATOM    369 HD21 LEU A 274     -14.518   6.967  -3.191  1.00  1.61           H  
-ATOM    370 HD22 LEU A 274     -14.283   8.687  -2.881  1.00  1.62           H  
-ATOM    371 HD23 LEU A 274     -12.996   7.541  -2.506  1.00  1.44           H  
-ATOM    372  N   PRO A 275      -9.188   4.993  -5.097  1.00  1.27           N  
-ATOM    373  CA  PRO A 275      -7.802   4.730  -5.591  1.00  1.52           C  
-ATOM    374  C   PRO A 275      -7.533   5.085  -7.070  1.00  1.56           C  
-ATOM    375  O   PRO A 275      -6.994   4.270  -7.820  1.00  2.24           O  
-ATOM    376  CB  PRO A 275      -7.667   3.215  -5.410  1.00  1.88           C  
-ATOM    377  CG  PRO A 275      -8.557   2.912  -4.269  1.00  1.87           C  
-ATOM    378  CD  PRO A 275      -9.765   3.768  -4.490  1.00  1.52           C  
-ATOM    379  HA  PRO A 275      -7.071   5.220  -4.971  1.00  1.57           H  
-ATOM    380  HB2 PRO A 275      -7.989   2.713  -6.310  1.00  1.96           H  
-ATOM    381  HB3 PRO A 275      -6.640   2.960  -5.193  1.00  2.10           H  
-ATOM    382  HG2 PRO A 275      -8.822   1.866  -4.265  1.00  2.08           H  
-ATOM    383  HG3 PRO A 275      -8.068   3.188  -3.347  1.00  1.98           H  
-ATOM    384  HD2 PRO A 275     -10.462   3.289  -5.160  1.00  1.55           H  
-ATOM    385  HD3 PRO A 275     -10.252   3.998  -3.537  1.00  1.53           H  
-ATOM    386  N   LYS A 276      -7.897   6.291  -7.485  1.00  1.39           N  
-ATOM    387  CA  LYS A 276      -7.696   6.740  -8.851  1.00  1.65           C  
-ATOM    388  C   LYS A 276      -7.734   8.278  -8.921  1.00  1.63           C  
-ATOM    389  O   LYS A 276      -7.377   8.845  -9.980  1.00  2.33           O  
-ATOM    390  CB  LYS A 276      -8.782   6.130  -9.744  1.00  2.30           C  
-ATOM    391  CG  LYS A 276      -8.712   6.558 -11.196  1.00  2.74           C  
-ATOM    392  CD  LYS A 276     -10.080   6.956 -11.712  1.00  3.55           C  
-ATOM    393  CE  LYS A 276     -10.608   8.208 -11.024  1.00  3.69           C  
-ATOM    394  NZ  LYS A 276      -9.659   9.358 -11.131  1.00  3.38           N  
-ATOM    395  OXT LYS A 276      -8.140   8.911  -7.919  1.00  1.33           O  
-ATOM    396  H   LYS A 276      -8.331   6.906  -6.849  1.00  1.59           H  
-ATOM    397  HA  LYS A 276      -6.728   6.393  -9.178  1.00  1.74           H  
-ATOM    398  HB2 LYS A 276      -8.689   5.055  -9.708  1.00  2.56           H  
-ATOM    399  HB3 LYS A 276      -9.751   6.409  -9.356  1.00  2.43           H  
-ATOM    400  HG2 LYS A 276      -8.046   7.404 -11.284  1.00  2.41           H  
-ATOM    401  HG3 LYS A 276      -8.338   5.737 -11.788  1.00  3.02           H  
-ATOM    402  HD2 LYS A 276     -10.015   7.140 -12.770  1.00  3.85           H  
-ATOM    403  HD3 LYS A 276     -10.767   6.143 -11.526  1.00  3.96           H  
-ATOM    404  HE2 LYS A 276     -11.545   8.485 -11.485  1.00  4.35           H  
-ATOM    405  HE3 LYS A 276     -10.774   7.985  -9.981  1.00  3.57           H  
-ATOM    406  HZ1 LYS A 276      -8.711   9.088 -10.759  1.00  2.89           H  
-ATOM    407  HZ2 LYS A 276     -10.010  10.164 -10.579  1.00  3.83           H  
-ATOM    408  HZ3 LYS A 276      -9.559   9.649 -12.123  1.00  3.41           H  
-TER
-ENDMDL
-MODEL      7
-ATOM      1  N   ALA A 249      -1.976  -1.121 -13.700  1.00  2.96           N  
-ATOM      2  CA  ALA A 249      -1.072  -1.555 -12.605  1.00  2.32           C  
-ATOM      3  C   ALA A 249      -1.161  -3.067 -12.390  1.00  1.85           C  
-ATOM      4  O   ALA A 249      -2.142  -3.692 -12.786  1.00  2.08           O  
-ATOM      5  CB  ALA A 249      -1.399  -0.811 -11.321  1.00  2.41           C  
-ATOM      6  H1  ALA A 249      -1.648  -1.504 -14.608  1.00  3.21           H  
-ATOM      7  H2  ALA A 249      -1.994  -0.083 -13.759  1.00  3.50           H  
-ATOM      8  H3  ALA A 249      -2.942  -1.464 -13.519  1.00  3.05           H  
-ATOM      9  HA  ALA A 249      -0.059  -1.305 -12.884  1.00  2.27           H  
-ATOM     10  HB1 ALA A 249      -0.514  -0.771 -10.700  1.00  2.64           H  
-ATOM     11  HB2 ALA A 249      -2.189  -1.327 -10.796  1.00  2.70           H  
-ATOM     12  HB3 ALA A 249      -1.718   0.193 -11.557  1.00  2.64           H  
-ATOM     13  N   PRO A 250      -0.127  -3.672 -11.772  1.00  1.32           N  
-ATOM     14  CA  PRO A 250      -0.080  -5.120 -11.509  1.00  1.13           C  
-ATOM     15  C   PRO A 250      -1.019  -5.575 -10.384  1.00  0.86           C  
-ATOM     16  O   PRO A 250      -2.166  -5.142 -10.298  1.00  0.92           O  
-ATOM     17  CB  PRO A 250       1.384  -5.328 -11.113  1.00  0.96           C  
-ATOM     18  CG  PRO A 250       1.771  -4.050 -10.477  1.00  0.75           C  
-ATOM     19  CD  PRO A 250       1.090  -2.995 -11.285  1.00  1.15           C  
-ATOM     20  HA  PRO A 250      -0.292  -5.691 -12.396  1.00  1.58           H  
-ATOM     21  HB2 PRO A 250       1.464  -6.150 -10.425  1.00  1.09           H  
-ATOM     22  HB3 PRO A 250       1.974  -5.518 -11.994  1.00  1.32           H  
-ATOM     23  HG2 PRO A 250       1.425  -4.025  -9.455  1.00  0.85           H  
-ATOM     24  HG3 PRO A 250       2.843  -3.924 -10.519  1.00  0.80           H  
-ATOM     25  HD2 PRO A 250       0.840  -2.152 -10.663  1.00  1.42           H  
-ATOM     26  HD3 PRO A 250       1.715  -2.695 -12.109  1.00  1.36           H  
-ATOM     27  N   ALA A 251      -0.525  -6.469  -9.535  1.00  0.74           N  
-ATOM     28  CA  ALA A 251      -1.317  -7.006  -8.425  1.00  0.65           C  
-ATOM     29  C   ALA A 251      -1.454  -6.019  -7.249  1.00  0.55           C  
-ATOM     30  O   ALA A 251      -1.489  -4.800  -7.432  1.00  0.55           O  
-ATOM     31  CB  ALA A 251      -0.710  -8.320  -7.948  1.00  0.85           C  
-ATOM     32  H   ALA A 251       0.390  -6.790  -9.668  1.00  0.89           H  
-ATOM     33  HA  ALA A 251      -2.304  -7.221  -8.806  1.00  0.76           H  
-ATOM     34  HB1 ALA A 251      -1.208  -9.144  -8.435  1.00  1.37           H  
-ATOM     35  HB2 ALA A 251      -0.834  -8.406  -6.878  1.00  1.38           H  
-ATOM     36  HB3 ALA A 251       0.343  -8.341  -8.191  1.00  1.38           H  
-ATOM     37  N   ARG A 252      -1.545  -6.572  -6.036  1.00  0.59           N  
-ATOM     38  CA  ARG A 252      -1.700  -5.779  -4.811  1.00  0.57           C  
-ATOM     39  C   ARG A 252      -0.536  -4.798  -4.614  1.00  0.43           C  
-ATOM     40  O   ARG A 252      -0.746  -3.650  -4.224  1.00  0.40           O  
-ATOM     41  CB  ARG A 252      -1.807  -6.720  -3.600  1.00  0.72           C  
-ATOM     42  CG  ARG A 252      -2.738  -6.234  -2.493  1.00  0.83           C  
-ATOM     43  CD  ARG A 252      -2.214  -4.984  -1.803  1.00  0.68           C  
-ATOM     44  NE  ARG A 252      -3.130  -4.507  -0.770  1.00  0.92           N  
-ATOM     45  CZ  ARG A 252      -2.949  -3.401  -0.082  1.00  0.96           C  
-ATOM     46  NH1 ARG A 252      -1.908  -2.640  -0.303  1.00  0.90           N  
-ATOM     47  NH2 ARG A 252      -3.818  -3.052   0.827  1.00  1.26           N  
-ATOM     48  H   ARG A 252      -1.521  -7.548  -5.964  1.00  0.70           H  
-ATOM     49  HA  ARG A 252      -2.616  -5.218  -4.897  1.00  0.62           H  
-ATOM     50  HB2 ARG A 252      -2.164  -7.680  -3.939  1.00  0.85           H  
-ATOM     51  HB3 ARG A 252      -0.821  -6.846  -3.177  1.00  0.69           H  
-ATOM     52  HG2 ARG A 252      -3.704  -6.014  -2.921  1.00  0.98           H  
-ATOM     53  HG3 ARG A 252      -2.841  -7.019  -1.759  1.00  1.02           H  
-ATOM     54  HD2 ARG A 252      -1.263  -5.212  -1.348  1.00  0.68           H  
-ATOM     55  HD3 ARG A 252      -2.083  -4.208  -2.542  1.00  0.61           H  
-ATOM     56  HE  ARG A 252      -3.921  -5.050  -0.583  1.00  1.18           H  
-ATOM     57 HH11 ARG A 252      -1.243  -2.898  -0.998  1.00  0.82           H  
-ATOM     58 HH12 ARG A 252      -1.781  -1.803   0.223  1.00  1.09           H  
-ATOM     59 HH21 ARG A 252      -4.616  -3.625   1.000  1.00  1.48           H  
-ATOM     60 HH22 ARG A 252      -3.684  -2.214   1.350  1.00  1.36           H  
-ATOM     61  N   VAL A 253       0.683  -5.262  -4.881  1.00  0.42           N  
-ATOM     62  CA  VAL A 253       1.881  -4.431  -4.731  1.00  0.38           C  
-ATOM     63  C   VAL A 253       1.775  -3.146  -5.557  1.00  0.31           C  
-ATOM     64  O   VAL A 253       1.915  -2.039  -5.033  1.00  0.33           O  
-ATOM     65  CB  VAL A 253       3.158  -5.216  -5.127  1.00  0.48           C  
-ATOM     66  CG1 VAL A 253       3.066  -5.785  -6.539  1.00  0.54           C  
-ATOM     67  CG2 VAL A 253       4.402  -4.347  -4.972  1.00  0.59           C  
-ATOM     68  H   VAL A 253       0.780  -6.187  -5.185  1.00  0.49           H  
-ATOM     69  HA  VAL A 253       1.963  -4.161  -3.690  1.00  0.40           H  
-ATOM     70  HB  VAL A 253       3.245  -6.043  -4.456  1.00  0.55           H  
-ATOM     71 HG11 VAL A 253       2.031  -5.984  -6.778  1.00  1.21           H  
-ATOM     72 HG12 VAL A 253       3.631  -6.703  -6.594  1.00  0.99           H  
-ATOM     73 HG13 VAL A 253       3.468  -5.071  -7.241  1.00  1.14           H  
-ATOM     74 HG21 VAL A 253       5.280  -4.976  -4.952  1.00  1.14           H  
-ATOM     75 HG22 VAL A 253       4.339  -3.790  -4.049  1.00  1.21           H  
-ATOM     76 HG23 VAL A 253       4.469  -3.662  -5.802  1.00  1.24           H  
-ATOM     77  N   GLY A 254       1.505  -3.315  -6.841  1.00  0.30           N  
-ATOM     78  CA  GLY A 254       1.358  -2.182  -7.742  1.00  0.32           C  
-ATOM     79  C   GLY A 254       0.223  -1.265  -7.331  1.00  0.31           C  
-ATOM     80  O   GLY A 254       0.385  -0.045  -7.290  1.00  0.35           O  
-ATOM     81  H   GLY A 254       1.398  -4.229  -7.176  1.00  0.33           H  
-ATOM     82  HA2 GLY A 254       2.280  -1.618  -7.748  1.00  0.36           H  
-ATOM     83  HA3 GLY A 254       1.167  -2.551  -8.739  1.00  0.39           H  
-ATOM     84  N   LEU A 255      -0.922  -1.857  -7.001  1.00  0.33           N  
-ATOM     85  CA  LEU A 255      -2.082  -1.087  -6.562  1.00  0.38           C  
-ATOM     86  C   LEU A 255      -1.758  -0.270  -5.318  1.00  0.34           C  
-ATOM     87  O   LEU A 255      -2.077   0.917  -5.235  1.00  0.40           O  
-ATOM     88  CB  LEU A 255      -3.251  -2.014  -6.259  1.00  0.47           C  
-ATOM     89  CG  LEU A 255      -4.191  -2.246  -7.428  1.00  0.62           C  
-ATOM     90  CD1 LEU A 255      -5.306  -3.189  -7.017  1.00  0.73           C  
-ATOM     91  CD2 LEU A 255      -4.750  -0.917  -7.913  1.00  0.72           C  
-ATOM     92  H   LEU A 255      -0.983  -2.838  -7.038  1.00  0.36           H  
-ATOM     93  HA  LEU A 255      -2.366  -0.422  -7.363  1.00  0.43           H  
-ATOM     94  HB2 LEU A 255      -2.856  -2.969  -5.942  1.00  0.47           H  
-ATOM     95  HB3 LEU A 255      -3.820  -1.589  -5.446  1.00  0.54           H  
-ATOM     96  HG  LEU A 255      -3.643  -2.699  -8.240  1.00  0.66           H  
-ATOM     97 HD11 LEU A 255      -5.364  -3.226  -5.939  1.00  1.28           H  
-ATOM     98 HD12 LEU A 255      -5.099  -4.178  -7.400  1.00  1.29           H  
-ATOM     99 HD13 LEU A 255      -6.242  -2.834  -7.417  1.00  1.26           H  
-ATOM    100 HD21 LEU A 255      -5.808  -0.872  -7.703  1.00  1.34           H  
-ATOM    101 HD22 LEU A 255      -4.588  -0.824  -8.976  1.00  1.29           H  
-ATOM    102 HD23 LEU A 255      -4.246  -0.109  -7.401  1.00  1.17           H  
-ATOM    103  N   GLY A 256      -1.122  -0.918  -4.354  1.00  0.30           N  
-ATOM    104  CA  GLY A 256      -0.763  -0.254  -3.123  1.00  0.30           C  
-ATOM    105  C   GLY A 256       0.231   0.882  -3.319  1.00  0.26           C  
-ATOM    106  O   GLY A 256      -0.006   1.995  -2.852  1.00  0.30           O  
-ATOM    107  H   GLY A 256      -0.896  -1.870  -4.481  1.00  0.31           H  
-ATOM    108  HA2 GLY A 256      -1.663   0.144  -2.678  1.00  0.37           H  
-ATOM    109  HA3 GLY A 256      -0.337  -0.983  -2.453  1.00  0.35           H  
-ATOM    110  N   ILE A 257       1.342   0.615  -4.011  1.00  0.26           N  
-ATOM    111  CA  ILE A 257       2.347   1.655  -4.242  1.00  0.31           C  
-ATOM    112  C   ILE A 257       1.780   2.816  -5.068  1.00  0.35           C  
-ATOM    113  O   ILE A 257       2.038   3.976  -4.760  1.00  0.41           O  
-ATOM    114  CB  ILE A 257       3.647   1.105  -4.891  1.00  0.39           C  
-ATOM    115  CG1 ILE A 257       3.382   0.456  -6.253  1.00  0.42           C  
-ATOM    116  CG2 ILE A 257       4.321   0.112  -3.956  1.00  0.47           C  
-ATOM    117  CD1 ILE A 257       3.767   1.329  -7.429  1.00  0.54           C  
-ATOM    118  H   ILE A 257       1.488  -0.292  -4.370  1.00  0.29           H  
-ATOM    119  HA  ILE A 257       2.614   2.048  -3.269  1.00  0.34           H  
-ATOM    120  HB  ILE A 257       4.324   1.936  -5.027  1.00  0.47           H  
-ATOM    121 HG12 ILE A 257       3.948  -0.462  -6.323  1.00  0.48           H  
-ATOM    122 HG13 ILE A 257       2.329   0.229  -6.336  1.00  0.45           H  
-ATOM    123 HG21 ILE A 257       4.828   0.648  -3.167  1.00  1.11           H  
-ATOM    124 HG22 ILE A 257       5.037  -0.477  -4.510  1.00  1.14           H  
-ATOM    125 HG23 ILE A 257       3.575  -0.540  -3.525  1.00  1.16           H  
-ATOM    126 HD11 ILE A 257       4.696   1.834  -7.214  1.00  1.14           H  
-ATOM    127 HD12 ILE A 257       2.991   2.061  -7.603  1.00  1.19           H  
-ATOM    128 HD13 ILE A 257       3.885   0.715  -8.309  1.00  1.18           H  
-ATOM    129  N   THR A 258       0.985   2.512  -6.099  1.00  0.35           N  
-ATOM    130  CA  THR A 258       0.381   3.563  -6.923  1.00  0.44           C  
-ATOM    131  C   THR A 258      -0.627   4.381  -6.115  1.00  0.44           C  
-ATOM    132  O   THR A 258      -0.670   5.608  -6.225  1.00  0.50           O  
-ATOM    133  CB  THR A 258      -0.312   3.011  -8.189  1.00  0.51           C  
-ATOM    134  OG1 THR A 258      -1.194   1.935  -7.851  1.00  0.49           O  
-ATOM    135  CG2 THR A 258       0.713   2.536  -9.208  1.00  0.57           C  
-ATOM    136  H   THR A 258       0.790   1.568  -6.297  1.00  0.32           H  
-ATOM    137  HA  THR A 258       1.179   4.221  -7.240  1.00  0.49           H  
-ATOM    138  HB  THR A 258      -0.890   3.807  -8.634  1.00  0.58           H  
-ATOM    139  HG1 THR A 258      -0.682   1.119  -7.741  1.00  0.48           H  
-ATOM    140 HG21 THR A 258       1.210   3.390  -9.645  1.00  1.16           H  
-ATOM    141 HG22 THR A 258       0.215   1.974  -9.985  1.00  1.16           H  
-ATOM    142 HG23 THR A 258       1.443   1.907  -8.719  1.00  1.21           H  
-ATOM    143  N   THR A 259      -1.422   3.702  -5.282  1.00  0.40           N  
-ATOM    144  CA  THR A 259      -2.406   4.388  -4.441  1.00  0.43           C  
-ATOM    145  C   THR A 259      -1.697   5.300  -3.441  1.00  0.39           C  
-ATOM    146  O   THR A 259      -2.035   6.479  -3.313  1.00  0.45           O  
-ATOM    147  CB  THR A 259      -3.302   3.388  -3.674  1.00  0.47           C  
-ATOM    148  OG1 THR A 259      -3.990   2.534  -4.597  1.00  0.54           O  
-ATOM    149  CG2 THR A 259      -4.320   4.116  -2.806  1.00  0.57           C  
-ATOM    150  H   THR A 259      -1.333   2.723  -5.220  1.00  0.38           H  
-ATOM    151  HA  THR A 259      -3.034   4.989  -5.082  1.00  0.51           H  
-ATOM    152  HB  THR A 259      -2.674   2.783  -3.035  1.00  0.44           H  
-ATOM    153  HG1 THR A 259      -3.370   1.875  -4.948  1.00  0.53           H  
-ATOM    154 HG21 THR A 259      -4.443   3.585  -1.874  1.00  1.19           H  
-ATOM    155 HG22 THR A 259      -5.267   4.161  -3.323  1.00  1.18           H  
-ATOM    156 HG23 THR A 259      -3.970   5.118  -2.605  1.00  1.18           H  
-ATOM    157  N   VAL A 260      -0.690   4.753  -2.757  1.00  0.33           N  
-ATOM    158  CA  VAL A 260       0.091   5.524  -1.789  1.00  0.34           C  
-ATOM    159  C   VAL A 260       0.782   6.709  -2.466  1.00  0.35           C  
-ATOM    160  O   VAL A 260       0.694   7.839  -1.982  1.00  0.39           O  
-ATOM    161  CB  VAL A 260       1.151   4.645  -1.089  1.00  0.39           C  
-ATOM    162  CG1 VAL A 260       2.068   5.490  -0.217  1.00  0.50           C  
-ATOM    163  CG2 VAL A 260       0.482   3.561  -0.259  1.00  0.47           C  
-ATOM    164  H   VAL A 260      -0.455   3.810  -2.921  1.00  0.33           H  
-ATOM    165  HA  VAL A 260      -0.588   5.899  -1.038  1.00  0.38           H  
-ATOM    166  HB  VAL A 260       1.754   4.166  -1.847  1.00  0.42           H  
-ATOM    167 HG11 VAL A 260       3.085   5.400  -0.569  1.00  1.21           H  
-ATOM    168 HG12 VAL A 260       2.009   5.148   0.806  1.00  1.14           H  
-ATOM    169 HG13 VAL A 260       1.760   6.524  -0.269  1.00  1.10           H  
-ATOM    170 HG21 VAL A 260       1.135   2.704  -0.190  1.00  1.12           H  
-ATOM    171 HG22 VAL A 260      -0.446   3.269  -0.729  1.00  1.16           H  
-ATOM    172 HG23 VAL A 260       0.279   3.939   0.732  1.00  1.12           H  
-ATOM    173  N   LEU A 261       1.452   6.446  -3.594  1.00  0.38           N  
-ATOM    174  CA  LEU A 261       2.142   7.498  -4.349  1.00  0.46           C  
-ATOM    175  C   LEU A 261       1.165   8.609  -4.726  1.00  0.49           C  
-ATOM    176  O   LEU A 261       1.471   9.794  -4.595  1.00  0.57           O  
-ATOM    177  CB  LEU A 261       2.776   6.913  -5.613  1.00  0.52           C  
-ATOM    178  CG  LEU A 261       4.269   7.201  -5.794  1.00  0.69           C  
-ATOM    179  CD1 LEU A 261       4.520   8.696  -5.930  1.00  1.07           C  
-ATOM    180  CD2 LEU A 261       5.065   6.627  -4.630  1.00  1.15           C  
-ATOM    181  H   LEU A 261       1.475   5.521  -3.935  1.00  0.37           H  
-ATOM    182  HA  LEU A 261       2.917   7.909  -3.720  1.00  0.49           H  
-ATOM    183  HB2 LEU A 261       2.638   5.842  -5.596  1.00  0.75           H  
-ATOM    184  HB3 LEU A 261       2.252   7.312  -6.469  1.00  0.68           H  
-ATOM    185  HG  LEU A 261       4.608   6.724  -6.699  1.00  0.71           H  
-ATOM    186 HD11 LEU A 261       5.121   9.038  -5.101  1.00  1.57           H  
-ATOM    187 HD12 LEU A 261       3.576   9.221  -5.932  1.00  1.44           H  
-ATOM    188 HD13 LEU A 261       5.041   8.889  -6.855  1.00  1.68           H  
-ATOM    189 HD21 LEU A 261       6.044   7.081  -4.605  1.00  1.72           H  
-ATOM    190 HD22 LEU A 261       5.167   5.558  -4.757  1.00  1.46           H  
-ATOM    191 HD23 LEU A 261       4.548   6.831  -3.704  1.00  1.74           H  
-ATOM    192  N   THR A 262      -0.020   8.206  -5.180  1.00  0.49           N  
-ATOM    193  CA  THR A 262      -1.068   9.155  -5.556  1.00  0.56           C  
-ATOM    194  C   THR A 262      -1.475  10.017  -4.357  1.00  0.55           C  
-ATOM    195  O   THR A 262      -1.545  11.242  -4.453  1.00  0.64           O  
-ATOM    196  CB  THR A 262      -2.317   8.420  -6.089  1.00  0.60           C  
-ATOM    197  OG1 THR A 262      -1.979   7.636  -7.239  1.00  0.65           O  
-ATOM    198  CG2 THR A 262      -3.421   9.404  -6.448  1.00  0.72           C  
-ATOM    199  H   THR A 262      -0.199   7.240  -5.252  1.00  0.47           H  
-ATOM    200  HA  THR A 262      -0.683   9.791  -6.339  1.00  0.63           H  
-ATOM    201  HB  THR A 262      -2.684   7.763  -5.313  1.00  0.56           H  
-ATOM    202  HG1 THR A 262      -1.551   6.813  -6.953  1.00  0.68           H  
-ATOM    203 HG21 THR A 262      -3.491   9.488  -7.522  1.00  1.28           H  
-ATOM    204 HG22 THR A 262      -3.192  10.370  -6.024  1.00  1.26           H  
-ATOM    205 HG23 THR A 262      -4.360   9.050  -6.050  1.00  1.25           H  
-ATOM    206  N   MET A 263      -1.742   9.361  -3.229  1.00  0.46           N  
-ATOM    207  CA  MET A 263      -2.142  10.059  -2.004  1.00  0.46           C  
-ATOM    208  C   MET A 263      -1.036  10.983  -1.468  1.00  0.44           C  
-ATOM    209  O   MET A 263      -1.269  12.170  -1.246  1.00  0.48           O  
-ATOM    210  CB  MET A 263      -2.536   9.049  -0.923  1.00  0.47           C  
-ATOM    211  CG  MET A 263      -3.767   8.230  -1.275  1.00  0.53           C  
-ATOM    212  SD  MET A 263      -4.182   7.016  -0.008  1.00  0.64           S  
-ATOM    213  CE  MET A 263      -4.485   8.079   1.400  1.00  0.78           C  
-ATOM    214  H   MET A 263      -1.671   8.378  -3.222  1.00  0.43           H  
-ATOM    215  HA  MET A 263      -3.005  10.663  -2.241  1.00  0.50           H  
-ATOM    216  HB2 MET A 263      -1.711   8.371  -0.765  1.00  0.49           H  
-ATOM    217  HB3 MET A 263      -2.735   9.582  -0.006  1.00  0.52           H  
-ATOM    218  HG2 MET A 263      -4.605   8.899  -1.400  1.00  0.61           H  
-ATOM    219  HG3 MET A 263      -3.583   7.710  -2.205  1.00  0.63           H  
-ATOM    220  HE1 MET A 263      -5.519   8.390   1.402  1.00  1.23           H  
-ATOM    221  HE2 MET A 263      -3.848   8.949   1.339  1.00  1.22           H  
-ATOM    222  HE3 MET A 263      -4.270   7.538   2.311  1.00  1.38           H  
-ATOM    223  N   THR A 264       0.159  10.438  -1.249  1.00  0.42           N  
-ATOM    224  CA  THR A 264       1.268  11.234  -0.729  1.00  0.42           C  
-ATOM    225  C   THR A 264       2.516  11.129  -1.618  1.00  0.52           C  
-ATOM    226  O   THR A 264       2.898  10.043  -2.056  1.00  0.59           O  
-ATOM    227  CB  THR A 264       1.610  10.816   0.717  1.00  0.36           C  
-ATOM    228  OG1 THR A 264       2.550  11.736   1.298  1.00  0.38           O  
-ATOM    229  CG2 THR A 264       2.168   9.399   0.777  1.00  0.42           C  
-ATOM    230  H   THR A 264       0.297   9.482  -1.430  1.00  0.43           H  
-ATOM    231  HA  THR A 264       0.949  12.265  -0.710  1.00  0.46           H  
-ATOM    232  HB  THR A 264       0.695  10.840   1.288  1.00  0.35           H  
-ATOM    233  HG1 THR A 264       3.396  11.279   1.465  1.00  0.43           H  
-ATOM    234 HG21 THR A 264       2.884   9.327   1.582  1.00  1.11           H  
-ATOM    235 HG22 THR A 264       2.654   9.164  -0.159  1.00  1.16           H  
-ATOM    236 HG23 THR A 264       1.361   8.702   0.948  1.00  1.07           H  
-ATOM    237  N   THR A 265       3.144  12.272  -1.889  1.00  0.58           N  
-ATOM    238  CA  THR A 265       4.340  12.310  -2.736  1.00  0.71           C  
-ATOM    239  C   THR A 265       5.517  11.559  -2.103  1.00  0.71           C  
-ATOM    240  O   THR A 265       5.982  11.903  -1.013  1.00  0.66           O  
-ATOM    241  CB  THR A 265       4.767  13.760  -3.060  1.00  0.79           C  
-ATOM    242  OG1 THR A 265       6.038  13.763  -3.723  1.00  0.93           O  
-ATOM    243  CG2 THR A 265       4.839  14.618  -1.802  1.00  0.73           C  
-ATOM    244  H   THR A 265       2.790  13.110  -1.520  1.00  0.57           H  
-ATOM    245  HA  THR A 265       4.090  11.824  -3.668  1.00  0.80           H  
-ATOM    246  HB  THR A 265       4.030  14.189  -3.724  1.00  0.87           H  
-ATOM    247  HG1 THR A 265       6.583  14.474  -3.372  1.00  1.30           H  
-ATOM    248 HG21 THR A 265       4.954  13.981  -0.938  1.00  1.27           H  
-ATOM    249 HG22 THR A 265       3.929  15.194  -1.707  1.00  1.27           H  
-ATOM    250 HG23 THR A 265       5.684  15.289  -1.872  1.00  1.22           H  
-ATOM    251  N   GLN A 266       5.982  10.519  -2.812  1.00  0.83           N  
-ATOM    252  CA  GLN A 266       7.101   9.668  -2.373  1.00  0.91           C  
-ATOM    253  C   GLN A 266       6.747   8.832  -1.133  1.00  0.86           C  
-ATOM    254  O   GLN A 266       6.752   7.602  -1.182  1.00  0.97           O  
-ATOM    255  CB  GLN A 266       8.352  10.513  -2.092  1.00  0.97           C  
-ATOM    256  CG  GLN A 266       9.559   9.691  -1.664  1.00  1.11           C  
-ATOM    257  CD  GLN A 266      10.738  10.552  -1.257  1.00  1.21           C  
-ATOM    258  OE1 GLN A 266      11.235  11.357  -2.036  1.00  1.32           O  
-ATOM    259  NE2 GLN A 266      11.200  10.383  -0.031  1.00  1.29           N  
-ATOM    260  H   GLN A 266       5.550  10.312  -3.667  1.00  0.89           H  
-ATOM    261  HA  GLN A 266       7.322   8.991  -3.179  1.00  1.03           H  
-ATOM    262  HB2 GLN A 266       8.612  11.056  -2.987  1.00  1.04           H  
-ATOM    263  HB3 GLN A 266       8.126  11.218  -1.305  1.00  0.91           H  
-ATOM    264  HG2 GLN A 266       9.278   9.075  -0.823  1.00  1.11           H  
-ATOM    265  HG3 GLN A 266       9.858   9.060  -2.488  1.00  1.26           H  
-ATOM    266 HE21 GLN A 266      10.764   9.722   0.541  1.00  1.30           H  
-ATOM    267 HE22 GLN A 266      11.961  10.929   0.250  1.00  1.41           H  
-ATOM    268  N   SER A 267       6.445   9.511  -0.033  1.00  0.73           N  
-ATOM    269  CA  SER A 267       6.094   8.854   1.227  1.00  0.72           C  
-ATOM    270  C   SER A 267       5.615   9.890   2.251  1.00  0.61           C  
-ATOM    271  O   SER A 267       4.915  10.840   1.895  1.00  0.50           O  
-ATOM    272  CB  SER A 267       7.308   8.082   1.773  1.00  0.89           C  
-ATOM    273  OG  SER A 267       6.983   7.349   2.946  1.00  0.98           O  
-ATOM    274  H   SER A 267       6.457  10.496  -0.072  1.00  0.68           H  
-ATOM    275  HA  SER A 267       5.291   8.159   1.027  1.00  0.72           H  
-ATOM    276  HB2 SER A 267       7.657   7.390   1.022  1.00  0.99           H  
-ATOM    277  HB3 SER A 267       8.096   8.781   2.010  1.00  0.93           H  
-ATOM    278  HG  SER A 267       7.051   6.406   2.765  1.00  1.22           H  
-ATOM    279  N   SER A 268       5.994   9.703   3.516  1.00  0.72           N  
-ATOM    280  CA  SER A 268       5.611  10.613   4.603  1.00  0.72           C  
-ATOM    281  C   SER A 268       4.085  10.669   4.773  1.00  0.64           C  
-ATOM    282  O   SER A 268       3.404   9.650   4.667  1.00  0.77           O  
-ATOM    283  CB  SER A 268       6.189  12.019   4.350  1.00  0.70           C  
-ATOM    284  OG  SER A 268       5.916  12.905   5.429  1.00  0.76           O  
-ATOM    285  H   SER A 268       6.550   8.916   3.726  1.00  0.86           H  
-ATOM    286  HA  SER A 268       6.040  10.224   5.515  1.00  0.89           H  
-ATOM    287  HB2 SER A 268       7.258  11.948   4.226  1.00  0.84           H  
-ATOM    288  HB3 SER A 268       5.751  12.426   3.449  1.00  0.58           H  
-ATOM    289  HG  SER A 268       6.734  13.317   5.720  1.00  0.96           H  
-ATOM    290  N   GLY A 269       3.556  11.855   5.042  1.00  0.57           N  
-ATOM    291  CA  GLY A 269       2.124  12.008   5.228  1.00  0.58           C  
-ATOM    292  C   GLY A 269       1.603  13.348   4.736  1.00  0.49           C  
-ATOM    293  O   GLY A 269       1.375  14.260   5.528  1.00  0.65           O  
-ATOM    294  H   GLY A 269       4.154  12.636   5.126  1.00  0.64           H  
-ATOM    295  HA2 GLY A 269       1.618  11.219   4.690  1.00  0.55           H  
-ATOM    296  HA3 GLY A 269       1.898  11.912   6.280  1.00  0.76           H  
-ATOM    297  N   SER A 270       1.423  13.464   3.421  1.00  0.38           N  
-ATOM    298  CA  SER A 270       0.927  14.702   2.810  1.00  0.45           C  
-ATOM    299  C   SER A 270      -0.463  15.070   3.334  1.00  0.59           C  
-ATOM    300  O   SER A 270      -1.342  14.214   3.453  1.00  0.59           O  
-ATOM    301  CB  SER A 270       0.872  14.566   1.286  1.00  0.47           C  
-ATOM    302  OG  SER A 270       2.148  14.258   0.747  1.00  0.45           O  
-ATOM    303  H   SER A 270       1.631  12.696   2.843  1.00  0.39           H  
-ATOM    304  HA  SER A 270       1.615  15.494   3.066  1.00  0.51           H  
-ATOM    305  HB2 SER A 270       0.185  13.776   1.022  1.00  0.48           H  
-ATOM    306  HB3 SER A 270       0.529  15.497   0.857  1.00  0.62           H  
-ATOM    307  HG  SER A 270       2.369  13.332   0.951  1.00  0.38           H  
-ATOM    308  N   ARG A 271      -0.655  16.349   3.647  1.00  0.78           N  
-ATOM    309  CA  ARG A 271      -1.939  16.834   4.161  1.00  0.97           C  
-ATOM    310  C   ARG A 271      -3.069  16.634   3.143  1.00  0.95           C  
-ATOM    311  O   ARG A 271      -4.194  16.296   3.511  1.00  1.02           O  
-ATOM    312  CB  ARG A 271      -1.838  18.317   4.538  1.00  1.21           C  
-ATOM    313  CG  ARG A 271      -3.129  18.889   5.109  1.00  1.39           C  
-ATOM    314  CD  ARG A 271      -3.011  20.378   5.402  1.00  1.62           C  
-ATOM    315  NE  ARG A 271      -4.261  20.931   5.921  1.00  1.85           N  
-ATOM    316  CZ  ARG A 271      -4.441  22.200   6.225  1.00  2.08           C  
-ATOM    317  NH1 ARG A 271      -3.473  23.065   6.066  1.00  2.17           N  
-ATOM    318  NH2 ARG A 271      -5.594  22.602   6.688  1.00  2.28           N  
-ATOM    319  H   ARG A 271       0.083  16.983   3.533  1.00  0.83           H  
-ATOM    320  HA  ARG A 271      -2.171  16.265   5.049  1.00  1.00           H  
-ATOM    321  HB2 ARG A 271      -1.059  18.437   5.275  1.00  1.27           H  
-ATOM    322  HB3 ARG A 271      -1.578  18.884   3.656  1.00  1.26           H  
-ATOM    323  HG2 ARG A 271      -3.924  18.738   4.394  1.00  1.38           H  
-ATOM    324  HG3 ARG A 271      -3.365  18.370   6.026  1.00  1.43           H  
-ATOM    325  HD2 ARG A 271      -2.231  20.529   6.133  1.00  1.67           H  
-ATOM    326  HD3 ARG A 271      -2.752  20.893   4.488  1.00  1.65           H  
-ATOM    327  HE  ARG A 271      -5.007  20.312   6.049  1.00  1.87           H  
-ATOM    328 HH11 ARG A 271      -2.592  22.763   5.710  1.00  2.08           H  
-ATOM    329 HH12 ARG A 271      -3.617  24.023   6.299  1.00  2.37           H  
-ATOM    330 HH21 ARG A 271      -6.337  21.948   6.810  1.00  2.30           H  
-ATOM    331 HH22 ARG A 271      -5.730  23.562   6.920  1.00  2.46           H  
-ATOM    332  N   ALA A 272      -2.770  16.855   1.866  1.00  0.96           N  
-ATOM    333  CA  ALA A 272      -3.769  16.711   0.806  1.00  1.02           C  
-ATOM    334  C   ALA A 272      -3.976  15.254   0.366  1.00  0.71           C  
-ATOM    335  O   ALA A 272      -4.269  14.992  -0.800  1.00  0.82           O  
-ATOM    336  CB  ALA A 272      -3.378  17.568  -0.388  1.00  1.38           C  
-ATOM    337  H   ALA A 272      -1.860  17.129   1.630  1.00  1.00           H  
-ATOM    338  HA  ALA A 272      -4.705  17.090   1.188  1.00  1.15           H  
-ATOM    339  HB1 ALA A 272      -2.349  17.879  -0.288  1.00  1.73           H  
-ATOM    340  HB2 ALA A 272      -4.015  18.439  -0.429  1.00  1.66           H  
-ATOM    341  HB3 ALA A 272      -3.493  16.995  -1.296  1.00  1.97           H  
-ATOM    342  N   SER A 273      -3.858  14.306   1.296  1.00  0.47           N  
-ATOM    343  CA  SER A 273      -4.065  12.891   0.970  1.00  0.37           C  
-ATOM    344  C   SER A 273      -5.544  12.543   0.920  1.00  0.40           C  
-ATOM    345  O   SER A 273      -6.009  11.622   1.593  1.00  0.60           O  
-ATOM    346  CB  SER A 273      -3.365  11.968   1.962  1.00  0.42           C  
-ATOM    347  OG  SER A 273      -1.964  12.184   1.983  1.00  0.43           O  
-ATOM    348  H   SER A 273      -3.645  14.564   2.219  1.00  0.57           H  
-ATOM    349  HA  SER A 273      -3.658  12.712  -0.017  1.00  0.57           H  
-ATOM    350  HB2 SER A 273      -3.763  12.143   2.947  1.00  0.48           H  
-ATOM    351  HB3 SER A 273      -3.554  10.944   1.674  1.00  0.62           H  
-ATOM    352  HG  SER A 273      -1.756  12.905   2.599  1.00  0.46           H  
-ATOM    353  N   LEU A 274      -6.269  13.256   0.079  1.00  0.42           N  
-ATOM    354  CA  LEU A 274      -7.682  12.995  -0.114  1.00  0.55           C  
-ATOM    355  C   LEU A 274      -8.001  12.823  -1.615  1.00  0.80           C  
-ATOM    356  O   LEU A 274      -9.000  13.361  -2.105  1.00  0.85           O  
-ATOM    357  CB  LEU A 274      -8.508  14.138   0.489  1.00  0.51           C  
-ATOM    358  CG  LEU A 274     -10.003  13.855   0.657  1.00  0.75           C  
-ATOM    359  CD1 LEU A 274     -10.226  12.682   1.599  1.00  1.08           C  
-ATOM    360  CD2 LEU A 274     -10.721  15.096   1.169  1.00  0.89           C  
-ATOM    361  H   LEU A 274      -5.829  13.956  -0.457  1.00  0.49           H  
-ATOM    362  HA  LEU A 274      -7.923  12.077   0.401  1.00  0.75           H  
-ATOM    363  HB2 LEU A 274      -8.098  14.375   1.459  1.00  0.61           H  
-ATOM    364  HB3 LEU A 274      -8.400  15.004  -0.148  1.00  0.54           H  
-ATOM    365  HG  LEU A 274     -10.425  13.597  -0.304  1.00  0.82           H  
-ATOM    366 HD11 LEU A 274      -9.272  12.325   1.961  1.00  1.55           H  
-ATOM    367 HD12 LEU A 274     -10.731  11.887   1.072  1.00  1.59           H  
-ATOM    368 HD13 LEU A 274     -10.830  13.001   2.435  1.00  1.51           H  
-ATOM    369 HD21 LEU A 274     -10.618  15.154   2.243  1.00  1.36           H  
-ATOM    370 HD22 LEU A 274     -11.769  15.037   0.911  1.00  1.57           H  
-ATOM    371 HD23 LEU A 274     -10.287  15.975   0.716  1.00  1.21           H  
-ATOM    372  N   PRO A 275      -7.150  12.085  -2.388  1.00  1.08           N  
-ATOM    373  CA  PRO A 275      -7.368  11.878  -3.834  1.00  1.45           C  
-ATOM    374  C   PRO A 275      -8.638  11.083  -4.144  1.00  1.66           C  
-ATOM    375  O   PRO A 275      -8.653   9.855  -4.049  1.00  2.20           O  
-ATOM    376  CB  PRO A 275      -6.131  11.086  -4.274  1.00  1.76           C  
-ATOM    377  CG  PRO A 275      -5.125  11.405  -3.237  1.00  1.50           C  
-ATOM    378  CD  PRO A 275      -5.901  11.412  -1.960  1.00  1.13           C  
-ATOM    379  HA  PRO A 275      -7.389  12.819  -4.360  1.00  1.46           H  
-ATOM    380  HB2 PRO A 275      -6.362  10.031  -4.296  1.00  1.96           H  
-ATOM    381  HB3 PRO A 275      -5.812  11.416  -5.251  1.00  1.99           H  
-ATOM    382  HG2 PRO A 275      -4.351  10.651  -3.216  1.00  1.69           H  
-ATOM    383  HG3 PRO A 275      -4.711  12.383  -3.427  1.00  1.47           H  
-ATOM    384  HD2 PRO A 275      -6.088  10.404  -1.622  1.00  1.29           H  
-ATOM    385  HD3 PRO A 275      -5.383  11.986  -1.189  1.00  0.85           H  
-ATOM    386  N   LYS A 276      -9.695  11.800  -4.524  1.00  1.39           N  
-ATOM    387  CA  LYS A 276     -10.987  11.192  -4.863  1.00  1.61           C  
-ATOM    388  C   LYS A 276     -11.937  12.240  -5.451  1.00  1.62           C  
-ATOM    389  O   LYS A 276     -11.711  13.449  -5.194  1.00  1.47           O  
-ATOM    390  CB  LYS A 276     -11.620  10.520  -3.628  1.00  1.64           C  
-ATOM    391  CG  LYS A 276     -11.711  11.412  -2.392  1.00  1.42           C  
-ATOM    392  CD  LYS A 276     -12.909  12.349  -2.441  1.00  1.50           C  
-ATOM    393  CE  LYS A 276     -12.538  13.741  -1.959  1.00  1.38           C  
-ATOM    394  NZ  LYS A 276     -11.462  14.337  -2.799  1.00  1.24           N  
-ATOM    395  OXT LYS A 276     -12.893  11.852  -6.151  1.00  1.88           O  
-ATOM    396  H   LYS A 276      -9.605  12.775  -4.583  1.00  1.19           H  
-ATOM    397  HA  LYS A 276     -10.803  10.438  -5.614  1.00  1.86           H  
-ATOM    398  HB2 LYS A 276     -12.620  10.202  -3.883  1.00  1.83           H  
-ATOM    399  HB3 LYS A 276     -11.035   9.649  -3.371  1.00  1.76           H  
-ATOM    400  HG2 LYS A 276     -11.796  10.785  -1.517  1.00  1.56           H  
-ATOM    401  HG3 LYS A 276     -10.807  12.002  -2.323  1.00  1.20           H  
-ATOM    402  HD2 LYS A 276     -13.265  12.413  -3.458  1.00  1.57           H  
-ATOM    403  HD3 LYS A 276     -13.689  11.955  -1.805  1.00  1.73           H  
-ATOM    404  HE2 LYS A 276     -13.412  14.372  -2.007  1.00  1.57           H  
-ATOM    405  HE3 LYS A 276     -12.193  13.677  -0.938  1.00  1.40           H  
-ATOM    406  HZ1 LYS A 276     -11.468  15.371  -2.711  1.00  1.37           H  
-ATOM    407  HZ2 LYS A 276     -11.611  14.081  -3.811  1.00  1.28           H  
-ATOM    408  HZ3 LYS A 276     -10.528  13.973  -2.498  1.00  1.13           H  
-TER
-ENDMDL
-MODEL      8
-ATOM      1  N   ALA A 249       1.445  -4.274 -13.954  1.00  1.31           N  
-ATOM      2  CA  ALA A 249       1.757  -3.868 -12.556  1.00  1.10           C  
-ATOM      3  C   ALA A 249       1.809  -5.083 -11.630  1.00  1.01           C  
-ATOM      4  O   ALA A 249       1.178  -6.104 -11.904  1.00  1.07           O  
-ATOM      5  CB  ALA A 249       0.733  -2.862 -12.050  1.00  1.05           C  
-ATOM      6  H1  ALA A 249       2.290  -4.175 -14.553  1.00  1.62           H  
-ATOM      7  H2  ALA A 249       0.688  -3.675 -14.341  1.00  1.66           H  
-ATOM      8  H3  ALA A 249       1.131  -5.267 -13.974  1.00  1.50           H  
-ATOM      9  HA  ALA A 249       2.726  -3.388 -12.552  1.00  1.18           H  
-ATOM     10  HB1 ALA A 249       1.230  -2.128 -11.431  1.00  1.46           H  
-ATOM     11  HB2 ALA A 249      -0.016  -3.375 -11.466  1.00  1.48           H  
-ATOM     12  HB3 ALA A 249       0.266  -2.371 -12.887  1.00  1.45           H  
-ATOM     13  N   PRO A 250       2.580  -4.991 -10.530  1.00  0.98           N  
-ATOM     14  CA  PRO A 250       2.749  -6.080  -9.550  1.00  1.04           C  
-ATOM     15  C   PRO A 250       1.495  -6.364  -8.703  1.00  0.95           C  
-ATOM     16  O   PRO A 250       1.578  -6.462  -7.477  1.00  1.02           O  
-ATOM     17  CB  PRO A 250       3.889  -5.567  -8.653  1.00  1.14           C  
-ATOM     18  CG  PRO A 250       4.508  -4.445  -9.406  1.00  1.19           C  
-ATOM     19  CD  PRO A 250       3.379  -3.821 -10.153  1.00  1.04           C  
-ATOM     20  HA  PRO A 250       3.061  -6.994 -10.033  1.00  1.17           H  
-ATOM     21  HB2 PRO A 250       3.481  -5.224  -7.715  1.00  1.08           H  
-ATOM     22  HB3 PRO A 250       4.597  -6.357  -8.479  1.00  1.32           H  
-ATOM     23  HG2 PRO A 250       4.947  -3.735  -8.720  1.00  1.25           H  
-ATOM     24  HG3 PRO A 250       5.251  -4.823 -10.091  1.00  1.36           H  
-ATOM     25  HD2 PRO A 250       2.818  -3.162  -9.508  1.00  0.95           H  
-ATOM     26  HD3 PRO A 250       3.739  -3.301 -11.023  1.00  1.15           H  
-ATOM     27  N   ALA A 251       0.345  -6.512  -9.368  1.00  0.91           N  
-ATOM     28  CA  ALA A 251      -0.933  -6.809  -8.704  1.00  0.96           C  
-ATOM     29  C   ALA A 251      -1.208  -5.912  -7.481  1.00  0.86           C  
-ATOM     30  O   ALA A 251      -1.231  -4.683  -7.588  1.00  0.74           O  
-ATOM     31  CB  ALA A 251      -0.979  -8.284  -8.314  1.00  1.19           C  
-ATOM     32  H   ALA A 251       0.360  -6.433 -10.350  1.00  0.94           H  
-ATOM     33  HA  ALA A 251      -1.717  -6.642  -9.427  1.00  1.02           H  
-ATOM     34  HB1 ALA A 251      -0.798  -8.382  -7.253  1.00  1.60           H  
-ATOM     35  HB2 ALA A 251      -0.221  -8.824  -8.860  1.00  1.52           H  
-ATOM     36  HB3 ALA A 251      -1.951  -8.690  -8.552  1.00  1.70           H  
-ATOM     37  N   ARG A 252      -1.430  -6.549  -6.326  1.00  0.99           N  
-ATOM     38  CA  ARG A 252      -1.720  -5.845  -5.073  1.00  1.01           C  
-ATOM     39  C   ARG A 252      -0.608  -4.853  -4.712  1.00  0.88           C  
-ATOM     40  O   ARG A 252      -0.883  -3.737  -4.266  1.00  0.82           O  
-ATOM     41  CB  ARG A 252      -1.903  -6.863  -3.941  1.00  1.27           C  
-ATOM     42  CG  ARG A 252      -3.113  -6.603  -3.051  1.00  1.43           C  
-ATOM     43  CD  ARG A 252      -2.992  -5.293  -2.288  1.00  1.40           C  
-ATOM     44  NE  ARG A 252      -4.126  -5.068  -1.395  1.00  1.65           N  
-ATOM     45  CZ  ARG A 252      -4.265  -3.999  -0.642  1.00  1.79           C  
-ATOM     46  NH1 ARG A 252      -3.372  -3.047  -0.676  1.00  1.76           N  
-ATOM     47  NH2 ARG A 252      -5.303  -3.878   0.144  1.00  2.07           N  
-ATOM     48  H   ARG A 252      -1.406  -7.528  -6.318  1.00  1.13           H  
-ATOM     49  HA  ARG A 252      -2.641  -5.300  -5.206  1.00  1.01           H  
-ATOM     50  HB2 ARG A 252      -2.010  -7.846  -4.374  1.00  1.39           H  
-ATOM     51  HB3 ARG A 252      -1.020  -6.851  -3.319  1.00  1.32           H  
-ATOM     52  HG2 ARG A 252      -3.998  -6.565  -3.669  1.00  1.48           H  
-ATOM     53  HG3 ARG A 252      -3.204  -7.414  -2.343  1.00  1.62           H  
-ATOM     54  HD2 ARG A 252      -2.084  -5.315  -1.704  1.00  1.44           H  
-ATOM     55  HD3 ARG A 252      -2.940  -4.482  -3.000  1.00  1.29           H  
-ATOM     56  HE  ARG A 252      -4.814  -5.761  -1.355  1.00  1.80           H  
-ATOM     57 HH11 ARG A 252      -2.580  -3.130  -1.276  1.00  1.62           H  
-ATOM     58 HH12 ARG A 252      -3.481  -2.241  -0.103  1.00  1.96           H  
-ATOM     59 HH21 ARG A 252      -5.992  -4.596   0.175  1.00  2.18           H  
-ATOM     60 HH22 ARG A 252      -5.405  -3.064   0.712  1.00  2.22           H  
-ATOM     61  N   VAL A 253       0.642  -5.262  -4.920  1.00  0.91           N  
-ATOM     62  CA  VAL A 253       1.791  -4.404  -4.629  1.00  0.87           C  
-ATOM     63  C   VAL A 253       1.755  -3.153  -5.507  1.00  0.66           C  
-ATOM     64  O   VAL A 253       1.942  -2.034  -5.026  1.00  0.61           O  
-ATOM     65  CB  VAL A 253       3.126  -5.150  -4.851  1.00  1.03           C  
-ATOM     66  CG1 VAL A 253       4.312  -4.275  -4.470  1.00  1.10           C  
-ATOM     67  CG2 VAL A 253       3.150  -6.452  -4.065  1.00  1.26           C  
-ATOM     68  H   VAL A 253       0.793  -6.156  -5.290  1.00  1.01           H  
-ATOM     69  HA  VAL A 253       1.732  -4.107  -3.594  1.00  0.93           H  
-ATOM     70  HB  VAL A 253       3.210  -5.390  -5.902  1.00  1.01           H  
-ATOM     71 HG11 VAL A 253       5.194  -4.615  -4.993  1.00  1.33           H  
-ATOM     72 HG12 VAL A 253       4.479  -4.340  -3.405  1.00  1.53           H  
-ATOM     73 HG13 VAL A 253       4.106  -3.250  -4.740  1.00  1.35           H  
-ATOM     74 HG21 VAL A 253       4.172  -6.778  -3.939  1.00  1.74           H  
-ATOM     75 HG22 VAL A 253       2.595  -7.207  -4.601  1.00  1.62           H  
-ATOM     76 HG23 VAL A 253       2.701  -6.296  -3.096  1.00  1.63           H  
-ATOM     77  N   GLY A 254       1.479  -3.358  -6.794  1.00  0.60           N  
-ATOM     78  CA  GLY A 254       1.388  -2.248  -7.730  1.00  0.47           C  
-ATOM     79  C   GLY A 254       0.279  -1.278  -7.359  1.00  0.36           C  
-ATOM     80  O   GLY A 254       0.489  -0.065  -7.332  1.00  0.36           O  
-ATOM     81  H   GLY A 254       1.316  -4.274  -7.104  1.00  0.68           H  
-ATOM     82  HA2 GLY A 254       2.329  -1.718  -7.739  1.00  0.53           H  
-ATOM     83  HA3 GLY A 254       1.195  -2.639  -8.718  1.00  0.51           H  
-ATOM     84  N   LEU A 255      -0.897  -1.818  -7.045  1.00  0.41           N  
-ATOM     85  CA  LEU A 255      -2.036  -0.996  -6.644  1.00  0.48           C  
-ATOM     86  C   LEU A 255      -1.717  -0.240  -5.351  1.00  0.48           C  
-ATOM     87  O   LEU A 255      -2.039   0.942  -5.208  1.00  0.52           O  
-ATOM     88  CB  LEU A 255      -3.274  -1.858  -6.454  1.00  0.67           C  
-ATOM     89  CG  LEU A 255      -4.578  -1.075  -6.494  1.00  0.84           C  
-ATOM     90  CD1 LEU A 255      -4.772  -0.446  -7.867  1.00  0.91           C  
-ATOM     91  CD2 LEU A 255      -5.751  -1.968  -6.136  1.00  1.05           C  
-ATOM     92  H   LEU A 255      -0.996  -2.797  -7.069  1.00  0.48           H  
-ATOM     93  HA  LEU A 255      -2.236  -0.279  -7.426  1.00  0.51           H  
-ATOM     94  HB2 LEU A 255      -3.295  -2.606  -7.234  1.00  0.71           H  
-ATOM     95  HB3 LEU A 255      -3.204  -2.355  -5.499  1.00  0.73           H  
-ATOM     96  HG  LEU A 255      -4.524  -0.276  -5.772  1.00  0.84           H  
-ATOM     97 HD11 LEU A 255      -4.085   0.380  -7.984  1.00  1.40           H  
-ATOM     98 HD12 LEU A 255      -5.785  -0.087  -7.960  1.00  1.36           H  
-ATOM     99 HD13 LEU A 255      -4.578  -1.184  -8.631  1.00  1.40           H  
-ATOM    100 HD21 LEU A 255      -5.891  -2.704  -6.913  1.00  1.58           H  
-ATOM    101 HD22 LEU A 255      -6.642  -1.369  -6.041  1.00  1.44           H  
-ATOM    102 HD23 LEU A 255      -5.549  -2.467  -5.200  1.00  1.49           H  
-ATOM    103  N   GLY A 256      -1.063  -0.932  -4.419  1.00  0.54           N  
-ATOM    104  CA  GLY A 256      -0.689  -0.321  -3.157  1.00  0.61           C  
-ATOM    105  C   GLY A 256       0.271   0.845  -3.333  1.00  0.49           C  
-ATOM    106  O   GLY A 256       0.007   1.944  -2.854  1.00  0.51           O  
-ATOM    107  H   GLY A 256      -0.823  -1.870  -4.597  1.00  0.58           H  
-ATOM    108  HA2 GLY A 256      -1.582   0.034  -2.664  1.00  0.71           H  
-ATOM    109  HA3 GLY A 256      -0.219  -1.068  -2.534  1.00  0.72           H  
-ATOM    110  N   ILE A 257       1.383   0.621  -4.034  1.00  0.45           N  
-ATOM    111  CA  ILE A 257       2.356   1.692  -4.256  1.00  0.46           C  
-ATOM    112  C   ILE A 257       1.759   2.824  -5.102  1.00  0.40           C  
-ATOM    113  O   ILE A 257       2.052   3.996  -4.870  1.00  0.49           O  
-ATOM    114  CB  ILE A 257       3.680   1.178  -4.881  1.00  0.60           C  
-ATOM    115  CG1 ILE A 257       3.454   0.499  -6.238  1.00  0.60           C  
-ATOM    116  CG2 ILE A 257       4.373   0.220  -3.925  1.00  0.81           C  
-ATOM    117  CD1 ILE A 257       3.687   1.408  -7.427  1.00  0.70           C  
-ATOM    118  H   ILE A 257       1.551  -0.276  -4.410  1.00  0.50           H  
-ATOM    119  HA  ILE A 257       2.594   2.101  -3.283  1.00  0.53           H  
-ATOM    120  HB  ILE A 257       4.330   2.030  -5.022  1.00  0.69           H  
-ATOM    121 HG12 ILE A 257       4.126  -0.341  -6.328  1.00  0.75           H  
-ATOM    122 HG13 ILE A 257       2.434   0.142  -6.287  1.00  0.58           H  
-ATOM    123 HG21 ILE A 257       4.053   0.428  -2.914  1.00  1.33           H  
-ATOM    124 HG22 ILE A 257       5.443   0.351  -3.997  1.00  1.34           H  
-ATOM    125 HG23 ILE A 257       4.116  -0.796  -4.183  1.00  1.35           H  
-ATOM    126 HD11 ILE A 257       3.052   2.277  -7.347  1.00  1.21           H  
-ATOM    127 HD12 ILE A 257       3.456   0.876  -8.339  1.00  1.28           H  
-ATOM    128 HD13 ILE A 257       4.722   1.718  -7.445  1.00  1.29           H  
-ATOM    129  N   THR A 258       0.905   2.474  -6.067  1.00  0.39           N  
-ATOM    130  CA  THR A 258       0.263   3.477  -6.918  1.00  0.52           C  
-ATOM    131  C   THR A 258      -0.661   4.375  -6.096  1.00  0.59           C  
-ATOM    132  O   THR A 258      -0.607   5.602  -6.201  1.00  0.71           O  
-ATOM    133  CB  THR A 258      -0.557   2.829  -8.058  1.00  0.61           C  
-ATOM    134  OG1 THR A 258       0.288   2.001  -8.866  1.00  0.62           O  
-ATOM    135  CG2 THR A 258      -1.214   3.889  -8.934  1.00  0.86           C  
-ATOM    136  H   THR A 258       0.695   1.521  -6.202  1.00  0.36           H  
-ATOM    137  HA  THR A 258       1.039   4.086  -7.360  1.00  0.61           H  
-ATOM    138  HB  THR A 258      -1.331   2.218  -7.619  1.00  0.61           H  
-ATOM    139  HG1 THR A 258       0.437   1.157  -8.414  1.00  0.54           H  
-ATOM    140 HG21 THR A 258      -2.274   3.693  -9.001  1.00  1.38           H  
-ATOM    141 HG22 THR A 258      -0.779   3.860  -9.922  1.00  1.44           H  
-ATOM    142 HG23 THR A 258      -1.057   4.865  -8.499  1.00  1.29           H  
-ATOM    143  N   THR A 259      -1.498   3.760  -5.264  1.00  0.60           N  
-ATOM    144  CA  THR A 259      -2.421   4.515  -4.416  1.00  0.74           C  
-ATOM    145  C   THR A 259      -1.661   5.362  -3.400  1.00  0.70           C  
-ATOM    146  O   THR A 259      -1.960   6.545  -3.228  1.00  0.84           O  
-ATOM    147  CB  THR A 259      -3.425   3.600  -3.685  1.00  0.83           C  
-ATOM    148  OG1 THR A 259      -2.740   2.525  -3.031  1.00  0.78           O  
-ATOM    149  CG2 THR A 259      -4.450   3.038  -4.659  1.00  0.97           C  
-ATOM    150  H   THR A 259      -1.487   2.777  -5.207  1.00  0.55           H  
-ATOM    151  HA  THR A 259      -2.981   5.178  -5.061  1.00  0.86           H  
-ATOM    152  HB  THR A 259      -3.945   4.187  -2.941  1.00  0.93           H  
-ATOM    153  HG1 THR A 259      -2.533   1.836  -3.676  1.00  0.96           H  
-ATOM    154 HG21 THR A 259      -4.003   2.238  -5.230  1.00  1.41           H  
-ATOM    155 HG22 THR A 259      -4.777   3.820  -5.330  1.00  1.45           H  
-ATOM    156 HG23 THR A 259      -5.299   2.658  -4.110  1.00  1.45           H  
-ATOM    157  N   VAL A 260      -0.662   4.764  -2.746  1.00  0.56           N  
-ATOM    158  CA  VAL A 260       0.151   5.489  -1.768  1.00  0.56           C  
-ATOM    159  C   VAL A 260       0.843   6.694  -2.419  1.00  0.56           C  
-ATOM    160  O   VAL A 260       0.779   7.808  -1.895  1.00  0.67           O  
-ATOM    161  CB  VAL A 260       1.208   4.572  -1.116  1.00  0.56           C  
-ATOM    162  CG1 VAL A 260       2.152   5.374  -0.232  1.00  0.69           C  
-ATOM    163  CG2 VAL A 260       0.532   3.474  -0.309  1.00  0.72           C  
-ATOM    164  H   VAL A 260      -0.460   3.815  -2.935  1.00  0.50           H  
-ATOM    165  HA  VAL A 260      -0.511   5.848  -0.992  1.00  0.68           H  
-ATOM    166  HB  VAL A 260       1.789   4.109  -1.900  1.00  0.51           H  
-ATOM    167 HG11 VAL A 260       2.277   6.363  -0.646  1.00  1.22           H  
-ATOM    168 HG12 VAL A 260       3.109   4.878  -0.187  1.00  1.26           H  
-ATOM    169 HG13 VAL A 260       1.737   5.448   0.762  1.00  1.28           H  
-ATOM    170 HG21 VAL A 260      -0.517   3.705  -0.198  1.00  1.32           H  
-ATOM    171 HG22 VAL A 260       0.991   3.406   0.664  1.00  1.20           H  
-ATOM    172 HG23 VAL A 260       0.640   2.531  -0.826  1.00  1.19           H  
-ATOM    173  N   LEU A 261       1.478   6.467  -3.574  1.00  0.53           N  
-ATOM    174  CA  LEU A 261       2.156   7.540  -4.304  1.00  0.66           C  
-ATOM    175  C   LEU A 261       1.156   8.614  -4.744  1.00  0.81           C  
-ATOM    176  O   LEU A 261       1.452   9.808  -4.709  1.00  0.94           O  
-ATOM    177  CB  LEU A 261       2.887   6.979  -5.522  1.00  0.74           C  
-ATOM    178  CG  LEU A 261       3.856   7.954  -6.188  1.00  0.96           C  
-ATOM    179  CD1 LEU A 261       4.864   8.474  -5.173  1.00  1.08           C  
-ATOM    180  CD2 LEU A 261       4.563   7.280  -7.350  1.00  1.09           C  
-ATOM    181  H   LEU A 261       1.479   5.557  -3.950  1.00  0.48           H  
-ATOM    182  HA  LEU A 261       2.883   7.989  -3.644  1.00  0.68           H  
-ATOM    183  HB2 LEU A 261       3.440   6.103  -5.214  1.00  0.73           H  
-ATOM    184  HB3 LEU A 261       2.150   6.681  -6.254  1.00  0.80           H  
-ATOM    185  HG  LEU A 261       3.301   8.798  -6.573  1.00  1.08           H  
-ATOM    186 HD11 LEU A 261       4.339   8.920  -4.340  1.00  1.43           H  
-ATOM    187 HD12 LEU A 261       5.496   9.215  -5.638  1.00  1.65           H  
-ATOM    188 HD13 LEU A 261       5.472   7.655  -4.818  1.00  1.44           H  
-ATOM    189 HD21 LEU A 261       4.356   6.221  -7.330  1.00  1.61           H  
-ATOM    190 HD22 LEU A 261       5.627   7.442  -7.265  1.00  1.46           H  
-ATOM    191 HD23 LEU A 261       4.207   7.700  -8.278  1.00  1.48           H  
-ATOM    192  N   THR A 262      -0.034   8.173  -5.151  1.00  0.84           N  
-ATOM    193  CA  THR A 262      -1.093   9.089  -5.584  1.00  1.05           C  
-ATOM    194  C   THR A 262      -1.553   9.979  -4.426  1.00  1.10           C  
-ATOM    195  O   THR A 262      -1.649  11.198  -4.565  1.00  1.25           O  
-ATOM    196  CB  THR A 262      -2.312   8.317  -6.136  1.00  1.14           C  
-ATOM    197  OG1 THR A 262      -1.931   7.542  -7.281  1.00  1.14           O  
-ATOM    198  CG2 THR A 262      -3.442   9.263  -6.516  1.00  1.39           C  
-ATOM    199  H   THR A 262      -0.208   7.204  -5.153  1.00  0.76           H  
-ATOM    200  HA  THR A 262      -0.696   9.712  -6.373  1.00  1.15           H  
-ATOM    201  HB  THR A 262      -2.670   7.648  -5.365  1.00  1.07           H  
-ATOM    202  HG1 THR A 262      -1.418   6.771  -6.991  1.00  1.00           H  
-ATOM    203 HG21 THR A 262      -4.054   9.462  -5.648  1.00  1.84           H  
-ATOM    204 HG22 THR A 262      -4.046   8.808  -7.286  1.00  1.73           H  
-ATOM    205 HG23 THR A 262      -3.027  10.189  -6.886  1.00  1.75           H  
-ATOM    206  N   MET A 263      -1.839   9.356  -3.284  1.00  1.01           N  
-ATOM    207  CA  MET A 263      -2.291  10.089  -2.098  1.00  1.10           C  
-ATOM    208  C   MET A 263      -1.190  10.995  -1.525  1.00  1.03           C  
-ATOM    209  O   MET A 263      -1.426  12.174  -1.262  1.00  1.17           O  
-ATOM    210  CB  MET A 263      -2.771   9.115  -1.019  1.00  1.09           C  
-ATOM    211  CG  MET A 263      -3.977   8.288  -1.436  1.00  1.22           C  
-ATOM    212  SD  MET A 263      -4.563   7.203  -0.120  1.00  1.32           S  
-ATOM    213  CE  MET A 263      -3.126   6.165   0.139  1.00  1.21           C  
-ATOM    214  H   MET A 263      -1.746   8.376  -3.239  1.00  0.92           H  
-ATOM    215  HA  MET A 263      -3.122  10.709  -2.397  1.00  1.26           H  
-ATOM    216  HB2 MET A 263      -1.965   8.440  -0.776  1.00  0.97           H  
-ATOM    217  HB3 MET A 263      -3.037   9.678  -0.137  1.00  1.18           H  
-ATOM    218  HG2 MET A 263      -4.776   8.956  -1.716  1.00  1.36           H  
-ATOM    219  HG3 MET A 263      -3.702   7.681  -2.287  1.00  1.20           H  
-ATOM    220  HE1 MET A 263      -2.236   6.777   0.134  1.00  1.68           H  
-ATOM    221  HE2 MET A 263      -3.063   5.433  -0.652  1.00  1.44           H  
-ATOM    222  HE3 MET A 263      -3.212   5.662   1.090  1.00  1.60           H  
-ATOM    223  N   THR A 264       0.006  10.445  -1.314  1.00  0.88           N  
-ATOM    224  CA  THR A 264       1.110  11.223  -0.758  1.00  0.88           C  
-ATOM    225  C   THR A 264       2.380  11.120  -1.609  1.00  0.83           C  
-ATOM    226  O   THR A 264       2.777  10.035  -2.039  1.00  0.79           O  
-ATOM    227  CB  THR A 264       1.417  10.781   0.688  1.00  0.86           C  
-ATOM    228  OG1 THR A 264       2.393  11.649   1.286  1.00  0.97           O  
-ATOM    229  CG2 THR A 264       1.918   9.342   0.742  1.00  0.80           C  
-ATOM    230  H   THR A 264       0.146   9.496  -1.519  1.00  0.81           H  
-ATOM    231  HA  THR A 264       0.798  12.258  -0.729  1.00  1.00           H  
-ATOM    232  HB  THR A 264       0.499  10.837   1.250  1.00  0.92           H  
-ATOM    233  HG1 THR A 264       1.988  12.135   2.014  1.00  1.14           H  
-ATOM    234 HG21 THR A 264       2.995   9.332   0.663  1.00  1.30           H  
-ATOM    235 HG22 THR A 264       1.492   8.784  -0.080  1.00  1.30           H  
-ATOM    236 HG23 THR A 264       1.620   8.890   1.676  1.00  1.17           H  
-ATOM    237  N   THR A 265       3.023  12.261  -1.848  1.00  0.92           N  
-ATOM    238  CA  THR A 265       4.253  12.297  -2.643  1.00  0.99           C  
-ATOM    239  C   THR A 265       5.468  11.814  -1.834  1.00  0.92           C  
-ATOM    240  O   THR A 265       6.432  12.555  -1.611  1.00  1.05           O  
-ATOM    241  CB  THR A 265       4.527  13.704  -3.228  1.00  1.20           C  
-ATOM    242  OG1 THR A 265       5.827  13.743  -3.834  1.00  1.31           O  
-ATOM    243  CG2 THR A 265       4.420  14.787  -2.159  1.00  1.28           C  
-ATOM    244  H   THR A 265       2.666  13.095  -1.477  1.00  1.01           H  
-ATOM    245  HA  THR A 265       4.113  11.618  -3.472  1.00  1.01           H  
-ATOM    246  HB  THR A 265       3.786  13.903  -3.990  1.00  1.29           H  
-ATOM    247  HG1 THR A 265       6.491  13.494  -3.177  1.00  1.49           H  
-ATOM    248 HG21 THR A 265       3.698  15.529  -2.469  1.00  1.66           H  
-ATOM    249 HG22 THR A 265       5.383  15.257  -2.024  1.00  1.64           H  
-ATOM    250 HG23 THR A 265       4.103  14.343  -1.227  1.00  1.67           H  
-ATOM    251  N   GLN A 266       5.405  10.559  -1.394  1.00  0.83           N  
-ATOM    252  CA  GLN A 266       6.476   9.943  -0.610  1.00  0.87           C  
-ATOM    253  C   GLN A 266       7.766   9.810  -1.432  1.00  0.99           C  
-ATOM    254  O   GLN A 266       7.974   8.815  -2.127  1.00  1.22           O  
-ATOM    255  CB  GLN A 266       6.027   8.562  -0.118  1.00  0.93           C  
-ATOM    256  CG  GLN A 266       7.022   7.898   0.821  1.00  1.06           C  
-ATOM    257  CD  GLN A 266       7.048   8.529   2.200  1.00  0.89           C  
-ATOM    258  OE1 GLN A 266       8.061   8.480   2.892  1.00  1.14           O  
-ATOM    259  NE2 GLN A 266       5.928   9.099   2.624  1.00  1.29           N  
-ATOM    260  H   GLN A 266       4.603  10.027  -1.607  1.00  0.82           H  
-ATOM    261  HA  GLN A 266       6.670  10.571   0.244  1.00  0.89           H  
-ATOM    262  HB2 GLN A 266       5.087   8.669   0.405  1.00  0.95           H  
-ATOM    263  HB3 GLN A 266       5.882   7.918  -0.972  1.00  0.98           H  
-ATOM    264  HG2 GLN A 266       6.758   6.857   0.926  1.00  1.49           H  
-ATOM    265  HG3 GLN A 266       8.008   7.976   0.387  1.00  1.51           H  
-ATOM    266 HE21 GLN A 266       5.147   9.083   2.033  1.00  1.49           H  
-ATOM    267 HE22 GLN A 266       5.927   9.516   3.516  1.00  1.66           H  
-ATOM    268  N   SER A 267       8.634  10.815  -1.348  1.00  0.98           N  
-ATOM    269  CA  SER A 267       9.900  10.805  -2.089  1.00  1.15           C  
-ATOM    270  C   SER A 267      10.953   9.927  -1.407  1.00  1.08           C  
-ATOM    271  O   SER A 267      12.105  10.340  -1.251  1.00  1.16           O  
-ATOM    272  CB  SER A 267      10.436  12.231  -2.249  1.00  1.26           C  
-ATOM    273  OG  SER A 267      10.707  12.822  -0.989  1.00  1.15           O  
-ATOM    274  H   SER A 267       8.418  11.586  -0.775  1.00  0.95           H  
-ATOM    275  HA  SER A 267       9.699  10.398  -3.069  1.00  1.40           H  
-ATOM    276  HB2 SER A 267      11.349  12.207  -2.825  1.00  1.41           H  
-ATOM    277  HB3 SER A 267       9.702  12.832  -2.763  1.00  1.44           H  
-ATOM    278  HG  SER A 267      11.656  12.949  -0.893  1.00  1.36           H  
-ATOM    279  N   SER A 268      10.544   8.714  -1.014  1.00  1.29           N  
-ATOM    280  CA  SER A 268      11.421   7.734  -0.348  1.00  1.54           C  
-ATOM    281  C   SER A 268      11.855   8.192   1.055  1.00  1.49           C  
-ATOM    282  O   SER A 268      11.659   7.474   2.036  1.00  1.96           O  
-ATOM    283  CB  SER A 268      12.655   7.431  -1.211  1.00  1.68           C  
-ATOM    284  OG  SER A 268      13.508   6.487  -0.583  1.00  1.92           O  
-ATOM    285  H   SER A 268       9.607   8.463  -1.187  1.00  1.46           H  
-ATOM    286  HA  SER A 268      10.850   6.822  -0.239  1.00  1.98           H  
-ATOM    287  HB2 SER A 268      12.337   7.028  -2.162  1.00  2.05           H  
-ATOM    288  HB3 SER A 268      13.208   8.344  -1.376  1.00  1.48           H  
-ATOM    289  HG  SER A 268      12.986   5.881  -0.051  1.00  1.97           H  
-ATOM    290  N   GLY A 269      12.440   9.383   1.148  1.00  1.35           N  
-ATOM    291  CA  GLY A 269      12.881   9.902   2.434  1.00  1.81           C  
-ATOM    292  C   GLY A 269      12.096  11.128   2.882  1.00  1.48           C  
-ATOM    293  O   GLY A 269      12.641  12.007   3.552  1.00  1.52           O  
-ATOM    294  H   GLY A 269      12.572   9.917   0.327  1.00  1.27           H  
-ATOM    295  HA2 GLY A 269      12.775   9.127   3.179  1.00  2.34           H  
-ATOM    296  HA3 GLY A 269      13.925  10.169   2.357  1.00  2.11           H  
-ATOM    297  N   SER A 270      10.818  11.190   2.514  1.00  1.27           N  
-ATOM    298  CA  SER A 270       9.966  12.324   2.885  1.00  0.98           C  
-ATOM    299  C   SER A 270       9.152  12.037   4.151  1.00  1.04           C  
-ATOM    300  O   SER A 270       9.023  12.904   5.018  1.00  1.01           O  
-ATOM    301  CB  SER A 270       9.017  12.685   1.736  1.00  0.75           C  
-ATOM    302  OG  SER A 270       8.060  11.660   1.512  1.00  0.89           O  
-ATOM    303  H   SER A 270      10.440  10.460   1.979  1.00  1.38           H  
-ATOM    304  HA  SER A 270      10.612  13.168   3.078  1.00  1.06           H  
-ATOM    305  HB2 SER A 270       8.495  13.600   1.975  1.00  0.69           H  
-ATOM    306  HB3 SER A 270       9.591  12.825   0.832  1.00  0.79           H  
-ATOM    307  HG  SER A 270       7.162  12.038   1.537  1.00  0.94           H  
-ATOM    308  N   ARG A 271       8.590  10.824   4.231  1.00  1.28           N  
-ATOM    309  CA  ARG A 271       7.760  10.394   5.365  1.00  1.48           C  
-ATOM    310  C   ARG A 271       6.436  11.160   5.418  1.00  1.36           C  
-ATOM    311  O   ARG A 271       5.390  10.624   5.054  1.00  1.57           O  
-ATOM    312  CB  ARG A 271       8.517  10.540   6.685  1.00  1.66           C  
-ATOM    313  CG  ARG A 271       9.631   9.522   6.850  1.00  2.01           C  
-ATOM    314  CD  ARG A 271      10.561   9.896   7.994  1.00  2.18           C  
-ATOM    315  NE  ARG A 271      11.230  11.177   7.765  1.00  2.07           N  
-ATOM    316  CZ  ARG A 271      12.155  11.379   6.847  1.00  2.15           C  
-ATOM    317  NH1 ARG A 271      12.579  10.391   6.103  1.00  2.31           N  
-ATOM    318  NH2 ARG A 271      12.671  12.570   6.689  1.00  2.18           N  
-ATOM    319  H   ARG A 271       8.723  10.194   3.485  1.00  1.39           H  
-ATOM    320  HA  ARG A 271       7.539   9.347   5.215  1.00  1.72           H  
-ATOM    321  HB2 ARG A 271       8.951  11.528   6.731  1.00  1.53           H  
-ATOM    322  HB3 ARG A 271       7.823  10.421   7.503  1.00  1.76           H  
-ATOM    323  HG2 ARG A 271       9.194   8.556   7.054  1.00  2.23           H  
-ATOM    324  HG3 ARG A 271      10.199   9.477   5.934  1.00  1.99           H  
-ATOM    325  HD2 ARG A 271       9.983   9.960   8.905  1.00  2.22           H  
-ATOM    326  HD3 ARG A 271      11.309   9.123   8.098  1.00  2.46           H  
-ATOM    327  HE  ARG A 271      10.957  11.932   8.320  1.00  2.01           H  
-ATOM    328 HH11 ARG A 271      12.203   9.477   6.229  1.00  2.38           H  
-ATOM    329 HH12 ARG A 271      13.267  10.557   5.401  1.00  2.42           H  
-ATOM    330 HH21 ARG A 271      12.366  13.326   7.263  1.00  2.14           H  
-ATOM    331 HH22 ARG A 271      13.367  12.722   5.990  1.00  2.31           H  
-ATOM    332  N   ALA A 272       6.496  12.415   5.848  1.00  1.16           N  
-ATOM    333  CA  ALA A 272       5.310  13.263   5.928  1.00  1.18           C  
-ATOM    334  C   ALA A 272       4.915  13.788   4.548  1.00  1.11           C  
-ATOM    335  O   ALA A 272       3.731  13.880   4.224  1.00  1.41           O  
-ATOM    336  CB  ALA A 272       5.556  14.420   6.886  1.00  1.15           C  
-ATOM    337  H   ALA A 272       7.369  12.786   6.105  1.00  1.13           H  
-ATOM    338  HA  ALA A 272       4.501  12.666   6.321  1.00  1.44           H  
-ATOM    339  HB1 ALA A 272       6.566  14.368   7.265  1.00  1.71           H  
-ATOM    340  HB2 ALA A 272       4.860  14.363   7.710  1.00  1.54           H  
-ATOM    341  HB3 ALA A 272       5.417  15.355   6.364  1.00  1.26           H  
-ATOM    342  N   SER A 273       5.927  14.131   3.745  1.00  0.84           N  
-ATOM    343  CA  SER A 273       5.724  14.657   2.385  1.00  0.89           C  
-ATOM    344  C   SER A 273       5.038  16.010   2.394  1.00  1.10           C  
-ATOM    345  O   SER A 273       4.002  16.201   1.758  1.00  1.43           O  
-ATOM    346  CB  SER A 273       4.923  13.697   1.507  1.00  1.18           C  
-ATOM    347  OG  SER A 273       5.492  12.397   1.487  1.00  1.25           O  
-ATOM    348  H   SER A 273       6.843  14.025   4.079  1.00  0.75           H  
-ATOM    349  HA  SER A 273       6.698  14.799   1.937  1.00  0.75           H  
-ATOM    350  HB2 SER A 273       3.916  13.631   1.884  1.00  1.38           H  
-ATOM    351  HB3 SER A 273       4.902  14.082   0.497  1.00  1.30           H  
-ATOM    352  HG  SER A 273       4.792  11.751   1.328  1.00  1.72           H  
-ATOM    353  N   LEU A 274       5.655  16.967   3.073  1.00  1.07           N  
-ATOM    354  CA  LEU A 274       5.132  18.330   3.107  1.00  1.43           C  
-ATOM    355  C   LEU A 274       6.160  19.358   2.598  1.00  1.52           C  
-ATOM    356  O   LEU A 274       6.340  20.414   3.211  1.00  1.74           O  
-ATOM    357  CB  LEU A 274       4.685  18.701   4.514  1.00  1.61           C  
-ATOM    358  CG  LEU A 274       3.199  18.489   4.796  1.00  2.05           C  
-ATOM    359  CD1 LEU A 274       2.346  19.177   3.739  1.00  2.49           C  
-ATOM    360  CD2 LEU A 274       2.855  17.010   4.883  1.00  2.27           C  
-ATOM    361  H   LEU A 274       6.500  16.765   3.524  1.00  0.90           H  
-ATOM    362  HA  LEU A 274       4.272  18.359   2.455  1.00  1.63           H  
-ATOM    363  HB2 LEU A 274       5.255  18.111   5.218  1.00  1.49           H  
-ATOM    364  HB3 LEU A 274       4.913  19.743   4.677  1.00  1.76           H  
-ATOM    365  HG  LEU A 274       2.971  18.939   5.739  1.00  2.15           H  
-ATOM    366 HD11 LEU A 274       2.046  18.455   2.992  1.00  2.79           H  
-ATOM    367 HD12 LEU A 274       2.920  19.962   3.270  1.00  2.81           H  
-ATOM    368 HD13 LEU A 274       1.468  19.601   4.204  1.00  2.85           H  
-ATOM    369 HD21 LEU A 274       1.783  16.888   4.839  1.00  2.56           H  
-ATOM    370 HD22 LEU A 274       3.225  16.607   5.813  1.00  2.59           H  
-ATOM    371 HD23 LEU A 274       3.311  16.485   4.056  1.00  2.57           H  
-ATOM    372  N   PRO A 275       6.854  19.074   1.467  1.00  1.45           N  
-ATOM    373  CA  PRO A 275       7.857  19.995   0.895  1.00  1.67           C  
-ATOM    374  C   PRO A 275       7.282  21.383   0.590  1.00  2.04           C  
-ATOM    375  O   PRO A 275       7.905  22.411   0.880  1.00  2.26           O  
-ATOM    376  CB  PRO A 275       8.264  19.300  -0.407  1.00  1.64           C  
-ATOM    377  CG  PRO A 275       8.007  17.866  -0.135  1.00  1.31           C  
-ATOM    378  CD  PRO A 275       6.734  17.847   0.655  1.00  1.25           C  
-ATOM    379  HA  PRO A 275       8.717  20.087   1.536  1.00  1.67           H  
-ATOM    380  HB2 PRO A 275       7.658  19.668  -1.223  1.00  1.81           H  
-ATOM    381  HB3 PRO A 275       9.308  19.486  -0.610  1.00  1.76           H  
-ATOM    382  HG2 PRO A 275       7.895  17.320  -1.060  1.00  1.35           H  
-ATOM    383  HG3 PRO A 275       8.815  17.462   0.455  1.00  1.23           H  
-ATOM    384  HD2 PRO A 275       5.873  17.890   0.007  1.00  1.42           H  
-ATOM    385  HD3 PRO A 275       6.682  16.963   1.295  1.00  1.04           H  
-ATOM    386  N   LYS A 276       6.080  21.381   0.014  1.00  2.19           N  
-ATOM    387  CA  LYS A 276       5.353  22.601  -0.348  1.00  2.58           C  
-ATOM    388  C   LYS A 276       3.855  22.305  -0.438  1.00  2.73           C  
-ATOM    389  O   LYS A 276       3.488  21.108  -0.376  1.00  2.57           O  
-ATOM    390  CB  LYS A 276       5.840  23.163  -1.693  1.00  2.82           C  
-ATOM    391  CG  LYS A 276       7.191  23.858  -1.626  1.00  2.95           C  
-ATOM    392  CD  LYS A 276       7.128  25.119  -0.780  1.00  3.20           C  
-ATOM    393  CE  LYS A 276       8.518  25.661  -0.494  1.00  3.34           C  
-ATOM    394  NZ  LYS A 276       9.316  24.714   0.335  1.00  3.07           N  
-ATOM    395  OXT LYS A 276       3.065  23.263  -0.574  1.00  3.09           O  
-ATOM    396  H   LYS A 276       5.652  20.517  -0.169  1.00  2.07           H  
-ATOM    397  HA  LYS A 276       5.521  23.333   0.428  1.00  2.68           H  
-ATOM    398  HB2 LYS A 276       5.915  22.351  -2.400  1.00  2.72           H  
-ATOM    399  HB3 LYS A 276       5.113  23.874  -2.053  1.00  3.10           H  
-ATOM    400  HG2 LYS A 276       7.911  23.182  -1.191  1.00  2.73           H  
-ATOM    401  HG3 LYS A 276       7.500  24.121  -2.628  1.00  3.15           H  
-ATOM    402  HD2 LYS A 276       6.562  25.871  -1.308  1.00  3.47           H  
-ATOM    403  HD3 LYS A 276       6.642  24.890   0.157  1.00  3.08           H  
-ATOM    404  HE2 LYS A 276       9.028  25.821  -1.433  1.00  3.49           H  
-ATOM    405  HE3 LYS A 276       8.427  26.600   0.031  1.00  3.55           H  
-ATOM    406  HZ1 LYS A 276       8.730  23.887   0.601  1.00  2.88           H  
-ATOM    407  HZ2 LYS A 276       9.642  25.186   1.202  1.00  3.26           H  
-ATOM    408  HZ3 LYS A 276      10.143  24.382  -0.198  1.00  3.15           H  
-TER
-ENDMDL
-MODEL      9
-ATOM      1  N   ALA A 249       1.028 -12.240 -10.406  1.00  1.89           N  
-ATOM      2  CA  ALA A 249       1.678 -10.906 -10.514  1.00  1.98           C  
-ATOM      3  C   ALA A 249       1.362 -10.048  -9.290  1.00  1.58           C  
-ATOM      4  O   ALA A 249       0.329 -10.236  -8.646  1.00  1.21           O  
-ATOM      5  CB  ALA A 249       1.240 -10.200 -11.790  1.00  2.17           C  
-ATOM      6  H1  ALA A 249       0.503 -12.455 -11.277  1.00  1.90           H  
-ATOM      7  H2  ALA A 249       0.365 -12.249  -9.603  1.00  1.79           H  
-ATOM      8  H3  ALA A 249       1.747 -12.976 -10.254  1.00  2.21           H  
-ATOM      9  HA  ALA A 249       2.747 -11.055 -10.566  1.00  2.34           H  
-ATOM     10  HB1 ALA A 249       2.050 -10.217 -12.505  1.00  2.46           H  
-ATOM     11  HB2 ALA A 249       0.985  -9.174 -11.563  1.00  2.48           H  
-ATOM     12  HB3 ALA A 249       0.381 -10.703 -12.205  1.00  2.44           H  
-ATOM     13  N   PRO A 250       2.256  -9.102  -8.947  1.00  1.75           N  
-ATOM     14  CA  PRO A 250       2.100  -8.205  -7.789  1.00  1.53           C  
-ATOM     15  C   PRO A 250       0.998  -7.150  -7.972  1.00  1.04           C  
-ATOM     16  O   PRO A 250       1.256  -5.949  -7.864  1.00  1.05           O  
-ATOM     17  CB  PRO A 250       3.473  -7.515  -7.694  1.00  1.99           C  
-ATOM     18  CG  PRO A 250       4.378  -8.321  -8.551  1.00  2.47           C  
-ATOM     19  CD  PRO A 250       3.510  -8.827  -9.650  1.00  2.29           C  
-ATOM     20  HA  PRO A 250       1.913  -8.759  -6.881  1.00  1.57           H  
-ATOM     21  HB2 PRO A 250       3.392  -6.505  -8.062  1.00  1.88           H  
-ATOM     22  HB3 PRO A 250       3.806  -7.510  -6.672  1.00  2.15           H  
-ATOM     23  HG2 PRO A 250       5.167  -7.697  -8.947  1.00  2.77           H  
-ATOM     24  HG3 PRO A 250       4.790  -9.144  -7.987  1.00  2.70           H  
-ATOM     25  HD2 PRO A 250       3.370  -8.064 -10.400  1.00  2.34           H  
-ATOM     26  HD3 PRO A 250       3.918  -9.725 -10.077  1.00  2.59           H  
-ATOM     27  N   ALA A 251      -0.228  -7.600  -8.250  1.00  0.75           N  
-ATOM     28  CA  ALA A 251      -1.359  -6.689  -8.448  1.00  0.73           C  
-ATOM     29  C   ALA A 251      -1.587  -5.808  -7.218  1.00  0.65           C  
-ATOM     30  O   ALA A 251      -1.741  -4.592  -7.334  1.00  0.72           O  
-ATOM     31  CB  ALA A 251      -2.622  -7.473  -8.780  1.00  1.05           C  
-ATOM     32  H   ALA A 251      -0.372  -8.571  -8.326  1.00  0.80           H  
-ATOM     33  HA  ALA A 251      -1.128  -6.055  -9.292  1.00  1.00           H  
-ATOM     34  HB1 ALA A 251      -2.384  -8.523  -8.856  1.00  1.56           H  
-ATOM     35  HB2 ALA A 251      -3.023  -7.127  -9.720  1.00  1.59           H  
-ATOM     36  HB3 ALA A 251      -3.354  -7.324  -8.001  1.00  1.41           H  
-ATOM     37  N   ARG A 252      -1.584  -6.425  -6.035  1.00  0.90           N  
-ATOM     38  CA  ARG A 252      -1.769  -5.688  -4.787  1.00  1.28           C  
-ATOM     39  C   ARG A 252      -0.606  -4.722  -4.551  1.00  1.12           C  
-ATOM     40  O   ARG A 252      -0.815  -3.566  -4.191  1.00  1.14           O  
-ATOM     41  CB  ARG A 252      -1.919  -6.656  -3.598  1.00  1.87           C  
-ATOM     42  CG  ARG A 252      -1.853  -5.984  -2.226  1.00  2.35           C  
-ATOM     43  CD  ARG A 252      -2.707  -4.727  -2.170  1.00  2.40           C  
-ATOM     44  NE  ARG A 252      -2.479  -3.950  -0.954  1.00  2.83           N  
-ATOM     45  CZ  ARG A 252      -2.880  -2.706  -0.799  1.00  2.95           C  
-ATOM     46  NH1 ARG A 252      -3.511  -2.089  -1.764  1.00  2.76           N  
-ATOM     47  NH2 ARG A 252      -2.638  -2.072   0.318  1.00  3.35           N  
-ATOM     48  H   ARG A 252      -1.440  -7.393  -6.005  1.00  1.00           H  
-ATOM     49  HA  ARG A 252      -2.675  -5.112  -4.883  1.00  1.43           H  
-ATOM     50  HB2 ARG A 252      -2.871  -7.159  -3.678  1.00  2.07           H  
-ATOM     51  HB3 ARG A 252      -1.130  -7.391  -3.650  1.00  1.89           H  
-ATOM     52  HG2 ARG A 252      -2.206  -6.679  -1.479  1.00  2.81           H  
-ATOM     53  HG3 ARG A 252      -0.826  -5.720  -2.015  1.00  2.32           H  
-ATOM     54  HD2 ARG A 252      -2.464  -4.112  -3.025  1.00  2.01           H  
-ATOM     55  HD3 ARG A 252      -3.745  -5.013  -2.217  1.00  2.58           H  
-ATOM     56  HE  ARG A 252      -2.002  -4.385  -0.220  1.00  3.11           H  
-ATOM     57 HH11 ARG A 252      -3.691  -2.562  -2.622  1.00  2.52           H  
-ATOM     58 HH12 ARG A 252      -3.813  -1.147  -1.640  1.00  2.95           H  
-ATOM     59 HH21 ARG A 252      -2.152  -2.532   1.057  1.00  3.60           H  
-ATOM     60 HH22 ARG A 252      -2.936  -1.127   0.428  1.00  3.46           H  
-ATOM     61  N   VAL A 253       0.611  -5.197  -4.761  1.00  1.19           N  
-ATOM     62  CA  VAL A 253       1.798  -4.365  -4.572  1.00  1.43           C  
-ATOM     63  C   VAL A 253       1.765  -3.148  -5.499  1.00  1.07           C  
-ATOM     64  O   VAL A 253       1.971  -2.016  -5.057  1.00  1.11           O  
-ATOM     65  CB  VAL A 253       3.088  -5.174  -4.800  1.00  1.89           C  
-ATOM     66  CG1 VAL A 253       4.313  -4.342  -4.452  1.00  2.33           C  
-ATOM     67  CG2 VAL A 253       3.047  -6.458  -3.982  1.00  2.23           C  
-ATOM     68  H   VAL A 253       0.717  -6.123  -5.054  1.00  1.24           H  
-ATOM     69  HA  VAL A 253       1.793  -4.018  -3.551  1.00  1.70           H  
-ATOM     70  HB  VAL A 253       3.143  -5.440  -5.845  1.00  1.77           H  
-ATOM     71 HG11 VAL A 253       5.179  -4.983  -4.395  1.00  2.64           H  
-ATOM     72 HG12 VAL A 253       4.158  -3.858  -3.497  1.00  2.64           H  
-ATOM     73 HG13 VAL A 253       4.467  -3.593  -5.213  1.00  2.64           H  
-ATOM     74 HG21 VAL A 253       3.999  -6.602  -3.491  1.00  2.57           H  
-ATOM     75 HG22 VAL A 253       2.847  -7.295  -4.634  1.00  2.51           H  
-ATOM     76 HG23 VAL A 253       2.266  -6.386  -3.239  1.00  2.52           H  
-ATOM     77  N   GLY A 254       1.471  -3.387  -6.777  1.00  0.90           N  
-ATOM     78  CA  GLY A 254       1.379  -2.299  -7.741  1.00  1.00           C  
-ATOM     79  C   GLY A 254       0.285  -1.307  -7.373  1.00  0.65           C  
-ATOM     80  O   GLY A 254       0.519  -0.097  -7.324  1.00  0.78           O  
-ATOM     81  H   GLY A 254       1.293  -4.313  -7.063  1.00  0.91           H  
-ATOM     82  HA2 GLY A 254       2.325  -1.781  -7.778  1.00  1.34           H  
-ATOM     83  HA3 GLY A 254       1.164  -2.711  -8.716  1.00  1.20           H  
-ATOM     84  N   LEU A 255      -0.909  -1.826  -7.081  1.00  0.51           N  
-ATOM     85  CA  LEU A 255      -2.040  -0.988  -6.682  1.00  0.85           C  
-ATOM     86  C   LEU A 255      -1.715  -0.231  -5.392  1.00  0.71           C  
-ATOM     87  O   LEU A 255      -2.054   0.942  -5.238  1.00  0.85           O  
-ATOM     88  CB  LEU A 255      -3.286  -1.839  -6.485  1.00  1.30           C  
-ATOM     89  CG  LEU A 255      -4.582  -1.041  -6.478  1.00  1.86           C  
-ATOM     90  CD1 LEU A 255      -4.819  -0.401  -7.841  1.00  2.13           C  
-ATOM     91  CD2 LEU A 255      -5.750  -1.926  -6.082  1.00  2.34           C  
-ATOM     92  H   LEU A 255      -1.027  -2.803  -7.117  1.00  0.49           H  
-ATOM     93  HA  LEU A 255      -2.238  -0.273  -7.466  1.00  1.12           H  
-ATOM     94  HB2 LEU A 255      -3.335  -2.569  -7.282  1.00  1.35           H  
-ATOM     95  HB3 LEU A 255      -3.203  -2.359  -5.542  1.00  1.32           H  
-ATOM     96  HG  LEU A 255      -4.494  -0.248  -5.754  1.00  1.85           H  
-ATOM     97 HD11 LEU A 255      -4.420  -1.041  -8.614  1.00  2.42           H  
-ATOM     98 HD12 LEU A 255      -4.324   0.559  -7.879  1.00  2.45           H  
-ATOM     99 HD13 LEU A 255      -5.878  -0.265  -7.995  1.00  2.44           H  
-ATOM    100 HD21 LEU A 255      -6.670  -1.368  -6.166  1.00  2.65           H  
-ATOM    101 HD22 LEU A 255      -5.618  -2.256  -5.062  1.00  2.67           H  
-ATOM    102 HD23 LEU A 255      -5.787  -2.784  -6.736  1.00  2.66           H  
-ATOM    103  N   GLY A 256      -1.040  -0.917  -4.472  1.00  0.78           N  
-ATOM    104  CA  GLY A 256      -0.658  -0.313  -3.210  1.00  1.13           C  
-ATOM    105  C   GLY A 256       0.299   0.853  -3.381  1.00  0.92           C  
-ATOM    106  O   GLY A 256       0.024   1.951  -2.904  1.00  0.97           O  
-ATOM    107  H   GLY A 256      -0.790  -1.852  -4.658  1.00  0.77           H  
-ATOM    108  HA2 GLY A 256      -1.548   0.036  -2.709  1.00  1.42           H  
-ATOM    109  HA3 GLY A 256      -0.185  -1.064  -2.596  1.00  1.45           H  
-ATOM    110  N   ILE A 257       1.417   0.634  -4.070  1.00  0.91           N  
-ATOM    111  CA  ILE A 257       2.383   1.712  -4.286  1.00  1.10           C  
-ATOM    112  C   ILE A 257       1.768   2.845  -5.117  1.00  0.75           C  
-ATOM    113  O   ILE A 257       2.050   4.018  -4.878  1.00  0.83           O  
-ATOM    114  CB  ILE A 257       3.706   1.209  -4.927  1.00  1.60           C  
-ATOM    115  CG1 ILE A 257       3.476   0.536  -6.286  1.00  1.55           C  
-ATOM    116  CG2 ILE A 257       4.413   0.250  -3.982  1.00  2.03           C  
-ATOM    117  CD1 ILE A 257       3.698   1.451  -7.472  1.00  1.81           C  
-ATOM    118  H   ILE A 257       1.593  -0.262  -4.445  1.00  0.97           H  
-ATOM    119  HA  ILE A 257       2.626   2.113  -3.310  1.00  1.35           H  
-ATOM    120  HB  ILE A 257       4.352   2.066  -5.067  1.00  1.83           H  
-ATOM    121 HG12 ILE A 257       4.153  -0.299  -6.384  1.00  1.85           H  
-ATOM    122 HG13 ILE A 257       2.460   0.174  -6.331  1.00  1.18           H  
-ATOM    123 HG21 ILE A 257       4.161  -0.766  -4.246  1.00  2.37           H  
-ATOM    124 HG22 ILE A 257       4.098   0.446  -2.968  1.00  2.36           H  
-ATOM    125 HG23 ILE A 257       5.481   0.388  -4.060  1.00  2.40           H  
-ATOM    126 HD11 ILE A 257       3.265   1.007  -8.356  1.00  2.16           H  
-ATOM    127 HD12 ILE A 257       4.758   1.594  -7.625  1.00  2.14           H  
-ATOM    128 HD13 ILE A 257       3.231   2.407  -7.283  1.00  2.16           H  
-ATOM    129  N   THR A 258       0.907   2.487  -6.075  1.00  0.60           N  
-ATOM    130  CA  THR A 258       0.237   3.478  -6.923  1.00  0.89           C  
-ATOM    131  C   THR A 258      -0.690   4.375  -6.097  1.00  0.77           C  
-ATOM    132  O   THR A 258      -0.657   5.601  -6.221  1.00  0.94           O  
-ATOM    133  CB  THR A 258      -0.594   2.805  -8.036  1.00  1.28           C  
-ATOM    134  OG1 THR A 258       0.246   1.965  -8.837  1.00  1.50           O  
-ATOM    135  CG2 THR A 258      -1.267   3.841  -8.926  1.00  1.79           C  
-ATOM    136  H   THR A 258       0.711   1.530  -6.208  1.00  0.55           H  
-ATOM    137  HA  THR A 258       0.995   4.091  -7.387  1.00  1.19           H  
-ATOM    138  HB  THR A 258      -1.361   2.197  -7.575  1.00  1.16           H  
-ATOM    139  HG1 THR A 258       0.431   1.143  -8.357  1.00  1.29           H  
-ATOM    140 HG21 THR A 258      -0.535   4.564  -9.254  1.00  2.14           H  
-ATOM    141 HG22 THR A 258      -2.044   4.343  -8.369  1.00  2.17           H  
-ATOM    142 HG23 THR A 258      -1.700   3.351  -9.786  1.00  2.20           H  
-ATOM    143  N   THR A 259      -1.508   3.757  -5.247  1.00  0.78           N  
-ATOM    144  CA  THR A 259      -2.433   4.507  -4.398  1.00  1.16           C  
-ATOM    145  C   THR A 259      -1.675   5.352  -3.385  1.00  0.97           C  
-ATOM    146  O   THR A 259      -1.968   6.534  -3.211  1.00  1.13           O  
-ATOM    147  CB  THR A 259      -3.425   3.582  -3.662  1.00  1.61           C  
-ATOM    148  OG1 THR A 259      -2.727   2.521  -2.999  1.00  1.50           O  
-ATOM    149  CG2 THR A 259      -4.437   3.003  -4.638  1.00  1.94           C  
-ATOM    150  H   THR A 259      -1.484   2.777  -5.182  1.00  0.72           H  
-ATOM    151  HA  THR A 259      -3.002   5.168  -5.037  1.00  1.43           H  
-ATOM    152  HB  THR A 259      -3.956   4.167  -2.923  1.00  1.93           H  
-ATOM    153  HG1 THR A 259      -2.582   1.793  -3.620  1.00  1.30           H  
-ATOM    154 HG21 THR A 259      -4.718   3.760  -5.355  1.00  2.08           H  
-ATOM    155 HG22 THR A 259      -5.312   2.676  -4.097  1.00  2.36           H  
-ATOM    156 HG23 THR A 259      -3.997   2.163  -5.155  1.00  1.89           H  
-ATOM    157  N   VAL A 260      -0.680   4.749  -2.735  1.00  0.86           N  
-ATOM    158  CA  VAL A 260       0.135   5.468  -1.762  1.00  1.06           C  
-ATOM    159  C   VAL A 260       0.839   6.660  -2.422  1.00  0.74           C  
-ATOM    160  O   VAL A 260       0.785   7.774  -1.903  1.00  0.84           O  
-ATOM    161  CB  VAL A 260       1.173   4.540  -1.096  1.00  1.44           C  
-ATOM    162  CG1 VAL A 260       2.134   5.334  -0.222  1.00  1.85           C  
-ATOM    163  CG2 VAL A 260       0.468   3.471  -0.273  1.00  1.82           C  
-ATOM    164  H   VAL A 260      -0.482   3.803  -2.928  1.00  0.82           H  
-ATOM    165  HA  VAL A 260      -0.527   5.841  -0.994  1.00  1.38           H  
-ATOM    166  HB  VAL A 260       1.742   4.050  -1.872  1.00  1.29           H  
-ATOM    167 HG11 VAL A 260       2.128   4.930   0.779  1.00  2.14           H  
-ATOM    168 HG12 VAL A 260       1.824   6.368  -0.196  1.00  2.20           H  
-ATOM    169 HG13 VAL A 260       3.130   5.267  -0.632  1.00  2.25           H  
-ATOM    170 HG21 VAL A 260       0.215   2.636  -0.911  1.00  2.16           H  
-ATOM    171 HG22 VAL A 260      -0.434   3.883   0.156  1.00  2.17           H  
-ATOM    172 HG23 VAL A 260       1.123   3.135   0.516  1.00  2.18           H  
-ATOM    173  N   LEU A 261       1.466   6.430  -3.584  1.00  0.60           N  
-ATOM    174  CA  LEU A 261       2.143   7.502  -4.318  1.00  0.82           C  
-ATOM    175  C   LEU A 261       1.147   8.604  -4.686  1.00  0.72           C  
-ATOM    176  O   LEU A 261       1.420   9.787  -4.497  1.00  0.83           O  
-ATOM    177  CB  LEU A 261       2.808   6.959  -5.582  1.00  1.24           C  
-ATOM    178  CG  LEU A 261       3.684   7.967  -6.327  1.00  1.75           C  
-ATOM    179  CD1 LEU A 261       4.724   8.565  -5.388  1.00  1.96           C  
-ATOM    180  CD2 LEU A 261       4.359   7.303  -7.514  1.00  2.23           C  
-ATOM    181  H   LEU A 261       1.456   5.522  -3.965  1.00  0.60           H  
-ATOM    182  HA  LEU A 261       2.907   7.919  -3.678  1.00  1.06           H  
-ATOM    183  HB2 LEU A 261       3.421   6.111  -5.309  1.00  1.38           H  
-ATOM    184  HB3 LEU A 261       2.035   6.620  -6.257  1.00  1.20           H  
-ATOM    185  HG  LEU A 261       3.065   8.770  -6.698  1.00  1.74           H  
-ATOM    186 HD11 LEU A 261       4.245   9.272  -4.726  1.00  2.29           H  
-ATOM    187 HD12 LEU A 261       5.484   9.068  -5.963  1.00  2.28           H  
-ATOM    188 HD13 LEU A 261       5.176   7.776  -4.804  1.00  2.24           H  
-ATOM    189 HD21 LEU A 261       4.036   7.781  -8.426  1.00  2.57           H  
-ATOM    190 HD22 LEU A 261       4.091   6.257  -7.540  1.00  2.53           H  
-ATOM    191 HD23 LEU A 261       5.430   7.397  -7.417  1.00  2.57           H  
-ATOM    192  N   THR A 262      -0.016   8.199  -5.189  1.00  0.83           N  
-ATOM    193  CA  THR A 262      -1.074   9.141  -5.565  1.00  1.26           C  
-ATOM    194  C   THR A 262      -1.539   9.967  -4.360  1.00  1.21           C  
-ATOM    195  O   THR A 262      -1.693  11.185  -4.441  1.00  1.40           O  
-ATOM    196  CB  THR A 262      -2.287   8.394  -6.156  1.00  1.65           C  
-ATOM    197  OG1 THR A 262      -1.900   7.684  -7.339  1.00  1.80           O  
-ATOM    198  CG2 THR A 262      -3.420   9.353  -6.482  1.00  2.17           C  
-ATOM    199  H   THR A 262      -0.176   7.232  -5.301  1.00  0.77           H  
-ATOM    200  HA  THR A 262      -0.680   9.806  -6.320  1.00  1.50           H  
-ATOM    201  HB  THR A 262      -2.641   7.682  -5.424  1.00  1.57           H  
-ATOM    202  HG1 THR A 262      -1.432   6.872  -7.090  1.00  1.55           H  
-ATOM    203 HG21 THR A 262      -3.113  10.013  -7.279  1.00  2.51           H  
-ATOM    204 HG22 THR A 262      -3.658   9.936  -5.604  1.00  2.48           H  
-ATOM    205 HG23 THR A 262      -4.289   8.792  -6.789  1.00  2.54           H  
-ATOM    206  N   MET A 263      -1.765   9.285  -3.248  1.00  1.15           N  
-ATOM    207  CA  MET A 263      -2.216   9.927  -2.013  1.00  1.42           C  
-ATOM    208  C   MET A 263      -1.142  10.844  -1.408  1.00  1.19           C  
-ATOM    209  O   MET A 263      -1.451  11.939  -0.940  1.00  1.40           O  
-ATOM    210  CB  MET A 263      -2.620   8.854  -0.998  1.00  1.73           C  
-ATOM    211  CG  MET A 263      -3.049   9.402   0.353  1.00  2.19           C  
-ATOM    212  SD  MET A 263      -3.368   8.098   1.555  1.00  2.72           S  
-ATOM    213  CE  MET A 263      -1.752   7.332   1.660  1.00  2.53           C  
-ATOM    214  H   MET A 263      -1.633   8.312  -3.257  1.00  1.05           H  
-ATOM    215  HA  MET A 263      -3.084  10.525  -2.250  1.00  1.75           H  
-ATOM    216  HB2 MET A 263      -3.444   8.286  -1.403  1.00  1.91           H  
-ATOM    217  HB3 MET A 263      -1.783   8.190  -0.842  1.00  1.55           H  
-ATOM    218  HG2 MET A 263      -2.262  10.037   0.734  1.00  2.08           H  
-ATOM    219  HG3 MET A 263      -3.949   9.982   0.224  1.00  2.43           H  
-ATOM    220  HE1 MET A 263      -1.208   7.517   0.745  1.00  2.24           H  
-ATOM    221  HE2 MET A 263      -1.866   6.268   1.804  1.00  2.75           H  
-ATOM    222  HE3 MET A 263      -1.208   7.753   2.493  1.00  2.74           H  
-ATOM    223  N   THR A 264       0.112  10.389  -1.393  1.00  0.90           N  
-ATOM    224  CA  THR A 264       1.201  11.187  -0.810  1.00  0.97           C  
-ATOM    225  C   THR A 264       1.552  12.421  -1.649  1.00  0.73           C  
-ATOM    226  O   THR A 264       2.032  13.419  -1.107  1.00  0.87           O  
-ATOM    227  CB  THR A 264       2.484  10.361  -0.559  1.00  1.28           C  
-ATOM    228  OG1 THR A 264       3.445  11.163   0.139  1.00  1.64           O  
-ATOM    229  CG2 THR A 264       3.102   9.864  -1.857  1.00  1.20           C  
-ATOM    230  H   THR A 264       0.310   9.494  -1.760  1.00  0.80           H  
-ATOM    231  HA  THR A 264       0.849  11.536   0.152  1.00  1.27           H  
-ATOM    232  HB  THR A 264       2.230   9.506   0.053  1.00  1.52           H  
-ATOM    233  HG1 THR A 264       3.276  12.095  -0.041  1.00  1.57           H  
-ATOM    234 HG21 THR A 264       2.555   9.002  -2.207  1.00  1.53           H  
-ATOM    235 HG22 THR A 264       4.131   9.591  -1.685  1.00  1.60           H  
-ATOM    236 HG23 THR A 264       3.056  10.645  -2.599  1.00  1.62           H  
-ATOM    237  N   THR A 265       1.328  12.362  -2.963  1.00  0.70           N  
-ATOM    238  CA  THR A 265       1.636  13.506  -3.829  1.00  1.02           C  
-ATOM    239  C   THR A 265       0.493  14.541  -3.805  1.00  1.18           C  
-ATOM    240  O   THR A 265      -0.102  14.785  -2.755  1.00  1.14           O  
-ATOM    241  CB  THR A 265       1.962  13.063  -5.285  1.00  1.45           C  
-ATOM    242  OG1 THR A 265       2.358  14.193  -6.078  1.00  1.89           O  
-ATOM    243  CG2 THR A 265       0.784  12.373  -5.955  1.00  1.57           C  
-ATOM    244  H   THR A 265       0.949  11.544  -3.353  1.00  0.70           H  
-ATOM    245  HA  THR A 265       2.522  13.978  -3.422  1.00  1.09           H  
-ATOM    246  HB  THR A 265       2.786  12.365  -5.247  1.00  1.49           H  
-ATOM    247  HG1 THR A 265       3.132  13.961  -6.601  1.00  2.21           H  
-ATOM    248 HG21 THR A 265      -0.138  12.809  -5.601  1.00  1.92           H  
-ATOM    249 HG22 THR A 265       0.798  11.319  -5.716  1.00  1.91           H  
-ATOM    250 HG23 THR A 265       0.854  12.499  -7.025  1.00  1.93           H  
-ATOM    251  N   GLN A 266       0.202  15.160  -4.953  1.00  1.61           N  
-ATOM    252  CA  GLN A 266      -0.855  16.175  -5.057  1.00  1.95           C  
-ATOM    253  C   GLN A 266      -0.534  17.388  -4.176  1.00  1.88           C  
-ATOM    254  O   GLN A 266      -1.428  18.048  -3.645  1.00  1.98           O  
-ATOM    255  CB  GLN A 266      -2.225  15.588  -4.673  1.00  2.08           C  
-ATOM    256  CG  GLN A 266      -2.644  14.393  -5.517  1.00  2.18           C  
-ATOM    257  CD  GLN A 266      -3.996  13.829  -5.112  1.00  2.37           C  
-ATOM    258  OE1 GLN A 266      -5.014  14.506  -5.192  1.00  2.76           O  
-ATOM    259  NE2 GLN A 266      -4.015  12.583  -4.669  1.00  2.18           N  
-ATOM    260  H   GLN A 266       0.726  14.933  -5.756  1.00  1.79           H  
-ATOM    261  HA  GLN A 266      -0.892  16.502  -6.086  1.00  2.29           H  
-ATOM    262  HB2 GLN A 266      -2.191  15.278  -3.641  1.00  1.86           H  
-ATOM    263  HB3 GLN A 266      -2.975  16.360  -4.783  1.00  2.41           H  
-ATOM    264  HG2 GLN A 266      -2.696  14.699  -6.551  1.00  2.47           H  
-ATOM    265  HG3 GLN A 266      -1.901  13.617  -5.411  1.00  1.90           H  
-ATOM    266 HE21 GLN A 266      -3.160  12.092  -4.623  1.00  1.88           H  
-ATOM    267 HE22 GLN A 266      -4.876  12.203  -4.404  1.00  2.37           H  
-ATOM    268  N   SER A 267       0.757  17.676  -4.033  1.00  1.78           N  
-ATOM    269  CA  SER A 267       1.211  18.805  -3.223  1.00  1.70           C  
-ATOM    270  C   SER A 267       1.828  19.898  -4.094  1.00  1.36           C  
-ATOM    271  O   SER A 267       2.623  19.622  -4.992  1.00  1.54           O  
-ATOM    272  CB  SER A 267       2.228  18.346  -2.173  1.00  2.20           C  
-ATOM    273  OG  SER A 267       2.684  19.436  -1.383  1.00  2.14           O  
-ATOM    274  H   SER A 267       1.421  17.116  -4.484  1.00  1.81           H  
-ATOM    275  HA  SER A 267       0.349  19.214  -2.717  1.00  1.67           H  
-ATOM    276  HB2 SER A 267       1.764  17.618  -1.523  1.00  2.52           H  
-ATOM    277  HB3 SER A 267       3.075  17.897  -2.668  1.00  2.40           H  
-ATOM    278  HG  SER A 267       3.215  19.099  -0.654  1.00  2.51           H  
-ATOM    279  N   SER A 268       1.448  21.142  -3.819  1.00  1.07           N  
-ATOM    280  CA  SER A 268       1.950  22.298  -4.566  1.00  1.05           C  
-ATOM    281  C   SER A 268       3.468  22.454  -4.425  1.00  1.50           C  
-ATOM    282  O   SER A 268       4.164  22.771  -5.390  1.00  1.86           O  
-ATOM    283  CB  SER A 268       1.245  23.565  -4.082  1.00  0.92           C  
-ATOM    284  OG  SER A 268       1.783  24.725  -4.688  1.00  1.28           O  
-ATOM    285  H   SER A 268       0.808  21.289  -3.091  1.00  1.08           H  
-ATOM    286  HA  SER A 268       1.713  22.145  -5.608  1.00  1.16           H  
-ATOM    287  HB2 SER A 268       0.195  23.503  -4.325  1.00  1.01           H  
-ATOM    288  HB3 SER A 268       1.360  23.649  -3.011  1.00  0.85           H  
-ATOM    289  HG  SER A 268       1.559  25.498  -4.144  1.00  1.48           H  
-ATOM    290  N   GLY A 269       3.978  22.220  -3.219  1.00  1.68           N  
-ATOM    291  CA  GLY A 269       5.412  22.328  -2.977  1.00  2.22           C  
-ATOM    292  C   GLY A 269       5.906  23.763  -2.824  1.00  2.26           C  
-ATOM    293  O   GLY A 269       6.562  24.090  -1.834  1.00  2.51           O  
-ATOM    294  H   GLY A 269       3.377  21.962  -2.489  1.00  1.57           H  
-ATOM    295  HA2 GLY A 269       5.646  21.785  -2.072  1.00  2.43           H  
-ATOM    296  HA3 GLY A 269       5.938  21.866  -3.801  1.00  2.49           H  
-ATOM    297  N   SER A 270       5.592  24.617  -3.799  1.00  2.15           N  
-ATOM    298  CA  SER A 270       6.011  26.026  -3.760  1.00  2.32           C  
-ATOM    299  C   SER A 270       5.476  26.718  -2.502  1.00  1.91           C  
-ATOM    300  O   SER A 270       4.268  26.765  -2.285  1.00  1.48           O  
-ATOM    301  CB  SER A 270       5.517  26.767  -5.010  1.00  2.52           C  
-ATOM    302  OG  SER A 270       5.997  26.156  -6.199  1.00  2.95           O  
-ATOM    303  H   SER A 270       5.067  24.294  -4.566  1.00  2.05           H  
-ATOM    304  HA  SER A 270       7.089  26.052  -3.738  1.00  2.75           H  
-ATOM    305  HB2 SER A 270       4.437  26.760  -5.030  1.00  2.21           H  
-ATOM    306  HB3 SER A 270       5.866  27.789  -4.981  1.00  2.74           H  
-ATOM    307  HG  SER A 270       5.252  25.858  -6.730  1.00  3.37           H  
-ATOM    308  N   ARG A 271       6.386  27.235  -1.670  1.00  2.11           N  
-ATOM    309  CA  ARG A 271       6.013  27.903  -0.413  1.00  1.84           C  
-ATOM    310  C   ARG A 271       5.005  29.036  -0.621  1.00  1.59           C  
-ATOM    311  O   ARG A 271       4.051  29.164   0.151  1.00  1.28           O  
-ATOM    312  CB  ARG A 271       7.246  28.443   0.302  1.00  2.30           C  
-ATOM    313  CG  ARG A 271       6.921  28.978   1.683  1.00  2.15           C  
-ATOM    314  CD  ARG A 271       6.575  27.856   2.652  1.00  2.19           C  
-ATOM    315  NE  ARG A 271       6.056  28.358   3.925  1.00  2.27           N  
-ATOM    316  CZ  ARG A 271       4.864  28.906   4.078  1.00  2.05           C  
-ATOM    317  NH1 ARG A 271       4.051  29.039   3.057  1.00  1.71           N  
-ATOM    318  NH2 ARG A 271       4.483  29.318   5.260  1.00  2.37           N  
-ATOM    319  H   ARG A 271       7.335  27.148  -1.896  1.00  2.52           H  
-ATOM    320  HA  ARG A 271       5.553  27.167   0.225  1.00  1.64           H  
-ATOM    321  HB2 ARG A 271       7.973  27.649   0.403  1.00  2.64           H  
-ATOM    322  HB3 ARG A 271       7.672  29.243  -0.283  1.00  2.55           H  
-ATOM    323  HG2 ARG A 271       7.768  29.529   2.063  1.00  2.48           H  
-ATOM    324  HG3 ARG A 271       6.068  29.633   1.597  1.00  1.84           H  
-ATOM    325  HD2 ARG A 271       5.826  27.226   2.197  1.00  1.92           H  
-ATOM    326  HD3 ARG A 271       7.465  27.275   2.841  1.00  2.60           H  
-ATOM    327  HE  ARG A 271       6.634  28.278   4.709  1.00  2.62           H  
-ATOM    328 HH11 ARG A 271       4.329  28.726   2.149  1.00  1.54           H  
-ATOM    329 HH12 ARG A 271       3.155  29.453   3.186  1.00  1.78           H  
-ATOM    330 HH21 ARG A 271       5.091  29.219   6.044  1.00  2.68           H  
-ATOM    331 HH22 ARG A 271       3.584  29.733   5.375  1.00  2.40           H  
-ATOM    332  N   ALA A 272       5.216  29.853  -1.651  1.00  1.96           N  
-ATOM    333  CA  ALA A 272       4.309  30.965  -1.942  1.00  2.09           C  
-ATOM    334  C   ALA A 272       2.903  30.451  -2.262  1.00  1.91           C  
-ATOM    335  O   ALA A 272       1.904  30.992  -1.789  1.00  2.05           O  
-ATOM    336  CB  ALA A 272       4.850  31.801  -3.093  1.00  2.72           C  
-ATOM    337  H   ALA A 272       5.993  29.703  -2.228  1.00  2.29           H  
-ATOM    338  HA  ALA A 272       4.261  31.592  -1.064  1.00  2.01           H  
-ATOM    339  HB1 ALA A 272       5.581  32.501  -2.717  1.00  3.07           H  
-ATOM    340  HB2 ALA A 272       4.038  32.343  -3.557  1.00  3.04           H  
-ATOM    341  HB3 ALA A 272       5.314  31.153  -3.822  1.00  3.02           H  
-ATOM    342  N   SER A 273       2.838  29.390  -3.053  1.00  1.75           N  
-ATOM    343  CA  SER A 273       1.564  28.787  -3.423  1.00  1.75           C  
-ATOM    344  C   SER A 273       1.233  27.610  -2.522  1.00  1.15           C  
-ATOM    345  O   SER A 273       0.900  26.528  -2.998  1.00  1.03           O  
-ATOM    346  CB  SER A 273       1.584  28.317  -4.871  1.00  2.12           C  
-ATOM    347  OG  SER A 273       2.128  29.307  -5.731  1.00  2.82           O  
-ATOM    348  H   SER A 273       3.669  28.992  -3.386  1.00  1.77           H  
-ATOM    349  HA  SER A 273       0.789  29.533  -3.304  1.00  2.10           H  
-ATOM    350  HB2 SER A 273       2.177  27.422  -4.942  1.00  1.92           H  
-ATOM    351  HB3 SER A 273       0.569  28.104  -5.184  1.00  2.19           H  
-ATOM    352  HG  SER A 273       1.829  30.176  -5.450  1.00  3.16           H  
-ATOM    353  N   LEU A 274       1.285  27.833  -1.216  1.00  1.04           N  
-ATOM    354  CA  LEU A 274       0.944  26.787  -0.262  1.00  1.14           C  
-ATOM    355  C   LEU A 274      -0.327  27.113   0.551  1.00  1.86           C  
-ATOM    356  O   LEU A 274      -0.367  26.813   1.744  1.00  2.22           O  
-ATOM    357  CB  LEU A 274       2.108  26.536   0.704  1.00  1.20           C  
-ATOM    358  CG  LEU A 274       3.173  25.553   0.218  1.00  1.22           C  
-ATOM    359  CD1 LEU A 274       4.166  25.255   1.329  1.00  1.84           C  
-ATOM    360  CD2 LEU A 274       2.531  24.267  -0.280  1.00  1.42           C  
-ATOM    361  H   LEU A 274       1.533  28.724  -0.891  1.00  1.23           H  
-ATOM    362  HA  LEU A 274       0.767  25.882  -0.823  1.00  1.04           H  
-ATOM    363  HB2 LEU A 274       2.590  27.484   0.898  1.00  1.22           H  
-ATOM    364  HB3 LEU A 274       1.702  26.164   1.631  1.00  1.67           H  
-ATOM    365  HG  LEU A 274       3.717  25.997  -0.603  1.00  1.05           H  
-ATOM    366 HD11 LEU A 274       4.298  26.134   1.942  1.00  2.28           H  
-ATOM    367 HD12 LEU A 274       5.115  24.970   0.897  1.00  2.23           H  
-ATOM    368 HD13 LEU A 274       3.791  24.445   1.940  1.00  2.21           H  
-ATOM    369 HD21 LEU A 274       1.523  24.196   0.099  1.00  1.80           H  
-ATOM    370 HD22 LEU A 274       3.106  23.420   0.065  1.00  1.77           H  
-ATOM    371 HD23 LEU A 274       2.509  24.271  -1.360  1.00  1.81           H  
-ATOM    372  N   PRO A 275      -1.395  27.724  -0.046  1.00  2.27           N  
-ATOM    373  CA  PRO A 275      -2.626  28.042   0.710  1.00  2.99           C  
-ATOM    374  C   PRO A 275      -3.265  26.795   1.333  1.00  3.32           C  
-ATOM    375  O   PRO A 275      -3.804  26.851   2.439  1.00  3.84           O  
-ATOM    376  CB  PRO A 275      -3.547  28.654  -0.351  1.00  3.37           C  
-ATOM    377  CG  PRO A 275      -2.600  29.204  -1.350  1.00  3.02           C  
-ATOM    378  CD  PRO A 275      -1.525  28.167  -1.455  1.00  2.29           C  
-ATOM    379  HA  PRO A 275      -2.433  28.771   1.480  1.00  3.15           H  
-ATOM    380  HB2 PRO A 275      -4.177  27.887  -0.777  1.00  3.48           H  
-ATOM    381  HB3 PRO A 275      -4.153  29.432   0.091  1.00  3.85           H  
-ATOM    382  HG2 PRO A 275      -3.093  29.349  -2.300  1.00  3.29           H  
-ATOM    383  HG3 PRO A 275      -2.189  30.132  -0.985  1.00  3.11           H  
-ATOM    384  HD2 PRO A 275      -1.840  27.358  -2.096  1.00  2.21           H  
-ATOM    385  HD3 PRO A 275      -0.596  28.604  -1.822  1.00  2.03           H  
-ATOM    386  N   LYS A 276      -3.168  25.672   0.609  1.00  3.09           N  
-ATOM    387  CA  LYS A 276      -3.700  24.375   1.054  1.00  3.47           C  
-ATOM    388  C   LYS A 276      -5.220  24.427   1.280  1.00  4.14           C  
-ATOM    389  O   LYS A 276      -5.653  24.511   2.456  1.00  4.77           O  
-ATOM    390  CB  LYS A 276      -2.969  23.919   2.328  1.00  3.72           C  
-ATOM    391  CG  LYS A 276      -2.862  22.404   2.482  1.00  4.01           C  
-ATOM    392  CD  LYS A 276      -4.178  21.754   2.895  1.00  4.67           C  
-ATOM    393  CE  LYS A 276      -4.510  22.014   4.364  1.00  5.37           C  
-ATOM    394  NZ  LYS A 276      -4.903  23.431   4.625  1.00  5.46           N  
-ATOM    395  OXT LYS A 276      -5.962  24.363   0.276  1.00  4.17           O  
-ATOM    396  H   LYS A 276      -2.707  25.714  -0.253  1.00  2.71           H  
-ATOM    397  HA  LYS A 276      -3.501  23.660   0.269  1.00  3.19           H  
-ATOM    398  HB2 LYS A 276      -1.971  24.328   2.317  1.00  3.39           H  
-ATOM    399  HB3 LYS A 276      -3.497  24.308   3.186  1.00  4.23           H  
-ATOM    400  HG2 LYS A 276      -2.547  21.981   1.541  1.00  3.69           H  
-ATOM    401  HG3 LYS A 276      -2.118  22.188   3.235  1.00  4.22           H  
-ATOM    402  HD2 LYS A 276      -4.972  22.153   2.282  1.00  4.58           H  
-ATOM    403  HD3 LYS A 276      -4.104  20.686   2.736  1.00  4.88           H  
-ATOM    404  HE2 LYS A 276      -5.322  21.366   4.655  1.00  5.88           H  
-ATOM    405  HE3 LYS A 276      -3.639  21.780   4.956  1.00  5.43           H  
-ATOM    406  HZ1 LYS A 276      -5.317  23.859   3.752  1.00  5.15           H  
-ATOM    407  HZ2 LYS A 276      -4.072  23.989   4.907  1.00  5.41           H  
-ATOM    408  HZ3 LYS A 276      -5.611  23.473   5.384  1.00  6.01           H  
-TER
-ENDMDL
-MODEL      10
-ATOM      1  N   ALA A 249       1.888 -10.410 -12.513  1.00  3.22           N  
-ATOM      2  CA  ALA A 249       1.926  -8.921 -12.567  1.00  2.63           C  
-ATOM      3  C   ALA A 249       1.754  -8.312 -11.171  1.00  1.94           C  
-ATOM      4  O   ALA A 249       1.173  -8.939 -10.286  1.00  2.04           O  
-ATOM      5  CB  ALA A 249       0.853  -8.399 -13.518  1.00  2.94           C  
-ATOM      6  H1  ALA A 249       2.516 -10.812 -13.240  1.00  3.65           H  
-ATOM      7  H2  ALA A 249       0.919 -10.752 -12.680  1.00  3.25           H  
-ATOM      8  H3  ALA A 249       2.201 -10.744 -11.578  1.00  3.59           H  
-ATOM      9  HA  ALA A 249       2.888  -8.625 -12.957  1.00  2.64           H  
-ATOM     10  HB1 ALA A 249       0.244  -7.669 -13.007  1.00  3.23           H  
-ATOM     11  HB2 ALA A 249       0.235  -9.219 -13.848  1.00  3.16           H  
-ATOM     12  HB3 ALA A 249       1.325  -7.937 -14.373  1.00  3.22           H  
-ATOM     13  N   PRO A 250       2.271  -7.085 -10.956  1.00  1.47           N  
-ATOM     14  CA  PRO A 250       2.190  -6.376  -9.665  1.00  1.05           C  
-ATOM     15  C   PRO A 250       0.762  -5.933  -9.308  1.00  0.93           C  
-ATOM     16  O   PRO A 250       0.458  -4.744  -9.312  1.00  1.17           O  
-ATOM     17  CB  PRO A 250       3.085  -5.140  -9.889  1.00  1.21           C  
-ATOM     18  CG  PRO A 250       3.889  -5.452 -11.096  1.00  1.50           C  
-ATOM     19  CD  PRO A 250       2.988  -6.276 -11.949  1.00  1.75           C  
-ATOM     20  HA  PRO A 250       2.592  -6.972  -8.858  1.00  1.23           H  
-ATOM     21  HB2 PRO A 250       2.465  -4.274 -10.053  1.00  1.43           H  
-ATOM     22  HB3 PRO A 250       3.715  -4.986  -9.029  1.00  1.35           H  
-ATOM     23  HG2 PRO A 250       4.164  -4.539 -11.604  1.00  1.86           H  
-ATOM     24  HG3 PRO A 250       4.769  -6.014 -10.821  1.00  1.47           H  
-ATOM     25  HD2 PRO A 250       2.305  -5.643 -12.494  1.00  2.00           H  
-ATOM     26  HD3 PRO A 250       3.558  -6.897 -12.616  1.00  2.10           H  
-ATOM     27  N   ALA A 251      -0.114  -6.893  -9.018  1.00  0.77           N  
-ATOM     28  CA  ALA A 251      -1.508  -6.585  -8.681  1.00  0.88           C  
-ATOM     29  C   ALA A 251      -1.643  -5.841  -7.345  1.00  0.70           C  
-ATOM     30  O   ALA A 251      -1.935  -4.645  -7.320  1.00  0.66           O  
-ATOM     31  CB  ALA A 251      -2.335  -7.864  -8.659  1.00  1.11           C  
-ATOM     32  H   ALA A 251       0.179  -7.831  -9.045  1.00  0.77           H  
-ATOM     33  HA  ALA A 251      -1.900  -5.955  -9.467  1.00  1.08           H  
-ATOM     34  HB1 ALA A 251      -2.633  -8.083  -7.643  1.00  1.18           H  
-ATOM     35  HB2 ALA A 251      -1.745  -8.683  -9.044  1.00  1.14           H  
-ATOM     36  HB3 ALA A 251      -3.214  -7.735  -9.272  1.00  1.40           H  
-ATOM     37  N   ARG A 252      -1.444  -6.553  -6.236  1.00  0.74           N  
-ATOM     38  CA  ARG A 252      -1.564  -5.954  -4.901  1.00  0.70           C  
-ATOM     39  C   ARG A 252      -0.476  -4.900  -4.655  1.00  0.54           C  
-ATOM     40  O   ARG A 252      -0.773  -3.771  -4.260  1.00  0.54           O  
-ATOM     41  CB  ARG A 252      -1.490  -7.050  -3.827  1.00  0.83           C  
-ATOM     42  CG  ARG A 252      -2.313  -6.764  -2.572  1.00  0.98           C  
-ATOM     43  CD  ARG A 252      -1.863  -5.496  -1.861  1.00  0.99           C  
-ATOM     44  NE  ARG A 252      -2.630  -5.239  -0.645  1.00  1.28           N  
-ATOM     45  CZ  ARG A 252      -2.569  -4.113   0.030  1.00  1.43           C  
-ATOM     46  NH1 ARG A 252      -1.842  -3.116  -0.410  1.00  1.39           N  
-ATOM     47  NH2 ARG A 252      -3.250  -3.976   1.140  1.00  1.74           N  
-ATOM     48  H   ARG A 252      -1.224  -7.505  -6.315  1.00  0.89           H  
-ATOM     49  HA  ARG A 252      -2.526  -5.474  -4.843  1.00  0.76           H  
-ATOM     50  HB2 ARG A 252      -1.842  -7.976  -4.254  1.00  0.93           H  
-ATOM     51  HB3 ARG A 252      -0.458  -7.174  -3.531  1.00  0.80           H  
-ATOM     52  HG2 ARG A 252      -3.348  -6.655  -2.853  1.00  1.10           H  
-ATOM     53  HG3 ARG A 252      -2.211  -7.600  -1.893  1.00  1.09           H  
-ATOM     54  HD2 ARG A 252      -0.820  -5.596  -1.601  1.00  0.99           H  
-ATOM     55  HD3 ARG A 252      -1.985  -4.660  -2.535  1.00  0.92           H  
-ATOM     56  HE  ARG A 252      -3.202  -5.956  -0.310  1.00  1.42           H  
-ATOM     57 HH11 ARG A 252      -1.331  -3.213  -1.261  1.00  1.23           H  
-ATOM     58 HH12 ARG A 252      -1.799  -2.262   0.103  1.00  1.60           H  
-ATOM     59 HH21 ARG A 252      -3.816  -4.727   1.474  1.00  1.85           H  
-ATOM     60 HH22 ARG A 252      -3.204  -3.122   1.651  1.00  1.90           H  
-ATOM     61  N   VAL A 253       0.776  -5.280  -4.890  1.00  0.53           N  
-ATOM     62  CA  VAL A 253       1.908  -4.373  -4.693  1.00  0.51           C  
-ATOM     63  C   VAL A 253       1.760  -3.113  -5.546  1.00  0.45           C  
-ATOM     64  O   VAL A 253       1.858  -1.993  -5.044  1.00  0.45           O  
-ATOM     65  CB  VAL A 253       3.249  -5.077  -5.008  1.00  0.64           C  
-ATOM     66  CG1 VAL A 253       3.268  -5.671  -6.410  1.00  0.76           C  
-ATOM     67  CG2 VAL A 253       4.423  -4.130  -4.806  1.00  0.77           C  
-ATOM     68  H   VAL A 253       0.943  -6.191  -5.199  1.00  0.62           H  
-ATOM     69  HA  VAL A 253       1.918  -4.083  -3.653  1.00  0.57           H  
-ATOM     70  HB  VAL A 253       3.353  -5.888  -4.320  1.00  0.72           H  
-ATOM     71 HG11 VAL A 253       3.103  -4.889  -7.135  1.00  1.37           H  
-ATOM     72 HG12 VAL A 253       2.489  -6.414  -6.496  1.00  1.27           H  
-ATOM     73 HG13 VAL A 253       4.228  -6.134  -6.592  1.00  1.26           H  
-ATOM     74 HG21 VAL A 253       5.323  -4.583  -5.198  1.00  1.30           H  
-ATOM     75 HG22 VAL A 253       4.549  -3.932  -3.753  1.00  1.31           H  
-ATOM     76 HG23 VAL A 253       4.232  -3.203  -5.326  1.00  1.30           H  
-ATOM     77  N   GLY A 254       1.499  -3.323  -6.827  1.00  0.51           N  
-ATOM     78  CA  GLY A 254       1.311  -2.218  -7.758  1.00  0.56           C  
-ATOM     79  C   GLY A 254       0.192  -1.283  -7.330  1.00  0.49           C  
-ATOM     80  O   GLY A 254       0.376  -0.066  -7.285  1.00  0.47           O  
-ATOM     81  H   GLY A 254       1.427  -4.249  -7.140  1.00  0.59           H  
-ATOM     82  HA2 GLY A 254       2.231  -1.656  -7.824  1.00  0.60           H  
-ATOM     83  HA3 GLY A 254       1.077  -2.620  -8.733  1.00  0.68           H  
-ATOM     84  N   LEU A 255      -0.963  -1.851  -6.987  1.00  0.52           N  
-ATOM     85  CA  LEU A 255      -2.102  -1.054  -6.529  1.00  0.54           C  
-ATOM     86  C   LEU A 255      -1.748  -0.266  -5.274  1.00  0.44           C  
-ATOM     87  O   LEU A 255      -2.066   0.920  -5.150  1.00  0.46           O  
-ATOM     88  CB  LEU A 255      -3.300  -1.949  -6.235  1.00  0.68           C  
-ATOM     89  CG  LEU A 255      -4.315  -2.041  -7.364  1.00  0.86           C  
-ATOM     90  CD1 LEU A 255      -5.464  -2.947  -6.961  1.00  1.00           C  
-ATOM     91  CD2 LEU A 255      -4.819  -0.651  -7.725  1.00  0.99           C  
-ATOM     92  H   LEU A 255      -1.048  -2.831  -7.024  1.00  0.57           H  
-ATOM     93  HA  LEU A 255      -2.367  -0.367  -7.318  1.00  0.61           H  
-ATOM     94  HB2 LEU A 255      -2.937  -2.944  -6.019  1.00  0.74           H  
-ATOM     95  HB3 LEU A 255      -3.803  -1.568  -5.359  1.00  0.72           H  
-ATOM     96  HG  LEU A 255      -3.841  -2.465  -8.236  1.00  0.92           H  
-ATOM     97 HD11 LEU A 255      -6.124  -2.414  -6.292  1.00  1.58           H  
-ATOM     98 HD12 LEU A 255      -5.073  -3.820  -6.459  1.00  1.38           H  
-ATOM     99 HD13 LEU A 255      -6.010  -3.251  -7.841  1.00  1.41           H  
-ATOM    100 HD21 LEU A 255      -4.233   0.089  -7.198  1.00  1.36           H  
-ATOM    101 HD22 LEU A 255      -5.855  -0.558  -7.441  1.00  1.42           H  
-ATOM    102 HD23 LEU A 255      -4.720  -0.497  -8.789  1.00  1.58           H  
-ATOM    103  N   GLY A 256      -1.084  -0.935  -4.344  1.00  0.42           N  
-ATOM    104  CA  GLY A 256      -0.695  -0.293  -3.111  1.00  0.44           C  
-ATOM    105  C   GLY A 256       0.280   0.855  -3.321  1.00  0.40           C  
-ATOM    106  O   GLY A 256       0.041   1.962  -2.847  1.00  0.44           O  
-ATOM    107  H   GLY A 256      -0.858  -1.881  -4.498  1.00  0.44           H  
-ATOM    108  HA2 GLY A 256      -1.584   0.087  -2.629  1.00  0.53           H  
-ATOM    109  HA3 GLY A 256      -0.238  -1.029  -2.468  1.00  0.49           H  
-ATOM    110  N   ILE A 257       1.378   0.607  -4.037  1.00  0.38           N  
-ATOM    111  CA  ILE A 257       2.367   1.658  -4.283  1.00  0.44           C  
-ATOM    112  C   ILE A 257       1.783   2.815  -5.104  1.00  0.41           C  
-ATOM    113  O   ILE A 257       2.065   3.977  -4.823  1.00  0.47           O  
-ATOM    114  CB  ILE A 257       3.660   1.119  -4.952  1.00  0.54           C  
-ATOM    115  CG1 ILE A 257       3.380   0.464  -6.310  1.00  0.55           C  
-ATOM    116  CG2 ILE A 257       4.355   0.132  -4.028  1.00  0.62           C  
-ATOM    117  CD1 ILE A 257       3.718   1.346  -7.495  1.00  0.68           C  
-ATOM    118  H   ILE A 257       1.525  -0.297  -4.403  1.00  0.39           H  
-ATOM    119  HA  ILE A 257       2.645   2.053  -3.316  1.00  0.51           H  
-ATOM    120  HB  ILE A 257       4.329   1.954  -5.099  1.00  0.62           H  
-ATOM    121 HG12 ILE A 257       3.964  -0.439  -6.394  1.00  0.64           H  
-ATOM    122 HG13 ILE A 257       2.330   0.216  -6.371  1.00  0.53           H  
-ATOM    123 HG21 ILE A 257       5.408   0.366  -3.977  1.00  1.21           H  
-ATOM    124 HG22 ILE A 257       4.227  -0.871  -4.409  1.00  1.19           H  
-ATOM    125 HG23 ILE A 257       3.923   0.199  -3.040  1.00  1.23           H  
-ATOM    126 HD11 ILE A 257       3.257   2.315  -7.366  1.00  1.22           H  
-ATOM    127 HD12 ILE A 257       3.347   0.890  -8.401  1.00  1.21           H  
-ATOM    128 HD13 ILE A 257       4.789   1.464  -7.562  1.00  1.31           H  
-ATOM    129  N   THR A 258       0.958   2.501  -6.108  1.00  0.38           N  
-ATOM    130  CA  THR A 258       0.343   3.541  -6.939  1.00  0.42           C  
-ATOM    131  C   THR A 258      -0.628   4.404  -6.134  1.00  0.38           C  
-ATOM    132  O   THR A 258      -0.594   5.632  -6.231  1.00  0.44           O  
-ATOM    133  CB  THR A 258      -0.399   2.963  -8.163  1.00  0.51           C  
-ATOM    134  OG1 THR A 258      -1.270   1.899  -7.766  1.00  0.49           O  
-ATOM    135  CG2 THR A 258       0.587   2.463  -9.212  1.00  0.64           C  
-ATOM    136  H   THR A 258       0.754   1.554  -6.287  1.00  0.39           H  
-ATOM    137  HA  THR A 258       1.139   4.176  -7.301  1.00  0.48           H  
-ATOM    138  HB  THR A 258      -0.992   3.751  -8.603  1.00  0.57           H  
-ATOM    139  HG1 THR A 258      -0.759   1.080  -7.679  1.00  0.54           H  
-ATOM    140 HG21 THR A 258       1.133   3.300  -9.619  1.00  1.29           H  
-ATOM    141 HG22 THR A 258       0.048   1.964 -10.002  1.00  1.17           H  
-ATOM    142 HG23 THR A 258       1.277   1.770  -8.753  1.00  1.22           H  
-ATOM    143  N   THR A 259      -1.485   3.773  -5.330  1.00  0.37           N  
-ATOM    144  CA  THR A 259      -2.442   4.521  -4.511  1.00  0.43           C  
-ATOM    145  C   THR A 259      -1.713   5.362  -3.467  1.00  0.43           C  
-ATOM    146  O   THR A 259      -2.014   6.543  -3.297  1.00  0.48           O  
-ATOM    147  CB  THR A 259      -3.466   3.603  -3.809  1.00  0.54           C  
-ATOM    148  OG1 THR A 259      -2.796   2.539  -3.122  1.00  0.58           O  
-ATOM    149  CG2 THR A 259      -4.454   3.026  -4.813  1.00  0.65           C  
-ATOM    150  H   THR A 259      -1.466   2.789  -5.276  1.00  0.38           H  
-ATOM    151  HA  THR A 259      -2.983   5.187  -5.170  1.00  0.47           H  
-ATOM    152  HB  THR A 259      -4.015   4.192  -3.088  1.00  0.61           H  
-ATOM    153  HG1 THR A 259      -2.599   1.827  -3.749  1.00  0.67           H  
-ATOM    154 HG21 THR A 259      -4.681   2.005  -4.548  1.00  1.25           H  
-ATOM    155 HG22 THR A 259      -4.020   3.054  -5.801  1.00  1.24           H  
-ATOM    156 HG23 THR A 259      -5.362   3.611  -4.802  1.00  1.21           H  
-ATOM    157  N   VAL A 260      -0.737   4.758  -2.790  1.00  0.43           N  
-ATOM    158  CA  VAL A 260       0.048   5.473  -1.784  1.00  0.52           C  
-ATOM    159  C   VAL A 260       0.783   6.659  -2.417  1.00  0.52           C  
-ATOM    160  O   VAL A 260       0.738   7.772  -1.893  1.00  0.59           O  
-ATOM    161  CB  VAL A 260       1.060   4.540  -1.081  1.00  0.58           C  
-ATOM    162  CG1 VAL A 260       2.012   5.333  -0.199  1.00  0.75           C  
-ATOM    163  CG2 VAL A 260       0.328   3.488  -0.259  1.00  0.65           C  
-ATOM    164  H   VAL A 260      -0.529   3.811  -2.980  1.00  0.42           H  
-ATOM    165  HA  VAL A 260      -0.639   5.850  -1.039  1.00  0.60           H  
-ATOM    166  HB  VAL A 260       1.642   4.034  -1.838  1.00  0.56           H  
-ATOM    167 HG11 VAL A 260       2.662   5.933  -0.820  1.00  1.24           H  
-ATOM    168 HG12 VAL A 260       2.604   4.653   0.393  1.00  1.30           H  
-ATOM    169 HG13 VAL A 260       1.442   5.978   0.455  1.00  1.35           H  
-ATOM    170 HG21 VAL A 260      -0.289   3.976   0.483  1.00  1.18           H  
-ATOM    171 HG22 VAL A 260       1.046   2.849   0.233  1.00  1.29           H  
-ATOM    172 HG23 VAL A 260      -0.297   2.893  -0.909  1.00  1.15           H  
-ATOM    173  N   LEU A 261       1.432   6.416  -3.560  1.00  0.49           N  
-ATOM    174  CA  LEU A 261       2.152   7.472  -4.276  1.00  0.56           C  
-ATOM    175  C   LEU A 261       1.181   8.594  -4.657  1.00  0.54           C  
-ATOM    176  O   LEU A 261       1.461   9.774  -4.452  1.00  0.63           O  
-ATOM    177  CB  LEU A 261       2.818   6.895  -5.536  1.00  0.60           C  
-ATOM    178  CG  LEU A 261       4.114   7.584  -5.995  1.00  0.95           C  
-ATOM    179  CD1 LEU A 261       3.852   9.000  -6.486  1.00  1.39           C  
-ATOM    180  CD2 LEU A 261       5.141   7.594  -4.869  1.00  1.33           C  
-ATOM    181  H   LEU A 261       1.415   5.506  -3.937  1.00  0.47           H  
-ATOM    182  HA  LEU A 261       2.912   7.868  -3.620  1.00  0.65           H  
-ATOM    183  HB2 LEU A 261       3.040   5.854  -5.349  1.00  0.67           H  
-ATOM    184  HB3 LEU A 261       2.106   6.949  -6.346  1.00  0.74           H  
-ATOM    185  HG  LEU A 261       4.533   7.024  -6.818  1.00  0.96           H  
-ATOM    186 HD11 LEU A 261       3.604   8.979  -7.537  1.00  1.76           H  
-ATOM    187 HD12 LEU A 261       4.737   9.602  -6.337  1.00  1.74           H  
-ATOM    188 HD13 LEU A 261       3.029   9.428  -5.931  1.00  1.63           H  
-ATOM    189 HD21 LEU A 261       5.852   8.389  -5.036  1.00  1.75           H  
-ATOM    190 HD22 LEU A 261       5.658   6.646  -4.847  1.00  1.81           H  
-ATOM    191 HD23 LEU A 261       4.641   7.751  -3.924  1.00  1.77           H  
-ATOM    192  N   THR A 262       0.026   8.202  -5.190  1.00  0.47           N  
-ATOM    193  CA  THR A 262      -1.014   9.153  -5.582  1.00  0.49           C  
-ATOM    194  C   THR A 262      -1.490   9.969  -4.379  1.00  0.51           C  
-ATOM    195  O   THR A 262      -1.558  11.197  -4.427  1.00  0.58           O  
-ATOM    196  CB  THR A 262      -2.229   8.421  -6.196  1.00  0.52           C  
-ATOM    197  OG1 THR A 262      -1.818   7.639  -7.324  1.00  0.59           O  
-ATOM    198  CG2 THR A 262      -3.304   9.408  -6.623  1.00  0.59           C  
-ATOM    199  H   THR A 262      -0.137   7.240  -5.310  1.00  0.45           H  
-ATOM    200  HA  THR A 262      -0.603   9.820  -6.326  1.00  0.55           H  
-ATOM    201  HB  THR A 262      -2.645   7.762  -5.447  1.00  0.54           H  
-ATOM    202  HG1 THR A 262      -1.385   6.827  -7.015  1.00  0.90           H  
-ATOM    203 HG21 THR A 262      -4.268   9.054  -6.288  1.00  1.20           H  
-ATOM    204 HG22 THR A 262      -3.305   9.496  -7.698  1.00  1.26           H  
-ATOM    205 HG23 THR A 262      -3.101  10.372  -6.181  1.00  1.09           H  
-ATOM    206  N   MET A 263      -1.817   9.271  -3.299  1.00  0.50           N  
-ATOM    207  CA  MET A 263      -2.286   9.914  -2.075  1.00  0.59           C  
-ATOM    208  C   MET A 263      -1.216  10.831  -1.461  1.00  0.62           C  
-ATOM    209  O   MET A 263      -1.511  11.966  -1.082  1.00  0.66           O  
-ATOM    210  CB  MET A 263      -2.704   8.851  -1.058  1.00  0.70           C  
-ATOM    211  CG  MET A 263      -3.297   9.421   0.221  1.00  0.88           C  
-ATOM    212  SD  MET A 263      -3.745   8.140   1.407  1.00  1.11           S  
-ATOM    213  CE  MET A 263      -4.426   9.132   2.734  1.00  1.45           C  
-ATOM    214  H   MET A 263      -1.746   8.289  -3.326  1.00  0.48           H  
-ATOM    215  HA  MET A 263      -3.148  10.512  -2.328  1.00  0.62           H  
-ATOM    216  HB2 MET A 263      -3.440   8.204  -1.512  1.00  0.73           H  
-ATOM    217  HB3 MET A 263      -1.837   8.262  -0.795  1.00  0.75           H  
-ATOM    218  HG2 MET A 263      -2.570  10.076   0.678  1.00  0.94           H  
-ATOM    219  HG3 MET A 263      -4.182   9.986  -0.029  1.00  0.94           H  
-ATOM    220  HE1 MET A 263      -4.139   8.705   3.685  1.00  1.83           H  
-ATOM    221  HE2 MET A 263      -5.503   9.145   2.657  1.00  1.83           H  
-ATOM    222  HE3 MET A 263      -4.046  10.140   2.662  1.00  1.86           H  
-ATOM    223  N   THR A 264       0.019  10.335  -1.346  1.00  0.64           N  
-ATOM    224  CA  THR A 264       1.099  11.129  -0.750  1.00  0.74           C  
-ATOM    225  C   THR A 264       1.465  12.360  -1.587  1.00  0.72           C  
-ATOM    226  O   THR A 264       1.788  13.408  -1.028  1.00  0.82           O  
-ATOM    227  CB  THR A 264       2.380  10.303  -0.471  1.00  0.82           C  
-ATOM    228  OG1 THR A 264       3.309  11.092   0.284  1.00  0.98           O  
-ATOM    229  CG2 THR A 264       3.055   9.841  -1.755  1.00  0.78           C  
-ATOM    230  H   THR A 264       0.204   9.412  -1.652  1.00  0.64           H  
-ATOM    231  HA  THR A 264       0.731  11.481   0.205  1.00  0.80           H  
-ATOM    232  HB  THR A 264       2.108   9.432   0.109  1.00  0.87           H  
-ATOM    233  HG1 THR A 264       3.224  12.018   0.032  1.00  1.37           H  
-ATOM    234 HG21 THR A 264       2.489   9.028  -2.188  1.00  1.32           H  
-ATOM    235 HG22 THR A 264       4.056   9.503  -1.533  1.00  1.25           H  
-ATOM    236 HG23 THR A 264       3.098  10.663  -2.454  1.00  1.28           H  
-ATOM    237  N   THR A 265       1.426  12.247  -2.918  1.00  0.64           N  
-ATOM    238  CA  THR A 265       1.768  13.389  -3.770  1.00  0.68           C  
-ATOM    239  C   THR A 265       0.663  14.448  -3.753  1.00  0.64           C  
-ATOM    240  O   THR A 265       0.947  15.646  -3.749  1.00  0.71           O  
-ATOM    241  CB  THR A 265       2.090  12.982  -5.231  1.00  0.74           C  
-ATOM    242  OG1 THR A 265       2.596  14.116  -5.946  1.00  0.83           O  
-ATOM    243  CG2 THR A 265       0.866  12.437  -5.958  1.00  0.73           C  
-ATOM    244  H   THR A 265       1.169  11.389  -3.327  1.00  0.61           H  
-ATOM    245  HA  THR A 265       2.660  13.835  -3.352  1.00  0.75           H  
-ATOM    246  HB  THR A 265       2.849  12.214  -5.213  1.00  0.81           H  
-ATOM    247  HG1 THR A 265       3.232  14.587  -5.395  1.00  1.10           H  
-ATOM    248 HG21 THR A 265       0.997  12.559  -7.024  1.00  1.18           H  
-ATOM    249 HG22 THR A 265      -0.013  12.977  -5.638  1.00  1.15           H  
-ATOM    250 HG23 THR A 265       0.749  11.388  -5.727  1.00  1.08           H  
-ATOM    251  N   GLN A 266      -0.595  14.003  -3.726  1.00  0.58           N  
-ATOM    252  CA  GLN A 266      -1.736  14.919  -3.690  1.00  0.57           C  
-ATOM    253  C   GLN A 266      -1.901  15.541  -2.300  1.00  0.56           C  
-ATOM    254  O   GLN A 266      -2.218  16.727  -2.177  1.00  0.57           O  
-ATOM    255  CB  GLN A 266      -3.020  14.190  -4.088  1.00  0.61           C  
-ATOM    256  CG  GLN A 266      -4.235  15.102  -4.138  1.00  0.74           C  
-ATOM    257  CD  GLN A 266      -4.120  16.178  -5.200  1.00  0.90           C  
-ATOM    258  OE1 GLN A 266      -4.104  15.894  -6.394  1.00  1.06           O  
-ATOM    259  NE2 GLN A 266      -4.028  17.426  -4.772  1.00  1.02           N  
-ATOM    260  H   GLN A 266      -0.760  13.034  -3.721  1.00  0.60           H  
-ATOM    261  HA  GLN A 266      -1.553  15.707  -4.398  1.00  0.61           H  
-ATOM    262  HB2 GLN A 266      -2.885  13.749  -5.064  1.00  0.68           H  
-ATOM    263  HB3 GLN A 266      -3.214  13.406  -3.370  1.00  0.61           H  
-ATOM    264  HG2 GLN A 266      -5.107  14.505  -4.348  1.00  0.82           H  
-ATOM    265  HG3 GLN A 266      -4.347  15.579  -3.176  1.00  0.78           H  
-ATOM    266 HE21 GLN A 266      -4.035  17.586  -3.806  1.00  1.04           H  
-ATOM    267 HE22 GLN A 266      -3.953  18.135  -5.441  1.00  1.17           H  
-ATOM    268  N   SER A 267      -1.695  14.726  -1.262  1.00  0.62           N  
-ATOM    269  CA  SER A 267      -1.827  15.172   0.130  1.00  0.70           C  
-ATOM    270  C   SER A 267      -3.269  15.609   0.427  1.00  0.68           C  
-ATOM    271  O   SER A 267      -4.219  14.988  -0.050  1.00  0.74           O  
-ATOM    272  CB  SER A 267      -0.833  16.312   0.423  1.00  0.77           C  
-ATOM    273  OG  SER A 267      -0.857  16.694   1.791  1.00  0.93           O  
-ATOM    274  H   SER A 267      -1.455  13.786  -1.437  1.00  0.68           H  
-ATOM    275  HA  SER A 267      -1.587  14.331   0.764  1.00  0.83           H  
-ATOM    276  HB2 SER A 267       0.165  15.985   0.174  1.00  0.85           H  
-ATOM    277  HB3 SER A 267      -1.090  17.170  -0.182  1.00  0.73           H  
-ATOM    278  HG  SER A 267       0.031  16.631   2.155  1.00  1.14           H  
-ATOM    279  N   SER A 268      -3.431  16.670   1.212  1.00  0.72           N  
-ATOM    280  CA  SER A 268      -4.760  17.176   1.560  1.00  0.80           C  
-ATOM    281  C   SER A 268      -5.401  17.918   0.383  1.00  0.81           C  
-ATOM    282  O   SER A 268      -6.573  17.712   0.070  1.00  0.95           O  
-ATOM    283  CB  SER A 268      -4.677  18.101   2.777  1.00  0.93           C  
-ATOM    284  OG  SER A 268      -3.779  19.175   2.545  1.00  0.95           O  
-ATOM    285  H   SER A 268      -2.631  17.125   1.570  1.00  0.76           H  
-ATOM    286  HA  SER A 268      -5.379  16.327   1.810  1.00  0.86           H  
-ATOM    287  HB2 SER A 268      -5.656  18.506   2.987  1.00  1.05           H  
-ATOM    288  HB3 SER A 268      -4.331  17.538   3.631  1.00  1.04           H  
-ATOM    289  HG  SER A 268      -3.912  19.854   3.214  1.00  1.10           H  
-ATOM    290  N   GLY A 269      -4.625  18.782  -0.264  1.00  0.80           N  
-ATOM    291  CA  GLY A 269      -5.137  19.536  -1.398  1.00  0.91           C  
-ATOM    292  C   GLY A 269      -4.248  20.711  -1.775  1.00  0.92           C  
-ATOM    293  O   GLY A 269      -3.022  20.615  -1.706  1.00  0.79           O  
-ATOM    294  H   GLY A 269      -3.699  18.908   0.029  1.00  0.79           H  
-ATOM    295  HA2 GLY A 269      -5.217  18.874  -2.247  1.00  0.92           H  
-ATOM    296  HA3 GLY A 269      -6.121  19.908  -1.153  1.00  1.07           H  
-ATOM    297  N   SER A 270      -4.876  21.822  -2.176  1.00  1.14           N  
-ATOM    298  CA  SER A 270      -4.155  23.040  -2.573  1.00  1.22           C  
-ATOM    299  C   SER A 270      -3.148  22.745  -3.702  1.00  1.02           C  
-ATOM    300  O   SER A 270      -3.501  22.118  -4.701  1.00  1.03           O  
-ATOM    301  CB  SER A 270      -3.455  23.669  -1.355  1.00  1.31           C  
-ATOM    302  OG  SER A 270      -2.891  24.933  -1.671  1.00  1.44           O  
-ATOM    303  H   SER A 270      -5.855  21.824  -2.205  1.00  1.30           H  
-ATOM    304  HA  SER A 270      -4.888  23.739  -2.949  1.00  1.43           H  
-ATOM    305  HB2 SER A 270      -4.174  23.801  -0.561  1.00  1.49           H  
-ATOM    306  HB3 SER A 270      -2.667  23.011  -1.018  1.00  1.17           H  
-ATOM    307  HG  SER A 270      -3.337  25.621  -1.166  1.00  1.64           H  
-ATOM    308  N   ARG A 271      -1.905  23.193  -3.545  1.00  0.92           N  
-ATOM    309  CA  ARG A 271      -0.871  22.968  -4.560  1.00  0.77           C  
-ATOM    310  C   ARG A 271      -0.206  21.586  -4.403  1.00  0.61           C  
-ATOM    311  O   ARG A 271       0.791  21.290  -5.063  1.00  0.49           O  
-ATOM    312  CB  ARG A 271       0.186  24.074  -4.482  1.00  0.83           C  
-ATOM    313  CG  ARG A 271       1.058  24.157  -5.721  1.00  0.75           C  
-ATOM    314  CD  ARG A 271       0.291  24.712  -6.914  1.00  0.81           C  
-ATOM    315  NE  ARG A 271       1.008  24.516  -8.175  1.00  0.83           N  
-ATOM    316  CZ  ARG A 271       2.170  25.066  -8.469  1.00  0.82           C  
-ATOM    317  NH1 ARG A 271       2.740  25.919  -7.665  1.00  0.82           N  
-ATOM    318  NH2 ARG A 271       2.765  24.781  -9.593  1.00  1.00           N  
-ATOM    319  H   ARG A 271      -1.679  23.695  -2.729  1.00  1.02           H  
-ATOM    320  HA  ARG A 271      -1.343  23.012  -5.529  1.00  0.84           H  
-ATOM    321  HB2 ARG A 271      -0.310  25.025  -4.351  1.00  1.00           H  
-ATOM    322  HB3 ARG A 271       0.824  23.889  -3.630  1.00  0.87           H  
-ATOM    323  HG2 ARG A 271       1.903  24.798  -5.518  1.00  0.82           H  
-ATOM    324  HG3 ARG A 271       1.405  23.164  -5.963  1.00  0.73           H  
-ATOM    325  HD2 ARG A 271      -0.664  24.210  -6.974  1.00  0.93           H  
-ATOM    326  HD3 ARG A 271       0.131  25.769  -6.762  1.00  0.91           H  
-ATOM    327  HE  ARG A 271       0.603  23.915  -8.830  1.00  0.99           H  
-ATOM    328 HH11 ARG A 271       2.302  26.178  -6.812  1.00  0.87           H  
-ATOM    329 HH12 ARG A 271       3.649  26.303  -7.915  1.00  0.92           H  
-ATOM    330 HH21 ARG A 271       2.347  24.150 -10.237  1.00  1.16           H  
-ATOM    331 HH22 ARG A 271       3.656  25.203  -9.797  1.00  1.07           H  
-ATOM    332  N   ALA A 272      -0.769  20.745  -3.527  1.00  0.77           N  
-ATOM    333  CA  ALA A 272      -0.243  19.397  -3.275  1.00  0.72           C  
-ATOM    334  C   ALA A 272       1.255  19.429  -2.942  1.00  0.59           C  
-ATOM    335  O   ALA A 272       1.721  20.321  -2.231  1.00  0.79           O  
-ATOM    336  CB  ALA A 272      -0.519  18.487  -4.467  1.00  0.78           C  
-ATOM    337  H   ALA A 272      -1.566  21.041  -3.034  1.00  0.99           H  
-ATOM    338  HA  ALA A 272      -0.773  18.996  -2.421  1.00  0.91           H  
-ATOM    339  HB1 ALA A 272      -1.563  18.206  -4.471  1.00  1.39           H  
-ATOM    340  HB2 ALA A 272       0.091  17.599  -4.390  1.00  1.25           H  
-ATOM    341  HB3 ALA A 272      -0.282  19.010  -5.382  1.00  1.28           H  
-ATOM    342  N   SER A 273       2.017  18.466  -3.457  1.00  0.55           N  
-ATOM    343  CA  SER A 273       3.455  18.432  -3.196  1.00  0.69           C  
-ATOM    344  C   SER A 273       4.270  18.943  -4.363  1.00  0.77           C  
-ATOM    345  O   SER A 273       5.422  18.554  -4.552  1.00  1.04           O  
-ATOM    346  CB  SER A 273       3.928  17.042  -2.818  1.00  0.90           C  
-ATOM    347  OG  SER A 273       3.524  16.071  -3.777  1.00  0.99           O  
-ATOM    348  H   SER A 273       1.608  17.775  -4.023  1.00  0.67           H  
-ATOM    349  HA  SER A 273       3.644  19.097  -2.362  1.00  0.80           H  
-ATOM    350  HB2 SER A 273       5.007  17.052  -2.763  1.00  1.13           H  
-ATOM    351  HB3 SER A 273       3.519  16.786  -1.856  1.00  0.95           H  
-ATOM    352  HG  SER A 273       2.572  15.905  -3.687  1.00  1.32           H  
-ATOM    353  N   LEU A 274       3.706  19.881  -5.095  1.00  0.65           N  
-ATOM    354  CA  LEU A 274       4.431  20.507  -6.183  1.00  0.85           C  
-ATOM    355  C   LEU A 274       4.485  22.032  -5.986  1.00  0.87           C  
-ATOM    356  O   LEU A 274       4.222  22.784  -6.927  1.00  0.87           O  
-ATOM    357  CB  LEU A 274       3.816  20.154  -7.547  1.00  0.94           C  
-ATOM    358  CG  LEU A 274       2.297  20.309  -7.657  1.00  0.86           C  
-ATOM    359  CD1 LEU A 274       1.914  20.839  -9.030  1.00  1.05           C  
-ATOM    360  CD2 LEU A 274       1.605  18.975  -7.404  1.00  1.04           C  
-ATOM    361  H   LEU A 274       2.806  20.200  -4.862  1.00  0.55           H  
-ATOM    362  HA  LEU A 274       5.442  20.128  -6.153  1.00  1.10           H  
-ATOM    363  HB2 LEU A 274       4.274  20.784  -8.295  1.00  1.10           H  
-ATOM    364  HB3 LEU A 274       4.065  19.126  -7.771  1.00  1.14           H  
-ATOM    365  HG  LEU A 274       1.954  21.014  -6.914  1.00  0.94           H  
-ATOM    366 HD11 LEU A 274       1.480  20.042  -9.615  1.00  1.56           H  
-ATOM    367 HD12 LEU A 274       2.795  21.214  -9.530  1.00  1.49           H  
-ATOM    368 HD13 LEU A 274       1.196  21.637  -8.920  1.00  1.51           H  
-ATOM    369 HD21 LEU A 274       2.187  18.180  -7.846  1.00  1.57           H  
-ATOM    370 HD22 LEU A 274       0.621  18.991  -7.848  1.00  1.48           H  
-ATOM    371 HD23 LEU A 274       1.520  18.811  -6.340  1.00  1.50           H  
-ATOM    372  N   PRO A 275       4.807  22.526  -4.748  1.00  1.05           N  
-ATOM    373  CA  PRO A 275       4.871  23.980  -4.450  1.00  1.30           C  
-ATOM    374  C   PRO A 275       5.974  24.736  -5.219  1.00  1.52           C  
-ATOM    375  O   PRO A 275       6.880  25.312  -4.615  1.00  2.03           O  
-ATOM    376  CB  PRO A 275       5.165  24.019  -2.942  1.00  1.64           C  
-ATOM    377  CG  PRO A 275       4.687  22.704  -2.454  1.00  1.44           C  
-ATOM    378  CD  PRO A 275       5.106  21.741  -3.520  1.00  1.17           C  
-ATOM    379  HA  PRO A 275       3.918  24.451  -4.631  1.00  1.21           H  
-ATOM    380  HB2 PRO A 275       6.226  24.142  -2.780  1.00  1.89           H  
-ATOM    381  HB3 PRO A 275       4.623  24.833  -2.485  1.00  1.82           H  
-ATOM    382  HG2 PRO A 275       5.148  22.462  -1.508  1.00  1.68           H  
-ATOM    383  HG3 PRO A 275       3.612  22.722  -2.370  1.00  1.35           H  
-ATOM    384  HD2 PRO A 275       6.160  21.519  -3.435  1.00  1.40           H  
-ATOM    385  HD3 PRO A 275       4.512  20.818  -3.471  1.00  0.95           H  
-ATOM    386  N   LYS A 276       5.888  24.732  -6.546  1.00  1.24           N  
-ATOM    387  CA  LYS A 276       6.873  25.413  -7.391  1.00  1.48           C  
-ATOM    388  C   LYS A 276       6.248  25.926  -8.697  1.00  1.27           C  
-ATOM    389  O   LYS A 276       6.991  26.098  -9.684  1.00  1.51           O  
-ATOM    390  CB  LYS A 276       8.063  24.479  -7.688  1.00  1.85           C  
-ATOM    391  CG  LYS A 276       7.682  23.036  -8.039  1.00  1.88           C  
-ATOM    392  CD  LYS A 276       7.072  22.903  -9.432  1.00  1.90           C  
-ATOM    393  CE  LYS A 276       8.051  23.313 -10.526  1.00  2.23           C  
-ATOM    394  NZ  LYS A 276       9.299  22.497 -10.496  1.00  2.76           N  
-ATOM    395  OXT LYS A 276       5.016  26.150  -8.719  1.00  1.15           O  
-ATOM    396  H   LYS A 276       5.139  24.252  -6.972  1.00  0.97           H  
-ATOM    397  HA  LYS A 276       7.239  26.264  -6.835  1.00  1.78           H  
-ATOM    398  HB2 LYS A 276       8.621  24.887  -8.517  1.00  1.98           H  
-ATOM    399  HB3 LYS A 276       8.703  24.454  -6.819  1.00  2.18           H  
-ATOM    400  HG2 LYS A 276       8.570  22.423  -7.994  1.00  2.28           H  
-ATOM    401  HG3 LYS A 276       6.968  22.681  -7.309  1.00  1.70           H  
-ATOM    402  HD2 LYS A 276       6.787  21.873  -9.590  1.00  2.15           H  
-ATOM    403  HD3 LYS A 276       6.196  23.533  -9.490  1.00  1.65           H  
-ATOM    404  HE2 LYS A 276       7.571  23.183 -11.483  1.00  2.36           H  
-ATOM    405  HE3 LYS A 276       8.308  24.353 -10.391  1.00  2.13           H  
-ATOM    406  HZ1 LYS A 276       9.265  21.822  -9.707  1.00  3.15           H  
-ATOM    407  HZ2 LYS A 276      10.128  23.116 -10.372  1.00  3.01           H  
-ATOM    408  HZ3 LYS A 276       9.404  21.971 -11.387  1.00  3.01           H  
-TER
-ENDMDL
-MODEL      11
-ATOM      1  N   ALA A 249      -1.950  -2.063 -13.217  1.00  1.34           N  
-ATOM      2  CA  ALA A 249      -0.885  -2.100 -12.178  1.00  1.01           C  
-ATOM      3  C   ALA A 249      -0.476  -3.535 -11.869  1.00  0.81           C  
-ATOM      4  O   ALA A 249      -1.262  -4.467 -12.046  1.00  0.96           O  
-ATOM      5  CB  ALA A 249      -1.333  -1.392 -10.910  1.00  1.10           C  
-ATOM      6  H1  ALA A 249      -2.787  -1.563 -12.854  1.00  1.62           H  
-ATOM      7  H2  ALA A 249      -2.223  -3.033 -13.480  1.00  1.77           H  
-ATOM      8  H3  ALA A 249      -1.605  -1.571 -14.066  1.00  1.78           H  
-ATOM      9  HA  ALA A 249      -0.022  -1.576 -12.565  1.00  1.00           H  
-ATOM     10  HB1 ALA A 249      -0.462  -1.031 -10.378  1.00  1.55           H  
-ATOM     11  HB2 ALA A 249      -1.873  -2.084 -10.283  1.00  1.53           H  
-ATOM     12  HB3 ALA A 249      -1.968  -0.560 -11.167  1.00  1.50           H  
-ATOM     13  N   PRO A 250       0.771  -3.726 -11.416  1.00  0.65           N  
-ATOM     14  CA  PRO A 250       1.315  -5.048 -11.089  1.00  0.66           C  
-ATOM     15  C   PRO A 250       0.711  -5.664  -9.818  1.00  0.50           C  
-ATOM     16  O   PRO A 250       1.361  -5.737  -8.775  1.00  0.46           O  
-ATOM     17  CB  PRO A 250       2.807  -4.764 -10.918  1.00  0.86           C  
-ATOM     18  CG  PRO A 250       2.874  -3.353 -10.478  1.00  0.84           C  
-ATOM     19  CD  PRO A 250       1.762  -2.659 -11.199  1.00  0.75           C  
-ATOM     20  HA  PRO A 250       1.177  -5.738 -11.910  1.00  0.84           H  
-ATOM     21  HB2 PRO A 250       3.221  -5.424 -10.179  1.00  1.13           H  
-ATOM     22  HB3 PRO A 250       3.307  -4.898 -11.862  1.00  1.12           H  
-ATOM     23  HG2 PRO A 250       2.727  -3.292  -9.409  1.00  1.14           H  
-ATOM     24  HG3 PRO A 250       3.825  -2.925 -10.755  1.00  1.00           H  
-ATOM     25  HD2 PRO A 250       1.348  -1.877 -10.583  1.00  0.78           H  
-ATOM     26  HD3 PRO A 250       2.110  -2.265 -12.138  1.00  0.95           H  
-ATOM     27  N   ALA A 251      -0.541  -6.111  -9.932  1.00  0.64           N  
-ATOM     28  CA  ALA A 251      -1.273  -6.748  -8.832  1.00  0.70           C  
-ATOM     29  C   ALA A 251      -1.465  -5.833  -7.611  1.00  0.56           C  
-ATOM     30  O   ALA A 251      -1.571  -4.611  -7.738  1.00  0.55           O  
-ATOM     31  CB  ALA A 251      -0.583  -8.046  -8.434  1.00  0.81           C  
-ATOM     32  H   ALA A 251      -0.989  -6.020 -10.801  1.00  0.82           H  
-ATOM     33  HA  ALA A 251      -2.251  -7.006  -9.212  1.00  0.91           H  
-ATOM     34  HB1 ALA A 251      -1.038  -8.435  -7.537  1.00  1.34           H  
-ATOM     35  HB2 ALA A 251       0.464  -7.855  -8.256  1.00  1.32           H  
-ATOM     36  HB3 ALA A 251      -0.688  -8.766  -9.233  1.00  1.33           H  
-ATOM     37  N   ARG A 252      -1.541  -6.447  -6.425  1.00  0.61           N  
-ATOM     38  CA  ARG A 252      -1.761  -5.713  -5.178  1.00  0.59           C  
-ATOM     39  C   ARG A 252      -0.602  -4.768  -4.851  1.00  0.50           C  
-ATOM     40  O   ARG A 252      -0.830  -3.611  -4.503  1.00  0.50           O  
-ATOM     41  CB  ARG A 252      -1.988  -6.692  -4.017  1.00  0.77           C  
-ATOM     42  CG  ARG A 252      -2.981  -6.192  -2.969  1.00  0.95           C  
-ATOM     43  CD  ARG A 252      -2.506  -4.909  -2.298  1.00  0.99           C  
-ATOM     44  NE  ARG A 252      -3.510  -4.337  -1.403  1.00  1.30           N  
-ATOM     45  CZ  ARG A 252      -3.853  -4.860  -0.243  1.00  1.59           C  
-ATOM     46  NH1 ARG A 252      -3.278  -5.950   0.190  1.00  1.68           N  
-ATOM     47  NH2 ARG A 252      -4.766  -4.282   0.489  1.00  1.92           N  
-ATOM     48  H   ARG A 252      -1.474  -7.423  -6.394  1.00  0.75           H  
-ATOM     49  HA  ARG A 252      -2.650  -5.120  -5.306  1.00  0.63           H  
-ATOM     50  HB2 ARG A 252      -2.362  -7.623  -4.416  1.00  0.85           H  
-ATOM     51  HB3 ARG A 252      -1.044  -6.875  -3.527  1.00  0.81           H  
-ATOM     52  HG2 ARG A 252      -3.929  -6.003  -3.448  1.00  0.99           H  
-ATOM     53  HG3 ARG A 252      -3.104  -6.956  -2.216  1.00  1.12           H  
-ATOM     54  HD2 ARG A 252      -1.617  -5.129  -1.726  1.00  1.07           H  
-ATOM     55  HD3 ARG A 252      -2.268  -4.187  -3.064  1.00  0.91           H  
-ATOM     56  HE  ARG A 252      -3.952  -3.515  -1.692  1.00  1.39           H  
-ATOM     57 HH11 ARG A 252      -2.575  -6.393  -0.358  1.00  1.52           H  
-ATOM     58 HH12 ARG A 252      -3.543  -6.335   1.070  1.00  1.98           H  
-ATOM     59 HH21 ARG A 252      -5.204  -3.444   0.174  1.00  1.98           H  
-ATOM     60 HH22 ARG A 252      -5.024  -4.680   1.366  1.00  2.16           H  
-ATOM     61  N   VAL A 253       0.632  -5.256  -4.961  1.00  0.52           N  
-ATOM     62  CA  VAL A 253       1.805  -4.429  -4.669  1.00  0.55           C  
-ATOM     63  C   VAL A 253       1.783  -3.159  -5.515  1.00  0.45           C  
-ATOM     64  O   VAL A 253       1.951  -2.047  -5.006  1.00  0.45           O  
-ATOM     65  CB  VAL A 253       3.124  -5.210  -4.892  1.00  0.66           C  
-ATOM     66  CG1 VAL A 253       3.290  -5.672  -6.333  1.00  0.62           C  
-ATOM     67  CG2 VAL A 253       4.323  -4.387  -4.446  1.00  0.82           C  
-ATOM     68  H   VAL A 253       0.755  -6.183  -5.242  1.00  0.57           H  
-ATOM     69  HA  VAL A 253       1.753  -4.147  -3.627  1.00  0.63           H  
-ATOM     70  HB  VAL A 253       3.080  -6.089  -4.283  1.00  0.76           H  
-ATOM     71 HG11 VAL A 253       3.526  -4.824  -6.957  1.00  1.20           H  
-ATOM     72 HG12 VAL A 253       2.372  -6.127  -6.673  1.00  1.16           H  
-ATOM     73 HG13 VAL A 253       4.092  -6.394  -6.389  1.00  1.22           H  
-ATOM     74 HG21 VAL A 253       5.233  -4.906  -4.707  1.00  1.28           H  
-ATOM     75 HG22 VAL A 253       4.284  -4.245  -3.375  1.00  1.33           H  
-ATOM     76 HG23 VAL A 253       4.302  -3.425  -4.937  1.00  1.41           H  
-ATOM     77  N   GLY A 254       1.531  -3.347  -6.801  1.00  0.43           N  
-ATOM     78  CA  GLY A 254       1.440  -2.230  -7.720  1.00  0.42           C  
-ATOM     79  C   GLY A 254       0.320  -1.276  -7.350  1.00  0.35           C  
-ATOM     80  O   GLY A 254       0.533  -0.068  -7.256  1.00  0.39           O  
-ATOM     81  H   GLY A 254       1.387  -4.267  -7.119  1.00  0.48           H  
-ATOM     82  HA2 GLY A 254       2.377  -1.692  -7.713  1.00  0.49           H  
-ATOM     83  HA3 GLY A 254       1.261  -2.610  -8.716  1.00  0.46           H  
-ATOM     84  N   LEU A 255      -0.873  -1.820  -7.113  1.00  0.34           N  
-ATOM     85  CA  LEU A 255      -2.019  -1.005  -6.721  1.00  0.37           C  
-ATOM     86  C   LEU A 255      -1.738  -0.246  -5.431  1.00  0.31           C  
-ATOM     87  O   LEU A 255      -2.056   0.938  -5.309  1.00  0.33           O  
-ATOM     88  CB  LEU A 255      -3.255  -1.875  -6.531  1.00  0.50           C  
-ATOM     89  CG  LEU A 255      -4.195  -1.904  -7.725  1.00  0.70           C  
-ATOM     90  CD1 LEU A 255      -5.400  -2.775  -7.421  1.00  0.86           C  
-ATOM     91  CD2 LEU A 255      -4.622  -0.488  -8.080  1.00  0.87           C  
-ATOM     92  H   LEU A 255      -0.979  -2.796  -7.186  1.00  0.38           H  
-ATOM     93  HA  LEU A 255      -2.212  -0.297  -7.512  1.00  0.43           H  
-ATOM     94  HB2 LEU A 255      -2.930  -2.886  -6.325  1.00  0.61           H  
-ATOM     95  HB3 LEU A 255      -3.802  -1.508  -5.678  1.00  0.56           H  
-ATOM     96  HG  LEU A 255      -3.678  -2.324  -8.576  1.00  0.83           H  
-ATOM     97 HD11 LEU A 255      -5.381  -3.062  -6.378  1.00  1.33           H  
-ATOM     98 HD12 LEU A 255      -5.369  -3.660  -8.038  1.00  1.37           H  
-ATOM     99 HD13 LEU A 255      -6.303  -2.223  -7.625  1.00  1.39           H  
-ATOM    100 HD21 LEU A 255      -4.754   0.084  -7.172  1.00  1.37           H  
-ATOM    101 HD22 LEU A 255      -5.552  -0.515  -8.626  1.00  1.29           H  
-ATOM    102 HD23 LEU A 255      -3.859  -0.023  -8.687  1.00  1.39           H  
-ATOM    103  N   GLY A 256      -1.142  -0.939  -4.472  1.00  0.33           N  
-ATOM    104  CA  GLY A 256      -0.826  -0.332  -3.200  1.00  0.36           C  
-ATOM    105  C   GLY A 256       0.149   0.829  -3.323  1.00  0.33           C  
-ATOM    106  O   GLY A 256      -0.139   1.931  -2.852  1.00  0.35           O  
-ATOM    107  H   GLY A 256      -0.915  -1.887  -4.632  1.00  0.38           H  
-ATOM    108  HA2 GLY A 256      -1.743   0.029  -2.759  1.00  0.40           H  
-ATOM    109  HA3 GLY A 256      -0.399  -1.083  -2.556  1.00  0.45           H  
-ATOM    110  N   ILE A 257       1.300   0.600  -3.966  1.00  0.37           N  
-ATOM    111  CA  ILE A 257       2.289   1.669  -4.138  1.00  0.43           C  
-ATOM    112  C   ILE A 257       1.713   2.819  -4.973  1.00  0.38           C  
-ATOM    113  O   ILE A 257       1.960   3.989  -4.678  1.00  0.43           O  
-ATOM    114  CB  ILE A 257       3.623   1.165  -4.752  1.00  0.53           C  
-ATOM    115  CG1 ILE A 257       3.418   0.524  -6.129  1.00  0.52           C  
-ATOM    116  CG2 ILE A 257       4.297   0.182  -3.807  1.00  0.64           C  
-ATOM    117  CD1 ILE A 257       3.737   1.448  -7.288  1.00  0.63           C  
-ATOM    118  H   ILE A 257       1.482  -0.298  -4.336  1.00  0.39           H  
-ATOM    119  HA  ILE A 257       2.507   2.054  -3.151  1.00  0.48           H  
-ATOM    120  HB  ILE A 257       4.279   2.016  -4.858  1.00  0.60           H  
-ATOM    121 HG12 ILE A 257       4.056  -0.343  -6.214  1.00  0.60           H  
-ATOM    122 HG13 ILE A 257       2.386   0.216  -6.224  1.00  0.50           H  
-ATOM    123 HG21 ILE A 257       5.067   0.692  -3.248  1.00  1.20           H  
-ATOM    124 HG22 ILE A 257       4.738  -0.621  -4.379  1.00  1.25           H  
-ATOM    125 HG23 ILE A 257       3.564  -0.222  -3.124  1.00  1.22           H  
-ATOM    126 HD11 ILE A 257       3.748   2.471  -6.939  1.00  1.20           H  
-ATOM    127 HD12 ILE A 257       2.986   1.337  -8.055  1.00  1.25           H  
-ATOM    128 HD13 ILE A 257       4.706   1.195  -7.693  1.00  1.20           H  
-ATOM    129  N   THR A 258       0.923   2.478  -5.997  1.00  0.35           N  
-ATOM    130  CA  THR A 258       0.291   3.486  -6.850  1.00  0.38           C  
-ATOM    131  C   THR A 258      -0.665   4.345  -6.023  1.00  0.35           C  
-ATOM    132  O   THR A 258      -0.683   5.572  -6.145  1.00  0.40           O  
-ATOM    133  CB  THR A 258      -0.494   2.848  -8.019  1.00  0.40           C  
-ATOM    134  OG1 THR A 258       0.355   1.968  -8.766  1.00  0.47           O  
-ATOM    135  CG2 THR A 258      -1.054   3.914  -8.951  1.00  0.54           C  
-ATOM    136  H   THR A 258       0.750   1.524  -6.173  1.00  0.36           H  
-ATOM    137  HA  THR A 258       1.068   4.115  -7.261  1.00  0.44           H  
-ATOM    138  HB  THR A 258      -1.319   2.280  -7.612  1.00  0.38           H  
-ATOM    139  HG1 THR A 258       0.465   1.137  -8.278  1.00  0.48           H  
-ATOM    140 HG21 THR A 258      -0.697   4.885  -8.641  1.00  1.15           H  
-ATOM    141 HG22 THR A 258      -2.133   3.897  -8.910  1.00  1.25           H  
-ATOM    142 HG23 THR A 258      -0.728   3.716  -9.961  1.00  1.12           H  
-ATOM    143  N   THR A 259      -1.442   3.691  -5.159  1.00  0.32           N  
-ATOM    144  CA  THR A 259      -2.385   4.389  -4.289  1.00  0.32           C  
-ATOM    145  C   THR A 259      -1.633   5.312  -3.334  1.00  0.32           C  
-ATOM    146  O   THR A 259      -1.989   6.482  -3.172  1.00  0.37           O  
-ATOM    147  CB  THR A 259      -3.248   3.403  -3.471  1.00  0.35           C  
-ATOM    148  OG1 THR A 259      -3.909   2.479  -4.347  1.00  0.39           O  
-ATOM    149  CG2 THR A 259      -4.291   4.145  -2.644  1.00  0.49           C  
-ATOM    150  H   THR A 259      -1.366   2.711  -5.094  1.00  0.32           H  
-ATOM    151  HA  THR A 259      -3.039   4.984  -4.911  1.00  0.37           H  
-ATOM    152  HB  THR A 259      -2.604   2.855  -2.800  1.00  0.38           H  
-ATOM    153  HG1 THR A 259      -3.257   1.867  -4.723  1.00  0.51           H  
-ATOM    154 HG21 THR A 259      -3.955   5.157  -2.464  1.00  1.14           H  
-ATOM    155 HG22 THR A 259      -4.429   3.639  -1.700  1.00  1.15           H  
-ATOM    156 HG23 THR A 259      -5.227   4.167  -3.181  1.00  1.17           H  
-ATOM    157  N   VAL A 260      -0.571   4.784  -2.720  1.00  0.32           N  
-ATOM    158  CA  VAL A 260       0.247   5.569  -1.800  1.00  0.36           C  
-ATOM    159  C   VAL A 260       0.880   6.764  -2.518  1.00  0.36           C  
-ATOM    160  O   VAL A 260       0.773   7.895  -2.048  1.00  0.41           O  
-ATOM    161  CB  VAL A 260       1.359   4.715  -1.152  1.00  0.43           C  
-ATOM    162  CG1 VAL A 260       2.252   5.571  -0.266  1.00  0.55           C  
-ATOM    163  CG2 VAL A 260       0.755   3.572  -0.350  1.00  0.49           C  
-ATOM    164  H   VAL A 260      -0.327   3.847  -2.904  1.00  0.32           H  
-ATOM    165  HA  VAL A 260      -0.397   5.938  -1.015  1.00  0.39           H  
-ATOM    166  HB  VAL A 260       1.967   4.291  -1.939  1.00  0.47           H  
-ATOM    167 HG11 VAL A 260       1.688   5.915   0.588  1.00  1.21           H  
-ATOM    168 HG12 VAL A 260       2.608   6.422  -0.829  1.00  1.19           H  
-ATOM    169 HG13 VAL A 260       3.093   4.984   0.070  1.00  1.13           H  
-ATOM    170 HG21 VAL A 260       1.543   3.016   0.137  1.00  1.14           H  
-ATOM    171 HG22 VAL A 260       0.208   2.918  -1.013  1.00  1.14           H  
-ATOM    172 HG23 VAL A 260       0.083   3.972   0.395  1.00  1.16           H  
-ATOM    173  N   LEU A 261       1.516   6.506  -3.668  1.00  0.36           N  
-ATOM    174  CA  LEU A 261       2.151   7.562  -4.463  1.00  0.41           C  
-ATOM    175  C   LEU A 261       1.133   8.648  -4.822  1.00  0.40           C  
-ATOM    176  O   LEU A 261       1.416   9.843  -4.719  1.00  0.46           O  
-ATOM    177  CB  LEU A 261       2.758   6.972  -5.741  1.00  0.46           C  
-ATOM    178  CG  LEU A 261       4.221   7.341  -6.012  1.00  0.67           C  
-ATOM    179  CD1 LEU A 261       4.387   8.845  -6.173  1.00  1.18           C  
-ATOM    180  CD2 LEU A 261       5.117   6.823  -4.897  1.00  1.24           C  
-ATOM    181  H   LEU A 261       1.552   5.576  -3.997  1.00  0.37           H  
-ATOM    182  HA  LEU A 261       2.938   8.002  -3.867  1.00  0.45           H  
-ATOM    183  HB2 LEU A 261       2.688   5.895  -5.682  1.00  0.73           H  
-ATOM    184  HB3 LEU A 261       2.168   7.308  -6.579  1.00  0.73           H  
-ATOM    185  HG  LEU A 261       4.533   6.874  -6.934  1.00  0.66           H  
-ATOM    186 HD11 LEU A 261       4.148   9.128  -7.187  1.00  1.65           H  
-ATOM    187 HD12 LEU A 261       5.408   9.121  -5.953  1.00  1.67           H  
-ATOM    188 HD13 LEU A 261       3.724   9.354  -5.490  1.00  1.73           H  
-ATOM    189 HD21 LEU A 261       5.533   7.659  -4.352  1.00  1.70           H  
-ATOM    190 HD22 LEU A 261       5.917   6.235  -5.321  1.00  1.77           H  
-ATOM    191 HD23 LEU A 261       4.538   6.208  -4.225  1.00  1.73           H  
-ATOM    192  N   THR A 262      -0.057   8.215  -5.223  1.00  0.39           N  
-ATOM    193  CA  THR A 262      -1.137   9.137  -5.576  1.00  0.42           C  
-ATOM    194  C   THR A 262      -1.544   9.979  -4.365  1.00  0.42           C  
-ATOM    195  O   THR A 262      -1.608  11.205  -4.437  1.00  0.46           O  
-ATOM    196  CB  THR A 262      -2.378   8.375  -6.089  1.00  0.46           C  
-ATOM    197  OG1 THR A 262      -2.026   7.550  -7.208  1.00  0.51           O  
-ATOM    198  CG2 THR A 262      -3.481   9.340  -6.494  1.00  0.54           C  
-ATOM    199  H   THR A 262      -0.219   7.245  -5.273  1.00  0.39           H  
-ATOM    200  HA  THR A 262      -0.782   9.789  -6.360  1.00  0.45           H  
-ATOM    201  HB  THR A 262      -2.747   7.746  -5.292  1.00  0.44           H  
-ATOM    202  HG1 THR A 262      -1.593   6.742  -6.890  1.00  0.57           H  
-ATOM    203 HG21 THR A 262      -3.485  10.183  -5.819  1.00  1.21           H  
-ATOM    204 HG22 THR A 262      -4.435   8.835  -6.444  1.00  1.10           H  
-ATOM    205 HG23 THR A 262      -3.308   9.684  -7.502  1.00  1.20           H  
-ATOM    206  N   MET A 263      -1.807   9.301  -3.250  1.00  0.42           N  
-ATOM    207  CA  MET A 263      -2.202   9.961  -2.004  1.00  0.46           C  
-ATOM    208  C   MET A 263      -1.121  10.930  -1.500  1.00  0.45           C  
-ATOM    209  O   MET A 263      -1.421  12.060  -1.120  1.00  0.49           O  
-ATOM    210  CB  MET A 263      -2.492   8.906  -0.932  1.00  0.52           C  
-ATOM    211  CG  MET A 263      -2.943   9.488   0.401  1.00  0.59           C  
-ATOM    212  SD  MET A 263      -3.177   8.226   1.669  1.00  0.73           S  
-ATOM    213  CE  MET A 263      -1.525   7.546   1.775  1.00  0.83           C  
-ATOM    214  H   MET A 263      -1.733   8.318  -3.266  1.00  0.41           H  
-ATOM    215  HA  MET A 263      -3.105  10.519  -2.196  1.00  0.50           H  
-ATOM    216  HB2 MET A 263      -3.269   8.249  -1.293  1.00  0.59           H  
-ATOM    217  HB3 MET A 263      -1.596   8.328  -0.763  1.00  0.54           H  
-ATOM    218  HG2 MET A 263      -2.193  10.185   0.744  1.00  0.63           H  
-ATOM    219  HG3 MET A 263      -3.877  10.010   0.254  1.00  0.69           H  
-ATOM    220  HE1 MET A 263      -1.009   7.713   0.842  1.00  0.90           H  
-ATOM    221  HE2 MET A 263      -1.584   6.485   1.969  1.00  1.07           H  
-ATOM    222  HE3 MET A 263      -0.986   8.030   2.577  1.00  0.93           H  
-ATOM    223  N   THR A 264       0.137  10.484  -1.490  1.00  0.43           N  
-ATOM    224  CA  THR A 264       1.243  11.332  -1.019  1.00  0.45           C  
-ATOM    225  C   THR A 264       1.631  12.414  -2.037  1.00  0.40           C  
-ATOM    226  O   THR A 264       2.400  13.318  -1.709  1.00  0.43           O  
-ATOM    227  CB  THR A 264       2.504  10.511  -0.659  1.00  0.50           C  
-ATOM    228  OG1 THR A 264       3.503  11.371  -0.100  1.00  0.60           O  
-ATOM    229  CG2 THR A 264       3.079   9.810  -1.880  1.00  0.47           C  
-ATOM    230  H   THR A 264       0.326   9.562  -1.799  1.00  0.44           H  
-ATOM    231  HA  THR A 264       0.904  11.824  -0.118  1.00  0.53           H  
-ATOM    232  HB  THR A 264       2.232   9.764   0.074  1.00  0.59           H  
-ATOM    233  HG1 THR A 264       3.429  12.250  -0.496  1.00  0.63           H  
-ATOM    234 HG21 THR A 264       2.575   8.866  -2.024  1.00  1.05           H  
-ATOM    235 HG22 THR A 264       4.134   9.637  -1.730  1.00  1.08           H  
-ATOM    236 HG23 THR A 264       2.937  10.432  -2.751  1.00  1.01           H  
-ATOM    237  N   THR A 265       1.080  12.309  -3.261  1.00  0.39           N  
-ATOM    238  CA  THR A 265       1.322  13.252  -4.373  1.00  0.39           C  
-ATOM    239  C   THR A 265       2.743  13.841  -4.381  1.00  0.38           C  
-ATOM    240  O   THR A 265       2.943  15.058  -4.321  1.00  0.43           O  
-ATOM    241  CB  THR A 265       0.250  14.368  -4.419  1.00  0.44           C  
-ATOM    242  OG1 THR A 265       0.655  15.431  -5.294  1.00  0.50           O  
-ATOM    243  CG2 THR A 265      -0.048  14.918  -3.034  1.00  0.45           C  
-ATOM    244  H   THR A 265       0.470  11.568  -3.425  1.00  0.45           H  
-ATOM    245  HA  THR A 265       1.214  12.676  -5.283  1.00  0.44           H  
-ATOM    246  HB  THR A 265      -0.660  13.934  -4.809  1.00  0.52           H  
-ATOM    247  HG1 THR A 265       1.522  15.757  -5.012  1.00  0.64           H  
-ATOM    248 HG21 THR A 265      -0.832  15.654  -3.099  1.00  1.02           H  
-ATOM    249 HG22 THR A 265       0.845  15.370  -2.629  1.00  1.01           H  
-ATOM    250 HG23 THR A 265      -0.364  14.108  -2.391  1.00  1.06           H  
-ATOM    251  N   GLN A 266       3.730  12.953  -4.452  1.00  0.43           N  
-ATOM    252  CA  GLN A 266       5.139  13.354  -4.462  1.00  0.50           C  
-ATOM    253  C   GLN A 266       5.543  14.049  -5.769  1.00  0.49           C  
-ATOM    254  O   GLN A 266       6.487  14.838  -5.790  1.00  0.57           O  
-ATOM    255  CB  GLN A 266       6.030  12.136  -4.224  1.00  0.65           C  
-ATOM    256  CG  GLN A 266       5.936  11.580  -2.813  1.00  0.78           C  
-ATOM    257  CD  GLN A 266       6.469  12.534  -1.758  1.00  0.82           C  
-ATOM    258  OE1 GLN A 266       5.982  13.652  -1.599  1.00  0.84           O  
-ATOM    259  NE2 GLN A 266       7.481  12.098  -1.031  1.00  0.96           N  
-ATOM    260  H   GLN A 266       3.505  11.997  -4.491  1.00  0.47           H  
-ATOM    261  HA  GLN A 266       5.284  14.049  -3.649  1.00  0.53           H  
-ATOM    262  HB2 GLN A 266       5.741  11.357  -4.913  1.00  0.71           H  
-ATOM    263  HB3 GLN A 266       7.055  12.413  -4.413  1.00  0.70           H  
-ATOM    264  HG2 GLN A 266       4.900  11.371  -2.593  1.00  0.84           H  
-ATOM    265  HG3 GLN A 266       6.504  10.662  -2.765  1.00  0.87           H  
-ATOM    266 HE21 GLN A 266       7.826  11.199  -1.212  1.00  1.03           H  
-ATOM    267 HE22 GLN A 266       7.839  12.688  -0.339  1.00  1.04           H  
-ATOM    268  N   SER A 267       4.830  13.737  -6.854  1.00  0.53           N  
-ATOM    269  CA  SER A 267       5.106  14.321  -8.176  1.00  0.59           C  
-ATOM    270  C   SER A 267       6.491  13.915  -8.695  1.00  0.55           C  
-ATOM    271  O   SER A 267       7.267  14.755  -9.149  1.00  0.60           O  
-ATOM    272  CB  SER A 267       4.980  15.852  -8.130  1.00  0.73           C  
-ATOM    273  OG  SER A 267       3.696  16.247  -7.665  1.00  0.95           O  
-ATOM    274  H   SER A 267       4.097  13.091  -6.765  1.00  0.60           H  
-ATOM    275  HA  SER A 267       4.362  13.935  -8.858  1.00  0.69           H  
-ATOM    276  HB2 SER A 267       5.728  16.252  -7.463  1.00  0.74           H  
-ATOM    277  HB3 SER A 267       5.133  16.253  -9.122  1.00  0.81           H  
-ATOM    278  HG  SER A 267       3.255  15.485  -7.272  1.00  1.20           H  
-ATOM    279  N   SER A 268       6.783  12.610  -8.623  1.00  0.61           N  
-ATOM    280  CA  SER A 268       8.065  12.047  -9.082  1.00  0.72           C  
-ATOM    281  C   SER A 268       9.260  12.600  -8.288  1.00  0.67           C  
-ATOM    282  O   SER A 268      10.355  12.780  -8.828  1.00  0.83           O  
-ATOM    283  CB  SER A 268       8.260  12.300 -10.587  1.00  0.85           C  
-ATOM    284  OG  SER A 268       9.478  11.741 -11.051  1.00  0.99           O  
-ATOM    285  H   SER A 268       6.110  12.001  -8.254  1.00  0.69           H  
-ATOM    286  HA  SER A 268       8.018  10.980  -8.921  1.00  0.83           H  
-ATOM    287  HB2 SER A 268       7.444  11.853 -11.133  1.00  0.93           H  
-ATOM    288  HB3 SER A 268       8.274  13.365 -10.771  1.00  0.84           H  
-ATOM    289  HG  SER A 268      10.197  12.014 -10.463  1.00  0.96           H  
-ATOM    290  N   GLY A 269       9.046  12.855  -7.000  1.00  0.62           N  
-ATOM    291  CA  GLY A 269      10.109  13.372  -6.152  1.00  0.71           C  
-ATOM    292  C   GLY A 269       9.685  13.525  -4.699  1.00  0.69           C  
-ATOM    293  O   GLY A 269       9.842  12.604  -3.899  1.00  0.86           O  
-ATOM    294  H   GLY A 269       8.158  12.688  -6.620  1.00  0.64           H  
-ATOM    295  HA2 GLY A 269      10.951  12.698  -6.197  1.00  0.85           H  
-ATOM    296  HA3 GLY A 269      10.415  14.337  -6.528  1.00  0.78           H  
-ATOM    297  N   SER A 270       9.139  14.692  -4.364  1.00  0.64           N  
-ATOM    298  CA  SER A 270       8.681  14.978  -2.999  1.00  0.69           C  
-ATOM    299  C   SER A 270       7.968  16.325  -2.951  1.00  0.62           C  
-ATOM    300  O   SER A 270       8.434  17.294  -3.550  1.00  0.79           O  
-ATOM    301  CB  SER A 270       9.860  15.013  -2.023  1.00  0.92           C  
-ATOM    302  OG  SER A 270      10.648  16.178  -2.216  1.00  1.03           O  
-ATOM    303  H   SER A 270       9.038  15.382  -5.052  1.00  0.70           H  
-ATOM    304  HA  SER A 270       7.992  14.202  -2.702  1.00  0.71           H  
-ATOM    305  HB2 SER A 270       9.487  15.009  -1.009  1.00  1.00           H  
-ATOM    306  HB3 SER A 270      10.480  14.143  -2.182  1.00  1.02           H  
-ATOM    307  HG  SER A 270      10.155  16.817  -2.745  1.00  1.25           H  
-ATOM    308  N   ARG A 271       6.850  16.398  -2.240  1.00  0.59           N  
-ATOM    309  CA  ARG A 271       6.112  17.654  -2.143  1.00  0.58           C  
-ATOM    310  C   ARG A 271       5.741  17.984  -0.693  1.00  0.70           C  
-ATOM    311  O   ARG A 271       5.221  17.141   0.035  1.00  0.80           O  
-ATOM    312  CB  ARG A 271       4.845  17.595  -3.004  1.00  0.51           C  
-ATOM    313  CG  ARG A 271       4.168  18.947  -3.161  1.00  0.67           C  
-ATOM    314  CD  ARG A 271       2.785  18.838  -3.786  1.00  0.59           C  
-ATOM    315  NE  ARG A 271       2.809  18.379  -5.175  1.00  0.44           N  
-ATOM    316  CZ  ARG A 271       1.873  18.693  -6.050  1.00  0.41           C  
-ATOM    317  NH1 ARG A 271       0.932  19.539  -5.727  1.00  0.49           N  
-ATOM    318  NH2 ARG A 271       1.896  18.187  -7.253  1.00  0.57           N  
-ATOM    319  H   ARG A 271       6.513  15.598  -1.774  1.00  0.73           H  
-ATOM    320  HA  ARG A 271       6.751  18.438  -2.521  1.00  0.66           H  
-ATOM    321  HB2 ARG A 271       5.105  17.227  -3.984  1.00  0.52           H  
-ATOM    322  HB3 ARG A 271       4.143  16.915  -2.545  1.00  0.56           H  
-ATOM    323  HG2 ARG A 271       4.070  19.401  -2.188  1.00  0.86           H  
-ATOM    324  HG3 ARG A 271       4.786  19.572  -3.789  1.00  0.80           H  
-ATOM    325  HD2 ARG A 271       2.200  18.140  -3.205  1.00  0.68           H  
-ATOM    326  HD3 ARG A 271       2.315  19.810  -3.749  1.00  0.77           H  
-ATOM    327  HE  ARG A 271       3.534  17.785  -5.452  1.00  0.52           H  
-ATOM    328 HH11 ARG A 271       0.923  19.959  -4.816  1.00  0.55           H  
-ATOM    329 HH12 ARG A 271       0.194  19.762  -6.396  1.00  0.61           H  
-ATOM    330 HH21 ARG A 271       2.631  17.548  -7.518  1.00  0.64           H  
-ATOM    331 HH22 ARG A 271       1.185  18.437  -7.909  1.00  0.71           H  
-ATOM    332  N   ALA A 272       6.001  19.226  -0.286  1.00  0.81           N  
-ATOM    333  CA  ALA A 272       5.681  19.677   1.066  1.00  0.97           C  
-ATOM    334  C   ALA A 272       4.238  20.203   1.140  1.00  0.79           C  
-ATOM    335  O   ALA A 272       3.324  19.606   0.569  1.00  0.72           O  
-ATOM    336  CB  ALA A 272       6.674  20.744   1.505  1.00  1.24           C  
-ATOM    337  H   ALA A 272       6.409  19.856  -0.915  1.00  0.85           H  
-ATOM    338  HA  ALA A 272       5.779  18.831   1.731  1.00  1.11           H  
-ATOM    339  HB1 ALA A 272       7.613  20.278   1.765  1.00  1.66           H  
-ATOM    340  HB2 ALA A 272       6.282  21.270   2.364  1.00  1.65           H  
-ATOM    341  HB3 ALA A 272       6.830  21.443   0.697  1.00  1.66           H  
-ATOM    342  N   SER A 273       4.032  21.328   1.829  1.00  0.81           N  
-ATOM    343  CA  SER A 273       2.695  21.918   1.948  1.00  0.70           C  
-ATOM    344  C   SER A 273       2.274  22.638   0.678  1.00  0.60           C  
-ATOM    345  O   SER A 273       1.957  23.828   0.688  1.00  0.71           O  
-ATOM    346  CB  SER A 273       2.608  22.867   3.138  1.00  0.87           C  
-ATOM    347  OG  SER A 273       3.599  23.880   3.071  1.00  1.05           O  
-ATOM    348  H   SER A 273       4.791  21.776   2.259  1.00  0.94           H  
-ATOM    349  HA  SER A 273       1.991  21.110   2.101  1.00  0.68           H  
-ATOM    350  HB2 SER A 273       1.633  23.334   3.143  1.00  0.90           H  
-ATOM    351  HB3 SER A 273       2.741  22.304   4.048  1.00  0.95           H  
-ATOM    352  HG  SER A 273       3.384  24.487   2.354  1.00  1.08           H  
-ATOM    353  N   LEU A 274       2.223  21.881  -0.403  1.00  0.48           N  
-ATOM    354  CA  LEU A 274       1.781  22.402  -1.681  1.00  0.48           C  
-ATOM    355  C   LEU A 274       0.691  21.487  -2.281  1.00  0.41           C  
-ATOM    356  O   LEU A 274       0.768  21.111  -3.453  1.00  0.48           O  
-ATOM    357  CB  LEU A 274       2.973  22.527  -2.638  1.00  0.63           C  
-ATOM    358  CG  LEU A 274       2.703  23.320  -3.921  1.00  0.80           C  
-ATOM    359  CD1 LEU A 274       2.349  24.763  -3.593  1.00  1.02           C  
-ATOM    360  CD2 LEU A 274       3.907  23.260  -4.847  1.00  0.99           C  
-ATOM    361  H   LEU A 274       2.455  20.926  -0.325  1.00  0.49           H  
-ATOM    362  HA  LEU A 274       1.360  23.383  -1.512  1.00  0.59           H  
-ATOM    363  HB2 LEU A 274       3.782  23.008  -2.108  1.00  0.78           H  
-ATOM    364  HB3 LEU A 274       3.289  21.533  -2.917  1.00  0.60           H  
-ATOM    365  HG  LEU A 274       1.861  22.880  -4.436  1.00  0.78           H  
-ATOM    366 HD11 LEU A 274       3.099  25.420  -4.006  1.00  1.44           H  
-ATOM    367 HD12 LEU A 274       2.312  24.888  -2.521  1.00  1.53           H  
-ATOM    368 HD13 LEU A 274       1.385  25.003  -4.016  1.00  1.49           H  
-ATOM    369 HD21 LEU A 274       4.384  24.228  -4.880  1.00  1.56           H  
-ATOM    370 HD22 LEU A 274       3.585  22.983  -5.840  1.00  1.46           H  
-ATOM    371 HD23 LEU A 274       4.610  22.526  -4.479  1.00  1.36           H  
-ATOM    372  N   PRO A 275      -0.338  21.088  -1.476  1.00  0.46           N  
-ATOM    373  CA  PRO A 275      -1.423  20.202  -1.952  1.00  0.65           C  
-ATOM    374  C   PRO A 275      -2.070  20.669  -3.265  1.00  0.75           C  
-ATOM    375  O   PRO A 275      -2.299  21.865  -3.460  1.00  0.83           O  
-ATOM    376  CB  PRO A 275      -2.442  20.264  -0.815  1.00  0.83           C  
-ATOM    377  CG  PRO A 275      -1.614  20.492   0.392  1.00  0.77           C  
-ATOM    378  CD  PRO A 275      -0.529  21.434  -0.041  1.00  0.53           C  
-ATOM    379  HA  PRO A 275      -1.076  19.186  -2.062  1.00  0.74           H  
-ATOM    380  HB2 PRO A 275      -3.131  21.078  -0.986  1.00  0.88           H  
-ATOM    381  HB3 PRO A 275      -2.980  19.330  -0.755  1.00  1.02           H  
-ATOM    382  HG2 PRO A 275      -2.211  20.932   1.177  1.00  0.90           H  
-ATOM    383  HG3 PRO A 275      -1.185  19.556   0.718  1.00  0.87           H  
-ATOM    384  HD2 PRO A 275      -0.846  22.459   0.078  1.00  0.60           H  
-ATOM    385  HD3 PRO A 275       0.385  21.255   0.530  1.00  0.51           H  
-ATOM    386  N   LYS A 276      -2.356  19.701  -4.153  1.00  0.85           N  
-ATOM    387  CA  LYS A 276      -2.975  19.965  -5.467  1.00  1.02           C  
-ATOM    388  C   LYS A 276      -2.000  20.665  -6.426  1.00  0.97           C  
-ATOM    389  O   LYS A 276      -2.064  21.905  -6.546  1.00  1.12           O  
-ATOM    390  CB  LYS A 276      -4.259  20.796  -5.324  1.00  1.23           C  
-ATOM    391  CG  LYS A 276      -5.348  20.121  -4.506  1.00  1.37           C  
-ATOM    392  CD  LYS A 276      -6.562  21.025  -4.356  1.00  1.61           C  
-ATOM    393  CE  LYS A 276      -7.638  20.383  -3.497  1.00  1.83           C  
-ATOM    394  NZ  LYS A 276      -8.810  21.285  -3.317  1.00  2.08           N  
-ATOM    395  OXT LYS A 276      -1.175  19.957  -7.054  1.00  0.90           O  
-ATOM    396  H   LYS A 276      -2.139  18.775  -3.919  1.00  0.88           H  
-ATOM    397  HA  LYS A 276      -3.234  19.007  -5.892  1.00  1.12           H  
-ATOM    398  HB2 LYS A 276      -4.014  21.734  -4.848  1.00  1.20           H  
-ATOM    399  HB3 LYS A 276      -4.653  20.997  -6.310  1.00  1.36           H  
-ATOM    400  HG2 LYS A 276      -5.648  19.211  -5.003  1.00  1.48           H  
-ATOM    401  HG3 LYS A 276      -4.958  19.889  -3.525  1.00  1.30           H  
-ATOM    402  HD2 LYS A 276      -6.253  21.951  -3.895  1.00  1.61           H  
-ATOM    403  HD3 LYS A 276      -6.969  21.228  -5.336  1.00  1.74           H  
-ATOM    404  HE2 LYS A 276      -7.967  19.472  -3.975  1.00  1.96           H  
-ATOM    405  HE3 LYS A 276      -7.219  20.152  -2.529  1.00  1.80           H  
-ATOM    406  HZ1 LYS A 276      -9.489  21.150  -4.093  1.00  2.33           H  
-ATOM    407  HZ2 LYS A 276      -8.498  22.277  -3.311  1.00  2.34           H  
-ATOM    408  HZ3 LYS A 276      -9.285  21.078  -2.414  1.00  2.36           H  
-TER
-ENDMDL
-MODEL      12
-ATOM      1  N   ALA A 249       4.884  -8.025 -12.298  1.00  1.90           N  
-ATOM      2  CA  ALA A 249       4.838  -7.531 -10.896  1.00  1.62           C  
-ATOM      3  C   ALA A 249       3.487  -7.837 -10.248  1.00  1.21           C  
-ATOM      4  O   ALA A 249       2.478  -7.971 -10.938  1.00  1.23           O  
-ATOM      5  CB  ALA A 249       5.124  -6.036 -10.847  1.00  1.91           C  
-ATOM      6  H1  ALA A 249       5.824  -7.850 -12.710  1.00  2.23           H  
-ATOM      7  H2  ALA A 249       4.169  -7.534 -12.875  1.00  2.06           H  
-ATOM      8  H3  ALA A 249       4.689  -9.047 -12.324  1.00  2.31           H  
-ATOM      9  HA  ALA A 249       5.611  -8.036 -10.336  1.00  1.64           H  
-ATOM     10  HB1 ALA A 249       4.196  -5.496 -10.740  1.00  2.25           H  
-ATOM     11  HB2 ALA A 249       5.616  -5.734 -11.757  1.00  2.25           H  
-ATOM     12  HB3 ALA A 249       5.762  -5.821 -10.000  1.00  2.21           H  
-ATOM     13  N   PRO A 250       3.454  -7.955  -8.910  1.00  1.02           N  
-ATOM     14  CA  PRO A 250       2.227  -8.250  -8.158  1.00  0.77           C  
-ATOM     15  C   PRO A 250       1.179  -7.136  -8.263  1.00  0.63           C  
-ATOM     16  O   PRO A 250       1.493  -5.953  -8.104  1.00  0.69           O  
-ATOM     17  CB  PRO A 250       2.728  -8.381  -6.712  1.00  1.05           C  
-ATOM     18  CG  PRO A 250       4.188  -8.615  -6.830  1.00  1.29           C  
-ATOM     19  CD  PRO A 250       4.606  -7.812  -8.013  1.00  1.31           C  
-ATOM     20  HA  PRO A 250       1.786  -9.185  -8.473  1.00  0.78           H  
-ATOM     21  HB2 PRO A 250       2.525  -7.465  -6.177  1.00  1.15           H  
-ATOM     22  HB3 PRO A 250       2.238  -9.206  -6.229  1.00  1.13           H  
-ATOM     23  HG2 PRO A 250       4.693  -8.269  -5.940  1.00  1.59           H  
-ATOM     24  HG3 PRO A 250       4.385  -9.662  -6.998  1.00  1.29           H  
-ATOM     25  HD2 PRO A 250       4.751  -6.779  -7.733  1.00  1.51           H  
-ATOM     26  HD3 PRO A 250       5.496  -8.222  -8.458  1.00  1.48           H  
-ATOM     27  N   ALA A 251      -0.067  -7.526  -8.536  1.00  0.58           N  
-ATOM     28  CA  ALA A 251      -1.170  -6.574  -8.677  1.00  0.55           C  
-ATOM     29  C   ALA A 251      -1.390  -5.748  -7.407  1.00  0.44           C  
-ATOM     30  O   ALA A 251      -1.483  -4.524  -7.467  1.00  0.45           O  
-ATOM     31  CB  ALA A 251      -2.446  -7.308  -9.050  1.00  0.68           C  
-ATOM     32  H   ALA A 251      -0.248  -8.482  -8.656  1.00  0.66           H  
-ATOM     33  HA  ALA A 251      -0.924  -5.903  -9.488  1.00  0.62           H  
-ATOM     34  HB1 ALA A 251      -2.331  -7.759 -10.023  1.00  1.35           H  
-ATOM     35  HB2 ALA A 251      -3.270  -6.611  -9.068  1.00  1.30           H  
-ATOM     36  HB3 ALA A 251      -2.643  -8.078  -8.317  1.00  1.07           H  
-ATOM     37  N   ARG A 252      -1.474  -6.424  -6.260  1.00  0.42           N  
-ATOM     38  CA  ARG A 252      -1.685  -5.740  -4.983  1.00  0.40           C  
-ATOM     39  C   ARG A 252      -0.567  -4.743  -4.689  1.00  0.35           C  
-ATOM     40  O   ARG A 252      -0.828  -3.596  -4.333  1.00  0.36           O  
-ATOM     41  CB  ARG A 252      -1.796  -6.750  -3.832  1.00  0.51           C  
-ATOM     42  CG  ARG A 252      -1.740  -6.108  -2.445  1.00  0.60           C  
-ATOM     43  CD  ARG A 252      -2.729  -4.957  -2.321  1.00  0.60           C  
-ATOM     44  NE  ARG A 252      -2.470  -4.117  -1.153  1.00  0.79           N  
-ATOM     45  CZ  ARG A 252      -3.073  -2.966  -0.927  1.00  0.81           C  
-ATOM     46  NH1 ARG A 252      -3.982  -2.522  -1.751  1.00  0.75           N  
-ATOM     47  NH2 ARG A 252      -2.769  -2.255   0.127  1.00  1.09           N  
-ATOM     48  H   ARG A 252      -1.393  -7.399  -6.274  1.00  0.49           H  
-ATOM     49  HA  ARG A 252      -2.613  -5.197  -5.058  1.00  0.44           H  
-ATOM     50  HB2 ARG A 252      -2.732  -7.282  -3.921  1.00  0.58           H  
-ATOM     51  HB3 ARG A 252      -0.983  -7.457  -3.908  1.00  0.60           H  
-ATOM     52  HG2 ARG A 252      -1.979  -6.856  -1.703  1.00  0.75           H  
-ATOM     53  HG3 ARG A 252      -0.741  -5.734  -2.274  1.00  0.65           H  
-ATOM     54  HD2 ARG A 252      -2.653  -4.347  -3.209  1.00  0.53           H  
-ATOM     55  HD3 ARG A 252      -3.723  -5.363  -2.248  1.00  0.78           H  
-ATOM     56  HE  ARG A 252      -1.803  -4.427  -0.510  1.00  1.00           H  
-ATOM     57 HH11 ARG A 252      -4.224  -3.046  -2.559  1.00  0.78           H  
-ATOM     58 HH12 ARG A 252      -4.455  -1.639  -1.553  1.00  0.82           H  
-ATOM     59 HH21 ARG A 252      -2.082  -2.581   0.770  1.00  1.33           H  
-ATOM     60 HH22 ARG A 252      -3.217  -1.368   0.277  1.00  1.12           H  
-ATOM     61  N   VAL A 253       0.670  -5.186  -4.837  1.00  0.40           N  
-ATOM     62  CA  VAL A 253       1.817  -4.325  -4.578  1.00  0.45           C  
-ATOM     63  C   VAL A 253       1.817  -3.122  -5.516  1.00  0.39           C  
-ATOM     64  O   VAL A 253       1.991  -1.988  -5.073  1.00  0.41           O  
-ATOM     65  CB  VAL A 253       3.140  -5.101  -4.696  1.00  0.57           C  
-ATOM     66  CG1 VAL A 253       4.322  -4.204  -4.363  1.00  0.70           C  
-ATOM     67  CG2 VAL A 253       3.107  -6.313  -3.777  1.00  0.66           C  
-ATOM     68  H   VAL A 253       0.814  -6.109  -5.125  1.00  0.45           H  
-ATOM     69  HA  VAL A 253       1.729  -3.967  -3.565  1.00  0.50           H  
-ATOM     70  HB  VAL A 253       3.248  -5.447  -5.713  1.00  0.59           H  
-ATOM     71 HG11 VAL A 253       4.034  -3.170  -4.488  1.00  1.18           H  
-ATOM     72 HG12 VAL A 253       5.144  -4.432  -5.024  1.00  1.20           H  
-ATOM     73 HG13 VAL A 253       4.623  -4.372  -3.340  1.00  1.05           H  
-ATOM     74 HG21 VAL A 253       4.088  -6.755  -3.727  1.00  1.25           H  
-ATOM     75 HG22 VAL A 253       2.403  -7.037  -4.165  1.00  1.20           H  
-ATOM     76 HG23 VAL A 253       2.796  -6.005  -2.789  1.00  1.25           H  
-ATOM     77  N   GLY A 254       1.576  -3.371  -6.802  1.00  0.39           N  
-ATOM     78  CA  GLY A 254       1.511  -2.286  -7.768  1.00  0.40           C  
-ATOM     79  C   GLY A 254       0.394  -1.310  -7.434  1.00  0.33           C  
-ATOM     80  O   GLY A 254       0.607  -0.094  -7.385  1.00  0.33           O  
-ATOM     81  H   GLY A 254       1.412  -4.297  -7.090  1.00  0.43           H  
-ATOM     82  HA2 GLY A 254       2.454  -1.759  -7.768  1.00  0.46           H  
-ATOM     83  HA3 GLY A 254       1.337  -2.698  -8.749  1.00  0.45           H  
-ATOM     84  N   LEU A 255      -0.794  -1.845  -7.169  1.00  0.32           N  
-ATOM     85  CA  LEU A 255      -1.938  -1.020  -6.804  1.00  0.31           C  
-ATOM     86  C   LEU A 255      -1.660  -0.238  -5.528  1.00  0.26           C  
-ATOM     87  O   LEU A 255      -1.866   0.971  -5.481  1.00  0.28           O  
-ATOM     88  CB  LEU A 255      -3.181  -1.879  -6.611  1.00  0.39           C  
-ATOM     89  CG  LEU A 255      -4.103  -1.936  -7.820  1.00  0.63           C  
-ATOM     90  CD1 LEU A 255      -5.315  -2.796  -7.513  1.00  0.77           C  
-ATOM     91  CD2 LEU A 255      -4.522  -0.529  -8.224  1.00  0.81           C  
-ATOM     92  H   LEU A 255      -0.899  -2.825  -7.203  1.00  0.35           H  
-ATOM     93  HA  LEU A 255      -2.119  -0.325  -7.611  1.00  0.34           H  
-ATOM     94  HB2 LEU A 255      -2.866  -2.885  -6.374  1.00  0.55           H  
-ATOM     95  HB3 LEU A 255      -3.742  -1.486  -5.777  1.00  0.45           H  
-ATOM     96  HG  LEU A 255      -3.573  -2.381  -8.649  1.00  0.78           H  
-ATOM     97 HD11 LEU A 255      -5.888  -2.338  -6.721  1.00  1.27           H  
-ATOM     98 HD12 LEU A 255      -4.988  -3.777  -7.201  1.00  1.31           H  
-ATOM     99 HD13 LEU A 255      -5.925  -2.884  -8.398  1.00  1.32           H  
-ATOM    100 HD21 LEU A 255      -5.598  -0.474  -8.277  1.00  1.36           H  
-ATOM    101 HD22 LEU A 255      -4.099  -0.290  -9.188  1.00  1.36           H  
-ATOM    102 HD23 LEU A 255      -4.161   0.178  -7.489  1.00  1.31           H  
-ATOM    103  N   GLY A 256      -1.182  -0.941  -4.502  1.00  0.29           N  
-ATOM    104  CA  GLY A 256      -0.878  -0.309  -3.232  1.00  0.34           C  
-ATOM    105  C   GLY A 256       0.117   0.834  -3.357  1.00  0.32           C  
-ATOM    106  O   GLY A 256      -0.143   1.942  -2.875  1.00  0.35           O  
-ATOM    107  H   GLY A 256      -1.032  -1.908  -4.610  1.00  0.33           H  
-ATOM    108  HA2 GLY A 256      -1.798   0.074  -2.813  1.00  0.37           H  
-ATOM    109  HA3 GLY A 256      -0.475  -1.054  -2.565  1.00  0.41           H  
-ATOM    110  N   ILE A 257       1.250   0.588  -4.022  1.00  0.32           N  
-ATOM    111  CA  ILE A 257       2.248   1.640  -4.209  1.00  0.35           C  
-ATOM    112  C   ILE A 257       1.660   2.776  -5.043  1.00  0.30           C  
-ATOM    113  O   ILE A 257       1.971   3.944  -4.814  1.00  0.35           O  
-ATOM    114  CB  ILE A 257       3.576   1.128  -4.831  1.00  0.42           C  
-ATOM    115  CG1 ILE A 257       3.373   0.529  -6.227  1.00  0.42           C  
-ATOM    116  CG2 ILE A 257       4.229   0.109  -3.910  1.00  0.53           C  
-ATOM    117  CD1 ILE A 257       3.675   1.491  -7.359  1.00  0.50           C  
-ATOM    118  H   ILE A 257       1.406  -0.307  -4.404  1.00  0.34           H  
-ATOM    119  HA  ILE A 257       2.476   2.034  -3.226  1.00  0.41           H  
-ATOM    120  HB  ILE A 257       4.246   1.973  -4.910  1.00  0.48           H  
-ATOM    121 HG12 ILE A 257       4.020  -0.327  -6.340  1.00  0.49           H  
-ATOM    122 HG13 ILE A 257       2.346   0.213  -6.327  1.00  0.44           H  
-ATOM    123 HG21 ILE A 257       5.299   0.118  -4.068  1.00  1.16           H  
-ATOM    124 HG22 ILE A 257       3.841  -0.875  -4.129  1.00  1.21           H  
-ATOM    125 HG23 ILE A 257       4.015   0.363  -2.883  1.00  1.12           H  
-ATOM    126 HD11 ILE A 257       4.308   2.288  -6.995  1.00  1.19           H  
-ATOM    127 HD12 ILE A 257       2.752   1.907  -7.735  1.00  1.13           H  
-ATOM    128 HD13 ILE A 257       4.182   0.963  -8.153  1.00  1.13           H  
-ATOM    129  N   THR A 258       0.777   2.434  -5.986  1.00  0.28           N  
-ATOM    130  CA  THR A 258       0.123   3.444  -6.815  1.00  0.31           C  
-ATOM    131  C   THR A 258      -0.754   4.360  -5.955  1.00  0.27           C  
-ATOM    132  O   THR A 258      -0.678   5.583  -6.075  1.00  0.29           O  
-ATOM    133  CB  THR A 258      -0.747   2.817  -7.930  1.00  0.39           C  
-ATOM    134  OG1 THR A 258       0.060   1.995  -8.784  1.00  0.46           O  
-ATOM    135  CG2 THR A 258      -1.422   3.894  -8.765  1.00  0.54           C  
-ATOM    136  H   THR A 258       0.547   1.481  -6.112  1.00  0.31           H  
-ATOM    137  HA  THR A 258       0.896   4.040  -7.282  1.00  0.36           H  
-ATOM    138  HB  THR A 258      -1.511   2.206  -7.470  1.00  0.40           H  
-ATOM    139  HG1 THR A 258       0.320   1.191  -8.306  1.00  0.44           H  
-ATOM    140 HG21 THR A 258      -2.405   3.559  -9.061  1.00  1.02           H  
-ATOM    141 HG22 THR A 258      -0.829   4.092  -9.646  1.00  1.16           H  
-ATOM    142 HG23 THR A 258      -1.511   4.799  -8.182  1.00  1.08           H  
-ATOM    143  N   THR A 259      -1.577   3.774  -5.075  1.00  0.25           N  
-ATOM    144  CA  THR A 259      -2.445   4.571  -4.200  1.00  0.26           C  
-ATOM    145  C   THR A 259      -1.615   5.425  -3.249  1.00  0.25           C  
-ATOM    146  O   THR A 259      -1.907   6.605  -3.046  1.00  0.28           O  
-ATOM    147  CB  THR A 259      -3.413   3.715  -3.356  1.00  0.32           C  
-ATOM    148  OG1 THR A 259      -3.349   2.341  -3.736  1.00  0.94           O  
-ATOM    149  CG2 THR A 259      -4.839   4.215  -3.506  1.00  1.13           C  
-ATOM    150  H   THR A 259      -1.596   2.788  -5.006  1.00  0.29           H  
-ATOM    151  HA  THR A 259      -3.033   5.224  -4.831  1.00  0.28           H  
-ATOM    152  HB  THR A 259      -3.129   3.802  -2.318  1.00  0.87           H  
-ATOM    153  HG1 THR A 259      -3.469   1.780  -2.937  1.00  1.05           H  
-ATOM    154 HG21 THR A 259      -5.218   3.940  -4.480  1.00  1.68           H  
-ATOM    155 HG22 THR A 259      -4.854   5.289  -3.403  1.00  1.70           H  
-ATOM    156 HG23 THR A 259      -5.457   3.771  -2.741  1.00  1.72           H  
-ATOM    157  N   VAL A 260      -0.569   4.827  -2.677  1.00  0.26           N  
-ATOM    158  CA  VAL A 260       0.315   5.546  -1.759  1.00  0.31           C  
-ATOM    159  C   VAL A 260       0.987   6.729  -2.466  1.00  0.32           C  
-ATOM    160  O   VAL A 260       0.976   7.858  -1.964  1.00  0.36           O  
-ATOM    161  CB  VAL A 260       1.387   4.605  -1.172  1.00  0.39           C  
-ATOM    162  CG1 VAL A 260       2.418   5.386  -0.372  1.00  0.55           C  
-ATOM    163  CG2 VAL A 260       0.729   3.543  -0.303  1.00  0.42           C  
-ATOM    164  H   VAL A 260      -0.382   3.879  -2.886  1.00  0.28           H  
-ATOM    165  HA  VAL A 260      -0.289   5.923  -0.945  1.00  0.32           H  
-ATOM    166  HB  VAL A 260       1.893   4.110  -1.987  1.00  0.41           H  
-ATOM    167 HG11 VAL A 260       3.305   4.785  -0.243  1.00  1.17           H  
-ATOM    168 HG12 VAL A 260       2.008   5.636   0.596  1.00  1.23           H  
-ATOM    169 HG13 VAL A 260       2.669   6.293  -0.902  1.00  1.14           H  
-ATOM    170 HG21 VAL A 260       0.938   2.565  -0.711  1.00  1.10           H  
-ATOM    171 HG22 VAL A 260      -0.339   3.705  -0.286  1.00  1.08           H  
-ATOM    172 HG23 VAL A 260       1.119   3.606   0.701  1.00  1.16           H  
-ATOM    173  N   LEU A 261       1.542   6.474  -3.647  1.00  0.36           N  
-ATOM    174  CA  LEU A 261       2.183   7.522  -4.434  1.00  0.43           C  
-ATOM    175  C   LEU A 261       1.169   8.595  -4.821  1.00  0.43           C  
-ATOM    176  O   LEU A 261       1.458   9.790  -4.775  1.00  0.49           O  
-ATOM    177  CB  LEU A 261       2.810   6.929  -5.695  1.00  0.50           C  
-ATOM    178  CG  LEU A 261       4.337   6.916  -5.722  1.00  0.83           C  
-ATOM    179  CD1 LEU A 261       4.886   8.334  -5.671  1.00  1.36           C  
-ATOM    180  CD2 LEU A 261       4.891   6.091  -4.569  1.00  1.26           C  
-ATOM    181  H   LEU A 261       1.499   5.557  -4.009  1.00  0.37           H  
-ATOM    182  HA  LEU A 261       2.956   7.970  -3.829  1.00  0.48           H  
-ATOM    183  HB2 LEU A 261       2.460   5.913  -5.797  1.00  0.60           H  
-ATOM    184  HB3 LEU A 261       2.463   7.496  -6.546  1.00  0.77           H  
-ATOM    185  HG  LEU A 261       4.663   6.465  -6.646  1.00  0.83           H  
-ATOM    186 HD11 LEU A 261       4.091   9.035  -5.881  1.00  1.81           H  
-ATOM    187 HD12 LEU A 261       5.668   8.444  -6.407  1.00  1.81           H  
-ATOM    188 HD13 LEU A 261       5.287   8.529  -4.687  1.00  1.87           H  
-ATOM    189 HD21 LEU A 261       5.497   6.721  -3.936  1.00  1.66           H  
-ATOM    190 HD22 LEU A 261       5.493   5.286  -4.961  1.00  1.75           H  
-ATOM    191 HD23 LEU A 261       4.073   5.683  -3.993  1.00  1.78           H  
-ATOM    192  N   THR A 262      -0.022   8.147  -5.205  1.00  0.39           N  
-ATOM    193  CA  THR A 262      -1.099   9.052  -5.608  1.00  0.42           C  
-ATOM    194  C   THR A 262      -1.552   9.958  -4.458  1.00  0.35           C  
-ATOM    195  O   THR A 262      -1.638  11.174  -4.622  1.00  0.42           O  
-ATOM    196  CB  THR A 262      -2.330   8.274  -6.131  1.00  0.48           C  
-ATOM    197  OG1 THR A 262      -1.966   7.456  -7.251  1.00  0.61           O  
-ATOM    198  CG2 THR A 262      -3.444   9.224  -6.541  1.00  0.58           C  
-ATOM    199  H   THR A 262      -0.176   7.175  -5.226  1.00  0.37           H  
-ATOM    200  HA  THR A 262      -0.728   9.670  -6.412  1.00  0.53           H  
-ATOM    201  HB  THR A 262      -2.696   7.639  -5.336  1.00  0.46           H  
-ATOM    202  HG1 THR A 262      -1.507   6.663  -6.930  1.00  0.58           H  
-ATOM    203 HG21 THR A 262      -3.507  10.034  -5.830  1.00  1.23           H  
-ATOM    204 HG22 THR A 262      -4.384   8.691  -6.562  1.00  1.13           H  
-ATOM    205 HG23 THR A 262      -3.234   9.623  -7.523  1.00  1.18           H  
-ATOM    206  N   MET A 263      -1.869   9.370  -3.299  1.00  0.28           N  
-ATOM    207  CA  MET A 263      -2.338  10.165  -2.168  1.00  0.30           C  
-ATOM    208  C   MET A 263      -2.190   9.460  -0.805  1.00  0.28           C  
-ATOM    209  O   MET A 263      -3.168   9.308  -0.070  1.00  0.35           O  
-ATOM    210  CB  MET A 263      -3.800  10.549  -2.401  1.00  0.46           C  
-ATOM    211  CG  MET A 263      -4.720   9.369  -2.681  1.00  0.58           C  
-ATOM    212  SD  MET A 263      -6.426   9.876  -2.967  1.00  0.84           S  
-ATOM    213  CE  MET A 263      -6.233  10.910  -4.417  1.00  0.95           C  
-ATOM    214  H   MET A 263      -1.812   8.394  -3.215  1.00  0.29           H  
-ATOM    215  HA  MET A 263      -1.751  11.069  -2.146  1.00  0.35           H  
-ATOM    216  HB2 MET A 263      -4.163  11.059  -1.527  1.00  0.53           H  
-ATOM    217  HB3 MET A 263      -3.844  11.218  -3.247  1.00  0.54           H  
-ATOM    218  HG2 MET A 263      -4.362   8.851  -3.558  1.00  0.63           H  
-ATOM    219  HG3 MET A 263      -4.694   8.701  -1.833  1.00  0.60           H  
-ATOM    220  HE1 MET A 263      -5.320  11.480  -4.331  1.00  1.33           H  
-ATOM    221  HE2 MET A 263      -7.073  11.585  -4.492  1.00  1.42           H  
-ATOM    222  HE3 MET A 263      -6.189  10.289  -5.299  1.00  1.52           H  
-ATOM    223  N   THR A 264      -0.971   9.047  -0.453  1.00  0.28           N  
-ATOM    224  CA  THR A 264      -0.729   8.381   0.841  1.00  0.35           C  
-ATOM    225  C   THR A 264      -1.214   9.231   2.031  1.00  0.32           C  
-ATOM    226  O   THR A 264      -1.687   8.693   3.034  1.00  0.39           O  
-ATOM    227  CB  THR A 264       0.767   8.042   1.046  1.00  0.49           C  
-ATOM    228  OG1 THR A 264       0.959   7.398   2.314  1.00  0.68           O  
-ATOM    229  CG2 THR A 264       1.639   9.290   0.968  1.00  0.52           C  
-ATOM    230  H   THR A 264      -0.212   9.191  -1.070  1.00  0.31           H  
-ATOM    231  HA  THR A 264      -1.284   7.453   0.837  1.00  0.44           H  
-ATOM    232  HB  THR A 264       1.071   7.363   0.262  1.00  0.55           H  
-ATOM    233  HG1 THR A 264       1.900   7.350   2.513  1.00  1.11           H  
-ATOM    234 HG21 THR A 264       2.189   9.404   1.891  1.00  1.17           H  
-ATOM    235 HG22 THR A 264       1.013  10.156   0.814  1.00  1.14           H  
-ATOM    236 HG23 THR A 264       2.332   9.195   0.145  1.00  1.16           H  
-ATOM    237  N   THR A 265      -1.097  10.556   1.906  1.00  0.34           N  
-ATOM    238  CA  THR A 265      -1.514  11.485   2.965  1.00  0.44           C  
-ATOM    239  C   THR A 265      -3.041  11.576   3.125  1.00  0.38           C  
-ATOM    240  O   THR A 265      -3.529  12.241   4.039  1.00  0.53           O  
-ATOM    241  CB  THR A 265      -0.954  12.905   2.721  1.00  0.63           C  
-ATOM    242  OG1 THR A 265      -1.334  13.779   3.793  1.00  0.79           O  
-ATOM    243  CG2 THR A 265      -1.454  13.473   1.398  1.00  0.68           C  
-ATOM    244  H   THR A 265      -0.716  10.919   1.079  1.00  0.37           H  
-ATOM    245  HA  THR A 265      -1.098  11.124   3.893  1.00  0.56           H  
-ATOM    246  HB  THR A 265       0.125  12.846   2.683  1.00  0.75           H  
-ATOM    247  HG1 THR A 265      -2.199  13.515   4.133  1.00  0.95           H  
-ATOM    248 HG21 THR A 265      -1.575  14.543   1.488  1.00  1.32           H  
-ATOM    249 HG22 THR A 265      -2.403  13.023   1.149  1.00  1.19           H  
-ATOM    250 HG23 THR A 265      -0.737  13.257   0.619  1.00  1.21           H  
-ATOM    251  N   GLN A 266      -3.792  10.914   2.242  1.00  0.47           N  
-ATOM    252  CA  GLN A 266      -5.257  10.933   2.313  1.00  0.50           C  
-ATOM    253  C   GLN A 266      -5.755  10.282   3.608  1.00  0.43           C  
-ATOM    254  O   GLN A 266      -5.423   9.132   3.906  1.00  0.51           O  
-ATOM    255  CB  GLN A 266      -5.859  10.202   1.107  1.00  0.68           C  
-ATOM    256  CG  GLN A 266      -7.369  10.027   1.181  1.00  0.87           C  
-ATOM    257  CD  GLN A 266      -7.894   9.041   0.153  1.00  1.12           C  
-ATOM    258  OE1 GLN A 266      -7.437   7.904   0.081  1.00  1.25           O  
-ATOM    259  NE2 GLN A 266      -8.862   9.466  -0.642  1.00  1.42           N  
-ATOM    260  H   GLN A 266      -3.352  10.398   1.531  1.00  0.64           H  
-ATOM    261  HA  GLN A 266      -5.576  11.964   2.295  1.00  0.60           H  
-ATOM    262  HB2 GLN A 266      -5.627  10.762   0.213  1.00  0.80           H  
-ATOM    263  HB3 GLN A 266      -5.408   9.224   1.034  1.00  0.73           H  
-ATOM    264  HG2 GLN A 266      -7.629   9.670   2.165  1.00  0.86           H  
-ATOM    265  HG3 GLN A 266      -7.837  10.986   1.011  1.00  1.03           H  
-ATOM    266 HE21 GLN A 266      -9.188  10.382  -0.529  1.00  1.52           H  
-ATOM    267 HE22 GLN A 266      -9.211   8.842  -1.313  1.00  1.62           H  
-ATOM    268  N   SER A 267      -6.550  11.021   4.370  1.00  0.53           N  
-ATOM    269  CA  SER A 267      -7.095  10.517   5.629  1.00  0.64           C  
-ATOM    270  C   SER A 267      -8.337   9.654   5.405  1.00  0.64           C  
-ATOM    271  O   SER A 267      -9.174   9.949   4.548  1.00  0.81           O  
-ATOM    272  CB  SER A 267      -7.441  11.676   6.563  1.00  0.92           C  
-ATOM    273  OG  SER A 267      -8.376  12.556   5.962  1.00  1.06           O  
-ATOM    274  H   SER A 267      -6.781  11.929   4.081  1.00  0.67           H  
-ATOM    275  HA  SER A 267      -6.333   9.910   6.096  1.00  0.71           H  
-ATOM    276  HB2 SER A 267      -7.867  11.285   7.475  1.00  1.09           H  
-ATOM    277  HB3 SER A 267      -6.544  12.230   6.795  1.00  1.00           H  
-ATOM    278  HG  SER A 267      -9.263  12.194   6.052  1.00  1.33           H  
-ATOM    279  N   SER A 268      -8.455   8.589   6.190  1.00  0.66           N  
-ATOM    280  CA  SER A 268      -9.593   7.670   6.104  1.00  0.77           C  
-ATOM    281  C   SER A 268      -9.713   6.848   7.384  1.00  0.77           C  
-ATOM    282  O   SER A 268      -8.705   6.470   7.981  1.00  1.35           O  
-ATOM    283  CB  SER A 268      -9.457   6.741   4.891  1.00  0.96           C  
-ATOM    284  OG  SER A 268      -9.613   7.463   3.678  1.00  1.05           O  
-ATOM    285  H   SER A 268      -7.757   8.414   6.856  1.00  0.72           H  
-ATOM    286  HA  SER A 268     -10.487   8.264   5.990  1.00  0.84           H  
-ATOM    287  HB2 SER A 268      -8.478   6.282   4.900  1.00  1.00           H  
-ATOM    288  HB3 SER A 268     -10.214   5.973   4.941  1.00  1.13           H  
-ATOM    289  HG  SER A 268      -9.520   8.414   3.853  1.00  1.19           H  
-ATOM    290  N   GLY A 269     -10.948   6.595   7.815  1.00  0.57           N  
-ATOM    291  CA  GLY A 269     -11.167   5.842   9.040  1.00  0.56           C  
-ATOM    292  C   GLY A 269     -10.783   6.638  10.277  1.00  0.55           C  
-ATOM    293  O   GLY A 269     -11.164   7.801  10.416  1.00  0.73           O  
-ATOM    294  H   GLY A 269     -11.714   6.937   7.309  1.00  0.93           H  
-ATOM    295  HA2 GLY A 269     -12.211   5.575   9.104  1.00  0.66           H  
-ATOM    296  HA3 GLY A 269     -10.575   4.939   9.006  1.00  0.56           H  
-ATOM    297  N   SER A 270     -10.024   6.017  11.175  1.00  0.47           N  
-ATOM    298  CA  SER A 270      -9.580   6.682  12.404  1.00  0.56           C  
-ATOM    299  C   SER A 270      -8.423   5.923  13.043  1.00  0.53           C  
-ATOM    300  O   SER A 270      -8.490   4.703  13.198  1.00  0.48           O  
-ATOM    301  CB  SER A 270     -10.727   6.803  13.417  1.00  0.70           C  
-ATOM    302  OG  SER A 270     -11.718   7.718  12.976  1.00  0.93           O  
-ATOM    303  H   SER A 270      -9.746   5.090  11.006  1.00  0.48           H  
-ATOM    304  HA  SER A 270      -9.242   7.673  12.139  1.00  0.65           H  
-ATOM    305  HB2 SER A 270     -11.187   5.836  13.552  1.00  0.70           H  
-ATOM    306  HB3 SER A 270     -10.333   7.147  14.362  1.00  0.80           H  
-ATOM    307  HG  SER A 270     -11.608   7.877  12.024  1.00  0.92           H  
-ATOM    308  N   ARG A 271      -7.364   6.658  13.407  1.00  0.71           N  
-ATOM    309  CA  ARG A 271      -6.163   6.083  14.041  1.00  0.82           C  
-ATOM    310  C   ARG A 271      -5.339   5.216  13.070  1.00  0.81           C  
-ATOM    311  O   ARG A 271      -4.118   5.340  13.002  1.00  1.06           O  
-ATOM    312  CB  ARG A 271      -6.558   5.269  15.278  1.00  0.85           C  
-ATOM    313  CG  ARG A 271      -5.382   4.702  16.054  1.00  1.01           C  
-ATOM    314  CD  ARG A 271      -5.860   3.849  17.216  1.00  1.10           C  
-ATOM    315  NE  ARG A 271      -6.721   2.758  16.768  1.00  1.02           N  
-ATOM    316  CZ  ARG A 271      -7.332   1.924  17.574  1.00  1.19           C  
-ATOM    317  NH1 ARG A 271      -7.209   2.045  18.868  1.00  1.41           N  
-ATOM    318  NH2 ARG A 271      -8.074   0.970  17.084  1.00  1.29           N  
-ATOM    319  H   ARG A 271      -7.388   7.624  13.245  1.00  0.84           H  
-ATOM    320  HA  ARG A 271      -5.544   6.909  14.360  1.00  1.03           H  
-ATOM    321  HB2 ARG A 271      -7.125   5.903  15.942  1.00  0.98           H  
-ATOM    322  HB3 ARG A 271      -7.183   4.445  14.963  1.00  0.75           H  
-ATOM    323  HG2 ARG A 271      -4.784   4.092  15.393  1.00  1.00           H  
-ATOM    324  HG3 ARG A 271      -4.787   5.517  16.437  1.00  1.19           H  
-ATOM    325  HD2 ARG A 271      -5.000   3.433  17.721  1.00  1.25           H  
-ATOM    326  HD3 ARG A 271      -6.413   4.472  17.903  1.00  1.23           H  
-ATOM    327  HE  ARG A 271      -6.841   2.645  15.806  1.00  0.98           H  
-ATOM    328 HH11 ARG A 271      -6.645   2.773  19.248  1.00  1.47           H  
-ATOM    329 HH12 ARG A 271      -7.680   1.411  19.473  1.00  1.59           H  
-ATOM    330 HH21 ARG A 271      -8.178   0.875  16.096  1.00  1.28           H  
-ATOM    331 HH22 ARG A 271      -8.535   0.334  17.699  1.00  1.47           H  
-ATOM    332  N   ALA A 272      -6.011   4.341  12.329  1.00  0.66           N  
-ATOM    333  CA  ALA A 272      -5.342   3.460  11.376  1.00  0.81           C  
-ATOM    334  C   ALA A 272      -6.215   3.211  10.140  1.00  0.65           C  
-ATOM    335  O   ALA A 272      -6.813   4.143   9.602  1.00  0.61           O  
-ATOM    336  CB  ALA A 272      -4.982   2.144  12.054  1.00  1.12           C  
-ATOM    337  H   ALA A 272      -6.990   4.287  12.429  1.00  0.58           H  
-ATOM    338  HA  ALA A 272      -4.425   3.939  11.066  1.00  0.95           H  
-ATOM    339  HB1 ALA A 272      -4.000   2.222  12.493  1.00  1.58           H  
-ATOM    340  HB2 ALA A 272      -4.988   1.349  11.321  1.00  1.56           H  
-ATOM    341  HB3 ALA A 272      -5.707   1.927  12.824  1.00  1.58           H  
-ATOM    342  N   SER A 273      -6.285   1.946   9.703  1.00  0.81           N  
-ATOM    343  CA  SER A 273      -7.085   1.544   8.530  1.00  0.85           C  
-ATOM    344  C   SER A 273      -6.745   2.346   7.281  1.00  0.66           C  
-ATOM    345  O   SER A 273      -7.607   2.970   6.666  1.00  0.74           O  
-ATOM    346  CB  SER A 273      -8.595   1.600   8.809  1.00  1.01           C  
-ATOM    347  OG  SER A 273      -9.022   2.888   9.234  1.00  0.90           O  
-ATOM    348  H   SER A 273      -5.786   1.256  10.187  1.00  1.01           H  
-ATOM    349  HA  SER A 273      -6.828   0.515   8.311  1.00  1.05           H  
-ATOM    350  HB2 SER A 273      -9.126   1.346   7.903  1.00  1.17           H  
-ATOM    351  HB3 SER A 273      -8.837   0.882   9.576  1.00  1.18           H  
-ATOM    352  HG  SER A 273      -8.277   3.510   9.180  1.00  0.90           H  
-ATOM    353  N   LEU A 274      -5.484   2.272   6.878  1.00  0.61           N  
-ATOM    354  CA  LEU A 274      -5.034   2.941   5.666  1.00  0.51           C  
-ATOM    355  C   LEU A 274      -4.421   1.943   4.656  1.00  0.61           C  
-ATOM    356  O   LEU A 274      -3.381   2.239   4.068  1.00  0.68           O  
-ATOM    357  CB  LEU A 274      -4.002   4.022   6.009  1.00  0.67           C  
-ATOM    358  CG  LEU A 274      -4.505   5.162   6.901  1.00  0.78           C  
-ATOM    359  CD1 LEU A 274      -3.353   6.063   7.308  1.00  1.16           C  
-ATOM    360  CD2 LEU A 274      -5.577   5.974   6.186  1.00  0.91           C  
-ATOM    361  H   LEU A 274      -4.851   1.725   7.386  1.00  0.76           H  
-ATOM    362  HA  LEU A 274      -5.893   3.412   5.211  1.00  0.53           H  
-ATOM    363  HB2 LEU A 274      -3.170   3.546   6.506  1.00  0.86           H  
-ATOM    364  HB3 LEU A 274      -3.646   4.451   5.084  1.00  0.79           H  
-ATOM    365  HG  LEU A 274      -4.940   4.746   7.798  1.00  0.78           H  
-ATOM    366 HD11 LEU A 274      -3.405   6.988   6.752  1.00  1.68           H  
-ATOM    367 HD12 LEU A 274      -2.417   5.568   7.096  1.00  1.36           H  
-ATOM    368 HD13 LEU A 274      -3.417   6.274   8.365  1.00  1.68           H  
-ATOM    369 HD21 LEU A 274      -5.107   6.655   5.492  1.00  1.43           H  
-ATOM    370 HD22 LEU A 274      -6.145   6.537   6.912  1.00  1.40           H  
-ATOM    371 HD23 LEU A 274      -6.236   5.309   5.649  1.00  1.41           H  
-ATOM    372  N   PRO A 275      -5.034   0.742   4.433  1.00  0.81           N  
-ATOM    373  CA  PRO A 275      -4.490  -0.255   3.483  1.00  1.04           C  
-ATOM    374  C   PRO A 275      -4.256   0.316   2.080  1.00  0.90           C  
-ATOM    375  O   PRO A 275      -3.174   0.162   1.510  1.00  0.98           O  
-ATOM    376  CB  PRO A 275      -5.577  -1.329   3.440  1.00  1.31           C  
-ATOM    377  CG  PRO A 275      -6.220  -1.232   4.772  1.00  1.34           C  
-ATOM    378  CD  PRO A 275      -6.272   0.235   5.075  1.00  0.99           C  
-ATOM    379  HA  PRO A 275      -3.577  -0.689   3.853  1.00  1.21           H  
-ATOM    380  HB2 PRO A 275      -6.273  -1.113   2.642  1.00  1.32           H  
-ATOM    381  HB3 PRO A 275      -5.128  -2.298   3.287  1.00  1.55           H  
-ATOM    382  HG2 PRO A 275      -7.216  -1.651   4.737  1.00  1.52           H  
-ATOM    383  HG3 PRO A 275      -5.614  -1.743   5.502  1.00  1.47           H  
-ATOM    384  HD2 PRO A 275      -7.154   0.683   4.643  1.00  1.01           H  
-ATOM    385  HD3 PRO A 275      -6.252   0.402   6.151  1.00  0.98           H  
-ATOM    386  N   LYS A 276      -5.281   0.991   1.553  1.00  0.82           N  
-ATOM    387  CA  LYS A 276      -5.228   1.630   0.225  1.00  0.81           C  
-ATOM    388  C   LYS A 276      -4.673   0.695  -0.866  1.00  0.89           C  
-ATOM    389  O   LYS A 276      -3.723   1.093  -1.580  1.00  1.32           O  
-ATOM    390  CB  LYS A 276      -4.404   2.919   0.304  1.00  0.74           C  
-ATOM    391  CG  LYS A 276      -4.984   3.943   1.269  1.00  0.78           C  
-ATOM    392  CD  LYS A 276      -4.162   5.221   1.300  1.00  0.92           C  
-ATOM    393  CE  LYS A 276      -4.758   6.237   2.259  1.00  1.07           C  
-ATOM    394  NZ  LYS A 276      -4.046   7.540   2.208  1.00  1.35           N  
-ATOM    395  OXT LYS A 276      -5.193  -0.434  -1.002  1.00  0.68           O  
-ATOM    396  H   LYS A 276      -6.101   1.077   2.082  1.00  0.89           H  
-ATOM    397  HA  LYS A 276      -6.241   1.891  -0.042  1.00  0.91           H  
-ATOM    398  HB2 LYS A 276      -3.402   2.676   0.629  1.00  0.74           H  
-ATOM    399  HB3 LYS A 276      -4.357   3.367  -0.677  1.00  0.84           H  
-ATOM    400  HG2 LYS A 276      -5.989   4.184   0.959  1.00  0.98           H  
-ATOM    401  HG3 LYS A 276      -5.006   3.515   2.262  1.00  0.79           H  
-ATOM    402  HD2 LYS A 276      -3.157   4.985   1.620  1.00  0.98           H  
-ATOM    403  HD3 LYS A 276      -4.135   5.646   0.308  1.00  1.12           H  
-ATOM    404  HE2 LYS A 276      -5.793   6.394   1.998  1.00  1.25           H  
-ATOM    405  HE3 LYS A 276      -4.697   5.843   3.263  1.00  1.07           H  
-ATOM    406  HZ1 LYS A 276      -3.047   7.418   2.486  1.00  1.65           H  
-ATOM    407  HZ2 LYS A 276      -4.496   8.219   2.868  1.00  1.85           H  
-ATOM    408  HZ3 LYS A 276      -4.082   7.934   1.243  1.00  1.50           H  
-TER
-ENDMDL
-MODEL      13
-ATOM      1  N   ALA A 249       1.543 -11.826 -11.005  1.00  3.74           N  
-ATOM      2  CA  ALA A 249       2.138 -10.463 -10.975  1.00  3.58           C  
-ATOM      3  C   ALA A 249       1.770  -9.729  -9.686  1.00  3.24           C  
-ATOM      4  O   ALA A 249       0.738 -10.010  -9.077  1.00  3.10           O  
-ATOM      5  CB  ALA A 249       1.694  -9.660 -12.189  1.00  3.64           C  
-ATOM      6  H1  ALA A 249       1.278 -12.122 -10.041  1.00  3.69           H  
-ATOM      7  H2  ALA A 249       2.229 -12.507 -11.389  1.00  4.32           H  
-ATOM      8  H3  ALA A 249       0.692 -11.833 -11.604  1.00  3.64           H  
-ATOM      9  HA  ALA A 249       3.212 -10.565 -11.019  1.00  3.72           H  
-ATOM     10  HB1 ALA A 249       0.639  -9.814 -12.357  1.00  3.80           H  
-ATOM     11  HB2 ALA A 249       2.250  -9.984 -13.056  1.00  3.78           H  
-ATOM     12  HB3 ALA A 249       1.883  -8.610 -12.013  1.00  3.80           H  
-ATOM     13  N   PRO A 250       2.624  -8.788  -9.248  1.00  3.15           N  
-ATOM     14  CA  PRO A 250       2.420  -8.000  -8.021  1.00  2.87           C  
-ATOM     15  C   PRO A 250       1.276  -6.980  -8.133  1.00  2.62           C  
-ATOM     16  O   PRO A 250       1.488  -5.780  -7.958  1.00  2.49           O  
-ATOM     17  CB  PRO A 250       3.763  -7.270  -7.847  1.00  3.00           C  
-ATOM     18  CG  PRO A 250       4.711  -7.964  -8.756  1.00  3.33           C  
-ATOM     19  CD  PRO A 250       3.877  -8.410  -9.907  1.00  3.39           C  
-ATOM     20  HA  PRO A 250       2.245  -8.639  -7.168  1.00  2.84           H  
-ATOM     21  HB2 PRO A 250       3.647  -6.235  -8.128  1.00  2.92           H  
-ATOM     22  HB3 PRO A 250       4.084  -7.339  -6.823  1.00  2.97           H  
-ATOM     23  HG2 PRO A 250       5.477  -7.278  -9.087  1.00  3.46           H  
-ATOM     24  HG3 PRO A 250       5.150  -8.813  -8.256  1.00  3.44           H  
-ATOM     25  HD2 PRO A 250       3.720  -7.594 -10.597  1.00  3.39           H  
-ATOM     26  HD3 PRO A 250       4.325  -9.254 -10.400  1.00  3.62           H  
-ATOM     27  N   ALA A 251       0.068  -7.462  -8.429  1.00  2.59           N  
-ATOM     28  CA  ALA A 251      -1.103  -6.590  -8.568  1.00  2.42           C  
-ATOM     29  C   ALA A 251      -1.383  -5.802  -7.286  1.00  2.13           C  
-ATOM     30  O   ALA A 251      -1.587  -4.589  -7.328  1.00  1.97           O  
-ATOM     31  CB  ALA A 251      -2.323  -7.406  -8.965  1.00  2.54           C  
-ATOM     32  H   ALA A 251      -0.038  -8.432  -8.562  1.00  2.73           H  
-ATOM     33  HA  ALA A 251      -0.897  -5.890  -9.366  1.00  2.47           H  
-ATOM     34  HB1 ALA A 251      -3.216  -6.820  -8.801  1.00  2.81           H  
-ATOM     35  HB2 ALA A 251      -2.367  -8.305  -8.368  1.00  2.71           H  
-ATOM     36  HB3 ALA A 251      -2.255  -7.672 -10.011  1.00  2.79           H  
-ATOM     37  N   ARG A 252      -1.380  -6.495  -6.147  1.00  2.08           N  
-ATOM     38  CA  ARG A 252      -1.623  -5.853  -4.852  1.00  1.85           C  
-ATOM     39  C   ARG A 252      -0.523  -4.828  -4.554  1.00  1.77           C  
-ATOM     40  O   ARG A 252      -0.797  -3.720  -4.091  1.00  1.55           O  
-ATOM     41  CB  ARG A 252      -1.680  -6.912  -3.740  1.00  1.96           C  
-ATOM     42  CG  ARG A 252      -2.671  -6.599  -2.623  1.00  1.86           C  
-ATOM     43  CD  ARG A 252      -2.314  -5.322  -1.881  1.00  1.70           C  
-ATOM     44  NE  ARG A 252      -3.238  -5.036  -0.787  1.00  1.83           N  
-ATOM     45  CZ  ARG A 252      -3.242  -3.905  -0.117  1.00  1.88           C  
-ATOM     46  NH1 ARG A 252      -2.432  -2.935  -0.459  1.00  1.83           N  
-ATOM     47  NH2 ARG A 252      -4.071  -3.734   0.880  1.00  2.14           N  
-ATOM     48  H   ARG A 252      -1.203  -7.457  -6.176  1.00  2.24           H  
-ATOM     49  HA  ARG A 252      -2.571  -5.343  -4.906  1.00  1.75           H  
-ATOM     50  HB2 ARG A 252      -1.953  -7.859  -4.177  1.00  2.15           H  
-ATOM     51  HB3 ARG A 252      -0.697  -7.001  -3.300  1.00  2.01           H  
-ATOM     52  HG2 ARG A 252      -3.655  -6.487  -3.052  1.00  1.92           H  
-ATOM     53  HG3 ARG A 252      -2.676  -7.422  -1.922  1.00  1.98           H  
-ATOM     54  HD2 ARG A 252      -1.317  -5.425  -1.477  1.00  1.75           H  
-ATOM     55  HD3 ARG A 252      -2.333  -4.499  -2.579  1.00  1.58           H  
-ATOM     56  HE  ARG A 252      -3.871  -5.735  -0.533  1.00  2.01           H  
-ATOM     57 HH11 ARG A 252      -1.809  -3.057  -1.228  1.00  1.73           H  
-ATOM     58 HH12 ARG A 252      -2.439  -2.077   0.046  1.00  1.99           H  
-ATOM     59 HH21 ARG A 252      -4.699  -4.463   1.138  1.00  2.28           H  
-ATOM     60 HH22 ARG A 252      -4.071  -2.874   1.386  1.00  2.26           H  
-ATOM     61  N   VAL A 253       0.721  -5.204  -4.846  1.00  2.00           N  
-ATOM     62  CA  VAL A 253       1.867  -4.322  -4.632  1.00  2.06           C  
-ATOM     63  C   VAL A 253       1.767  -3.087  -5.526  1.00  1.95           C  
-ATOM     64  O   VAL A 253       1.899  -1.956  -5.055  1.00  1.84           O  
-ATOM     65  CB  VAL A 253       3.199  -5.052  -4.912  1.00  2.40           C  
-ATOM     66  CG1 VAL A 253       4.390  -4.145  -4.629  1.00  2.54           C  
-ATOM     67  CG2 VAL A 253       3.291  -6.329  -4.090  1.00  2.53           C  
-ATOM     68  H   VAL A 253       0.868  -6.092  -5.226  1.00  2.18           H  
-ATOM     69  HA  VAL A 253       1.859  -4.008  -3.601  1.00  1.98           H  
-ATOM     70  HB  VAL A 253       3.224  -5.322  -5.957  1.00  2.50           H  
-ATOM     71 HG11 VAL A 253       4.050  -3.126  -4.518  1.00  2.66           H  
-ATOM     72 HG12 VAL A 253       5.090  -4.205  -5.450  1.00  2.77           H  
-ATOM     73 HG13 VAL A 253       4.877  -4.464  -3.719  1.00  2.78           H  
-ATOM     74 HG21 VAL A 253       3.473  -7.166  -4.746  1.00  2.67           H  
-ATOM     75 HG22 VAL A 253       2.362  -6.483  -3.561  1.00  2.84           H  
-ATOM     76 HG23 VAL A 253       4.100  -6.243  -3.380  1.00  2.78           H  
-ATOM     77  N   GLY A 254       1.507  -3.317  -6.813  1.00  2.03           N  
-ATOM     78  CA  GLY A 254       1.363  -2.222  -7.763  1.00  1.98           C  
-ATOM     79  C   GLY A 254       0.247  -1.270  -7.368  1.00  1.68           C  
-ATOM     80  O   GLY A 254       0.441  -0.053  -7.335  1.00  1.58           O  
-ATOM     81  H   GLY A 254       1.396  -4.248  -7.120  1.00  2.15           H  
-ATOM     82  HA2 GLY A 254       2.293  -1.674  -7.808  1.00  2.08           H  
-ATOM     83  HA3 GLY A 254       1.147  -2.630  -8.739  1.00  2.10           H  
-ATOM     84  N   LEU A 255      -0.913  -1.832  -7.032  1.00  1.58           N  
-ATOM     85  CA  LEU A 255      -2.056  -1.034  -6.598  1.00  1.34           C  
-ATOM     86  C   LEU A 255      -1.723  -0.250  -5.338  1.00  1.15           C  
-ATOM     87  O   LEU A 255      -2.044   0.934  -5.215  1.00  1.00           O  
-ATOM     88  CB  LEU A 255      -3.257  -1.928  -6.325  1.00  1.39           C  
-ATOM     89  CG  LEU A 255      -4.225  -2.060  -7.489  1.00  1.58           C  
-ATOM     90  CD1 LEU A 255      -5.374  -2.977  -7.111  1.00  1.74           C  
-ATOM     91  CD2 LEU A 255      -4.736  -0.685  -7.891  1.00  1.53           C  
-ATOM     92  H   LEU A 255      -0.995  -2.813  -7.057  1.00  1.70           H  
-ATOM     93  HA  LEU A 255      -2.306  -0.345  -7.391  1.00  1.35           H  
-ATOM     94  HB2 LEU A 255      -2.896  -2.914  -6.067  1.00  1.55           H  
-ATOM     95  HB3 LEU A 255      -3.796  -1.527  -5.480  1.00  1.26           H  
-ATOM     96  HG  LEU A 255      -3.709  -2.490  -8.334  1.00  1.77           H  
-ATOM     97 HD11 LEU A 255      -5.303  -3.224  -6.062  1.00  2.07           H  
-ATOM     98 HD12 LEU A 255      -5.320  -3.882  -7.698  1.00  2.06           H  
-ATOM     99 HD13 LEU A 255      -6.311  -2.477  -7.300  1.00  2.05           H  
-ATOM    100 HD21 LEU A 255      -3.950  -0.144  -8.398  1.00  1.69           H  
-ATOM    101 HD22 LEU A 255      -5.032  -0.139  -7.005  1.00  1.84           H  
-ATOM    102 HD23 LEU A 255      -5.583  -0.794  -8.548  1.00  1.97           H  
-ATOM    103  N   GLY A 256      -1.077  -0.926  -4.398  1.00  1.22           N  
-ATOM    104  CA  GLY A 256      -0.707  -0.298  -3.153  1.00  1.17           C  
-ATOM    105  C   GLY A 256       0.267   0.856  -3.335  1.00  1.25           C  
-ATOM    106  O   GLY A 256       0.015   1.956  -2.852  1.00  1.15           O  
-ATOM    107  H   GLY A 256      -0.852  -1.872  -4.553  1.00  1.36           H  
-ATOM    108  HA2 GLY A 256      -1.604   0.074  -2.681  1.00  1.01           H  
-ATOM    109  HA3 GLY A 256      -0.257  -1.041  -2.514  1.00  1.29           H  
-ATOM    110  N   ILE A 257       1.374   0.625  -4.041  1.00  1.48           N  
-ATOM    111  CA  ILE A 257       2.351   1.692  -4.262  1.00  1.64           C  
-ATOM    112  C   ILE A 257       1.747   2.836  -5.086  1.00  1.47           C  
-ATOM    113  O   ILE A 257       2.021   4.005  -4.822  1.00  1.51           O  
-ATOM    114  CB  ILE A 257       3.663   1.180  -4.918  1.00  1.96           C  
-ATOM    115  CG1 ILE A 257       3.415   0.531  -6.285  1.00  2.01           C  
-ATOM    116  CG2 ILE A 257       4.364   0.198  -3.992  1.00  2.15           C  
-ATOM    117  CD1 ILE A 257       3.674   1.456  -7.457  1.00  2.09           C  
-ATOM    118  H   ILE A 257       1.534  -0.272  -4.421  1.00  1.59           H  
-ATOM    119  HA  ILE A 257       2.607   2.086  -3.288  1.00  1.68           H  
-ATOM    120  HB  ILE A 257       4.319   2.029  -5.048  1.00  2.08           H  
-ATOM    121 HG12 ILE A 257       4.064  -0.325  -6.392  1.00  2.21           H  
-ATOM    122 HG13 ILE A 257       2.386   0.206  -6.337  1.00  1.86           H  
-ATOM    123 HG21 ILE A 257       3.704  -0.632  -3.781  1.00  2.38           H  
-ATOM    124 HG22 ILE A 257       4.625   0.694  -3.069  1.00  2.42           H  
-ATOM    125 HG23 ILE A 257       5.261  -0.170  -4.469  1.00  2.47           H  
-ATOM    126 HD11 ILE A 257       3.762   0.873  -8.362  1.00  2.18           H  
-ATOM    127 HD12 ILE A 257       4.590   2.003  -7.290  1.00  2.25           H  
-ATOM    128 HD13 ILE A 257       2.852   2.151  -7.555  1.00  2.12           H  
-ATOM    129  N   THR A 258       0.912   2.493  -6.069  1.00  1.36           N  
-ATOM    130  CA  THR A 258       0.261   3.498  -6.914  1.00  1.25           C  
-ATOM    131  C   THR A 258      -0.687   4.377  -6.096  1.00  0.98           C  
-ATOM    132  O   THR A 258      -0.654   5.605  -6.196  1.00  0.98           O  
-ATOM    133  CB  THR A 258      -0.531   2.847  -8.069  1.00  1.30           C  
-ATOM    134  OG1 THR A 258       0.338   2.031  -8.864  1.00  1.59           O  
-ATOM    135  CG2 THR A 258      -1.184   3.902  -8.950  1.00  1.28           C  
-ATOM    136  H   THR A 258       0.722   1.539  -6.227  1.00  1.39           H  
-ATOM    137  HA  THR A 258       1.032   4.123  -7.342  1.00  1.40           H  
-ATOM    138  HB  THR A 258      -1.306   2.224  -7.645  1.00  1.21           H  
-ATOM    139  HG1 THR A 258       0.463   1.175  -8.429  1.00  1.58           H  
-ATOM    140 HG21 THR A 258      -1.756   3.417  -9.726  1.00  1.62           H  
-ATOM    141 HG22 THR A 258      -0.419   4.518  -9.399  1.00  1.67           H  
-ATOM    142 HG23 THR A 258      -1.838   4.518  -8.350  1.00  1.63           H  
-ATOM    143  N   THR A 259      -1.524   3.744  -5.274  1.00  0.82           N  
-ATOM    144  CA  THR A 259      -2.467   4.480  -4.433  1.00  0.65           C  
-ATOM    145  C   THR A 259      -1.726   5.332  -3.410  1.00  0.78           C  
-ATOM    146  O   THR A 259      -2.046   6.506  -3.225  1.00  0.80           O  
-ATOM    147  CB  THR A 259      -3.459   3.545  -3.712  1.00  0.60           C  
-ATOM    148  OG1 THR A 259      -2.762   2.472  -3.070  1.00  0.70           O  
-ATOM    149  CG2 THR A 259      -4.478   2.983  -4.691  1.00  0.70           C  
-ATOM    150  H   THR A 259      -1.498   2.763  -5.222  1.00  0.88           H  
-ATOM    151  HA  THR A 259      -3.033   5.137  -5.079  1.00  0.61           H  
-ATOM    152  HB  THR A 259      -3.986   4.118  -2.961  1.00  0.61           H  
-ATOM    153  HG1 THR A 259      -2.579   1.775  -3.716  1.00  0.99           H  
-ATOM    154 HG21 THR A 259      -4.099   2.068  -5.121  1.00  1.18           H  
-ATOM    155 HG22 THR A 259      -4.657   3.704  -5.475  1.00  1.10           H  
-ATOM    156 HG23 THR A 259      -5.403   2.781  -4.172  1.00  1.32           H  
-ATOM    157  N   VAL A 260      -0.715   4.747  -2.769  1.00  0.98           N  
-ATOM    158  CA  VAL A 260       0.088   5.472  -1.791  1.00  1.22           C  
-ATOM    159  C   VAL A 260       0.799   6.655  -2.455  1.00  1.34           C  
-ATOM    160  O   VAL A 260       0.719   7.782  -1.965  1.00  1.44           O  
-ATOM    161  CB  VAL A 260       1.127   4.553  -1.115  1.00  1.46           C  
-ATOM    162  CG1 VAL A 260       2.100   5.361  -0.269  1.00  1.77           C  
-ATOM    163  CG2 VAL A 260       0.432   3.503  -0.262  1.00  1.43           C  
-ATOM    164  H   VAL A 260      -0.494   3.807  -2.974  1.00  1.00           H  
-ATOM    165  HA  VAL A 260      -0.580   5.849  -1.029  1.00  1.22           H  
-ATOM    166  HB  VAL A 260       1.688   4.046  -1.885  1.00  1.51           H  
-ATOM    167 HG11 VAL A 260       2.365   4.796   0.612  1.00  2.13           H  
-ATOM    168 HG12 VAL A 260       1.634   6.290   0.026  1.00  1.97           H  
-ATOM    169 HG13 VAL A 260       2.988   5.571  -0.845  1.00  2.20           H  
-ATOM    170 HG21 VAL A 260       0.555   2.531  -0.718  1.00  1.75           H  
-ATOM    171 HG22 VAL A 260      -0.622   3.735  -0.194  1.00  1.76           H  
-ATOM    172 HG23 VAL A 260       0.865   3.495   0.726  1.00  1.74           H  
-ATOM    173  N   LEU A 261       1.467   6.397  -3.586  1.00  1.39           N  
-ATOM    174  CA  LEU A 261       2.169   7.447  -4.331  1.00  1.54           C  
-ATOM    175  C   LEU A 261       1.203   8.565  -4.726  1.00  1.37           C  
-ATOM    176  O   LEU A 261       1.521   9.748  -4.604  1.00  1.51           O  
-ATOM    177  CB  LEU A 261       2.829   6.870  -5.581  1.00  1.66           C  
-ATOM    178  CG  LEU A 261       3.761   7.832  -6.312  1.00  1.90           C  
-ATOM    179  CD1 LEU A 261       4.829   8.361  -5.365  1.00  2.18           C  
-ATOM    180  CD2 LEU A 261       4.397   7.144  -7.506  1.00  2.08           C  
-ATOM    181  H   LEU A 261       1.477   5.473  -3.937  1.00  1.36           H  
-ATOM    182  HA  LEU A 261       2.938   7.858  -3.694  1.00  1.76           H  
-ATOM    183  HB2 LEU A 261       3.398   5.996  -5.292  1.00  1.79           H  
-ATOM    184  HB3 LEU A 261       2.052   6.563  -6.264  1.00  1.50           H  
-ATOM    185  HG  LEU A 261       3.186   8.675  -6.674  1.00  1.81           H  
-ATOM    186 HD11 LEU A 261       4.452   9.229  -4.844  1.00  2.47           H  
-ATOM    187 HD12 LEU A 261       5.709   8.633  -5.928  1.00  2.50           H  
-ATOM    188 HD13 LEU A 261       5.083   7.595  -4.646  1.00  2.47           H  
-ATOM    189 HD21 LEU A 261       4.278   7.763  -8.382  1.00  2.37           H  
-ATOM    190 HD22 LEU A 261       3.913   6.191  -7.668  1.00  2.38           H  
-ATOM    191 HD23 LEU A 261       5.446   6.988  -7.313  1.00  2.33           H  
-ATOM    192  N   THR A 262       0.013   8.174  -5.178  1.00  1.10           N  
-ATOM    193  CA  THR A 262      -1.021   9.132  -5.568  1.00  0.96           C  
-ATOM    194  C   THR A 262      -1.436   9.983  -4.367  1.00  1.02           C  
-ATOM    195  O   THR A 262      -1.488  11.212  -4.442  1.00  1.11           O  
-ATOM    196  CB  THR A 262      -2.268   8.409  -6.124  1.00  0.79           C  
-ATOM    197  OG1 THR A 262      -1.918   7.626  -7.275  1.00  0.89           O  
-ATOM    198  CG2 THR A 262      -3.359   9.401  -6.493  1.00  0.80           C  
-ATOM    199  H   THR A 262      -0.180   7.209  -5.238  1.00  1.03           H  
-ATOM    200  HA  THR A 262      -0.618   9.773  -6.339  1.00  1.04           H  
-ATOM    201  HB  THR A 262      -2.650   7.750  -5.359  1.00  0.76           H  
-ATOM    202  HG1 THR A 262      -1.443   6.830  -6.993  1.00  0.98           H  
-ATOM    203 HG21 THR A 262      -3.115  10.369  -6.081  1.00  1.32           H  
-ATOM    204 HG22 THR A 262      -4.302   9.065  -6.088  1.00  1.29           H  
-ATOM    205 HG23 THR A 262      -3.432   9.474  -7.567  1.00  1.31           H  
-ATOM    206  N   MET A 263      -1.717   9.307  -3.255  1.00  1.03           N  
-ATOM    207  CA  MET A 263      -2.116   9.971  -2.014  1.00  1.19           C  
-ATOM    208  C   MET A 263      -1.030  10.933  -1.513  1.00  1.40           C  
-ATOM    209  O   MET A 263      -1.318  12.080  -1.169  1.00  1.50           O  
-ATOM    210  CB  MET A 263      -2.416   8.918  -0.943  1.00  1.24           C  
-ATOM    211  CG  MET A 263      -2.851   9.499   0.392  1.00  1.45           C  
-ATOM    212  SD  MET A 263      -3.166   8.226   1.631  1.00  1.57           S  
-ATOM    213  CE  MET A 263      -1.560   7.441   1.736  1.00  1.67           C  
-ATOM    214  H   MET A 263      -1.649   8.324  -3.270  1.00  0.98           H  
-ATOM    215  HA  MET A 263      -3.016  10.534  -2.213  1.00  1.16           H  
-ATOM    216  HB2 MET A 263      -3.203   8.272  -1.302  1.00  1.13           H  
-ATOM    217  HB3 MET A 263      -1.526   8.327  -0.778  1.00  1.30           H  
-ATOM    218  HG2 MET A 263      -2.072  10.152   0.757  1.00  1.60           H  
-ATOM    219  HG3 MET A 263      -3.757  10.069   0.243  1.00  1.47           H  
-ATOM    220  HE1 MET A 263      -1.276   7.069   0.762  1.00  1.96           H  
-ATOM    221  HE2 MET A 263      -1.606   6.619   2.435  1.00  1.96           H  
-ATOM    222  HE3 MET A 263      -0.828   8.161   2.072  1.00  2.06           H  
-ATOM    223  N   THR A 264       0.220  10.467  -1.476  1.00  1.52           N  
-ATOM    224  CA  THR A 264       1.331  11.308  -1.012  1.00  1.75           C  
-ATOM    225  C   THR A 264       1.809  12.292  -2.095  1.00  1.62           C  
-ATOM    226  O   THR A 264       3.012  12.454  -2.321  1.00  1.66           O  
-ATOM    227  CB  THR A 264       2.529  10.466  -0.500  1.00  2.02           C  
-ATOM    228  OG1 THR A 264       3.558  11.328   0.000  1.00  2.22           O  
-ATOM    229  CG2 THR A 264       3.109   9.579  -1.594  1.00  1.96           C  
-ATOM    230  H   THR A 264       0.399   9.539  -1.766  1.00  1.49           H  
-ATOM    231  HA  THR A 264       0.960  11.888  -0.177  1.00  1.87           H  
-ATOM    232  HB  THR A 264       2.181   9.833   0.305  1.00  2.15           H  
-ATOM    233  HG1 THR A 264       3.778  11.984  -0.675  1.00  2.13           H  
-ATOM    234 HG21 THR A 264       2.421   8.773  -1.808  1.00  2.20           H  
-ATOM    235 HG22 THR A 264       4.052   9.169  -1.262  1.00  2.22           H  
-ATOM    236 HG23 THR A 264       3.265  10.165  -2.487  1.00  2.21           H  
-ATOM    237  N   THR A 265       0.861  12.967  -2.748  1.00  1.51           N  
-ATOM    238  CA  THR A 265       1.187  13.949  -3.790  1.00  1.42           C  
-ATOM    239  C   THR A 265       2.081  15.066  -3.239  1.00  1.25           C  
-ATOM    240  O   THR A 265       3.019  15.509  -3.901  1.00  1.20           O  
-ATOM    241  CB  THR A 265      -0.086  14.563  -4.420  1.00  1.50           C  
-ATOM    242  OG1 THR A 265       0.263  15.650  -5.289  1.00  1.46           O  
-ATOM    243  CG2 THR A 265      -1.058  15.052  -3.353  1.00  1.52           C  
-ATOM    244  H   THR A 265      -0.082  12.809  -2.515  1.00  1.52           H  
-ATOM    245  HA  THR A 265       1.729  13.431  -4.569  1.00  1.48           H  
-ATOM    246  HB  THR A 265      -0.579  13.798  -5.005  1.00  1.63           H  
-ATOM    247  HG1 THR A 265       1.160  15.530  -5.612  1.00  1.55           H  
-ATOM    248 HG21 THR A 265      -0.958  14.442  -2.468  1.00  2.08           H  
-ATOM    249 HG22 THR A 265      -2.069  14.980  -3.728  1.00  1.64           H  
-ATOM    250 HG23 THR A 265      -0.836  16.081  -3.110  1.00  1.75           H  
-ATOM    251  N   GLN A 266       1.798  15.494  -2.015  1.00  1.26           N  
-ATOM    252  CA  GLN A 266       2.589  16.528  -1.363  1.00  1.15           C  
-ATOM    253  C   GLN A 266       3.740  15.896  -0.577  1.00  0.88           C  
-ATOM    254  O   GLN A 266       3.528  14.968   0.205  1.00  0.93           O  
-ATOM    255  CB  GLN A 266       1.710  17.359  -0.428  1.00  1.42           C  
-ATOM    256  CG  GLN A 266       2.468  18.448   0.311  1.00  1.45           C  
-ATOM    257  CD  GLN A 266       1.574  19.258   1.224  1.00  1.84           C  
-ATOM    258  OE1 GLN A 266       0.940  18.725   2.126  1.00  1.97           O  
-ATOM    259  NE2 GLN A 266       1.522  20.558   0.997  1.00  2.12           N  
-ATOM    260  H   GLN A 266       1.051  15.087  -1.528  1.00  1.41           H  
-ATOM    261  HA  GLN A 266       2.998  17.169  -2.129  1.00  1.22           H  
-ATOM    262  HB2 GLN A 266       0.927  17.824  -1.007  1.00  1.67           H  
-ATOM    263  HB3 GLN A 266       1.263  16.701   0.304  1.00  1.46           H  
-ATOM    264  HG2 GLN A 266       3.245  17.991   0.905  1.00  1.28           H  
-ATOM    265  HG3 GLN A 266       2.913  19.112  -0.414  1.00  1.48           H  
-ATOM    266 HE21 GLN A 266       2.057  20.920   0.263  1.00  2.09           H  
-ATOM    267 HE22 GLN A 266       0.949  21.100   1.574  1.00  2.40           H  
-ATOM    268  N   SER A 267       4.953  16.397  -0.787  1.00  0.77           N  
-ATOM    269  CA  SER A 267       6.129  15.868  -0.095  1.00  0.79           C  
-ATOM    270  C   SER A 267       7.318  16.820  -0.229  1.00  0.79           C  
-ATOM    271  O   SER A 267       7.357  17.656  -1.132  1.00  0.88           O  
-ATOM    272  CB  SER A 267       6.503  14.491  -0.654  1.00  1.05           C  
-ATOM    273  OG  SER A 267       7.527  13.882   0.115  1.00  1.33           O  
-ATOM    274  H   SER A 267       5.063  17.134  -1.425  1.00  0.84           H  
-ATOM    275  HA  SER A 267       5.879  15.767   0.950  1.00  0.88           H  
-ATOM    276  HB2 SER A 267       5.634  13.851  -0.640  1.00  1.10           H  
-ATOM    277  HB3 SER A 267       6.851  14.600  -1.672  1.00  1.17           H  
-ATOM    278  HG  SER A 267       7.295  12.963   0.279  1.00  1.58           H  
-ATOM    279  N   SER A 268       8.284  16.690   0.677  1.00  0.86           N  
-ATOM    280  CA  SER A 268       9.480  17.540   0.662  1.00  0.98           C  
-ATOM    281  C   SER A 268      10.291  17.360  -0.622  1.00  1.03           C  
-ATOM    282  O   SER A 268      10.879  18.316  -1.137  1.00  1.18           O  
-ATOM    283  CB  SER A 268      10.374  17.229   1.866  1.00  1.19           C  
-ATOM    284  OG  SER A 268      11.604  17.940   1.791  1.00  1.37           O  
-ATOM    285  H   SER A 268       8.193  16.004   1.372  1.00  0.93           H  
-ATOM    286  HA  SER A 268       9.155  18.567   0.725  1.00  1.03           H  
-ATOM    287  HB2 SER A 268       9.864  17.513   2.775  1.00  1.31           H  
-ATOM    288  HB3 SER A 268      10.586  16.170   1.889  1.00  1.21           H  
-ATOM    289  HG  SER A 268      12.327  17.316   1.622  1.00  1.67           H  
-ATOM    290  N   GLY A 269      10.343  16.128  -1.122  1.00  1.09           N  
-ATOM    291  CA  GLY A 269      11.111  15.835  -2.325  1.00  1.29           C  
-ATOM    292  C   GLY A 269      12.604  15.796  -2.041  1.00  1.44           C  
-ATOM    293  O   GLY A 269      13.253  14.767  -2.213  1.00  1.69           O  
-ATOM    294  H   GLY A 269       9.868  15.404  -0.659  1.00  1.14           H  
-ATOM    295  HA2 GLY A 269      10.801  14.878  -2.716  1.00  1.44           H  
-ATOM    296  HA3 GLY A 269      10.915  16.598  -3.064  1.00  1.34           H  
-ATOM    297  N   SER A 270      13.136  16.917  -1.568  1.00  1.46           N  
-ATOM    298  CA  SER A 270      14.549  17.010  -1.218  1.00  1.75           C  
-ATOM    299  C   SER A 270      14.716  16.988   0.309  1.00  1.72           C  
-ATOM    300  O   SER A 270      13.882  16.415   1.020  1.00  1.61           O  
-ATOM    301  CB  SER A 270      15.166  18.285  -1.816  1.00  1.90           C  
-ATOM    302  OG  SER A 270      16.572  18.339  -1.602  1.00  2.26           O  
-ATOM    303  H   SER A 270      12.550  17.698  -1.430  1.00  1.41           H  
-ATOM    304  HA  SER A 270      15.050  16.149  -1.633  1.00  1.97           H  
-ATOM    305  HB2 SER A 270      14.978  18.308  -2.879  1.00  1.95           H  
-ATOM    306  HB3 SER A 270      14.713  19.150  -1.353  1.00  1.80           H  
-ATOM    307  HG  SER A 270      17.026  18.340  -2.453  1.00  2.43           H  
-ATOM    308  N   ARG A 271      15.794  17.610   0.797  1.00  1.92           N  
-ATOM    309  CA  ARG A 271      16.113  17.679   2.236  1.00  2.03           C  
-ATOM    310  C   ARG A 271      16.536  16.320   2.807  1.00  2.13           C  
-ATOM    311  O   ARG A 271      17.567  16.224   3.473  1.00  2.43           O  
-ATOM    312  CB  ARG A 271      14.940  18.229   3.059  1.00  1.86           C  
-ATOM    313  CG  ARG A 271      15.014  19.724   3.330  1.00  2.18           C  
-ATOM    314  CD  ARG A 271      14.806  20.542   2.069  1.00  2.15           C  
-ATOM    315  NE  ARG A 271      13.488  20.316   1.481  1.00  1.72           N  
-ATOM    316  CZ  ARG A 271      13.007  21.008   0.476  1.00  1.69           C  
-ATOM    317  NH1 ARG A 271      13.711  21.972  -0.059  1.00  2.02           N  
-ATOM    318  NH2 ARG A 271      11.824  20.730   0.001  1.00  1.41           N  
-ATOM    319  H   ARG A 271      16.410  18.041   0.156  1.00  2.08           H  
-ATOM    320  HA  ARG A 271      16.948  18.357   2.340  1.00  2.31           H  
-ATOM    321  HB2 ARG A 271      14.020  18.030   2.528  1.00  1.62           H  
-ATOM    322  HB3 ARG A 271      14.912  17.715   4.008  1.00  1.83           H  
-ATOM    323  HG2 ARG A 271      14.249  19.986   4.044  1.00  2.17           H  
-ATOM    324  HG3 ARG A 271      15.986  19.956   3.743  1.00  2.52           H  
-ATOM    325  HD2 ARG A 271      14.907  21.590   2.312  1.00  2.42           H  
-ATOM    326  HD3 ARG A 271      15.562  20.267   1.348  1.00  2.28           H  
-ATOM    327  HE  ARG A 271      12.934  19.600   1.862  1.00  1.52           H  
-ATOM    328 HH11 ARG A 271      14.616  22.185   0.302  1.00  2.29           H  
-ATOM    329 HH12 ARG A 271      13.342  22.495  -0.822  1.00  2.04           H  
-ATOM    330 HH21 ARG A 271      11.286  19.989   0.405  1.00  1.24           H  
-ATOM    331 HH22 ARG A 271      11.455  21.255  -0.760  1.00  1.46           H  
-ATOM    332  N   ALA A 272      15.728  15.284   2.558  1.00  2.02           N  
-ATOM    333  CA  ALA A 272      16.001  13.932   3.056  1.00  2.27           C  
-ATOM    334  C   ALA A 272      16.085  13.920   4.584  1.00  2.34           C  
-ATOM    335  O   ALA A 272      16.960  13.278   5.167  1.00  2.68           O  
-ATOM    336  CB  ALA A 272      17.282  13.377   2.445  1.00  2.61           C  
-ATOM    337  H   ALA A 272      14.913  15.440   2.024  1.00  1.87           H  
-ATOM    338  HA  ALA A 272      15.181  13.296   2.751  1.00  2.28           H  
-ATOM    339  HB1 ALA A 272      18.064  13.380   3.189  1.00  2.85           H  
-ATOM    340  HB2 ALA A 272      17.578  13.995   1.609  1.00  2.90           H  
-ATOM    341  HB3 ALA A 272      17.110  12.367   2.105  1.00  2.89           H  
-ATOM    342  N   SER A 273      15.169  14.646   5.225  1.00  2.10           N  
-ATOM    343  CA  SER A 273      15.131  14.739   6.687  1.00  2.19           C  
-ATOM    344  C   SER A 273      14.602  13.470   7.331  1.00  2.35           C  
-ATOM    345  O   SER A 273      13.625  13.491   8.079  1.00  2.31           O  
-ATOM    346  CB  SER A 273      14.289  15.926   7.143  1.00  2.02           C  
-ATOM    347  OG  SER A 273      14.673  17.116   6.476  1.00  2.11           O  
-ATOM    348  H   SER A 273      14.507  15.139   4.701  1.00  1.93           H  
-ATOM    349  HA  SER A 273      16.145  14.882   7.035  1.00  2.42           H  
-ATOM    350  HB2 SER A 273      13.252  15.726   6.936  1.00  1.92           H  
-ATOM    351  HB3 SER A 273      14.426  16.064   8.206  1.00  2.14           H  
-ATOM    352  HG  SER A 273      15.426  17.506   6.927  1.00  2.40           H  
-ATOM    353  N   LEU A 274      15.285  12.374   7.074  1.00  2.68           N  
-ATOM    354  CA  LEU A 274      14.926  11.099   7.660  1.00  3.01           C  
-ATOM    355  C   LEU A 274      16.103  10.482   8.446  1.00  3.41           C  
-ATOM    356  O   LEU A 274      16.326   9.274   8.373  1.00  3.82           O  
-ATOM    357  CB  LEU A 274      14.432  10.152   6.561  1.00  3.24           C  
-ATOM    358  CG  LEU A 274      15.219  10.201   5.247  1.00  3.30           C  
-ATOM    359  CD1 LEU A 274      16.313   9.149   5.226  1.00  3.97           C  
-ATOM    360  CD2 LEU A 274      14.285  10.022   4.061  1.00  3.35           C  
-ATOM    361  H   LEU A 274      16.080  12.432   6.492  1.00  2.77           H  
-ATOM    362  HA  LEU A 274      14.114  11.279   8.351  1.00  2.90           H  
-ATOM    363  HB2 LEU A 274      14.475   9.144   6.944  1.00  3.63           H  
-ATOM    364  HB3 LEU A 274      13.402  10.392   6.345  1.00  3.12           H  
-ATOM    365  HG  LEU A 274      15.691  11.170   5.155  1.00  3.12           H  
-ATOM    366 HD11 LEU A 274      15.917   8.211   5.590  1.00  4.31           H  
-ATOM    367 HD12 LEU A 274      17.130   9.465   5.856  1.00  4.24           H  
-ATOM    368 HD13 LEU A 274      16.667   9.020   4.215  1.00  4.27           H  
-ATOM    369 HD21 LEU A 274      14.138  10.975   3.574  1.00  3.37           H  
-ATOM    370 HD22 LEU A 274      13.336   9.644   4.409  1.00  3.59           H  
-ATOM    371 HD23 LEU A 274      14.719   9.324   3.362  1.00  3.67           H  
-ATOM    372  N   PRO A 275      16.883  11.296   9.224  1.00  3.40           N  
-ATOM    373  CA  PRO A 275      18.020  10.777  10.007  1.00  3.87           C  
-ATOM    374  C   PRO A 275      17.608   9.683  10.989  1.00  4.12           C  
-ATOM    375  O   PRO A 275      18.309   8.682  11.141  1.00  4.56           O  
-ATOM    376  CB  PRO A 275      18.521  12.007  10.768  1.00  3.83           C  
-ATOM    377  CG  PRO A 275      18.099  13.142   9.915  1.00  3.45           C  
-ATOM    378  CD  PRO A 275      16.743  12.758   9.419  1.00  3.07           C  
-ATOM    379  HA  PRO A 275      18.804  10.414   9.363  1.00  4.12           H  
-ATOM    380  HB2 PRO A 275      18.056  12.047  11.743  1.00  3.78           H  
-ATOM    381  HB3 PRO A 275      19.594  11.966  10.870  1.00  4.18           H  
-ATOM    382  HG2 PRO A 275      18.053  14.051  10.496  1.00  3.40           H  
-ATOM    383  HG3 PRO A 275      18.783  13.245   9.089  1.00  3.58           H  
-ATOM    384  HD2 PRO A 275      15.991  12.988  10.158  1.00  2.92           H  
-ATOM    385  HD3 PRO A 275      16.519  13.261   8.480  1.00  2.85           H  
-ATOM    386  N   LYS A 276      16.448   9.888  11.628  1.00  3.89           N  
-ATOM    387  CA  LYS A 276      15.884   8.943  12.588  1.00  4.16           C  
-ATOM    388  C   LYS A 276      16.900   8.507  13.658  1.00  4.54           C  
-ATOM    389  O   LYS A 276      17.203   9.330  14.549  1.00  4.48           O  
-ATOM    390  CB  LYS A 276      15.344   7.740  11.832  1.00  4.45           C  
-ATOM    391  CG  LYS A 276      14.564   6.769  12.686  1.00  4.74           C  
-ATOM    392  CD  LYS A 276      14.535   5.418  12.018  1.00  5.24           C  
-ATOM    393  CE  LYS A 276      15.749   4.573  12.399  1.00  5.69           C  
-ATOM    394  NZ  LYS A 276      17.038   5.269  12.115  1.00  5.61           N  
-ATOM    395  OXT LYS A 276      17.377   7.344  13.599  1.00  4.98           O  
-ATOM    396  H   LYS A 276      15.942  10.692  11.427  1.00  3.57           H  
-ATOM    397  HA  LYS A 276      15.061   9.434  13.083  1.00  3.94           H  
-ATOM    398  HB2 LYS A 276      14.693   8.090  11.044  1.00  4.25           H  
-ATOM    399  HB3 LYS A 276      16.173   7.207  11.388  1.00  4.73           H  
-ATOM    400  HG2 LYS A 276      15.040   6.681  13.651  1.00  4.84           H  
-ATOM    401  HG3 LYS A 276      13.554   7.130  12.802  1.00  4.56           H  
-ATOM    402  HD2 LYS A 276      13.636   4.907  12.309  1.00  5.40           H  
-ATOM    403  HD3 LYS A 276      14.539   5.572  10.948  1.00  5.20           H  
-ATOM    404  HE2 LYS A 276      15.697   4.347  13.453  1.00  5.78           H  
-ATOM    405  HE3 LYS A 276      15.717   3.652  11.835  1.00  6.05           H  
-ATOM    406  HZ1 LYS A 276      17.836   4.614  12.235  1.00  5.97           H  
-ATOM    407  HZ2 LYS A 276      17.167   6.082  12.777  1.00  5.34           H  
-ATOM    408  HZ3 LYS A 276      17.042   5.633  11.140  1.00  5.52           H  
-TER
-ENDMDL
-MODEL      14
-ATOM      1  N   ALA A 249       3.924  -7.852 -12.947  1.00  1.72           N  
-ATOM      2  CA  ALA A 249       4.121  -7.389 -11.548  1.00  1.46           C  
-ATOM      3  C   ALA A 249       2.889  -7.692 -10.696  1.00  1.23           C  
-ATOM      4  O   ALA A 249       1.782  -7.802 -11.219  1.00  1.27           O  
-ATOM      5  CB  ALA A 249       4.432  -5.901 -11.520  1.00  1.49           C  
-ATOM      6  H1  ALA A 249       3.949  -7.041 -13.599  1.00  1.97           H  
-ATOM      7  H2  ALA A 249       3.001  -8.326 -13.037  1.00  2.16           H  
-ATOM      8  H3  ALA A 249       4.675  -8.522 -13.212  1.00  1.90           H  
-ATOM      9  HA  ALA A 249       4.968  -7.916 -11.133  1.00  1.52           H  
-ATOM     10  HB1 ALA A 249       5.427  -5.749 -11.126  1.00  1.79           H  
-ATOM     11  HB2 ALA A 249       3.715  -5.396 -10.888  1.00  1.85           H  
-ATOM     12  HB3 ALA A 249       4.376  -5.502 -12.520  1.00  1.82           H  
-ATOM     13  N   PRO A 250       3.069  -7.840  -9.373  1.00  1.10           N  
-ATOM     14  CA  PRO A 250       1.971  -8.141  -8.443  1.00  1.00           C  
-ATOM     15  C   PRO A 250       0.933  -7.014  -8.362  1.00  0.83           C  
-ATOM     16  O   PRO A 250       1.271  -5.862  -8.081  1.00  0.83           O  
-ATOM     17  CB  PRO A 250       2.690  -8.303  -7.096  1.00  1.08           C  
-ATOM     18  CG  PRO A 250       4.110  -8.559  -7.448  1.00  1.21           C  
-ATOM     19  CD  PRO A 250       4.351  -7.736  -8.667  1.00  1.19           C  
-ATOM     20  HA  PRO A 250       1.476  -9.065  -8.702  1.00  1.11           H  
-ATOM     21  HB2 PRO A 250       2.591  -7.395  -6.524  1.00  1.05           H  
-ATOM     22  HB3 PRO A 250       2.267  -9.130  -6.555  1.00  1.20           H  
-ATOM     23  HG2 PRO A 250       4.755  -8.242  -6.641  1.00  1.34           H  
-ATOM     24  HG3 PRO A 250       4.257  -9.606  -7.665  1.00  1.34           H  
-ATOM     25  HD2 PRO A 250       4.558  -6.712  -8.394  1.00  1.22           H  
-ATOM     26  HD3 PRO A 250       5.151  -8.150  -9.254  1.00  1.36           H  
-ATOM     27  N   ALA A 251      -0.330  -7.359  -8.612  1.00  0.79           N  
-ATOM     28  CA  ALA A 251      -1.426  -6.388  -8.579  1.00  0.73           C  
-ATOM     29  C   ALA A 251      -1.547  -5.696  -7.219  1.00  0.61           C  
-ATOM     30  O   ALA A 251      -1.672  -4.476  -7.149  1.00  0.58           O  
-ATOM     31  CB  ALA A 251      -2.737  -7.069  -8.934  1.00  0.84           C  
-ATOM     32  H   ALA A 251      -0.531  -8.292  -8.834  1.00  0.88           H  
-ATOM     33  HA  ALA A 251      -1.228  -5.640  -9.332  1.00  0.80           H  
-ATOM     34  HB1 ALA A 251      -2.590  -7.710  -9.790  1.00  1.32           H  
-ATOM     35  HB2 ALA A 251      -3.480  -6.322  -9.167  1.00  1.37           H  
-ATOM     36  HB3 ALA A 251      -3.074  -7.661  -8.095  1.00  1.34           H  
-ATOM     37  N   ARG A 252      -1.503  -6.482  -6.144  1.00  0.63           N  
-ATOM     38  CA  ARG A 252      -1.604  -5.945  -4.783  1.00  0.60           C  
-ATOM     39  C   ARG A 252      -0.522  -4.898  -4.506  1.00  0.56           C  
-ATOM     40  O   ARG A 252      -0.802  -3.811  -3.987  1.00  0.53           O  
-ATOM     41  CB  ARG A 252      -1.494  -7.087  -3.764  1.00  0.71           C  
-ATOM     42  CG  ARG A 252      -1.301  -6.619  -2.325  1.00  0.78           C  
-ATOM     43  CD  ARG A 252      -2.449  -5.737  -1.862  1.00  0.78           C  
-ATOM     44  NE  ARG A 252      -2.249  -5.233  -0.504  1.00  0.90           N  
-ATOM     45  CZ  ARG A 252      -1.463  -4.219  -0.201  1.00  0.89           C  
-ATOM     46  NH1 ARG A 252      -0.864  -3.532  -1.143  1.00  0.78           N  
-ATOM     47  NH2 ARG A 252      -1.298  -3.874   1.049  1.00  1.06           N  
-ATOM     48  H   ARG A 252      -1.400  -7.448  -6.268  1.00  0.71           H  
-ATOM     49  HA  ARG A 252      -2.570  -5.479  -4.683  1.00  0.60           H  
-ATOM     50  HB2 ARG A 252      -2.397  -7.678  -3.805  1.00  0.76           H  
-ATOM     51  HB3 ARG A 252      -0.655  -7.711  -4.031  1.00  0.77           H  
-ATOM     52  HG2 ARG A 252      -1.242  -7.483  -1.681  1.00  0.91           H  
-ATOM     53  HG3 ARG A 252      -0.379  -6.058  -2.262  1.00  0.79           H  
-ATOM     54  HD2 ARG A 252      -2.527  -4.897  -2.538  1.00  0.69           H  
-ATOM     55  HD3 ARG A 252      -3.360  -6.312  -1.897  1.00  0.86           H  
-ATOM     56  HE  ARG A 252      -2.714  -5.696   0.220  1.00  1.03           H  
-ATOM     57 HH11 ARG A 252      -1.001  -3.776  -2.106  1.00  0.70           H  
-ATOM     58 HH12 ARG A 252      -0.275  -2.765  -0.903  1.00  0.82           H  
-ATOM     59 HH21 ARG A 252      -1.765  -4.378   1.772  1.00  1.18           H  
-ATOM     60 HH22 ARG A 252      -0.702  -3.110   1.278  1.00  1.09           H  
-ATOM     61  N   VAL A 253       0.709  -5.236  -4.852  1.00  0.64           N  
-ATOM     62  CA  VAL A 253       1.835  -4.337  -4.642  1.00  0.69           C  
-ATOM     63  C   VAL A 253       1.739  -3.121  -5.561  1.00  0.62           C  
-ATOM     64  O   VAL A 253       1.922  -1.987  -5.118  1.00  0.62           O  
-ATOM     65  CB  VAL A 253       3.176  -5.061  -4.859  1.00  0.85           C  
-ATOM     66  CG1 VAL A 253       4.339  -4.153  -4.495  1.00  0.94           C  
-ATOM     67  CG2 VAL A 253       3.214  -6.344  -4.041  1.00  0.95           C  
-ATOM     68  H   VAL A 253       0.863  -6.113  -5.260  1.00  0.71           H  
-ATOM     69  HA  VAL A 253       1.796  -3.998  -3.619  1.00  0.70           H  
-ATOM     70  HB  VAL A 253       3.260  -5.320  -5.904  1.00  0.89           H  
-ATOM     71 HG11 VAL A 253       3.969  -3.157  -4.299  1.00  1.38           H  
-ATOM     72 HG12 VAL A 253       5.041  -4.122  -5.315  1.00  1.54           H  
-ATOM     73 HG13 VAL A 253       4.831  -4.534  -3.612  1.00  1.26           H  
-ATOM     74 HG21 VAL A 253       3.734  -6.164  -3.113  1.00  1.42           H  
-ATOM     75 HG22 VAL A 253       3.726  -7.113  -4.600  1.00  1.40           H  
-ATOM     76 HG23 VAL A 253       2.203  -6.666  -3.831  1.00  1.41           H  
-ATOM     77  N   GLY A 254       1.422  -3.367  -6.832  1.00  0.64           N  
-ATOM     78  CA  GLY A 254       1.274  -2.281  -7.789  1.00  0.67           C  
-ATOM     79  C   GLY A 254       0.194  -1.302  -7.362  1.00  0.56           C  
-ATOM     80  O   GLY A 254       0.417  -0.091  -7.325  1.00  0.56           O  
-ATOM     81  H   GLY A 254       1.267  -4.297  -7.118  1.00  0.68           H  
-ATOM     82  HA2 GLY A 254       2.214  -1.753  -7.872  1.00  0.75           H  
-ATOM     83  HA3 GLY A 254       1.014  -2.692  -8.751  1.00  0.75           H  
-ATOM     84  N   LEU A 255      -0.971  -1.837  -7.000  1.00  0.51           N  
-ATOM     85  CA  LEU A 255      -2.087  -1.019  -6.533  1.00  0.49           C  
-ATOM     86  C   LEU A 255      -1.708  -0.256  -5.272  1.00  0.42           C  
-ATOM     87  O   LEU A 255      -2.011   0.930  -5.129  1.00  0.45           O  
-ATOM     88  CB  LEU A 255      -3.304  -1.885  -6.242  1.00  0.56           C  
-ATOM     89  CG  LEU A 255      -4.318  -1.958  -7.371  1.00  0.71           C  
-ATOM     90  CD1 LEU A 255      -5.478  -2.850  -6.970  1.00  0.80           C  
-ATOM     91  CD2 LEU A 255      -4.802  -0.560  -7.724  1.00  0.89           C  
-ATOM     92  H   LEU A 255      -1.078  -2.819  -7.028  1.00  0.54           H  
-ATOM     93  HA  LEU A 255      -2.338  -0.315  -7.312  1.00  0.57           H  
-ATOM     94  HB2 LEU A 255      -2.963  -2.888  -6.024  1.00  0.60           H  
-ATOM     95  HB3 LEU A 255      -3.800  -1.493  -5.366  1.00  0.60           H  
-ATOM     96  HG  LEU A 255      -3.849  -2.384  -8.245  1.00  0.76           H  
-ATOM     97 HD11 LEU A 255      -5.752  -2.641  -5.947  1.00  1.36           H  
-ATOM     98 HD12 LEU A 255      -5.182  -3.885  -7.059  1.00  1.29           H  
-ATOM     99 HD13 LEU A 255      -6.320  -2.658  -7.617  1.00  1.27           H  
-ATOM    100 HD21 LEU A 255      -3.994   0.144  -7.586  1.00  1.45           H  
-ATOM    101 HD22 LEU A 255      -5.626  -0.291  -7.079  1.00  1.31           H  
-ATOM    102 HD23 LEU A 255      -5.126  -0.539  -8.753  1.00  1.41           H  
-ATOM    103  N   GLY A 256      -1.045  -0.948  -4.356  1.00  0.41           N  
-ATOM    104  CA  GLY A 256      -0.640  -0.322  -3.120  1.00  0.44           C  
-ATOM    105  C   GLY A 256       0.327   0.837  -3.329  1.00  0.41           C  
-ATOM    106  O   GLY A 256       0.083   1.942  -2.847  1.00  0.42           O  
-ATOM    107  H   GLY A 256      -0.834  -1.897  -4.521  1.00  0.43           H  
-ATOM    108  HA2 GLY A 256      -1.523   0.047  -2.620  1.00  0.49           H  
-ATOM    109  HA3 GLY A 256      -0.170  -1.064  -2.496  1.00  0.52           H  
-ATOM    110  N   ILE A 257       1.421   0.602  -4.053  1.00  0.46           N  
-ATOM    111  CA  ILE A 257       2.398   1.665  -4.297  1.00  0.51           C  
-ATOM    112  C   ILE A 257       1.801   2.815  -5.118  1.00  0.45           C  
-ATOM    113  O   ILE A 257       2.067   3.979  -4.830  1.00  0.46           O  
-ATOM    114  CB  ILE A 257       3.702   1.139  -4.957  1.00  0.66           C  
-ATOM    115  CG1 ILE A 257       3.436   0.476  -6.313  1.00  0.70           C  
-ATOM    116  CG2 ILE A 257       4.406   0.166  -4.025  1.00  0.80           C  
-ATOM    117  CD1 ILE A 257       3.757   1.362  -7.499  1.00  0.78           C  
-ATOM    118  H   ILE A 257       1.574  -0.296  -4.428  1.00  0.50           H  
-ATOM    119  HA  ILE A 257       2.666   2.062  -3.328  1.00  0.55           H  
-ATOM    120  HB  ILE A 257       4.359   1.984  -5.105  1.00  0.73           H  
-ATOM    121 HG12 ILE A 257       4.040  -0.415  -6.394  1.00  0.80           H  
-ATOM    122 HG13 ILE A 257       2.393   0.204  -6.374  1.00  0.65           H  
-ATOM    123 HG21 ILE A 257       4.125  -0.845  -4.281  1.00  1.39           H  
-ATOM    124 HG22 ILE A 257       4.118   0.372  -3.004  1.00  1.22           H  
-ATOM    125 HG23 ILE A 257       5.476   0.280  -4.127  1.00  1.29           H  
-ATOM    126 HD11 ILE A 257       3.402   0.892  -8.405  1.00  1.24           H  
-ATOM    127 HD12 ILE A 257       4.826   1.506  -7.562  1.00  1.31           H  
-ATOM    128 HD13 ILE A 257       3.272   2.319  -7.375  1.00  1.17           H  
-ATOM    129  N   THR A 258       0.983   2.496  -6.127  1.00  0.45           N  
-ATOM    130  CA  THR A 258       0.359   3.534  -6.956  1.00  0.50           C  
-ATOM    131  C   THR A 258      -0.606   4.401  -6.146  1.00  0.45           C  
-ATOM    132  O   THR A 258      -0.578   5.630  -6.248  1.00  0.48           O  
-ATOM    133  CB  THR A 258      -0.396   2.946  -8.167  1.00  0.61           C  
-ATOM    134  OG1 THR A 258      -1.240   1.864  -7.753  1.00  0.57           O  
-ATOM    135  CG2 THR A 258       0.574   2.467  -9.236  1.00  0.77           C  
-ATOM    136  H   THR A 258       0.790   1.546  -6.313  1.00  0.47           H  
-ATOM    137  HA  THR A 258       1.151   4.166  -7.332  1.00  0.57           H  
-ATOM    138  HB  THR A 258      -1.014   3.725  -8.591  1.00  0.70           H  
-ATOM    139  HG1 THR A 258      -0.714   1.051  -7.690  1.00  0.75           H  
-ATOM    140 HG21 THR A 258       0.073   2.451 -10.194  1.00  1.30           H  
-ATOM    141 HG22 THR A 258       0.915   1.472  -8.992  1.00  1.32           H  
-ATOM    142 HG23 THR A 258       1.418   3.138  -9.283  1.00  1.29           H  
-ATOM    143  N   THR A 259      -1.452   3.769  -5.335  1.00  0.42           N  
-ATOM    144  CA  THR A 259      -2.410   4.510  -4.512  1.00  0.47           C  
-ATOM    145  C   THR A 259      -1.691   5.348  -3.461  1.00  0.43           C  
-ATOM    146  O   THR A 259      -1.999   6.526  -3.290  1.00  0.48           O  
-ATOM    147  CB  THR A 259      -3.431   3.586  -3.816  1.00  0.53           C  
-ATOM    148  OG1 THR A 259      -2.761   2.513  -3.144  1.00  0.50           O  
-ATOM    149  CG2 THR A 259      -4.426   3.024  -4.821  1.00  0.62           C  
-ATOM    150  H   THR A 259      -1.428   2.787  -5.282  1.00  0.41           H  
-ATOM    151  HA  THR A 259      -2.952   5.176  -5.167  1.00  0.54           H  
-ATOM    152  HB  THR A 259      -3.976   4.168  -3.087  1.00  0.62           H  
-ATOM    153  HG1 THR A 259      -2.535   1.822  -3.786  1.00  0.53           H  
-ATOM    154 HG21 THR A 259      -4.414   3.629  -5.717  1.00  1.13           H  
-ATOM    155 HG22 THR A 259      -5.417   3.035  -4.393  1.00  1.30           H  
-ATOM    156 HG23 THR A 259      -4.151   2.009  -5.069  1.00  1.17           H  
-ATOM    157  N   VAL A 260      -0.718   4.748  -2.776  1.00  0.39           N  
-ATOM    158  CA  VAL A 260       0.052   5.467  -1.763  1.00  0.44           C  
-ATOM    159  C   VAL A 260       0.763   6.675  -2.381  1.00  0.39           C  
-ATOM    160  O   VAL A 260       0.670   7.788  -1.860  1.00  0.45           O  
-ATOM    161  CB  VAL A 260       1.080   4.546  -1.071  1.00  0.53           C  
-ATOM    162  CG1 VAL A 260       2.014   5.345  -0.176  1.00  0.69           C  
-ATOM    163  CG2 VAL A 260       0.365   3.471  -0.266  1.00  0.63           C  
-ATOM    164  H   VAL A 260      -0.503   3.804  -2.966  1.00  0.38           H  
-ATOM    165  HA  VAL A 260      -0.643   5.821  -1.013  1.00  0.52           H  
-ATOM    166  HB  VAL A 260       1.672   4.061  -1.833  1.00  0.53           H  
-ATOM    167 HG11 VAL A 260       2.775   4.692   0.223  1.00  1.25           H  
-ATOM    168 HG12 VAL A 260       1.450   5.779   0.636  1.00  1.22           H  
-ATOM    169 HG13 VAL A 260       2.478   6.131  -0.753  1.00  1.28           H  
-ATOM    170 HG21 VAL A 260       1.052   3.035   0.444  1.00  0.81           H  
-ATOM    171 HG22 VAL A 260       0.003   2.702  -0.934  1.00  0.65           H  
-ATOM    172 HG23 VAL A 260      -0.468   3.910   0.262  1.00  0.80           H  
-ATOM    173  N   LEU A 261       1.444   6.455  -3.508  1.00  0.36           N  
-ATOM    174  CA  LEU A 261       2.143   7.536  -4.208  1.00  0.40           C  
-ATOM    175  C   LEU A 261       1.166   8.622  -4.664  1.00  0.40           C  
-ATOM    176  O   LEU A 261       1.459   9.811  -4.570  1.00  0.45           O  
-ATOM    177  CB  LEU A 261       2.917   6.991  -5.405  1.00  0.50           C  
-ATOM    178  CG  LEU A 261       4.165   6.183  -5.047  1.00  0.63           C  
-ATOM    179  CD1 LEU A 261       4.888   5.739  -6.305  1.00  0.78           C  
-ATOM    180  CD2 LEU A 261       5.087   6.999  -4.152  1.00  0.79           C  
-ATOM    181  H   LEU A 261       1.465   5.546  -3.889  1.00  0.37           H  
-ATOM    182  HA  LEU A 261       2.848   7.971  -3.515  1.00  0.45           H  
-ATOM    183  HB2 LEU A 261       2.254   6.358  -5.977  1.00  0.54           H  
-ATOM    184  HB3 LEU A 261       3.219   7.822  -6.023  1.00  0.58           H  
-ATOM    185  HG  LEU A 261       3.868   5.298  -4.503  1.00  0.69           H  
-ATOM    186 HD11 LEU A 261       4.805   6.510  -7.058  1.00  1.36           H  
-ATOM    187 HD12 LEU A 261       4.442   4.827  -6.673  1.00  1.24           H  
-ATOM    188 HD13 LEU A 261       5.930   5.566  -6.080  1.00  1.33           H  
-ATOM    189 HD21 LEU A 261       6.115   6.769  -4.388  1.00  1.35           H  
-ATOM    190 HD22 LEU A 261       4.892   6.758  -3.117  1.00  1.28           H  
-ATOM    191 HD23 LEU A 261       4.907   8.052  -4.315  1.00  1.35           H  
-ATOM    192  N   THR A 262       0.001   8.202  -5.153  1.00  0.42           N  
-ATOM    193  CA  THR A 262      -1.026   9.140  -5.611  1.00  0.54           C  
-ATOM    194  C   THR A 262      -1.604   9.949  -4.442  1.00  0.56           C  
-ATOM    195  O   THR A 262      -1.760  11.168  -4.525  1.00  0.63           O  
-ATOM    196  CB  THR A 262      -2.180   8.395  -6.314  1.00  0.64           C  
-ATOM    197  OG1 THR A 262      -1.685   7.682  -7.453  1.00  0.69           O  
-ATOM    198  CG2 THR A 262      -3.273   9.357  -6.746  1.00  0.83           C  
-ATOM    199  H   THR A 262      -0.176   7.234  -5.202  1.00  0.40           H  
-ATOM    200  HA  THR A 262      -0.574   9.818  -6.322  1.00  0.59           H  
-ATOM    201  HB  THR A 262      -2.603   7.685  -5.615  1.00  0.63           H  
-ATOM    202  HG1 THR A 262      -1.278   6.851  -7.161  1.00  0.65           H  
-ATOM    203 HG21 THR A 262      -3.583   9.950  -5.896  1.00  1.44           H  
-ATOM    204 HG22 THR A 262      -4.116   8.798  -7.121  1.00  1.33           H  
-ATOM    205 HG23 THR A 262      -2.895  10.007  -7.519  1.00  1.25           H  
-ATOM    206  N   MET A 263      -1.934   9.246  -3.363  1.00  0.55           N  
-ATOM    207  CA  MET A 263      -2.514   9.867  -2.170  1.00  0.65           C  
-ATOM    208  C   MET A 263      -1.542  10.842  -1.489  1.00  0.56           C  
-ATOM    209  O   MET A 263      -1.941  11.933  -1.077  1.00  0.62           O  
-ATOM    210  CB  MET A 263      -2.944   8.783  -1.177  1.00  0.77           C  
-ATOM    211  CG  MET A 263      -3.720   9.316   0.016  1.00  0.97           C  
-ATOM    212  SD  MET A 263      -5.270  10.105  -0.458  1.00  1.22           S  
-ATOM    213  CE  MET A 263      -5.905  10.590   1.144  1.00  1.49           C  
-ATOM    214  H   MET A 263      -1.800   8.271  -3.377  1.00  0.52           H  
-ATOM    215  HA  MET A 263      -3.389  10.416  -2.481  1.00  0.76           H  
-ATOM    216  HB2 MET A 263      -3.568   8.067  -1.693  1.00  0.83           H  
-ATOM    217  HB3 MET A 263      -2.063   8.278  -0.810  1.00  0.72           H  
-ATOM    218  HG2 MET A 263      -3.941   8.493   0.681  1.00  1.09           H  
-ATOM    219  HG3 MET A 263      -3.107  10.039   0.533  1.00  0.98           H  
-ATOM    220  HE1 MET A 263      -6.257  11.610   1.099  1.00  1.77           H  
-ATOM    221  HE2 MET A 263      -5.120  10.513   1.881  1.00  1.94           H  
-ATOM    222  HE3 MET A 263      -6.722   9.939   1.420  1.00  1.85           H  
-ATOM    223  N   THR A 264      -0.272  10.451  -1.357  1.00  0.47           N  
-ATOM    224  CA  THR A 264       0.717  11.320  -0.709  1.00  0.45           C  
-ATOM    225  C   THR A 264       1.065  12.531  -1.584  1.00  0.34           C  
-ATOM    226  O   THR A 264       1.264  12.412  -2.794  1.00  0.37           O  
-ATOM    227  CB  THR A 264       2.014  10.566  -0.325  1.00  0.53           C  
-ATOM    228  OG1 THR A 264       2.865  11.424   0.445  1.00  0.68           O  
-ATOM    229  CG2 THR A 264       2.772  10.078  -1.552  1.00  0.46           C  
-ATOM    230  H   THR A 264       0.002   9.566  -1.693  1.00  0.47           H  
-ATOM    231  HA  THR A 264       0.269  11.686   0.203  1.00  0.56           H  
-ATOM    232  HB  THR A 264       1.746   9.710   0.276  1.00  0.66           H  
-ATOM    233  HG1 THR A 264       3.784  11.221   0.255  1.00  0.95           H  
-ATOM    234 HG21 THR A 264       2.230   9.264  -2.010  1.00  1.16           H  
-ATOM    235 HG22 THR A 264       3.753   9.736  -1.256  1.00  1.11           H  
-ATOM    236 HG23 THR A 264       2.871  10.888  -2.260  1.00  1.09           H  
-ATOM    237  N   THR A 265       1.131  13.701  -0.957  1.00  0.39           N  
-ATOM    238  CA  THR A 265       1.445  14.940  -1.665  1.00  0.42           C  
-ATOM    239  C   THR A 265       2.925  15.020  -2.042  1.00  0.40           C  
-ATOM    240  O   THR A 265       3.797  14.614  -1.268  1.00  0.48           O  
-ATOM    241  CB  THR A 265       1.062  16.186  -0.830  1.00  0.59           C  
-ATOM    242  OG1 THR A 265       1.459  17.384  -1.511  1.00  0.70           O  
-ATOM    243  CG2 THR A 265       1.705  16.147   0.551  1.00  0.69           C  
-ATOM    244  H   THR A 265       0.963  13.732   0.005  1.00  0.51           H  
-ATOM    245  HA  THR A 265       0.859  14.955  -2.573  1.00  0.46           H  
-ATOM    246  HB  THR A 265      -0.011  16.195  -0.706  1.00  0.66           H  
-ATOM    247  HG1 THR A 265       0.842  18.092  -1.300  1.00  1.00           H  
-ATOM    248 HG21 THR A 265       1.108  16.724   1.242  1.00  1.26           H  
-ATOM    249 HG22 THR A 265       2.699  16.566   0.497  1.00  1.23           H  
-ATOM    250 HG23 THR A 265       1.763  15.125   0.893  1.00  1.27           H  
-ATOM    251  N   GLN A 266       3.193  15.547  -3.242  1.00  0.45           N  
-ATOM    252  CA  GLN A 266       4.559  15.709  -3.768  1.00  0.54           C  
-ATOM    253  C   GLN A 266       5.235  14.375  -4.119  1.00  0.51           C  
-ATOM    254  O   GLN A 266       5.746  14.212  -5.228  1.00  0.54           O  
-ATOM    255  CB  GLN A 266       5.428  16.490  -2.777  1.00  0.69           C  
-ATOM    256  CG  GLN A 266       5.011  17.942  -2.613  1.00  0.84           C  
-ATOM    257  CD  GLN A 266       5.873  18.684  -1.612  1.00  1.03           C  
-ATOM    258  OE1 GLN A 266       7.092  18.723  -1.736  1.00  1.17           O  
-ATOM    259  NE2 GLN A 266       5.247  19.281  -0.613  1.00  1.18           N  
-ATOM    260  H   GLN A 266       2.442  15.845  -3.797  1.00  0.52           H  
-ATOM    261  HA  GLN A 266       4.481  16.288  -4.674  1.00  0.63           H  
-ATOM    262  HB2 GLN A 266       5.367  16.009  -1.810  1.00  0.68           H  
-ATOM    263  HB3 GLN A 266       6.452  16.466  -3.116  1.00  0.78           H  
-ATOM    264  HG2 GLN A 266       5.091  18.437  -3.569  1.00  0.94           H  
-ATOM    265  HG3 GLN A 266       3.985  17.973  -2.275  1.00  0.88           H  
-ATOM    266 HE21 GLN A 266       4.272  19.215  -0.570  1.00  1.18           H  
-ATOM    267 HE22 GLN A 266       5.790  19.766   0.041  1.00  1.35           H  
-ATOM    268  N   SER A 267       5.236  13.435  -3.173  1.00  0.54           N  
-ATOM    269  CA  SER A 267       5.852  12.105  -3.357  1.00  0.63           C  
-ATOM    270  C   SER A 267       7.328  12.220  -3.770  1.00  0.71           C  
-ATOM    271  O   SER A 267       7.725  11.758  -4.843  1.00  0.74           O  
-ATOM    272  CB  SER A 267       5.067  11.252  -4.373  1.00  0.62           C  
-ATOM    273  OG  SER A 267       5.160  11.766  -5.692  1.00  0.57           O  
-ATOM    274  H   SER A 267       4.806  13.643  -2.312  1.00  0.58           H  
-ATOM    275  HA  SER A 267       5.815  11.608  -2.397  1.00  0.74           H  
-ATOM    276  HB2 SER A 267       5.460  10.246  -4.369  1.00  0.74           H  
-ATOM    277  HB3 SER A 267       4.026  11.227  -4.084  1.00  0.68           H  
-ATOM    278  HG  SER A 267       5.409  12.706  -5.655  1.00  0.57           H  
-ATOM    279  N   SER A 268       8.124  12.855  -2.900  1.00  0.83           N  
-ATOM    280  CA  SER A 268       9.565  13.070  -3.130  1.00  0.98           C  
-ATOM    281  C   SER A 268       9.809  14.153  -4.190  1.00  0.97           C  
-ATOM    282  O   SER A 268      10.544  13.943  -5.158  1.00  1.04           O  
-ATOM    283  CB  SER A 268      10.285  11.768  -3.525  1.00  1.10           C  
-ATOM    284  OG  SER A 268      10.252  10.817  -2.469  1.00  1.19           O  
-ATOM    285  H   SER A 268       7.728  13.197  -2.072  1.00  0.85           H  
-ATOM    286  HA  SER A 268       9.984  13.420  -2.198  1.00  1.08           H  
-ATOM    287  HB2 SER A 268       9.801  11.341  -4.390  1.00  1.04           H  
-ATOM    288  HB3 SER A 268      11.315  11.988  -3.762  1.00  1.25           H  
-ATOM    289  HG  SER A 268      10.760  10.041  -2.722  1.00  1.48           H  
-ATOM    290  N   GLY A 269       9.191  15.317  -3.990  1.00  0.96           N  
-ATOM    291  CA  GLY A 269       9.350  16.431  -4.918  1.00  1.02           C  
-ATOM    292  C   GLY A 269       8.454  16.342  -6.148  1.00  0.97           C  
-ATOM    293  O   GLY A 269       7.740  17.291  -6.469  1.00  1.15           O  
-ATOM    294  H   GLY A 269       8.626  15.426  -3.199  1.00  0.96           H  
-ATOM    295  HA2 GLY A 269       9.126  17.349  -4.396  1.00  1.15           H  
-ATOM    296  HA3 GLY A 269      10.380  16.463  -5.243  1.00  1.10           H  
-ATOM    297  N   SER A 270       8.499  15.210  -6.840  1.00  0.87           N  
-ATOM    298  CA  SER A 270       7.691  15.012  -8.046  1.00  0.92           C  
-ATOM    299  C   SER A 270       7.526  13.526  -8.352  1.00  0.76           C  
-ATOM    300  O   SER A 270       8.381  12.712  -8.000  1.00  0.72           O  
-ATOM    301  CB  SER A 270       8.336  15.712  -9.245  1.00  1.12           C  
-ATOM    302  OG  SER A 270       9.611  15.164  -9.538  1.00  1.17           O  
-ATOM    303  H   SER A 270       9.095  14.489  -6.537  1.00  0.88           H  
-ATOM    304  HA  SER A 270       6.718  15.442  -7.868  1.00  1.04           H  
-ATOM    305  HB2 SER A 270       7.702  15.594 -10.112  1.00  1.25           H  
-ATOM    306  HB3 SER A 270       8.452  16.763  -9.026  1.00  1.23           H  
-ATOM    307  HG  SER A 270      10.292  15.690  -9.107  1.00  1.43           H  
-ATOM    308  N   ARG A 271       6.424  13.167  -9.010  1.00  0.77           N  
-ATOM    309  CA  ARG A 271       6.165  11.767  -9.349  1.00  0.68           C  
-ATOM    310  C   ARG A 271       6.962  11.311 -10.582  1.00  0.73           C  
-ATOM    311  O   ARG A 271       6.419  10.712 -11.509  1.00  1.15           O  
-ATOM    312  CB  ARG A 271       4.664  11.524  -9.561  1.00  0.81           C  
-ATOM    313  CG  ARG A 271       4.272  10.049  -9.499  1.00  0.80           C  
-ATOM    314  CD  ARG A 271       4.261   9.508  -8.068  1.00  0.75           C  
-ATOM    315  NE  ARG A 271       5.519   9.750  -7.353  1.00  0.64           N  
-ATOM    316  CZ  ARG A 271       6.670   9.172  -7.638  1.00  0.66           C  
-ATOM    317  NH1 ARG A 271       6.753   8.283  -8.592  1.00  0.76           N  
-ATOM    318  NH2 ARG A 271       7.747   9.500  -6.974  1.00  0.70           N  
-ATOM    319  H   ARG A 271       5.774  13.854  -9.267  1.00  0.90           H  
-ATOM    320  HA  ARG A 271       6.485  11.171  -8.509  1.00  0.62           H  
-ATOM    321  HB2 ARG A 271       4.113  12.055  -8.800  1.00  0.89           H  
-ATOM    322  HB3 ARG A 271       4.383  11.907 -10.531  1.00  0.96           H  
-ATOM    323  HG2 ARG A 271       3.286   9.931  -9.921  1.00  0.95           H  
-ATOM    324  HG3 ARG A 271       4.983   9.480 -10.079  1.00  0.81           H  
-ATOM    325  HD2 ARG A 271       3.459   9.986  -7.525  1.00  0.85           H  
-ATOM    326  HD3 ARG A 271       4.081   8.443  -8.103  1.00  0.84           H  
-ATOM    327  HE  ARG A 271       5.499  10.420  -6.625  1.00  0.64           H  
-ATOM    328 HH11 ARG A 271       5.940   8.032  -9.110  1.00  0.82           H  
-ATOM    329 HH12 ARG A 271       7.643   7.864  -8.813  1.00  0.85           H  
-ATOM    330 HH21 ARG A 271       7.700  10.193  -6.251  1.00  0.70           H  
-ATOM    331 HH22 ARG A 271       8.618   9.059  -7.188  1.00  0.82           H  
-ATOM    332  N   ALA A 272       8.260  11.586 -10.584  1.00  0.81           N  
-ATOM    333  CA  ALA A 272       9.123  11.188 -11.692  1.00  0.96           C  
-ATOM    334  C   ALA A 272       9.841   9.870 -11.382  1.00  0.89           C  
-ATOM    335  O   ALA A 272      10.850   9.858 -10.675  1.00  1.09           O  
-ATOM    336  CB  ALA A 272      10.130  12.287 -11.999  1.00  1.24           C  
-ATOM    337  H   ALA A 272       8.651  12.060  -9.814  1.00  1.07           H  
-ATOM    338  HA  ALA A 272       8.500  11.049 -12.566  1.00  1.14           H  
-ATOM    339  HB1 ALA A 272      11.096  12.011 -11.601  1.00  1.46           H  
-ATOM    340  HB2 ALA A 272       9.804  13.211 -11.544  1.00  1.77           H  
-ATOM    341  HB3 ALA A 272      10.204  12.418 -13.068  1.00  1.68           H  
-ATOM    342  N   SER A 273       9.312   8.762 -11.914  1.00  1.02           N  
-ATOM    343  CA  SER A 273       9.896   7.429 -11.696  1.00  1.21           C  
-ATOM    344  C   SER A 273       9.965   7.070 -10.213  1.00  1.07           C  
-ATOM    345  O   SER A 273       9.135   7.502  -9.411  1.00  0.98           O  
-ATOM    346  CB  SER A 273      11.292   7.344 -12.327  1.00  1.55           C  
-ATOM    347  OG  SER A 273      11.235   7.529 -13.731  1.00  1.85           O  
-ATOM    348  H   SER A 273       8.507   8.841 -12.466  1.00  1.21           H  
-ATOM    349  HA  SER A 273       9.259   6.696 -12.175  1.00  1.46           H  
-ATOM    350  HB2 SER A 273      11.925   8.110 -11.904  1.00  1.51           H  
-ATOM    351  HB3 SER A 273      11.719   6.372 -12.122  1.00  1.79           H  
-ATOM    352  HG  SER A 273      12.100   7.793 -14.056  1.00  2.13           H  
-ATOM    353  N   LEU A 274      10.946   6.238  -9.874  1.00  1.30           N  
-ATOM    354  CA  LEU A 274      11.145   5.758  -8.507  1.00  1.40           C  
-ATOM    355  C   LEU A 274       9.940   4.960  -7.934  1.00  1.31           C  
-ATOM    356  O   LEU A 274       9.721   5.001  -6.724  1.00  1.41           O  
-ATOM    357  CB  LEU A 274      11.478   6.935  -7.578  1.00  1.53           C  
-ATOM    358  CG  LEU A 274      12.742   7.724  -7.938  1.00  1.83           C  
-ATOM    359  CD1 LEU A 274      12.925   8.900  -6.992  1.00  2.10           C  
-ATOM    360  CD2 LEU A 274      13.968   6.825  -7.903  1.00  2.22           C  
-ATOM    361  H   LEU A 274      11.550   5.915 -10.573  1.00  1.52           H  
-ATOM    362  HA  LEU A 274      12.000   5.098  -8.527  1.00  1.66           H  
-ATOM    363  HB2 LEU A 274      10.640   7.618  -7.587  1.00  1.39           H  
-ATOM    364  HB3 LEU A 274      11.594   6.551  -6.577  1.00  1.73           H  
-ATOM    365  HG  LEU A 274      12.641   8.114  -8.941  1.00  1.76           H  
-ATOM    366 HD11 LEU A 274      12.494   8.660  -6.030  1.00  2.38           H  
-ATOM    367 HD12 LEU A 274      12.433   9.771  -7.399  1.00  2.47           H  
-ATOM    368 HD13 LEU A 274      13.979   9.105  -6.873  1.00  2.39           H  
-ATOM    369 HD21 LEU A 274      13.720   5.863  -8.325  1.00  2.58           H  
-ATOM    370 HD22 LEU A 274      14.293   6.700  -6.881  1.00  2.48           H  
-ATOM    371 HD23 LEU A 274      14.761   7.278  -8.480  1.00  2.58           H  
-ATOM    372  N   PRO A 275       9.133   4.214  -8.758  1.00  1.37           N  
-ATOM    373  CA  PRO A 275       7.988   3.432  -8.232  1.00  1.53           C  
-ATOM    374  C   PRO A 275       8.386   2.475  -7.100  1.00  1.63           C  
-ATOM    375  O   PRO A 275       7.615   2.235  -6.170  1.00  1.82           O  
-ATOM    376  CB  PRO A 275       7.519   2.634  -9.446  1.00  1.78           C  
-ATOM    377  CG  PRO A 275       7.895   3.489 -10.593  1.00  1.75           C  
-ATOM    378  CD  PRO A 275       9.233   4.064 -10.231  1.00  1.54           C  
-ATOM    379  HA  PRO A 275       7.193   4.078  -7.899  1.00  1.56           H  
-ATOM    380  HB2 PRO A 275       8.025   1.679  -9.475  1.00  1.90           H  
-ATOM    381  HB3 PRO A 275       6.451   2.485  -9.396  1.00  1.97           H  
-ATOM    382  HG2 PRO A 275       7.963   2.898 -11.494  1.00  2.00           H  
-ATOM    383  HG3 PRO A 275       7.170   4.279 -10.706  1.00  1.76           H  
-ATOM    384  HD2 PRO A 275      10.028   3.395 -10.511  1.00  1.72           H  
-ATOM    385  HD3 PRO A 275       9.364   5.032 -10.705  1.00  1.48           H  
-ATOM    386  N   LYS A 276       9.604   1.948  -7.193  1.00  1.67           N  
-ATOM    387  CA  LYS A 276      10.150   1.033  -6.191  1.00  1.84           C  
-ATOM    388  C   LYS A 276      11.670   0.956  -6.306  1.00  2.07           C  
-ATOM    389  O   LYS A 276      12.290   0.251  -5.476  1.00  2.32           O  
-ATOM    390  CB  LYS A 276       9.552  -0.369  -6.331  1.00  2.03           C  
-ATOM    391  CG  LYS A 276       8.620  -0.742  -5.190  1.00  2.32           C  
-ATOM    392  CD  LYS A 276       9.333  -0.758  -3.841  1.00  2.52           C  
-ATOM    393  CE  LYS A 276      10.203  -1.998  -3.657  1.00  2.62           C  
-ATOM    394  NZ  LYS A 276      11.495  -1.919  -4.399  1.00  2.58           N  
-ATOM    395  OXT LYS A 276      12.225   1.595  -7.226  1.00  2.24           O  
-ATOM    396  H   LYS A 276      10.169   2.196  -7.955  1.00  1.71           H  
-ATOM    397  HA  LYS A 276       9.897   1.425  -5.216  1.00  1.90           H  
-ATOM    398  HB2 LYS A 276       8.998  -0.423  -7.255  1.00  2.07           H  
-ATOM    399  HB3 LYS A 276      10.356  -1.090  -6.359  1.00  2.17           H  
-ATOM    400  HG2 LYS A 276       7.820  -0.020  -5.147  1.00  2.43           H  
-ATOM    401  HG3 LYS A 276       8.211  -1.723  -5.380  1.00  2.50           H  
-ATOM    402  HD2 LYS A 276       9.961   0.116  -3.770  1.00  2.49           H  
-ATOM    403  HD3 LYS A 276       8.592  -0.733  -3.058  1.00  2.84           H  
-ATOM    404  HE2 LYS A 276      10.413  -2.116  -2.605  1.00  2.91           H  
-ATOM    405  HE3 LYS A 276       9.651  -2.858  -4.008  1.00  2.73           H  
-ATOM    406  HZ1 LYS A 276      11.665  -0.936  -4.745  1.00  2.40           H  
-ATOM    407  HZ2 LYS A 276      11.482  -2.560  -5.216  1.00  2.77           H  
-ATOM    408  HZ3 LYS A 276      12.279  -2.189  -3.774  1.00  2.91           H  
-TER
-ENDMDL
-MODEL      15
-ATOM      1  N   ALA A 249       1.481  -4.140 -13.751  1.00  2.02           N  
-ATOM      2  CA  ALA A 249       1.920  -3.911 -12.347  1.00  1.70           C  
-ATOM      3  C   ALA A 249       1.799  -5.191 -11.517  1.00  1.50           C  
-ATOM      4  O   ALA A 249       1.042  -6.094 -11.874  1.00  1.53           O  
-ATOM      5  CB  ALA A 249       1.111  -2.790 -11.712  1.00  1.51           C  
-ATOM      6  H1  ALA A 249       1.150  -5.122 -13.866  1.00  2.43           H  
-ATOM      7  H2  ALA A 249       2.272  -3.974 -14.408  1.00  1.91           H  
-ATOM      8  H3  ALA A 249       0.703  -3.494 -13.993  1.00  2.37           H  
-ATOM      9  HA  ALA A 249       2.958  -3.607 -12.364  1.00  1.82           H  
-ATOM     10  HB1 ALA A 249       0.632  -3.157 -10.816  1.00  1.87           H  
-ATOM     11  HB2 ALA A 249       0.364  -2.446 -12.408  1.00  1.73           H  
-ATOM     12  HB3 ALA A 249       1.771  -1.973 -11.458  1.00  1.77           H  
-ATOM     13  N   PRO A 250       2.557  -5.291 -10.407  1.00  1.35           N  
-ATOM     14  CA  PRO A 250       2.548  -6.473  -9.527  1.00  1.22           C  
-ATOM     15  C   PRO A 250       1.270  -6.615  -8.674  1.00  0.90           C  
-ATOM     16  O   PRO A 250       1.345  -6.867  -7.470  1.00  0.79           O  
-ATOM     17  CB  PRO A 250       3.770  -6.232  -8.639  1.00  1.34           C  
-ATOM     18  CG  PRO A 250       3.888  -4.757  -8.565  1.00  1.34           C  
-ATOM     19  CD  PRO A 250       3.501  -4.266  -9.924  1.00  1.43           C  
-ATOM     20  HA  PRO A 250       2.694  -7.381 -10.093  1.00  1.35           H  
-ATOM     21  HB2 PRO A 250       3.608  -6.662  -7.668  1.00  1.24           H  
-ATOM     22  HB3 PRO A 250       4.643  -6.667  -9.100  1.00  1.57           H  
-ATOM     23  HG2 PRO A 250       3.212  -4.370  -7.816  1.00  1.13           H  
-ATOM     24  HG3 PRO A 250       4.906  -4.477  -8.339  1.00  1.56           H  
-ATOM     25  HD2 PRO A 250       3.017  -3.306  -9.851  1.00  1.34           H  
-ATOM     26  HD3 PRO A 250       4.367  -4.213 -10.564  1.00  1.70           H  
-ATOM     27  N   ALA A 251       0.108  -6.474  -9.320  1.00  0.91           N  
-ATOM     28  CA  ALA A 251      -1.203  -6.608  -8.661  1.00  0.80           C  
-ATOM     29  C   ALA A 251      -1.319  -5.794  -7.358  1.00  0.57           C  
-ATOM     30  O   ALA A 251      -1.276  -4.563  -7.376  1.00  0.57           O  
-ATOM     31  CB  ALA A 251      -1.505  -8.080  -8.405  1.00  0.86           C  
-ATOM     32  H   ALA A 251       0.131  -6.293 -10.289  1.00  1.11           H  
-ATOM     33  HA  ALA A 251      -1.947  -6.242  -9.353  1.00  1.00           H  
-ATOM     34  HB1 ALA A 251      -2.419  -8.166  -7.837  1.00  1.34           H  
-ATOM     35  HB2 ALA A 251      -0.692  -8.523  -7.849  1.00  1.32           H  
-ATOM     36  HB3 ALA A 251      -1.617  -8.594  -9.349  1.00  1.40           H  
-ATOM     37  N   ARG A 252      -1.485  -6.508  -6.236  1.00  0.52           N  
-ATOM     38  CA  ARG A 252      -1.631  -5.902  -4.904  1.00  0.61           C  
-ATOM     39  C   ARG A 252      -0.540  -4.868  -4.609  1.00  0.52           C  
-ATOM     40  O   ARG A 252      -0.829  -3.753  -4.169  1.00  0.57           O  
-ATOM     41  CB  ARG A 252      -1.604  -7.015  -3.842  1.00  0.85           C  
-ATOM     42  CG  ARG A 252      -1.544  -6.531  -2.396  1.00  1.17           C  
-ATOM     43  CD  ARG A 252      -2.715  -5.626  -2.047  1.00  1.32           C  
-ATOM     44  NE  ARG A 252      -2.902  -5.478  -0.598  1.00  1.76           N  
-ATOM     45  CZ  ARG A 252      -3.352  -6.436   0.201  1.00  2.17           C  
-ATOM     46  NH1 ARG A 252      -3.695  -7.600  -0.286  1.00  2.22           N  
-ATOM     47  NH2 ARG A 252      -3.469  -6.224   1.490  1.00  2.58           N  
-ATOM     48  H   ARG A 252      -1.522  -7.484  -6.309  1.00  0.57           H  
-ATOM     49  HA  ARG A 252      -2.589  -5.412  -4.868  1.00  0.77           H  
-ATOM     50  HB2 ARG A 252      -2.494  -7.616  -3.953  1.00  0.96           H  
-ATOM     51  HB3 ARG A 252      -0.741  -7.639  -4.024  1.00  0.82           H  
-ATOM     52  HG2 ARG A 252      -1.561  -7.389  -1.740  1.00  1.41           H  
-ATOM     53  HG3 ARG A 252      -0.622  -5.985  -2.251  1.00  1.15           H  
-ATOM     54  HD2 ARG A 252      -2.528  -4.651  -2.476  1.00  1.13           H  
-ATOM     55  HD3 ARG A 252      -3.611  -6.041  -2.480  1.00  1.45           H  
-ATOM     56  HE  ARG A 252      -2.674  -4.613  -0.203  1.00  1.79           H  
-ATOM     57 HH11 ARG A 252      -3.622  -7.771  -1.264  1.00  1.97           H  
-ATOM     58 HH12 ARG A 252      -4.031  -8.315   0.323  1.00  2.56           H  
-ATOM     59 HH21 ARG A 252      -3.220  -5.337   1.872  1.00  2.60           H  
-ATOM     60 HH22 ARG A 252      -3.808  -6.948   2.086  1.00  2.92           H  
-ATOM     61  N   VAL A 253       0.706  -5.243  -4.853  1.00  0.58           N  
-ATOM     62  CA  VAL A 253       1.835  -4.351  -4.609  1.00  0.74           C  
-ATOM     63  C   VAL A 253       1.775  -3.125  -5.520  1.00  0.61           C  
-ATOM     64  O   VAL A 253       1.971  -1.996  -5.068  1.00  0.64           O  
-ATOM     65  CB  VAL A 253       3.177  -5.083  -4.803  1.00  1.02           C  
-ATOM     66  CG1 VAL A 253       4.335  -4.208  -4.347  1.00  1.30           C  
-ATOM     67  CG2 VAL A 253       3.170  -6.407  -4.050  1.00  1.19           C  
-ATOM     68  H   VAL A 253       0.871  -6.142  -5.206  1.00  0.61           H  
-ATOM     69  HA  VAL A 253       1.776  -4.023  -3.584  1.00  0.88           H  
-ATOM     70  HB  VAL A 253       3.305  -5.292  -5.855  1.00  1.01           H  
-ATOM     71 HG11 VAL A 253       5.268  -4.713  -4.546  1.00  1.68           H  
-ATOM     72 HG12 VAL A 253       4.246  -4.019  -3.287  1.00  1.72           H  
-ATOM     73 HG13 VAL A 253       4.309  -3.271  -4.883  1.00  1.71           H  
-ATOM     74 HG21 VAL A 253       4.186  -6.734  -3.887  1.00  1.64           H  
-ATOM     75 HG22 VAL A 253       2.642  -7.148  -4.631  1.00  1.60           H  
-ATOM     76 HG23 VAL A 253       2.675  -6.278  -3.098  1.00  1.58           H  
-ATOM     77  N   GLY A 254       1.472  -3.354  -6.795  1.00  0.58           N  
-ATOM     78  CA  GLY A 254       1.360  -2.259  -7.748  1.00  0.66           C  
-ATOM     79  C   GLY A 254       0.261  -1.282  -7.366  1.00  0.47           C  
-ATOM     80  O   GLY A 254       0.481  -0.071  -7.332  1.00  0.49           O  
-ATOM     81  H   GLY A 254       1.303  -4.274  -7.085  1.00  0.58           H  
-ATOM     82  HA2 GLY A 254       2.302  -1.730  -7.787  1.00  0.82           H  
-ATOM     83  HA3 GLY A 254       1.145  -2.665  -8.726  1.00  0.80           H  
-ATOM     84  N   LEU A 255      -0.918  -1.814  -7.050  1.00  0.41           N  
-ATOM     85  CA  LEU A 255      -2.048  -0.985  -6.638  1.00  0.52           C  
-ATOM     86  C   LEU A 255      -1.712  -0.223  -5.355  1.00  0.42           C  
-ATOM     87  O   LEU A 255      -2.011   0.968  -5.227  1.00  0.50           O  
-ATOM     88  CB  LEU A 255      -3.291  -1.839  -6.426  1.00  0.76           C  
-ATOM     89  CG  LEU A 255      -4.588  -1.043  -6.431  1.00  1.06           C  
-ATOM     90  CD1 LEU A 255      -4.815  -0.410  -7.796  1.00  1.25           C  
-ATOM     91  CD2 LEU A 255      -5.759  -1.926  -6.041  1.00  1.33           C  
-ATOM     92  H   LEU A 255      -1.026  -2.794  -7.078  1.00  0.43           H  
-ATOM     93  HA  LEU A 255      -2.257  -0.273  -7.421  1.00  0.67           H  
-ATOM     94  HB2 LEU A 255      -3.336  -2.581  -7.211  1.00  0.84           H  
-ATOM     95  HB3 LEU A 255      -3.204  -2.343  -5.475  1.00  0.75           H  
-ATOM     96  HG  LEU A 255      -4.508  -0.247  -5.709  1.00  1.04           H  
-ATOM     97 HD11 LEU A 255      -5.806  -0.652  -8.148  1.00  1.63           H  
-ATOM     98 HD12 LEU A 255      -4.082  -0.789  -8.495  1.00  1.68           H  
-ATOM     99 HD13 LEU A 255      -4.712   0.663  -7.716  1.00  1.65           H  
-ATOM    100 HD21 LEU A 255      -6.028  -1.734  -5.014  1.00  1.72           H  
-ATOM    101 HD22 LEU A 255      -5.480  -2.963  -6.152  1.00  1.72           H  
-ATOM    102 HD23 LEU A 255      -6.601  -1.710  -6.681  1.00  1.77           H  
-ATOM    103  N   GLY A 256      -1.074  -0.916  -4.410  1.00  0.45           N  
-ATOM    104  CA  GLY A 256      -0.690  -0.294  -3.154  1.00  0.66           C  
-ATOM    105  C   GLY A 256       0.273   0.869  -3.342  1.00  0.60           C  
-ATOM    106  O   GLY A 256       0.004   1.976  -2.882  1.00  0.68           O  
-ATOM    107  H   GLY A 256      -0.855  -1.863  -4.572  1.00  0.43           H  
-ATOM    108  HA2 GLY A 256      -1.579   0.066  -2.659  1.00  0.81           H  
-ATOM    109  HA3 GLY A 256      -0.219  -1.037  -2.527  1.00  0.81           H  
-ATOM    110  N   ILE A 257       1.390   0.634  -4.031  1.00  0.59           N  
-ATOM    111  CA  ILE A 257       2.364   1.703  -4.265  1.00  0.71           C  
-ATOM    112  C   ILE A 257       1.763   2.830  -5.114  1.00  0.52           C  
-ATOM    113  O   ILE A 257       2.060   4.000  -4.893  1.00  0.60           O  
-ATOM    114  CB  ILE A 257       3.684   1.183  -4.901  1.00  0.95           C  
-ATOM    115  CG1 ILE A 257       3.447   0.506  -6.256  1.00  0.90           C  
-ATOM    116  CG2 ILE A 257       4.382   0.223  -3.951  1.00  1.18           C  
-ATOM    117  CD1 ILE A 257       3.677   1.414  -7.448  1.00  1.05           C  
-ATOM    118  H   ILE A 257       1.559  -0.268  -4.391  1.00  0.59           H  
-ATOM    119  HA  ILE A 257       2.611   2.116  -3.296  1.00  0.86           H  
-ATOM    120  HB  ILE A 257       4.336   2.033  -5.044  1.00  1.07           H  
-ATOM    121 HG12 ILE A 257       4.116  -0.335  -6.350  1.00  1.06           H  
-ATOM    122 HG13 ILE A 257       2.427   0.154  -6.300  1.00  0.71           H  
-ATOM    123 HG21 ILE A 257       3.643  -0.298  -3.359  1.00  1.66           H  
-ATOM    124 HG22 ILE A 257       5.040   0.777  -3.297  1.00  1.63           H  
-ATOM    125 HG23 ILE A 257       4.958  -0.492  -4.520  1.00  1.51           H  
-ATOM    126 HD11 ILE A 257       2.749   1.900  -7.715  1.00  1.49           H  
-ATOM    127 HD12 ILE A 257       4.030   0.829  -8.283  1.00  1.50           H  
-ATOM    128 HD13 ILE A 257       4.414   2.162  -7.194  1.00  1.51           H  
-ATOM    129  N   THR A 258       0.903   2.474  -6.073  1.00  0.38           N  
-ATOM    130  CA  THR A 258       0.255   3.470  -6.934  1.00  0.46           C  
-ATOM    131  C   THR A 258      -0.677   4.374  -6.123  1.00  0.37           C  
-ATOM    132  O   THR A 258      -0.637   5.601  -6.253  1.00  0.43           O  
-ATOM    133  CB  THR A 258      -0.556   2.807  -8.071  1.00  0.69           C  
-ATOM    134  OG1 THR A 258       0.300   1.980  -8.870  1.00  0.86           O  
-ATOM    135  CG2 THR A 258      -1.216   3.852  -8.962  1.00  0.97           C  
-ATOM    136  H   THR A 258       0.692   1.519  -6.200  1.00  0.36           H  
-ATOM    137  HA  THR A 258       1.030   4.078  -7.379  1.00  0.64           H  
-ATOM    138  HB  THR A 258      -1.329   2.194  -7.631  1.00  0.65           H  
-ATOM    139  HG1 THR A 258       0.464   1.146  -8.403  1.00  0.75           H  
-ATOM    140 HG21 THR A 258      -0.653   4.774  -8.913  1.00  1.45           H  
-ATOM    141 HG22 THR A 258      -2.226   4.029  -8.622  1.00  1.44           H  
-ATOM    142 HG23 THR A 258      -1.237   3.497  -9.981  1.00  1.48           H  
-ATOM    143  N   THR A 259      -1.505   3.766  -5.273  1.00  0.42           N  
-ATOM    144  CA  THR A 259      -2.431   4.526  -4.434  1.00  0.65           C  
-ATOM    145  C   THR A 259      -1.670   5.363  -3.411  1.00  0.62           C  
-ATOM    146  O   THR A 259      -1.945   6.552  -3.249  1.00  0.70           O  
-ATOM    147  CB  THR A 259      -3.444   3.616  -3.709  1.00  0.92           C  
-ATOM    148  OG1 THR A 259      -2.764   2.552  -3.034  1.00  0.89           O  
-ATOM    149  CG2 THR A 259      -4.455   3.040  -4.691  1.00  1.09           C  
-ATOM    150  H   THR A 259      -1.483   2.785  -5.199  1.00  0.42           H  
-ATOM    151  HA  THR A 259      -2.983   5.195  -5.082  1.00  0.76           H  
-ATOM    152  HB  THR A 259      -3.976   4.210  -2.979  1.00  1.13           H  
-ATOM    153  HG1 THR A 259      -2.619   1.820  -3.648  1.00  0.86           H  
-ATOM    154 HG21 THR A 259      -4.421   3.602  -5.614  1.00  1.54           H  
-ATOM    155 HG22 THR A 259      -5.446   3.104  -4.267  1.00  1.50           H  
-ATOM    156 HG23 THR A 259      -4.215   2.006  -4.890  1.00  1.55           H  
-ATOM    157  N   VAL A 260      -0.694   4.748  -2.741  1.00  0.64           N  
-ATOM    158  CA  VAL A 260       0.124   5.458  -1.757  1.00  0.82           C  
-ATOM    159  C   VAL A 260       0.840   6.645  -2.410  1.00  0.69           C  
-ATOM    160  O   VAL A 260       0.811   7.758  -1.882  1.00  0.78           O  
-ATOM    161  CB  VAL A 260       1.159   4.523  -1.091  1.00  1.02           C  
-ATOM    162  CG1 VAL A 260       2.128   5.310  -0.220  1.00  1.29           C  
-ATOM    163  CG2 VAL A 260       0.455   3.456  -0.266  1.00  1.20           C  
-ATOM    164  H   VAL A 260      -0.509   3.795  -2.924  1.00  0.62           H  
-ATOM    165  HA  VAL A 260      -0.539   5.833  -0.988  1.00  0.99           H  
-ATOM    166  HB  VAL A 260       1.725   4.032  -1.868  1.00  0.93           H  
-ATOM    167 HG11 VAL A 260       2.988   4.697   0.006  1.00  1.67           H  
-ATOM    168 HG12 VAL A 260       1.636   5.593   0.700  1.00  1.72           H  
-ATOM    169 HG13 VAL A 260       2.445   6.198  -0.746  1.00  1.69           H  
-ATOM    170 HG21 VAL A 260       1.161   3.001   0.413  1.00  1.45           H  
-ATOM    171 HG22 VAL A 260       0.053   2.700  -0.924  1.00  1.19           H  
-ATOM    172 HG23 VAL A 260      -0.348   3.907   0.298  1.00  1.39           H  
-ATOM    173  N   LEU A 261       1.453   6.410  -3.576  1.00  0.55           N  
-ATOM    174  CA  LEU A 261       2.143   7.474  -4.308  1.00  0.59           C  
-ATOM    175  C   LEU A 261       1.157   8.579  -4.695  1.00  0.44           C  
-ATOM    176  O   LEU A 261       1.440   9.764  -4.528  1.00  0.53           O  
-ATOM    177  CB  LEU A 261       2.816   6.919  -5.562  1.00  0.74           C  
-ATOM    178  CG  LEU A 261       3.733   7.908  -6.282  1.00  1.00           C  
-ATOM    179  CD1 LEU A 261       4.788   8.447  -5.326  1.00  1.21           C  
-ATOM    180  CD2 LEU A 261       4.389   7.244  -7.480  1.00  1.26           C  
-ATOM    181  H   LEU A 261       1.424   5.503  -3.961  1.00  0.51           H  
-ATOM    182  HA  LEU A 261       2.902   7.892  -3.665  1.00  0.78           H  
-ATOM    183  HB2 LEU A 261       3.399   6.054  -5.282  1.00  0.87           H  
-ATOM    184  HB3 LEU A 261       2.047   6.608  -6.254  1.00  0.67           H  
-ATOM    185  HG  LEU A 261       3.147   8.742  -6.638  1.00  0.96           H  
-ATOM    186 HD11 LEU A 261       5.469   7.653  -5.055  1.00  1.67           H  
-ATOM    187 HD12 LEU A 261       4.305   8.824  -4.435  1.00  1.64           H  
-ATOM    188 HD13 LEU A 261       5.335   9.245  -5.803  1.00  1.59           H  
-ATOM    189 HD21 LEU A 261       3.819   6.371  -7.763  1.00  1.61           H  
-ATOM    190 HD22 LEU A 261       5.395   6.950  -7.222  1.00  1.70           H  
-ATOM    191 HD23 LEU A 261       4.417   7.939  -8.305  1.00  1.71           H  
-ATOM    192  N   THR A 262      -0.011   8.175  -5.193  1.00  0.36           N  
-ATOM    193  CA  THR A 262      -1.058   9.122  -5.585  1.00  0.52           C  
-ATOM    194  C   THR A 262      -1.513   9.970  -4.393  1.00  0.58           C  
-ATOM    195  O   THR A 262      -1.612  11.194  -4.483  1.00  0.67           O  
-ATOM    196  CB  THR A 262      -2.283   8.381  -6.167  1.00  0.76           C  
-ATOM    197  OG1 THR A 262      -1.904   7.635  -7.332  1.00  0.84           O  
-ATOM    198  CG2 THR A 262      -3.397   9.353  -6.525  1.00  1.07           C  
-ATOM    199  H   THR A 262      -0.179   7.208  -5.290  1.00  0.33           H  
-ATOM    200  HA  THR A 262      -0.657   9.771  -6.350  1.00  0.60           H  
-ATOM    201  HB  THR A 262      -2.655   7.694  -5.420  1.00  0.77           H  
-ATOM    202  HG1 THR A 262      -1.439   6.828  -7.062  1.00  0.70           H  
-ATOM    203 HG21 THR A 262      -3.102   9.938  -7.381  1.00  1.52           H  
-ATOM    204 HG22 THR A 262      -3.583  10.008  -5.686  1.00  1.51           H  
-ATOM    205 HG23 THR A 262      -4.295   8.800  -6.756  1.00  1.56           H  
-ATOM    206  N   MET A 263      -1.788   9.304  -3.275  1.00  0.68           N  
-ATOM    207  CA  MET A 263      -2.235   9.987  -2.059  1.00  0.93           C  
-ATOM    208  C   MET A 263      -1.160  10.913  -1.476  1.00  0.88           C  
-ATOM    209  O   MET A 263      -1.469  12.024  -1.037  1.00  0.99           O  
-ATOM    210  CB  MET A 263      -2.666   8.968  -1.001  1.00  1.21           C  
-ATOM    211  CG  MET A 263      -3.931   8.206  -1.366  1.00  1.43           C  
-ATOM    212  SD  MET A 263      -5.347   9.290  -1.636  1.00  1.85           S  
-ATOM    213  CE  MET A 263      -5.501  10.074  -0.032  1.00  2.19           C  
-ATOM    214  H   MET A 263      -1.696   8.322  -3.270  1.00  0.65           H  
-ATOM    215  HA  MET A 263      -3.092  10.586  -2.324  1.00  1.04           H  
-ATOM    216  HB2 MET A 263      -1.868   8.252  -0.862  1.00  1.16           H  
-ATOM    217  HB3 MET A 263      -2.840   9.484  -0.068  1.00  1.47           H  
-ATOM    218  HG2 MET A 263      -3.747   7.646  -2.271  1.00  1.34           H  
-ATOM    219  HG3 MET A 263      -4.167   7.523  -0.564  1.00  1.60           H  
-ATOM    220  HE1 MET A 263      -6.303   9.607   0.522  1.00  2.58           H  
-ATOM    221  HE2 MET A 263      -5.718  11.124  -0.162  1.00  2.68           H  
-ATOM    222  HE3 MET A 263      -4.575   9.964   0.512  1.00  2.16           H  
-ATOM    223  N   THR A 264       0.097  10.461  -1.445  1.00  0.83           N  
-ATOM    224  CA  THR A 264       1.177  11.282  -0.882  1.00  0.98           C  
-ATOM    225  C   THR A 264       1.490  12.521  -1.731  1.00  0.83           C  
-ATOM    226  O   THR A 264       1.868  13.558  -1.186  1.00  0.99           O  
-ATOM    227  CB  THR A 264       2.485  10.491  -0.640  1.00  1.14           C  
-ATOM    228  OG1 THR A 264       3.440  11.335   0.014  1.00  1.42           O  
-ATOM    229  CG2 THR A 264       3.087   9.977  -1.937  1.00  1.00           C  
-ATOM    230  H   THR A 264       0.299   9.556  -1.789  1.00  0.77           H  
-ATOM    231  HA  THR A 264       0.830  11.627   0.084  1.00  1.17           H  
-ATOM    232  HB  THR A 264       2.266   9.647  -0.001  1.00  1.26           H  
-ATOM    233  HG1 THR A 264       3.249  12.259  -0.190  1.00  1.66           H  
-ATOM    234 HG21 THR A 264       4.157   9.887  -1.826  1.00  1.40           H  
-ATOM    235 HG22 THR A 264       2.864  10.668  -2.737  1.00  1.44           H  
-ATOM    236 HG23 THR A 264       2.668   9.009  -2.170  1.00  1.43           H  
-ATOM    237  N   THR A 265       1.343  12.428  -3.055  1.00  0.61           N  
-ATOM    238  CA  THR A 265       1.629  13.577  -3.928  1.00  0.60           C  
-ATOM    239  C   THR A 265       0.420  14.522  -4.047  1.00  0.44           C  
-ATOM    240  O   THR A 265       0.210  15.164  -5.080  1.00  0.52           O  
-ATOM    241  CB  THR A 265       2.085  13.136  -5.343  1.00  0.74           C  
-ATOM    242  OG1 THR A 265       2.445  14.283  -6.123  1.00  0.90           O  
-ATOM    243  CG2 THR A 265       0.993  12.362  -6.069  1.00  0.71           C  
-ATOM    244  H   THR A 265       1.042  11.578  -3.450  1.00  0.55           H  
-ATOM    245  HA  THR A 265       2.443  14.127  -3.477  1.00  0.77           H  
-ATOM    246  HB  THR A 265       2.950  12.496  -5.242  1.00  0.90           H  
-ATOM    247  HG1 THR A 265       1.714  14.915  -6.113  1.00  0.81           H  
-ATOM    248 HG21 THR A 265       0.765  12.851  -7.004  1.00  1.28           H  
-ATOM    249 HG22 THR A 265       0.105  12.330  -5.454  1.00  1.22           H  
-ATOM    250 HG23 THR A 265       1.332  11.355  -6.263  1.00  1.25           H  
-ATOM    251  N   GLN A 266      -0.362  14.618  -2.976  1.00  0.47           N  
-ATOM    252  CA  GLN A 266      -1.541  15.487  -2.949  1.00  0.53           C  
-ATOM    253  C   GLN A 266      -1.145  16.955  -2.709  1.00  0.50           C  
-ATOM    254  O   GLN A 266      -1.606  17.594  -1.759  1.00  0.61           O  
-ATOM    255  CB  GLN A 266      -2.514  15.005  -1.867  1.00  0.79           C  
-ATOM    256  CG  GLN A 266      -3.894  15.643  -1.938  1.00  0.95           C  
-ATOM    257  CD  GLN A 266      -4.826  15.159  -0.839  1.00  1.23           C  
-ATOM    258  OE1 GLN A 266      -5.956  15.621  -0.722  1.00  1.40           O  
-ATOM    259  NE2 GLN A 266      -4.363  14.221  -0.030  1.00  1.35           N  
-ATOM    260  H   GLN A 266      -0.138  14.093  -2.178  1.00  0.62           H  
-ATOM    261  HA  GLN A 266      -2.025  15.413  -3.911  1.00  0.59           H  
-ATOM    262  HB2 GLN A 266      -2.633  13.936  -1.960  1.00  0.89           H  
-ATOM    263  HB3 GLN A 266      -2.090  15.225  -0.897  1.00  0.88           H  
-ATOM    264  HG2 GLN A 266      -3.786  16.714  -1.849  1.00  0.91           H  
-ATOM    265  HG3 GLN A 266      -4.337  15.406  -2.895  1.00  1.01           H  
-ATOM    266 HE21 GLN A 266      -3.455  13.885  -0.179  1.00  1.28           H  
-ATOM    267 HE22 GLN A 266      -4.949  13.906   0.685  1.00  1.57           H  
-ATOM    268  N   SER A 267      -0.282  17.480  -3.574  1.00  0.51           N  
-ATOM    269  CA  SER A 267       0.191  18.865  -3.468  1.00  0.64           C  
-ATOM    270  C   SER A 267      -0.880  19.869  -3.906  1.00  0.70           C  
-ATOM    271  O   SER A 267      -0.653  20.681  -4.802  1.00  0.84           O  
-ATOM    272  CB  SER A 267       1.455  19.063  -4.312  1.00  0.80           C  
-ATOM    273  OG  SER A 267       2.488  18.182  -3.905  1.00  0.81           O  
-ATOM    274  H   SER A 267       0.055  16.915  -4.307  1.00  0.53           H  
-ATOM    275  HA  SER A 267       0.433  19.051  -2.432  1.00  0.68           H  
-ATOM    276  HB2 SER A 267       1.226  18.871  -5.348  1.00  0.84           H  
-ATOM    277  HB3 SER A 267       1.801  20.080  -4.203  1.00  0.95           H  
-ATOM    278  HG  SER A 267       3.342  18.580  -4.097  1.00  1.20           H  
-ATOM    279  N   SER A 268      -2.046  19.810  -3.271  1.00  0.65           N  
-ATOM    280  CA  SER A 268      -3.150  20.714  -3.600  1.00  0.74           C  
-ATOM    281  C   SER A 268      -4.165  20.780  -2.462  1.00  0.72           C  
-ATOM    282  O   SER A 268      -4.463  19.766  -1.826  1.00  0.72           O  
-ATOM    283  CB  SER A 268      -3.856  20.264  -4.883  1.00  0.80           C  
-ATOM    284  OG  SER A 268      -4.449  18.985  -4.723  1.00  0.78           O  
-ATOM    285  H   SER A 268      -2.168  19.137  -2.560  1.00  0.62           H  
-ATOM    286  HA  SER A 268      -2.736  21.699  -3.755  1.00  0.83           H  
-ATOM    287  HB2 SER A 268      -4.629  20.974  -5.134  1.00  0.91           H  
-ATOM    288  HB3 SER A 268      -3.137  20.215  -5.688  1.00  0.86           H  
-ATOM    289  HG  SER A 268      -4.267  18.654  -3.838  1.00  1.08           H  
-ATOM    290  N   GLY A 269      -4.693  21.978  -2.215  1.00  0.79           N  
-ATOM    291  CA  GLY A 269      -5.677  22.173  -1.156  1.00  0.81           C  
-ATOM    292  C   GLY A 269      -7.045  21.589  -1.487  1.00  0.83           C  
-ATOM    293  O   GLY A 269      -7.988  22.326  -1.763  1.00  0.97           O  
-ATOM    294  H   GLY A 269      -4.413  22.744  -2.759  1.00  0.87           H  
-ATOM    295  HA2 GLY A 269      -5.312  21.704  -0.253  1.00  0.79           H  
-ATOM    296  HA3 GLY A 269      -5.786  23.231  -0.975  1.00  0.88           H  
-ATOM    297  N   SER A 270      -7.142  20.264  -1.470  1.00  0.82           N  
-ATOM    298  CA  SER A 270      -8.394  19.566  -1.778  1.00  0.92           C  
-ATOM    299  C   SER A 270      -9.480  19.847  -0.732  1.00  0.75           C  
-ATOM    300  O   SER A 270     -10.586  20.266  -1.068  1.00  0.77           O  
-ATOM    301  CB  SER A 270      -8.141  18.060  -1.853  1.00  1.08           C  
-ATOM    302  OG  SER A 270      -7.615  17.575  -0.627  1.00  1.02           O  
-ATOM    303  H   SER A 270      -6.342  19.734  -1.254  1.00  0.84           H  
-ATOM    304  HA  SER A 270      -8.739  19.912  -2.740  1.00  1.05           H  
-ATOM    305  HB2 SER A 270      -9.071  17.550  -2.059  1.00  1.19           H  
-ATOM    306  HB3 SER A 270      -7.433  17.853  -2.643  1.00  1.22           H  
-ATOM    307  HG  SER A 270      -7.018  16.826  -0.798  1.00  1.13           H  
-ATOM    308  N   ARG A 271      -9.164  19.601   0.536  1.00  0.67           N  
-ATOM    309  CA  ARG A 271     -10.118  19.814   1.625  1.00  0.54           C  
-ATOM    310  C   ARG A 271     -10.259  21.305   1.973  1.00  0.55           C  
-ATOM    311  O   ARG A 271      -9.802  21.757   3.026  1.00  0.74           O  
-ATOM    312  CB  ARG A 271      -9.702  19.008   2.862  1.00  0.68           C  
-ATOM    313  CG  ARG A 271     -10.800  18.893   3.909  1.00  0.70           C  
-ATOM    314  CD  ARG A 271     -10.345  18.106   5.129  1.00  0.91           C  
-ATOM    315  NE  ARG A 271      -9.282  18.784   5.865  1.00  1.07           N  
-ATOM    316  CZ  ARG A 271      -8.797  18.351   7.008  1.00  1.33           C  
-ATOM    317  NH1 ARG A 271      -9.252  17.252   7.546  1.00  1.44           N  
-ATOM    318  NH2 ARG A 271      -7.856  19.016   7.620  1.00  1.59           N  
-ATOM    319  H   ARG A 271      -8.270  19.252   0.743  1.00  0.75           H  
-ATOM    320  HA  ARG A 271     -11.079  19.453   1.288  1.00  0.58           H  
-ATOM    321  HB2 ARG A 271      -9.424  18.011   2.552  1.00  0.86           H  
-ATOM    322  HB3 ARG A 271      -8.846  19.484   3.319  1.00  0.80           H  
-ATOM    323  HG2 ARG A 271     -11.087  19.885   4.223  1.00  0.71           H  
-ATOM    324  HG3 ARG A 271     -11.651  18.394   3.468  1.00  0.80           H  
-ATOM    325  HD2 ARG A 271     -11.191  17.968   5.787  1.00  0.99           H  
-ATOM    326  HD3 ARG A 271      -9.986  17.141   4.803  1.00  1.08           H  
-ATOM    327  HE  ARG A 271      -8.921  19.608   5.484  1.00  1.08           H  
-ATOM    328 HH11 ARG A 271      -9.965  16.731   7.089  1.00  1.38           H  
-ATOM    329 HH12 ARG A 271      -8.888  16.940   8.446  1.00  1.67           H  
-ATOM    330 HH21 ARG A 271      -7.500  19.858   7.225  1.00  1.63           H  
-ATOM    331 HH22 ARG A 271      -7.493  18.673   8.487  1.00  1.81           H  
-ATOM    332  N   ALA A 272     -10.888  22.059   1.073  1.00  0.74           N  
-ATOM    333  CA  ALA A 272     -11.096  23.500   1.257  1.00  1.03           C  
-ATOM    334  C   ALA A 272     -11.908  23.823   2.521  1.00  1.05           C  
-ATOM    335  O   ALA A 272     -11.734  24.881   3.126  1.00  1.30           O  
-ATOM    336  CB  ALA A 272     -11.781  24.087   0.031  1.00  1.36           C  
-ATOM    337  H   ALA A 272     -11.216  21.633   0.249  1.00  0.86           H  
-ATOM    338  HA  ALA A 272     -10.124  23.962   1.344  1.00  1.14           H  
-ATOM    339  HB1 ALA A 272     -12.721  23.581  -0.133  1.00  1.78           H  
-ATOM    340  HB2 ALA A 272     -11.146  23.958  -0.834  1.00  1.78           H  
-ATOM    341  HB3 ALA A 272     -11.962  25.140   0.191  1.00  1.76           H  
-ATOM    342  N   SER A 273     -12.796  22.917   2.919  1.00  0.91           N  
-ATOM    343  CA  SER A 273     -13.618  23.128   4.114  1.00  1.00           C  
-ATOM    344  C   SER A 273     -13.856  21.830   4.868  1.00  0.86           C  
-ATOM    345  O   SER A 273     -13.267  20.797   4.552  1.00  0.76           O  
-ATOM    346  CB  SER A 273     -14.953  23.776   3.758  1.00  1.39           C  
-ATOM    347  OG  SER A 273     -14.759  25.040   3.145  1.00  1.65           O  
-ATOM    348  H   SER A 273     -12.897  22.088   2.406  1.00  0.89           H  
-ATOM    349  HA  SER A 273     -13.077  23.793   4.778  1.00  1.02           H  
-ATOM    350  HB2 SER A 273     -15.491  23.135   3.079  1.00  1.46           H  
-ATOM    351  HB3 SER A 273     -15.531  23.913   4.662  1.00  1.49           H  
-ATOM    352  HG  SER A 273     -13.817  25.251   3.137  1.00  1.72           H  
-ATOM    353  N   LEU A 274     -14.685  21.921   5.906  1.00  1.03           N  
-ATOM    354  CA  LEU A 274     -14.974  20.789   6.786  1.00  1.12           C  
-ATOM    355  C   LEU A 274     -13.688  20.240   7.458  1.00  0.95           C  
-ATOM    356  O   LEU A 274     -13.550  19.021   7.605  1.00  1.11           O  
-ATOM    357  CB  LEU A 274     -15.682  19.667   6.009  1.00  1.32           C  
-ATOM    358  CG  LEU A 274     -17.047  20.032   5.415  1.00  1.65           C  
-ATOM    359  CD1 LEU A 274     -17.606  18.867   4.613  1.00  1.90           C  
-ATOM    360  CD2 LEU A 274     -18.022  20.434   6.512  1.00  1.91           C  
-ATOM    361  H   LEU A 274     -15.081  22.792   6.117  1.00  1.19           H  
-ATOM    362  HA  LEU A 274     -15.638  21.143   7.561  1.00  1.30           H  
-ATOM    363  HB2 LEU A 274     -15.036  19.357   5.201  1.00  1.19           H  
-ATOM    364  HB3 LEU A 274     -15.821  18.830   6.677  1.00  1.47           H  
-ATOM    365  HG  LEU A 274     -16.929  20.872   4.746  1.00  1.65           H  
-ATOM    366 HD11 LEU A 274     -18.518  19.172   4.122  1.00  2.28           H  
-ATOM    367 HD12 LEU A 274     -17.816  18.041   5.278  1.00  2.17           H  
-ATOM    368 HD13 LEU A 274     -16.884  18.559   3.872  1.00  2.18           H  
-ATOM    369 HD21 LEU A 274     -18.915  19.833   6.437  1.00  2.19           H  
-ATOM    370 HD22 LEU A 274     -18.280  21.478   6.400  1.00  2.26           H  
-ATOM    371 HD23 LEU A 274     -17.562  20.280   7.477  1.00  2.21           H  
-ATOM    372  N   PRO A 275     -12.717  21.119   7.868  1.00  0.81           N  
-ATOM    373  CA  PRO A 275     -11.451  20.679   8.503  1.00  0.89           C  
-ATOM    374  C   PRO A 275     -11.634  19.722   9.687  1.00  1.16           C  
-ATOM    375  O   PRO A 275     -12.459  19.952  10.572  1.00  1.30           O  
-ATOM    376  CB  PRO A 275     -10.833  21.991   8.987  1.00  1.00           C  
-ATOM    377  CG  PRO A 275     -11.323  22.990   8.012  1.00  0.94           C  
-ATOM    378  CD  PRO A 275     -12.744  22.600   7.730  1.00  0.86           C  
-ATOM    379  HA  PRO A 275     -10.791  20.228   7.779  1.00  0.89           H  
-ATOM    380  HB2 PRO A 275     -11.175  22.208   9.989  1.00  1.13           H  
-ATOM    381  HB3 PRO A 275      -9.757  21.916   8.971  1.00  1.14           H  
-ATOM    382  HG2 PRO A 275     -11.276  23.982   8.438  1.00  1.15           H  
-ATOM    383  HG3 PRO A 275     -10.734  22.934   7.108  1.00  0.92           H  
-ATOM    384  HD2 PRO A 275     -13.410  23.050   8.446  1.00  1.04           H  
-ATOM    385  HD3 PRO A 275     -13.025  22.887   6.716  1.00  0.85           H  
-ATOM    386  N   LYS A 276     -10.838  18.649   9.688  1.00  1.34           N  
-ATOM    387  CA  LYS A 276     -10.877  17.635  10.749  1.00  1.67           C  
-ATOM    388  C   LYS A 276      -9.479  17.053  10.994  1.00  1.92           C  
-ATOM    389  O   LYS A 276      -9.173  16.709  12.154  1.00  2.26           O  
-ATOM    390  CB  LYS A 276     -11.837  16.498  10.372  1.00  1.78           C  
-ATOM    391  CG  LYS A 276     -13.287  16.935  10.236  1.00  1.79           C  
-ATOM    392  CD  LYS A 276     -14.153  15.827   9.664  1.00  2.02           C  
-ATOM    393  CE  LYS A 276     -15.582  16.298   9.444  1.00  2.20           C  
-ATOM    394  NZ  LYS A 276     -15.644  17.497   8.563  1.00  2.05           N  
-ATOM    395  OXT LYS A 276      -8.703  16.946  10.010  1.00  1.88           O  
-ATOM    396  H   LYS A 276     -10.200  18.536   8.954  1.00  1.31           H  
-ATOM    397  HA  LYS A 276     -11.227  18.108  11.656  1.00  1.77           H  
-ATOM    398  HB2 LYS A 276     -11.521  16.077   9.429  1.00  1.71           H  
-ATOM    399  HB3 LYS A 276     -11.785  15.733  11.131  1.00  2.07           H  
-ATOM    400  HG2 LYS A 276     -13.663  17.205  11.210  1.00  1.98           H  
-ATOM    401  HG3 LYS A 276     -13.333  17.792   9.579  1.00  1.60           H  
-ATOM    402  HD2 LYS A 276     -13.739  15.510   8.719  1.00  1.95           H  
-ATOM    403  HD3 LYS A 276     -14.159  14.997  10.354  1.00  2.26           H  
-ATOM    404  HE2 LYS A 276     -16.144  15.498   8.986  1.00  2.41           H  
-ATOM    405  HE3 LYS A 276     -16.019  16.543  10.402  1.00  2.38           H  
-ATOM    406  HZ1 LYS A 276     -16.127  18.276   9.054  1.00  2.17           H  
-ATOM    407  HZ2 LYS A 276     -16.165  17.277   7.692  1.00  2.38           H  
-ATOM    408  HZ3 LYS A 276     -14.678  17.809   8.307  1.00  2.23           H  
-TER
-ENDMDL
-MODEL      16
-ATOM      1  N   ALA A 249       4.871  -8.982  -0.495  1.00  2.03           N  
-ATOM      2  CA  ALA A 249       3.957  -7.845  -0.781  1.00  1.79           C  
-ATOM      3  C   ALA A 249       2.499  -8.243  -0.558  1.00  1.42           C  
-ATOM      4  O   ALA A 249       2.144  -9.415  -0.677  1.00  1.27           O  
-ATOM      5  CB  ALA A 249       4.154  -7.340  -2.203  1.00  1.71           C  
-ATOM      6  H1  ALA A 249       5.399  -8.805   0.384  1.00  2.32           H  
-ATOM      7  H2  ALA A 249       5.549  -9.105  -1.274  1.00  2.28           H  
-ATOM      8  H3  ALA A 249       4.322  -9.861  -0.386  1.00  1.87           H  
-ATOM      9  HA  ALA A 249       4.201  -7.038  -0.104  1.00  2.05           H  
-ATOM     10  HB1 ALA A 249       4.354  -8.172  -2.858  1.00  1.99           H  
-ATOM     11  HB2 ALA A 249       4.985  -6.651  -2.226  1.00  1.95           H  
-ATOM     12  HB3 ALA A 249       3.258  -6.832  -2.529  1.00  2.00           H  
-ATOM     13  N   PRO A 250       1.640  -7.266  -0.219  1.00  1.44           N  
-ATOM     14  CA  PRO A 250       0.207  -7.481   0.041  1.00  1.36           C  
-ATOM     15  C   PRO A 250      -0.606  -7.783  -1.227  1.00  1.00           C  
-ATOM     16  O   PRO A 250      -1.639  -7.152  -1.469  1.00  1.14           O  
-ATOM     17  CB  PRO A 250      -0.243  -6.140   0.647  1.00  1.73           C  
-ATOM     18  CG  PRO A 250       1.010  -5.413   0.978  1.00  1.99           C  
-ATOM     19  CD  PRO A 250       1.990  -5.855  -0.052  1.00  1.76           C  
-ATOM     20  HA  PRO A 250       0.051  -8.271   0.761  1.00  1.45           H  
-ATOM     21  HB2 PRO A 250      -0.823  -5.595  -0.081  1.00  1.66           H  
-ATOM     22  HB3 PRO A 250      -0.832  -6.321   1.528  1.00  1.96           H  
-ATOM     23  HG2 PRO A 250       0.850  -4.347   0.914  1.00  2.18           H  
-ATOM     24  HG3 PRO A 250       1.352  -5.689   1.965  1.00  2.23           H  
-ATOM     25  HD2 PRO A 250       1.844  -5.309  -0.972  1.00  1.63           H  
-ATOM     26  HD3 PRO A 250       2.999  -5.746   0.308  1.00  2.03           H  
-ATOM     27  N   ALA A 251      -0.133  -8.755  -2.018  1.00  0.76           N  
-ATOM     28  CA  ALA A 251      -0.788  -9.178  -3.266  1.00  0.75           C  
-ATOM     29  C   ALA A 251      -0.831  -8.062  -4.324  1.00  0.62           C  
-ATOM     30  O   ALA A 251      -0.178  -8.152  -5.363  1.00  0.69           O  
-ATOM     31  CB  ALA A 251      -2.186  -9.704  -2.976  1.00  1.12           C  
-ATOM     32  H   ALA A 251       0.691  -9.216  -1.744  1.00  0.85           H  
-ATOM     33  HA  ALA A 251      -0.208  -9.998  -3.668  1.00  0.88           H  
-ATOM     34  HB1 ALA A 251      -2.123 -10.514  -2.264  1.00  1.59           H  
-ATOM     35  HB2 ALA A 251      -2.634 -10.061  -3.891  1.00  1.58           H  
-ATOM     36  HB3 ALA A 251      -2.790  -8.910  -2.563  1.00  1.62           H  
-ATOM     37  N   ARG A 252      -1.584  -7.002  -4.052  1.00  0.55           N  
-ATOM     38  CA  ARG A 252      -1.690  -5.878  -4.978  1.00  0.50           C  
-ATOM     39  C   ARG A 252      -0.607  -4.832  -4.728  1.00  0.39           C  
-ATOM     40  O   ARG A 252      -0.898  -3.673  -4.427  1.00  0.37           O  
-ATOM     41  CB  ARG A 252      -3.079  -5.249  -4.911  1.00  0.62           C  
-ATOM     42  CG  ARG A 252      -4.127  -6.071  -5.637  1.00  0.78           C  
-ATOM     43  CD  ARG A 252      -3.664  -6.418  -7.046  1.00  0.84           C  
-ATOM     44  NE  ARG A 252      -4.639  -7.244  -7.757  1.00  1.04           N  
-ATOM     45  CZ  ARG A 252      -4.463  -7.718  -8.973  1.00  1.19           C  
-ATOM     46  NH1 ARG A 252      -3.362  -7.464  -9.631  1.00  1.25           N  
-ATOM     47  NH2 ARG A 252      -5.389  -8.456  -9.529  1.00  1.39           N  
-ATOM     48  H   ARG A 252      -2.071  -6.970  -3.197  1.00  0.64           H  
-ATOM     49  HA  ARG A 252      -1.547  -6.274  -5.973  1.00  0.58           H  
-ATOM     50  HB2 ARG A 252      -3.374  -5.156  -3.875  1.00  0.66           H  
-ATOM     51  HB3 ARG A 252      -3.045  -4.268  -5.359  1.00  0.64           H  
-ATOM     52  HG2 ARG A 252      -4.300  -6.985  -5.088  1.00  0.82           H  
-ATOM     53  HG3 ARG A 252      -5.043  -5.502  -5.696  1.00  0.89           H  
-ATOM     54  HD2 ARG A 252      -3.507  -5.501  -7.597  1.00  0.87           H  
-ATOM     55  HD3 ARG A 252      -2.727  -6.958  -6.979  1.00  0.79           H  
-ATOM     56  HE  ARG A 252      -5.472  -7.456  -7.292  1.00  1.13           H  
-ATOM     57 HH11 ARG A 252      -2.649  -6.909  -9.213  1.00  1.16           H  
-ATOM     58 HH12 ARG A 252      -3.239  -7.825 -10.553  1.00  1.44           H  
-ATOM     59 HH21 ARG A 252      -6.231  -8.659  -9.035  1.00  1.43           H  
-ATOM     60 HH22 ARG A 252      -5.253  -8.818 -10.448  1.00  1.53           H  
-ATOM     61  N   VAL A 253       0.639  -5.262  -4.869  1.00  0.41           N  
-ATOM     62  CA  VAL A 253       1.802  -4.396  -4.678  1.00  0.46           C  
-ATOM     63  C   VAL A 253       1.709  -3.122  -5.522  1.00  0.46           C  
-ATOM     64  O   VAL A 253       1.766  -2.010  -4.996  1.00  0.46           O  
-ATOM     65  CB  VAL A 253       3.099  -5.162  -5.028  1.00  0.63           C  
-ATOM     66  CG1 VAL A 253       2.985  -5.871  -6.373  1.00  0.73           C  
-ATOM     67  CG2 VAL A 253       4.302  -4.232  -5.002  1.00  0.79           C  
-ATOM     68  H   VAL A 253       0.784  -6.201  -5.115  1.00  0.48           H  
-ATOM     69  HA  VAL A 253       1.843  -4.122  -3.636  1.00  0.46           H  
-ATOM     70  HB  VAL A 253       3.242  -5.913  -4.281  1.00  0.66           H  
-ATOM     71 HG11 VAL A 253       2.878  -6.934  -6.213  1.00  1.26           H  
-ATOM     72 HG12 VAL A 253       3.872  -5.681  -6.957  1.00  1.27           H  
-ATOM     73 HG13 VAL A 253       2.119  -5.500  -6.903  1.00  1.28           H  
-ATOM     74 HG21 VAL A 253       4.276  -3.587  -5.868  1.00  1.23           H  
-ATOM     75 HG22 VAL A 253       5.209  -4.818  -5.015  1.00  1.31           H  
-ATOM     76 HG23 VAL A 253       4.274  -3.632  -4.105  1.00  1.40           H  
-ATOM     77  N   GLY A 254       1.559  -3.307  -6.827  1.00  0.55           N  
-ATOM     78  CA  GLY A 254       1.453  -2.184  -7.754  1.00  0.63           C  
-ATOM     79  C   GLY A 254       0.316  -1.235  -7.404  1.00  0.54           C  
-ATOM     80  O   GLY A 254       0.502  -0.018  -7.370  1.00  0.55           O  
-ATOM     81  H   GLY A 254       1.527  -4.228  -7.162  1.00  0.59           H  
-ATOM     82  HA2 GLY A 254       2.381  -1.634  -7.741  1.00  0.70           H  
-ATOM     83  HA3 GLY A 254       1.289  -2.570  -8.749  1.00  0.74           H  
-ATOM     84  N   LEU A 255      -0.856  -1.795  -7.116  1.00  0.50           N  
-ATOM     85  CA  LEU A 255      -2.017  -0.992  -6.740  1.00  0.49           C  
-ATOM     86  C   LEU A 255      -1.757  -0.225  -5.451  1.00  0.36           C  
-ATOM     87  O   LEU A 255      -2.057   0.967  -5.350  1.00  0.38           O  
-ATOM     88  CB  LEU A 255      -3.246  -1.878  -6.561  1.00  0.58           C  
-ATOM     89  CG  LEU A 255      -4.195  -1.900  -7.751  1.00  0.78           C  
-ATOM     90  CD1 LEU A 255      -5.398  -2.773  -7.445  1.00  0.89           C  
-ATOM     91  CD2 LEU A 255      -4.627  -0.483  -8.100  1.00  0.88           C  
-ATOM     92  H   LEU A 255      -0.935  -2.769  -7.140  1.00  0.53           H  
-ATOM     93  HA  LEU A 255      -2.209  -0.290  -7.537  1.00  0.59           H  
-ATOM     94  HB2 LEU A 255      -2.912  -2.888  -6.371  1.00  0.58           H  
-ATOM     95  HB3 LEU A 255      -3.793  -1.528  -5.699  1.00  0.59           H  
-ATOM     96  HG  LEU A 255      -3.682  -2.316  -8.606  1.00  0.85           H  
-ATOM     97 HD11 LEU A 255      -5.063  -3.716  -7.040  1.00  1.41           H  
-ATOM     98 HD12 LEU A 255      -5.956  -2.947  -8.352  1.00  1.36           H  
-ATOM     99 HD13 LEU A 255      -6.028  -2.274  -6.723  1.00  1.35           H  
-ATOM    100 HD21 LEU A 255      -3.819   0.025  -8.605  1.00  1.27           H  
-ATOM    101 HD22 LEU A 255      -4.874   0.051  -7.194  1.00  1.37           H  
-ATOM    102 HD23 LEU A 255      -5.491  -0.518  -8.744  1.00  1.44           H  
-ATOM    103  N   GLY A 256      -1.195  -0.918  -4.469  1.00  0.28           N  
-ATOM    104  CA  GLY A 256      -0.897  -0.299  -3.196  1.00  0.27           C  
-ATOM    105  C   GLY A 256       0.115   0.827  -3.313  1.00  0.27           C  
-ATOM    106  O   GLY A 256      -0.134   1.936  -2.838  1.00  0.30           O  
-ATOM    107  H   GLY A 256      -0.978  -1.869  -4.611  1.00  0.31           H  
-ATOM    108  HA2 GLY A 256      -1.815   0.098  -2.786  1.00  0.33           H  
-ATOM    109  HA3 GLY A 256      -0.512  -1.051  -2.527  1.00  0.34           H  
-ATOM    110  N   ILE A 257       1.255   0.559  -3.959  1.00  0.34           N  
-ATOM    111  CA  ILE A 257       2.282   1.590  -4.129  1.00  0.46           C  
-ATOM    112  C   ILE A 257       1.755   2.759  -4.966  1.00  0.43           C  
-ATOM    113  O   ILE A 257       2.052   3.918  -4.676  1.00  0.46           O  
-ATOM    114  CB  ILE A 257       3.604   1.036  -4.727  1.00  0.64           C  
-ATOM    115  CG1 ILE A 257       3.397   0.417  -6.114  1.00  0.69           C  
-ATOM    116  CG2 ILE A 257       4.220   0.016  -3.783  1.00  0.73           C  
-ATOM    117  CD1 ILE A 257       3.750   1.345  -7.259  1.00  0.82           C  
-ATOM    118  H   ILE A 257       1.402  -0.343  -4.333  1.00  0.37           H  
-ATOM    119  HA  ILE A 257       2.507   1.970  -3.140  1.00  0.50           H  
-ATOM    120  HB  ILE A 257       4.296   1.861  -4.813  1.00  0.75           H  
-ATOM    121 HG12 ILE A 257       4.015  -0.464  -6.202  1.00  0.79           H  
-ATOM    122 HG13 ILE A 257       2.360   0.136  -6.222  1.00  0.64           H  
-ATOM    123 HG21 ILE A 257       3.471  -0.329  -3.085  1.00  1.23           H  
-ATOM    124 HG22 ILE A 257       5.035   0.474  -3.240  1.00  1.29           H  
-ATOM    125 HG23 ILE A 257       4.594  -0.822  -4.353  1.00  1.29           H  
-ATOM    126 HD11 ILE A 257       2.988   1.282  -8.021  1.00  1.38           H  
-ATOM    127 HD12 ILE A 257       4.703   1.056  -7.677  1.00  1.34           H  
-ATOM    128 HD13 ILE A 257       3.811   2.360  -6.894  1.00  1.28           H  
-ATOM    129  N   THR A 258       0.944   2.455  -5.985  1.00  0.42           N  
-ATOM    130  CA  THR A 258       0.357   3.498  -6.828  1.00  0.47           C  
-ATOM    131  C   THR A 258      -0.593   4.364  -6.003  1.00  0.38           C  
-ATOM    132  O   THR A 258      -0.593   5.590  -6.118  1.00  0.41           O  
-ATOM    133  CB  THR A 258      -0.410   2.910  -8.034  1.00  0.56           C  
-ATOM    134  OG1 THR A 258       0.465   2.100  -8.825  1.00  0.67           O  
-ATOM    135  CG2 THR A 258      -0.999   4.011  -8.904  1.00  0.70           C  
-ATOM    136  H   THR A 258       0.722   1.511  -6.160  1.00  0.43           H  
-ATOM    137  HA  THR A 258       1.160   4.118  -7.202  1.00  0.57           H  
-ATOM    138  HB  THR A 258      -1.217   2.295  -7.661  1.00  0.52           H  
-ATOM    139  HG1 THR A 258       0.560   1.228  -8.410  1.00  0.67           H  
-ATOM    140 HG21 THR A 258      -0.207   4.655  -9.256  1.00  1.22           H  
-ATOM    141 HG22 THR A 258      -1.704   4.590  -8.324  1.00  1.25           H  
-ATOM    142 HG23 THR A 258      -1.507   3.570  -9.749  1.00  1.30           H  
-ATOM    143  N   THR A 259      -1.386   3.716  -5.149  1.00  0.31           N  
-ATOM    144  CA  THR A 259      -2.325   4.425  -4.280  1.00  0.31           C  
-ATOM    145  C   THR A 259      -1.563   5.331  -3.314  1.00  0.29           C  
-ATOM    146  O   THR A 259      -1.901   6.506  -3.153  1.00  0.33           O  
-ATOM    147  CB  THR A 259      -3.211   3.448  -3.475  1.00  0.38           C  
-ATOM    148  OG1 THR A 259      -3.888   2.553  -4.366  1.00  0.46           O  
-ATOM    149  CG2 THR A 259      -4.241   4.199  -2.644  1.00  0.52           C  
-ATOM    150  H   THR A 259      -1.325   2.735  -5.089  1.00  0.31           H  
-ATOM    151  HA  THR A 259      -2.964   5.035  -4.904  1.00  0.38           H  
-ATOM    152  HB  THR A 259      -2.580   2.876  -2.810  1.00  0.39           H  
-ATOM    153  HG1 THR A 259      -3.257   1.907  -4.719  1.00  0.45           H  
-ATOM    154 HG21 THR A 259      -4.091   3.977  -1.598  1.00  1.18           H  
-ATOM    155 HG22 THR A 259      -5.234   3.893  -2.938  1.00  1.17           H  
-ATOM    156 HG23 THR A 259      -4.129   5.262  -2.805  1.00  1.13           H  
-ATOM    157  N   VAL A 260      -0.511   4.784  -2.697  1.00  0.31           N  
-ATOM    158  CA  VAL A 260       0.321   5.552  -1.776  1.00  0.39           C  
-ATOM    159  C   VAL A 260       0.937   6.751  -2.496  1.00  0.40           C  
-ATOM    160  O   VAL A 260       0.859   7.878  -2.012  1.00  0.45           O  
-ATOM    161  CB  VAL A 260       1.441   4.683  -1.160  1.00  0.49           C  
-ATOM    162  CG1 VAL A 260       2.361   5.524  -0.286  1.00  0.67           C  
-ATOM    163  CG2 VAL A 260       0.847   3.537  -0.355  1.00  0.50           C  
-ATOM    164  H   VAL A 260      -0.280   3.844  -2.885  1.00  0.32           H  
-ATOM    165  HA  VAL A 260      -0.311   5.911  -0.975  1.00  0.43           H  
-ATOM    166  HB  VAL A 260       2.029   4.264  -1.964  1.00  0.51           H  
-ATOM    167 HG11 VAL A 260       2.168   6.571  -0.467  1.00  1.24           H  
-ATOM    168 HG12 VAL A 260       3.390   5.300  -0.527  1.00  1.26           H  
-ATOM    169 HG13 VAL A 260       2.176   5.297   0.754  1.00  1.25           H  
-ATOM    170 HG21 VAL A 260       1.121   2.596  -0.811  1.00  1.14           H  
-ATOM    171 HG22 VAL A 260      -0.229   3.628  -0.338  1.00  1.17           H  
-ATOM    172 HG23 VAL A 260       1.227   3.570   0.655  1.00  1.12           H  
-ATOM    173  N   LEU A 261       1.516   6.506  -3.673  1.00  0.40           N  
-ATOM    174  CA  LEU A 261       2.111   7.574  -4.476  1.00  0.47           C  
-ATOM    175  C   LEU A 261       1.061   8.628  -4.828  1.00  0.42           C  
-ATOM    176  O   LEU A 261       1.308   9.828  -4.717  1.00  0.49           O  
-ATOM    177  CB  LEU A 261       2.719   7.002  -5.758  1.00  0.55           C  
-ATOM    178  CG  LEU A 261       4.248   7.022  -5.826  1.00  0.73           C  
-ATOM    179  CD1 LEU A 261       4.767   8.453  -5.796  1.00  1.10           C  
-ATOM    180  CD2 LEU A 261       4.853   6.211  -4.689  1.00  1.20           C  
-ATOM    181  H   LEU A 261       1.525   5.584  -4.021  1.00  0.40           H  
-ATOM    182  HA  LEU A 261       2.891   8.037  -3.891  1.00  0.55           H  
-ATOM    183  HB2 LEU A 261       2.390   5.977  -5.859  1.00  0.81           H  
-ATOM    184  HB3 LEU A 261       2.337   7.567  -6.595  1.00  0.73           H  
-ATOM    185  HG  LEU A 261       4.559   6.577  -6.758  1.00  0.78           H  
-ATOM    186 HD11 LEU A 261       5.784   8.474  -6.158  1.00  1.39           H  
-ATOM    187 HD12 LEU A 261       4.738   8.825  -4.782  1.00  1.61           H  
-ATOM    188 HD13 LEU A 261       4.148   9.074  -6.425  1.00  1.62           H  
-ATOM    189 HD21 LEU A 261       5.053   5.207  -5.032  1.00  1.71           H  
-ATOM    190 HD22 LEU A 261       4.158   6.178  -3.862  1.00  1.67           H  
-ATOM    191 HD23 LEU A 261       5.774   6.671  -4.367  1.00  1.66           H  
-ATOM    192  N   THR A 262      -0.114   8.160  -5.238  1.00  0.36           N  
-ATOM    193  CA  THR A 262      -1.225   9.045  -5.596  1.00  0.40           C  
-ATOM    194  C   THR A 262      -1.620   9.952  -4.425  1.00  0.40           C  
-ATOM    195  O   THR A 262      -1.758  11.164  -4.583  1.00  0.47           O  
-ATOM    196  CB  THR A 262      -2.462   8.231  -6.038  1.00  0.43           C  
-ATOM    197  OG1 THR A 262      -2.141   7.419  -7.175  1.00  0.49           O  
-ATOM    198  CG2 THR A 262      -3.629   9.146  -6.378  1.00  0.55           C  
-ATOM    199  H   THR A 262      -0.240   7.185  -5.297  1.00  0.35           H  
-ATOM    200  HA  THR A 262      -0.909   9.659  -6.425  1.00  0.47           H  
-ATOM    201  HB  THR A 262      -2.758   7.588  -5.222  1.00  0.41           H  
-ATOM    202  HG1 THR A 262      -1.583   6.678  -6.894  1.00  0.50           H  
-ATOM    203 HG21 THR A 262      -3.520  10.080  -5.846  1.00  1.15           H  
-ATOM    204 HG22 THR A 262      -4.555   8.671  -6.084  1.00  1.23           H  
-ATOM    205 HG23 THR A 262      -3.641   9.333  -7.441  1.00  1.13           H  
-ATOM    206  N   MET A 263      -1.810   9.364  -3.249  1.00  0.38           N  
-ATOM    207  CA  MET A 263      -2.195  10.142  -2.069  1.00  0.45           C  
-ATOM    208  C   MET A 263      -1.040  10.998  -1.516  1.00  0.47           C  
-ATOM    209  O   MET A 263      -1.274  12.081  -0.983  1.00  0.55           O  
-ATOM    210  CB  MET A 263      -2.750   9.228  -0.966  1.00  0.52           C  
-ATOM    211  CG  MET A 263      -1.752   8.210  -0.435  1.00  0.56           C  
-ATOM    212  SD  MET A 263      -2.386   7.268   0.964  1.00  0.73           S  
-ATOM    213  CE  MET A 263      -3.822   6.485   0.237  1.00  0.86           C  
-ATOM    214  H   MET A 263      -1.698   8.389  -3.175  1.00  0.36           H  
-ATOM    215  HA  MET A 263      -2.984  10.811  -2.379  1.00  0.50           H  
-ATOM    216  HB2 MET A 263      -3.074   9.841  -0.138  1.00  0.60           H  
-ATOM    217  HB3 MET A 263      -3.601   8.692  -1.357  1.00  0.59           H  
-ATOM    218  HG2 MET A 263      -1.506   7.521  -1.230  1.00  0.59           H  
-ATOM    219  HG3 MET A 263      -0.857   8.732  -0.125  1.00  0.62           H  
-ATOM    220  HE1 MET A 263      -4.584   7.228   0.054  1.00  1.44           H  
-ATOM    221  HE2 MET A 263      -4.206   5.735   0.914  1.00  1.40           H  
-ATOM    222  HE3 MET A 263      -3.544   6.017  -0.697  1.00  1.26           H  
-ATOM    223  N   THR A 264       0.201  10.505  -1.602  1.00  0.45           N  
-ATOM    224  CA  THR A 264       1.349  11.251  -1.057  1.00  0.52           C  
-ATOM    225  C   THR A 264       1.862  12.366  -1.976  1.00  0.50           C  
-ATOM    226  O   THR A 264       2.608  13.232  -1.516  1.00  0.54           O  
-ATOM    227  CB  THR A 264       2.546  10.342  -0.691  1.00  0.60           C  
-ATOM    228  OG1 THR A 264       3.522  11.105   0.033  1.00  0.67           O  
-ATOM    229  CG2 THR A 264       3.203   9.752  -1.930  1.00  0.61           C  
-ATOM    230  H   THR A 264       0.346   9.617  -2.008  1.00  0.44           H  
-ATOM    231  HA  THR A 264       1.009  11.714  -0.142  1.00  0.57           H  
-ATOM    232  HB  THR A 264       2.192   9.535  -0.066  1.00  0.67           H  
-ATOM    233  HG1 THR A 264       3.461  12.035  -0.235  1.00  0.76           H  
-ATOM    234 HG21 THR A 264       3.482  10.550  -2.603  1.00  1.21           H  
-ATOM    235 HG22 THR A 264       2.510   9.089  -2.425  1.00  1.23           H  
-ATOM    236 HG23 THR A 264       4.085   9.200  -1.640  1.00  1.11           H  
-ATOM    237  N   THR A 265       1.493  12.354  -3.260  1.00  0.53           N  
-ATOM    238  CA  THR A 265       1.965  13.391  -4.194  1.00  0.56           C  
-ATOM    239  C   THR A 265       1.237  14.728  -3.976  1.00  0.50           C  
-ATOM    240  O   THR A 265       0.741  15.364  -4.910  1.00  0.56           O  
-ATOM    241  CB  THR A 265       1.855  12.950  -5.677  1.00  0.67           C  
-ATOM    242  OG1 THR A 265       2.373  13.973  -6.536  1.00  0.74           O  
-ATOM    243  CG2 THR A 265       0.420  12.633  -6.072  1.00  0.71           C  
-ATOM    244  H   THR A 265       0.907  11.639  -3.584  1.00  0.60           H  
-ATOM    245  HA  THR A 265       3.013  13.549  -3.979  1.00  0.60           H  
-ATOM    246  HB  THR A 265       2.449  12.055  -5.808  1.00  0.76           H  
-ATOM    247  HG1 THR A 265       1.648  14.533  -6.836  1.00  1.00           H  
-ATOM    248 HG21 THR A 265       0.338  12.619  -7.149  1.00  1.09           H  
-ATOM    249 HG22 THR A 265      -0.239  13.388  -5.671  1.00  1.39           H  
-ATOM    250 HG23 THR A 265       0.141  11.667  -5.678  1.00  1.24           H  
-ATOM    251  N   GLN A 266       1.200  15.151  -2.720  1.00  0.46           N  
-ATOM    252  CA  GLN A 266       0.566  16.405  -2.322  1.00  0.50           C  
-ATOM    253  C   GLN A 266       1.438  17.603  -2.707  1.00  0.51           C  
-ATOM    254  O   GLN A 266       2.003  18.275  -1.842  1.00  0.53           O  
-ATOM    255  CB  GLN A 266       0.327  16.401  -0.813  1.00  0.53           C  
-ATOM    256  CG  GLN A 266      -0.548  15.257  -0.331  1.00  0.57           C  
-ATOM    257  CD  GLN A 266      -0.666  15.216   1.179  1.00  0.59           C  
-ATOM    258  OE1 GLN A 266       0.323  15.051   1.889  1.00  0.66           O  
-ATOM    259  NE2 GLN A 266      -1.875  15.374   1.684  1.00  0.73           N  
-ATOM    260  H   GLN A 266       1.631  14.598  -2.027  1.00  0.46           H  
-ATOM    261  HA  GLN A 266      -0.382  16.478  -2.832  1.00  0.57           H  
-ATOM    262  HB2 GLN A 266       1.281  16.327  -0.312  1.00  0.52           H  
-ATOM    263  HB3 GLN A 266      -0.146  17.328  -0.534  1.00  0.62           H  
-ATOM    264  HG2 GLN A 266      -1.535  15.374  -0.752  1.00  0.71           H  
-ATOM    265  HG3 GLN A 266      -0.119  14.324  -0.670  1.00  0.62           H  
-ATOM    266 HE21 GLN A 266      -2.621  15.506   1.067  1.00  0.86           H  
-ATOM    267 HE22 GLN A 266      -1.970  15.351   2.657  1.00  0.79           H  
-ATOM    268  N   SER A 267       1.559  17.845  -4.013  1.00  0.62           N  
-ATOM    269  CA  SER A 267       2.380  18.943  -4.539  1.00  0.70           C  
-ATOM    270  C   SER A 267       3.854  18.737  -4.185  1.00  0.57           C  
-ATOM    271  O   SER A 267       4.548  19.673  -3.788  1.00  0.58           O  
-ATOM    272  CB  SER A 267       1.896  20.299  -4.010  1.00  0.82           C  
-ATOM    273  OG  SER A 267       0.552  20.546  -4.386  1.00  1.05           O  
-ATOM    274  H   SER A 267       1.094  17.252  -4.647  1.00  0.71           H  
-ATOM    275  HA  SER A 267       2.283  18.935  -5.615  1.00  0.82           H  
-ATOM    276  HB2 SER A 267       1.962  20.306  -2.931  1.00  0.74           H  
-ATOM    277  HB3 SER A 267       2.519  21.083  -4.413  1.00  0.91           H  
-ATOM    278  HG  SER A 267       0.108  21.035  -3.688  1.00  1.42           H  
-ATOM    279  N   SER A 268       4.315  17.493  -4.325  1.00  0.54           N  
-ATOM    280  CA  SER A 268       5.700  17.129  -4.018  1.00  0.53           C  
-ATOM    281  C   SER A 268       6.694  17.896  -4.892  1.00  0.66           C  
-ATOM    282  O   SER A 268       7.737  18.350  -4.409  1.00  0.70           O  
-ATOM    283  CB  SER A 268       5.901  15.620  -4.198  1.00  0.62           C  
-ATOM    284  OG  SER A 268       5.012  14.880  -3.372  1.00  0.56           O  
-ATOM    285  H   SER A 268       3.701  16.794  -4.635  1.00  0.60           H  
-ATOM    286  HA  SER A 268       5.883  17.383  -2.984  1.00  0.45           H  
-ATOM    287  HB2 SER A 268       5.717  15.356  -5.228  1.00  0.76           H  
-ATOM    288  HB3 SER A 268       6.916  15.361  -3.935  1.00  0.69           H  
-ATOM    289  HG  SER A 268       5.140  15.135  -2.445  1.00  0.63           H  
-ATOM    290  N   GLY A 269       6.360  18.042  -6.175  1.00  0.81           N  
-ATOM    291  CA  GLY A 269       7.220  18.759  -7.112  1.00  0.97           C  
-ATOM    292  C   GLY A 269       7.195  20.270  -6.915  1.00  0.97           C  
-ATOM    293  O   GLY A 269       6.832  21.016  -7.822  1.00  1.45           O  
-ATOM    294  H   GLY A 269       5.515  17.662  -6.490  1.00  0.84           H  
-ATOM    295  HA2 GLY A 269       8.235  18.410  -6.988  1.00  1.02           H  
-ATOM    296  HA3 GLY A 269       6.897  18.536  -8.118  1.00  1.09           H  
-ATOM    297  N   SER A 270       7.579  20.714  -5.725  1.00  0.73           N  
-ATOM    298  CA  SER A 270       7.603  22.139  -5.394  1.00  0.75           C  
-ATOM    299  C   SER A 270       8.741  22.449  -4.419  1.00  0.76           C  
-ATOM    300  O   SER A 270       9.526  23.369  -4.639  1.00  1.12           O  
-ATOM    301  CB  SER A 270       6.262  22.566  -4.786  1.00  0.71           C  
-ATOM    302  OG  SER A 270       5.973  21.824  -3.611  1.00  0.59           O  
-ATOM    303  H   SER A 270       7.852  20.058  -5.044  1.00  0.87           H  
-ATOM    304  HA  SER A 270       7.766  22.690  -6.309  1.00  0.89           H  
-ATOM    305  HB2 SER A 270       6.303  23.615  -4.532  1.00  0.78           H  
-ATOM    306  HB3 SER A 270       5.474  22.400  -5.506  1.00  0.80           H  
-ATOM    307  HG  SER A 270       5.364  21.098  -3.827  1.00  0.64           H  
-ATOM    308  N   ARG A 271       8.827  21.664  -3.346  1.00  0.60           N  
-ATOM    309  CA  ARG A 271       9.874  21.842  -2.340  1.00  0.71           C  
-ATOM    310  C   ARG A 271      10.059  20.553  -1.527  1.00  0.69           C  
-ATOM    311  O   ARG A 271      10.128  20.584  -0.297  1.00  0.77           O  
-ATOM    312  CB  ARG A 271       9.539  23.018  -1.411  1.00  0.78           C  
-ATOM    313  CG  ARG A 271      10.765  23.631  -0.745  1.00  1.16           C  
-ATOM    314  CD  ARG A 271      10.402  24.810   0.145  1.00  1.32           C  
-ATOM    315  NE  ARG A 271       9.670  24.402   1.342  1.00  1.09           N  
-ATOM    316  CZ  ARG A 271       9.345  25.224   2.319  1.00  1.25           C  
-ATOM    317  NH1 ARG A 271       9.667  26.491   2.253  1.00  1.60           N  
-ATOM    318  NH2 ARG A 271       8.700  24.778   3.365  1.00  1.36           N  
-ATOM    319  H   ARG A 271       8.169  20.947  -3.229  1.00  0.67           H  
-ATOM    320  HA  ARG A 271      10.796  22.057  -2.861  1.00  0.82           H  
-ATOM    321  HB2 ARG A 271       9.044  23.787  -1.986  1.00  1.14           H  
-ATOM    322  HB3 ARG A 271       8.870  22.672  -0.637  1.00  0.80           H  
-ATOM    323  HG2 ARG A 271      11.247  22.877  -0.142  1.00  1.34           H  
-ATOM    324  HG3 ARG A 271      11.445  23.969  -1.512  1.00  1.53           H  
-ATOM    325  HD2 ARG A 271      11.311  25.310   0.446  1.00  1.75           H  
-ATOM    326  HD3 ARG A 271       9.789  25.494  -0.424  1.00  1.46           H  
-ATOM    327  HE  ARG A 271       9.414  23.462   1.415  1.00  1.07           H  
-ATOM    328 HH11 ARG A 271      10.157  26.838   1.458  1.00  1.85           H  
-ATOM    329 HH12 ARG A 271       9.421  27.109   2.997  1.00  1.75           H  
-ATOM    330 HH21 ARG A 271       8.452  23.814   3.424  1.00  1.50           H  
-ATOM    331 HH22 ARG A 271       8.460  25.402   4.104  1.00  1.49           H  
-ATOM    332  N   ALA A 272      10.135  19.421  -2.241  1.00  0.69           N  
-ATOM    333  CA  ALA A 272      10.312  18.099  -1.625  1.00  0.73           C  
-ATOM    334  C   ALA A 272       9.155  17.733  -0.685  1.00  0.58           C  
-ATOM    335  O   ALA A 272       9.371  17.273   0.438  1.00  0.66           O  
-ATOM    336  CB  ALA A 272      11.646  18.023  -0.889  1.00  0.95           C  
-ATOM    337  H   ALA A 272      10.071  19.478  -3.217  1.00  0.73           H  
-ATOM    338  HA  ALA A 272      10.339  17.373  -2.425  1.00  0.78           H  
-ATOM    339  HB1 ALA A 272      11.950  19.016  -0.592  1.00  1.36           H  
-ATOM    340  HB2 ALA A 272      12.394  17.598  -1.543  1.00  1.45           H  
-ATOM    341  HB3 ALA A 272      11.538  17.401  -0.013  1.00  1.47           H  
-ATOM    342  N   SER A 273       7.925  17.932  -1.152  1.00  0.44           N  
-ATOM    343  CA  SER A 273       6.737  17.613  -0.349  1.00  0.38           C  
-ATOM    344  C   SER A 273       6.441  16.121  -0.350  1.00  0.40           C  
-ATOM    345  O   SER A 273       5.400  15.675  -0.834  1.00  0.49           O  
-ATOM    346  CB  SER A 273       5.509  18.377  -0.843  1.00  0.40           C  
-ATOM    347  OG  SER A 273       5.793  19.755  -1.019  1.00  0.55           O  
-ATOM    348  H   SER A 273       7.810  18.297  -2.054  1.00  0.47           H  
-ATOM    349  HA  SER A 273       6.936  17.902   0.675  1.00  0.45           H  
-ATOM    350  HB2 SER A 273       5.189  17.961  -1.784  1.00  0.48           H  
-ATOM    351  HB3 SER A 273       4.717  18.274  -0.114  1.00  0.48           H  
-ATOM    352  HG  SER A 273       5.299  20.091  -1.777  1.00  0.78           H  
-ATOM    353  N   LEU A 274       7.350  15.356   0.230  1.00  0.47           N  
-ATOM    354  CA  LEU A 274       7.172  13.919   0.342  1.00  0.54           C  
-ATOM    355  C   LEU A 274       7.341  13.454   1.807  1.00  0.59           C  
-ATOM    356  O   LEU A 274       7.996  12.440   2.060  1.00  0.73           O  
-ATOM    357  CB  LEU A 274       8.175  13.199  -0.568  1.00  0.75           C  
-ATOM    358  CG  LEU A 274       7.866  11.725  -0.851  1.00  0.88           C  
-ATOM    359  CD1 LEU A 274       6.583  11.591  -1.657  1.00  0.91           C  
-ATOM    360  CD2 LEU A 274       9.028  11.070  -1.581  1.00  1.14           C  
-ATOM    361  H   LEU A 274       8.148  15.777   0.622  1.00  0.54           H  
-ATOM    362  HA  LEU A 274       6.170  13.684   0.015  1.00  0.53           H  
-ATOM    363  HB2 LEU A 274       8.211  13.725  -1.512  1.00  0.79           H  
-ATOM    364  HB3 LEU A 274       9.150  13.256  -0.108  1.00  0.89           H  
-ATOM    365  HG  LEU A 274       7.726  11.208   0.087  1.00  0.94           H  
-ATOM    366 HD11 LEU A 274       5.814  12.207  -1.217  1.00  1.41           H  
-ATOM    367 HD12 LEU A 274       6.263  10.559  -1.655  1.00  1.36           H  
-ATOM    368 HD13 LEU A 274       6.763  11.910  -2.674  1.00  1.38           H  
-ATOM    369 HD21 LEU A 274       8.661  10.563  -2.460  1.00  1.55           H  
-ATOM    370 HD22 LEU A 274       9.508  10.357  -0.928  1.00  1.59           H  
-ATOM    371 HD23 LEU A 274       9.741  11.827  -1.873  1.00  1.60           H  
-ATOM    372  N   PRO A 275       6.756  14.181   2.806  1.00  0.59           N  
-ATOM    373  CA  PRO A 275       6.870  13.807   4.234  1.00  0.76           C  
-ATOM    374  C   PRO A 275       6.459  12.359   4.527  1.00  0.80           C  
-ATOM    375  O   PRO A 275       5.502  11.841   3.939  1.00  0.75           O  
-ATOM    376  CB  PRO A 275       5.903  14.771   4.924  1.00  0.87           C  
-ATOM    377  CG  PRO A 275       5.910  15.965   4.046  1.00  0.79           C  
-ATOM    378  CD  PRO A 275       5.948  15.420   2.650  1.00  0.55           C  
-ATOM    379  HA  PRO A 275       7.867  13.982   4.604  1.00  0.88           H  
-ATOM    380  HB2 PRO A 275       4.921  14.325   4.984  1.00  0.91           H  
-ATOM    381  HB3 PRO A 275       6.264  15.006   5.914  1.00  1.06           H  
-ATOM    382  HG2 PRO A 275       5.017  16.551   4.204  1.00  0.89           H  
-ATOM    383  HG3 PRO A 275       6.794  16.552   4.241  1.00  0.89           H  
-ATOM    384  HD2 PRO A 275       4.952  15.199   2.300  1.00  0.55           H  
-ATOM    385  HD3 PRO A 275       6.436  16.123   1.976  1.00  0.51           H  
-ATOM    386  N   LYS A 276       7.185  11.714   5.439  1.00  0.98           N  
-ATOM    387  CA  LYS A 276       6.906  10.326   5.825  1.00  1.09           C  
-ATOM    388  C   LYS A 276       7.118  10.124   7.327  1.00  1.29           C  
-ATOM    389  O   LYS A 276       7.853  10.936   7.933  1.00  1.38           O  
-ATOM    390  CB  LYS A 276       7.804   9.354   5.048  1.00  1.15           C  
-ATOM    391  CG  LYS A 276       7.456   9.245   3.574  1.00  1.05           C  
-ATOM    392  CD  LYS A 276       6.063   8.670   3.373  1.00  1.01           C  
-ATOM    393  CE  LYS A 276       5.567   8.908   1.959  1.00  0.93           C  
-ATOM    394  NZ  LYS A 276       5.482  10.361   1.647  1.00  0.78           N  
-ATOM    395  OXT LYS A 276       6.549   9.160   7.881  1.00  1.43           O  
-ATOM    396  H   LYS A 276       7.929  12.182   5.875  1.00  1.08           H  
-ATOM    397  HA  LYS A 276       5.873  10.118   5.588  1.00  1.05           H  
-ATOM    398  HB2 LYS A 276       8.828   9.686   5.129  1.00  1.23           H  
-ATOM    399  HB3 LYS A 276       7.717   8.373   5.489  1.00  1.26           H  
-ATOM    400  HG2 LYS A 276       7.495  10.229   3.130  1.00  0.96           H  
-ATOM    401  HG3 LYS A 276       8.175   8.600   3.091  1.00  1.18           H  
-ATOM    402  HD2 LYS A 276       6.092   7.607   3.560  1.00  1.16           H  
-ATOM    403  HD3 LYS A 276       5.385   9.142   4.068  1.00  0.98           H  
-ATOM    404  HE2 LYS A 276       6.250   8.438   1.266  1.00  1.06           H  
-ATOM    405  HE3 LYS A 276       4.588   8.466   1.854  1.00  1.00           H  
-ATOM    406  HZ1 LYS A 276       5.296  10.905   2.523  1.00  1.00           H  
-ATOM    407  HZ2 LYS A 276       4.705  10.541   0.968  1.00  0.91           H  
-ATOM    408  HZ3 LYS A 276       6.376  10.690   1.233  1.00  1.18           H  
-TER
-ENDMDL
-MODEL      17
-ATOM      1  N   ALA A 249       1.651 -11.307 -11.781  1.00  2.28           N  
-ATOM      2  CA  ALA A 249       2.257  -9.991 -11.436  1.00  1.79           C  
-ATOM      3  C   ALA A 249       1.777  -9.498 -10.069  1.00  1.37           C  
-ATOM      4  O   ALA A 249       0.681  -9.842  -9.627  1.00  1.54           O  
-ATOM      5  CB  ALA A 249       1.947  -8.961 -12.512  1.00  2.15           C  
-ATOM      6  H1  ALA A 249       2.084 -11.683 -12.650  1.00  2.54           H  
-ATOM      7  H2  ALA A 249       0.627 -11.201 -11.935  1.00  2.76           H  
-ATOM      8  H3  ALA A 249       1.804 -11.987 -11.010  1.00  2.49           H  
-ATOM      9  HA  ALA A 249       3.329 -10.118 -11.398  1.00  1.76           H  
-ATOM     10  HB1 ALA A 249       2.787  -8.887 -13.190  1.00  2.49           H  
-ATOM     11  HB2 ALA A 249       1.776  -8.000 -12.050  1.00  2.49           H  
-ATOM     12  HB3 ALA A 249       1.068  -9.262 -13.059  1.00  2.47           H  
-ATOM     13  N   PRO A 250       2.600  -8.687  -9.381  1.00  1.16           N  
-ATOM     14  CA  PRO A 250       2.282  -8.136  -8.055  1.00  1.10           C  
-ATOM     15  C   PRO A 250       1.165  -7.077  -8.090  1.00  0.84           C  
-ATOM     16  O   PRO A 250       1.410  -5.901  -7.821  1.00  0.92           O  
-ATOM     17  CB  PRO A 250       3.612  -7.496  -7.615  1.00  1.57           C  
-ATOM     18  CG  PRO A 250       4.637  -8.043  -8.541  1.00  1.77           C  
-ATOM     19  CD  PRO A 250       3.918  -8.233  -9.831  1.00  1.51           C  
-ATOM     20  HA  PRO A 250       2.011  -8.916  -7.359  1.00  1.34           H  
-ATOM     21  HB2 PRO A 250       3.543  -6.423  -7.710  1.00  1.66           H  
-ATOM     22  HB3 PRO A 250       3.826  -7.763  -6.596  1.00  1.89           H  
-ATOM     23  HG2 PRO A 250       5.446  -7.336  -8.658  1.00  2.16           H  
-ATOM     24  HG3 PRO A 250       5.006  -8.987  -8.172  1.00  1.91           H  
-ATOM     25  HD2 PRO A 250       3.840  -7.293 -10.359  1.00  1.72           H  
-ATOM     26  HD3 PRO A 250       4.400  -8.980 -10.433  1.00  1.67           H  
-ATOM     27  N   ALA A 251      -0.058  -7.502  -8.432  1.00  0.78           N  
-ATOM     28  CA  ALA A 251      -1.208  -6.589  -8.511  1.00  0.84           C  
-ATOM     29  C   ALA A 251      -1.422  -5.826  -7.197  1.00  0.70           C  
-ATOM     30  O   ALA A 251      -1.609  -4.609  -7.203  1.00  0.78           O  
-ATOM     31  CB  ALA A 251      -2.467  -7.355  -8.888  1.00  1.12           C  
-ATOM     32  H   ALA A 251      -0.187  -8.454  -8.649  1.00  0.89           H  
-ATOM     33  HA  ALA A 251      -1.005  -5.874  -9.298  1.00  0.98           H  
-ATOM     34  HB1 ALA A 251      -2.587  -8.198  -8.224  1.00  1.22           H  
-ATOM     35  HB2 ALA A 251      -2.384  -7.706  -9.907  1.00  1.53           H  
-ATOM     36  HB3 ALA A 251      -3.323  -6.703  -8.803  1.00  1.76           H  
-ATOM     37  N   ARG A 252      -1.375  -6.548  -6.074  1.00  0.65           N  
-ATOM     38  CA  ARG A 252      -1.544  -5.941  -4.749  1.00  0.69           C  
-ATOM     39  C   ARG A 252      -0.473  -4.876  -4.497  1.00  0.54           C  
-ATOM     40  O   ARG A 252      -0.771  -3.770  -4.040  1.00  0.58           O  
-ATOM     41  CB  ARG A 252      -1.486  -7.034  -3.666  1.00  0.84           C  
-ATOM     42  CG  ARG A 252      -1.319  -6.523  -2.235  1.00  1.05           C  
-ATOM     43  CD  ARG A 252      -2.414  -5.540  -1.837  1.00  1.23           C  
-ATOM     44  NE  ARG A 252      -2.489  -5.344  -0.384  1.00  1.54           N  
-ATOM     45  CZ  ARG A 252      -2.913  -6.261   0.468  1.00  1.82           C  
-ATOM     46  NH1 ARG A 252      -3.339  -7.420   0.034  1.00  1.84           N  
-ATOM     47  NH2 ARG A 252      -2.923  -6.014   1.755  1.00  2.16           N  
-ATOM     48  H   ARG A 252      -1.211  -7.511  -6.140  1.00  0.70           H  
-ATOM     49  HA  ARG A 252      -2.514  -5.470  -4.720  1.00  0.85           H  
-ATOM     50  HB2 ARG A 252      -2.401  -7.606  -3.708  1.00  0.99           H  
-ATOM     51  HB3 ARG A 252      -0.657  -7.690  -3.887  1.00  0.80           H  
-ATOM     52  HG2 ARG A 252      -1.348  -7.364  -1.559  1.00  1.26           H  
-ATOM     53  HG3 ARG A 252      -0.360  -6.030  -2.153  1.00  1.02           H  
-ATOM     54  HD2 ARG A 252      -2.207  -4.589  -2.307  1.00  1.16           H  
-ATOM     55  HD3 ARG A 252      -3.360  -5.916  -2.190  1.00  1.35           H  
-ATOM     56  HE  ARG A 252      -2.198  -4.479  -0.032  1.00  1.61           H  
-ATOM     57 HH11 ARG A 252      -3.343  -7.610  -0.944  1.00  1.66           H  
-ATOM     58 HH12 ARG A 252      -3.658  -8.109   0.680  1.00  2.11           H  
-ATOM     59 HH21 ARG A 252      -2.608  -5.129   2.093  1.00  2.21           H  
-ATOM     60 HH22 ARG A 252      -3.244  -6.707   2.397  1.00  2.40           H  
-ATOM     61  N   VAL A 253       0.768  -5.217  -4.814  1.00  0.47           N  
-ATOM     62  CA  VAL A 253       1.887  -4.298  -4.639  1.00  0.47           C  
-ATOM     63  C   VAL A 253       1.748  -3.085  -5.558  1.00  0.37           C  
-ATOM     64  O   VAL A 253       1.868  -1.946  -5.107  1.00  0.38           O  
-ATOM     65  CB  VAL A 253       3.230  -5.007  -4.900  1.00  0.59           C  
-ATOM     66  CG1 VAL A 253       4.399  -4.048  -4.723  1.00  1.48           C  
-ATOM     67  CG2 VAL A 253       3.366  -6.206  -3.975  1.00  1.44           C  
-ATOM     68  H   VAL A 253       0.936  -6.105  -5.183  1.00  0.51           H  
-ATOM     69  HA  VAL A 253       1.877  -3.959  -3.615  1.00  0.58           H  
-ATOM     70  HB  VAL A 253       3.234  -5.365  -5.920  1.00  0.83           H  
-ATOM     71 HG11 VAL A 253       5.129  -4.488  -4.061  1.00  2.04           H  
-ATOM     72 HG12 VAL A 253       4.042  -3.119  -4.301  1.00  2.01           H  
-ATOM     73 HG13 VAL A 253       4.855  -3.855  -5.683  1.00  2.03           H  
-ATOM     74 HG21 VAL A 253       3.855  -5.900  -3.061  1.00  1.99           H  
-ATOM     75 HG22 VAL A 253       3.951  -6.971  -4.458  1.00  1.99           H  
-ATOM     76 HG23 VAL A 253       2.383  -6.593  -3.744  1.00  1.98           H  
-ATOM     77  N   GLY A 254       1.467  -3.340  -6.837  1.00  0.42           N  
-ATOM     78  CA  GLY A 254       1.287  -2.257  -7.797  1.00  0.52           C  
-ATOM     79  C   GLY A 254       0.187  -1.298  -7.370  1.00  0.50           C  
-ATOM     80  O   GLY A 254       0.391  -0.081  -7.332  1.00  0.51           O  
-ATOM     81  H   GLY A 254       1.364  -4.277  -7.128  1.00  0.47           H  
-ATOM     82  HA2 GLY A 254       2.215  -1.710  -7.887  1.00  0.57           H  
-ATOM     83  HA3 GLY A 254       1.032  -2.676  -8.759  1.00  0.67           H  
-ATOM     84  N   LEU A 255      -0.970  -1.853  -7.015  1.00  0.57           N  
-ATOM     85  CA  LEU A 255      -2.100  -1.047  -6.550  1.00  0.69           C  
-ATOM     86  C   LEU A 255      -1.731  -0.258  -5.302  1.00  0.60           C  
-ATOM     87  O   LEU A 255      -2.025   0.933  -5.188  1.00  0.66           O  
-ATOM     88  CB  LEU A 255      -3.295  -1.938  -6.240  1.00  0.88           C  
-ATOM     89  CG  LEU A 255      -4.301  -2.071  -7.371  1.00  1.10           C  
-ATOM     90  CD1 LEU A 255      -5.437  -2.985  -6.952  1.00  1.29           C  
-ATOM     91  CD2 LEU A 255      -4.825  -0.697  -7.764  1.00  1.26           C  
-ATOM     92  H   LEU A 255      -1.061  -2.834  -7.044  1.00  0.59           H  
-ATOM     93  HA  LEU A 255      -2.372  -0.363  -7.338  1.00  0.79           H  
-ATOM     94  HB2 LEU A 255      -2.929  -2.925  -5.993  1.00  0.92           H  
-ATOM     95  HB3 LEU A 255      -3.805  -1.535  -5.378  1.00  0.96           H  
-ATOM     96  HG  LEU A 255      -3.814  -2.505  -8.230  1.00  1.15           H  
-ATOM     97 HD11 LEU A 255      -5.057  -3.984  -6.803  1.00  1.47           H  
-ATOM     98 HD12 LEU A 255      -6.192  -2.996  -7.723  1.00  1.50           H  
-ATOM     99 HD13 LEU A 255      -5.867  -2.622  -6.029  1.00  1.84           H  
-ATOM    100 HD21 LEU A 255      -4.265  -0.325  -8.609  1.00  1.57           H  
-ATOM    101 HD22 LEU A 255      -4.710  -0.018  -6.930  1.00  1.74           H  
-ATOM    102 HD23 LEU A 255      -5.869  -0.770  -8.025  1.00  1.67           H  
-ATOM    103  N   GLY A 256      -1.089  -0.936  -4.362  1.00  0.54           N  
-ATOM    104  CA  GLY A 256      -0.693  -0.297  -3.130  1.00  0.60           C  
-ATOM    105  C   GLY A 256       0.287   0.850  -3.333  1.00  0.51           C  
-ATOM    106  O   GLY A 256       0.050   1.958  -2.854  1.00  0.59           O  
-ATOM    107  H   GLY A 256      -0.886  -1.889  -4.506  1.00  0.53           H  
-ATOM    108  HA2 GLY A 256      -1.579   0.084  -2.645  1.00  0.75           H  
-ATOM    109  HA3 GLY A 256      -0.238  -1.038  -2.491  1.00  0.65           H  
-ATOM    110  N   ILE A 257       1.388   0.604  -4.043  1.00  0.41           N  
-ATOM    111  CA  ILE A 257       2.373   1.660  -4.274  1.00  0.44           C  
-ATOM    112  C   ILE A 257       1.784   2.815  -5.092  1.00  0.38           C  
-ATOM    113  O   ILE A 257       2.044   3.978  -4.792  1.00  0.44           O  
-ATOM    114  CB  ILE A 257       3.679   1.136  -4.929  1.00  0.56           C  
-ATOM    115  CG1 ILE A 257       3.422   0.481  -6.291  1.00  0.56           C  
-ATOM    116  CG2 ILE A 257       4.378   0.158  -3.998  1.00  0.71           C  
-ATOM    117  CD1 ILE A 257       3.762   1.370  -7.469  1.00  0.73           C  
-ATOM    118  H   ILE A 257       1.539  -0.298  -4.414  1.00  0.40           H  
-ATOM    119  HA  ILE A 257       2.636   2.052  -3.300  1.00  0.55           H  
-ATOM    120  HB  ILE A 257       4.338   1.981  -5.068  1.00  0.67           H  
-ATOM    121 HG12 ILE A 257       4.020  -0.414  -6.371  1.00  0.66           H  
-ATOM    122 HG13 ILE A 257       2.376   0.216  -6.364  1.00  0.52           H  
-ATOM    123 HG21 ILE A 257       3.640  -0.435  -3.477  1.00  1.24           H  
-ATOM    124 HG22 ILE A 257       4.972   0.705  -3.280  1.00  1.29           H  
-ATOM    125 HG23 ILE A 257       5.020  -0.493  -4.574  1.00  1.26           H  
-ATOM    126 HD11 ILE A 257       3.317   2.344  -7.325  1.00  1.23           H  
-ATOM    127 HD12 ILE A 257       3.376   0.929  -8.377  1.00  1.31           H  
-ATOM    128 HD13 ILE A 257       4.834   1.473  -7.546  1.00  1.30           H  
-ATOM    129  N   THR A 258       0.974   2.501  -6.107  1.00  0.39           N  
-ATOM    130  CA  THR A 258       0.355   3.546  -6.929  1.00  0.51           C  
-ATOM    131  C   THR A 258      -0.627   4.394  -6.120  1.00  0.53           C  
-ATOM    132  O   THR A 258      -0.603   5.624  -6.203  1.00  0.55           O  
-ATOM    133  CB  THR A 258      -0.374   2.975  -8.165  1.00  0.68           C  
-ATOM    134  OG1 THR A 258      -1.249   1.909  -7.781  1.00  0.69           O  
-ATOM    135  CG2 THR A 258       0.620   2.481  -9.205  1.00  0.78           C  
-ATOM    136  H   THR A 258       0.783   1.554  -6.300  1.00  0.41           H  
-ATOM    137  HA  THR A 258       1.149   4.190  -7.282  1.00  0.55           H  
-ATOM    138  HB  THR A 258      -0.964   3.766  -8.606  1.00  0.81           H  
-ATOM    139  HG1 THR A 258      -0.741   1.087  -7.700  1.00  0.78           H  
-ATOM    140 HG21 THR A 258       1.364   1.862  -8.727  1.00  1.21           H  
-ATOM    141 HG22 THR A 258       1.102   3.326  -9.673  1.00  1.23           H  
-ATOM    142 HG23 THR A 258       0.099   1.903  -9.955  1.00  1.31           H  
-ATOM    143  N   THR A 259      -1.477   3.745  -5.322  1.00  0.60           N  
-ATOM    144  CA  THR A 259      -2.442   4.472  -4.497  1.00  0.74           C  
-ATOM    145  C   THR A 259      -1.727   5.320  -3.453  1.00  0.70           C  
-ATOM    146  O   THR A 259      -2.063   6.488  -3.262  1.00  0.77           O  
-ATOM    147  CB  THR A 259      -3.449   3.535  -3.798  1.00  0.90           C  
-ATOM    148  OG1 THR A 259      -2.761   2.468  -3.136  1.00  0.84           O  
-ATOM    149  CG2 THR A 259      -4.444   2.967  -4.799  1.00  1.04           C  
-ATOM    150  H   THR A 259      -1.446   2.763  -5.277  1.00  0.60           H  
-ATOM    151  HA  THR A 259      -2.996   5.131  -5.151  1.00  0.82           H  
-ATOM    152  HB  THR A 259      -3.994   4.108  -3.061  1.00  1.03           H  
-ATOM    153  HG1 THR A 259      -2.535   1.780  -3.780  1.00  0.84           H  
-ATOM    154 HG21 THR A 259      -4.423   3.557  -5.703  1.00  1.43           H  
-ATOM    155 HG22 THR A 259      -5.437   2.995  -4.375  1.00  1.55           H  
-ATOM    156 HG23 THR A 259      -4.180   1.945  -5.029  1.00  1.35           H  
-ATOM    157  N   VAL A 260      -0.718   4.742  -2.798  1.00  0.63           N  
-ATOM    158  CA  VAL A 260       0.056   5.475  -1.800  1.00  0.70           C  
-ATOM    159  C   VAL A 260       0.756   6.679  -2.438  1.00  0.60           C  
-ATOM    160  O   VAL A 260       0.613   7.806  -1.961  1.00  0.69           O  
-ATOM    161  CB  VAL A 260       1.094   4.568  -1.107  1.00  0.76           C  
-ATOM    162  CG1 VAL A 260       2.037   5.385  -0.236  1.00  0.96           C  
-ATOM    163  CG2 VAL A 260       0.393   3.505  -0.276  1.00  0.91           C  
-ATOM    164  H   VAL A 260      -0.480   3.807  -3.006  1.00  0.59           H  
-ATOM    165  HA  VAL A 260      -0.635   5.835  -1.049  1.00  0.84           H  
-ATOM    166  HB  VAL A 260       1.678   4.073  -1.868  1.00  0.68           H  
-ATOM    167 HG11 VAL A 260       2.836   5.781  -0.846  1.00  1.25           H  
-ATOM    168 HG12 VAL A 260       2.451   4.755   0.537  1.00  1.45           H  
-ATOM    169 HG13 VAL A 260       1.492   6.201   0.215  1.00  1.44           H  
-ATOM    170 HG21 VAL A 260      -0.488   3.930   0.183  1.00  1.38           H  
-ATOM    171 HG22 VAL A 260       1.062   3.147   0.492  1.00  1.04           H  
-ATOM    172 HG23 VAL A 260       0.104   2.683  -0.913  1.00  1.27           H  
-ATOM    173  N   LEU A 261       1.485   6.440  -3.535  1.00  0.48           N  
-ATOM    174  CA  LEU A 261       2.179   7.517  -4.250  1.00  0.48           C  
-ATOM    175  C   LEU A 261       1.197   8.601  -4.699  1.00  0.49           C  
-ATOM    176  O   LEU A 261       1.505   9.788  -4.640  1.00  0.54           O  
-ATOM    177  CB  LEU A 261       2.937   6.964  -5.455  1.00  0.53           C  
-ATOM    178  CG  LEU A 261       4.171   6.128  -5.108  1.00  0.68           C  
-ATOM    179  CD1 LEU A 261       4.887   5.692  -6.373  1.00  0.84           C  
-ATOM    180  CD2 LEU A 261       5.108   6.916  -4.201  1.00  0.86           C  
-ATOM    181  H   LEU A 261       1.544   5.519  -3.883  1.00  0.46           H  
-ATOM    182  HA  LEU A 261       2.894   7.954  -3.568  1.00  0.57           H  
-ATOM    183  HB2 LEU A 261       2.260   6.349  -6.029  1.00  0.52           H  
-ATOM    184  HB3 LEU A 261       3.253   7.794  -6.069  1.00  0.62           H  
-ATOM    185  HG  LEU A 261       3.859   5.241  -4.579  1.00  0.68           H  
-ATOM    186 HD11 LEU A 261       5.119   4.639  -6.310  1.00  1.37           H  
-ATOM    187 HD12 LEU A 261       5.801   6.256  -6.482  1.00  1.43           H  
-ATOM    188 HD13 LEU A 261       4.250   5.870  -7.227  1.00  1.26           H  
-ATOM    189 HD21 LEU A 261       5.783   7.505  -4.803  1.00  1.38           H  
-ATOM    190 HD22 LEU A 261       5.675   6.231  -3.588  1.00  1.43           H  
-ATOM    191 HD23 LEU A 261       4.527   7.569  -3.566  1.00  1.29           H  
-ATOM    192  N   THR A 262       0.010   8.182  -5.132  1.00  0.54           N  
-ATOM    193  CA  THR A 262      -1.029   9.118  -5.568  1.00  0.68           C  
-ATOM    194  C   THR A 262      -1.514   9.968  -4.393  1.00  0.74           C  
-ATOM    195  O   THR A 262      -1.593  11.194  -4.482  1.00  0.80           O  
-ATOM    196  CB  THR A 262      -2.239   8.372  -6.176  1.00  0.83           C  
-ATOM    197  OG1 THR A 262      -1.827   7.610  -7.317  1.00  0.84           O  
-ATOM    198  CG2 THR A 262      -3.337   9.342  -6.581  1.00  1.03           C  
-ATOM    199  H   THR A 262      -0.180   7.214  -5.147  1.00  0.54           H  
-ATOM    200  HA  THR A 262      -0.607   9.765  -6.324  1.00  0.70           H  
-ATOM    201  HB  THR A 262      -2.635   7.699  -5.429  1.00  0.85           H  
-ATOM    202  HG1 THR A 262      -1.322   6.837  -7.020  1.00  0.75           H  
-ATOM    203 HG21 THR A 262      -3.061   9.837  -7.500  1.00  1.46           H  
-ATOM    204 HG22 THR A 262      -3.472  10.078  -5.801  1.00  1.53           H  
-ATOM    205 HG23 THR A 262      -4.260   8.800  -6.726  1.00  1.47           H  
-ATOM    206  N   MET A 263      -1.831   9.301  -3.286  1.00  0.77           N  
-ATOM    207  CA  MET A 263      -2.302   9.976  -2.078  1.00  0.90           C  
-ATOM    208  C   MET A 263      -1.245  10.945  -1.524  1.00  0.84           C  
-ATOM    209  O   MET A 263      -1.537  12.113  -1.272  1.00  0.97           O  
-ATOM    210  CB  MET A 263      -2.674   8.940  -1.014  1.00  1.00           C  
-ATOM    211  CG  MET A 263      -3.269   9.544   0.249  1.00  1.15           C  
-ATOM    212  SD  MET A 263      -3.684   8.295   1.480  1.00  1.25           S  
-ATOM    213  CE  MET A 263      -4.309   9.328   2.802  1.00  1.42           C  
-ATOM    214  H   MET A 263      -1.743   8.319  -3.281  1.00  0.75           H  
-ATOM    215  HA  MET A 263      -3.184  10.540  -2.340  1.00  1.00           H  
-ATOM    216  HB2 MET A 263      -3.397   8.256  -1.433  1.00  1.07           H  
-ATOM    217  HB3 MET A 263      -1.788   8.389  -0.741  1.00  0.96           H  
-ATOM    218  HG2 MET A 263      -2.552  10.226   0.678  1.00  1.20           H  
-ATOM    219  HG3 MET A 263      -4.166  10.082  -0.014  1.00  1.26           H  
-ATOM    220  HE1 MET A 263      -3.539  10.018   3.115  1.00  1.87           H  
-ATOM    221  HE2 MET A 263      -4.596   8.709   3.639  1.00  1.71           H  
-ATOM    222  HE3 MET A 263      -5.168   9.881   2.453  1.00  1.74           H  
-ATOM    223  N   THR A 264      -0.012  10.467  -1.347  1.00  0.71           N  
-ATOM    224  CA  THR A 264       1.062  11.326  -0.832  1.00  0.72           C  
-ATOM    225  C   THR A 264       1.882  11.954  -1.970  1.00  0.56           C  
-ATOM    226  O   THR A 264       3.108  12.044  -1.899  1.00  0.59           O  
-ATOM    227  CB  THR A 264       2.004  10.568   0.142  1.00  0.84           C  
-ATOM    228  OG1 THR A 264       2.986  11.466   0.687  1.00  0.92           O  
-ATOM    229  CG2 THR A 264       2.711   9.402  -0.539  1.00  0.82           C  
-ATOM    230  H   THR A 264       0.181   9.525  -1.574  1.00  0.67           H  
-ATOM    231  HA  THR A 264       0.591  12.126  -0.278  1.00  0.85           H  
-ATOM    232  HB  THR A 264       1.407  10.177   0.954  1.00  1.00           H  
-ATOM    233  HG1 THR A 264       3.498  11.861  -0.041  1.00  0.85           H  
-ATOM    234 HG21 THR A 264       3.532   9.778  -1.133  1.00  1.31           H  
-ATOM    235 HG22 THR A 264       2.012   8.882  -1.177  1.00  1.30           H  
-ATOM    236 HG23 THR A 264       3.089   8.724   0.210  1.00  1.33           H  
-ATOM    237  N   THR A 265       1.189  12.395  -3.019  1.00  0.53           N  
-ATOM    238  CA  THR A 265       1.840  13.016  -4.182  1.00  0.50           C  
-ATOM    239  C   THR A 265       2.603  14.296  -3.815  1.00  0.47           C  
-ATOM    240  O   THR A 265       3.609  14.629  -4.444  1.00  0.54           O  
-ATOM    241  CB  THR A 265       0.822  13.345  -5.302  1.00  0.62           C  
-ATOM    242  OG1 THR A 265       1.468  14.054  -6.369  1.00  0.70           O  
-ATOM    243  CG2 THR A 265      -0.340  14.174  -4.768  1.00  0.73           C  
-ATOM    244  H   THR A 265       0.211  12.295  -3.017  1.00  0.63           H  
-ATOM    245  HA  THR A 265       2.546  12.300  -4.578  1.00  0.51           H  
-ATOM    246  HB  THR A 265       0.430  12.414  -5.689  1.00  0.67           H  
-ATOM    247  HG1 THR A 265       2.347  14.334  -6.087  1.00  1.20           H  
-ATOM    248 HG21 THR A 265      -1.263  13.815  -5.196  1.00  1.26           H  
-ATOM    249 HG22 THR A 265      -0.196  15.210  -5.036  1.00  1.21           H  
-ATOM    250 HG23 THR A 265      -0.383  14.084  -3.692  1.00  1.34           H  
-ATOM    251  N   GLN A 266       2.117  15.013  -2.808  1.00  0.46           N  
-ATOM    252  CA  GLN A 266       2.746  16.259  -2.363  1.00  0.49           C  
-ATOM    253  C   GLN A 266       3.965  16.003  -1.462  1.00  0.49           C  
-ATOM    254  O   GLN A 266       4.013  16.477  -0.326  1.00  0.52           O  
-ATOM    255  CB  GLN A 266       1.721  17.123  -1.622  1.00  0.55           C  
-ATOM    256  CG  GLN A 266       0.561  17.580  -2.492  1.00  0.62           C  
-ATOM    257  CD  GLN A 266       0.954  18.650  -3.492  1.00  0.74           C  
-ATOM    258  OE1 GLN A 266       1.793  18.434  -4.360  1.00  0.83           O  
-ATOM    259  NE2 GLN A 266       0.347  19.817  -3.377  1.00  0.95           N  
-ATOM    260  H   GLN A 266       1.308  14.699  -2.352  1.00  0.49           H  
-ATOM    261  HA  GLN A 266       3.071  16.792  -3.244  1.00  0.52           H  
-ATOM    262  HB2 GLN A 266       1.321  16.555  -0.796  1.00  0.59           H  
-ATOM    263  HB3 GLN A 266       2.219  17.999  -1.236  1.00  0.61           H  
-ATOM    264  HG2 GLN A 266       0.180  16.728  -3.036  1.00  0.66           H  
-ATOM    265  HG3 GLN A 266      -0.216  17.973  -1.853  1.00  0.71           H  
-ATOM    266 HE21 GLN A 266      -0.315  19.928  -2.664  1.00  1.02           H  
-ATOM    267 HE22 GLN A 266       0.587  20.523  -4.011  1.00  1.12           H  
-ATOM    268  N   SER A 267       4.940  15.248  -1.975  1.00  0.54           N  
-ATOM    269  CA  SER A 267       6.163  14.922  -1.221  1.00  0.60           C  
-ATOM    270  C   SER A 267       5.841  14.103   0.044  1.00  0.58           C  
-ATOM    271  O   SER A 267       5.211  13.047  -0.037  1.00  0.74           O  
-ATOM    272  CB  SER A 267       6.935  16.207  -0.863  1.00  0.68           C  
-ATOM    273  OG  SER A 267       8.191  15.912  -0.271  1.00  0.81           O  
-ATOM    274  H   SER A 267       4.836  14.899  -2.890  1.00  0.57           H  
-ATOM    275  HA  SER A 267       6.784  14.316  -1.865  1.00  0.67           H  
-ATOM    276  HB2 SER A 267       7.103  16.783  -1.761  1.00  0.75           H  
-ATOM    277  HB3 SER A 267       6.351  16.790  -0.167  1.00  0.69           H  
-ATOM    278  HG  SER A 267       8.872  16.465  -0.667  1.00  1.10           H  
-ATOM    279  N   SER A 268       6.284  14.579   1.209  1.00  0.57           N  
-ATOM    280  CA  SER A 268       6.043  13.878   2.480  1.00  0.61           C  
-ATOM    281  C   SER A 268       6.175  14.824   3.675  1.00  0.75           C  
-ATOM    282  O   SER A 268       6.941  15.785   3.637  1.00  1.21           O  
-ATOM    283  CB  SER A 268       7.014  12.703   2.654  1.00  0.89           C  
-ATOM    284  OG  SER A 268       6.732  11.659   1.736  1.00  1.04           O  
-ATOM    285  H   SER A 268       6.795  15.419   1.218  1.00  0.66           H  
-ATOM    286  HA  SER A 268       5.035  13.495   2.455  1.00  0.56           H  
-ATOM    287  HB2 SER A 268       8.024  13.045   2.487  1.00  1.07           H  
-ATOM    288  HB3 SER A 268       6.927  12.315   3.659  1.00  0.97           H  
-ATOM    289  HG  SER A 268       6.270  12.026   0.968  1.00  1.06           H  
-ATOM    290  N   GLY A 269       5.424  14.540   4.737  1.00  0.77           N  
-ATOM    291  CA  GLY A 269       5.474  15.372   5.932  1.00  0.97           C  
-ATOM    292  C   GLY A 269       4.717  14.768   7.108  1.00  0.88           C  
-ATOM    293  O   GLY A 269       4.846  13.579   7.387  1.00  0.90           O  
-ATOM    294  H   GLY A 269       4.835  13.758   4.713  1.00  0.95           H  
-ATOM    295  HA2 GLY A 269       6.507  15.507   6.217  1.00  1.21           H  
-ATOM    296  HA3 GLY A 269       5.048  16.338   5.702  1.00  1.13           H  
-ATOM    297  N   SER A 270       3.928  15.590   7.798  1.00  0.98           N  
-ATOM    298  CA  SER A 270       3.145  15.134   8.959  1.00  1.04           C  
-ATOM    299  C   SER A 270       1.969  14.229   8.562  1.00  0.97           C  
-ATOM    300  O   SER A 270       1.341  13.617   9.427  1.00  1.12           O  
-ATOM    301  CB  SER A 270       2.615  16.336   9.748  1.00  1.29           C  
-ATOM    302  OG  SER A 270       1.794  15.921  10.830  1.00  1.42           O  
-ATOM    303  H   SER A 270       3.870  16.531   7.526  1.00  1.12           H  
-ATOM    304  HA  SER A 270       3.810  14.571   9.596  1.00  1.11           H  
-ATOM    305  HB2 SER A 270       3.447  16.899  10.144  1.00  1.43           H  
-ATOM    306  HB3 SER A 270       2.033  16.967   9.092  1.00  1.39           H  
-ATOM    307  HG  SER A 270       1.421  15.048  10.639  1.00  1.50           H  
-ATOM    308  N   ARG A 271       1.665  14.171   7.262  1.00  0.88           N  
-ATOM    309  CA  ARG A 271       0.550  13.362   6.744  1.00  0.92           C  
-ATOM    310  C   ARG A 271      -0.798  13.874   7.267  1.00  1.11           C  
-ATOM    311  O   ARG A 271      -0.991  15.079   7.421  1.00  1.19           O  
-ATOM    312  CB  ARG A 271       0.718  11.872   7.100  1.00  1.01           C  
-ATOM    313  CG  ARG A 271       1.525  11.063   6.093  1.00  0.92           C  
-ATOM    314  CD  ARG A 271       3.001  11.431   6.101  1.00  0.80           C  
-ATOM    315  NE  ARG A 271       3.754  10.706   5.075  1.00  0.85           N  
-ATOM    316  CZ  ARG A 271       3.620  10.911   3.778  1.00  0.79           C  
-ATOM    317  NH1 ARG A 271       2.840  11.856   3.331  1.00  0.68           N  
-ATOM    318  NH2 ARG A 271       4.282  10.185   2.921  1.00  1.01           N  
-ATOM    319  H   ARG A 271       2.196  14.700   6.634  1.00  0.89           H  
-ATOM    320  HA  ARG A 271       0.554  13.461   5.669  1.00  0.86           H  
-ATOM    321  HB2 ARG A 271       1.213  11.800   8.058  1.00  1.09           H  
-ATOM    322  HB3 ARG A 271      -0.262  11.426   7.182  1.00  1.22           H  
-ATOM    323  HG2 ARG A 271       1.430  10.015   6.334  1.00  1.17           H  
-ATOM    324  HG3 ARG A 271       1.125  11.241   5.104  1.00  0.89           H  
-ATOM    325  HD2 ARG A 271       3.095  12.491   5.922  1.00  0.72           H  
-ATOM    326  HD3 ARG A 271       3.412  11.193   7.071  1.00  1.00           H  
-ATOM    327  HE  ARG A 271       4.375  10.014   5.376  1.00  1.06           H  
-ATOM    328 HH11 ARG A 271       2.338  12.432   3.966  1.00  0.65           H  
-ATOM    329 HH12 ARG A 271       2.747  11.994   2.336  1.00  0.78           H  
-ATOM    330 HH21 ARG A 271       4.897   9.469   3.238  1.00  1.20           H  
-ATOM    331 HH22 ARG A 271       4.171  10.354   1.939  1.00  1.08           H  
-ATOM    332  N   ALA A 272      -1.718  12.941   7.535  1.00  1.28           N  
-ATOM    333  CA  ALA A 272      -3.055  13.266   8.048  1.00  1.53           C  
-ATOM    334  C   ALA A 272      -3.793  14.283   7.168  1.00  1.42           C  
-ATOM    335  O   ALA A 272      -4.492  15.164   7.675  1.00  1.62           O  
-ATOM    336  CB  ALA A 272      -2.960  13.769   9.481  1.00  1.80           C  
-ATOM    337  H   ALA A 272      -1.487  12.001   7.390  1.00  1.30           H  
-ATOM    338  HA  ALA A 272      -3.627  12.349   8.061  1.00  1.67           H  
-ATOM    339  HB1 ALA A 272      -2.446  14.719   9.496  1.00  2.12           H  
-ATOM    340  HB2 ALA A 272      -2.413  13.055  10.079  1.00  2.14           H  
-ATOM    341  HB3 ALA A 272      -3.953  13.893   9.887  1.00  2.11           H  
-ATOM    342  N   SER A 273      -3.641  14.154   5.849  1.00  1.15           N  
-ATOM    343  CA  SER A 273      -4.299  15.054   4.891  1.00  1.07           C  
-ATOM    344  C   SER A 273      -4.218  14.503   3.481  1.00  0.85           C  
-ATOM    345  O   SER A 273      -3.911  13.327   3.276  1.00  0.89           O  
-ATOM    346  CB  SER A 273      -3.682  16.458   4.917  1.00  1.13           C  
-ATOM    347  OG  SER A 273      -4.013  17.156   6.105  1.00  1.45           O  
-ATOM    348  H   SER A 273      -3.078  13.427   5.505  1.00  1.07           H  
-ATOM    349  HA  SER A 273      -5.348  15.128   5.155  1.00  1.23           H  
-ATOM    350  HB2 SER A 273      -2.608  16.377   4.851  1.00  1.09           H  
-ATOM    351  HB3 SER A 273      -4.052  17.021   4.071  1.00  1.11           H  
-ATOM    352  HG  SER A 273      -4.181  16.517   6.817  1.00  1.50           H  
-ATOM    353  N   LEU A 274      -4.537  15.366   2.521  1.00  0.79           N  
-ATOM    354  CA  LEU A 274      -4.560  15.004   1.106  1.00  0.74           C  
-ATOM    355  C   LEU A 274      -5.608  13.909   0.792  1.00  0.76           C  
-ATOM    356  O   LEU A 274      -5.355  13.059  -0.062  1.00  0.87           O  
-ATOM    357  CB  LEU A 274      -3.170  14.537   0.648  1.00  0.80           C  
-ATOM    358  CG  LEU A 274      -2.045  15.563   0.807  1.00  0.96           C  
-ATOM    359  CD1 LEU A 274      -0.710  14.950   0.417  1.00  1.11           C  
-ATOM    360  CD2 LEU A 274      -2.326  16.801  -0.031  1.00  1.23           C  
-ATOM    361  H   LEU A 274      -4.799  16.275   2.775  1.00  0.92           H  
-ATOM    362  HA  LEU A 274      -4.823  15.892   0.551  1.00  0.85           H  
-ATOM    363  HB2 LEU A 274      -2.907  13.657   1.216  1.00  0.80           H  
-ATOM    364  HB3 LEU A 274      -3.232  14.265  -0.395  1.00  0.94           H  
-ATOM    365  HG  LEU A 274      -1.984  15.864   1.842  1.00  0.99           H  
-ATOM    366 HD11 LEU A 274      -0.008  15.734   0.176  1.00  1.48           H  
-ATOM    367 HD12 LEU A 274      -0.845  14.311  -0.443  1.00  1.59           H  
-ATOM    368 HD13 LEU A 274      -0.328  14.365   1.242  1.00  1.55           H  
-ATOM    369 HD21 LEU A 274      -3.168  16.613  -0.680  1.00  1.53           H  
-ATOM    370 HD22 LEU A 274      -1.457  17.035  -0.629  1.00  1.64           H  
-ATOM    371 HD23 LEU A 274      -2.550  17.633   0.619  1.00  1.79           H  
-ATOM    372  N   PRO A 275      -6.801  13.884   1.470  1.00  0.85           N  
-ATOM    373  CA  PRO A 275      -7.830  12.854   1.207  1.00  1.03           C  
-ATOM    374  C   PRO A 275      -8.197  12.734  -0.279  1.00  1.04           C  
-ATOM    375  O   PRO A 275      -8.489  13.732  -0.940  1.00  1.08           O  
-ATOM    376  CB  PRO A 275      -9.032  13.349   2.012  1.00  1.16           C  
-ATOM    377  CG  PRO A 275      -8.420  14.088   3.140  1.00  1.17           C  
-ATOM    378  CD  PRO A 275      -7.245  14.809   2.546  1.00  0.96           C  
-ATOM    379  HA  PRO A 275      -7.522  11.890   1.583  1.00  1.18           H  
-ATOM    380  HB2 PRO A 275      -9.645  13.994   1.398  1.00  1.13           H  
-ATOM    381  HB3 PRO A 275      -9.613  12.508   2.359  1.00  1.37           H  
-ATOM    382  HG2 PRO A 275      -9.129  14.789   3.557  1.00  1.30           H  
-ATOM    383  HG3 PRO A 275      -8.087  13.388   3.891  1.00  1.30           H  
-ATOM    384  HD2 PRO A 275      -7.545  15.764   2.148  1.00  1.01           H  
-ATOM    385  HD3 PRO A 275      -6.461  14.935   3.291  1.00  1.01           H  
-ATOM    386  N   LYS A 276      -8.175  11.506  -0.795  1.00  1.25           N  
-ATOM    387  CA  LYS A 276      -8.498  11.258  -2.200  1.00  1.41           C  
-ATOM    388  C   LYS A 276      -8.942   9.808  -2.423  1.00  1.82           C  
-ATOM    389  O   LYS A 276      -9.811   9.583  -3.303  1.00  2.00           O  
-ATOM    390  CB  LYS A 276      -7.283  11.578  -3.083  1.00  1.55           C  
-ATOM    391  CG  LYS A 276      -7.509  11.288  -4.558  1.00  1.85           C  
-ATOM    392  CD  LYS A 276      -8.698  12.061  -5.104  1.00  1.79           C  
-ATOM    393  CE  LYS A 276      -9.351  11.323  -6.260  1.00  2.03           C  
-ATOM    394  NZ  LYS A 276      -9.870   9.989  -5.839  1.00  2.16           N  
-ATOM    395  OXT LYS A 276      -8.420   8.914  -1.723  1.00  2.09           O  
-ATOM    396  H   LYS A 276      -7.931  10.748  -0.223  1.00  1.42           H  
-ATOM    397  HA  LYS A 276      -9.310  11.916  -2.473  1.00  1.36           H  
-ATOM    398  HB2 LYS A 276      -7.041  12.625  -2.976  1.00  1.49           H  
-ATOM    399  HB3 LYS A 276      -6.443  10.987  -2.747  1.00  1.73           H  
-ATOM    400  HG2 LYS A 276      -6.625  11.572  -5.109  1.00  2.09           H  
-ATOM    401  HG3 LYS A 276      -7.689  10.231  -4.683  1.00  2.03           H  
-ATOM    402  HD2 LYS A 276      -9.424  12.190  -4.316  1.00  1.63           H  
-ATOM    403  HD3 LYS A 276      -8.362  13.028  -5.448  1.00  1.92           H  
-ATOM    404  HE2 LYS A 276     -10.170  11.917  -6.635  1.00  2.10           H  
-ATOM    405  HE3 LYS A 276      -8.619  11.184  -7.041  1.00  2.24           H  
-ATOM    406  HZ1 LYS A 276      -9.299   9.233  -6.262  1.00  2.39           H  
-ATOM    407  HZ2 LYS A 276     -10.857   9.878  -6.143  1.00  2.31           H  
-ATOM    408  HZ3 LYS A 276      -9.828   9.896  -4.787  1.00  2.05           H  
-TER
-ENDMDL
-MODEL      18
-ATOM      1  N   ALA A 249       5.603 -10.030   0.927  1.00  3.16           N  
-ATOM      2  CA  ALA A 249       5.263  -8.800   0.161  1.00  2.80           C  
-ATOM      3  C   ALA A 249       3.761  -8.717  -0.104  1.00  2.34           C  
-ATOM      4  O   ALA A 249       3.078  -9.737  -0.153  1.00  2.34           O  
-ATOM      5  CB  ALA A 249       6.037  -8.755  -1.148  1.00  2.94           C  
-ATOM      6  H1  ALA A 249       5.184 -10.864   0.465  1.00  3.09           H  
-ATOM      7  H2  ALA A 249       5.231  -9.965   1.896  1.00  3.58           H  
-ATOM      8  H3  ALA A 249       6.634 -10.153   0.970  1.00  3.46           H  
-ATOM      9  HA  ALA A 249       5.558  -7.945   0.752  1.00  2.87           H  
-ATOM     10  HB1 ALA A 249       5.496  -9.301  -1.905  1.00  3.19           H  
-ATOM     11  HB2 ALA A 249       7.011  -9.199  -1.007  1.00  3.14           H  
-ATOM     12  HB3 ALA A 249       6.152  -7.726  -1.460  1.00  3.14           H  
-ATOM     13  N   PRO A 250       3.229  -7.496  -0.270  1.00  2.07           N  
-ATOM     14  CA  PRO A 250       1.799  -7.272  -0.525  1.00  1.73           C  
-ATOM     15  C   PRO A 250       1.342  -7.844  -1.870  1.00  1.46           C  
-ATOM     16  O   PRO A 250       1.983  -7.618  -2.898  1.00  1.46           O  
-ATOM     17  CB  PRO A 250       1.684  -5.739  -0.532  1.00  1.74           C  
-ATOM     18  CG  PRO A 250       2.903  -5.259   0.165  1.00  2.10           C  
-ATOM     19  CD  PRO A 250       3.967  -6.228  -0.222  1.00  2.25           C  
-ATOM     20  HA  PRO A 250       1.186  -7.678   0.265  1.00  1.84           H  
-ATOM     21  HB2 PRO A 250       1.654  -5.385  -1.550  1.00  1.61           H  
-ATOM     22  HB3 PRO A 250       0.792  -5.439  -0.010  1.00  1.81           H  
-ATOM     23  HG2 PRO A 250       3.155  -4.263  -0.172  1.00  2.22           H  
-ATOM     24  HG3 PRO A 250       2.751  -5.272   1.234  1.00  2.27           H  
-ATOM     25  HD2 PRO A 250       4.367  -5.979  -1.193  1.00  2.28           H  
-ATOM     26  HD3 PRO A 250       4.743  -6.260   0.521  1.00  2.55           H  
-ATOM     27  N   ALA A 251       0.231  -8.587  -1.858  1.00  1.39           N  
-ATOM     28  CA  ALA A 251      -0.308  -9.186  -3.081  1.00  1.26           C  
-ATOM     29  C   ALA A 251      -0.568  -8.119  -4.147  1.00  0.99           C  
-ATOM     30  O   ALA A 251      -0.144  -8.248  -5.294  1.00  1.00           O  
-ATOM     31  CB  ALA A 251      -1.584  -9.957  -2.774  1.00  1.42           C  
-ATOM     32  H   ALA A 251      -0.235  -8.735  -1.008  1.00  1.54           H  
-ATOM     33  HA  ALA A 251       0.424  -9.886  -3.459  1.00  1.39           H  
-ATOM     34  HB1 ALA A 251      -1.392 -11.017  -2.848  1.00  1.78           H  
-ATOM     35  HB2 ALA A 251      -2.351  -9.680  -3.482  1.00  1.79           H  
-ATOM     36  HB3 ALA A 251      -1.915  -9.721  -1.773  1.00  1.79           H  
-ATOM     37  N   ARG A 252      -1.245  -7.047  -3.754  1.00  0.89           N  
-ATOM     38  CA  ARG A 252      -1.528  -5.952  -4.674  1.00  0.71           C  
-ATOM     39  C   ARG A 252      -0.468  -4.856  -4.578  1.00  0.67           C  
-ATOM     40  O   ARG A 252      -0.779  -3.688  -4.348  1.00  0.64           O  
-ATOM     41  CB  ARG A 252      -2.925  -5.384  -4.435  1.00  0.81           C  
-ATOM     42  CG  ARG A 252      -4.019  -6.271  -5.001  1.00  0.95           C  
-ATOM     43  CD  ARG A 252      -3.663  -6.731  -6.409  1.00  0.92           C  
-ATOM     44  NE  ARG A 252      -4.695  -7.585  -6.993  1.00  1.09           N  
-ATOM     45  CZ  ARG A 252      -4.561  -8.217  -8.140  1.00  1.21           C  
-ATOM     46  NH1 ARG A 252      -3.444  -8.131  -8.815  1.00  1.27           N  
-ATOM     47  NH2 ARG A 252      -5.541  -8.946  -8.605  1.00  1.38           N  
-ATOM     48  H   ARG A 252      -1.544  -6.982  -2.823  1.00  1.04           H  
-ATOM     49  HA  ARG A 252      -1.493  -6.362  -5.673  1.00  0.71           H  
-ATOM     50  HB2 ARG A 252      -3.085  -5.280  -3.371  1.00  0.94           H  
-ATOM     51  HB3 ARG A 252      -2.996  -4.414  -4.902  1.00  0.81           H  
-ATOM     52  HG2 ARG A 252      -4.136  -7.136  -4.365  1.00  1.07           H  
-ATOM     53  HG3 ARG A 252      -4.943  -5.714  -5.035  1.00  1.08           H  
-ATOM     54  HD2 ARG A 252      -3.530  -5.860  -7.035  1.00  0.92           H  
-ATOM     55  HD3 ARG A 252      -2.732  -7.282  -6.367  1.00  0.92           H  
-ATOM     56  HE  ARG A 252      -5.534  -7.682  -6.502  1.00  1.20           H  
-ATOM     57 HH11 ARG A 252      -2.689  -7.585  -8.460  1.00  1.20           H  
-ATOM     58 HH12 ARG A 252      -3.348  -8.610  -9.683  1.00  1.46           H  
-ATOM     59 HH21 ARG A 252      -6.391  -9.026  -8.091  1.00  1.44           H  
-ATOM     60 HH22 ARG A 252      -5.436  -9.425  -9.474  1.00  1.51           H  
-ATOM     61  N   VAL A 253       0.785  -5.262  -4.761  1.00  0.79           N  
-ATOM     62  CA  VAL A 253       1.935  -4.358  -4.706  1.00  0.90           C  
-ATOM     63  C   VAL A 253       1.738  -3.109  -5.573  1.00  0.81           C  
-ATOM     64  O   VAL A 253       1.808  -1.983  -5.079  1.00  0.75           O  
-ATOM     65  CB  VAL A 253       3.214  -5.104  -5.148  1.00  1.18           C  
-ATOM     66  CG1 VAL A 253       2.991  -5.865  -6.453  1.00  1.24           C  
-ATOM     67  CG2 VAL A 253       4.391  -4.150  -5.268  1.00  1.38           C  
-ATOM     68  H   VAL A 253       0.947  -6.216  -4.937  1.00  0.87           H  
-ATOM     69  HA  VAL A 253       2.065  -4.051  -3.682  1.00  0.92           H  
-ATOM     70  HB  VAL A 253       3.444  -5.826  -4.394  1.00  1.29           H  
-ATOM     71 HG11 VAL A 253       3.914  -5.902  -7.011  1.00  1.55           H  
-ATOM     72 HG12 VAL A 253       2.236  -5.361  -7.037  1.00  1.66           H  
-ATOM     73 HG13 VAL A 253       2.662  -6.871  -6.231  1.00  1.64           H  
-ATOM     74 HG21 VAL A 253       4.026  -3.152  -5.459  1.00  1.62           H  
-ATOM     75 HG22 VAL A 253       5.025  -4.462  -6.084  1.00  1.61           H  
-ATOM     76 HG23 VAL A 253       4.956  -4.157  -4.348  1.00  1.99           H  
-ATOM     77  N   GLY A 254       1.493  -3.329  -6.857  1.00  0.88           N  
-ATOM     78  CA  GLY A 254       1.284  -2.228  -7.795  1.00  0.95           C  
-ATOM     79  C   GLY A 254       0.172  -1.288  -7.357  1.00  0.79           C  
-ATOM     80  O   GLY A 254       0.350  -0.068  -7.328  1.00  0.84           O  
-ATOM     81  H   GLY A 254       1.460  -4.257  -7.170  1.00  0.93           H  
-ATOM     82  HA2 GLY A 254       2.202  -1.665  -7.881  1.00  1.10           H  
-ATOM     83  HA3 GLY A 254       1.035  -2.637  -8.762  1.00  1.07           H  
-ATOM     84  N   LEU A 255      -0.970  -1.859  -6.986  1.00  0.66           N  
-ATOM     85  CA  LEU A 255      -2.106  -1.073  -6.517  1.00  0.66           C  
-ATOM     86  C   LEU A 255      -1.743  -0.269  -5.275  1.00  0.52           C  
-ATOM     87  O   LEU A 255      -2.054   0.918  -5.170  1.00  0.61           O  
-ATOM     88  CB  LEU A 255      -3.286  -1.982  -6.201  1.00  0.71           C  
-ATOM     89  CG  LEU A 255      -4.308  -2.107  -7.320  1.00  0.97           C  
-ATOM     90  CD1 LEU A 255      -5.443  -3.022  -6.894  1.00  1.09           C  
-ATOM     91  CD2 LEU A 255      -4.836  -0.732  -7.699  1.00  1.21           C  
-ATOM     92  H   LEU A 255      -1.043  -2.835  -7.009  1.00  0.65           H  
-ATOM     93  HA  LEU A 255      -2.393  -0.394  -7.306  1.00  0.84           H  
-ATOM     94  HB2 LEU A 255      -2.905  -2.967  -5.974  1.00  0.66           H  
-ATOM     95  HB3 LEU A 255      -3.788  -1.596  -5.326  1.00  0.77           H  
-ATOM     96  HG  LEU A 255      -3.834  -2.537  -8.189  1.00  1.03           H  
-ATOM     97 HD11 LEU A 255      -5.823  -3.549  -7.755  1.00  1.56           H  
-ATOM     98 HD12 LEU A 255      -6.232  -2.433  -6.452  1.00  1.46           H  
-ATOM     99 HD13 LEU A 255      -5.075  -3.733  -6.168  1.00  1.54           H  
-ATOM    100 HD21 LEU A 255      -4.468  -0.461  -8.678  1.00  1.66           H  
-ATOM    101 HD22 LEU A 255      -4.493  -0.005  -6.974  1.00  1.64           H  
-ATOM    102 HD23 LEU A 255      -5.914  -0.748  -7.710  1.00  1.61           H  
-ATOM    103  N   GLY A 256      -1.083  -0.927  -4.333  1.00  0.40           N  
-ATOM    104  CA  GLY A 256      -0.691  -0.267  -3.109  1.00  0.39           C  
-ATOM    105  C   GLY A 256       0.299   0.867  -3.329  1.00  0.35           C  
-ATOM    106  O   GLY A 256       0.085   1.977  -2.849  1.00  0.37           O  
-ATOM    107  H   GLY A 256      -0.863  -1.877  -4.471  1.00  0.44           H  
-ATOM    108  HA2 GLY A 256      -1.578   0.132  -2.639  1.00  0.50           H  
-ATOM    109  HA3 GLY A 256      -0.249  -0.998  -2.452  1.00  0.50           H  
-ATOM    110  N   ILE A 257       1.383   0.602  -4.058  1.00  0.50           N  
-ATOM    111  CA  ILE A 257       2.384   1.640  -4.310  1.00  0.67           C  
-ATOM    112  C   ILE A 257       1.807   2.809  -5.119  1.00  0.74           C  
-ATOM    113  O   ILE A 257       2.103   3.966  -4.828  1.00  0.80           O  
-ATOM    114  CB  ILE A 257       3.666   1.085  -4.989  1.00  0.95           C  
-ATOM    115  CG1 ILE A 257       3.367   0.431  -6.341  1.00  1.07           C  
-ATOM    116  CG2 ILE A 257       4.357   0.093  -4.067  1.00  1.06           C  
-ATOM    117  CD1 ILE A 257       3.704   1.306  -7.530  1.00  1.31           C  
-ATOM    118  H   ILE A 257       1.512  -0.304  -4.429  1.00  0.58           H  
-ATOM    119  HA  ILE A 257       2.675   2.026  -3.342  1.00  0.64           H  
-ATOM    120  HB  ILE A 257       4.341   1.913  -5.143  1.00  1.10           H  
-ATOM    121 HG12 ILE A 257       3.944  -0.479  -6.428  1.00  1.19           H  
-ATOM    122 HG13 ILE A 257       2.315   0.190  -6.392  1.00  0.96           H  
-ATOM    123 HG21 ILE A 257       4.416  -0.869  -4.556  1.00  1.48           H  
-ATOM    124 HG22 ILE A 257       3.795  -0.002  -3.151  1.00  1.48           H  
-ATOM    125 HG23 ILE A 257       5.353   0.444  -3.844  1.00  1.55           H  
-ATOM    126 HD11 ILE A 257       3.489   0.772  -8.443  1.00  1.69           H  
-ATOM    127 HD12 ILE A 257       4.751   1.565  -7.502  1.00  1.75           H  
-ATOM    128 HD13 ILE A 257       3.108   2.207  -7.493  1.00  1.68           H  
-ATOM    129  N   THR A 258       0.971   2.515  -6.119  1.00  0.82           N  
-ATOM    130  CA  THR A 258       0.363   3.575  -6.934  1.00  1.01           C  
-ATOM    131  C   THR A 258      -0.634   4.408  -6.127  1.00  0.95           C  
-ATOM    132  O   THR A 258      -0.629   5.639  -6.214  1.00  1.08           O  
-ATOM    133  CB  THR A 258      -0.348   3.024  -8.187  1.00  1.20           C  
-ATOM    134  OG1 THR A 258      -1.240   1.966  -7.828  1.00  1.04           O  
-ATOM    135  CG2 THR A 258       0.659   2.526  -9.213  1.00  1.40           C  
-ATOM    136  H   THR A 258       0.753   1.572  -6.307  1.00  0.78           H  
-ATOM    137  HA  THR A 258       1.162   4.224  -7.264  1.00  1.14           H  
-ATOM    138  HB  THR A 258      -0.920   3.825  -8.633  1.00  1.39           H  
-ATOM    139  HG1 THR A 258      -0.744   1.136  -7.756  1.00  0.98           H  
-ATOM    140 HG21 THR A 258       1.493   3.209  -9.262  1.00  1.78           H  
-ATOM    141 HG22 THR A 258       0.186   2.466 -10.183  1.00  1.77           H  
-ATOM    142 HG23 THR A 258       1.012   1.545  -8.925  1.00  1.80           H  
-ATOM    143  N   THR A 259      -1.479   3.746  -5.331  1.00  0.83           N  
-ATOM    144  CA  THR A 259      -2.459   4.460  -4.508  1.00  0.91           C  
-ATOM    145  C   THR A 259      -1.757   5.315  -3.461  1.00  0.81           C  
-ATOM    146  O   THR A 259      -2.099   6.485  -3.278  1.00  0.96           O  
-ATOM    147  CB  THR A 259      -3.456   3.510  -3.807  1.00  0.93           C  
-ATOM    148  OG1 THR A 259      -2.760   2.437  -3.164  1.00  0.74           O  
-ATOM    149  CG2 THR A 259      -4.462   2.949  -4.802  1.00  1.10           C  
-ATOM    150  H   THR A 259      -1.434   2.762  -5.286  1.00  0.74           H  
-ATOM    151  HA  THR A 259      -3.020   5.111  -5.163  1.00  1.12           H  
-ATOM    152  HB  THR A 259      -3.995   4.074  -3.058  1.00  1.05           H  
-ATOM    153  HG1 THR A 259      -2.554   1.750  -3.817  1.00  0.73           H  
-ATOM    154 HG21 THR A 259      -4.294   1.890  -4.927  1.00  1.36           H  
-ATOM    155 HG22 THR A 259      -4.343   3.446  -5.754  1.00  1.50           H  
-ATOM    156 HG23 THR A 259      -5.464   3.114  -4.434  1.00  1.64           H  
-ATOM    157  N   VAL A 260      -0.756   4.740  -2.796  1.00  0.59           N  
-ATOM    158  CA  VAL A 260       0.009   5.473  -1.792  1.00  0.54           C  
-ATOM    159  C   VAL A 260       0.730   6.667  -2.428  1.00  0.63           C  
-ATOM    160  O   VAL A 260       0.617   7.791  -1.938  1.00  0.72           O  
-ATOM    161  CB  VAL A 260       1.031   4.560  -1.080  1.00  0.47           C  
-ATOM    162  CG1 VAL A 260       1.971   5.374  -0.205  1.00  0.61           C  
-ATOM    163  CG2 VAL A 260       0.309   3.509  -0.250  1.00  0.55           C  
-ATOM    164  H   VAL A 260      -0.516   3.803  -2.999  1.00  0.52           H  
-ATOM    165  HA  VAL A 260      -0.687   5.842  -1.053  1.00  0.65           H  
-ATOM    166  HB  VAL A 260       1.619   4.053  -1.831  1.00  0.48           H  
-ATOM    167 HG11 VAL A 260       2.700   4.719   0.246  1.00  1.18           H  
-ATOM    168 HG12 VAL A 260       1.403   5.868   0.568  1.00  1.19           H  
-ATOM    169 HG13 VAL A 260       2.475   6.114  -0.810  1.00  1.25           H  
-ATOM    170 HG21 VAL A 260       0.340   2.560  -0.765  1.00  1.20           H  
-ATOM    171 HG22 VAL A 260      -0.720   3.808  -0.109  1.00  1.16           H  
-ATOM    172 HG23 VAL A 260       0.792   3.414   0.709  1.00  1.18           H  
-ATOM    173  N   LEU A 261       1.442   6.421  -3.534  1.00  0.72           N  
-ATOM    174  CA  LEU A 261       2.158   7.483  -4.250  1.00  0.92           C  
-ATOM    175  C   LEU A 261       1.201   8.605  -4.648  1.00  1.07           C  
-ATOM    176  O   LEU A 261       1.497   9.786  -4.462  1.00  1.16           O  
-ATOM    177  CB  LEU A 261       2.832   6.917  -5.502  1.00  1.09           C  
-ATOM    178  CG  LEU A 261       4.359   7.016  -5.529  1.00  1.11           C  
-ATOM    179  CD1 LEU A 261       4.804   8.471  -5.529  1.00  1.35           C  
-ATOM    180  CD2 LEU A 261       4.968   6.273  -4.350  1.00  1.31           C  
-ATOM    181  H   LEU A 261       1.475   5.502  -3.887  1.00  0.70           H  
-ATOM    182  HA  LEU A 261       2.913   7.882  -3.590  1.00  0.90           H  
-ATOM    183  HB2 LEU A 261       2.560   5.875  -5.590  1.00  1.27           H  
-ATOM    184  HB3 LEU A 261       2.445   7.444  -6.361  1.00  1.30           H  
-ATOM    185  HG  LEU A 261       4.722   6.557  -6.438  1.00  1.26           H  
-ATOM    186 HD11 LEU A 261       5.013   8.783  -4.517  1.00  1.87           H  
-ATOM    187 HD12 LEU A 261       4.018   9.086  -5.941  1.00  1.63           H  
-ATOM    188 HD13 LEU A 261       5.694   8.574  -6.130  1.00  1.81           H  
-ATOM    189 HD21 LEU A 261       4.564   5.273  -4.306  1.00  1.78           H  
-ATOM    190 HD22 LEU A 261       4.733   6.797  -3.434  1.00  1.69           H  
-ATOM    191 HD23 LEU A 261       6.040   6.224  -4.471  1.00  1.74           H  
-ATOM    192  N   THR A 262       0.044   8.217  -5.177  1.00  1.13           N  
-ATOM    193  CA  THR A 262      -0.985   9.174  -5.583  1.00  1.34           C  
-ATOM    194  C   THR A 262      -1.461   9.994  -4.384  1.00  1.32           C  
-ATOM    195  O   THR A 262      -1.560  11.217  -4.451  1.00  1.46           O  
-ATOM    196  CB  THR A 262      -2.198   8.450  -6.205  1.00  1.43           C  
-ATOM    197  OG1 THR A 262      -1.794   7.724  -7.372  1.00  1.50           O  
-ATOM    198  CG2 THR A 262      -3.299   9.434  -6.569  1.00  1.65           C  
-ATOM    199  H   THR A 262      -0.128   7.254  -5.286  1.00  1.06           H  
-ATOM    200  HA  THR A 262      -0.561   9.836  -6.324  1.00  1.48           H  
-ATOM    201  HB  THR A 262      -2.589   7.753  -5.478  1.00  1.35           H  
-ATOM    202  HG1 THR A 262      -1.312   6.927  -7.101  1.00  1.38           H  
-ATOM    203 HG21 THR A 262      -3.316  10.235  -5.843  1.00  1.95           H  
-ATOM    204 HG22 THR A 262      -4.250   8.925  -6.565  1.00  1.99           H  
-ATOM    205 HG23 THR A 262      -3.109   9.840  -7.550  1.00  2.00           H  
-ATOM    206  N   MET A 263      -1.747   9.302  -3.286  1.00  1.18           N  
-ATOM    207  CA  MET A 263      -2.206   9.948  -2.058  1.00  1.22           C  
-ATOM    208  C   MET A 263      -1.144  10.902  -1.493  1.00  1.18           C  
-ATOM    209  O   MET A 263      -1.412  12.084  -1.266  1.00  1.30           O  
-ATOM    210  CB  MET A 263      -2.555   8.882  -1.018  1.00  1.13           C  
-ATOM    211  CG  MET A 263      -3.101   9.446   0.283  1.00  1.28           C  
-ATOM    212  SD  MET A 263      -3.428   8.163   1.505  1.00  1.32           S  
-ATOM    213  CE  MET A 263      -4.058   9.146   2.863  1.00  1.60           C  
-ATOM    214  H   MET A 263      -1.647   8.324  -3.303  1.00  1.09           H  
-ATOM    215  HA  MET A 263      -3.094  10.515  -2.292  1.00  1.41           H  
-ATOM    216  HB2 MET A 263      -3.297   8.219  -1.436  1.00  1.20           H  
-ATOM    217  HB3 MET A 263      -1.665   8.314  -0.792  1.00  0.98           H  
-ATOM    218  HG2 MET A 263      -2.380  10.138   0.692  1.00  1.27           H  
-ATOM    219  HG3 MET A 263      -4.023   9.969   0.074  1.00  1.48           H  
-ATOM    220  HE1 MET A 263      -3.668  10.150   2.791  1.00  2.10           H  
-ATOM    221  HE2 MET A 263      -3.749   8.706   3.799  1.00  1.96           H  
-ATOM    222  HE3 MET A 263      -5.137   9.175   2.816  1.00  1.76           H  
-ATOM    223  N   THR A 264       0.072  10.394  -1.275  1.00  1.05           N  
-ATOM    224  CA  THR A 264       1.155  11.228  -0.744  1.00  1.04           C  
-ATOM    225  C   THR A 264       1.905  11.961  -1.865  1.00  1.00           C  
-ATOM    226  O   THR A 264       3.127  12.121  -1.818  1.00  1.04           O  
-ATOM    227  CB  THR A 264       2.158  10.414   0.116  1.00  1.11           C  
-ATOM    228  OG1 THR A 264       3.157  11.286   0.663  1.00  1.12           O  
-ATOM    229  CG2 THR A 264       2.840   9.318  -0.692  1.00  1.15           C  
-ATOM    230  H   THR A 264       0.243   9.443  -1.483  1.00  0.99           H  
-ATOM    231  HA  THR A 264       0.700  11.970  -0.103  1.00  1.05           H  
-ATOM    232  HB  THR A 264       1.615   9.953   0.930  1.00  1.17           H  
-ATOM    233  HG1 THR A 264       3.601  11.751  -0.060  1.00  1.14           H  
-ATOM    234 HG21 THR A 264       2.337   8.377  -0.519  1.00  1.53           H  
-ATOM    235 HG22 THR A 264       3.872   9.234  -0.388  1.00  1.49           H  
-ATOM    236 HG23 THR A 264       2.792   9.562  -1.743  1.00  1.63           H  
-ATOM    237  N   THR A 265       1.158  12.420  -2.869  1.00  0.98           N  
-ATOM    238  CA  THR A 265       1.741  13.148  -4.000  1.00  0.98           C  
-ATOM    239  C   THR A 265       2.359  14.474  -3.556  1.00  0.88           C  
-ATOM    240  O   THR A 265       3.305  14.960  -4.175  1.00  0.92           O  
-ATOM    241  CB  THR A 265       0.703  13.415  -5.111  1.00  1.06           C  
-ATOM    242  OG1 THR A 265       1.303  14.155  -6.183  1.00  1.13           O  
-ATOM    243  CG2 THR A 265      -0.498  14.181  -4.573  1.00  1.05           C  
-ATOM    244  H   THR A 265       0.187  12.267  -2.846  1.00  1.00           H  
-ATOM    245  HA  THR A 265       2.524  12.528  -4.415  1.00  1.05           H  
-ATOM    246  HB  THR A 265       0.358  12.464  -5.493  1.00  1.15           H  
-ATOM    247  HG1 THR A 265       2.204  14.403  -5.945  1.00  1.10           H  
-ATOM    248 HG21 THR A 265      -0.278  15.238  -4.563  1.00  1.49           H  
-ATOM    249 HG22 THR A 265      -0.717  13.849  -3.569  1.00  1.45           H  
-ATOM    250 HG23 THR A 265      -1.355  13.997  -5.205  1.00  1.48           H  
-ATOM    251  N   GLN A 266       1.816  15.043  -2.473  1.00  0.84           N  
-ATOM    252  CA  GLN A 266       2.304  16.310  -1.913  1.00  0.80           C  
-ATOM    253  C   GLN A 266       2.317  17.440  -2.952  1.00  0.58           C  
-ATOM    254  O   GLN A 266       1.263  17.949  -3.339  1.00  0.61           O  
-ATOM    255  CB  GLN A 266       3.698  16.117  -1.303  1.00  0.98           C  
-ATOM    256  CG  GLN A 266       3.717  15.138  -0.140  1.00  1.22           C  
-ATOM    257  CD  GLN A 266       5.108  14.912   0.418  1.00  1.45           C  
-ATOM    258  OE1 GLN A 266       6.004  14.453  -0.279  1.00  1.58           O  
-ATOM    259  NE2 GLN A 266       5.299  15.237   1.683  1.00  1.61           N  
-ATOM    260  H   GLN A 266       1.070  14.589  -2.031  1.00  0.90           H  
-ATOM    261  HA  GLN A 266       1.624  16.589  -1.122  1.00  0.86           H  
-ATOM    262  HB2 GLN A 266       4.366  15.749  -2.068  1.00  1.03           H  
-ATOM    263  HB3 GLN A 266       4.059  17.071  -0.950  1.00  0.98           H  
-ATOM    264  HG2 GLN A 266       3.090  15.525   0.649  1.00  1.27           H  
-ATOM    265  HG3 GLN A 266       3.323  14.191  -0.478  1.00  1.31           H  
-ATOM    266 HE21 GLN A 266       4.543  15.602   2.186  1.00  1.61           H  
-ATOM    267 HE22 GLN A 266       6.187  15.090   2.064  1.00  1.77           H  
-ATOM    268  N   SER A 267       3.509  17.836  -3.390  1.00  0.59           N  
-ATOM    269  CA  SER A 267       3.657  18.909  -4.375  1.00  0.49           C  
-ATOM    270  C   SER A 267       5.038  18.873  -5.027  1.00  0.77           C  
-ATOM    271  O   SER A 267       6.049  18.673  -4.351  1.00  0.94           O  
-ATOM    272  CB  SER A 267       3.427  20.284  -3.730  1.00  0.40           C  
-ATOM    273  OG  SER A 267       2.067  20.460  -3.359  1.00  0.46           O  
-ATOM    274  H   SER A 267       4.314  17.397  -3.041  1.00  0.80           H  
-ATOM    275  HA  SER A 267       2.910  18.755  -5.141  1.00  0.49           H  
-ATOM    276  HB2 SER A 267       4.041  20.369  -2.845  1.00  0.54           H  
-ATOM    277  HB3 SER A 267       3.699  21.058  -4.432  1.00  0.51           H  
-ATOM    278  HG  SER A 267       1.653  19.589  -3.240  1.00  0.52           H  
-ATOM    279  N   SER A 268       5.075  19.067  -6.343  1.00  0.91           N  
-ATOM    280  CA  SER A 268       6.331  19.056  -7.102  1.00  1.23           C  
-ATOM    281  C   SER A 268       7.130  20.356  -6.928  1.00  1.28           C  
-ATOM    282  O   SER A 268       7.626  20.927  -7.901  1.00  1.49           O  
-ATOM    283  CB  SER A 268       6.039  18.823  -8.587  1.00  1.42           C  
-ATOM    284  OG  SER A 268       5.105  19.772  -9.082  1.00  1.35           O  
-ATOM    285  H   SER A 268       4.228  19.222  -6.828  1.00  0.84           H  
-ATOM    286  HA  SER A 268       6.927  18.235  -6.735  1.00  1.34           H  
-ATOM    287  HB2 SER A 268       6.955  18.914  -9.150  1.00  1.65           H  
-ATOM    288  HB3 SER A 268       5.632  17.832  -8.720  1.00  1.46           H  
-ATOM    289  HG  SER A 268       4.527  19.342  -9.752  1.00  1.45           H  
-ATOM    290  N   GLY A 269       7.266  20.816  -5.690  1.00  1.15           N  
-ATOM    291  CA  GLY A 269       8.016  22.035  -5.438  1.00  1.26           C  
-ATOM    292  C   GLY A 269       7.684  22.678  -4.101  1.00  1.11           C  
-ATOM    293  O   GLY A 269       7.314  21.997  -3.147  1.00  1.03           O  
-ATOM    294  H   GLY A 269       6.864  20.319  -4.942  1.00  1.04           H  
-ATOM    295  HA2 GLY A 269       9.071  21.805  -5.458  1.00  1.48           H  
-ATOM    296  HA3 GLY A 269       7.801  22.742  -6.225  1.00  1.30           H  
-ATOM    297  N   SER A 270       7.822  23.999  -4.039  1.00  1.17           N  
-ATOM    298  CA  SER A 270       7.541  24.750  -2.812  1.00  1.15           C  
-ATOM    299  C   SER A 270       6.093  25.240  -2.767  1.00  1.03           C  
-ATOM    300  O   SER A 270       5.560  25.517  -1.693  1.00  1.19           O  
-ATOM    301  CB  SER A 270       8.491  25.945  -2.685  1.00  1.44           C  
-ATOM    302  OG  SER A 270       8.214  26.703  -1.517  1.00  1.54           O  
-ATOM    303  H   SER A 270       8.126  24.483  -4.835  1.00  1.29           H  
-ATOM    304  HA  SER A 270       7.706  24.086  -1.977  1.00  1.14           H  
-ATOM    305  HB2 SER A 270       9.510  25.588  -2.634  1.00  1.61           H  
-ATOM    306  HB3 SER A 270       8.378  26.584  -3.550  1.00  1.52           H  
-ATOM    307  HG  SER A 270       7.335  26.484  -1.188  1.00  1.71           H  
-ATOM    308  N   ARG A 271       5.470  25.361  -3.936  1.00  0.98           N  
-ATOM    309  CA  ARG A 271       4.087  25.831  -4.032  1.00  0.97           C  
-ATOM    310  C   ARG A 271       3.102  24.846  -3.397  1.00  0.83           C  
-ATOM    311  O   ARG A 271       3.130  23.650  -3.682  1.00  0.66           O  
-ATOM    312  CB  ARG A 271       3.707  26.061  -5.497  1.00  1.06           C  
-ATOM    313  CG  ARG A 271       2.237  26.406  -5.700  1.00  1.18           C  
-ATOM    314  CD  ARG A 271       1.895  26.564  -7.173  1.00  1.33           C  
-ATOM    315  NE  ARG A 271       2.567  27.713  -7.778  1.00  1.50           N  
-ATOM    316  CZ  ARG A 271       2.249  28.966  -7.535  1.00  1.67           C  
-ATOM    317  NH1 ARG A 271       1.238  29.258  -6.757  1.00  1.76           N  
-ATOM    318  NH2 ARG A 271       2.935  29.928  -8.087  1.00  1.86           N  
-ATOM    319  H   ARG A 271       5.952  25.133  -4.756  1.00  1.11           H  
-ATOM    320  HA  ARG A 271       4.023  26.771  -3.504  1.00  1.14           H  
-ATOM    321  HB2 ARG A 271       4.303  26.873  -5.888  1.00  1.26           H  
-ATOM    322  HB3 ARG A 271       3.925  25.164  -6.059  1.00  0.99           H  
-ATOM    323  HG2 ARG A 271       1.631  25.617  -5.284  1.00  1.13           H  
-ATOM    324  HG3 ARG A 271       2.024  27.334  -5.189  1.00  1.37           H  
-ATOM    325  HD2 ARG A 271       2.196  25.668  -7.696  1.00  1.30           H  
-ATOM    326  HD3 ARG A 271       0.827  26.693  -7.268  1.00  1.47           H  
-ATOM    327  HE  ARG A 271       3.309  27.530  -8.384  1.00  1.55           H  
-ATOM    328 HH11 ARG A 271       0.703  28.525  -6.344  1.00  1.68           H  
-ATOM    329 HH12 ARG A 271       1.003  30.210  -6.580  1.00  1.95           H  
-ATOM    330 HH21 ARG A 271       3.700  29.711  -8.688  1.00  1.89           H  
-ATOM    331 HH22 ARG A 271       2.694  30.878  -7.907  1.00  2.01           H  
-ATOM    332  N   ALA A 272       2.219  25.369  -2.551  1.00  1.02           N  
-ATOM    333  CA  ALA A 272       1.207  24.550  -1.893  1.00  1.09           C  
-ATOM    334  C   ALA A 272       0.162  24.059  -2.898  1.00  1.09           C  
-ATOM    335  O   ALA A 272      -0.305  24.828  -3.741  1.00  1.23           O  
-ATOM    336  CB  ALA A 272       0.541  25.338  -0.774  1.00  1.45           C  
-ATOM    337  H   ALA A 272       2.240  26.331  -2.378  1.00  1.19           H  
-ATOM    338  HA  ALA A 272       1.702  23.695  -1.454  1.00  0.98           H  
-ATOM    339  HB1 ALA A 272       1.242  26.055  -0.372  1.00  1.89           H  
-ATOM    340  HB2 ALA A 272       0.229  24.661   0.007  1.00  1.78           H  
-ATOM    341  HB3 ALA A 272      -0.321  25.858  -1.165  1.00  1.86           H  
-ATOM    342  N   SER A 273      -0.185  22.773  -2.804  1.00  0.99           N  
-ATOM    343  CA  SER A 273      -1.164  22.149  -3.706  1.00  1.06           C  
-ATOM    344  C   SER A 273      -0.665  22.130  -5.136  1.00  0.93           C  
-ATOM    345  O   SER A 273      -1.365  22.530  -6.066  1.00  1.11           O  
-ATOM    346  CB  SER A 273      -2.527  22.836  -3.650  1.00  1.40           C  
-ATOM    347  OG  SER A 273      -3.038  22.877  -2.326  1.00  1.62           O  
-ATOM    348  H   SER A 273       0.238  22.221  -2.115  1.00  0.95           H  
-ATOM    349  HA  SER A 273      -1.285  21.117  -3.402  1.00  1.03           H  
-ATOM    350  HB2 SER A 273      -2.430  23.842  -4.022  1.00  1.47           H  
-ATOM    351  HB3 SER A 273      -3.219  22.286  -4.273  1.00  1.46           H  
-ATOM    352  HG  SER A 273      -3.927  23.245  -2.335  1.00  1.96           H  
-ATOM    353  N   LEU A 274       0.535  21.606  -5.307  1.00  0.69           N  
-ATOM    354  CA  LEU A 274       1.119  21.466  -6.627  1.00  0.73           C  
-ATOM    355  C   LEU A 274       1.475  19.991  -6.893  1.00  0.73           C  
-ATOM    356  O   LEU A 274       2.600  19.686  -7.287  1.00  0.82           O  
-ATOM    357  CB  LEU A 274       2.365  22.353  -6.752  1.00  0.77           C  
-ATOM    358  CG  LEU A 274       2.953  22.469  -8.161  1.00  1.05           C  
-ATOM    359  CD1 LEU A 274       1.957  23.119  -9.109  1.00  1.28           C  
-ATOM    360  CD2 LEU A 274       4.254  23.256  -8.132  1.00  1.20           C  
-ATOM    361  H   LEU A 274       1.030  21.266  -4.521  1.00  0.58           H  
-ATOM    362  HA  LEU A 274       0.384  21.784  -7.352  1.00  0.91           H  
-ATOM    363  HB2 LEU A 274       2.109  23.345  -6.410  1.00  0.85           H  
-ATOM    364  HB3 LEU A 274       3.129  21.954  -6.100  1.00  0.70           H  
-ATOM    365  HG  LEU A 274       3.169  21.479  -8.535  1.00  1.14           H  
-ATOM    366 HD11 LEU A 274       1.414  23.892  -8.585  1.00  1.77           H  
-ATOM    367 HD12 LEU A 274       1.263  22.374  -9.471  1.00  1.69           H  
-ATOM    368 HD13 LEU A 274       2.485  23.554  -9.944  1.00  1.61           H  
-ATOM    369 HD21 LEU A 274       5.067  22.598  -7.865  1.00  1.55           H  
-ATOM    370 HD22 LEU A 274       4.179  24.050  -7.402  1.00  1.11           H  
-ATOM    371 HD23 LEU A 274       4.440  23.681  -9.108  1.00  1.56           H  
-ATOM    372  N   PRO A 275       0.526  19.038  -6.660  1.00  0.78           N  
-ATOM    373  CA  PRO A 275       0.779  17.596  -6.872  1.00  0.90           C  
-ATOM    374  C   PRO A 275       1.417  17.284  -8.230  1.00  1.12           C  
-ATOM    375  O   PRO A 275       2.365  16.506  -8.318  1.00  1.27           O  
-ATOM    376  CB  PRO A 275      -0.622  16.993  -6.794  1.00  1.05           C  
-ATOM    377  CG  PRO A 275      -1.330  17.875  -5.834  1.00  1.00           C  
-ATOM    378  CD  PRO A 275      -0.853  19.264  -6.151  1.00  0.89           C  
-ATOM    379  HA  PRO A 275       1.389  17.187  -6.086  1.00  0.86           H  
-ATOM    380  HB2 PRO A 275      -1.083  17.012  -7.771  1.00  1.19           H  
-ATOM    381  HB3 PRO A 275      -0.564  15.978  -6.432  1.00  1.12           H  
-ATOM    382  HG2 PRO A 275      -2.397  17.797  -5.973  1.00  1.19           H  
-ATOM    383  HG3 PRO A 275      -1.054  17.608  -4.825  1.00  0.96           H  
-ATOM    384  HD2 PRO A 275      -1.480  19.716  -6.903  1.00  1.03           H  
-ATOM    385  HD3 PRO A 275      -0.843  19.884  -5.250  1.00  0.85           H  
-ATOM    386  N   LYS A 276       0.896  17.914  -9.278  1.00  1.23           N  
-ATOM    387  CA  LYS A 276       1.419  17.724 -10.630  1.00  1.49           C  
-ATOM    388  C   LYS A 276       2.404  18.844 -11.004  1.00  1.57           C  
-ATOM    389  O   LYS A 276       3.621  18.662 -10.790  1.00  1.66           O  
-ATOM    390  CB  LYS A 276       0.275  17.683 -11.645  1.00  1.68           C  
-ATOM    391  CG  LYS A 276       0.741  17.399 -13.066  1.00  1.98           C  
-ATOM    392  CD  LYS A 276      -0.208  17.988 -14.097  1.00  2.16           C  
-ATOM    393  CE  LYS A 276      -0.317  19.501 -13.953  1.00  2.12           C  
-ATOM    394  NZ  LYS A 276       1.023  20.153 -13.922  1.00  2.12           N  
-ATOM    395  OXT LYS A 276       1.944  19.896 -11.517  1.00  1.66           O  
-ATOM    396  H   LYS A 276       0.151  18.532  -9.135  1.00  1.19           H  
-ATOM    397  HA  LYS A 276       1.942  16.779 -10.652  1.00  1.61           H  
-ATOM    398  HB2 LYS A 276      -0.422  16.909 -11.355  1.00  1.72           H  
-ATOM    399  HB3 LYS A 276      -0.233  18.635 -11.638  1.00  1.67           H  
-ATOM    400  HG2 LYS A 276       1.719  17.833 -13.205  1.00  2.02           H  
-ATOM    401  HG3 LYS A 276       0.795  16.330 -13.209  1.00  2.11           H  
-ATOM    402  HD2 LYS A 276       0.161  17.756 -15.086  1.00  2.37           H  
-ATOM    403  HD3 LYS A 276      -1.187  17.551 -13.964  1.00  2.21           H  
-ATOM    404  HE2 LYS A 276      -0.877  19.891 -14.790  1.00  2.34           H  
-ATOM    405  HE3 LYS A 276      -0.839  19.726 -13.035  1.00  1.98           H  
-ATOM    406  HZ1 LYS A 276       1.672  19.669 -14.575  1.00  2.28           H  
-ATOM    407  HZ2 LYS A 276       1.426  20.105 -12.946  1.00  1.93           H  
-ATOM    408  HZ3 LYS A 276       0.943  21.151 -14.200  1.00  2.23           H  
-TER
-ENDMDL
-MODEL      19
-ATOM      1  N   ALA A 249       1.328 -11.693 -11.317  1.00  1.62           N  
-ATOM      2  CA  ALA A 249       2.017 -10.389 -11.129  1.00  1.44           C  
-ATOM      3  C   ALA A 249       1.612  -9.741  -9.805  1.00  1.14           C  
-ATOM      4  O   ALA A 249       0.543 -10.032  -9.267  1.00  1.20           O  
-ATOM      5  CB  ALA A 249       1.719  -9.455 -12.292  1.00  1.70           C  
-ATOM      6  H1  ALA A 249       1.986 -12.477 -11.134  1.00  1.82           H  
-ATOM      7  H2  ALA A 249       0.973 -11.773 -12.290  1.00  1.94           H  
-ATOM      8  H3  ALA A 249       0.524 -11.769 -10.658  1.00  1.98           H  
-ATOM      9  HA  ALA A 249       3.082 -10.571 -11.112  1.00  1.47           H  
-ATOM     10  HB1 ALA A 249       0.652  -9.333 -12.393  1.00  2.02           H  
-ATOM     11  HB2 ALA A 249       2.125  -9.874 -13.201  1.00  2.05           H  
-ATOM     12  HB3 ALA A 249       2.177  -8.494 -12.105  1.00  2.06           H  
-ATOM     13  N   PRO A 250       2.468  -8.859  -9.263  1.00  1.00           N  
-ATOM     14  CA  PRO A 250       2.218  -8.162  -7.993  1.00  0.82           C  
-ATOM     15  C   PRO A 250       1.150  -7.063  -8.099  1.00  0.76           C  
-ATOM     16  O   PRO A 250       1.415  -5.896  -7.806  1.00  0.73           O  
-ATOM     17  CB  PRO A 250       3.588  -7.560  -7.676  1.00  0.97           C  
-ATOM     18  CG  PRO A 250       4.193  -7.311  -9.003  1.00  1.18           C  
-ATOM     19  CD  PRO A 250       3.764  -8.468  -9.849  1.00  1.22           C  
-ATOM     20  HA  PRO A 250       1.941  -8.855  -7.211  1.00  0.90           H  
-ATOM     21  HB2 PRO A 250       3.466  -6.647  -7.121  1.00  0.97           H  
-ATOM     22  HB3 PRO A 250       4.172  -8.268  -7.110  1.00  1.11           H  
-ATOM     23  HG2 PRO A 250       3.819  -6.384  -9.412  1.00  1.21           H  
-ATOM     24  HG3 PRO A 250       5.269  -7.284  -8.920  1.00  1.42           H  
-ATOM     25  HD2 PRO A 250       3.641  -8.158 -10.873  1.00  1.42           H  
-ATOM     26  HD3 PRO A 250       4.475  -9.273  -9.772  1.00  1.34           H  
-ATOM     27  N   ALA A 251      -0.057  -7.445  -8.523  1.00  0.91           N  
-ATOM     28  CA  ALA A 251      -1.168  -6.499  -8.677  1.00  1.09           C  
-ATOM     29  C   ALA A 251      -1.458  -5.739  -7.377  1.00  1.03           C  
-ATOM     30  O   ALA A 251      -1.625  -4.520  -7.388  1.00  1.05           O  
-ATOM     31  CB  ALA A 251      -2.416  -7.231  -9.151  1.00  1.42           C  
-ATOM     32  H   ALA A 251      -0.202  -8.393  -8.747  1.00  1.00           H  
-ATOM     33  HA  ALA A 251      -0.891  -5.787  -9.441  1.00  1.15           H  
-ATOM     34  HB1 ALA A 251      -3.243  -6.993  -8.498  1.00  1.80           H  
-ATOM     35  HB2 ALA A 251      -2.236  -8.296  -9.132  1.00  1.82           H  
-ATOM     36  HB3 ALA A 251      -2.652  -6.924 -10.159  1.00  1.84           H  
-ATOM     37  N   ARG A 252      -1.503  -6.464  -6.258  1.00  1.08           N  
-ATOM     38  CA  ARG A 252      -1.760  -5.853  -4.950  1.00  1.20           C  
-ATOM     39  C   ARG A 252      -0.648  -4.856  -4.603  1.00  1.00           C  
-ATOM     40  O   ARG A 252      -0.912  -3.755  -4.116  1.00  1.05           O  
-ATOM     41  CB  ARG A 252      -1.858  -6.945  -3.874  1.00  1.43           C  
-ATOM     42  CG  ARG A 252      -2.876  -6.662  -2.770  1.00  1.77           C  
-ATOM     43  CD  ARG A 252      -2.416  -5.564  -1.818  1.00  1.82           C  
-ATOM     44  NE  ARG A 252      -3.352  -5.362  -0.706  1.00  2.17           N  
-ATOM     45  CZ  ARG A 252      -3.563  -6.240   0.264  1.00  2.46           C  
-ATOM     46  NH1 ARG A 252      -2.874  -7.351   0.319  1.00  2.44           N  
-ATOM     47  NH2 ARG A 252      -4.454  -5.991   1.191  1.00  2.79           N  
-ATOM     48  H   ARG A 252      -1.354  -7.430  -6.314  1.00  1.10           H  
-ATOM     49  HA  ARG A 252      -2.698  -5.324  -5.007  1.00  1.37           H  
-ATOM     50  HB2 ARG A 252      -2.132  -7.875  -4.350  1.00  1.47           H  
-ATOM     51  HB3 ARG A 252      -0.888  -7.064  -3.414  1.00  1.39           H  
-ATOM     52  HG2 ARG A 252      -3.805  -6.356  -3.226  1.00  1.86           H  
-ATOM     53  HG3 ARG A 252      -3.035  -7.570  -2.205  1.00  1.94           H  
-ATOM     54  HD2 ARG A 252      -1.451  -5.834  -1.418  1.00  1.80           H  
-ATOM     55  HD3 ARG A 252      -2.327  -4.641  -2.373  1.00  1.66           H  
-ATOM     56  HE  ARG A 252      -3.862  -4.527  -0.696  1.00  2.23           H  
-ATOM     57 HH11 ARG A 252      -2.185  -7.542  -0.375  1.00  2.22           H  
-ATOM     58 HH12 ARG A 252      -3.038  -8.005   1.053  1.00  2.68           H  
-ATOM     59 HH21 ARG A 252      -4.974  -5.141   1.167  1.00  2.84           H  
-ATOM     60 HH22 ARG A 252      -4.612  -6.653   1.921  1.00  3.02           H  
-ATOM     61  N   VAL A 253       0.595  -5.245  -4.882  1.00  0.83           N  
-ATOM     62  CA  VAL A 253       1.750  -4.387  -4.625  1.00  0.78           C  
-ATOM     63  C   VAL A 253       1.699  -3.139  -5.507  1.00  0.58           C  
-ATOM     64  O   VAL A 253       1.838  -2.016  -5.019  1.00  0.60           O  
-ATOM     65  CB  VAL A 253       3.076  -5.140  -4.875  1.00  0.88           C  
-ATOM     66  CG1 VAL A 253       4.274  -4.259  -4.548  1.00  1.09           C  
-ATOM     67  CG2 VAL A 253       3.122  -6.427  -4.065  1.00  1.10           C  
-ATOM     68  H   VAL A 253       0.735  -6.124  -5.286  1.00  0.83           H  
-ATOM     69  HA  VAL A 253       1.718  -4.085  -3.590  1.00  0.97           H  
-ATOM     70  HB  VAL A 253       3.125  -5.398  -5.923  1.00  0.78           H  
-ATOM     71 HG11 VAL A 253       5.164  -4.870  -4.488  1.00  1.55           H  
-ATOM     72 HG12 VAL A 253       4.111  -3.765  -3.602  1.00  1.58           H  
-ATOM     73 HG13 VAL A 253       4.399  -3.518  -5.325  1.00  1.50           H  
-ATOM     74 HG21 VAL A 253       2.198  -6.539  -3.517  1.00  1.54           H  
-ATOM     75 HG22 VAL A 253       3.950  -6.388  -3.373  1.00  1.56           H  
-ATOM     76 HG23 VAL A 253       3.249  -7.266  -4.733  1.00  1.53           H  
-ATOM     77  N   GLY A 254       1.472  -3.350  -6.804  1.00  0.52           N  
-ATOM     78  CA  GLY A 254       1.377  -2.241  -7.744  1.00  0.60           C  
-ATOM     79  C   GLY A 254       0.267  -1.272  -7.376  1.00  0.62           C  
-ATOM     80  O   GLY A 254       0.478  -0.058  -7.332  1.00  0.60           O  
-ATOM     81  H   GLY A 254       1.354  -4.275  -7.123  1.00  0.56           H  
-ATOM     82  HA2 GLY A 254       2.318  -1.710  -7.756  1.00  0.66           H  
-ATOM     83  HA3 GLY A 254       1.184  -2.635  -8.731  1.00  0.79           H  
-ATOM     84  N   LEU A 255      -0.914  -1.811  -7.081  1.00  0.78           N  
-ATOM     85  CA  LEU A 255      -2.053  -0.991  -6.682  1.00  0.99           C  
-ATOM     86  C   LEU A 255      -1.735  -0.239  -5.390  1.00  0.90           C  
-ATOM     87  O   LEU A 255      -2.032   0.949  -5.258  1.00  0.95           O  
-ATOM     88  CB  LEU A 255      -3.294  -1.847  -6.491  1.00  1.28           C  
-ATOM     89  CG  LEU A 255      -4.589  -1.050  -6.473  1.00  1.59           C  
-ATOM     90  CD1 LEU A 255      -4.817  -0.383  -7.821  1.00  1.71           C  
-ATOM     91  CD2 LEU A 255      -5.758  -1.942  -6.101  1.00  1.91           C  
-ATOM     92  H   LEU A 255      -1.018  -2.791  -7.115  1.00  0.83           H  
-ATOM     93  HA  LEU A 255      -2.253  -0.272  -7.461  1.00  1.08           H  
-ATOM     94  HB2 LEU A 255      -3.340  -2.569  -7.294  1.00  1.33           H  
-ATOM     95  HB3 LEU A 255      -3.206  -2.376  -5.554  1.00  1.30           H  
-ATOM     96  HG  LEU A 255      -4.504  -0.271  -5.732  1.00  1.56           H  
-ATOM     97 HD11 LEU A 255      -5.795   0.072  -7.838  1.00  2.05           H  
-ATOM     98 HD12 LEU A 255      -4.748  -1.123  -8.606  1.00  2.01           H  
-ATOM     99 HD13 LEU A 255      -4.065   0.377  -7.978  1.00  2.00           H  
-ATOM    100 HD21 LEU A 255      -6.564  -1.334  -5.716  1.00  2.18           H  
-ATOM    101 HD22 LEU A 255      -5.445  -2.649  -5.348  1.00  2.21           H  
-ATOM    102 HD23 LEU A 255      -6.095  -2.472  -6.978  1.00  2.33           H  
-ATOM    103  N   GLY A 256      -1.115  -0.938  -4.443  1.00  0.88           N  
-ATOM    104  CA  GLY A 256      -0.752  -0.323  -3.180  1.00  0.97           C  
-ATOM    105  C   GLY A 256       0.233   0.826  -3.342  1.00  0.78           C  
-ATOM    106  O   GLY A 256      -0.022   1.930  -2.863  1.00  0.85           O  
-ATOM    107  H   GLY A 256      -0.894  -1.884  -4.607  1.00  0.87           H  
-ATOM    108  HA2 GLY A 256      -1.648   0.051  -2.707  1.00  1.17           H  
-ATOM    109  HA3 GLY A 256      -0.309  -1.075  -2.543  1.00  1.08           H  
-ATOM    110  N   ILE A 257       1.355   0.579  -4.022  1.00  0.61           N  
-ATOM    111  CA  ILE A 257       2.357   1.629  -4.221  1.00  0.59           C  
-ATOM    112  C   ILE A 257       1.795   2.800  -5.036  1.00  0.42           C  
-ATOM    113  O   ILE A 257       2.057   3.957  -4.716  1.00  0.48           O  
-ATOM    114  CB  ILE A 257       3.669   1.095  -4.859  1.00  0.73           C  
-ATOM    115  CG1 ILE A 257       3.433   0.456  -6.230  1.00  0.67           C  
-ATOM    116  CG2 ILE A 257       4.338   0.097  -3.926  1.00  0.97           C  
-ATOM    117  CD1 ILE A 257       3.785   1.358  -7.393  1.00  0.84           C  
-ATOM    118  H   ILE A 257       1.512  -0.323  -4.392  1.00  0.59           H  
-ATOM    119  HA  ILE A 257       2.606   2.006  -3.239  1.00  0.77           H  
-ATOM    120  HB  ILE A 257       4.342   1.933  -4.975  1.00  0.85           H  
-ATOM    121 HG12 ILE A 257       4.036  -0.436  -6.311  1.00  0.81           H  
-ATOM    122 HG13 ILE A 257       2.390   0.188  -6.319  1.00  0.55           H  
-ATOM    123 HG21 ILE A 257       5.286   0.492  -3.595  1.00  1.43           H  
-ATOM    124 HG22 ILE A 257       4.497  -0.833  -4.452  1.00  1.45           H  
-ATOM    125 HG23 ILE A 257       3.701  -0.078  -3.070  1.00  1.48           H  
-ATOM    126 HD11 ILE A 257       3.229   1.052  -8.267  1.00  1.30           H  
-ATOM    127 HD12 ILE A 257       4.843   1.288  -7.597  1.00  1.35           H  
-ATOM    128 HD13 ILE A 257       3.533   2.379  -7.145  1.00  1.43           H  
-ATOM    129  N   THR A 258       0.997   2.506  -6.069  1.00  0.41           N  
-ATOM    130  CA  THR A 258       0.395   3.568  -6.884  1.00  0.58           C  
-ATOM    131  C   THR A 258      -0.597   4.391  -6.064  1.00  0.63           C  
-ATOM    132  O   THR A 258      -0.656   5.615  -6.192  1.00  0.67           O  
-ATOM    133  CB  THR A 258      -0.314   3.022  -8.141  1.00  0.86           C  
-ATOM    134  OG1 THR A 258      -1.178   1.934  -7.798  1.00  0.93           O  
-ATOM    135  CG2 THR A 258       0.701   2.570  -9.180  1.00  0.99           C  
-ATOM    136  H   THR A 258       0.797   1.562  -6.274  1.00  0.44           H  
-ATOM    137  HA  THR A 258       1.194   4.220  -7.207  1.00  0.63           H  
-ATOM    138  HB  THR A 258      -0.909   3.817  -8.569  1.00  1.03           H  
-ATOM    139  HG1 THR A 258      -0.658   1.117  -7.736  1.00  0.87           H  
-ATOM    140 HG21 THR A 258       0.196   2.017  -9.959  1.00  1.50           H  
-ATOM    141 HG22 THR A 258       1.439   1.937  -8.710  1.00  1.10           H  
-ATOM    142 HG23 THR A 258       1.186   3.433  -9.609  1.00  1.50           H  
-ATOM    143  N   THR A 259      -1.363   3.716  -5.204  1.00  0.76           N  
-ATOM    144  CA  THR A 259      -2.333   4.395  -4.343  1.00  0.98           C  
-ATOM    145  C   THR A 259      -1.607   5.299  -3.354  1.00  0.91           C  
-ATOM    146  O   THR A 259      -1.956   6.470  -3.195  1.00  0.98           O  
-ATOM    147  CB  THR A 259      -3.210   3.395  -3.558  1.00  1.22           C  
-ATOM    148  OG1 THR A 259      -3.898   2.525  -4.464  1.00  1.33           O  
-ATOM    149  CG2 THR A 259      -4.225   4.121  -2.688  1.00  1.49           C  
-ATOM    150  H   THR A 259      -1.262   2.739  -5.136  1.00  0.75           H  
-ATOM    151  HA  THR A 259      -2.974   4.998  -4.970  1.00  1.09           H  
-ATOM    152  HB  THR A 259      -2.569   2.802  -2.921  1.00  1.19           H  
-ATOM    153  HG1 THR A 259      -3.274   1.873  -4.822  1.00  1.22           H  
-ATOM    154 HG21 THR A 259      -4.340   3.593  -1.752  1.00  1.74           H  
-ATOM    155 HG22 THR A 259      -5.176   4.159  -3.199  1.00  1.68           H  
-ATOM    156 HG23 THR A 259      -3.880   5.125  -2.495  1.00  1.45           H  
-ATOM    157  N   VAL A 260      -0.574   4.751  -2.708  1.00  0.85           N  
-ATOM    158  CA  VAL A 260       0.225   5.515  -1.750  1.00  0.92           C  
-ATOM    159  C   VAL A 260       0.860   6.728  -2.432  1.00  0.74           C  
-ATOM    160  O   VAL A 260       0.767   7.849  -1.928  1.00  0.82           O  
-ATOM    161  CB  VAL A 260       1.329   4.647  -1.107  1.00  1.04           C  
-ATOM    162  CG1 VAL A 260       2.239   5.486  -0.219  1.00  1.25           C  
-ATOM    163  CG2 VAL A 260       0.711   3.512  -0.305  1.00  1.24           C  
-ATOM    164  H   VAL A 260      -0.334   3.811  -2.892  1.00  0.81           H  
-ATOM    165  HA  VAL A 260      -0.435   5.862  -0.968  1.00  1.11           H  
-ATOM    166  HB  VAL A 260       1.929   4.216  -1.895  1.00  0.91           H  
-ATOM    167 HG11 VAL A 260       2.649   4.866   0.563  1.00  1.67           H  
-ATOM    168 HG12 VAL A 260       1.669   6.291   0.220  1.00  1.70           H  
-ATOM    169 HG13 VAL A 260       3.042   5.896  -0.813  1.00  1.61           H  
-ATOM    170 HG21 VAL A 260       0.822   2.585  -0.849  1.00  1.62           H  
-ATOM    171 HG22 VAL A 260      -0.338   3.713  -0.147  1.00  1.66           H  
-ATOM    172 HG23 VAL A 260       1.211   3.430   0.649  1.00  1.66           H  
-ATOM    173  N   LEU A 261       1.479   6.503  -3.594  1.00  0.54           N  
-ATOM    174  CA  LEU A 261       2.100   7.587  -4.354  1.00  0.49           C  
-ATOM    175  C   LEU A 261       1.066   8.650  -4.731  1.00  0.45           C  
-ATOM    176  O   LEU A 261       1.292   9.838  -4.529  1.00  0.50           O  
-ATOM    177  CB  LEU A 261       2.781   7.040  -5.607  1.00  0.57           C  
-ATOM    178  CG  LEU A 261       4.038   6.212  -5.341  1.00  0.78           C  
-ATOM    179  CD1 LEU A 261       4.672   5.777  -6.649  1.00  0.96           C  
-ATOM    180  CD2 LEU A 261       5.030   7.003  -4.498  1.00  1.03           C  
-ATOM    181  H   LEU A 261       1.504   5.585  -3.957  1.00  0.49           H  
-ATOM    182  HA  LEU A 261       2.852   8.039  -3.725  1.00  0.64           H  
-ATOM    183  HB2 LEU A 261       2.071   6.422  -6.136  1.00  0.53           H  
-ATOM    184  HB3 LEU A 261       3.051   7.873  -6.239  1.00  0.72           H  
-ATOM    185  HG  LEU A 261       3.766   5.323  -4.791  1.00  0.76           H  
-ATOM    186 HD11 LEU A 261       3.904   5.665  -7.400  1.00  1.44           H  
-ATOM    187 HD12 LEU A 261       5.177   4.833  -6.507  1.00  1.45           H  
-ATOM    188 HD13 LEU A 261       5.383   6.523  -6.970  1.00  1.45           H  
-ATOM    189 HD21 LEU A 261       4.491   7.652  -3.823  1.00  1.49           H  
-ATOM    190 HD22 LEU A 261       5.658   7.597  -5.144  1.00  1.50           H  
-ATOM    191 HD23 LEU A 261       5.643   6.320  -3.927  1.00  1.50           H  
-ATOM    192  N   THR A 262      -0.079   8.202  -5.253  1.00  0.53           N  
-ATOM    193  CA  THR A 262      -1.174   9.101  -5.639  1.00  0.73           C  
-ATOM    194  C   THR A 262      -1.654   9.933  -4.445  1.00  0.78           C  
-ATOM    195  O   THR A 262      -1.888  11.137  -4.556  1.00  0.83           O  
-ATOM    196  CB  THR A 262      -2.374   8.300  -6.191  1.00  0.98           C  
-ATOM    197  OG1 THR A 262      -1.989   7.567  -7.361  1.00  1.03           O  
-ATOM    198  CG2 THR A 262      -3.540   9.216  -6.522  1.00  1.23           C  
-ATOM    199  H   THR A 262      -0.199   7.232  -5.369  1.00  0.55           H  
-ATOM    200  HA  THR A 262      -0.815   9.762  -6.414  1.00  0.76           H  
-ATOM    201  HB  THR A 262      -2.695   7.600  -5.433  1.00  1.01           H  
-ATOM    202  HG1 THR A 262      -1.498   6.774  -7.096  1.00  0.92           H  
-ATOM    203 HG21 THR A 262      -3.553   9.412  -7.582  1.00  1.66           H  
-ATOM    204 HG22 THR A 262      -3.428  10.145  -5.983  1.00  1.63           H  
-ATOM    205 HG23 THR A 262      -4.463   8.740  -6.229  1.00  1.65           H  
-ATOM    206  N   MET A 263      -1.805   9.267  -3.307  1.00  0.88           N  
-ATOM    207  CA  MET A 263      -2.261   9.917  -2.080  1.00  1.07           C  
-ATOM    208  C   MET A 263      -1.219  10.907  -1.534  1.00  1.00           C  
-ATOM    209  O   MET A 263      -1.561  12.031  -1.167  1.00  1.11           O  
-ATOM    210  CB  MET A 263      -2.580   8.859  -1.019  1.00  1.27           C  
-ATOM    211  CG  MET A 263      -3.211   9.424   0.243  1.00  1.57           C  
-ATOM    212  SD  MET A 263      -4.779  10.252  -0.078  1.00  1.77           S  
-ATOM    213  CE  MET A 263      -5.241  10.754   1.578  1.00  2.12           C  
-ATOM    214  H   MET A 263      -1.614   8.303  -3.296  1.00  0.87           H  
-ATOM    215  HA  MET A 263      -3.164  10.460  -2.311  1.00  1.18           H  
-ATOM    216  HB2 MET A 263      -3.262   8.137  -1.443  1.00  1.32           H  
-ATOM    217  HB3 MET A 263      -1.664   8.357  -0.743  1.00  1.21           H  
-ATOM    218  HG2 MET A 263      -3.384   8.615   0.936  1.00  1.74           H  
-ATOM    219  HG3 MET A 263      -2.527  10.136   0.684  1.00  1.56           H  
-ATOM    220  HE1 MET A 263      -6.143  11.345   1.537  1.00  2.74           H  
-ATOM    221  HE2 MET A 263      -4.446  11.344   2.009  1.00  2.23           H  
-ATOM    222  HE3 MET A 263      -5.411   9.879   2.184  1.00  2.24           H  
-ATOM    223  N   THR A 264       0.050  10.487  -1.463  1.00  0.89           N  
-ATOM    224  CA  THR A 264       1.110  11.362  -0.938  1.00  0.96           C  
-ATOM    225  C   THR A 264       1.419  12.531  -1.881  1.00  0.81           C  
-ATOM    226  O   THR A 264       1.666  13.647  -1.426  1.00  0.96           O  
-ATOM    227  CB  THR A 264       2.419  10.597  -0.615  1.00  1.05           C  
-ATOM    228  OG1 THR A 264       3.372  11.486  -0.015  1.00  1.24           O  
-ATOM    229  CG2 THR A 264       3.038   9.972  -1.857  1.00  0.90           C  
-ATOM    230  H   THR A 264       0.275   9.571  -1.758  1.00  0.84           H  
-ATOM    231  HA  THR A 264       0.740  11.776  -0.011  1.00  1.14           H  
-ATOM    232  HB  THR A 264       2.190   9.807   0.087  1.00  1.19           H  
-ATOM    233  HG1 THR A 264       3.206  12.386  -0.313  1.00  1.34           H  
-ATOM    234 HG21 THR A 264       4.114   9.971  -1.761  1.00  1.33           H  
-ATOM    235 HG22 THR A 264       2.755  10.546  -2.727  1.00  1.25           H  
-ATOM    236 HG23 THR A 264       2.685   8.958  -1.963  1.00  1.07           H  
-ATOM    237  N   THR A 265       1.421  12.276  -3.188  1.00  0.62           N  
-ATOM    238  CA  THR A 265       1.709  13.316  -4.165  1.00  0.63           C  
-ATOM    239  C   THR A 265       0.794  13.201  -5.388  1.00  0.59           C  
-ATOM    240  O   THR A 265       0.516  12.109  -5.878  1.00  0.66           O  
-ATOM    241  CB  THR A 265       3.187  13.264  -4.600  1.00  0.80           C  
-ATOM    242  OG1 THR A 265       3.536  14.458  -5.312  1.00  0.93           O  
-ATOM    243  CG2 THR A 265       3.482  12.049  -5.471  1.00  0.85           C  
-ATOM    244  H   THR A 265       1.239  11.369  -3.503  1.00  0.57           H  
-ATOM    245  HA  THR A 265       1.533  14.271  -3.690  1.00  0.68           H  
-ATOM    246  HB  THR A 265       3.784  13.191  -3.707  1.00  0.90           H  
-ATOM    247  HG1 THR A 265       4.224  14.257  -5.952  1.00  1.31           H  
-ATOM    248 HG21 THR A 265       4.405  12.209  -6.010  1.00  1.40           H  
-ATOM    249 HG22 THR A 265       2.674  11.906  -6.174  1.00  1.28           H  
-ATOM    250 HG23 THR A 265       3.576  11.173  -4.847  1.00  1.35           H  
-ATOM    251  N   GLN A 266       0.317  14.338  -5.875  1.00  0.58           N  
-ATOM    252  CA  GLN A 266      -0.577  14.352  -7.033  1.00  0.68           C  
-ATOM    253  C   GLN A 266      -0.662  15.744  -7.647  1.00  0.76           C  
-ATOM    254  O   GLN A 266      -0.527  15.901  -8.858  1.00  0.93           O  
-ATOM    255  CB  GLN A 266      -1.975  13.874  -6.624  1.00  0.75           C  
-ATOM    256  CG  GLN A 266      -2.964  13.781  -7.778  1.00  0.96           C  
-ATOM    257  CD  GLN A 266      -4.318  13.240  -7.351  1.00  1.12           C  
-ATOM    258  OE1 GLN A 266      -5.237  13.134  -8.154  1.00  1.36           O  
-ATOM    259  NE2 GLN A 266      -4.451  12.888  -6.082  1.00  1.17           N  
-ATOM    260  H   GLN A 266       0.565  15.182  -5.446  1.00  0.59           H  
-ATOM    261  HA  GLN A 266      -0.176  13.673  -7.767  1.00  0.85           H  
-ATOM    262  HB2 GLN A 266      -1.891  12.895  -6.176  1.00  0.86           H  
-ATOM    263  HB3 GLN A 266      -2.376  14.559  -5.891  1.00  0.73           H  
-ATOM    264  HG2 GLN A 266      -3.106  14.769  -8.194  1.00  1.03           H  
-ATOM    265  HG3 GLN A 266      -2.555  13.130  -8.535  1.00  1.12           H  
-ATOM    266 HE21 GLN A 266      -3.677  12.993  -5.487  1.00  1.09           H  
-ATOM    267 HE22 GLN A 266      -5.315  12.531  -5.793  1.00  1.39           H  
-ATOM    268  N   SER A 267      -0.883  16.749  -6.796  1.00  0.80           N  
-ATOM    269  CA  SER A 267      -0.991  18.154  -7.229  1.00  1.02           C  
-ATOM    270  C   SER A 267      -2.010  18.325  -8.363  1.00  1.00           C  
-ATOM    271  O   SER A 267      -1.717  18.928  -9.396  1.00  1.22           O  
-ATOM    272  CB  SER A 267       0.378  18.723  -7.646  1.00  1.30           C  
-ATOM    273  OG  SER A 267       0.965  17.991  -8.711  1.00  1.38           O  
-ATOM    274  H   SER A 267      -0.978  16.543  -5.845  1.00  0.81           H  
-ATOM    275  HA  SER A 267      -1.347  18.717  -6.378  1.00  1.15           H  
-ATOM    276  HB2 SER A 267       0.253  19.748  -7.964  1.00  1.44           H  
-ATOM    277  HB3 SER A 267       1.046  18.694  -6.797  1.00  1.46           H  
-ATOM    278  HG  SER A 267       0.425  17.206  -8.902  1.00  1.20           H  
-ATOM    279  N   SER A 268      -3.210  17.787  -8.165  1.00  0.90           N  
-ATOM    280  CA  SER A 268      -4.273  17.878  -9.169  1.00  1.04           C  
-ATOM    281  C   SER A 268      -5.646  17.658  -8.534  1.00  1.13           C  
-ATOM    282  O   SER A 268      -5.830  17.888  -7.339  1.00  1.58           O  
-ATOM    283  CB  SER A 268      -4.051  16.845 -10.283  1.00  1.08           C  
-ATOM    284  OG  SER A 268      -2.892  17.142 -11.046  1.00  1.28           O  
-ATOM    285  H   SER A 268      -3.390  17.316  -7.323  1.00  0.87           H  
-ATOM    286  HA  SER A 268      -4.241  18.868  -9.597  1.00  1.22           H  
-ATOM    287  HB2 SER A 268      -3.931  15.867  -9.843  1.00  0.98           H  
-ATOM    288  HB3 SER A 268      -4.909  16.840 -10.940  1.00  1.25           H  
-ATOM    289  HG  SER A 268      -2.348  17.792 -10.573  1.00  1.33           H  
-ATOM    290  N   GLY A 269      -6.605  17.206  -9.335  1.00  1.11           N  
-ATOM    291  CA  GLY A 269      -7.942  16.963  -8.823  1.00  1.33           C  
-ATOM    292  C   GLY A 269      -8.945  16.649  -9.920  1.00  1.43           C  
-ATOM    293  O   GLY A 269      -8.666  15.845 -10.812  1.00  1.34           O  
-ATOM    294  H   GLY A 269      -6.405  17.034 -10.278  1.00  1.27           H  
-ATOM    295  HA2 GLY A 269      -7.904  16.129  -8.139  1.00  1.33           H  
-ATOM    296  HA3 GLY A 269      -8.275  17.839  -8.287  1.00  1.59           H  
-ATOM    297  N   SER A 270     -10.116  17.291  -9.841  1.00  1.81           N  
-ATOM    298  CA  SER A 270     -11.211  17.112 -10.816  1.00  2.06           C  
-ATOM    299  C   SER A 270     -11.830  15.707 -10.750  1.00  1.91           C  
-ATOM    300  O   SER A 270     -13.029  15.562 -10.517  1.00  2.18           O  
-ATOM    301  CB  SER A 270     -10.735  17.406 -12.246  1.00  2.25           C  
-ATOM    302  OG  SER A 270     -10.258  18.736 -12.367  1.00  2.54           O  
-ATOM    303  H   SER A 270     -10.254  17.915  -9.099  1.00  2.00           H  
-ATOM    304  HA  SER A 270     -11.980  17.826 -10.561  1.00  2.39           H  
-ATOM    305  HB2 SER A 270      -9.936  16.727 -12.504  1.00  2.03           H  
-ATOM    306  HB3 SER A 270     -11.558  17.268 -12.931  1.00  2.51           H  
-ATOM    307  HG  SER A 270      -9.316  18.721 -12.561  1.00  2.75           H  
-ATOM    308  N   ARG A 271     -11.008  14.680 -10.950  1.00  1.62           N  
-ATOM    309  CA  ARG A 271     -11.477  13.297 -10.907  1.00  1.54           C  
-ATOM    310  C   ARG A 271     -11.280  12.686  -9.513  1.00  1.61           C  
-ATOM    311  O   ARG A 271     -12.210  12.108  -8.948  1.00  1.89           O  
-ATOM    312  CB  ARG A 271     -10.750  12.454 -11.953  1.00  1.36           C  
-ATOM    313  CG  ARG A 271     -11.359  11.076 -12.108  1.00  1.38           C  
-ATOM    314  CD  ARG A 271     -12.712  11.136 -12.803  1.00  1.78           C  
-ATOM    315  NE  ARG A 271     -13.579  10.033 -12.396  1.00  1.90           N  
-ATOM    316  CZ  ARG A 271     -13.368   8.768 -12.694  1.00  1.80           C  
-ATOM    317  NH1 ARG A 271     -12.412   8.422 -13.515  1.00  1.66           N  
-ATOM    318  NH2 ARG A 271     -14.138   7.847 -12.185  1.00  2.09           N  
-ATOM    319  H   ARG A 271     -10.055  14.861 -11.128  1.00  1.58           H  
-ATOM    320  HA  ARG A 271     -12.531  13.292 -11.134  1.00  1.80           H  
-ATOM    321  HB2 ARG A 271     -10.793  12.960 -12.907  1.00  1.58           H  
-ATOM    322  HB3 ARG A 271      -9.717  12.339 -11.657  1.00  1.22           H  
-ATOM    323  HG2 ARG A 271     -10.692  10.455 -12.686  1.00  1.34           H  
-ATOM    324  HG3 ARG A 271     -11.494  10.653 -11.126  1.00  1.39           H  
-ATOM    325  HD2 ARG A 271     -13.190  12.071 -12.554  1.00  2.06           H  
-ATOM    326  HD3 ARG A 271     -12.556  11.085 -13.871  1.00  1.94           H  
-ATOM    327  HE  ARG A 271     -14.342  10.252 -11.826  1.00  2.20           H  
-ATOM    328 HH11 ARG A 271     -11.833   9.115 -13.928  1.00  1.64           H  
-ATOM    329 HH12 ARG A 271     -12.258   7.435 -13.727  1.00  1.76           H  
-ATOM    330 HH21 ARG A 271     -14.886   8.100 -11.580  1.00  2.38           H  
-ATOM    331 HH22 ARG A 271     -13.959   6.863 -12.392  1.00  2.11           H  
-ATOM    332  N   ALA A 272     -10.059  12.823  -8.978  1.00  1.59           N  
-ATOM    333  CA  ALA A 272      -9.686  12.301  -7.647  1.00  1.91           C  
-ATOM    334  C   ALA A 272      -9.648  10.764  -7.583  1.00  1.85           C  
-ATOM    335  O   ALA A 272      -8.663  10.179  -7.128  1.00  1.98           O  
-ATOM    336  CB  ALA A 272     -10.608  12.856  -6.566  1.00  2.40           C  
-ATOM    337  H   ALA A 272      -9.377  13.295  -9.498  1.00  1.49           H  
-ATOM    338  HA  ALA A 272      -8.692  12.663  -7.438  1.00  1.99           H  
-ATOM    339  HB1 ALA A 272     -11.368  12.126  -6.331  1.00  2.66           H  
-ATOM    340  HB2 ALA A 272     -11.077  13.761  -6.924  1.00  2.76           H  
-ATOM    341  HB3 ALA A 272     -10.032  13.075  -5.678  1.00  2.76           H  
-ATOM    342  N   SER A 273     -10.716  10.115  -8.027  1.00  1.78           N  
-ATOM    343  CA  SER A 273     -10.792   8.652  -8.008  1.00  1.83           C  
-ATOM    344  C   SER A 273      -9.941   8.025  -9.101  1.00  1.49           C  
-ATOM    345  O   SER A 273     -10.274   8.083 -10.287  1.00  1.28           O  
-ATOM    346  CB  SER A 273     -12.234   8.172  -8.163  1.00  2.04           C  
-ATOM    347  OG  SER A 273     -12.813   8.665  -9.359  1.00  1.86           O  
-ATOM    348  H   SER A 273     -11.478  10.634  -8.375  1.00  1.80           H  
-ATOM    349  HA  SER A 273     -10.419   8.310  -7.054  1.00  2.14           H  
-ATOM    350  HB2 SER A 273     -12.244   7.090  -8.194  1.00  2.15           H  
-ATOM    351  HB3 SER A 273     -12.817   8.512  -7.322  1.00  2.39           H  
-ATOM    352  HG  SER A 273     -12.182   8.563 -10.083  1.00  1.71           H  
-ATOM    353  N   LEU A 274      -8.856   7.400  -8.676  1.00  1.70           N  
-ATOM    354  CA  LEU A 274      -7.943   6.720  -9.582  1.00  1.73           C  
-ATOM    355  C   LEU A 274      -8.362   5.264  -9.941  1.00  1.78           C  
-ATOM    356  O   LEU A 274      -7.820   4.717 -10.900  1.00  1.92           O  
-ATOM    357  CB  LEU A 274      -6.517   6.715  -9.009  1.00  2.18           C  
-ATOM    358  CG  LEU A 274      -6.299   5.890  -7.733  1.00  2.55           C  
-ATOM    359  CD1 LEU A 274      -4.826   5.543  -7.587  1.00  3.04           C  
-ATOM    360  CD2 LEU A 274      -6.778   6.647  -6.501  1.00  2.72           C  
-ATOM    361  H   LEU A 274      -8.670   7.383  -7.715  1.00  2.00           H  
-ATOM    362  HA  LEU A 274      -7.927   7.292 -10.497  1.00  1.64           H  
-ATOM    363  HB2 LEU A 274      -5.852   6.334  -9.769  1.00  2.35           H  
-ATOM    364  HB3 LEU A 274      -6.239   7.737  -8.796  1.00  2.25           H  
-ATOM    365  HG  LEU A 274      -6.857   4.968  -7.804  1.00  2.52           H  
-ATOM    366 HD11 LEU A 274      -4.661   4.527  -7.917  1.00  3.33           H  
-ATOM    367 HD12 LEU A 274      -4.533   5.638  -6.552  1.00  3.33           H  
-ATOM    368 HD13 LEU A 274      -4.237   6.217  -8.192  1.00  3.32           H  
-ATOM    369 HD21 LEU A 274      -7.635   7.251  -6.761  1.00  2.95           H  
-ATOM    370 HD22 LEU A 274      -5.984   7.284  -6.139  1.00  2.95           H  
-ATOM    371 HD23 LEU A 274      -7.053   5.941  -5.731  1.00  2.97           H  
-ATOM    372  N   PRO A 275      -9.297   4.575  -9.206  1.00  1.90           N  
-ATOM    373  CA  PRO A 275      -9.671   3.182  -9.539  1.00  2.10           C  
-ATOM    374  C   PRO A 275     -10.232   3.005 -10.954  1.00  2.06           C  
-ATOM    375  O   PRO A 275      -9.909   2.032 -11.639  1.00  2.34           O  
-ATOM    376  CB  PRO A 275     -10.739   2.840  -8.496  1.00  2.38           C  
-ATOM    377  CG  PRO A 275     -10.422   3.739  -7.365  1.00  2.48           C  
-ATOM    378  CD  PRO A 275     -10.046   5.033  -8.013  1.00  2.10           C  
-ATOM    379  HA  PRO A 275      -8.833   2.520  -9.407  1.00  2.27           H  
-ATOM    380  HB2 PRO A 275     -11.722   3.034  -8.900  1.00  2.38           H  
-ATOM    381  HB3 PRO A 275     -10.655   1.801  -8.212  1.00  2.63           H  
-ATOM    382  HG2 PRO A 275     -11.287   3.864  -6.729  1.00  2.73           H  
-ATOM    383  HG3 PRO A 275      -9.587   3.343  -6.810  1.00  2.69           H  
-ATOM    384  HD2 PRO A 275     -10.933   5.587  -8.299  1.00  2.00           H  
-ATOM    385  HD3 PRO A 275      -9.425   5.629  -7.362  1.00  2.22           H  
-ATOM    386  N   LYS A 276     -11.074   3.944 -11.383  1.00  1.90           N  
-ATOM    387  CA  LYS A 276     -11.685   3.886 -12.716  1.00  2.12           C  
-ATOM    388  C   LYS A 276     -12.443   5.179 -13.039  1.00  2.01           C  
-ATOM    389  O   LYS A 276     -11.970   5.952 -13.902  1.00  1.99           O  
-ATOM    390  CB  LYS A 276     -12.642   2.691 -12.808  1.00  2.58           C  
-ATOM    391  CG  LYS A 276     -13.163   2.421 -14.213  1.00  2.97           C  
-ATOM    392  CD  LYS A 276     -12.033   2.153 -15.199  1.00  3.14           C  
-ATOM    393  CE  LYS A 276     -11.131   1.016 -14.739  1.00  3.34           C  
-ATOM    394  NZ  LYS A 276     -11.888  -0.247 -14.525  1.00  3.76           N  
-ATOM    395  OXT LYS A 276     -13.505   5.415 -12.426  1.00  2.16           O  
-ATOM    396  H   LYS A 276     -11.291   4.690 -10.788  1.00  1.77           H  
-ATOM    397  HA  LYS A 276     -10.893   3.758 -13.440  1.00  2.21           H  
-ATOM    398  HB2 LYS A 276     -12.126   1.807 -12.465  1.00  2.71           H  
-ATOM    399  HB3 LYS A 276     -13.489   2.874 -12.164  1.00  2.66           H  
-ATOM    400  HG2 LYS A 276     -13.812   1.559 -14.185  1.00  3.31           H  
-ATOM    401  HG3 LYS A 276     -13.722   3.283 -14.547  1.00  2.94           H  
-ATOM    402  HD2 LYS A 276     -12.461   1.892 -16.155  1.00  3.51           H  
-ATOM    403  HD3 LYS A 276     -11.441   3.051 -15.304  1.00  2.92           H  
-ATOM    404  HE2 LYS A 276     -10.375   0.848 -15.491  1.00  3.60           H  
-ATOM    405  HE3 LYS A 276     -10.656   1.303 -13.811  1.00  3.07           H  
-ATOM    406  HZ1 LYS A 276     -11.375  -1.048 -14.944  1.00  4.01           H  
-ATOM    407  HZ2 LYS A 276     -12.826  -0.181 -14.969  1.00  3.99           H  
-ATOM    408  HZ3 LYS A 276     -12.010  -0.422 -13.507  1.00  3.98           H  
-TER
-ENDMDL
-MODEL      20
-ATOM      1  N   ALA A 249       4.989 -11.131  -1.600  1.00  2.74           N  
-ATOM      2  CA  ALA A 249       4.588  -9.724  -1.871  1.00  2.30           C  
-ATOM      3  C   ALA A 249       3.234  -9.408  -1.236  1.00  1.71           C  
-ATOM      4  O   ALA A 249       2.438 -10.311  -0.981  1.00  1.73           O  
-ATOM      5  CB  ALA A 249       4.544  -9.460  -3.369  1.00  2.46           C  
-ATOM      6  H1  ALA A 249       5.334 -11.580  -2.474  1.00  2.96           H  
-ATOM      7  H2  ALA A 249       4.170 -11.671  -1.247  1.00  2.77           H  
-ATOM      8  H3  ALA A 249       5.745 -11.160  -0.885  1.00  3.04           H  
-ATOM      9  HA  ALA A 249       5.334  -9.071  -1.439  1.00  2.46           H  
-ATOM     10  HB1 ALA A 249       3.892 -10.177  -3.841  1.00  2.75           H  
-ATOM     11  HB2 ALA A 249       5.538  -9.548  -3.779  1.00  2.71           H  
-ATOM     12  HB3 ALA A 249       4.169  -8.462  -3.544  1.00  2.65           H  
-ATOM     13  N   PRO A 250       2.956  -8.119  -0.970  1.00  1.47           N  
-ATOM     14  CA  PRO A 250       1.694  -7.674  -0.357  1.00  1.23           C  
-ATOM     15  C   PRO A 250       0.507  -7.692  -1.329  1.00  1.00           C  
-ATOM     16  O   PRO A 250      -0.219  -6.705  -1.451  1.00  1.11           O  
-ATOM     17  CB  PRO A 250       2.028  -6.244   0.066  1.00  1.62           C  
-ATOM     18  CG  PRO A 250       3.004  -5.783  -0.949  1.00  1.80           C  
-ATOM     19  CD  PRO A 250       3.852  -6.981  -1.249  1.00  1.85           C  
-ATOM     20  HA  PRO A 250       1.451  -8.262   0.514  1.00  1.35           H  
-ATOM     21  HB2 PRO A 250       1.137  -5.643   0.058  1.00  1.71           H  
-ATOM     22  HB3 PRO A 250       2.466  -6.251   1.052  1.00  1.90           H  
-ATOM     23  HG2 PRO A 250       2.484  -5.454  -1.838  1.00  1.66           H  
-ATOM     24  HG3 PRO A 250       3.609  -4.984  -0.547  1.00  2.27           H  
-ATOM     25  HD2 PRO A 250       4.155  -6.977  -2.282  1.00  1.98           H  
-ATOM     26  HD3 PRO A 250       4.710  -7.005  -0.597  1.00  2.21           H  
-ATOM     27  N   ALA A 251       0.321  -8.828  -2.007  1.00  1.13           N  
-ATOM     28  CA  ALA A 251      -0.769  -9.008  -2.971  1.00  1.33           C  
-ATOM     29  C   ALA A 251      -0.737  -7.945  -4.077  1.00  1.06           C  
-ATOM     30  O   ALA A 251       0.055  -8.036  -5.017  1.00  1.00           O  
-ATOM     31  CB  ALA A 251      -2.116  -9.016  -2.253  1.00  1.70           C  
-ATOM     32  H   ALA A 251       0.940  -9.575  -1.851  1.00  1.35           H  
-ATOM     33  HA  ALA A 251      -0.636  -9.977  -3.430  1.00  1.67           H  
-ATOM     34  HB1 ALA A 251      -2.261  -9.973  -1.773  1.00  2.09           H  
-ATOM     35  HB2 ALA A 251      -2.906  -8.849  -2.968  1.00  1.87           H  
-ATOM     36  HB3 ALA A 251      -2.132  -8.234  -1.509  1.00  1.96           H  
-ATOM     37  N   ARG A 252      -1.594  -6.938  -3.958  1.00  1.00           N  
-ATOM     38  CA  ARG A 252      -1.660  -5.863  -4.941  1.00  0.79           C  
-ATOM     39  C   ARG A 252      -0.581  -4.805  -4.725  1.00  0.69           C  
-ATOM     40  O   ARG A 252      -0.879  -3.634  -4.490  1.00  0.65           O  
-ATOM     41  CB  ARG A 252      -3.044  -5.223  -4.939  1.00  0.86           C  
-ATOM     42  CG  ARG A 252      -4.061  -6.044  -5.701  1.00  0.96           C  
-ATOM     43  CD  ARG A 252      -3.563  -6.354  -7.107  1.00  0.91           C  
-ATOM     44  NE  ARG A 252      -4.508  -7.170  -7.868  1.00  1.07           N  
-ATOM     45  CZ  ARG A 252      -5.694  -6.758  -8.263  1.00  1.22           C  
-ATOM     46  NH1 ARG A 252      -6.077  -5.528  -8.039  1.00  1.26           N  
-ATOM     47  NH2 ARG A 252      -6.493  -7.577  -8.900  1.00  1.40           N  
-ATOM     48  H   ARG A 252      -2.189  -6.913  -3.186  1.00  1.16           H  
-ATOM     49  HA  ARG A 252      -1.497  -6.311  -5.909  1.00  0.74           H  
-ATOM     50  HB2 ARG A 252      -3.381  -5.119  -3.919  1.00  1.02           H  
-ATOM     51  HB3 ARG A 252      -2.982  -4.247  -5.395  1.00  0.79           H  
-ATOM     52  HG2 ARG A 252      -4.232  -6.972  -5.175  1.00  1.07           H  
-ATOM     53  HG3 ARG A 252      -4.984  -5.488  -5.769  1.00  1.06           H  
-ATOM     54  HD2 ARG A 252      -3.400  -5.423  -7.630  1.00  0.90           H  
-ATOM     55  HD3 ARG A 252      -2.623  -6.886  -7.029  1.00  0.93           H  
-ATOM     56  HE  ARG A 252      -4.241  -8.085  -8.078  1.00  1.13           H  
-ATOM     57 HH11 ARG A 252      -5.466  -4.895  -7.570  1.00  1.17           H  
-ATOM     58 HH12 ARG A 252      -6.977  -5.223  -8.337  1.00  1.44           H  
-ATOM     59 HH21 ARG A 252      -6.203  -8.512  -9.088  1.00  1.43           H  
-ATOM     60 HH22 ARG A 252      -7.392  -7.265  -9.198  1.00  1.57           H  
-ATOM     61  N   VAL A 253       0.669  -5.234  -4.823  1.00  0.72           N  
-ATOM     62  CA  VAL A 253       1.825  -4.350  -4.657  1.00  0.72           C  
-ATOM     63  C   VAL A 253       1.718  -3.096  -5.534  1.00  0.58           C  
-ATOM     64  O   VAL A 253       1.806  -1.972  -5.042  1.00  0.54           O  
-ATOM     65  CB  VAL A 253       3.133  -5.106  -4.985  1.00  0.85           C  
-ATOM     66  CG1 VAL A 253       3.052  -5.809  -6.335  1.00  0.82           C  
-ATOM     67  CG2 VAL A 253       4.332  -4.170  -4.930  1.00  1.00           C  
-ATOM     68  H   VAL A 253       0.819  -6.187  -5.016  1.00  0.80           H  
-ATOM     69  HA  VAL A 253       1.863  -4.047  -3.623  1.00  0.80           H  
-ATOM     70  HB  VAL A 253       3.265  -5.860  -4.240  1.00  0.96           H  
-ATOM     71 HG11 VAL A 253       3.683  -6.685  -6.324  1.00  1.31           H  
-ATOM     72 HG12 VAL A 253       3.381  -5.138  -7.113  1.00  1.31           H  
-ATOM     73 HG13 VAL A 253       2.030  -6.105  -6.525  1.00  1.31           H  
-ATOM     74 HG21 VAL A 253       4.170  -3.340  -5.601  1.00  1.48           H  
-ATOM     75 HG22 VAL A 253       5.221  -4.705  -5.229  1.00  1.48           H  
-ATOM     76 HG23 VAL A 253       4.455  -3.801  -3.924  1.00  1.42           H  
-ATOM     77  N   GLY A 254       1.524  -3.316  -6.828  1.00  0.57           N  
-ATOM     78  CA  GLY A 254       1.401  -2.216  -7.781  1.00  0.55           C  
-ATOM     79  C   GLY A 254       0.300  -1.234  -7.408  1.00  0.46           C  
-ATOM     80  O   GLY A 254       0.527  -0.024  -7.363  1.00  0.46           O  
-ATOM     81  H   GLY A 254       1.471  -4.244  -7.136  1.00  0.63           H  
-ATOM     82  HA2 GLY A 254       2.340  -1.686  -7.824  1.00  0.62           H  
-ATOM     83  HA3 GLY A 254       1.186  -2.625  -8.758  1.00  0.62           H  
-ATOM     84  N   LEU A 255      -0.888  -1.757  -7.113  1.00  0.48           N  
-ATOM     85  CA  LEU A 255      -2.017  -0.920  -6.715  1.00  0.50           C  
-ATOM     86  C   LEU A 255      -1.721  -0.201  -5.402  1.00  0.43           C  
-ATOM     87  O   LEU A 255      -2.024   0.982  -5.248  1.00  0.43           O  
-ATOM     88  CB  LEU A 255      -3.285  -1.756  -6.567  1.00  0.65           C  
-ATOM     89  CG  LEU A 255      -4.335  -1.520  -7.649  1.00  0.82           C  
-ATOM     90  CD1 LEU A 255      -5.593  -2.315  -7.347  1.00  1.01           C  
-ATOM     91  CD2 LEU A 255      -4.652  -0.036  -7.764  1.00  0.92           C  
-ATOM     92  H   LEU A 255      -1.002  -2.728  -7.147  1.00  0.54           H  
-ATOM     93  HA  LEU A 255      -2.174  -0.183  -7.488  1.00  0.54           H  
-ATOM     94  HB2 LEU A 255      -3.007  -2.801  -6.584  1.00  0.72           H  
-ATOM     95  HB3 LEU A 255      -3.730  -1.534  -5.610  1.00  0.71           H  
-ATOM     96  HG  LEU A 255      -3.948  -1.857  -8.599  1.00  0.89           H  
-ATOM     97 HD11 LEU A 255      -5.607  -3.208  -7.954  1.00  1.66           H  
-ATOM     98 HD12 LEU A 255      -6.461  -1.714  -7.569  1.00  1.00           H  
-ATOM     99 HD13 LEU A 255      -5.602  -2.590  -6.302  1.00  1.60           H  
-ATOM    100 HD21 LEU A 255      -4.541   0.431  -6.797  1.00  1.26           H  
-ATOM    101 HD22 LEU A 255      -5.666   0.091  -8.110  1.00  1.51           H  
-ATOM    102 HD23 LEU A 255      -3.972   0.423  -8.466  1.00  1.43           H  
-ATOM    103  N   GLY A 256      -1.118  -0.925  -4.462  1.00  0.46           N  
-ATOM    104  CA  GLY A 256      -0.780  -0.342  -3.179  1.00  0.50           C  
-ATOM    105  C   GLY A 256       0.193   0.818  -3.303  1.00  0.41           C  
-ATOM    106  O   GLY A 256      -0.075   1.908  -2.799  1.00  0.40           O  
-ATOM    107  H   GLY A 256      -0.896  -1.867  -4.645  1.00  0.52           H  
-ATOM    108  HA2 GLY A 256      -1.687   0.010  -2.708  1.00  0.56           H  
-ATOM    109  HA3 GLY A 256      -0.339  -1.105  -2.556  1.00  0.61           H  
-ATOM    110  N   ILE A 257       1.318   0.598  -3.991  1.00  0.42           N  
-ATOM    111  CA  ILE A 257       2.308   1.660  -4.173  1.00  0.46           C  
-ATOM    112  C   ILE A 257       1.736   2.813  -5.002  1.00  0.41           C  
-ATOM    113  O   ILE A 257       2.004   3.979  -4.716  1.00  0.46           O  
-ATOM    114  CB  ILE A 257       3.635   1.143  -4.794  1.00  0.61           C  
-ATOM    115  CG1 ILE A 257       3.421   0.507  -6.173  1.00  0.62           C  
-ATOM    116  CG2 ILE A 257       4.301   0.146  -3.857  1.00  0.75           C  
-ATOM    117  CD1 ILE A 257       3.720   1.438  -7.331  1.00  0.71           C  
-ATOM    118  H   ILE A 257       1.477  -0.291  -4.388  1.00  0.46           H  
-ATOM    119  HA  ILE A 257       2.537   2.045  -3.189  1.00  0.51           H  
-ATOM    120  HB  ILE A 257       4.301   1.987  -4.900  1.00  0.69           H  
-ATOM    121 HG12 ILE A 257       4.065  -0.354  -6.269  1.00  0.72           H  
-ATOM    122 HG13 ILE A 257       2.391   0.191  -6.259  1.00  0.57           H  
-ATOM    123 HG21 ILE A 257       4.219  -0.848  -4.270  1.00  1.33           H  
-ATOM    124 HG22 ILE A 257       3.814   0.177  -2.893  1.00  1.29           H  
-ATOM    125 HG23 ILE A 257       5.344   0.403  -3.741  1.00  1.14           H  
-ATOM    126 HD11 ILE A 257       3.578   0.911  -8.263  1.00  1.27           H  
-ATOM    127 HD12 ILE A 257       4.741   1.781  -7.263  1.00  1.25           H  
-ATOM    128 HD13 ILE A 257       3.052   2.285  -7.294  1.00  1.28           H  
-ATOM    129  N   THR A 258       0.925   2.487  -6.014  1.00  0.38           N  
-ATOM    130  CA  THR A 258       0.303   3.513  -6.855  1.00  0.45           C  
-ATOM    131  C   THR A 258      -0.668   4.358  -6.031  1.00  0.40           C  
-ATOM    132  O   THR A 258      -0.708   5.580  -6.165  1.00  0.49           O  
-ATOM    133  CB  THR A 258      -0.448   2.895  -8.055  1.00  0.55           C  
-ATOM    134  OG1 THR A 258       0.453   2.110  -8.846  1.00  0.64           O  
-ATOM    135  CG2 THR A 258      -1.075   3.974  -8.929  1.00  0.71           C  
-ATOM    136  H   THR A 258       0.730   1.536  -6.189  1.00  0.37           H  
-ATOM    137  HA  THR A 258       1.087   4.152  -7.235  1.00  0.56           H  
-ATOM    138  HB  THR A 258      -1.234   2.256  -7.679  1.00  0.52           H  
-ATOM    139  HG1 THR A 258       0.593   1.253  -8.416  1.00  0.59           H  
-ATOM    140 HG21 THR A 258      -1.068   4.915  -8.401  1.00  1.31           H  
-ATOM    141 HG22 THR A 258      -2.093   3.701  -9.164  1.00  1.22           H  
-ATOM    142 HG23 THR A 258      -0.508   4.071  -9.843  1.00  1.22           H  
-ATOM    143  N   THR A 259      -1.433   3.701  -5.159  1.00  0.34           N  
-ATOM    144  CA  THR A 259      -2.384   4.401  -4.295  1.00  0.39           C  
-ATOM    145  C   THR A 259      -1.636   5.315  -3.328  1.00  0.33           C  
-ATOM    146  O   THR A 259      -1.961   6.498  -3.199  1.00  0.38           O  
-ATOM    147  CB  THR A 259      -3.265   3.418  -3.490  1.00  0.50           C  
-ATOM    148  OG1 THR A 259      -3.940   2.520  -4.380  1.00  0.61           O  
-ATOM    149  CG2 THR A 259      -4.294   4.166  -2.653  1.00  0.63           C  
-ATOM    150  H   THR A 259      -1.345   2.724  -5.081  1.00  0.34           H  
-ATOM    151  HA  THR A 259      -3.026   5.004  -4.923  1.00  0.48           H  
-ATOM    152  HB  THR A 259      -2.630   2.848  -2.828  1.00  0.51           H  
-ATOM    153  HG1 THR A 259      -3.309   1.869  -4.720  1.00  0.59           H  
-ATOM    154 HG21 THR A 259      -5.238   4.188  -3.177  1.00  1.25           H  
-ATOM    155 HG22 THR A 259      -3.955   5.176  -2.481  1.00  1.20           H  
-ATOM    156 HG23 THR A 259      -4.420   3.664  -1.705  1.00  1.19           H  
-ATOM    157  N   VAL A 260      -0.612   4.765  -2.673  1.00  0.31           N  
-ATOM    158  CA  VAL A 260       0.203   5.542  -1.741  1.00  0.36           C  
-ATOM    159  C   VAL A 260       0.876   6.715  -2.461  1.00  0.36           C  
-ATOM    160  O   VAL A 260       0.840   7.850  -1.981  1.00  0.40           O  
-ATOM    161  CB  VAL A 260       1.280   4.666  -1.061  1.00  0.49           C  
-ATOM    162  CG1 VAL A 260       2.178   5.506  -0.166  1.00  0.63           C  
-ATOM    163  CG2 VAL A 260       0.629   3.548  -0.260  1.00  0.59           C  
-ATOM    164  H   VAL A 260      -0.388   3.818  -2.835  1.00  0.33           H  
-ATOM    165  HA  VAL A 260      -0.452   5.932  -0.974  1.00  0.40           H  
-ATOM    166  HB  VAL A 260       1.892   4.220  -1.830  1.00  0.53           H  
-ATOM    167 HG11 VAL A 260       3.126   5.007  -0.037  1.00  1.21           H  
-ATOM    168 HG12 VAL A 260       1.705   5.636   0.796  1.00  1.20           H  
-ATOM    169 HG13 VAL A 260       2.338   6.472  -0.621  1.00  1.25           H  
-ATOM    170 HG21 VAL A 260      -0.191   3.131  -0.825  1.00  1.17           H  
-ATOM    171 HG22 VAL A 260       0.258   3.943   0.674  1.00  1.29           H  
-ATOM    172 HG23 VAL A 260       1.357   2.777  -0.060  1.00  1.11           H  
-ATOM    173  N   LEU A 261       1.469   6.437  -3.627  1.00  0.41           N  
-ATOM    174  CA  LEU A 261       2.130   7.471  -4.424  1.00  0.55           C  
-ATOM    175  C   LEU A 261       1.137   8.562  -4.832  1.00  0.57           C  
-ATOM    176  O   LEU A 261       1.440   9.748  -4.758  1.00  0.66           O  
-ATOM    177  CB  LEU A 261       2.770   6.860  -5.669  1.00  0.67           C  
-ATOM    178  CG  LEU A 261       3.716   7.793  -6.421  1.00  0.86           C  
-ATOM    179  CD1 LEU A 261       4.808   8.304  -5.490  1.00  0.95           C  
-ATOM    180  CD2 LEU A 261       4.320   7.076  -7.614  1.00  0.99           C  
-ATOM    181  H   LEU A 261       1.452   5.512  -3.965  1.00  0.40           H  
-ATOM    182  HA  LEU A 261       2.910   7.916  -3.822  1.00  0.62           H  
-ATOM    183  HB2 LEU A 261       3.323   5.981  -5.371  1.00  0.67           H  
-ATOM    184  HB3 LEU A 261       1.983   6.561  -6.344  1.00  0.66           H  
-ATOM    185  HG  LEU A 261       3.162   8.645  -6.788  1.00  0.89           H  
-ATOM    186 HD11 LEU A 261       4.510   8.139  -4.464  1.00  1.46           H  
-ATOM    187 HD12 LEU A 261       4.955   9.360  -5.654  1.00  1.36           H  
-ATOM    188 HD13 LEU A 261       5.728   7.776  -5.688  1.00  1.42           H  
-ATOM    189 HD21 LEU A 261       3.549   6.880  -8.345  1.00  1.49           H  
-ATOM    190 HD22 LEU A 261       4.757   6.142  -7.290  1.00  1.41           H  
-ATOM    191 HD23 LEU A 261       5.084   7.698  -8.054  1.00  1.45           H  
-ATOM    192  N   THR A 262      -0.056   8.146  -5.244  1.00  0.53           N  
-ATOM    193  CA  THR A 262      -1.107   9.084  -5.643  1.00  0.62           C  
-ATOM    194  C   THR A 262      -1.542   9.951  -4.458  1.00  0.59           C  
-ATOM    195  O   THR A 262      -1.669  11.171  -4.572  1.00  0.69           O  
-ATOM    196  CB  THR A 262      -2.342   8.335  -6.191  1.00  0.65           C  
-ATOM    197  OG1 THR A 262      -1.984   7.565  -7.345  1.00  0.71           O  
-ATOM    198  CG2 THR A 262      -3.456   9.306  -6.549  1.00  0.81           C  
-ATOM    199  H   THR A 262      -0.242   7.180  -5.271  1.00  0.49           H  
-ATOM    200  HA  THR A 262      -0.716   9.718  -6.424  1.00  0.74           H  
-ATOM    201  HB  THR A 262      -2.704   7.667  -5.422  1.00  0.58           H  
-ATOM    202  HG1 THR A 262      -1.534   6.753  -7.063  1.00  0.70           H  
-ATOM    203 HG21 THR A 262      -4.408   8.868  -6.288  1.00  0.91           H  
-ATOM    204 HG22 THR A 262      -3.429   9.509  -7.607  1.00  0.97           H  
-ATOM    205 HG23 THR A 262      -3.320  10.226  -5.999  1.00  0.79           H  
-ATOM    206  N   MET A 263      -1.773   9.298  -3.322  1.00  0.49           N  
-ATOM    207  CA  MET A 263      -2.200   9.976  -2.100  1.00  0.52           C  
-ATOM    208  C   MET A 263      -1.141  10.967  -1.590  1.00  0.53           C  
-ATOM    209  O   MET A 263      -1.473  12.090  -1.212  1.00  0.58           O  
-ATOM    210  CB  MET A 263      -2.510   8.944  -1.013  1.00  0.53           C  
-ATOM    211  CG  MET A 263      -3.156   9.535   0.230  1.00  0.67           C  
-ATOM    212  SD  MET A 263      -4.776  10.251  -0.105  1.00  0.88           S  
-ATOM    213  CE  MET A 263      -5.676   8.813  -0.682  1.00  1.05           C  
-ATOM    214  H   MET A 263      -1.660   8.321  -3.310  1.00  0.44           H  
-ATOM    215  HA  MET A 263      -3.103  10.524  -2.325  1.00  0.62           H  
-ATOM    216  HB2 MET A 263      -3.178   8.200  -1.422  1.00  0.57           H  
-ATOM    217  HB3 MET A 263      -1.589   8.462  -0.719  1.00  0.51           H  
-ATOM    218  HG2 MET A 263      -3.270   8.753   0.966  1.00  0.76           H  
-ATOM    219  HG3 MET A 263      -2.510  10.305   0.623  1.00  0.68           H  
-ATOM    220  HE1 MET A 263      -6.042   8.253   0.166  1.00  1.18           H  
-ATOM    221  HE2 MET A 263      -5.018   8.189  -1.268  1.00  1.13           H  
-ATOM    222  HE3 MET A 263      -6.509   9.129  -1.292  1.00  1.17           H  
-ATOM    223  N   THR A 264       0.127  10.550  -1.572  1.00  0.52           N  
-ATOM    224  CA  THR A 264       1.201  11.428  -1.096  1.00  0.60           C  
-ATOM    225  C   THR A 264       1.434  12.605  -2.048  1.00  0.67           C  
-ATOM    226  O   THR A 264       1.304  12.478  -3.267  1.00  0.72           O  
-ATOM    227  CB  THR A 264       2.540  10.684  -0.877  1.00  0.65           C  
-ATOM    228  OG1 THR A 264       3.521  11.594  -0.363  1.00  0.77           O  
-ATOM    229  CG2 THR A 264       3.062  10.067  -2.163  1.00  0.68           C  
-ATOM    230  H   THR A 264       0.342   9.638  -1.879  1.00  0.51           H  
-ATOM    231  HA  THR A 264       0.887  11.828  -0.142  1.00  0.61           H  
-ATOM    232  HB  THR A 264       2.380   9.895  -0.154  1.00  0.65           H  
-ATOM    233  HG1 THR A 264       4.075  11.141   0.279  1.00  0.89           H  
-ATOM    234 HG21 THR A 264       3.927   9.457  -1.946  1.00  1.23           H  
-ATOM    235 HG22 THR A 264       3.339  10.851  -2.852  1.00  1.26           H  
-ATOM    236 HG23 THR A 264       2.293   9.454  -2.607  1.00  1.22           H  
-ATOM    237  N   THR A 265       1.778  13.758  -1.478  1.00  0.73           N  
-ATOM    238  CA  THR A 265       2.025  14.971  -2.263  1.00  0.83           C  
-ATOM    239  C   THR A 265       3.231  14.833  -3.199  1.00  0.92           C  
-ATOM    240  O   THR A 265       3.411  15.652  -4.100  1.00  1.02           O  
-ATOM    241  CB  THR A 265       2.232  16.209  -1.364  1.00  0.92           C  
-ATOM    242  OG1 THR A 265       2.469  17.370  -2.177  1.00  1.04           O  
-ATOM    243  CG2 THR A 265       3.401  16.004  -0.409  1.00  0.99           C  
-ATOM    244  H   THR A 265       1.862  13.796  -0.503  1.00  0.73           H  
-ATOM    245  HA  THR A 265       1.146  15.147  -2.867  1.00  0.84           H  
-ATOM    246  HB  THR A 265       1.337  16.364  -0.781  1.00  0.90           H  
-ATOM    247  HG1 THR A 265       3.120  17.154  -2.861  1.00  1.13           H  
-ATOM    248 HG21 THR A 265       3.921  16.940  -0.270  1.00  1.47           H  
-ATOM    249 HG22 THR A 265       4.081  15.274  -0.823  1.00  1.37           H  
-ATOM    250 HG23 THR A 265       3.033  15.654   0.544  1.00  1.48           H  
-ATOM    251  N   GLN A 266       4.054  13.804  -2.980  1.00  0.92           N  
-ATOM    252  CA  GLN A 266       5.241  13.572  -3.808  1.00  1.03           C  
-ATOM    253  C   GLN A 266       4.886  13.423  -5.291  1.00  1.05           C  
-ATOM    254  O   GLN A 266       5.538  14.014  -6.150  1.00  1.20           O  
-ATOM    255  CB  GLN A 266       6.001  12.338  -3.321  1.00  1.04           C  
-ATOM    256  CG  GLN A 266       6.533  12.470  -1.900  1.00  1.11           C  
-ATOM    257  CD  GLN A 266       7.668  13.476  -1.756  1.00  1.28           C  
-ATOM    258  OE1 GLN A 266       8.103  13.775  -0.649  1.00  1.41           O  
-ATOM    259  NE2 GLN A 266       8.171  13.997  -2.866  1.00  1.35           N  
-ATOM    260  H   GLN A 266       3.858  13.186  -2.242  1.00  0.86           H  
-ATOM    261  HA  GLN A 266       5.880  14.431  -3.696  1.00  1.13           H  
-ATOM    262  HB2 GLN A 266       5.341  11.485  -3.358  1.00  0.96           H  
-ATOM    263  HB3 GLN A 266       6.839  12.163  -3.980  1.00  1.14           H  
-ATOM    264  HG2 GLN A 266       5.721  12.783  -1.262  1.00  1.08           H  
-ATOM    265  HG3 GLN A 266       6.887  11.504  -1.577  1.00  1.13           H  
-ATOM    266 HE21 GLN A 266       7.796  13.714  -3.724  1.00  1.31           H  
-ATOM    267 HE22 GLN A 266       8.901  14.643  -2.778  1.00  1.49           H  
-ATOM    268  N   SER A 267       3.842  12.656  -5.591  1.00  0.95           N  
-ATOM    269  CA  SER A 267       3.408  12.467  -6.982  1.00  0.99           C  
-ATOM    270  C   SER A 267       2.499  13.619  -7.433  1.00  1.02           C  
-ATOM    271  O   SER A 267       1.420  13.392  -7.987  1.00  1.02           O  
-ATOM    272  CB  SER A 267       2.680  11.130  -7.145  1.00  0.93           C  
-ATOM    273  OG  SER A 267       2.292  10.911  -8.492  1.00  1.04           O  
-ATOM    274  H   SER A 267       3.343  12.223  -4.866  1.00  0.86           H  
-ATOM    275  HA  SER A 267       4.292  12.463  -7.603  1.00  1.09           H  
-ATOM    276  HB2 SER A 267       3.334  10.327  -6.840  1.00  0.92           H  
-ATOM    277  HB3 SER A 267       1.796  11.126  -6.523  1.00  0.87           H  
-ATOM    278  HG  SER A 267       1.667  11.598  -8.759  1.00  1.12           H  
-ATOM    279  N   SER A 268       2.946  14.852  -7.178  1.00  1.08           N  
-ATOM    280  CA  SER A 268       2.188  16.060  -7.534  1.00  1.12           C  
-ATOM    281  C   SER A 268       0.827  16.088  -6.833  1.00  1.06           C  
-ATOM    282  O   SER A 268      -0.183  16.461  -7.428  1.00  1.12           O  
-ATOM    283  CB  SER A 268       1.998  16.166  -9.053  1.00  1.22           C  
-ATOM    284  OG  SER A 268       3.246  16.242  -9.725  1.00  1.31           O  
-ATOM    285  H   SER A 268       3.814  14.955  -6.727  1.00  1.12           H  
-ATOM    286  HA  SER A 268       2.762  16.913  -7.197  1.00  1.17           H  
-ATOM    287  HB2 SER A 268       1.466  15.297  -9.408  1.00  1.22           H  
-ATOM    288  HB3 SER A 268       1.424  17.052  -9.279  1.00  1.26           H  
-ATOM    289  HG  SER A 268       3.305  15.531 -10.370  1.00  1.63           H  
-ATOM    290  N   GLY A 269       0.813  15.690  -5.560  1.00  0.99           N  
-ATOM    291  CA  GLY A 269      -0.424  15.674  -4.791  1.00  0.97           C  
-ATOM    292  C   GLY A 269      -0.967  17.069  -4.518  1.00  0.99           C  
-ATOM    293  O   GLY A 269      -2.164  17.318  -4.660  1.00  1.02           O  
-ATOM    294  H   GLY A 269       1.653  15.404  -5.140  1.00  0.98           H  
-ATOM    295  HA2 GLY A 269      -1.167  15.114  -5.340  1.00  0.99           H  
-ATOM    296  HA3 GLY A 269      -0.243  15.180  -3.848  1.00  0.94           H  
-ATOM    297  N   SER A 270      -0.081  17.984  -4.133  1.00  1.01           N  
-ATOM    298  CA  SER A 270      -0.468  19.371  -3.846  1.00  1.06           C  
-ATOM    299  C   SER A 270      -0.645  20.181  -5.133  1.00  1.00           C  
-ATOM    300  O   SER A 270      -0.110  21.284  -5.262  1.00  0.94           O  
-ATOM    301  CB  SER A 270       0.583  20.044  -2.977  1.00  1.14           C  
-ATOM    302  OG  SER A 270       0.778  19.340  -1.759  1.00  1.20           O  
-ATOM    303  H   SER A 270       0.860  17.724  -4.044  1.00  1.01           H  
-ATOM    304  HA  SER A 270      -1.406  19.353  -3.313  1.00  1.10           H  
-ATOM    305  HB2 SER A 270       1.517  20.073  -3.518  1.00  1.14           H  
-ATOM    306  HB3 SER A 270       0.263  21.049  -2.755  1.00  1.23           H  
-ATOM    307  HG  SER A 270       1.427  18.626  -1.894  1.00  1.24           H  
-ATOM    308  N   ARG A 271      -1.393  19.606  -6.068  1.00  1.10           N  
-ATOM    309  CA  ARG A 271      -1.680  20.211  -7.380  1.00  1.10           C  
-ATOM    310  C   ARG A 271      -0.428  20.248  -8.274  1.00  1.07           C  
-ATOM    311  O   ARG A 271       0.653  20.650  -7.844  1.00  1.02           O  
-ATOM    312  CB  ARG A 271      -2.264  21.619  -7.217  1.00  1.04           C  
-ATOM    313  CG  ARG A 271      -2.804  22.211  -8.508  1.00  1.15           C  
-ATOM    314  CD  ARG A 271      -3.275  23.640  -8.307  1.00  1.00           C  
-ATOM    315  NE  ARG A 271      -2.214  24.482  -7.770  1.00  0.87           N  
-ATOM    316  CZ  ARG A 271      -2.287  25.786  -7.673  1.00  0.93           C  
-ATOM    317  NH1 ARG A 271      -3.347  26.432  -8.070  1.00  1.13           N  
-ATOM    318  NH2 ARG A 271      -1.294  26.457  -7.182  1.00  1.37           N  
-ATOM    319  H   ARG A 271      -1.767  18.726  -5.865  1.00  1.20           H  
-ATOM    320  HA  ARG A 271      -2.419  19.588  -7.863  1.00  1.30           H  
-ATOM    321  HB2 ARG A 271      -3.068  21.582  -6.498  1.00  1.20           H  
-ATOM    322  HB3 ARG A 271      -1.490  22.274  -6.842  1.00  0.90           H  
-ATOM    323  HG2 ARG A 271      -2.021  22.203  -9.252  1.00  1.25           H  
-ATOM    324  HG3 ARG A 271      -3.635  21.612  -8.848  1.00  1.58           H  
-ATOM    325  HD2 ARG A 271      -3.594  24.041  -9.258  1.00  1.19           H  
-ATOM    326  HD3 ARG A 271      -4.107  23.641  -7.618  1.00  1.35           H  
-ATOM    327  HE  ARG A 271      -1.396  24.037  -7.462  1.00  1.21           H  
-ATOM    328 HH11 ARG A 271      -4.121  25.939  -8.451  1.00  1.41           H  
-ATOM    329 HH12 ARG A 271      -3.365  27.444  -7.996  1.00  1.34           H  
-ATOM    330 HH21 ARG A 271      -0.472  25.986  -6.873  1.00  1.76           H  
-ATOM    331 HH22 ARG A 271      -1.351  27.459  -7.131  1.00  1.51           H  
-ATOM    332  N   ALA A 272      -0.579  19.819  -9.529  1.00  1.27           N  
-ATOM    333  CA  ALA A 272       0.543  19.794 -10.477  1.00  1.37           C  
-ATOM    334  C   ALA A 272       0.902  21.195 -11.007  1.00  1.11           C  
-ATOM    335  O   ALA A 272       1.023  21.399 -12.214  1.00  1.35           O  
-ATOM    336  CB  ALA A 272       0.233  18.847 -11.631  1.00  1.78           C  
-ATOM    337  H   ALA A 272      -1.458  19.502  -9.823  1.00  1.45           H  
-ATOM    338  HA  ALA A 272       1.400  19.400  -9.952  1.00  1.48           H  
-ATOM    339  HB1 ALA A 272       0.847  19.102 -12.481  1.00  2.15           H  
-ATOM    340  HB2 ALA A 272      -0.809  18.934 -11.900  1.00  2.22           H  
-ATOM    341  HB3 ALA A 272       0.441  17.832 -11.328  1.00  2.02           H  
-ATOM    342  N   SER A 273       1.089  22.147 -10.092  1.00  0.87           N  
-ATOM    343  CA  SER A 273       1.455  23.522 -10.459  1.00  0.90           C  
-ATOM    344  C   SER A 273       1.825  24.355  -9.242  1.00  0.99           C  
-ATOM    345  O   SER A 273       0.989  24.667  -8.390  1.00  0.84           O  
-ATOM    346  CB  SER A 273       0.334  24.223 -11.234  1.00  0.90           C  
-ATOM    347  OG  SER A 273      -0.877  24.251 -10.499  1.00  0.79           O  
-ATOM    348  H   SER A 273       0.992  21.916  -9.140  1.00  0.94           H  
-ATOM    349  HA  SER A 273       2.326  23.478 -11.096  1.00  1.18           H  
-ATOM    350  HB2 SER A 273       0.634  25.239 -11.441  1.00  1.10           H  
-ATOM    351  HB3 SER A 273       0.169  23.704 -12.166  1.00  1.12           H  
-ATOM    352  HG  SER A 273      -0.759  24.782  -9.706  1.00  1.33           H  
-ATOM    353  N   LEU A 274       3.094  24.741  -9.216  1.00  1.45           N  
-ATOM    354  CA  LEU A 274       3.655  25.581  -8.165  1.00  1.79           C  
-ATOM    355  C   LEU A 274       3.100  27.028  -8.180  1.00  1.77           C  
-ATOM    356  O   LEU A 274       3.074  27.665  -7.126  1.00  1.91           O  
-ATOM    357  CB  LEU A 274       5.179  25.613  -8.306  1.00  2.40           C  
-ATOM    358  CG  LEU A 274       5.933  26.385  -7.218  1.00  2.87           C  
-ATOM    359  CD1 LEU A 274       5.746  25.731  -5.858  1.00  2.94           C  
-ATOM    360  CD2 LEU A 274       7.409  26.473  -7.566  1.00  3.47           C  
-ATOM    361  H   LEU A 274       3.677  24.467  -9.952  1.00  1.63           H  
-ATOM    362  HA  LEU A 274       3.406  25.128  -7.218  1.00  1.78           H  
-ATOM    363  HB2 LEU A 274       5.540  24.595  -8.308  1.00  2.50           H  
-ATOM    364  HB3 LEU A 274       5.417  26.060  -9.259  1.00  2.53           H  
-ATOM    365  HG  LEU A 274       5.542  27.390  -7.161  1.00  2.83           H  
-ATOM    366 HD11 LEU A 274       6.244  24.773  -5.848  1.00  3.10           H  
-ATOM    367 HD12 LEU A 274       4.694  25.593  -5.665  1.00  3.17           H  
-ATOM    368 HD13 LEU A 274       6.172  26.365  -5.093  1.00  3.16           H  
-ATOM    369 HD21 LEU A 274       7.950  25.705  -7.034  1.00  3.76           H  
-ATOM    370 HD22 LEU A 274       7.788  27.442  -7.282  1.00  3.77           H  
-ATOM    371 HD23 LEU A 274       7.537  26.332  -8.629  1.00  3.74           H  
-ATOM    372  N   PRO A 275       2.672  27.607  -9.350  1.00  1.77           N  
-ATOM    373  CA  PRO A 275       2.157  28.998  -9.388  1.00  1.99           C  
-ATOM    374  C   PRO A 275       0.896  29.212  -8.528  1.00  1.69           C  
-ATOM    375  O   PRO A 275       0.514  28.353  -7.740  1.00  1.66           O  
-ATOM    376  CB  PRO A 275       1.830  29.217 -10.872  1.00  2.24           C  
-ATOM    377  CG  PRO A 275       2.692  28.237 -11.576  1.00  2.29           C  
-ATOM    378  CD  PRO A 275       2.666  27.017 -10.709  1.00  1.79           C  
-ATOM    379  HA  PRO A 275       2.921  29.698  -9.092  1.00  2.37           H  
-ATOM    380  HB2 PRO A 275       0.781  29.024 -11.048  1.00  2.06           H  
-ATOM    381  HB3 PRO A 275       2.072  30.230 -11.155  1.00  2.67           H  
-ATOM    382  HG2 PRO A 275       2.294  28.023 -12.558  1.00  2.38           H  
-ATOM    383  HG3 PRO A 275       3.695  28.625 -11.642  1.00  2.68           H  
-ATOM    384  HD2 PRO A 275       1.761  26.441 -10.882  1.00  1.45           H  
-ATOM    385  HD3 PRO A 275       3.537  26.398 -10.874  1.00  1.95           H  
-ATOM    386  N   LYS A 276       0.242  30.363  -8.688  1.00  1.83           N  
-ATOM    387  CA  LYS A 276      -0.974  30.663  -7.922  1.00  1.89           C  
-ATOM    388  C   LYS A 276      -2.176  29.836  -8.411  1.00  1.70           C  
-ATOM    389  O   LYS A 276      -2.764  30.199  -9.460  1.00  2.09           O  
-ATOM    390  CB  LYS A 276      -1.312  32.150  -8.018  1.00  2.50           C  
-ATOM    391  CG  LYS A 276      -2.616  32.520  -7.324  1.00  2.85           C  
-ATOM    392  CD  LYS A 276      -3.260  33.734  -7.969  1.00  3.49           C  
-ATOM    393  CE  LYS A 276      -3.515  33.499  -9.450  1.00  3.56           C  
-ATOM    394  NZ  LYS A 276      -4.303  32.258  -9.687  1.00  3.27           N  
-ATOM    395  OXT LYS A 276      -2.535  28.847  -7.733  1.00  1.46           O  
-ATOM    396  H   LYS A 276       0.579  31.020  -9.331  1.00  2.11           H  
-ATOM    397  HA  LYS A 276      -0.780  30.415  -6.889  1.00  1.95           H  
-ATOM    398  HB2 LYS A 276      -0.515  32.719  -7.564  1.00  2.75           H  
-ATOM    399  HB3 LYS A 276      -1.393  32.425  -9.060  1.00  2.65           H  
-ATOM    400  HG2 LYS A 276      -3.297  31.684  -7.390  1.00  2.59           H  
-ATOM    401  HG3 LYS A 276      -2.411  32.739  -6.288  1.00  3.06           H  
-ATOM    402  HD2 LYS A 276      -4.202  33.934  -7.480  1.00  3.78           H  
-ATOM    403  HD3 LYS A 276      -2.604  34.581  -7.854  1.00  3.77           H  
-ATOM    404  HE2 LYS A 276      -4.060  34.342  -9.845  1.00  4.10           H  
-ATOM    405  HE3 LYS A 276      -2.564  33.415  -9.956  1.00  3.45           H  
-ATOM    406  HZ1 LYS A 276      -3.662  31.418  -9.709  1.00  3.16           H  
-ATOM    407  HZ2 LYS A 276      -4.804  32.321 -10.594  1.00  3.53           H  
-ATOM    408  HZ3 LYS A 276      -4.998  32.125  -8.927  1.00  3.13           H  
-TER
-ENDMDL
diff --git a/Tests/PDB/4zhl.cif b/Tests/PDB/4zhl.cif
new file mode 100644
index 0000000..0249b3b
--- /dev/null
+++ b/Tests/PDB/4zhl.cif
@@ -0,0 +1,3693 @@
+data_4ZHL
+# 
+_entry.id   4ZHL 
+# 
+_audit_conform.dict_name       mmcif_pdbx.dic 
+_audit_conform.dict_version    4.058 
+_audit_conform.dict_location   http://mmcif.pdb.org/dictionaries/ascii/mmcif_pdbx.dic 
+# 
+loop_
+_database_2.database_id 
+_database_2.database_code 
+PDB   4ZHL         
+WWPDB D_1000209290 
+# 
+loop_
+_database_PDB_rev.num 
+_database_PDB_rev.date 
+_database_PDB_rev.date_original 
+_database_PDB_rev.status 
+_database_PDB_rev.replaces 
+_database_PDB_rev.mod_type 
+1 2015-09-16 2015-04-25 ? 4ZHL 0 
+2 2015-10-14 ?          ? 4ZHL 1 
+# 
+_database_PDB_rev_record.rev_num   2 
+_database_PDB_rev_record.type      JRNL 
+_database_PDB_rev_record.details   ? 
+# 
+_pdbx_database_related.db_name        PDB 
+_pdbx_database_related.details        . 
+_pdbx_database_related.db_id          4ZHM 
+_pdbx_database_related.content_type   unspecified 
+# 
+_pdbx_database_status.status_code                      REL 
+_pdbx_database_status.status_code_sf                   REL 
+_pdbx_database_status.status_code_mr                   ? 
+_pdbx_database_status.entry_id                         4ZHL 
+_pdbx_database_status.date_begin_release_preparation   . 
+_pdbx_database_status.SG_entry                         N 
+_pdbx_database_status.deposit_site                     RCSB 
+_pdbx_database_status.process_site                     RCSB 
+_pdbx_database_status.methods_development_category     ? 
+_pdbx_database_status.pdb_format_compatible            Y 
+# 
+loop_
+_audit_author.address 
+_audit_author.name 
+_audit_author.pdbx_ordinal 
+? 'Jiang, L.'       1 
+? 'Andreasen, P.A.' 2 
+? 'Huang, M.'       3 
+# 
+_citation.abstract                  ? 
+_citation.abstract_id_CAS           ? 
+_citation.book_id_ISBN              ? 
+_citation.book_publisher            ? 
+_citation.book_publisher_city       ? 
+_citation.book_title                ? 
+_citation.coordinate_linkage        ? 
+_citation.country                   UK 
+_citation.database_id_Medline       ? 
+_citation.details                   ? 
+_citation.id                        primary 
+_citation.journal_abbrev            J.Mol.Biol. 
+_citation.journal_id_ASTM           JMOBAK 
+_citation.journal_id_CSD            ? 
+_citation.journal_id_ISSN           1089-8638 
+_citation.journal_full              ? 
+_citation.journal_issue             ? 
+_citation.journal_volume            427 
+_citation.language                  ? 
+_citation.page_first                3110 
+_citation.page_last                 3122 
+_citation.title                     
+;Selection of High-Affinity Peptidic Serine Protease Inhibitors with Increased Binding Entropy from a Back-Flip Library of Peptide-Protease Fusions.
+;
+_citation.year                      2015 
+_citation.database_id_CSD           ? 
+_citation.pdbx_database_id_DOI      10.1016/j.jmb.2015.08.005 
+_citation.pdbx_database_id_PubMed   26281711 
+_citation.unpublished_flag          ? 
+# 
+loop_
+_citation_author.citation_id 
+_citation_author.name 
+_citation_author.ordinal 
+primary 'Srensen, H.P.'      1 
+primary 'Xu, P.'             2 
+primary 'Jiang, L.'          3 
+primary 'Kromann-Hansen, T.' 4 
+primary 'Jensen, K.J.'       5 
+primary 'Huang, M.'          6 
+primary 'Andreasen, P.A.'    7 
+# 
+_cell.entry_id           4ZHL 
+_cell.length_a           122.057 
+_cell.length_b           122.057 
+_cell.length_c           42.555 
+_cell.angle_alpha        90.00 
+_cell.angle_beta         90.00 
+_cell.angle_gamma        120.00 
+_cell.Z_PDB              9 
+_cell.pdbx_unique_axis   ? 
+# 
+_symmetry.entry_id                         4ZHL 
+_symmetry.space_group_name_H-M             'H 3' 
+_symmetry.pdbx_full_space_group_name_H-M   ? 
+_symmetry.cell_setting                     ? 
+_symmetry.Int_Tables_number                146 
+# 
+loop_
+_entity.id 
+_entity.type 
+_entity.src_method 
+_entity.pdbx_description 
+_entity.formula_weight 
+_entity.pdbx_number_of_molecules 
+_entity.pdbx_ec 
+_entity.pdbx_mutation 
+_entity.pdbx_fragment 
+_entity.details 
+1 polymer man 'Urokinase-type plasminogen activator' 27869.742 1  3.4.21.73 'H99Y, C122A, N145Q' 'UNP RESIDUES 179-425' ? 
+2 polymer syn mupain-1-IG                            1133.322  1  ?         ?                    ?                      ? 
+3 water   nat water                                  18.015    50 ?         ?                    ?                      ? 
+# 
+_entity_name_com.entity_id   1 
+_entity_name_com.name        uPA 
+# 
+loop_
+_entity_poly.entity_id 
+_entity_poly.type 
+_entity_poly.nstd_linkage 
+_entity_poly.nstd_monomer 
+_entity_poly.pdbx_seq_one_letter_code 
+_entity_poly.pdbx_seq_one_letter_code_can 
+_entity_poly.pdbx_strand_id 
+_entity_poly.pdbx_target_identifier 
+1 'polypeptide(L)' no no 
+;IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVEN
+LILHKDYSADTLAYHNDIALLKIRSKEGRCAQPSRTIQTIALPSMYNDPQFGTSCEITGFGKEQSTDYLYPEQLKMTVVK
+LISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPW
+IRSHTKE
+;
+;IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVEN
+LILHKDYSADTLAYHNDIALLKIRSKEGRCAQPSRTIQTIALPSMYNDPQFGTSCEITGFGKEQSTDYLYPEQLKMTVVK
+LISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPW
+IRSHTKE
+;
+U ? 
+2 'polypeptide(L)' no no CPAYSRYIGC CPAYSRYIGC P ? 
+# 
+loop_
+_entity_poly_seq.entity_id 
+_entity_poly_seq.num 
+_entity_poly_seq.mon_id 
+_entity_poly_seq.hetero 
+1 1   ILE n 
+1 2   ILE n 
+1 3   GLY n 
+1 4   GLY n 
+1 5   GLU n 
+1 6   PHE n 
+1 7   THR n 
+1 8   THR n 
+1 9   ILE n 
+1 10  GLU n 
+1 11  ASN n 
+1 12  GLN n 
+1 13  PRO n 
+1 14  TRP n 
+1 15  PHE n 
+1 16  ALA n 
+1 17  ALA n 
+1 18  ILE n 
+1 19  TYR n 
+1 20  ARG n 
+1 21  ARG n 
+1 22  HIS n 
+1 23  ARG n 
+1 24  GLY n 
+1 25  GLY n 
+1 26  SER n 
+1 27  VAL n 
+1 28  THR n 
+1 29  TYR n 
+1 30  VAL n 
+1 31  CYS n 
+1 32  GLY n 
+1 33  GLY n 
+1 34  SER n 
+1 35  LEU n 
+1 36  ILE n 
+1 37  SER n 
+1 38  PRO n 
+1 39  CYS n 
+1 40  TRP n 
+1 41  VAL n 
+1 42  ILE n 
+1 43  SER n 
+1 44  ALA n 
+1 45  THR n 
+1 46  HIS n 
+1 47  CYS n 
+1 48  PHE n 
+1 49  ILE n 
+1 50  ASP n 
+1 51  TYR n 
+1 52  PRO n 
+1 53  LYS n 
+1 54  LYS n 
+1 55  GLU n 
+1 56  ASP n 
+1 57  TYR n 
+1 58  ILE n 
+1 59  VAL n 
+1 60  TYR n 
+1 61  LEU n 
+1 62  GLY n 
+1 63  ARG n 
+1 64  SER n 
+1 65  ARG n 
+1 66  LEU n 
+1 67  ASN n 
+1 68  SER n 
+1 69  ASN n 
+1 70  THR n 
+1 71  GLN n 
+1 72  GLY n 
+1 73  GLU n 
+1 74  MET n 
+1 75  LYS n 
+1 76  PHE n 
+1 77  GLU n 
+1 78  VAL n 
+1 79  GLU n 
+1 80  ASN n 
+1 81  LEU n 
+1 82  ILE n 
+1 83  LEU n 
+1 84  HIS n 
+1 85  LYS n 
+1 86  ASP n 
+1 87  TYR n 
+1 88  SER n 
+1 89  ALA n 
+1 90  ASP n 
+1 91  THR n 
+1 92  LEU n 
+1 93  ALA n 
+1 94  TYR n 
+1 95  HIS n 
+1 96  ASN n 
+1 97  ASP n 
+1 98  ILE n 
+1 99  ALA n 
+1 100 LEU n 
+1 101 LEU n 
+1 102 LYS n 
+1 103 ILE n 
+1 104 ARG n 
+1 105 SER n 
+1 106 LYS n 
+1 107 GLU n 
+1 108 GLY n 
+1 109 ARG n 
+1 110 CYS n 
+1 111 ALA n 
+1 112 GLN n 
+1 113 PRO n 
+1 114 SER n 
+1 115 ARG n 
+1 116 THR n 
+1 117 ILE n 
+1 118 GLN n 
+1 119 THR n 
+1 120 ILE n 
+1 121 ALA n 
+1 122 LEU n 
+1 123 PRO n 
+1 124 SER n 
+1 125 MET n 
+1 126 TYR n 
+1 127 ASN n 
+1 128 ASP n 
+1 129 PRO n 
+1 130 GLN n 
+1 131 PHE n 
+1 132 GLY n 
+1 133 THR n 
+1 134 SER n 
+1 135 CYS n 
+1 136 GLU n 
+1 137 ILE n 
+1 138 THR n 
+1 139 GLY n 
+1 140 PHE n 
+1 141 GLY n 
+1 142 LYS n 
+1 143 GLU n 
+1 144 GLN n 
+1 145 SER n 
+1 146 THR n 
+1 147 ASP n 
+1 148 TYR n 
+1 149 LEU n 
+1 150 TYR n 
+1 151 PRO n 
+1 152 GLU n 
+1 153 GLN n 
+1 154 LEU n 
+1 155 LYS n 
+1 156 MET n 
+1 157 THR n 
+1 158 VAL n 
+1 159 VAL n 
+1 160 LYS n 
+1 161 LEU n 
+1 162 ILE n 
+1 163 SER n 
+1 164 HIS n 
+1 165 ARG n 
+1 166 GLU n 
+1 167 CYS n 
+1 168 GLN n 
+1 169 GLN n 
+1 170 PRO n 
+1 171 HIS n 
+1 172 TYR n 
+1 173 TYR n 
+1 174 GLY n 
+1 175 SER n 
+1 176 GLU n 
+1 177 VAL n 
+1 178 THR n 
+1 179 THR n 
+1 180 LYS n 
+1 181 MET n 
+1 182 LEU n 
+1 183 CYS n 
+1 184 ALA n 
+1 185 ALA n 
+1 186 ASP n 
+1 187 PRO n 
+1 188 GLN n 
+1 189 TRP n 
+1 190 LYS n 
+1 191 THR n 
+1 192 ASP n 
+1 193 SER n 
+1 194 CYS n 
+1 195 GLN n 
+1 196 GLY n 
+1 197 ASP n 
+1 198 SER n 
+1 199 GLY n 
+1 200 GLY n 
+1 201 PRO n 
+1 202 LEU n 
+1 203 VAL n 
+1 204 CYS n 
+1 205 SER n 
+1 206 LEU n 
+1 207 GLN n 
+1 208 GLY n 
+1 209 ARG n 
+1 210 MET n 
+1 211 THR n 
+1 212 LEU n 
+1 213 THR n 
+1 214 GLY n 
+1 215 ILE n 
+1 216 VAL n 
+1 217 SER n 
+1 218 TRP n 
+1 219 GLY n 
+1 220 ARG n 
+1 221 GLY n 
+1 222 CYS n 
+1 223 ALA n 
+1 224 LEU n 
+1 225 LYS n 
+1 226 ASP n 
+1 227 LYS n 
+1 228 PRO n 
+1 229 GLY n 
+1 230 VAL n 
+1 231 TYR n 
+1 232 THR n 
+1 233 ARG n 
+1 234 VAL n 
+1 235 SER n 
+1 236 HIS n 
+1 237 PHE n 
+1 238 LEU n 
+1 239 PRO n 
+1 240 TRP n 
+1 241 ILE n 
+1 242 ARG n 
+1 243 SER n 
+1 244 HIS n 
+1 245 THR n 
+1 246 LYS n 
+1 247 GLU n 
+2 1   CYS n 
+2 2   PRO n 
+2 3   ALA n 
+2 4   TYR n 
+2 5   SER n 
+2 6   ARG n 
+2 7   TYR n 
+2 8   ILE n 
+2 9   GLY n 
+2 10  CYS n 
+# 
+_entity_src_gen.entity_id                          1 
+_entity_src_gen.pdbx_src_id                        1 
+_entity_src_gen.pdbx_alt_source_flag               sample 
+_entity_src_gen.pdbx_seq_type                      'Biological sequence' 
+_entity_src_gen.pdbx_beg_seq_num                   1 
+_entity_src_gen.pdbx_end_seq_num                   247 
+_entity_src_gen.gene_src_common_name               Human 
+_entity_src_gen.gene_src_genus                     ? 
+_entity_src_gen.pdbx_gene_src_gene                 PLAU 
+_entity_src_gen.gene_src_species                   ? 
+_entity_src_gen.gene_src_strain                    ? 
+_entity_src_gen.gene_src_tissue                    ? 
+_entity_src_gen.gene_src_tissue_fraction           ? 
+_entity_src_gen.gene_src_details                   ? 
+_entity_src_gen.pdbx_gene_src_fragment             ? 
+_entity_src_gen.pdbx_gene_src_scientific_name      'Homo sapiens' 
+_entity_src_gen.pdbx_gene_src_ncbi_taxonomy_id     9606 
+_entity_src_gen.pdbx_gene_src_variant              ? 
+_entity_src_gen.pdbx_gene_src_cell_line            ? 
+_entity_src_gen.pdbx_gene_src_atcc                 ? 
+_entity_src_gen.pdbx_gene_src_organ                ? 
+_entity_src_gen.pdbx_gene_src_organelle            ? 
+_entity_src_gen.pdbx_gene_src_cell                 ? 
+_entity_src_gen.pdbx_gene_src_cellular_location    ? 
+_entity_src_gen.host_org_common_name               ? 
+_entity_src_gen.pdbx_host_org_scientific_name      'Komagataella pastoris' 
+_entity_src_gen.pdbx_host_org_ncbi_taxonomy_id     4922 
+_entity_src_gen.host_org_genus                     ? 
+_entity_src_gen.pdbx_host_org_gene                 ? 
+_entity_src_gen.pdbx_host_org_organ                ? 
+_entity_src_gen.host_org_species                   ? 
+_entity_src_gen.pdbx_host_org_tissue               ? 
+_entity_src_gen.pdbx_host_org_tissue_fraction      ? 
+_entity_src_gen.pdbx_host_org_strain               ? 
+_entity_src_gen.pdbx_host_org_variant              ? 
+_entity_src_gen.pdbx_host_org_cell_line            ? 
+_entity_src_gen.pdbx_host_org_atcc                 ? 
+_entity_src_gen.pdbx_host_org_culture_collection   ? 
+_entity_src_gen.pdbx_host_org_cell                 ? 
+_entity_src_gen.pdbx_host_org_organelle            ? 
+_entity_src_gen.pdbx_host_org_cellular_location    ? 
+_entity_src_gen.pdbx_host_org_vector_type          ? 
+_entity_src_gen.pdbx_host_org_vector               ? 
+_entity_src_gen.host_org_details                   ? 
+_entity_src_gen.expression_system_id               ? 
+_entity_src_gen.plasmid_name                       ? 
+_entity_src_gen.plasmid_details                    ? 
+_entity_src_gen.pdbx_description                   ? 
+# 
+_pdbx_entity_src_syn.entity_id              2 
+_pdbx_entity_src_syn.pdbx_src_id            1 
+_pdbx_entity_src_syn.pdbx_alt_source_flag   sample 
+_pdbx_entity_src_syn.pdbx_beg_seq_num       1 
+_pdbx_entity_src_syn.pdbx_end_seq_num       10 
+_pdbx_entity_src_syn.organism_scientific    'synthetic construct' 
+_pdbx_entity_src_syn.organism_common_name   ? 
+_pdbx_entity_src_syn.ncbi_taxonomy_id       32630 
+_pdbx_entity_src_syn.details                ? 
+# 
+loop_
+_struct_ref.biol_id 
+_struct_ref.db_code 
+_struct_ref.db_name 
+_struct_ref.details 
+_struct_ref.entity_id 
+_struct_ref.id 
+_struct_ref.seq_align 
+_struct_ref.seq_dif 
+_struct_ref.pdbx_db_accession 
+_struct_ref.pdbx_db_isoform 
+_struct_ref.pdbx_seq_one_letter_code 
+_struct_ref.pdbx_align_begin 
+_struct_ref.pdbx_align_end 
+? UROK_HUMAN UNP ? 1 1 ? ? P00749 ? 
+;IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVEN
+LILHKDYSADTLAHHNDIALLKIRSKEGRCAQPSRTIQTICLPSMYNDPQFGTSCEITGFGKENSTDYLYPEQLKMTVVK
+LISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPW
+IRSHTKE
+;
+179 ? 
+? 4ZHL       PDB ? 2 2 ? ? 4ZHL   ? ? 1   ? 
+# 
+loop_
+_struct_ref_seq.align_id 
+_struct_ref_seq.ref_id 
+_struct_ref_seq.pdbx_PDB_id_code 
+_struct_ref_seq.pdbx_strand_id 
+_struct_ref_seq.seq_align_beg 
+_struct_ref_seq.pdbx_seq_align_beg_ins_code 
+_struct_ref_seq.seq_align_end 
+_struct_ref_seq.pdbx_seq_align_end_ins_code 
+_struct_ref_seq.pdbx_db_accession 
+_struct_ref_seq.db_align_beg 
+_struct_ref_seq.pdbx_db_align_beg_ins_code 
+_struct_ref_seq.db_align_end 
+_struct_ref_seq.pdbx_db_align_end_ins_code 
+_struct_ref_seq.pdbx_auth_seq_align_beg 
+_struct_ref_seq.pdbx_auth_seq_align_end 
+1 1 4ZHL U 1 ? 247 ? P00749 179 ? 425 ? 16 244 
+2 2 4ZHL P 1 ? 10  ? 4ZHL   1   ? 10  ? 1  10  
+# 
+loop_
+_struct_ref_seq_dif.align_id 
+_struct_ref_seq_dif.pdbx_pdb_id_code 
+_struct_ref_seq_dif.mon_id 
+_struct_ref_seq_dif.pdbx_pdb_strand_id 
+_struct_ref_seq_dif.seq_num 
+_struct_ref_seq_dif.pdbx_pdb_ins_code 
+_struct_ref_seq_dif.pdbx_seq_db_name 
+_struct_ref_seq_dif.pdbx_seq_db_accession_code 
+_struct_ref_seq_dif.db_mon_id 
+_struct_ref_seq_dif.pdbx_seq_db_seq_num 
+_struct_ref_seq_dif.details 
+_struct_ref_seq_dif.pdbx_auth_seq_num 
+_struct_ref_seq_dif.pdbx_ordinal 
+1 4ZHL TYR U 94  ? UNP P00749 HIS 272 'engineered mutation' 99  1 
+1 4ZHL ALA U 121 ? UNP P00749 CYS 299 'engineered mutation' 122 2 
+1 4ZHL GLN U 144 ? UNP P00749 ASN 322 'engineered mutation' 145 3 
+# 
+loop_
+_chem_comp.id 
+_chem_comp.type 
+_chem_comp.mon_nstd_flag 
+_chem_comp.name 
+_chem_comp.pdbx_synonyms 
+_chem_comp.formula 
+_chem_comp.formula_weight 
+ALA 'L-peptide linking' y ALANINE         ? 'C3 H7 N O2'     89.093  
+ARG 'L-peptide linking' y ARGININE        ? 'C6 H15 N4 O2 1' 175.209 
+ASN 'L-peptide linking' y ASPARAGINE      ? 'C4 H8 N2 O3'    132.118 
+ASP 'L-peptide linking' y 'ASPARTIC ACID' ? 'C4 H7 N O4'     133.103 
+CYS 'L-peptide linking' y CYSTEINE        ? 'C3 H7 N O2 S'   121.158 
+GLN 'L-peptide linking' y GLUTAMINE       ? 'C5 H10 N2 O3'   146.144 
+GLU 'L-peptide linking' y 'GLUTAMIC ACID' ? 'C5 H9 N O4'     147.129 
+GLY 'peptide linking'   y GLYCINE         ? 'C2 H5 N O2'     75.067  
+HIS 'L-peptide linking' y HISTIDINE       ? 'C6 H10 N3 O2 1' 156.162 
+HOH non-polymer         . WATER           ? 'H2 O'           18.015  
+ILE 'L-peptide linking' y ISOLEUCINE      ? 'C6 H13 N O2'    131.173 
+LEU 'L-peptide linking' y LEUCINE         ? 'C6 H13 N O2'    131.173 
+LYS 'L-peptide linking' y LYSINE          ? 'C6 H15 N2 O2 1' 147.195 
+MET 'L-peptide linking' y METHIONINE      ? 'C5 H11 N O2 S'  149.211 
+PHE 'L-peptide linking' y PHENYLALANINE   ? 'C9 H11 N O2'    165.189 
+PRO 'L-peptide linking' y PROLINE         ? 'C5 H9 N O2'     115.130 
+SER 'L-peptide linking' y SERINE          ? 'C3 H7 N O3'     105.093 
+THR 'L-peptide linking' y THREONINE       ? 'C4 H9 N O3'     119.119 
+TRP 'L-peptide linking' y TRYPTOPHAN      ? 'C11 H12 N2 O2'  204.225 
+TYR 'L-peptide linking' y TYROSINE        ? 'C9 H11 N O3'    181.189 
+VAL 'L-peptide linking' y VALINE          ? 'C5 H11 N O2'    117.146 
+# 
+_exptl.absorpt_coefficient_mu     ? 
+_exptl.absorpt_correction_T_max   ? 
+_exptl.absorpt_correction_T_min   ? 
+_exptl.absorpt_correction_type    ? 
+_exptl.absorpt_process_details    ? 
+_exptl.entry_id                   4ZHL 
+_exptl.crystals_number            ? 
+_exptl.details                    ? 
+_exptl.method                     'X-RAY DIFFRACTION' 
+_exptl.method_details             ? 
+# 
+_exptl_crystal.colour                      ? 
+_exptl_crystal.density_diffrn              ? 
+_exptl_crystal.density_Matthews            2.10 
+_exptl_crystal.density_method              ? 
+_exptl_crystal.density_percent_sol         41.52 
+_exptl_crystal.description                 ? 
+_exptl_crystal.F_000                       ? 
+_exptl_crystal.id                          1 
+_exptl_crystal.preparation                 ? 
+_exptl_crystal.size_max                    ? 
+_exptl_crystal.size_mid                    ? 
+_exptl_crystal.size_min                    ? 
+_exptl_crystal.size_rad                    ? 
+_exptl_crystal.colour_lustre               ? 
+_exptl_crystal.colour_modifier             ? 
+_exptl_crystal.colour_primary              ? 
+_exptl_crystal.density_meas                ? 
+_exptl_crystal.density_meas_esd            ? 
+_exptl_crystal.density_meas_gt             ? 
+_exptl_crystal.density_meas_lt             ? 
+_exptl_crystal.density_meas_temp           ? 
+_exptl_crystal.density_meas_temp_esd       ? 
+_exptl_crystal.density_meas_temp_gt        ? 
+_exptl_crystal.density_meas_temp_lt        ? 
+_exptl_crystal.pdbx_crystal_image_url      ? 
+_exptl_crystal.pdbx_crystal_image_format   ? 
+_exptl_crystal.pdbx_mosaicity              ? 
+_exptl_crystal.pdbx_mosaicity_esd          ? 
+# 
+_exptl_crystal_grow.apparatus       ? 
+_exptl_crystal_grow.atmosphere      ? 
+_exptl_crystal_grow.crystal_id      1 
+_exptl_crystal_grow.details         ? 
+_exptl_crystal_grow.method          'VAPOR DIFFUSION, SITTING DROP' 
+_exptl_crystal_grow.method_ref      ? 
+_exptl_crystal_grow.pH              4.6 
+_exptl_crystal_grow.pressure        ? 
+_exptl_crystal_grow.pressure_esd    ? 
+_exptl_crystal_grow.seeding         ? 
+_exptl_crystal_grow.seeding_ref     ? 
+_exptl_crystal_grow.temp            298 
+_exptl_crystal_grow.temp_details    ? 
+_exptl_crystal_grow.temp_esd        ? 
+_exptl_crystal_grow.time            ? 
+_exptl_crystal_grow.pdbx_details    '2.0M ammonium sulfate, 50mM sodium citrate, pH 4.6, 5% polyethylene glycol (PEG) 400' 
+_exptl_crystal_grow.pdbx_pH_range   ? 
+# 
+_diffrn.ambient_environment    ? 
+_diffrn.ambient_temp           100 
+_diffrn.ambient_temp_details   ? 
+_diffrn.ambient_temp_esd       ? 
+_diffrn.crystal_id             1 
+_diffrn.crystal_support        ? 
+_diffrn.crystal_treatment      ? 
+_diffrn.details                ? 
+_diffrn.id                     1 
+_diffrn.ambient_pressure       ? 
+_diffrn.ambient_pressure_esd   ? 
+_diffrn.ambient_pressure_gt    ? 
+_diffrn.ambient_pressure_lt    ? 
+_diffrn.ambient_temp_gt        ? 
+_diffrn.ambient_temp_lt        ? 
+# 
+_diffrn_detector.details                      ? 
+_diffrn_detector.detector                     CCD 
+_diffrn_detector.diffrn_id                    1 
+_diffrn_detector.type                         'ADSC QUANTUM 315r' 
+_diffrn_detector.area_resol_mean              ? 
+_diffrn_detector.dtime                        ? 
+_diffrn_detector.pdbx_frames_total            ? 
+_diffrn_detector.pdbx_collection_time_total   ? 
+_diffrn_detector.pdbx_collection_date         2013-07-10 
+# 
+_diffrn_radiation.collimation                      ? 
+_diffrn_radiation.diffrn_id                        1 
+_diffrn_radiation.filter_edge                      ? 
+_diffrn_radiation.inhomogeneity                    ? 
+_diffrn_radiation.monochromator                    ? 
+_diffrn_radiation.polarisn_norm                    ? 
+_diffrn_radiation.polarisn_ratio                   ? 
+_diffrn_radiation.probe                            ? 
+_diffrn_radiation.type                             ? 
+_diffrn_radiation.xray_symbol                      ? 
+_diffrn_radiation.wavelength_id                    1 
+_diffrn_radiation.pdbx_monochromatic_or_laue_m_l   M 
+_diffrn_radiation.pdbx_wavelength_list             ? 
+_diffrn_radiation.pdbx_wavelength                  ? 
+_diffrn_radiation.pdbx_diffrn_protocol             'SINGLE WAVELENGTH' 
+_diffrn_radiation.pdbx_analyzer                    ? 
+_diffrn_radiation.pdbx_scattering_type             x-ray 
+# 
+_diffrn_radiation_wavelength.id           1 
+_diffrn_radiation_wavelength.wavelength   0.979 
+_diffrn_radiation_wavelength.wt           1.0 
+# 
+_diffrn_source.current                     ? 
+_diffrn_source.details                     ? 
+_diffrn_source.diffrn_id                   1 
+_diffrn_source.power                       ? 
+_diffrn_source.size                        ? 
+_diffrn_source.source                      SYNCHROTRON 
+_diffrn_source.target                      ? 
+_diffrn_source.type                        'SSRF BEAMLINE BL17U' 
+_diffrn_source.voltage                     ? 
+_diffrn_source.take-off_angle              ? 
+_diffrn_source.pdbx_wavelength_list        0.979 
+_diffrn_source.pdbx_wavelength             ? 
+_diffrn_source.pdbx_synchrotron_beamline   BL17U 
+_diffrn_source.pdbx_synchrotron_site       SSRF 
+# 
+_reflns.B_iso_Wilson_estimate            ? 
+_reflns.entry_id                         4ZHL 
+_reflns.data_reduction_details           ? 
+_reflns.data_reduction_method            ? 
+_reflns.d_resolution_high                2.05 
+_reflns.d_resolution_low                 50 
+_reflns.details                          ? 
+_reflns.limit_h_max                      ? 
+_reflns.limit_h_min                      ? 
+_reflns.limit_k_max                      ? 
+_reflns.limit_k_min                      ? 
+_reflns.limit_l_max                      ? 
+_reflns.limit_l_min                      ? 
+_reflns.number_all                       ? 
+_reflns.number_obs                       14528 
+_reflns.observed_criterion               ? 
+_reflns.observed_criterion_F_max         ? 
+_reflns.observed_criterion_F_min         ? 
+_reflns.observed_criterion_I_max         ? 
+_reflns.observed_criterion_I_min         ? 
+_reflns.observed_criterion_sigma_F       ? 
+_reflns.observed_criterion_sigma_I       ? 
+_reflns.percent_possible_obs             99.1 
+_reflns.R_free_details                   ? 
+_reflns.Rmerge_F_all                     ? 
+_reflns.Rmerge_F_obs                     ? 
+_reflns.Friedel_coverage                 ? 
+_reflns.number_gt                        ? 
+_reflns.threshold_expression             ? 
+_reflns.pdbx_redundancy                  3.4 
+_reflns.pdbx_Rmerge_I_obs                ? 
+_reflns.pdbx_Rmerge_I_all                ? 
+_reflns.pdbx_Rsym_value                  ? 
+_reflns.pdbx_netI_over_av_sigmaI         ? 
+_reflns.pdbx_netI_over_sigmaI            22.7 
+_reflns.pdbx_res_netI_over_av_sigmaI_2   ? 
+_reflns.pdbx_res_netI_over_sigmaI_2      ? 
+_reflns.pdbx_chi_squared                 ? 
+_reflns.pdbx_scaling_rejects             ? 
+_reflns.pdbx_d_res_high_opt              ? 
+_reflns.pdbx_d_res_low_opt               ? 
+_reflns.pdbx_d_res_opt_method            ? 
+_reflns.phase_calculation_details        ? 
+_reflns.pdbx_Rrim_I_all                  ? 
+_reflns.pdbx_Rpim_I_all                  ? 
+_reflns.pdbx_d_opt                       ? 
+_reflns.pdbx_number_measured_all         ? 
+_reflns.pdbx_diffrn_id                   1 
+_reflns.pdbx_ordinal                     1 
+_reflns.pdbx_CC_half                     ? 
+_reflns.pdbx_R_split                     ? 
+# 
+_computing.entry_id                           4ZHL 
+_computing.cell_refinement                    ? 
+_computing.data_collection                    ? 
+_computing.data_reduction                     ? 
+_computing.molecular_graphics                 ? 
+_computing.publication_material               ? 
+_computing.structure_refinement               'REFMAC 5.7.0029' 
+_computing.structure_solution                 ? 
+_computing.pdbx_structure_refinement_method   ? 
+_computing.pdbx_data_reduction_ii             ? 
+_computing.pdbx_data_reduction_ds             ? 
+# 
+_refine.pdbx_refine_id                           'X-RAY DIFFRACTION' 
+_refine.entry_id                                 4ZHL 
+_refine.pdbx_diffrn_id                           1 
+_refine.pdbx_TLS_residual_ADP_flag               ? 
+_refine.ls_number_reflns_obs                     13804 
+_refine.ls_number_reflns_all                     ? 
+_refine.pdbx_ls_sigma_I                          ? 
+_refine.pdbx_ls_sigma_F                          ? 
+_refine.pdbx_data_cutoff_high_absF               ? 
+_refine.pdbx_data_cutoff_low_absF                ? 
+_refine.pdbx_data_cutoff_high_rms_absF           ? 
+_refine.ls_d_res_low                             30.53 
+_refine.ls_d_res_high                            2.06 
+_refine.ls_percent_reflns_obs                    99.02 
+_refine.ls_R_factor_obs                          0.22184 
+_refine.ls_R_factor_all                          ? 
+_refine.ls_R_factor_R_work                       0.21978 
+_refine.ls_R_factor_R_free                       0.26237 
+_refine.ls_R_factor_R_free_error                 ? 
+_refine.ls_R_factor_R_free_error_details         ? 
+_refine.ls_percent_reflns_R_free                 5.0 
+_refine.ls_number_reflns_R_free                  724 
+_refine.ls_number_parameters                     ? 
+_refine.ls_number_restraints                     ? 
+_refine.occupancy_min                            ? 
+_refine.occupancy_max                            ? 
+_refine.correlation_coeff_Fo_to_Fc               0.950 
+_refine.correlation_coeff_Fo_to_Fc_free          0.932 
+_refine.B_iso_mean                               46.977 
+_refine.aniso_B[1][1]                            -0.52 
+_refine.aniso_B[2][2]                            -0.52 
+_refine.aniso_B[3][3]                            1.69 
+_refine.aniso_B[1][2]                            -0.52 
+_refine.aniso_B[1][3]                            0.00 
+_refine.aniso_B[2][3]                            -0.00 
+_refine.solvent_model_details                    MASK 
+_refine.solvent_model_param_ksol                 ? 
+_refine.solvent_model_param_bsol                 ? 
+_refine.pdbx_solvent_vdw_probe_radii             1.20 
+_refine.pdbx_solvent_ion_probe_radii             0.80 
+_refine.pdbx_solvent_shrinkage_radii             0.80 
+_refine.pdbx_ls_cross_valid_method               THROUGHOUT 
+_refine.details                                  
+;HYDROGENS HAVE BEEN ADDED IN THE RIDING POSITIONS
+ U VALUES
+;
+_refine.pdbx_starting_model                      ? 
+_refine.pdbx_method_to_determine_struct          ? 
+_refine.pdbx_isotropic_thermal_model             ? 
+_refine.pdbx_stereochemistry_target_values       'MAXIMUM LIKELIHOOD' 
+_refine.pdbx_stereochem_target_val_spec_case     ? 
+_refine.pdbx_R_Free_selection_details            RANDOM 
+_refine.pdbx_overall_ESU_R                       0.284 
+_refine.pdbx_overall_ESU_R_Free                  0.214 
+_refine.overall_SU_ML                            0.177 
+_refine.pdbx_overall_phase_error                 ? 
+_refine.overall_SU_B                             6.512 
+_refine.overall_SU_R_Cruickshank_DPI             ? 
+_refine.pdbx_overall_SU_R_free_Cruickshank_DPI   ? 
+_refine.pdbx_overall_SU_R_Blow_DPI               ? 
+_refine.pdbx_overall_SU_R_free_Blow_DPI          ? 
+# 
+_refine_hist.pdbx_refine_id                   'X-RAY DIFFRACTION' 
+_refine_hist.cycle_id                         LAST 
+_refine_hist.pdbx_number_atoms_protein        2030 
+_refine_hist.pdbx_number_atoms_nucleic_acid   0 
+_refine_hist.pdbx_number_atoms_ligand         0 
+_refine_hist.number_atoms_solvent             50 
+_refine_hist.number_atoms_total               2080 
+_refine_hist.d_res_high                       2.06 
+_refine_hist.d_res_low                        30.53 
+# 
+loop_
+_refine_ls_restr.type 
+_refine_ls_restr.dev_ideal 
+_refine_ls_restr.dev_ideal_target 
+_refine_ls_restr.weight 
+_refine_ls_restr.number 
+_refine_ls_restr.pdbx_refine_id 
+_refine_ls_restr.pdbx_restraint_function 
+r_bond_refined_d             0.008  0.019  ? 2067 'X-RAY DIFFRACTION' ? 
+r_bond_other_d               0.001  0.020  ? 1943 'X-RAY DIFFRACTION' ? 
+r_angle_refined_deg          1.265  1.946  ? 2767 'X-RAY DIFFRACTION' ? 
+r_angle_other_deg            0.719  3.005  ? 4453 'X-RAY DIFFRACTION' ? 
+r_dihedral_angle_1_deg       6.549  5.000  ? 235  'X-RAY DIFFRACTION' ? 
+r_dihedral_angle_2_deg       33.531 23.077 ? 91   'X-RAY DIFFRACTION' ? 
+r_dihedral_angle_3_deg       16.759 15.000 ? 351  'X-RAY DIFFRACTION' ? 
+r_dihedral_angle_4_deg       18.334 15.000 ? 14   'X-RAY DIFFRACTION' ? 
+r_chiral_restr               0.079  0.200  ? 302  'X-RAY DIFFRACTION' ? 
+r_gen_planes_refined         0.005  0.021  ? 2196 'X-RAY DIFFRACTION' ? 
+r_gen_planes_other           0.001  0.020  ? 474  'X-RAY DIFFRACTION' ? 
+r_nbd_refined                ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_nbd_other                  ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_nbtor_refined              ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_nbtor_other                ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_xyhbond_nbd_refined        ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_xyhbond_nbd_other          ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_metal_ion_refined          ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_metal_ion_other            ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_symmetry_vdw_refined       ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_symmetry_vdw_other         ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_symmetry_hbond_refined     ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_symmetry_hbond_other       ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_symmetry_metal_ion_refined ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_symmetry_metal_ion_other   ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_mcbond_it                  ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_mcbond_other               ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_mcangle_it                 ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_mcangle_other              ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_scbond_it                  ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_scbond_other               ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_scangle_it                 ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_scangle_other              ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_long_range_B_refined       ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_long_range_B_other         ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_rigid_bond_restr           ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_sphericity_free            ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+r_sphericity_bonded          ?      ?      ? ?    'X-RAY DIFFRACTION' ? 
+# 
+_refine_ls_shell.pdbx_refine_id                   'X-RAY DIFFRACTION' 
+_refine_ls_shell.pdbx_total_number_of_bins_used   20 
+_refine_ls_shell.d_res_high                       2.057 
+_refine_ls_shell.d_res_low                        2.111 
+_refine_ls_shell.number_reflns_R_work             1025 
+_refine_ls_shell.R_factor_R_work                  0.302 
+_refine_ls_shell.percent_reflns_obs               98.35 
+_refine_ls_shell.R_factor_R_free                  0.307 
+_refine_ls_shell.R_factor_R_free_error            ? 
+_refine_ls_shell.percent_reflns_R_free            ? 
+_refine_ls_shell.number_reflns_R_free             46 
+_refine_ls_shell.number_reflns_all                ? 
+_refine_ls_shell.R_factor_all                     ? 
+# 
+_struct.entry_id                     4ZHL 
+_struct.title                        'The crystal structure of mupain-1-IG in complex with murinised human uPA at pH7.4' 
+_struct.pdbx_descriptor              'Urokinase-type plasminogen activator, mupain-1-IG' 
+_struct.pdbx_model_details           ? 
+_struct.pdbx_formula_weight          ? 
+_struct.pdbx_formula_weight_method   ? 
+_struct.pdbx_model_type_details      ? 
+_struct.pdbx_CASP_flag               ? 
+# 
+_struct_keywords.entry_id        4ZHL 
+_struct_keywords.text            'Peptides inhibitor, uPA, serine protease, HYDROLASE-HYDROLASE INHIBITOR complex' 
+_struct_keywords.pdbx_keywords   'HYDROLASE/HYDROLASE INHIBITOR' 
+# 
+loop_
+_struct_asym.id 
+_struct_asym.pdbx_blank_PDB_chainid_flag 
+_struct_asym.pdbx_modified 
+_struct_asym.entity_id 
+_struct_asym.details 
+A N N 1 ? 
+B N N 2 ? 
+C N N 3 ? 
+D N N 3 ? 
+# 
+loop_
+_struct_conf.conf_type_id 
+_struct_conf.id 
+_struct_conf.pdbx_PDB_helix_id 
+_struct_conf.beg_label_comp_id 
+_struct_conf.beg_label_asym_id 
+_struct_conf.beg_label_seq_id 
+_struct_conf.pdbx_beg_PDB_ins_code 
+_struct_conf.end_label_comp_id 
+_struct_conf.end_label_asym_id 
+_struct_conf.end_label_seq_id 
+_struct_conf.pdbx_end_PDB_ins_code 
+_struct_conf.beg_auth_comp_id 
+_struct_conf.beg_auth_asym_id 
+_struct_conf.beg_auth_seq_id 
+_struct_conf.end_auth_comp_id 
+_struct_conf.end_auth_asym_id 
+_struct_conf.end_auth_seq_id 
+_struct_conf.pdbx_PDB_helix_class 
+_struct_conf.details 
+_struct_conf.pdbx_PDB_helix_length 
+HELX_P HELX_P1 AA1 THR A 8   ? GLN A 12  ? THR U 23  GLN U 27  5 ? 5  
+HELX_P HELX_P2 AA2 ALA A 44  ? PHE A 48  ? ALA U 55  PHE U 59  5 ? 5  
+HELX_P HELX_P3 AA3 LYS A 53  ? GLU A 55  A LYS U 61  GLU U 62  5 ? 3  
+HELX_P HELX_P4 AA4 SER A 163 ? GLN A 168 ? SER U 164 GLN U 169 1 ? 6  
+HELX_P HELX_P5 AA5 TYR A 173 ? VAL A 177 ? TYR U 172 VAL U 176 5 ? 5  
+HELX_P HELX_P6 AA6 PHE A 237 ? LYS A 246 ? PHE U 234 LYS U 243 1 ? 10 
+# 
+_struct_conf_type.id          HELX_P 
+_struct_conf_type.criteria    ? 
+_struct_conf_type.reference   ? 
+# 
+loop_
+_struct_conn.id 
+_struct_conn.conn_type_id 
+_struct_conn.pdbx_leaving_atom_flag 
+_struct_conn.pdbx_PDB_id 
+_struct_conn.ptnr1_label_asym_id 
+_struct_conn.ptnr1_label_comp_id 
+_struct_conn.ptnr1_label_seq_id 
+_struct_conn.ptnr1_label_atom_id 
+_struct_conn.pdbx_ptnr1_label_alt_id 
+_struct_conn.pdbx_ptnr1_PDB_ins_code 
+_struct_conn.pdbx_ptnr1_standard_comp_id 
+_struct_conn.ptnr1_symmetry 
+_struct_conn.ptnr2_label_asym_id 
+_struct_conn.ptnr2_label_comp_id 
+_struct_conn.ptnr2_label_seq_id 
+_struct_conn.ptnr2_label_atom_id 
+_struct_conn.pdbx_ptnr2_label_alt_id 
+_struct_conn.pdbx_ptnr2_PDB_ins_code 
+_struct_conn.ptnr1_auth_asym_id 
+_struct_conn.ptnr1_auth_comp_id 
+_struct_conn.ptnr1_auth_seq_id 
+_struct_conn.ptnr2_auth_asym_id 
+_struct_conn.ptnr2_auth_comp_id 
+_struct_conn.ptnr2_auth_seq_id 
+_struct_conn.ptnr2_symmetry 
+_struct_conn.pdbx_ptnr3_label_atom_id 
+_struct_conn.pdbx_ptnr3_label_seq_id 
+_struct_conn.pdbx_ptnr3_label_comp_id 
+_struct_conn.pdbx_ptnr3_label_asym_id 
+_struct_conn.pdbx_ptnr3_label_alt_id 
+_struct_conn.pdbx_ptnr3_PDB_ins_code 
+_struct_conn.details 
+_struct_conn.pdbx_dist_value 
+_struct_conn.pdbx_value_order 
+disulf1 disulf ? ? A CYS 31  SG ? ? ? 1_555 A CYS 47  SG ? ? U CYS 42  U CYS 58  1_555 ? ? ? ? ? ? ? 2.027 ? 
+disulf2 disulf ? ? A CYS 167 SG ? ? ? 1_555 A CYS 183 SG ? ? U CYS 168 U CYS 182 1_555 ? ? ? ? ? ? ? 1.911 ? 
+disulf3 disulf ? ? A CYS 194 SG ? ? ? 1_555 A CYS 222 SG ? ? U CYS 191 U CYS 220 1_555 ? ? ? ? ? ? ? 1.876 ? 
+disulf4 disulf ? ? B CYS 1   SG ? ? ? 1_555 B CYS 10  SG ? ? P CYS 1   P CYS 10  1_555 ? ? ? ? ? ? ? 2.053 ? 
+# 
+_struct_conn_type.id          disulf 
+_struct_conn_type.criteria    ? 
+_struct_conn_type.reference   ? 
+# 
+loop_
+_struct_sheet.id 
+_struct_sheet.type 
+_struct_sheet.number_strands 
+_struct_sheet.details 
+AA1 ? 8 ? 
+AA2 ? 7 ? 
+AA3 ? 2 ? 
+# 
+loop_
+_struct_sheet_order.sheet_id 
+_struct_sheet_order.range_id_1 
+_struct_sheet_order.range_id_2 
+_struct_sheet_order.offset 
+_struct_sheet_order.sense 
+AA1 1 2 ? anti-parallel 
+AA1 2 3 ? anti-parallel 
+AA1 3 4 ? anti-parallel 
+AA1 4 5 ? anti-parallel 
+AA1 5 6 ? anti-parallel 
+AA1 6 7 ? anti-parallel 
+AA1 7 8 ? anti-parallel 
+AA2 1 2 ? anti-parallel 
+AA2 2 3 ? anti-parallel 
+AA2 3 4 ? anti-parallel 
+AA2 4 5 ? anti-parallel 
+AA2 5 6 ? anti-parallel 
+AA2 6 7 ? anti-parallel 
+AA3 1 2 ? anti-parallel 
+# 
+loop_
+_struct_sheet_range.sheet_id 
+_struct_sheet_range.id 
+_struct_sheet_range.beg_label_comp_id 
+_struct_sheet_range.beg_label_asym_id 
+_struct_sheet_range.beg_label_seq_id 
+_struct_sheet_range.pdbx_beg_PDB_ins_code 
+_struct_sheet_range.end_label_comp_id 
+_struct_sheet_range.end_label_asym_id 
+_struct_sheet_range.end_label_seq_id 
+_struct_sheet_range.pdbx_end_PDB_ins_code 
+_struct_sheet_range.beg_auth_comp_id 
+_struct_sheet_range.beg_auth_asym_id 
+_struct_sheet_range.beg_auth_seq_id 
+_struct_sheet_range.end_auth_comp_id 
+_struct_sheet_range.end_auth_asym_id 
+_struct_sheet_range.end_auth_seq_id 
+AA1 1 GLU A 5   ? PHE A 6   ? GLU U 20  PHE U 21  
+AA1 2 LYS A 155 ? ILE A 162 ? LYS U 156 ILE U 163 
+AA1 3 MET A 181 ? ALA A 185 ? MET U 180 ALA U 184 
+AA1 4 GLY A 229 ? ARG A 233 ? GLY U 226 ARG U 230 
+AA1 5 ARG A 209 ? TRP A 218 ? ARG U 206 TRP U 215 
+AA1 6 PRO A 201 ? LEU A 206 ? PRO U 198 LEU U 203 
+AA1 7 SER A 134 ? GLY A 139 ? SER U 135 GLY U 140 
+AA1 8 LYS A 155 ? ILE A 162 ? LYS U 156 ILE U 163 
+AA2 1 PHE A 15  ? ARG A 21  ? PHE U 30  ARG U 36  
+AA2 2 VAL A 27  ? SER A 37  ? VAL U 38  SER U 48  
+AA2 3 TRP A 40  ? SER A 43  ? TRP U 51  SER U 54  
+AA2 4 ALA A 99  ? ARG A 104 ? ALA U 104 ARG U 109 
+AA2 5 MET A 74  ? LEU A 83  ? MET U 81  LEU U 90  
+AA2 6 TYR A 57  ? LEU A 61  ? TYR U 64  LEU U 68  
+AA2 7 PHE A 15  ? ARG A 21  ? PHE U 30  ARG U 36  
+AA3 1 SER A 88  ? ALA A 89  ? SER U 95  ALA U 96  
+AA3 2 TYR A 94  ? HIS A 95  ? TYR U 99  HIS U 100 
+# 
+loop_
+_pdbx_struct_sheet_hbond.sheet_id 
+_pdbx_struct_sheet_hbond.range_id_1 
+_pdbx_struct_sheet_hbond.range_id_2 
+_pdbx_struct_sheet_hbond.range_1_label_atom_id 
+_pdbx_struct_sheet_hbond.range_1_label_comp_id 
+_pdbx_struct_sheet_hbond.range_1_label_asym_id 
+_pdbx_struct_sheet_hbond.range_1_label_seq_id 
+_pdbx_struct_sheet_hbond.range_1_PDB_ins_code 
+_pdbx_struct_sheet_hbond.range_1_auth_atom_id 
+_pdbx_struct_sheet_hbond.range_1_auth_comp_id 
+_pdbx_struct_sheet_hbond.range_1_auth_asym_id 
+_pdbx_struct_sheet_hbond.range_1_auth_seq_id 
+_pdbx_struct_sheet_hbond.range_2_label_atom_id 
+_pdbx_struct_sheet_hbond.range_2_label_comp_id 
+_pdbx_struct_sheet_hbond.range_2_label_asym_id 
+_pdbx_struct_sheet_hbond.range_2_label_seq_id 
+_pdbx_struct_sheet_hbond.range_2_PDB_ins_code 
+_pdbx_struct_sheet_hbond.range_2_auth_atom_id 
+_pdbx_struct_sheet_hbond.range_2_auth_comp_id 
+_pdbx_struct_sheet_hbond.range_2_auth_asym_id 
+_pdbx_struct_sheet_hbond.range_2_auth_seq_id 
+AA1 1 2 N GLU A 5   ? N GLU U 20  O MET A 156 ? O MET U 157 
+AA1 2 3 N ILE A 162 ? N ILE U 163 O CYS A 183 ? O CYS U 182 
+AA1 3 4 N LEU A 182 ? N LEU U 181 O TYR A 231 ? O TYR U 228 
+AA1 4 5 O VAL A 230 ? O VAL U 227 N TRP A 218 ? N TRP U 215 
+AA1 5 6 O ARG A 209 ? O ARG U 206 N LEU A 206 ? N LEU U 203 
+AA1 6 7 O VAL A 203 ? O VAL U 200 N GLU A 136 ? N GLU U 137 
+AA1 7 8 N CYS A 135 ? N CYS U 136 O VAL A 159 ? O VAL U 160 
+AA2 1 2 N ILE A 18  ? N ILE U 33  O VAL A 30  ? O VAL U 41  
+AA2 2 3 N SER A 34  ? N SER U 45  O ILE A 42  ? O ILE U 53  
+AA2 3 4 N VAL A 41  ? N VAL U 52  O LEU A 101 ? O LEU U 106 
+AA2 4 5 O LYS A 102 ? O LYS U 107 N GLU A 79  ? N GLU U 86  
+AA2 5 6 O PHE A 76  ? O PHE U 83  N VAL A 59  ? N VAL U 66  
+AA2 6 7 O ILE A 58  ? O ILE U 65  N TYR A 19  ? N TYR U 34  
+AA3 1 2 N SER A 88  ? N SER U 95  O HIS A 95  ? O HIS U 100 
+# 
+_atom_sites.entry_id                    4ZHL 
+_atom_sites.fract_transf_matrix[1][1]   0.008193 
+_atom_sites.fract_transf_matrix[1][2]   0.004730 
+_atom_sites.fract_transf_matrix[1][3]   0.000000 
+_atom_sites.fract_transf_matrix[2][1]   -0.000000 
+_atom_sites.fract_transf_matrix[2][2]   0.009460 
+_atom_sites.fract_transf_matrix[2][3]   0.000000 
+_atom_sites.fract_transf_matrix[3][1]   0.000000 
+_atom_sites.fract_transf_matrix[3][2]   -0.000000 
+_atom_sites.fract_transf_matrix[3][3]   0.023499 
+_atom_sites.fract_transf_vector[1]      0.00000 
+_atom_sites.fract_transf_vector[2]      0.00000 
+_atom_sites.fract_transf_vector[3]      0.00000 
+# 
+loop_
+_atom_type.symbol 
+C 
+N 
+O 
+S 
+# 
+loop_
+_atom_site.group_PDB 
+_atom_site.id 
+_atom_site.type_symbol 
+_atom_site.label_atom_id 
+_atom_site.label_alt_id 
+_atom_site.label_comp_id 
+_atom_site.label_asym_id 
+_atom_site.label_entity_id 
+_atom_site.label_seq_id 
+_atom_site.pdbx_PDB_ins_code 
+_atom_site.Cartn_x 
+_atom_site.Cartn_y 
+_atom_site.Cartn_z 
+_atom_site.occupancy 
+_atom_site.B_iso_or_equiv 
+_atom_site.Cartn_x_esd 
+_atom_site.Cartn_y_esd 
+_atom_site.Cartn_z_esd 
+_atom_site.occupancy_esd 
+_atom_site.B_iso_or_equiv_esd 
+_atom_site.pdbx_formal_charge 
+_atom_site.auth_seq_id 
+_atom_site.auth_comp_id 
+_atom_site.auth_asym_id 
+_atom_site.auth_atom_id 
+_atom_site.pdbx_PDB_model_num 
+ATOM   1    N N   . ILE A 1 1   ? -8.205  -34.800 -23.343 1.00 29.09  ? ? ? ? ? ? 16  ILE U N   1 
+ATOM   2    C CA  . ILE A 1 1   ? -8.506  -36.232 -23.305 1.00 29.78  ? ? ? ? ? ? 16  ILE U CA  1 
+ATOM   3    C C   . ILE A 1 1   ? -9.212  -36.712 -24.582 1.00 34.14  ? ? ? ? ? ? 16  ILE U C   1 
+ATOM   4    O O   . ILE A 1 1   ? -10.327 -36.280 -24.894 1.00 32.31  ? ? ? ? ? ? 16  ILE U O   1 
+ATOM   5    C CB  . ILE A 1 1   ? -9.382  -36.604 -22.100 1.00 30.71  ? ? ? ? ? ? 16  ILE U CB  1 
+ATOM   6    C CG1 . ILE A 1 1   ? -8.712  -36.199 -20.760 1.00 31.15  ? ? ? ? ? ? 16  ILE U CG1 1 
+ATOM   7    C CG2 . ILE A 1 1   ? -9.684  -38.091 -22.115 1.00 31.03  ? ? ? ? ? ? 16  ILE U CG2 1 
+ATOM   8    C CD1 . ILE A 1 1   ? -7.457  -36.945 -20.455 1.00 30.86  ? ? ? ? ? ? 16  ILE U CD1 1 
+ATOM   9    N N   . ILE A 1 2   ? -8.553  -37.638 -25.278 1.00 35.77  ? ? ? ? ? ? 17  ILE U N   1 
+ATOM   10   C CA  . ILE A 1 2   ? -9.098  -38.250 -26.487 1.00 39.69  ? ? ? ? ? ? 17  ILE U CA  1 
+ATOM   11   C C   . ILE A 1 2   ? -9.914  -39.457 -26.064 1.00 37.64  ? ? ? ? ? ? 17  ILE U C   1 
+ATOM   12   O O   . ILE A 1 2   ? -9.431  -40.304 -25.296 1.00 39.78  ? ? ? ? ? ? 17  ILE U O   1 
+ATOM   13   C CB  . ILE A 1 2   ? -7.999  -38.756 -27.451 1.00 40.53  ? ? ? ? ? ? 17  ILE U CB  1 
+ATOM   14   C CG1 . ILE A 1 2   ? -6.902  -37.721 -27.644 1.00 41.63  ? ? ? ? ? ? 17  ILE U CG1 1 
+ATOM   15   C CG2 . ILE A 1 2   ? -8.614  -39.138 -28.799 1.00 43.93  ? ? ? ? ? ? 17  ILE U CG2 1 
+ATOM   16   C CD1 . ILE A 1 2   ? -7.364  -36.417 -28.249 1.00 44.17  ? ? ? ? ? ? 17  ILE U CD1 1 
+ATOM   17   N N   . GLY A 1 3   ? -11.143 -39.537 -26.566 1.00 37.78  ? ? ? ? ? ? 18  GLY U N   1 
+ATOM   18   C CA  . GLY A 1 3   ? -12.083 -40.559 -26.142 1.00 38.70  ? ? ? ? ? ? 18  GLY U CA  1 
+ATOM   19   C C   . GLY A 1 3   ? -12.429 -40.358 -24.674 1.00 39.68  ? ? ? ? ? ? 18  GLY U C   1 
+ATOM   20   O O   . GLY A 1 3   ? -12.441 -39.238 -24.180 1.00 39.31  ? ? ? ? ? ? 18  GLY U O   1 
+ATOM   21   N N   . GLY A 1 4   ? -12.706 -41.445 -23.978 1.00 40.56  ? ? ? ? ? ? 19  GLY U N   1 
+ATOM   22   C CA  . GLY A 1 4   ? -13.076 -41.363 -22.583 1.00 41.99  ? ? ? ? ? ? 19  GLY U CA  1 
+ATOM   23   C C   . GLY A 1 4   ? -14.420 -40.710 -22.374 1.00 40.17  ? ? ? ? ? ? 19  GLY U C   1 
+ATOM   24   O O   . GLY A 1 4   ? -15.261 -40.624 -23.283 1.00 37.05  ? ? ? ? ? ? 19  GLY U O   1 
+ATOM   25   N N   . GLU A 1 5   ? -14.634 -40.246 -21.154 1.00 38.74  ? ? ? ? ? ? 20  GLU U N   1 
+ATOM   26   C CA  . GLU A 1 5   ? -15.945 -39.802 -20.761 1.00 37.20  ? ? ? ? ? ? 20  GLU U CA  1 
+ATOM   27   C C   . GLU A 1 5   ? -15.888 -38.463 -20.059 1.00 33.98  ? ? ? ? ? ? 20  GLU U C   1 
+ATOM   28   O O   . GLU A 1 5   ? -14.894 -38.131 -19.409 1.00 32.19  ? ? ? ? ? ? 20  GLU U O   1 
+ATOM   29   C CB  . GLU A 1 5   ? -16.575 -40.862 -19.873 1.00 43.14  ? ? ? ? ? ? 20  GLU U CB  1 
+ATOM   30   C CG  . GLU A 1 5   ? -16.972 -42.120 -20.634 1.00 52.22  ? ? ? ? ? ? 20  GLU U CG  1 
+ATOM   31   C CD  . GLU A 1 5   ? -17.512 -43.221 -19.733 1.00 59.51  ? ? ? ? ? ? 20  GLU U CD  1 
+ATOM   32   O OE1 . GLU A 1 5   ? -17.296 -44.413 -20.060 1.00 72.63  ? ? ? ? ? ? 20  GLU U OE1 1 
+ATOM   33   O OE2 . GLU A 1 5   ? -18.149 -42.908 -18.697 1.00 63.66  ? ? ? ? ? ? 20  GLU U OE2 1 
+ATOM   34   N N   . PHE A 1 6   ? -16.936 -37.670 -20.233 1.00 30.74  ? ? ? ? ? ? 21  PHE U N   1 
+ATOM   35   C CA  . PHE A 1 6   ? -17.120 -36.491 -19.399 1.00 30.35  ? ? ? ? ? ? 21  PHE U CA  1 
+ATOM   36   C C   . PHE A 1 6   ? -17.371 -36.954 -17.986 1.00 29.47  ? ? ? ? ? ? 21  PHE U C   1 
+ATOM   37   O O   . PHE A 1 6   ? -17.958 -38.015 -17.749 1.00 28.85  ? ? ? ? ? ? 21  PHE U O   1 
+ATOM   38   C CB  . PHE A 1 6   ? -18.254 -35.615 -19.907 1.00 30.52  ? ? ? ? ? ? 21  PHE U CB  1 
+ATOM   39   C CG  . PHE A 1 6   ? -17.897 -34.881 -21.155 1.00 30.79  ? ? ? ? ? ? 21  PHE U CG  1 
+ATOM   40   C CD1 . PHE A 1 6   ? -17.028 -33.808 -21.099 1.00 30.64  ? ? ? ? ? ? 21  PHE U CD1 1 
+ATOM   41   C CD2 . PHE A 1 6   ? -18.367 -35.297 -22.378 1.00 31.85  ? ? ? ? ? ? 21  PHE U CD2 1 
+ATOM   42   C CE1 . PHE A 1 6   ? -16.662 -33.128 -22.230 1.00 30.52  ? ? ? ? ? ? 21  PHE U CE1 1 
+ATOM   43   C CE2 . PHE A 1 6   ? -18.002 -34.627 -23.540 1.00 32.74  ? ? ? ? ? ? 21  PHE U CE2 1 
+ATOM   44   C CZ  . PHE A 1 6   ? -17.150 -33.537 -23.468 1.00 33.01  ? ? ? ? ? ? 21  PHE U CZ  1 
+ATOM   45   N N   . THR A 1 7   ? -16.842 -36.202 -17.040 1.00 33.35  ? ? ? ? ? ? 22  THR U N   1 
+ATOM   46   C CA  . THR A 1 7   ? -16.889 -36.639 -15.650 1.00 31.51  ? ? ? ? ? ? 22  THR U CA  1 
+ATOM   47   C C   . THR A 1 7   ? -17.075 -35.433 -14.775 1.00 32.09  ? ? ? ? ? ? 22  THR U C   1 
+ATOM   48   O O   . THR A 1 7   ? -17.268 -34.331 -15.286 1.00 29.52  ? ? ? ? ? ? 22  THR U O   1 
+ATOM   49   C CB  . THR A 1 7   ? -15.634 -37.460 -15.299 1.00 31.39  ? ? ? ? ? ? 22  THR U CB  1 
+ATOM   50   O OG1 . THR A 1 7   ? -15.792 -38.030 -13.996 1.00 33.51  ? ? ? ? ? ? 22  THR U OG1 1 
+ATOM   51   C CG2 . THR A 1 7   ? -14.332 -36.630 -15.371 1.00 31.85  ? ? ? ? ? ? 22  THR U CG2 1 
+ATOM   52   N N   . THR A 1 8   ? -17.093 -35.634 -13.456 1.00 32.21  ? ? ? ? ? ? 23  THR U N   1 
+ATOM   53   C CA  . THR A 1 8   ? -17.122 -34.517 -12.532 1.00 30.91  ? ? ? ? ? ? 23  THR U CA  1 
+ATOM   54   C C   . THR A 1 8   ? -15.986 -34.686 -11.521 1.00 28.53  ? ? ? ? ? ? 23  THR U C   1 
+ATOM   55   O O   . THR A 1 8   ? -15.423 -35.772 -11.355 1.00 25.30  ? ? ? ? ? ? 23  THR U O   1 
+ATOM   56   C CB  . THR A 1 8   ? -18.484 -34.404 -11.780 1.00 33.82  ? ? ? ? ? ? 23  THR U CB  1 
+ATOM   57   O OG1 . THR A 1 8   ? -18.682 -35.565 -10.979 1.00 32.46  ? ? ? ? ? ? 23  THR U OG1 1 
+ATOM   58   C CG2 . THR A 1 8   ? -19.670 -34.291 -12.760 1.00 35.38  ? ? ? ? ? ? 23  THR U CG2 1 
+ATOM   59   N N   . ILE A 1 9   ? -15.669 -33.598 -10.837 1.00 27.33  ? ? ? ? ? ? 24  ILE U N   1 
+ATOM   60   C CA  . ILE A 1 9   ? -14.538 -33.590 -9.911  1.00 29.72  ? ? ? ? ? ? 24  ILE U CA  1 
+ATOM   61   C C   . ILE A 1 9   ? -14.636 -34.724 -8.867  1.00 28.82  ? ? ? ? ? ? 24  ILE U C   1 
+ATOM   62   O O   . ILE A 1 9   ? -13.606 -35.309 -8.474  1.00 29.97  ? ? ? ? ? ? 24  ILE U O   1 
+ATOM   63   C CB  . ILE A 1 9   ? -14.334 -32.172 -9.302  1.00 27.49  ? ? ? ? ? ? 24  ILE U CB  1 
+ATOM   64   C CG1 . ILE A 1 9   ? -12.905 -31.987 -8.737  1.00 27.89  ? ? ? ? ? ? 24  ILE U CG1 1 
+ATOM   65   C CG2 . ILE A 1 9   ? -15.381 -31.869 -8.247  1.00 30.07  ? ? ? ? ? ? 24  ILE U CG2 1 
+ATOM   66   C CD1 . ILE A 1 9   ? -11.796 -32.250 -9.738  1.00 26.36  ? ? ? ? ? ? 24  ILE U CD1 1 
+ATOM   67   N N   . GLU A 1 10  ? -15.852 -35.120 -8.483  1.00 29.84  ? ? ? ? ? ? 25  GLU U N   1 
+ATOM   68   C CA  . GLU A 1 10  ? -16.001 -36.125 -7.431  1.00 30.45  ? ? ? ? ? ? 25  GLU U CA  1 
+ATOM   69   C C   . GLU A 1 10  ? -15.353 -37.456 -7.782  1.00 33.49  ? ? ? ? ? ? 25  GLU U C   1 
+ATOM   70   O O   . GLU A 1 10  ? -14.967 -38.212 -6.877  1.00 33.52  ? ? ? ? ? ? 25  GLU U O   1 
+ATOM   71   C CB  . GLU A 1 10  ? -17.482 -36.375 -7.070  1.00 33.92  ? ? ? ? ? ? 25  GLU U CB  1 
+ATOM   72   C CG  . GLU A 1 10  ? -18.197 -35.187 -6.460  1.00 34.07  ? ? ? ? ? ? 25  GLU U CG  1 
+ATOM   73   C CD  . GLU A 1 10  ? -18.835 -34.275 -7.494  1.00 33.18  ? ? ? ? ? ? 25  GLU U CD  1 
+ATOM   74   O OE1 . GLU A 1 10  ? -18.400 -34.261 -8.650  1.00 32.10  ? ? ? ? ? ? 25  GLU U OE1 1 
+ATOM   75   O OE2 . GLU A 1 10  ? -19.774 -33.558 -7.147  1.00 34.67  ? ? ? ? ? ? 25  GLU U OE2 1 
+ATOM   76   N N   . ASN A 1 11  ? -15.254 -37.773 -9.075  1.00 30.81  ? ? ? ? ? ? 26  ASN U N   1 
+ATOM   77   C CA  . ASN A 1 11  ? -14.607 -39.006 -9.494  1.00 35.54  ? ? ? ? ? ? 26  ASN U CA  1 
+ATOM   78   C C   . ASN A 1 11  ? -13.101 -38.902 -9.702  1.00 32.17  ? ? ? ? ? ? 26  ASN U C   1 
+ATOM   79   O O   . ASN A 1 11  ? -12.475 -39.875 -10.122 1.00 33.53  ? ? ? ? ? ? 26  ASN U O   1 
+ATOM   80   C CB  . ASN A 1 11  ? -15.269 -39.557 -10.761 1.00 38.17  ? ? ? ? ? ? 26  ASN U CB  1 
+ATOM   81   C CG  . ASN A 1 11  ? -16.610 -40.197 -10.469 1.00 42.29  ? ? ? ? ? ? 26  ASN U CG  1 
+ATOM   82   O OD1 . ASN A 1 11  ? -16.810 -40.772 -9.392  1.00 41.01  ? ? ? ? ? ? 26  ASN U OD1 1 
+ATOM   83   N ND2 . ASN A 1 11  ? -17.550 -40.079 -11.413 1.00 42.92  ? ? ? ? ? ? 26  ASN U ND2 1 
+ATOM   84   N N   . GLN A 1 12  ? -12.548 -37.731 -9.405  1.00 30.68  ? ? ? ? ? ? 27  GLN U N   1 
+ATOM   85   C CA  . GLN A 1 12  ? -11.117 -37.430 -9.514  1.00 32.45  ? ? ? ? ? ? 27  GLN U CA  1 
+ATOM   86   C C   . GLN A 1 12  ? -10.752 -36.236 -8.585  1.00 30.76  ? ? ? ? ? ? 27  GLN U C   1 
+ATOM   87   O O   . GLN A 1 12  ? -10.166 -35.252 -9.034  1.00 28.91  ? ? ? ? ? ? 27  GLN U O   1 
+ATOM   88   C CB  . GLN A 1 12  ? -10.791 -37.049 -10.962 1.00 35.45  ? ? ? ? ? ? 27  GLN U CB  1 
+ATOM   89   C CG  . GLN A 1 12  ? -10.952 -38.156 -11.978 1.00 40.44  ? ? ? ? ? ? 27  GLN U CG  1 
+ATOM   90   C CD  . GLN A 1 12  ? -9.850  -39.198 -11.861 1.00 46.52  ? ? ? ? ? ? 27  GLN U CD  1 
+ATOM   91   O OE1 . GLN A 1 12  ? -8.706  -38.858 -11.556 1.00 51.15  ? ? ? ? ? ? 27  GLN U OE1 1 
+ATOM   92   N NE2 . GLN A 1 12  ? -10.180 -40.464 -12.118 1.00 51.17  ? ? ? ? ? ? 27  GLN U NE2 1 
+ATOM   93   N N   . PRO A 1 13  ? -11.073 -36.341 -7.280  1.00 28.71  ? ? ? ? ? ? 28  PRO U N   1 
+ATOM   94   C CA  . PRO A 1 13  ? -11.068 -35.188 -6.382  1.00 27.48  ? ? ? ? ? ? 28  PRO U CA  1 
+ATOM   95   C C   . PRO A 1 13  ? -9.698  -34.638 -6.001  1.00 27.82  ? ? ? ? ? ? 28  PRO U C   1 
+ATOM   96   O O   . PRO A 1 13  ? -9.619  -33.569 -5.391  1.00 29.23  ? ? ? ? ? ? 28  PRO U O   1 
+ATOM   97   C CB  . PRO A 1 13  ? -11.872 -35.690 -5.169  1.00 28.82  ? ? ? ? ? ? 28  PRO U CB  1 
+ATOM   98   C CG  . PRO A 1 13  ? -11.627 -37.134 -5.141  1.00 27.55  ? ? ? ? ? ? 28  PRO U CG  1 
+ATOM   99   C CD  . PRO A 1 13  ? -11.583 -37.553 -6.604  1.00 28.27  ? ? ? ? ? ? 28  PRO U CD  1 
+ATOM   100  N N   . TRP A 1 14  ? -8.639  -35.332 -6.423  1.00 29.09  ? ? ? ? ? ? 29  TRP U N   1 
+ATOM   101  C CA  . TRP A 1 14  ? -7.261  -34.859 -6.346  1.00 28.48  ? ? ? ? ? ? 29  TRP U CA  1 
+ATOM   102  C C   . TRP A 1 14  ? -6.892  -33.930 -7.504  1.00 29.70  ? ? ? ? ? ? 29  TRP U C   1 
+ATOM   103  O O   . TRP A 1 14  ? -5.856  -33.247 -7.455  1.00 26.67  ? ? ? ? ? ? 29  TRP U O   1 
+ATOM   104  C CB  . TRP A 1 14  ? -6.281  -36.051 -6.370  1.00 30.34  ? ? ? ? ? ? 29  TRP U CB  1 
+ATOM   105  C CG  . TRP A 1 14  ? -6.604  -37.022 -7.437  1.00 30.03  ? ? ? ? ? ? 29  TRP U CG  1 
+ATOM   106  C CD1 . TRP A 1 14  ? -6.248  -36.951 -8.758  1.00 33.16  ? ? ? ? ? ? 29  TRP U CD1 1 
+ATOM   107  C CD2 . TRP A 1 14  ? -7.410  -38.186 -7.305  1.00 33.78  ? ? ? ? ? ? 29  TRP U CD2 1 
+ATOM   108  N NE1 . TRP A 1 14  ? -6.783  -38.004 -9.448  1.00 33.59  ? ? ? ? ? ? 29  TRP U NE1 1 
+ATOM   109  C CE2 . TRP A 1 14  ? -7.498  -38.782 -8.575  1.00 33.24  ? ? ? ? ? ? 29  TRP U CE2 1 
+ATOM   110  C CE3 . TRP A 1 14  ? -8.062  -38.790 -6.229  1.00 35.07  ? ? ? ? ? ? 29  TRP U CE3 1 
+ATOM   111  C CZ2 . TRP A 1 14  ? -8.199  -39.963 -8.792  1.00 35.05  ? ? ? ? ? ? 29  TRP U CZ2 1 
+ATOM   112  C CZ3 . TRP A 1 14  ? -8.769  -39.946 -6.456  1.00 35.80  ? ? ? ? ? ? 29  TRP U CZ3 1 
+ATOM   113  C CH2 . TRP A 1 14  ? -8.827  -40.524 -7.716  1.00 36.29  ? ? ? ? ? ? 29  TRP U CH2 1 
+ATOM   114  N N   . PHE A 1 15  ? -7.712  -33.903 -8.554  1.00 28.80  ? ? ? ? ? ? 30  PHE U N   1 
+ATOM   115  C CA  . PHE A 1 15  ? -7.365  -33.090 -9.703  1.00 24.17  ? ? ? ? ? ? 30  PHE U CA  1 
+ATOM   116  C C   . PHE A 1 15  ? -7.393  -31.581 -9.478  1.00 23.82  ? ? ? ? ? ? 30  PHE U C   1 
+ATOM   117  O O   . PHE A 1 15  ? -8.403  -30.996 -9.088  1.00 25.39  ? ? ? ? ? ? 30  PHE U O   1 
+ATOM   118  C CB  . PHE A 1 15  ? -8.187  -33.484 -10.926 1.00 26.92  ? ? ? ? ? ? 30  PHE U CB  1 
+ATOM   119  C CG  . PHE A 1 15  ? -7.892  -32.625 -12.108 1.00 25.03  ? ? ? ? ? ? 30  PHE U CG  1 
+ATOM   120  C CD1 . PHE A 1 15  ? -6.787  -32.872 -12.879 1.00 27.15  ? ? ? ? ? ? 30  PHE U CD1 1 
+ATOM   121  C CD2 . PHE A 1 15  ? -8.684  -31.518 -12.391 1.00 27.45  ? ? ? ? ? ? 30  PHE U CD2 1 
+ATOM   122  C CE1 . PHE A 1 15  ? -6.491  -32.072 -13.984 1.00 28.31  ? ? ? ? ? ? 30  PHE U CE1 1 
+ATOM   123  C CE2 . PHE A 1 15  ? -8.389  -30.705 -13.477 1.00 27.50  ? ? ? ? ? ? 30  PHE U CE2 1 
+ATOM   124  C CZ  . PHE A 1 15  ? -7.279  -30.986 -14.272 1.00 26.56  ? ? ? ? ? ? 30  PHE U CZ  1 
+ATOM   125  N N   . ALA A 1 16  ? -6.255  -30.939 -9.744  1.00 23.80  ? ? ? ? ? ? 31  ALA U N   1 
+ATOM   126  C CA  . ALA A 1 16  ? -6.089  -29.500 -9.595  1.00 24.25  ? ? ? ? ? ? 31  ALA U CA  1 
+ATOM   127  C C   . ALA A 1 16  ? -6.104  -28.829 -10.965 1.00 25.43  ? ? ? ? ? ? 31  ALA U C   1 
+ATOM   128  O O   . ALA A 1 16  ? -5.471  -29.354 -11.882 1.00 26.91  ? ? ? ? ? ? 31  ALA U O   1 
+ATOM   129  C CB  . ALA A 1 16  ? -4.756  -29.210 -8.945  1.00 24.05  ? ? ? ? ? ? 31  ALA U CB  1 
+ATOM   130  N N   . ALA A 1 17  ? -6.799  -27.685 -11.072 1.00 25.59  ? ? ? ? ? ? 32  ALA U N   1 
+ATOM   131  C CA  . ALA A 1 17  ? -6.861  -26.860 -12.293 1.00 28.20  ? ? ? ? ? ? 32  ALA U CA  1 
+ATOM   132  C C   . ALA A 1 17  ? -5.997  -25.607 -12.139 1.00 28.21  ? ? ? ? ? ? 32  ALA U C   1 
+ATOM   133  O O   . ALA A 1 17  ? -6.269  -24.758 -11.282 1.00 31.80  ? ? ? ? ? ? 32  ALA U O   1 
+ATOM   134  C CB  . ALA A 1 17  ? -8.308  -26.482 -12.604 1.00 26.88  ? ? ? ? ? ? 32  ALA U CB  1 
+ATOM   135  N N   . ILE A 1 18  ? -4.946  -25.492 -12.953 1.00 28.34  ? ? ? ? ? ? 33  ILE U N   1 
+ATOM   136  C CA  . ILE A 1 18  ? -3.972  -24.378 -12.813 1.00 30.06  ? ? ? ? ? ? 33  ILE U CA  1 
+ATOM   137  C C   . ILE A 1 18  ? -4.073  -23.331 -13.928 1.00 28.30  ? ? ? ? ? ? 33  ILE U C   1 
+ATOM   138  O O   . ILE A 1 18  ? -3.978  -23.656 -15.120 1.00 27.59  ? ? ? ? ? ? 33  ILE U O   1 
+ATOM   139  C CB  . ILE A 1 18  ? -2.519  -24.899 -12.750 1.00 30.88  ? ? ? ? ? ? 33  ILE U CB  1 
+ATOM   140  C CG1 . ILE A 1 18  ? -2.428  -26.073 -11.769 1.00 30.29  ? ? ? ? ? ? 33  ILE U CG1 1 
+ATOM   141  C CG2 . ILE A 1 18  ? -1.564  -23.750 -12.394 1.00 31.62  ? ? ? ? ? ? 33  ILE U CG2 1 
+ATOM   142  C CD1 . ILE A 1 18  ? -1.077  -26.746 -11.685 1.00 30.37  ? ? ? ? ? ? 33  ILE U CD1 1 
+ATOM   143  N N   . TYR A 1 19  ? -4.228  -22.096 -13.528 1.00 28.37  ? ? ? ? ? ? 34  TYR U N   1 
+ATOM   144  C CA  . TYR A 1 19  ? -4.441  -20.991 -14.420 1.00 27.98  ? ? ? ? ? ? 34  TYR U CA  1 
+ATOM   145  C C   . TYR A 1 19  ? -3.389  -19.924 -14.199 1.00 32.27  ? ? ? ? ? ? 34  TYR U C   1 
+ATOM   146  O O   . TYR A 1 19  ? -2.775  -19.850 -13.167 1.00 28.65  ? ? ? ? ? ? 34  TYR U O   1 
+ATOM   147  C CB  . TYR A 1 19  ? -5.807  -20.358 -14.201 1.00 27.03  ? ? ? ? ? ? 34  TYR U CB  1 
+ATOM   148  C CG  . TYR A 1 19  ? -7.026  -21.219 -14.421 1.00 26.58  ? ? ? ? ? ? 34  TYR U CG  1 
+ATOM   149  C CD1 . TYR A 1 19  ? -7.385  -22.172 -13.505 1.00 26.20  ? ? ? ? ? ? 34  TYR U CD1 1 
+ATOM   150  C CD2 . TYR A 1 19  ? -7.812  -21.075 -15.539 1.00 26.30  ? ? ? ? ? ? 34  TYR U CD2 1 
+ATOM   151  C CE1 . TYR A 1 19  ? -8.487  -22.955 -13.674 1.00 27.15  ? ? ? ? ? ? 34  TYR U CE1 1 
+ATOM   152  C CE2 . TYR A 1 19  ? -8.921  -21.861 -15.728 1.00 27.93  ? ? ? ? ? ? 34  TYR U CE2 1 
+ATOM   153  C CZ  . TYR A 1 19  ? -9.246  -22.816 -14.781 1.00 28.87  ? ? ? ? ? ? 34  TYR U CZ  1 
+ATOM   154  O OH  . TYR A 1 19  ? -10.334 -23.589 -14.898 1.00 26.72  ? ? ? ? ? ? 34  TYR U OH  1 
+ATOM   155  N N   . ARG A 1 20  ? -3.200  -19.095 -15.207 1.00 35.62  ? ? ? ? ? ? 35  ARG U N   1 
+ATOM   156  C CA  . ARG A 1 20  ? -2.311  -17.943 -15.124 1.00 42.15  ? ? ? ? ? ? 35  ARG U CA  1 
+ATOM   157  C C   . ARG A 1 20  ? -2.981  -16.588 -15.460 1.00 47.22  ? ? ? ? ? ? 35  ARG U C   1 
+ATOM   158  O O   . ARG A 1 20  ? -3.666  -16.473 -16.451 1.00 44.84  ? ? ? ? ? ? 35  ARG U O   1 
+ATOM   159  C CB  . ARG A 1 20  ? -1.090  -18.128 -16.010 1.00 44.92  ? ? ? ? ? ? 35  ARG U CB  1 
+ATOM   160  C CG  . ARG A 1 20  ? -0.140  -16.971 -15.879 1.00 50.76  ? ? ? ? ? ? 35  ARG U CG  1 
+ATOM   161  C CD  . ARG A 1 20  ? 1.079   -17.097 -16.741 1.00 53.58  ? ? ? ? ? ? 35  ARG U CD  1 
+ATOM   162  N NE  . ARG A 1 20  ? 0.744   -16.851 -18.124 1.00 57.38  ? ? ? ? ? ? 35  ARG U NE  1 
+ATOM   163  C CZ  . ARG A 1 20  ? 0.878   -17.761 -19.060 1.00 58.24  ? ? ? ? ? ? 35  ARG U CZ  1 
+ATOM   164  N NH1 . ARG A 1 20  ? 0.532   -17.500 -20.300 1.00 60.63  ? ? ? ? ? ? 35  ARG U NH1 1 
+ATOM   165  N NH2 . ARG A 1 20  ? 1.370   -18.932 -18.740 1.00 58.36  ? ? ? ? ? ? 35  ARG U NH2 1 
+ATOM   166  N N   . ARG A 1 21  ? -2.754  -15.565 -14.651 1.00 48.52  ? ? ? ? ? ? 36  ARG U N   1 
+ATOM   167  C CA  . ARG A 1 21  ? -3.295  -14.247 -14.967 1.00 53.45  ? ? ? ? ? ? 36  ARG U CA  1 
+ATOM   168  C C   . ARG A 1 21  ? -2.285  -13.421 -15.726 1.00 55.80  ? ? ? ? ? ? 36  ARG U C   1 
+ATOM   169  O O   . ARG A 1 21  ? -1.155  -13.317 -15.308 1.00 59.06  ? ? ? ? ? ? 36  ARG U O   1 
+ATOM   170  C CB  . ARG A 1 21  ? -3.775  -13.482 -13.745 1.00 54.23  ? ? ? ? ? ? 36  ARG U CB  1 
+ATOM   171  C CG  . ARG A 1 21  ? -3.452  -14.085 -12.411 1.00 56.79  ? ? ? ? ? ? 36  ARG U CG  1 
+ATOM   172  C CD  . ARG A 1 21  ? -3.833  -13.082 -11.351 1.00 58.90  ? ? ? ? ? ? 36  ARG U CD  1 
+ATOM   173  N NE  . ARG A 1 21  ? -4.120  -13.672 -10.061 1.00 60.54  ? ? ? ? ? ? 36  ARG U NE  1 
+ATOM   174  C CZ  . ARG A 1 21  ? -3.208  -14.189 -9.274  1.00 58.86  ? ? ? ? ? ? 36  ARG U CZ  1 
+ATOM   175  N NH1 . ARG A 1 21  ? -3.550  -14.698 -8.118  1.00 58.53  ? ? ? ? ? ? 36  ARG U NH1 1 
+ATOM   176  N NH2 . ARG A 1 21  ? -1.956  -14.219 -9.667  1.00 60.99  ? ? ? ? ? ? 36  ARG U NH2 1 
+ATOM   177  N N   . HIS A 1 22  ? -2.709  -12.870 -16.852 1.00 56.48  ? ? ? ? ? ? 37  HIS U N   1 
+ATOM   178  C CA  . HIS A 1 22  ? -1.880  -12.046 -17.729 1.00 60.64  ? ? ? ? ? ? 37  HIS U CA  1 
+ATOM   179  C C   . HIS A 1 22  ? -1.860  -10.563 -17.366 1.00 60.91  ? ? ? ? ? ? 37  HIS U C   1 
+ATOM   180  O O   . HIS A 1 22  ? -2.516  -10.135 -16.437 1.00 62.61  ? ? ? ? ? ? 37  HIS U O   1 
+ATOM   181  C CB  . HIS A 1 22  ? -2.272  -12.222 -19.174 1.00 61.91  ? ? ? ? ? ? 37  HIS U CB  1 
+ATOM   182  C CG  . HIS A 1 22  ? -2.435  -13.642 -19.572 1.00 63.77  ? ? ? ? ? ? 37  HIS U CG  1 
+ATOM   183  N ND1 . HIS A 1 22  ? -3.357  -14.467 -18.982 1.00 66.38  ? ? ? ? ? ? 37  HIS U ND1 1 
+ATOM   184  C CD2 . HIS A 1 22  ? -1.782  -14.399 -20.478 1.00 62.68  ? ? ? ? ? ? 37  HIS U CD2 1 
+ATOM   185  C CE1 . HIS A 1 22  ? -3.281  -15.664 -19.518 1.00 64.27  ? ? ? ? ? ? 37  HIS U CE1 1 
+ATOM   186  N NE2 . HIS A 1 22  ? -2.333  -15.651 -20.429 1.00 62.36  ? ? ? ? ? ? 37  HIS U NE2 1 
+ATOM   187  N N   . ARG A 1 23  A -1.085  -9.777  -18.102 1.00 94.28  ? ? ? ? ? ? 37  ARG U N   1 
+ATOM   188  C CA  . ARG A 1 23  A -0.915  -8.380  -17.724 1.00 95.71  ? ? ? ? ? ? 37  ARG U CA  1 
+ATOM   189  C C   . ARG A 1 23  A -2.225  -7.635  -17.710 1.00 91.18  ? ? ? ? ? ? 37  ARG U C   1 
+ATOM   190  O O   . ARG A 1 23  A -2.406  -6.794  -16.868 1.00 85.08  ? ? ? ? ? ? 37  ARG U O   1 
+ATOM   191  C CB  . ARG A 1 23  A 0.087   -7.636  -18.570 1.00 100.69 ? ? ? ? ? ? 37  ARG U CB  1 
+ATOM   192  C CG  . ARG A 1 23  A 0.150   -6.141  -18.231 1.00 103.98 ? ? ? ? ? ? 37  ARG U CG  1 
+ATOM   193  C CD  . ARG A 1 23  A 0.807   -5.878  -16.882 1.00 105.08 ? ? ? ? ? ? 37  ARG U CD  1 
+ATOM   194  N NE  . ARG A 1 23  A 0.535   -4.550  -16.333 1.00 106.39 ? ? ? ? ? ? 37  ARG U NE  1 
+ATOM   195  C CZ  . ARG A 1 23  A 0.470   -3.431  -17.045 1.00 105.57 ? ? ? ? ? ? 37  ARG U CZ  1 
+ATOM   196  N NH1 . ARG A 1 23  A 0.208   -2.279  -16.447 1.00 104.48 ? ? ? ? ? ? 37  ARG U NH1 1 
+ATOM   197  N NH2 . ARG A 1 23  A 0.641   -3.461  -18.356 1.00 105.86 ? ? ? ? ? ? 37  ARG U NH2 1 
+ATOM   198  N N   . GLY A 1 24  B -3.131  -7.922  -18.630 1.00 94.70  ? ? ? ? ? ? 37  GLY U N   1 
+ATOM   199  C CA  . GLY A 1 24  B -4.410  -7.233  -18.626 1.00 93.94  ? ? ? ? ? ? 37  GLY U CA  1 
+ATOM   200  C C   . GLY A 1 24  B -5.286  -7.423  -17.384 1.00 95.41  ? ? ? ? ? ? 37  GLY U C   1 
+ATOM   201  O O   . GLY A 1 24  B -5.869  -6.496  -16.850 1.00 96.75  ? ? ? ? ? ? 37  GLY U O   1 
+ATOM   202  N N   . GLY A 1 25  C -5.339  -8.645  -16.903 1.00 62.34  ? ? ? ? ? ? 37  GLY U N   1 
+ATOM   203  C CA  . GLY A 1 25  C -6.350  -9.082  -15.990 1.00 63.80  ? ? ? ? ? ? 37  GLY U CA  1 
+ATOM   204  C C   . GLY A 1 25  C -6.971  -10.356 -16.530 1.00 68.37  ? ? ? ? ? ? 37  GLY U C   1 
+ATOM   205  O O   . GLY A 1 25  C -7.661  -11.041 -15.790 1.00 69.92  ? ? ? ? ? ? 37  GLY U O   1 
+ATOM   206  N N   . SER A 1 26  D -6.707  -10.692 -17.793 1.00 59.58  ? ? ? ? ? ? 37  SER U N   1 
+ATOM   207  C CA  . SER A 1 26  D -7.216  -11.938 -18.376 1.00 56.06  ? ? ? ? ? ? 37  SER U CA  1 
+ATOM   208  C C   . SER A 1 26  D -6.558  -13.222 -17.834 1.00 54.35  ? ? ? ? ? ? 37  SER U C   1 
+ATOM   209  O O   . SER A 1 26  D -5.372  -13.274 -17.650 1.00 56.12  ? ? ? ? ? ? 37  SER U O   1 
+ATOM   210  C CB  . SER A 1 26  D -7.243  -11.894 -19.912 1.00 56.50  ? ? ? ? ? ? 37  SER U CB  1 
+ATOM   211  O OG  . SER A 1 26  D -5.978  -11.918 -20.510 1.00 57.93  ? ? ? ? ? ? 37  SER U OG  1 
+ATOM   212  N N   . VAL A 1 27  ? -7.359  -14.236 -17.557 1.00 51.14  ? ? ? ? ? ? 38  VAL U N   1 
+ATOM   213  C CA  . VAL A 1 27  ? -6.881  -15.450 -16.912 1.00 47.47  ? ? ? ? ? ? 38  VAL U CA  1 
+ATOM   214  C C   . VAL A 1 27  ? -7.122  -16.633 -17.809 1.00 45.26  ? ? ? ? ? ? 38  VAL U C   1 
+ATOM   215  O O   . VAL A 1 27  ? -8.221  -16.845 -18.227 1.00 42.71  ? ? ? ? ? ? 38  VAL U O   1 
+ATOM   216  C CB  . VAL A 1 27  ? -7.628  -15.708 -15.602 1.00 46.73  ? ? ? ? ? ? 38  VAL U CB  1 
+ATOM   217  C CG1 . VAL A 1 27  ? -7.099  -16.932 -14.894 1.00 45.35  ? ? ? ? ? ? 38  VAL U CG1 1 
+ATOM   218  C CG2 . VAL A 1 27  ? -7.564  -14.506 -14.700 1.00 46.89  ? ? ? ? ? ? 38  VAL U CG2 1 
+ATOM   219  N N   . THR A 1 28  ? -6.090  -17.410 -18.083 1.00 44.54  ? ? ? ? ? ? 39  THR U N   1 
+ATOM   220  C CA  . THR A 1 28  ? -6.239  -18.582 -18.922 1.00 42.57  ? ? ? ? ? ? 39  THR U CA  1 
+ATOM   221  C C   . THR A 1 28  ? -5.630  -19.851 -18.348 1.00 37.32  ? ? ? ? ? ? 39  THR U C   1 
+ATOM   222  O O   . THR A 1 28  ? -4.739  -19.832 -17.570 1.00 37.04  ? ? ? ? ? ? 39  THR U O   1 
+ATOM   223  C CB  . THR A 1 28  ? -5.620  -18.388 -20.284 1.00 41.48  ? ? ? ? ? ? 39  THR U CB  1 
+ATOM   224  O OG1 . THR A 1 28  ? -4.259  -18.104 -20.116 1.00 45.84  ? ? ? ? ? ? 39  THR U OG1 1 
+ATOM   225  C CG2 . THR A 1 28  ? -6.243  -17.294 -21.002 1.00 45.50  ? ? ? ? ? ? 39  THR U CG2 1 
+ATOM   226  N N   . TYR A 1 29  ? -6.152  -20.965 -18.781 1.00 34.13  ? ? ? ? ? ? 40  TYR U N   1 
+ATOM   227  C CA  . TYR A 1 29  ? -5.766  -22.266 -18.247 1.00 33.03  ? ? ? ? ? ? 40  TYR U CA  1 
+ATOM   228  C C   . TYR A 1 29  ? -4.364  -22.631 -18.691 1.00 32.95  ? ? ? ? ? ? 40  TYR U C   1 
+ATOM   229  O O   . TYR A 1 29  ? -3.931  -22.303 -19.797 1.00 36.85  ? ? ? ? ? ? 40  TYR U O   1 
+ATOM   230  C CB  . TYR A 1 29  ? -6.763  -23.320 -18.667 1.00 31.24  ? ? ? ? ? ? 40  TYR U CB  1 
+ATOM   231  C CG  . TYR A 1 29  ? -6.584  -24.677 -18.010 1.00 29.04  ? ? ? ? ? ? 40  TYR U CG  1 
+ATOM   232  C CD1 . TYR A 1 29  ? -6.975  -24.894 -16.698 1.00 26.35  ? ? ? ? ? ? 40  TYR U CD1 1 
+ATOM   233  C CD2 . TYR A 1 29  ? -6.093  -25.749 -18.724 1.00 25.67  ? ? ? ? ? ? 40  TYR U CD2 1 
+ATOM   234  C CE1 . TYR A 1 29  ? -6.862  -26.156 -16.098 1.00 25.41  ? ? ? ? ? ? 40  TYR U CE1 1 
+ATOM   235  C CE2 . TYR A 1 29  ? -5.967  -27.004 -18.155 1.00 24.71  ? ? ? ? ? ? 40  TYR U CE2 1 
+ATOM   236  C CZ  . TYR A 1 29  ? -6.339  -27.208 -16.816 1.00 25.61  ? ? ? ? ? ? 40  TYR U CZ  1 
+ATOM   237  O OH  . TYR A 1 29  ? -6.243  -28.480 -16.250 1.00 21.91  ? ? ? ? ? ? 40  TYR U OH  1 
+ATOM   238  N N   . VAL A 1 30  ? -3.624  -23.251 -17.794 1.00 32.69  ? ? ? ? ? ? 41  VAL U N   1 
+ATOM   239  C CA  . VAL A 1 30  ? -2.239  -23.543 -18.046 1.00 34.50  ? ? ? ? ? ? 41  VAL U CA  1 
+ATOM   240  C C   . VAL A 1 30  ? -2.081  -25.056 -18.149 1.00 34.30  ? ? ? ? ? ? 41  VAL U C   1 
+ATOM   241  O O   . VAL A 1 30  ? -1.752  -25.582 -19.214 1.00 31.21  ? ? ? ? ? ? 41  VAL U O   1 
+ATOM   242  C CB  . VAL A 1 30  ? -1.347  -22.936 -16.946 1.00 34.13  ? ? ? ? ? ? 41  VAL U CB  1 
+ATOM   243  C CG1 . VAL A 1 30  ? 0.096   -23.361 -17.105 1.00 35.74  ? ? ? ? ? ? 41  VAL U CG1 1 
+ATOM   244  C CG2 . VAL A 1 30  ? -1.444  -21.423 -16.983 1.00 36.98  ? ? ? ? ? ? 41  VAL U CG2 1 
+ATOM   245  N N   . CYS A 1 31  ? -2.336  -25.744 -17.041 1.00 31.48  ? ? ? ? ? ? 42  CYS U N   1 
+ATOM   246  C CA  . CYS A 1 31  ? -2.109  -27.178 -16.947 1.00 29.70  ? ? ? ? ? ? 42  CYS U CA  1 
+ATOM   247  C C   . CYS A 1 31  ? -2.929  -27.812 -15.824 1.00 28.02  ? ? ? ? ? ? 42  CYS U C   1 
+ATOM   248  O O   . CYS A 1 31  ? -3.457  -27.136 -14.943 1.00 23.83  ? ? ? ? ? ? 42  CYS U O   1 
+ATOM   249  C CB  . CYS A 1 31  ? -0.657  -27.443 -16.558 1.00 34.09  ? ? ? ? ? ? 42  CYS U CB  1 
+ATOM   250  S SG  . CYS A 1 31  ? 0.612   -27.294 -17.833 1.00 37.77  ? ? ? ? ? ? 42  CYS U SG  1 
+ATOM   251  N N   . GLY A 1 32  ? -2.961  -29.131 -15.844 1.00 26.79  ? ? ? ? ? ? 43  GLY U N   1 
+ATOM   252  C CA  . GLY A 1 32  ? -3.463  -29.891 -14.718 1.00 29.22  ? ? ? ? ? ? 43  GLY U CA  1 
+ATOM   253  C C   . GLY A 1 32  ? -2.403  -30.055 -13.636 1.00 26.88  ? ? ? ? ? ? 43  GLY U C   1 
+ATOM   254  O O   . GLY A 1 32  ? -1.266  -29.597 -13.760 1.00 25.22  ? ? ? ? ? ? 43  GLY U O   1 
+ATOM   255  N N   . GLY A 1 33  ? -2.819  -30.695 -12.552 1.00 27.51  ? ? ? ? ? ? 44  GLY U N   1 
+ATOM   256  C CA  . GLY A 1 33  ? -1.955  -31.049 -11.447 1.00 26.79  ? ? ? ? ? ? 44  GLY U CA  1 
+ATOM   257  C C   . GLY A 1 33  ? -2.722  -31.962 -10.507 1.00 27.66  ? ? ? ? ? ? 44  GLY U C   1 
+ATOM   258  O O   . GLY A 1 33  ? -3.878  -32.316 -10.785 1.00 26.70  ? ? ? ? ? ? 44  GLY U O   1 
+ATOM   259  N N   . SER A 1 34  ? -2.103  -32.306 -9.376  1.00 29.11  ? ? ? ? ? ? 45  SER U N   1 
+ATOM   260  C CA  . SER A 1 34  ? -2.669  -33.257 -8.429  1.00 27.38  ? ? ? ? ? ? 45  SER U CA  1 
+ATOM   261  C C   . SER A 1 34  ? -2.337  -32.889 -6.993  1.00 31.28  ? ? ? ? ? ? 45  SER U C   1 
+ATOM   262  O O   . SER A 1 34  ? -1.183  -32.588 -6.674  1.00 28.94  ? ? ? ? ? ? 45  SER U O   1 
+ATOM   263  C CB  . SER A 1 34  ? -2.097  -34.642 -8.658  1.00 29.15  ? ? ? ? ? ? 45  SER U CB  1 
+ATOM   264  O OG  . SER A 1 34  ? -2.387  -35.131 -9.939  1.00 33.87  ? ? ? ? ? ? 45  SER U OG  1 
+ATOM   265  N N   . LEU A 1 35  ? -3.351  -32.970 -6.139  1.00 30.66  ? ? ? ? ? ? 46  LEU U N   1 
+ATOM   266  C CA  . LEU A 1 35  ? -3.222  -32.684 -4.735  1.00 34.40  ? ? ? ? ? ? 46  LEU U CA  1 
+ATOM   267  C C   . LEU A 1 35  ? -2.629  -33.870 -3.971  1.00 36.06  ? ? ? ? ? ? 46  LEU U C   1 
+ATOM   268  O O   . LEU A 1 35  ? -3.288  -34.878 -3.748  1.00 39.21  ? ? ? ? ? ? 46  LEU U O   1 
+ATOM   269  C CB  . LEU A 1 35  ? -4.599  -32.356 -4.167  1.00 34.51  ? ? ? ? ? ? 46  LEU U CB  1 
+ATOM   270  C CG  . LEU A 1 35  ? -4.581  -31.738 -2.775  1.00 32.68  ? ? ? ? ? ? 46  LEU U CG  1 
+ATOM   271  C CD1 . LEU A 1 35  ? -3.791  -30.435 -2.815  1.00 31.78  ? ? ? ? ? ? 46  LEU U CD1 1 
+ATOM   272  C CD2 . LEU A 1 35  ? -5.998  -31.537 -2.261  1.00 32.49  ? ? ? ? ? ? 46  LEU U CD2 1 
+ATOM   273  N N   . ILE A 1 36  ? -1.385  -33.741 -3.546  1.00 34.37  ? ? ? ? ? ? 47  ILE U N   1 
+ATOM   274  C CA  . ILE A 1 36  ? -0.708  -34.869 -2.914  1.00 35.66  ? ? ? ? ? ? 47  ILE U CA  1 
+ATOM   275  C C   . ILE A 1 36  ? -0.583  -34.726 -1.390  1.00 37.01  ? ? ? ? ? ? 47  ILE U C   1 
+ATOM   276  O O   . ILE A 1 36  ? -0.149  -35.640 -0.709  1.00 38.42  ? ? ? ? ? ? 47  ILE U O   1 
+ATOM   277  C CB  . ILE A 1 36  ? 0.664   -35.102 -3.560  1.00 34.41  ? ? ? ? ? ? 47  ILE U CB  1 
+ATOM   278  C CG1 . ILE A 1 36  ? 1.597   -33.928 -3.289  1.00 34.48  ? ? ? ? ? ? 47  ILE U CG1 1 
+ATOM   279  C CG2 . ILE A 1 36  ? 0.504   -35.294 -5.067  1.00 36.85  ? ? ? ? ? ? 47  ILE U CG2 1 
+ATOM   280  C CD1 . ILE A 1 36  ? 3.050   -34.218 -3.610  1.00 35.77  ? ? ? ? ? ? 47  ILE U CD1 1 
+ATOM   281  N N   . SER A 1 37  ? -0.938  -33.563 -0.876  1.00 39.39  ? ? ? ? ? ? 48  SER U N   1 
+ATOM   282  C CA  . SER A 1 37  ? -1.088  -33.356 0.552   1.00 40.38  ? ? ? ? ? ? 48  SER U CA  1 
+ATOM   283  C C   . SER A 1 37  ? -1.840  -32.048 0.619   1.00 39.36  ? ? ? ? ? ? 48  SER U C   1 
+ATOM   284  O O   . SER A 1 37  ? -1.955  -31.376 -0.409  1.00 37.01  ? ? ? ? ? ? 48  SER U O   1 
+ATOM   285  C CB  . SER A 1 37  ? 0.272   -33.278 1.259   1.00 42.52  ? ? ? ? ? ? 48  SER U CB  1 
+ATOM   286  O OG  . SER A 1 37  ? 0.980   -32.107 0.912   1.00 42.03  ? ? ? ? ? ? 48  SER U OG  1 
+ATOM   287  N N   . PRO A 1 38  ? -2.371  -31.680 1.799   1.00 38.20  ? ? ? ? ? ? 49  PRO U N   1 
+ATOM   288  C CA  . PRO A 1 38  ? -3.238  -30.507 1.843   1.00 37.61  ? ? ? ? ? ? 49  PRO U CA  1 
+ATOM   289  C C   . PRO A 1 38  ? -2.636  -29.221 1.302   1.00 37.67  ? ? ? ? ? ? 49  PRO U C   1 
+ATOM   290  O O   . PRO A 1 38  ? -3.373  -28.420 0.744   1.00 36.64  ? ? ? ? ? ? 49  PRO U O   1 
+ATOM   291  C CB  . PRO A 1 38  ? -3.562  -30.354 3.334   1.00 38.26  ? ? ? ? ? ? 49  PRO U CB  1 
+ATOM   292  C CG  . PRO A 1 38  ? -3.426  -31.721 3.880   1.00 38.94  ? ? ? ? ? ? 49  PRO U CG  1 
+ATOM   293  C CD  . PRO A 1 38  ? -2.288  -32.341 3.117   1.00 41.02  ? ? ? ? ? ? 49  PRO U CD  1 
+ATOM   294  N N   . CYS A 1 39  ? -1.332  -29.007 1.485   1.00 38.12  ? ? ? ? ? ? 50  CYS U N   1 
+ATOM   295  C CA  . CYS A 1 39  ? -0.687  -27.760 1.060   1.00 40.13  ? ? ? ? ? ? 50  CYS U CA  1 
+ATOM   296  C C   . CYS A 1 39  ? 0.029   -27.853 -0.292  1.00 38.70  ? ? ? ? ? ? 50  CYS U C   1 
+ATOM   297  O O   . CYS A 1 39  ? 0.545   -26.838 -0.786  1.00 36.86  ? ? ? ? ? ? 50  CYS U O   1 
+ATOM   298  C CB  . CYS A 1 39  ? 0.328   -27.300 2.109   1.00 43.78  ? ? ? ? ? ? 50  CYS U CB  1 
+ATOM   299  S SG  . CYS A 1 39  ? -0.374  -27.057 3.754   1.00 48.95  ? ? ? ? ? ? 50  CYS U SG  1 
+ATOM   300  N N   . TRP A 1 40  ? 0.047   -29.046 -0.891  1.00 39.41  ? ? ? ? ? ? 51  TRP U N   1 
+ATOM   301  C CA  . TRP A 1 40  ? 0.929   -29.327 -2.028  1.00 40.08  ? ? ? ? ? ? 51  TRP U CA  1 
+ATOM   302  C C   . TRP A 1 40  ? 0.243   -29.937 -3.242  1.00 37.39  ? ? ? ? ? ? 51  TRP U C   1 
+ATOM   303  O O   . TRP A 1 40  ? -0.401  -30.986 -3.152  1.00 31.90  ? ? ? ? ? ? 51  TRP U O   1 
+ATOM   304  C CB  . TRP A 1 40  ? 2.049   -30.268 -1.593  1.00 42.82  ? ? ? ? ? ? 51  TRP U CB  1 
+ATOM   305  C CG  . TRP A 1 40  ? 3.028   -29.617 -0.683  1.00 45.78  ? ? ? ? ? ? 51  TRP U CG  1 
+ATOM   306  C CD1 . TRP A 1 40  ? 3.119   -29.764 0.672   1.00 47.19  ? ? ? ? ? ? 51  TRP U CD1 1 
+ATOM   307  C CD2 . TRP A 1 40  ? 4.057   -28.697 -1.057  1.00 46.26  ? ? ? ? ? ? 51  TRP U CD2 1 
+ATOM   308  N NE1 . TRP A 1 40  ? 4.147   -28.997 1.164   1.00 45.98  ? ? ? ? ? ? 51  TRP U NE1 1 
+ATOM   309  C CE2 . TRP A 1 40  ? 4.740   -28.329 0.129   1.00 46.38  ? ? ? ? ? ? 51  TRP U CE2 1 
+ATOM   310  C CE3 . TRP A 1 40  ? 4.470   -28.149 -2.271  1.00 44.74  ? ? ? ? ? ? 51  TRP U CE3 1 
+ATOM   311  C CZ2 . TRP A 1 40  ? 5.814   -27.437 0.131   1.00 45.63  ? ? ? ? ? ? 51  TRP U CZ2 1 
+ATOM   312  C CZ3 . TRP A 1 40  ? 5.534   -27.266 -2.276  1.00 45.72  ? ? ? ? ? ? 51  TRP U CZ3 1 
+ATOM   313  C CH2 . TRP A 1 40  ? 6.203   -26.919 -1.078  1.00 47.45  ? ? ? ? ? ? 51  TRP U CH2 1 
+ATOM   314  N N   . VAL A 1 41  ? 0.459   -29.300 -4.372  1.00 35.53  ? ? ? ? ? ? 52  VAL U N   1 
+ATOM   315  C CA  . VAL A 1 41  ? -0.013  -29.782 -5.628  1.00 36.42  ? ? ? ? ? ? 52  VAL U CA  1 
+ATOM   316  C C   . VAL A 1 41  ? 1.192   -30.090 -6.464  1.00 34.94  ? ? ? ? ? ? 52  VAL U C   1 
+ATOM   317  O O   . VAL A 1 41  ? 2.089   -29.314 -6.542  1.00 36.38  ? ? ? ? ? ? 52  VAL U O   1 
+ATOM   318  C CB  . VAL A 1 41  ? -0.920  -28.742 -6.314  1.00 36.44  ? ? ? ? ? ? 52  VAL U CB  1 
+ATOM   319  C CG1 . VAL A 1 41  ? -1.040  -28.962 -7.816  1.00 36.92  ? ? ? ? ? ? 52  VAL U CG1 1 
+ATOM   320  C CG2 . VAL A 1 41  ? -2.283  -28.709 -5.669  1.00 36.14  ? ? ? ? ? ? 52  VAL U CG2 1 
+ATOM   321  N N   . ILE A 1 42  ? 1.181   -31.223 -7.111  1.00 33.55  ? ? ? ? ? ? 53  ILE U N   1 
+ATOM   322  C CA  . ILE A 1 42  ? 2.256   -31.591 -7.996  1.00 32.87  ? ? ? ? ? ? 53  ILE U CA  1 
+ATOM   323  C C   . ILE A 1 42  ? 1.852   -31.547 -9.487  1.00 30.74  ? ? ? ? ? ? 53  ILE U C   1 
+ATOM   324  O O   . ILE A 1 42  ? 0.790   -31.921 -9.844  1.00 32.30  ? ? ? ? ? ? 53  ILE U O   1 
+ATOM   325  C CB  . ILE A 1 42  ? 2.812   -32.951 -7.627  1.00 33.61  ? ? ? ? ? ? 53  ILE U CB  1 
+ATOM   326  C CG1 . ILE A 1 42  ? 4.145   -33.162 -8.280  1.00 35.73  ? ? ? ? ? ? 53  ILE U CG1 1 
+ATOM   327  C CG2 . ILE A 1 42  ? 1.883   -34.041 -8.067  1.00 35.20  ? ? ? ? ? ? 53  ILE U CG2 1 
+ATOM   328  C CD1 . ILE A 1 42  ? 4.924   -34.290 -7.689  1.00 36.12  ? ? ? ? ? ? 53  ILE U CD1 1 
+ATOM   329  N N   . SER A 1 43  ? 2.743   -31.056 -10.320 1.00 29.70  ? ? ? ? ? ? 54  SER U N   1 
+ATOM   330  C CA  . SER A 1 43  ? 2.542   -30.862 -11.750 1.00 28.75  ? ? ? ? ? ? 54  SER U CA  1 
+ATOM   331  C C   . SER A 1 43  ? 3.849   -31.035 -12.524 1.00 32.02  ? ? ? ? ? ? 54  SER U C   1 
+ATOM   332  O O   . SER A 1 43  ? 4.732   -31.711 -12.091 1.00 33.14  ? ? ? ? ? ? 54  SER U O   1 
+ATOM   333  C CB  . SER A 1 43  ? 1.960   -29.488 -12.050 1.00 28.79  ? ? ? ? ? ? 54  SER U CB  1 
+ATOM   334  O OG  . SER A 1 43  ? 1.451   -29.379 -13.341 1.00 26.60  ? ? ? ? ? ? 54  SER U OG  1 
+ATOM   335  N N   . ALA A 1 44  ? 3.902   -30.448 -13.710 1.00 31.06  ? ? ? ? ? ? 55  ALA U N   1 
+ATOM   336  C CA  . ALA A 1 44  ? 5.008   -30.548 -14.638 1.00 31.99  ? ? ? ? ? ? 55  ALA U CA  1 
+ATOM   337  C C   . ALA A 1 44  ? 5.739   -29.218 -14.801 1.00 33.55  ? ? ? ? ? ? 55  ALA U C   1 
+ATOM   338  O O   . ALA A 1 44  ? 5.145   -28.191 -14.863 1.00 31.53  ? ? ? ? ? ? 55  ALA U O   1 
+ATOM   339  C CB  . ALA A 1 44  ? 4.511   -31.058 -15.974 1.00 30.04  ? ? ? ? ? ? 55  ALA U CB  1 
+ATOM   340  N N   . THR A 1 45  ? 7.049   -29.256 -14.824 1.00 36.32  ? ? ? ? ? ? 56  THR U N   1 
+ATOM   341  C CA  . THR A 1 45  ? 7.838   -28.027 -14.851 1.00 36.09  ? ? ? ? ? ? 56  THR U CA  1 
+ATOM   342  C C   . THR A 1 45  ? 7.602   -27.194 -16.111 1.00 35.59  ? ? ? ? ? ? 56  THR U C   1 
+ATOM   343  O O   . THR A 1 45  ? 7.688   -25.962 -16.066 1.00 35.76  ? ? ? ? ? ? 56  THR U O   1 
+ATOM   344  C CB  . THR A 1 45  ? 9.352   -28.325 -14.676 1.00 38.16  ? ? ? ? ? ? 56  THR U CB  1 
+ATOM   345  O OG1 . THR A 1 45  ? 9.659   -28.433 -13.281 1.00 38.04  ? ? ? ? ? ? 56  THR U OG1 1 
+ATOM   346  C CG2 . THR A 1 45  ? 10.213  -27.226 -15.247 1.00 40.24  ? ? ? ? ? ? 56  THR U CG2 1 
+ATOM   347  N N   . HIS A 1 46  ? 7.316   -27.851 -17.229 1.00 34.30  ? ? ? ? ? ? 57  HIS U N   1 
+ATOM   348  C CA  . HIS A 1 46  ? 7.181   -27.165 -18.502 1.00 33.05  ? ? ? ? ? ? 57  HIS U CA  1 
+ATOM   349  C C   . HIS A 1 46  ? 5.980   -26.221 -18.485 1.00 36.88  ? ? ? ? ? ? 57  HIS U C   1 
+ATOM   350  O O   . HIS A 1 46  ? 5.918   -25.274 -19.260 1.00 37.70  ? ? ? ? ? ? 57  HIS U O   1 
+ATOM   351  C CB  . HIS A 1 46  ? 7.069   -28.177 -19.651 1.00 33.66  ? ? ? ? ? ? 57  HIS U CB  1 
+ATOM   352  C CG  . HIS A 1 46  ? 5.737   -28.844 -19.746 1.00 35.43  ? ? ? ? ? ? 57  HIS U CG  1 
+ATOM   353  N ND1 . HIS A 1 46  ? 5.516   -30.144 -19.344 1.00 36.94  ? ? ? ? ? ? 57  HIS U ND1 1 
+ATOM   354  C CD2 . HIS A 1 46  ? 4.548   -28.383 -20.195 1.00 38.79  ? ? ? ? ? ? 57  HIS U CD2 1 
+ATOM   355  C CE1 . HIS A 1 46  ? 4.249   -30.457 -19.558 1.00 38.76  ? ? ? ? ? ? 57  HIS U CE1 1 
+ATOM   356  N NE2 . HIS A 1 46  ? 3.642   -29.405 -20.077 1.00 37.64  ? ? ? ? ? ? 57  HIS U NE2 1 
+ATOM   357  N N   . CYS A 1 47  ? 5.012   -26.480 -17.609 1.00 36.96  ? ? ? ? ? ? 58  CYS U N   1 
+ATOM   358  C CA  . CYS A 1 47  ? 3.859   -25.587 -17.464 1.00 35.30  ? ? ? ? ? ? 58  CYS U CA  1 
+ATOM   359  C C   . CYS A 1 47  ? 4.212   -24.175 -17.049 1.00 36.84  ? ? ? ? ? ? 58  CYS U C   1 
+ATOM   360  O O   . CYS A 1 47  ? 3.423   -23.259 -17.238 1.00 35.59  ? ? ? ? ? ? 58  CYS U O   1 
+ATOM   361  C CB  . CYS A 1 47  ? 2.936   -26.131 -16.391 1.00 36.48  ? ? ? ? ? ? 58  CYS U CB  1 
+ATOM   362  S SG  . CYS A 1 47  ? 2.276   -27.753 -16.770 1.00 35.39  ? ? ? ? ? ? 58  CYS U SG  1 
+ATOM   363  N N   . PHE A 1 48  ? 5.343   -24.036 -16.394 1.00 39.75  ? ? ? ? ? ? 59  PHE U N   1 
+ATOM   364  C CA  . PHE A 1 48  ? 5.699   -22.800 -15.746 1.00 38.36  ? ? ? ? ? ? 59  PHE U CA  1 
+ATOM   365  C C   . PHE A 1 48  ? 7.001   -22.172 -16.224 1.00 40.62  ? ? ? ? ? ? 59  PHE U C   1 
+ATOM   366  O O   . PHE A 1 48  ? 7.338   -21.077 -15.869 1.00 39.94  ? ? ? ? ? ? 59  PHE U O   1 
+ATOM   367  C CB  . PHE A 1 48  ? 5.757   -23.008 -14.240 1.00 36.41  ? ? ? ? ? ? 59  PHE U CB  1 
+ATOM   368  C CG  . PHE A 1 48  ? 4.612   -23.789 -13.705 1.00 35.50  ? ? ? ? ? ? 59  PHE U CG  1 
+ATOM   369  C CD1 . PHE A 1 48  ? 3.420   -23.193 -13.444 1.00 33.86  ? ? ? ? ? ? 59  PHE U CD1 1 
+ATOM   370  C CD2 . PHE A 1 48  ? 4.732   -25.129 -13.478 1.00 38.22  ? ? ? ? ? ? 59  PHE U CD2 1 
+ATOM   371  C CE1 . PHE A 1 48  ? 2.361   -23.910 -12.956 1.00 34.90  ? ? ? ? ? ? 59  PHE U CE1 1 
+ATOM   372  C CE2 . PHE A 1 48  ? 3.674   -25.858 -12.980 1.00 37.02  ? ? ? ? ? ? 59  PHE U CE2 1 
+ATOM   373  C CZ  . PHE A 1 48  ? 2.480   -25.244 -12.721 1.00 34.41  ? ? ? ? ? ? 59  PHE U CZ  1 
+ATOM   374  N N   . ILE A 1 49  ? 7.729   -22.899 -17.024 1.00 42.84  ? ? ? ? ? ? 60  ILE U N   1 
+ATOM   375  C CA  . ILE A 1 49  ? 9.076   -22.521 -17.354 1.00 47.48  ? ? ? ? ? ? 60  ILE U CA  1 
+ATOM   376  C C   . ILE A 1 49  ? 9.106   -21.178 -18.096 1.00 48.16  ? ? ? ? ? ? 60  ILE U C   1 
+ATOM   377  O O   . ILE A 1 49  ? 9.938   -20.373 -17.802 1.00 48.43  ? ? ? ? ? ? 60  ILE U O   1 
+ATOM   378  C CB  . ILE A 1 49  ? 9.842   -23.687 -18.024 1.00 47.93  ? ? ? ? ? ? 60  ILE U CB  1 
+ATOM   379  C CG1 . ILE A 1 49  ? 11.336  -23.456 -17.992 1.00 52.13  ? ? ? ? ? ? 60  ILE U CG1 1 
+ATOM   380  C CG2 . ILE A 1 49  ? 9.360   -23.938 -19.428 1.00 48.22  ? ? ? ? ? ? 60  ILE U CG2 1 
+ATOM   381  C CD1 . ILE A 1 49  ? 11.926  -23.294 -16.621 1.00 52.71  ? ? ? ? ? ? 60  ILE U CD1 1 
+ATOM   382  N N   . ASP A 1 50  A 8.163   -20.932 -18.993 1.00 41.35  ? ? ? ? ? ? 60  ASP U N   1 
+ATOM   383  C CA  . ASP A 1 50  A 8.058   -19.640 -19.623 1.00 44.13  ? ? ? ? ? ? 60  ASP U CA  1 
+ATOM   384  C C   . ASP A 1 50  A 7.706   -18.421 -18.760 1.00 44.67  ? ? ? ? ? ? 60  ASP U C   1 
+ATOM   385  O O   . ASP A 1 50  A 8.102   -17.354 -19.101 1.00 42.34  ? ? ? ? ? ? 60  ASP U O   1 
+ATOM   386  C CB  . ASP A 1 50  A 7.194   -19.662 -20.860 1.00 45.99  ? ? ? ? ? ? 60  ASP U CB  1 
+ATOM   387  C CG  . ASP A 1 50  A 7.483   -20.837 -21.731 1.00 49.54  ? ? ? ? ? ? 60  ASP U CG  1 
+ATOM   388  O OD1 . ASP A 1 50  A 8.585   -21.365 -21.626 1.00 56.51  ? ? ? ? ? ? 60  ASP U OD1 1 
+ATOM   389  O OD2 . ASP A 1 50  A 6.622   -21.229 -22.526 1.00 50.70  ? ? ? ? ? ? 60  ASP U OD2 1 
+ATOM   390  N N   . TYR A 1 51  B 6.895   -18.579 -17.749 1.00 52.86  ? ? ? ? ? ? 60  TYR U N   1 
+ATOM   391  C CA  . TYR A 1 51  B 6.498   -17.493 -16.882 1.00 57.14  ? ? ? ? ? ? 60  TYR U CA  1 
+ATOM   392  C C   . TYR A 1 51  B 6.672   -17.915 -15.403 1.00 54.75  ? ? ? ? ? ? 60  TYR U C   1 
+ATOM   393  O O   . TYR A 1 51  B 5.698   -18.216 -14.763 1.00 51.14  ? ? ? ? ? ? 60  TYR U O   1 
+ATOM   394  C CB  . TYR A 1 51  B 5.062   -17.052 -17.183 1.00 61.66  ? ? ? ? ? ? 60  TYR U CB  1 
+ATOM   395  C CG  . TYR A 1 51  B 4.707   -16.650 -18.634 1.00 65.97  ? ? ? ? ? ? 60  TYR U CG  1 
+ATOM   396  C CD1 . TYR A 1 51  B 4.767   -17.543 -19.680 1.00 69.56  ? ? ? ? ? ? 60  TYR U CD1 1 
+ATOM   397  C CD2 . TYR A 1 51  B 4.294   -15.366 -18.935 1.00 70.33  ? ? ? ? ? ? 60  TYR U CD2 1 
+ATOM   398  C CE1 . TYR A 1 51  B 4.468   -17.153 -20.977 1.00 71.17  ? ? ? ? ? ? 60  TYR U CE1 1 
+ATOM   399  C CE2 . TYR A 1 51  B 4.007   -14.973 -20.221 1.00 70.43  ? ? ? ? ? ? 60  TYR U CE2 1 
+ATOM   400  C CZ  . TYR A 1 51  B 4.089   -15.876 -21.227 1.00 72.28  ? ? ? ? ? ? 60  TYR U CZ  1 
+ATOM   401  O OH  . TYR A 1 51  B 3.777   -15.493 -22.492 1.00 73.97  ? ? ? ? ? ? 60  TYR U OH  1 
+ATOM   402  N N   . PRO A 1 52  C 7.979   -17.918 -14.891 1.00 49.51  ? ? ? ? ? ? 60  PRO U N   1 
+ATOM   403  C CA  . PRO A 1 52  C 8.094   -18.564 -13.558 1.00 47.83  ? ? ? ? ? ? 60  PRO U CA  1 
+ATOM   404  C C   . PRO A 1 52  C 7.733   -17.811 -12.282 1.00 46.55  ? ? ? ? ? ? 60  PRO U C   1 
+ATOM   405  O O   . PRO A 1 52  C 8.088   -18.242 -11.214 1.00 47.71  ? ? ? ? ? ? 60  PRO U O   1 
+ATOM   406  C CB  . PRO A 1 52  C 9.548   -19.009 -13.497 1.00 47.73  ? ? ? ? ? ? 60  PRO U CB  1 
+ATOM   407  C CG  . PRO A 1 52  C 10.288  -18.047 -14.304 1.00 49.36  ? ? ? ? ? ? 60  PRO U CG  1 
+ATOM   408  C CD  . PRO A 1 52  C 9.403   -17.636 -15.402 1.00 49.25  ? ? ? ? ? ? 60  PRO U CD  1 
+ATOM   409  N N   . LYS A 1 53  ? 7.098   -16.672 -12.396 1.00 48.29  ? ? ? ? ? ? 61  LYS U N   1 
+ATOM   410  C CA  . LYS A 1 53  ? 6.631   -15.968 -11.223 1.00 49.73  ? ? ? ? ? ? 61  LYS U CA  1 
+ATOM   411  C C   . LYS A 1 53  ? 5.361   -16.583 -10.633 1.00 45.85  ? ? ? ? ? ? 61  LYS U C   1 
+ATOM   412  O O   . LYS A 1 53  ? 4.347   -16.593 -11.253 1.00 44.19  ? ? ? ? ? ? 61  LYS U O   1 
+ATOM   413  C CB  . LYS A 1 53  ? 6.360   -14.515 -11.576 1.00 55.64  ? ? ? ? ? ? 61  LYS U CB  1 
+ATOM   414  C CG  . LYS A 1 53  ? 7.251   -13.948 -12.670 1.00 61.46  ? ? ? ? ? ? 61  LYS U CG  1 
+ATOM   415  C CD  . LYS A 1 53  ? 8.691   -13.950 -12.224 1.00 64.72  ? ? ? ? ? ? 61  LYS U CD  1 
+ATOM   416  C CE  . LYS A 1 53  ? 9.396   -12.700 -12.725 1.00 69.14  ? ? ? ? ? ? 61  LYS U CE  1 
+ATOM   417  N NZ  . LYS A 1 53  ? 10.710  -12.503 -12.074 1.00 71.19  ? ? ? ? ? ? 61  LYS U NZ  1 
+ATOM   418  N N   . LYS A 1 54  ? 5.483   -17.083 -9.418  1.00 44.97  ? ? ? ? ? ? 62  LYS U N   1 
+ATOM   419  C CA  . LYS A 1 54  ? 4.431   -17.730 -8.661  1.00 45.76  ? ? ? ? ? ? 62  LYS U CA  1 
+ATOM   420  C C   . LYS A 1 54  ? 3.191   -16.882 -8.407  1.00 44.59  ? ? ? ? ? ? 62  LYS U C   1 
+ATOM   421  O O   . LYS A 1 54  ? 2.111   -17.384 -8.189  1.00 41.78  ? ? ? ? ? ? 62  LYS U O   1 
+ATOM   422  C CB  . LYS A 1 54  ? 4.992   -18.183 -7.338  1.00 46.22  ? ? ? ? ? ? 62  LYS U CB  1 
+ATOM   423  C CG  . LYS A 1 54  ? 5.476   -17.057 -6.449  1.00 48.63  ? ? ? ? ? ? 62  LYS U CG  1 
+ATOM   424  C CD  . LYS A 1 54  ? 5.835   -17.577 -5.062  1.00 51.35  ? ? ? ? ? ? 62  LYS U CD  1 
+ATOM   425  C CE  . LYS A 1 54  ? 6.801   -16.689 -4.305  1.00 54.94  ? ? ? ? ? ? 62  LYS U CE  1 
+ATOM   426  N NZ  . LYS A 1 54  ? 6.408   -16.489 -2.895  1.00 55.54  ? ? ? ? ? ? 62  LYS U NZ  1 
+ATOM   427  N N   . GLU A 1 55  A 3.389   -15.580 -8.426  1.00 47.73  ? ? ? ? ? ? 62  GLU U N   1 
+ATOM   428  C CA  . GLU A 1 55  A 2.333   -14.602 -8.247  1.00 49.15  ? ? ? ? ? ? 62  GLU U CA  1 
+ATOM   429  C C   . GLU A 1 55  A 1.278   -14.665 -9.343  1.00 44.73  ? ? ? ? ? ? 62  GLU U C   1 
+ATOM   430  O O   . GLU A 1 55  A 0.139   -14.379 -9.109  1.00 43.01  ? ? ? ? ? ? 62  GLU U O   1 
+ATOM   431  C CB  . GLU A 1 55  A 2.917   -13.205 -8.206  1.00 51.22  ? ? ? ? ? ? 62  GLU U CB  1 
+ATOM   432  C CG  . GLU A 1 55  A 3.623   -12.856 -6.938  1.00 53.81  ? ? ? ? ? ? 62  GLU U CG  1 
+ATOM   433  C CD  . GLU A 1 55  A 5.017   -13.383 -6.891  1.00 57.28  ? ? ? ? ? ? 62  GLU U CD  1 
+ATOM   434  O OE1 . GLU A 1 55  A 5.689   -13.491 -7.922  1.00 62.30  ? ? ? ? ? ? 62  GLU U OE1 1 
+ATOM   435  O OE2 . GLU A 1 55  A 5.443   -13.682 -5.792  1.00 64.13  ? ? ? ? ? ? 62  GLU U OE2 1 
+ATOM   436  N N   . ASP A 1 56  ? 1.698   -14.955 -10.551 1.00 43.02  ? ? ? ? ? ? 63  ASP U N   1 
+ATOM   437  C CA  . ASP A 1 56  ? 0.780   -15.023 -11.656 1.00 45.68  ? ? ? ? ? ? 63  ASP U CA  1 
+ATOM   438  C C   . ASP A 1 56  ? -0.146  -16.232 -11.734 1.00 44.27  ? ? ? ? ? ? 63  ASP U C   1 
+ATOM   439  O O   . ASP A 1 56  ? -0.902  -16.337 -12.656 1.00 42.96  ? ? ? ? ? ? 63  ASP U O   1 
+ATOM   440  C CB  . ASP A 1 56  ? 1.544   -14.899 -12.942 1.00 47.77  ? ? ? ? ? ? 63  ASP U CB  1 
+ATOM   441  C CG  . ASP A 1 56  ? 2.149   -13.557 -13.101 1.00 50.56  ? ? ? ? ? ? 63  ASP U CG  1 
+ATOM   442  O OD1 . ASP A 1 56  ? 1.601   -12.585 -12.567 1.00 54.24  ? ? ? ? ? ? 63  ASP U OD1 1 
+ATOM   443  O OD2 . ASP A 1 56  ? 3.168   -13.478 -13.762 1.00 52.86  ? ? ? ? ? ? 63  ASP U OD2 1 
+ATOM   444  N N   . TYR A 1 57  ? -0.080  -17.129 -10.767 1.00 41.55  ? ? ? ? ? ? 64  TYR U N   1 
+ATOM   445  C CA  . TYR A 1 57  ? -0.798  -18.390 -10.855 1.00 40.93  ? ? ? ? ? ? 64  TYR U CA  1 
+ATOM   446  C C   . TYR A 1 57  ? -1.907  -18.527 -9.850  1.00 38.39  ? ? ? ? ? ? 64  TYR U C   1 
+ATOM   447  O O   . TYR A 1 57  ? -1.848  -17.992 -8.733  1.00 34.78  ? ? ? ? ? ? 64  TYR U O   1 
+ATOM   448  C CB  . TYR A 1 57  ? 0.172   -19.553 -10.711 1.00 41.79  ? ? ? ? ? ? 64  TYR U CB  1 
+ATOM   449  C CG  . TYR A 1 57  ? 1.068   -19.633 -11.889 1.00 39.30  ? ? ? ? ? ? 64  TYR U CG  1 
+ATOM   450  C CD1 . TYR A 1 57  ? 0.630   -20.217 -13.072 1.00 39.55  ? ? ? ? ? ? 64  TYR U CD1 1 
+ATOM   451  C CD2 . TYR A 1 57  ? 2.339   -19.064 -11.862 1.00 39.57  ? ? ? ? ? ? 64  TYR U CD2 1 
+ATOM   452  C CE1 . TYR A 1 57  ? 1.444   -20.271 -14.190 1.00 38.25  ? ? ? ? ? ? 64  TYR U CE1 1 
+ATOM   453  C CE2 . TYR A 1 57  ? 3.161   -19.118 -12.975 1.00 39.52  ? ? ? ? ? ? 64  TYR U CE2 1 
+ATOM   454  C CZ  . TYR A 1 57  ? 2.702   -19.719 -14.133 1.00 38.18  ? ? ? ? ? ? 64  TYR U CZ  1 
+ATOM   455  O OH  . TYR A 1 57  ? 3.494   -19.771 -15.230 1.00 40.37  ? ? ? ? ? ? 64  TYR U OH  1 
+ATOM   456  N N   . ILE A 1 58  ? -2.934  -19.248 -10.275 1.00 36.40  ? ? ? ? ? ? 65  ILE U N   1 
+ATOM   457  C CA  . ILE A 1 58  ? -4.035  -19.595 -9.415  1.00 35.67  ? ? ? ? ? ? 65  ILE U CA  1 
+ATOM   458  C C   . ILE A 1 58  ? -4.324  -21.076 -9.576  1.00 35.69  ? ? ? ? ? ? 65  ILE U C   1 
+ATOM   459  O O   . ILE A 1 58  ? -4.316  -21.607 -10.704 1.00 31.59  ? ? ? ? ? ? 65  ILE U O   1 
+ATOM   460  C CB  . ILE A 1 58  ? -5.319  -18.852 -9.791  1.00 39.32  ? ? ? ? ? ? 65  ILE U CB  1 
+ATOM   461  C CG1 . ILE A 1 58  ? -5.113  -17.336 -9.807  1.00 44.29  ? ? ? ? ? ? 65  ILE U CG1 1 
+ATOM   462  C CG2 . ILE A 1 58  ? -6.425  -19.216 -8.818  1.00 40.54  ? ? ? ? ? ? 65  ILE U CG2 1 
+ATOM   463  C CD1 . ILE A 1 58  ? -6.187  -16.613 -10.605 1.00 47.60  ? ? ? ? ? ? 65  ILE U CD1 1 
+ATOM   464  N N   . VAL A 1 59  ? -4.615  -21.728 -8.456  1.00 33.57  ? ? ? ? ? ? 66  VAL U N   1 
+ATOM   465  C CA  . VAL A 1 59  ? -4.941  -23.140 -8.451  1.00 31.83  ? ? ? ? ? ? 66  VAL U CA  1 
+ATOM   466  C C   . VAL A 1 59  ? -6.346  -23.321 -7.923  1.00 33.49  ? ? ? ? ? ? 66  VAL U C   1 
+ATOM   467  O O   . VAL A 1 59  ? -6.674  -22.819 -6.834  1.00 35.13  ? ? ? ? ? ? 66  VAL U O   1 
+ATOM   468  C CB  . VAL A 1 59  ? -3.974  -23.955 -7.593  1.00 31.78  ? ? ? ? ? ? 66  VAL U CB  1 
+ATOM   469  C CG1 . VAL A 1 59  ? -4.437  -25.412 -7.492  1.00 32.70  ? ? ? ? ? ? 66  VAL U CG1 1 
+ATOM   470  C CG2 . VAL A 1 59  ? -2.563  -23.884 -8.157  1.00 34.87  ? ? ? ? ? ? 66  VAL U CG2 1 
+ATOM   471  N N   . TYR A 1 60  ? -7.178  -24.031 -8.691  1.00 31.33  ? ? ? ? ? ? 67  TYR U N   1 
+ATOM   472  C CA  . TYR A 1 60  ? -8.505  -24.426 -8.214  1.00 30.41  ? ? ? ? ? ? 67  TYR U CA  1 
+ATOM   473  C C   . TYR A 1 60  ? -8.530  -25.903 -7.883  1.00 30.74  ? ? ? ? ? ? 67  TYR U C   1 
+ATOM   474  O O   . TYR A 1 60  ? -7.925  -26.733 -8.584  1.00 30.60  ? ? ? ? ? ? 67  TYR U O   1 
+ATOM   475  C CB  . TYR A 1 60  ? -9.597  -24.120 -9.233  1.00 31.58  ? ? ? ? ? ? 67  TYR U CB  1 
+ATOM   476  C CG  . TYR A 1 60  ? -9.948  -22.671 -9.383  1.00 33.34  ? ? ? ? ? ? 67  TYR U CG  1 
+ATOM   477  C CD1 . TYR A 1 60  ? -10.940 -22.077 -8.605  1.00 37.08  ? ? ? ? ? ? 67  TYR U CD1 1 
+ATOM   478  C CD2 . TYR A 1 60  ? -9.303  -21.885 -10.335 1.00 35.11  ? ? ? ? ? ? 67  TYR U CD2 1 
+ATOM   479  C CE1 . TYR A 1 60  ? -11.274 -20.736 -8.777  1.00 36.71  ? ? ? ? ? ? 67  TYR U CE1 1 
+ATOM   480  C CE2 . TYR A 1 60  ? -9.606  -20.555 -10.500 1.00 36.06  ? ? ? ? ? ? 67  TYR U CE2 1 
+ATOM   481  C CZ  . TYR A 1 60  ? -10.599 -19.979 -9.727  1.00 40.30  ? ? ? ? ? ? 67  TYR U CZ  1 
+ATOM   482  O OH  . TYR A 1 60  ? -10.877 -18.643 -9.933  1.00 42.27  ? ? ? ? ? ? 67  TYR U OH  1 
+ATOM   483  N N   . LEU A 1 61  ? -9.242  -26.220 -6.806  1.00 27.82  ? ? ? ? ? ? 68  LEU U N   1 
+ATOM   484  C CA  . LEU A 1 61  ? -9.547  -27.570 -6.441  1.00 27.14  ? ? ? ? ? ? 68  LEU U CA  1 
+ATOM   485  C C   . LEU A 1 61  ? -11.039 -27.656 -6.403  1.00 24.84  ? ? ? ? ? ? 68  LEU U C   1 
+ATOM   486  O O   . LEU A 1 61  ? -11.723 -26.659 -6.229  1.00 27.69  ? ? ? ? ? ? 68  LEU U O   1 
+ATOM   487  C CB  . LEU A 1 61  ? -8.953  -27.915 -5.065  1.00 29.78  ? ? ? ? ? ? 68  LEU U CB  1 
+ATOM   488  C CG  . LEU A 1 61  ? -7.436  -27.780 -4.959  1.00 29.69  ? ? ? ? ? ? 68  LEU U CG  1 
+ATOM   489  C CD1 . LEU A 1 61  ? -7.012  -28.056 -3.516  1.00 32.25  ? ? ? ? ? ? 68  LEU U CD1 1 
+ATOM   490  C CD2 . LEU A 1 61  ? -6.660  -28.690 -5.904  1.00 29.78  ? ? ? ? ? ? 68  LEU U CD2 1 
+ATOM   491  N N   . GLY A 1 62  ? -11.576 -28.832 -6.646  1.00 23.01  ? ? ? ? ? ? 69  GLY U N   1 
+ATOM   492  C CA  . GLY A 1 62  ? -13.028 -28.980 -6.588  1.00 23.48  ? ? ? ? ? ? 69  GLY U CA  1 
+ATOM   493  C C   . GLY A 1 62  ? -13.712 -28.482 -7.864  1.00 23.97  ? ? ? ? ? ? 69  GLY U C   1 
+ATOM   494  O O   . GLY A 1 62  ? -14.902 -28.204 -7.854  1.00 23.73  ? ? ? ? ? ? 69  GLY U O   1 
+ATOM   495  N N   . ARG A 1 63  ? -12.954 -28.342 -8.951  1.00 25.54  ? ? ? ? ? ? 70  ARG U N   1 
+ATOM   496  C CA  . ARG A 1 63  ? -13.453 -27.663 -10.174 1.00 28.15  ? ? ? ? ? ? 70  ARG U CA  1 
+ATOM   497  C C   . ARG A 1 63  ? -13.744 -28.703 -11.266 1.00 29.82  ? ? ? ? ? ? 70  ARG U C   1 
+ATOM   498  O O   . ARG A 1 63  ? -12.839 -29.478 -11.631 1.00 30.54  ? ? ? ? ? ? 70  ARG U O   1 
+ATOM   499  C CB  . ARG A 1 63  ? -12.382 -26.680 -10.633 1.00 31.47  ? ? ? ? ? ? 70  ARG U CB  1 
+ATOM   500  C CG  . ARG A 1 63  ? -12.684 -25.862 -11.866 1.00 33.99  ? ? ? ? ? ? 70  ARG U CG  1 
+ATOM   501  C CD  . ARG A 1 63  ? -12.419 -24.410 -11.566 1.00 36.45  ? ? ? ? ? ? 70  ARG U CD  1 
+ATOM   502  N NE  . ARG A 1 63  ? -12.533 -23.569 -12.730 1.00 39.24  ? ? ? ? ? ? 70  ARG U NE  1 
+ATOM   503  C CZ  . ARG A 1 63  ? -13.021 -22.333 -12.740 1.00 40.19  ? ? ? ? ? ? 70  ARG U CZ  1 
+ATOM   504  N NH1 . ARG A 1 63  ? -13.512 -21.750 -11.642 1.00 40.50  ? ? ? ? ? ? 70  ARG U NH1 1 
+ATOM   505  N NH2 . ARG A 1 63  ? -13.051 -21.684 -13.881 1.00 40.00  ? ? ? ? ? ? 70  ARG U NH2 1 
+ATOM   506  N N   . SER A 1 64  ? -14.984 -28.746 -11.769 1.00 29.34  ? ? ? ? ? ? 71  SER U N   1 
+ATOM   507  C CA  . SER A 1 64  ? -15.365 -29.682 -12.842 1.00 28.23  ? ? ? ? ? ? 71  SER U CA  1 
+ATOM   508  C C   . SER A 1 64  ? -15.496 -28.967 -14.234 1.00 30.84  ? ? ? ? ? ? 71  SER U C   1 
+ATOM   509  O O   . SER A 1 64  ? -15.666 -29.632 -15.263 1.00 29.05  ? ? ? ? ? ? 71  SER U O   1 
+ATOM   510  C CB  . SER A 1 64  ? -16.704 -30.403 -12.559 1.00 29.02  ? ? ? ? ? ? 71  SER U CB  1 
+ATOM   511  O OG  . SER A 1 64  ? -16.767 -31.131 -11.322 1.00 29.80  ? ? ? ? ? ? 71  SER U OG  1 
+ATOM   512  N N   . ARG A 1 65  ? -15.483 -27.641 -14.266 1.00 28.77  ? ? ? ? ? ? 72  ARG U N   1 
+ATOM   513  C CA  . ARG A 1 65  ? -15.612 -26.925 -15.544 1.00 29.41  ? ? ? ? ? ? 72  ARG U CA  1 
+ATOM   514  C C   . ARG A 1 65  ? -14.456 -25.974 -15.752 1.00 29.62  ? ? ? ? ? ? 72  ARG U C   1 
+ATOM   515  O O   . ARG A 1 65  ? -13.892 -25.445 -14.784 1.00 28.27  ? ? ? ? ? ? 72  ARG U O   1 
+ATOM   516  C CB  . ARG A 1 65  ? -16.959 -26.204 -15.620 1.00 31.78  ? ? ? ? ? ? 72  ARG U CB  1 
+ATOM   517  C CG  . ARG A 1 65  ? -18.123 -27.190 -15.498 1.00 35.75  ? ? ? ? ? ? 72  ARG U CG  1 
+ATOM   518  C CD  . ARG A 1 65  ? -19.475 -26.644 -15.947 1.00 40.14  ? ? ? ? ? ? 72  ARG U CD  1 
+ATOM   519  N NE  . ARG A 1 65  ? -19.496 -26.170 -17.345 1.00 44.42  ? ? ? ? ? ? 72  ARG U NE  1 
+ATOM   520  C CZ  . ARG A 1 65  ? -19.708 -26.926 -18.439 1.00 46.48  ? ? ? ? ? ? 72  ARG U CZ  1 
+ATOM   521  N NH1 . ARG A 1 65  ? -19.898 -28.234 -18.373 1.00 45.52  ? ? ? ? ? ? 72  ARG U NH1 1 
+ATOM   522  N NH2 . ARG A 1 65  ? -19.729 -26.355 -19.630 1.00 48.76  ? ? ? ? ? ? 72  ARG U NH2 1 
+ATOM   523  N N   . LEU A 1 66  ? -14.110 -25.761 -17.025 1.00 28.01  ? ? ? ? ? ? 73  LEU U N   1 
+ATOM   524  C CA  . LEU A 1 66  ? -12.915 -25.032 -17.408 1.00 26.47  ? ? ? ? ? ? 73  LEU U CA  1 
+ATOM   525  C C   . LEU A 1 66  ? -13.021 -23.524 -17.204 1.00 30.40  ? ? ? ? ? ? 73  LEU U C   1 
+ATOM   526  O O   . LEU A 1 66  ? -12.101 -22.890 -16.661 1.00 26.60  ? ? ? ? ? ? 73  LEU U O   1 
+ATOM   527  C CB  . LEU A 1 66  ? -12.599 -25.332 -18.897 1.00 28.67  ? ? ? ? ? ? 73  LEU U CB  1 
+ATOM   528  C CG  . LEU A 1 66  ? -11.300 -24.799 -19.493 1.00 29.10  ? ? ? ? ? ? 73  LEU U CG  1 
+ATOM   529  C CD1 . LEU A 1 66  ? -10.127 -25.385 -18.730 1.00 30.80  ? ? ? ? ? ? 73  LEU U CD1 1 
+ATOM   530  C CD2 . LEU A 1 66  ? -11.199 -25.090 -21.002 1.00 29.19  ? ? ? ? ? ? 73  LEU U CD2 1 
+ATOM   531  N N   . ASN A 1 67  ? -14.128 -22.942 -17.667 1.00 32.31  ? ? ? ? ? ? 74  ASN U N   1 
+ATOM   532  C CA  . ASN A 1 67  ? -14.293 -21.502 -17.663 1.00 36.04  ? ? ? ? ? ? 74  ASN U CA  1 
+ATOM   533  C C   . ASN A 1 67  ? -15.437 -21.006 -16.814 1.00 40.05  ? ? ? ? ? ? 74  ASN U C   1 
+ATOM   534  O O   . ASN A 1 67  ? -15.785 -19.828 -16.883 1.00 40.93  ? ? ? ? ? ? 74  ASN U O   1 
+ATOM   535  C CB  . ASN A 1 67  ? -14.527 -21.021 -19.079 1.00 39.03  ? ? ? ? ? ? 74  ASN U CB  1 
+ATOM   536  C CG  . ASN A 1 67  ? -13.363 -21.323 -19.979 1.00 36.50  ? ? ? ? ? ? 74  ASN U CG  1 
+ATOM   537  O OD1 . ASN A 1 67  ? -12.267 -20.846 -19.752 1.00 39.34  ? ? ? ? ? ? 74  ASN U OD1 1 
+ATOM   538  N ND2 . ASN A 1 67  ? -13.597 -22.123 -21.000 1.00 42.48  ? ? ? ? ? ? 74  ASN U ND2 1 
+ATOM   539  N N   . SER A 1 68  ? -16.033 -21.889 -16.026 1.00 40.10  ? ? ? ? ? ? 75  SER U N   1 
+ATOM   540  C CA  . SER A 1 68  ? -17.071 -21.474 -15.081 1.00 44.32  ? ? ? ? ? ? 75  SER U CA  1 
+ATOM   541  C C   . SER A 1 68  ? -16.848 -22.173 -13.746 1.00 42.02  ? ? ? ? ? ? 75  SER U C   1 
+ATOM   542  O O   . SER A 1 68  ? -15.977 -23.035 -13.640 1.00 40.38  ? ? ? ? ? ? 75  SER U O   1 
+ATOM   543  C CB  . SER A 1 68  ? -18.458 -21.770 -15.643 1.00 45.48  ? ? ? ? ? ? 75  SER U CB  1 
+ATOM   544  O OG  . SER A 1 68  ? -18.550 -23.123 -16.023 1.00 54.53  ? ? ? ? ? ? 75  SER U OG  1 
+ATOM   545  N N   . ASN A 1 69  ? -17.639 -21.782 -12.749 1.00 42.37  ? ? ? ? ? ? 76  ASN U N   1 
+ATOM   546  C CA  . ASN A 1 69  ? -17.487 -22.216 -11.364 1.00 42.76  ? ? ? ? ? ? 76  ASN U CA  1 
+ATOM   547  C C   . ASN A 1 69  ? -18.261 -23.506 -11.064 1.00 38.96  ? ? ? ? ? ? 76  ASN U C   1 
+ATOM   548  O O   . ASN A 1 69  ? -19.272 -23.781 -11.679 1.00 38.66  ? ? ? ? ? ? 76  ASN U O   1 
+ATOM   549  C CB  . ASN A 1 69  ? -17.964 -21.105 -10.423 1.00 44.67  ? ? ? ? ? ? 76  ASN U CB  1 
+ATOM   550  C CG  . ASN A 1 69  ? -17.266 -19.775 -10.676 1.00 50.73  ? ? ? ? ? ? 76  ASN U CG  1 
+ATOM   551  O OD1 . ASN A 1 69  ? -17.903 -18.793 -11.070 1.00 50.09  ? ? ? ? ? ? 76  ASN U OD1 1 
+ATOM   552  N ND2 . ASN A 1 69  ? -15.950 -19.736 -10.457 1.00 52.98  ? ? ? ? ? ? 76  ASN U ND2 1 
+ATOM   553  N N   . THR A 1 70  ? -17.748 -24.289 -10.125 1.00 34.70  ? ? ? ? ? ? 77  THR U N   1 
+ATOM   554  C CA  . THR A 1 70  ? -18.346 -25.547 -9.682  1.00 35.39  ? ? ? ? ? ? 77  THR U CA  1 
+ATOM   555  C C   . THR A 1 70  ? -18.674 -25.326 -8.213  1.00 37.56  ? ? ? ? ? ? 77  THR U C   1 
+ATOM   556  O O   . THR A 1 70  ? -17.883 -24.731 -7.497  1.00 35.42  ? ? ? ? ? ? 77  THR U O   1 
+ATOM   557  C CB  . THR A 1 70  ? -17.319 -26.694 -9.769  1.00 35.44  ? ? ? ? ? ? 77  THR U CB  1 
+ATOM   558  O OG1 . THR A 1 70  ? -17.053 -26.998 -11.134 1.00 31.33  ? ? ? ? ? ? 77  THR U OG1 1 
+ATOM   559  C CG2 . THR A 1 70  ? -17.794 -27.976 -9.033  1.00 35.31  ? ? ? ? ? ? 77  THR U CG2 1 
+ATOM   560  N N   . GLN A 1 71  ? -19.826 -25.808 -7.779  1.00 41.15  ? ? ? ? ? ? 78  GLN U N   1 
+ATOM   561  C CA  . GLN A 1 71  ? -20.211 -25.733 -6.378  1.00 47.23  ? ? ? ? ? ? 78  GLN U CA  1 
+ATOM   562  C C   . GLN A 1 71  ? -19.244 -26.576 -5.551  1.00 46.61  ? ? ? ? ? ? 78  GLN U C   1 
+ATOM   563  O O   . GLN A 1 71  ? -19.097 -27.780 -5.780  1.00 46.23  ? ? ? ? ? ? 78  GLN U O   1 
+ATOM   564  C CB  . GLN A 1 71  ? -21.656 -26.213 -6.180  1.00 51.41  ? ? ? ? ? ? 78  GLN U CB  1 
+ATOM   565  C CG  . GLN A 1 71  ? -22.492 -25.267 -5.323  1.00 59.61  ? ? ? ? ? ? 78  GLN U CG  1 
+ATOM   566  C CD  . GLN A 1 71  ? -23.980 -25.284 -5.662  1.00 65.95  ? ? ? ? ? ? 78  GLN U CD  1 
+ATOM   567  O OE1 . GLN A 1 71  ? -24.454 -26.111 -6.458  1.00 68.11  ? ? ? ? ? ? 78  GLN U OE1 1 
+ATOM   568  N NE2 . GLN A 1 71  ? -24.732 -24.371 -5.041  1.00 67.46  ? ? ? ? ? ? 78  GLN U NE2 1 
+ATOM   569  N N   . GLY A 1 72  ? -18.580 -25.927 -4.601  1.00 45.85  ? ? ? ? ? ? 79  GLY U N   1 
+ATOM   570  C CA  . GLY A 1 72  ? -17.627 -26.602 -3.729  1.00 45.06  ? ? ? ? ? ? 79  GLY U CA  1 
+ATOM   571  C C   . GLY A 1 72  ? -16.171 -26.332 -4.057  1.00 44.29  ? ? ? ? ? ? 79  GLY U C   1 
+ATOM   572  O O   . GLY A 1 72  ? -15.280 -26.776 -3.320  1.00 45.23  ? ? ? ? ? ? 79  GLY U O   1 
+ATOM   573  N N   . GLU A 1 73  ? -15.905 -25.607 -5.144  1.00 42.83  ? ? ? ? ? ? 80  GLU U N   1 
+ATOM   574  C CA  . GLU A 1 73  ? -14.520 -25.355 -5.536  1.00 42.05  ? ? ? ? ? ? 80  GLU U CA  1 
+ATOM   575  C C   . GLU A 1 73  ? -13.826 -24.366 -4.596  1.00 39.70  ? ? ? ? ? ? 80  GLU U C   1 
+ATOM   576  O O   . GLU A 1 73  ? -14.473 -23.532 -3.951  1.00 38.30  ? ? ? ? ? ? 80  GLU U O   1 
+ATOM   577  C CB  . GLU A 1 73  ? -14.429 -24.851 -6.972  1.00 42.73  ? ? ? ? ? ? 80  GLU U CB  1 
+ATOM   578  C CG  . GLU A 1 73  ? -14.719 -23.368 -7.130  1.00 44.81  ? ? ? ? ? ? 80  GLU U CG  1 
+ATOM   579  C CD  . GLU A 1 73  ? -14.891 -22.962 -8.579  1.00 45.13  ? ? ? ? ? ? 80  GLU U CD  1 
+ATOM   580  O OE1 . GLU A 1 73  ? -14.881 -23.849 -9.465  1.00 44.54  ? ? ? ? ? ? 80  GLU U OE1 1 
+ATOM   581  O OE2 . GLU A 1 73  ? -15.030 -21.758 -8.827  1.00 46.26  ? ? ? ? ? ? 80  GLU U OE2 1 
+ATOM   582  N N   . MET A 1 74  ? -12.502 -24.460 -4.535  1.00 37.32  ? ? ? ? ? ? 81  MET U N   1 
+ATOM   583  C CA  . MET A 1 74  ? -11.713 -23.562 -3.704  1.00 39.70  ? ? ? ? ? ? 81  MET U CA  1 
+ATOM   584  C C   . MET A 1 74  ? -10.539 -23.034 -4.516  1.00 38.38  ? ? ? ? ? ? 81  MET U C   1 
+ATOM   585  O O   . MET A 1 74  ? -9.808  -23.803 -5.168  1.00 34.71  ? ? ? ? ? ? 81  MET U O   1 
+ATOM   586  C CB  . MET A 1 74  ? -11.218 -24.292 -2.442  1.00 40.37  ? ? ? ? ? ? 81  MET U CB  1 
+ATOM   587  C CG  . MET A 1 74  ? -12.330 -24.942 -1.613  1.00 45.21  ? ? ? ? ? ? 81  MET U CG  1 
+ATOM   588  S SD  . MET A 1 74  ? -13.266 -23.733 -0.647  1.00 56.80  ? ? ? ? ? ? 81  MET U SD  1 
+ATOM   589  C CE  . MET A 1 74  ? -14.974 -24.174 -0.966  1.00 55.38  ? ? ? ? ? ? 81  MET U CE  1 
+ATOM   590  N N   . LYS A 1 75  ? -10.368 -21.722 -4.447  1.00 37.43  ? ? ? ? ? ? 82  LYS U N   1 
+ATOM   591  C CA  . LYS A 1 75  ? -9.313  -20.997 -5.141  1.00 39.98  ? ? ? ? ? ? 82  LYS U CA  1 
+ATOM   592  C C   . LYS A 1 75  ? -8.073  -20.783 -4.245  1.00 38.66  ? ? ? ? ? ? 82  LYS U C   1 
+ATOM   593  O O   . LYS A 1 75  ? -8.209  -20.418 -3.073  1.00 37.54  ? ? ? ? ? ? 82  LYS U O   1 
+ATOM   594  C CB  . LYS A 1 75  ? -9.898  -19.655 -5.582  1.00 40.85  ? ? ? ? ? ? 82  LYS U CB  1 
+ATOM   595  C CG  . LYS A 1 75  ? -8.985  -18.760 -6.407  1.00 45.62  ? ? ? ? ? ? 82  LYS U CG  1 
+ATOM   596  C CD  . LYS A 1 75  ? -9.735  -17.485 -6.766  1.00 48.39  ? ? ? ? ? ? 82  LYS U CD  1 
+ATOM   597  C CE  . LYS A 1 75  ? -8.909  -16.541 -7.614  1.00 53.37  ? ? ? ? ? ? 82  LYS U CE  1 
+ATOM   598  N NZ  . LYS A 1 75  ? -9.736  -15.382 -8.047  1.00 55.13  ? ? ? ? ? ? 82  LYS U NZ  1 
+ATOM   599  N N   . PHE A 1 76  ? -6.875  -20.995 -4.802  1.00 38.87  ? ? ? ? ? ? 83  PHE U N   1 
+ATOM   600  C CA  . PHE A 1 76  ? -5.602  -20.795 -4.080  1.00 37.85  ? ? ? ? ? ? 83  PHE U CA  1 
+ATOM   601  C C   . PHE A 1 76  ? -4.561  -20.000 -4.855  1.00 40.60  ? ? ? ? ? ? 83  PHE U C   1 
+ATOM   602  O O   . PHE A 1 76  ? -4.489  -20.054 -6.095  1.00 38.88  ? ? ? ? ? ? 83  PHE U O   1 
+ATOM   603  C CB  . PHE A 1 76  ? -4.973  -22.129 -3.673  1.00 38.85  ? ? ? ? ? ? 83  PHE U CB  1 
+ATOM   604  C CG  . PHE A 1 76  ? -5.889  -23.012 -2.897  1.00 35.69  ? ? ? ? ? ? 83  PHE U CG  1 
+ATOM   605  C CD1 . PHE A 1 76  ? -6.779  -23.843 -3.551  1.00 34.64  ? ? ? ? ? ? 83  PHE U CD1 1 
+ATOM   606  C CD2 . PHE A 1 76  ? -5.875  -23.001 -1.508  1.00 37.02  ? ? ? ? ? ? 83  PHE U CD2 1 
+ATOM   607  C CE1 . PHE A 1 76  ? -7.641  -24.643 -2.838  1.00 34.68  ? ? ? ? ? ? 83  PHE U CE1 1 
+ATOM   608  C CE2 . PHE A 1 76  ? -6.732  -23.810 -0.786  1.00 34.84  ? ? ? ? ? ? 83  PHE U CE2 1 
+ATOM   609  C CZ  . PHE A 1 76  ? -7.615  -24.629 -1.446  1.00 35.55  ? ? ? ? ? ? 83  PHE U CZ  1 
+ATOM   610  N N   . GLU A 1 77  ? -3.777  -19.237 -4.095  1.00 41.36  ? ? ? ? ? ? 84  GLU U N   1 
+ATOM   611  C CA  . GLU A 1 77  ? -2.550  -18.639 -4.572  1.00 43.68  ? ? ? ? ? ? 84  GLU U CA  1 
+ATOM   612  C C   . GLU A 1 77  ? -1.439  -19.664 -4.450  1.00 40.61  ? ? ? ? ? ? 84  GLU U C   1 
+ATOM   613  O O   . GLU A 1 77  ? -1.586  -20.677 -3.778  1.00 40.90  ? ? ? ? ? ? 84  GLU U O   1 
+ATOM   614  C CB  . GLU A 1 77  ? -2.176  -17.402 -3.739  1.00 49.28  ? ? ? ? ? ? 84  GLU U CB  1 
+ATOM   615  C CG  . GLU A 1 77  ? -3.204  -16.285 -3.744  1.00 54.72  ? ? ? ? ? ? 84  GLU U CG  1 
+ATOM   616  C CD  . GLU A 1 77  ? -3.625  -15.891 -5.142  1.00 58.53  ? ? ? ? ? ? 84  GLU U CD  1 
+ATOM   617  O OE1 . GLU A 1 77  ? -2.749  -15.482 -5.935  1.00 69.44  ? ? ? ? ? ? 84  GLU U OE1 1 
+ATOM   618  O OE2 . GLU A 1 77  ? -4.828  -16.003 -5.459  1.00 65.58  ? ? ? ? ? ? 84  GLU U OE2 1 
+ATOM   619  N N   . VAL A 1 78  ? -0.324  -19.376 -5.100  1.00 42.51  ? ? ? ? ? ? 85  VAL U N   1 
+ATOM   620  C CA  . VAL A 1 78  ? 0.849   -20.224 -5.060  1.00 43.38  ? ? ? ? ? ? 85  VAL U CA  1 
+ATOM   621  C C   . VAL A 1 78  ? 1.891   -19.522 -4.206  1.00 44.18  ? ? ? ? ? ? 85  VAL U C   1 
+ATOM   622  O O   . VAL A 1 78  ? 2.357   -18.455 -4.576  1.00 42.59  ? ? ? ? ? ? 85  VAL U O   1 
+ATOM   623  C CB  . VAL A 1 78  ? 1.428   -20.436 -6.466  1.00 43.37  ? ? ? ? ? ? 85  VAL U CB  1 
+ATOM   624  C CG1 . VAL A 1 78  ? 2.592   -21.426 -6.414  1.00 42.22  ? ? ? ? ? ? 85  VAL U CG1 1 
+ATOM   625  C CG2 . VAL A 1 78  ? 0.332   -20.879 -7.429  1.00 43.12  ? ? ? ? ? ? 85  VAL U CG2 1 
+ATOM   626  N N   . GLU A 1 79  ? 2.233   -20.126 -3.070  1.00 48.61  ? ? ? ? ? ? 86  GLU U N   1 
+ATOM   627  C CA  . GLU A 1 79  ? 3.183   -19.557 -2.109  1.00 51.82  ? ? ? ? ? ? 86  GLU U CA  1 
+ATOM   628  C C   . GLU A 1 79  ? 4.624   -19.956 -2.418  1.00 51.48  ? ? ? ? ? ? 86  GLU U C   1 
+ATOM   629  O O   . GLU A 1 79  ? 5.558   -19.260 -2.025  1.00 52.60  ? ? ? ? ? ? 86  GLU U O   1 
+ATOM   630  C CB  . GLU A 1 79  ? 2.831   -20.032 -0.699  1.00 53.85  ? ? ? ? ? ? 86  GLU U CB  1 
+ATOM   631  C CG  . GLU A 1 79  ? 3.588   -19.326 0.420   1.00 58.64  ? ? ? ? ? ? 86  GLU U CG  1 
+ATOM   632  C CD  . GLU A 1 79  ? 3.504   -20.059 1.744   1.00 58.86  ? ? ? ? ? ? 86  GLU U CD  1 
+ATOM   633  O OE1 . GLU A 1 79  ? 2.562   -20.863 1.921   1.00 57.48  ? ? ? ? ? ? 86  GLU U OE1 1 
+ATOM   634  O OE2 . GLU A 1 79  ? 4.380   -19.824 2.610   1.00 63.66  ? ? ? ? ? ? 86  GLU U OE2 1 
+ATOM   635  N N   . ASN A 1 80  ? 4.800   -21.098 -3.075  1.00 50.59  ? ? ? ? ? ? 87  ASN U N   1 
+ATOM   636  C CA  . ASN A 1 80  ? 6.115   -21.537 -3.553  1.00 50.47  ? ? ? ? ? ? 87  ASN U CA  1 
+ATOM   637  C C   . ASN A 1 80  ? 5.950   -22.295 -4.861  1.00 45.00  ? ? ? ? ? ? 87  ASN U C   1 
+ATOM   638  O O   . ASN A 1 80  ? 5.136   -23.200 -4.947  1.00 46.12  ? ? ? ? ? ? 87  ASN U O   1 
+ATOM   639  C CB  . ASN A 1 80  ? 6.794   -22.470 -2.543  1.00 52.00  ? ? ? ? ? ? 87  ASN U CB  1 
+ATOM   640  C CG  . ASN A 1 80  ? 7.708   -21.755 -1.567  1.00 59.19  ? ? ? ? ? ? 87  ASN U CG  1 
+ATOM   641  O OD1 . ASN A 1 80  ? 8.273   -22.398 -0.682  1.00 65.99  ? ? ? ? ? ? 87  ASN U OD1 1 
+ATOM   642  N ND2 . ASN A 1 80  ? 7.872   -20.442 -1.716  1.00 60.48  ? ? ? ? ? ? 87  ASN U ND2 1 
+ATOM   643  N N   . LEU A 1 81  ? 6.740   -21.935 -5.861  1.00 42.52  ? ? ? ? ? ? 88  LEU U N   1 
+ATOM   644  C CA  . LEU A 1 81  ? 6.693   -22.592 -7.169  1.00 39.98  ? ? ? ? ? ? 88  LEU U CA  1 
+ATOM   645  C C   . LEU A 1 81  ? 8.051   -23.220 -7.415  1.00 38.22  ? ? ? ? ? ? 88  LEU U C   1 
+ATOM   646  O O   . LEU A 1 81  ? 9.015   -22.508 -7.657  1.00 38.94  ? ? ? ? ? ? 88  LEU U O   1 
+ATOM   647  C CB  . LEU A 1 81  ? 6.376   -21.584 -8.263  1.00 36.16  ? ? ? ? ? ? 88  LEU U CB  1 
+ATOM   648  C CG  . LEU A 1 81  ? 6.456   -22.079 -9.716  1.00 35.70  ? ? ? ? ? ? 88  LEU U CG  1 
+ATOM   649  C CD1 . LEU A 1 81  ? 5.442   -23.187 -9.952  1.00 34.55  ? ? ? ? ? ? 88  LEU U CD1 1 
+ATOM   650  C CD2 . LEU A 1 81  ? 6.230   -20.931 -10.685 1.00 34.60  ? ? ? ? ? ? 88  LEU U CD2 1 
+ATOM   651  N N   . ILE A 1 82  ? 8.114   -24.548 -7.330  1.00 36.57  ? ? ? ? ? ? 89  ILE U N   1 
+ATOM   652  C CA  . ILE A 1 82  ? 9.382   -25.277 -7.351  1.00 37.65  ? ? ? ? ? ? 89  ILE U CA  1 
+ATOM   653  C C   . ILE A 1 82  ? 9.522   -26.064 -8.642  1.00 37.70  ? ? ? ? ? ? 89  ILE U C   1 
+ATOM   654  O O   . ILE A 1 82  ? 8.869   -27.080 -8.839  1.00 36.15  ? ? ? ? ? ? 89  ILE U O   1 
+ATOM   655  C CB  . ILE A 1 82  ? 9.505   -26.224 -6.145  1.00 38.48  ? ? ? ? ? ? 89  ILE U CB  1 
+ATOM   656  C CG1 . ILE A 1 82  ? 9.243   -25.459 -4.847  1.00 38.36  ? ? ? ? ? ? 89  ILE U CG1 1 
+ATOM   657  C CG2 . ILE A 1 82  ? 10.892  -26.836 -6.080  1.00 39.35  ? ? ? ? ? ? 89  ILE U CG2 1 
+ATOM   658  C CD1 . ILE A 1 82  ? 9.068   -26.348 -3.633  1.00 38.52  ? ? ? ? ? ? 89  ILE U CD1 1 
+ATOM   659  N N   . LEU A 1 83  ? 10.361  -25.551 -9.530  1.00 38.01  ? ? ? ? ? ? 90  LEU U N   1 
+ATOM   660  C CA  . LEU A 1 83  ? 10.698  -26.233 -10.758 1.00 40.07  ? ? ? ? ? ? 90  LEU U CA  1 
+ATOM   661  C C   . LEU A 1 83  ? 11.911  -27.116 -10.515 1.00 44.64  ? ? ? ? ? ? 90  LEU U C   1 
+ATOM   662  O O   . LEU A 1 83  ? 12.760  -26.820 -9.663  1.00 50.26  ? ? ? ? ? ? 90  LEU U O   1 
+ATOM   663  C CB  . LEU A 1 83  ? 10.981  -25.216 -11.864 1.00 39.28  ? ? ? ? ? ? 90  LEU U CB  1 
+ATOM   664  C CG  . LEU A 1 83  ? 9.855   -24.193 -12.047 1.00 39.59  ? ? ? ? ? ? 90  LEU U CG  1 
+ATOM   665  C CD1 . LEU A 1 83  ? 10.140  -23.246 -13.198 1.00 40.61  ? ? ? ? ? ? 90  LEU U CD1 1 
+ATOM   666  C CD2 . LEU A 1 83  ? 8.518   -24.898 -12.245 1.00 37.47  ? ? ? ? ? ? 90  LEU U CD2 1 
+ATOM   667  N N   . HIS A 1 84  ? 11.983  -28.206 -11.259 1.00 45.47  ? ? ? ? ? ? 91  HIS U N   1 
+ATOM   668  C CA  . HIS A 1 84  ? 13.091  -29.131 -11.130 1.00 49.93  ? ? ? ? ? ? 91  HIS U CA  1 
+ATOM   669  C C   . HIS A 1 84  ? 14.339  -28.563 -11.814 1.00 49.84  ? ? ? ? ? ? 91  HIS U C   1 
+ATOM   670  O O   . HIS A 1 84  ? 14.305  -28.206 -13.000 1.00 47.04  ? ? ? ? ? ? 91  HIS U O   1 
+ATOM   671  C CB  . HIS A 1 84  ? 12.726  -30.492 -11.719 1.00 48.77  ? ? ? ? ? ? 91  HIS U CB  1 
+ATOM   672  C CG  . HIS A 1 84  ? 13.624  -31.591 -11.263 1.00 51.64  ? ? ? ? ? ? 91  HIS U CG  1 
+ATOM   673  N ND1 . HIS A 1 84  ? 14.882  -31.785 -11.789 1.00 52.64  ? ? ? ? ? ? 91  HIS U ND1 1 
+ATOM   674  C CD2 . HIS A 1 84  ? 13.457  -32.546 -10.320 1.00 53.16  ? ? ? ? ? ? 91  HIS U CD2 1 
+ATOM   675  C CE1 . HIS A 1 84  ? 15.451  -32.818 -11.193 1.00 53.82  ? ? ? ? ? ? 91  HIS U CE1 1 
+ATOM   676  N NE2 . HIS A 1 84  ? 14.608  -33.294 -10.294 1.00 55.25  ? ? ? ? ? ? 91  HIS U NE2 1 
+ATOM   677  N N   . LYS A 1 85  ? 15.439  -28.510 -11.064 1.00 53.53  ? ? ? ? ? ? 92  LYS U N   1 
+ATOM   678  C CA  . LYS A 1 85  ? 16.693  -27.916 -11.546 1.00 58.53  ? ? ? ? ? ? 92  LYS U CA  1 
+ATOM   679  C C   . LYS A 1 85  ? 17.238  -28.583 -12.814 1.00 56.64  ? ? ? ? ? ? 92  LYS U C   1 
+ATOM   680  O O   . LYS A 1 85  ? 17.720  -27.908 -13.732 1.00 55.78  ? ? ? ? ? ? 92  LYS U O   1 
+ATOM   681  C CB  . LYS A 1 85  ? 17.747  -27.932 -10.430 1.00 63.87  ? ? ? ? ? ? 92  LYS U CB  1 
+ATOM   682  C CG  . LYS A 1 85  ? 17.404  -27.012 -9.259  1.00 68.59  ? ? ? ? ? ? 92  LYS U CG  1 
+ATOM   683  C CD  . LYS A 1 85  ? 17.457  -25.532 -9.651  1.00 70.32  ? ? ? ? ? ? 92  LYS U CD  1 
+ATOM   684  C CE  . LYS A 1 85  ? 16.200  -24.759 -9.247  1.00 73.74  ? ? ? ? ? ? 92  LYS U CE  1 
+ATOM   685  N NZ  . LYS A 1 85  ? 16.128  -24.441 -7.791  1.00 73.77  ? ? ? ? ? ? 92  LYS U NZ  1 
+ATOM   686  N N   . ASP A 1 86  ? 17.154  -29.904 -12.870 1.00 52.99  ? ? ? ? ? ? 93  ASP U N   1 
+ATOM   687  C CA  . ASP A 1 86  ? 17.554  -30.642 -14.069 1.00 51.95  ? ? ? ? ? ? 93  ASP U CA  1 
+ATOM   688  C C   . ASP A 1 86  ? 16.474  -30.757 -15.142 1.00 48.81  ? ? ? ? ? ? 93  ASP U C   1 
+ATOM   689  O O   . ASP A 1 86  ? 16.613  -31.564 -16.063 1.00 46.91  ? ? ? ? ? ? 93  ASP U O   1 
+ATOM   690  C CB  . ASP A 1 86  ? 18.079  -32.035 -13.681 1.00 54.81  ? ? ? ? ? ? 93  ASP U CB  1 
+ATOM   691  C CG  . ASP A 1 86  ? 19.234  -31.964 -12.699 1.00 54.20  ? ? ? ? ? ? 93  ASP U CG  1 
+ATOM   692  O OD1 . ASP A 1 86  ? 20.106  -31.089 -12.878 1.00 58.10  ? ? ? ? ? ? 93  ASP U OD1 1 
+ATOM   693  O OD2 . ASP A 1 86  ? 19.272  -32.775 -11.753 1.00 57.48  ? ? ? ? ? ? 93  ASP U OD2 1 
+ATOM   694  N N   . TYR A 1 87  ? 15.412  -29.954 -15.064 1.00 47.88  ? ? ? ? ? ? 94  TYR U N   1 
+ATOM   695  C CA  . TYR A 1 87  ? 14.448  -29.910 -16.163 1.00 46.86  ? ? ? ? ? ? 94  TYR U CA  1 
+ATOM   696  C C   . TYR A 1 87  ? 15.151  -29.568 -17.468 1.00 48.08  ? ? ? ? ? ? 94  TYR U C   1 
+ATOM   697  O O   . TYR A 1 87  ? 16.112  -28.796 -17.487 1.00 50.34  ? ? ? ? ? ? 94  TYR U O   1 
+ATOM   698  C CB  . TYR A 1 87  ? 13.316  -28.892 -15.918 1.00 46.92  ? ? ? ? ? ? 94  TYR U CB  1 
+ATOM   699  C CG  . TYR A 1 87  ? 12.475  -28.568 -17.160 1.00 45.16  ? ? ? ? ? ? 94  TYR U CG  1 
+ATOM   700  C CD1 . TYR A 1 87  ? 11.441  -29.412 -17.579 1.00 43.57  ? ? ? ? ? ? 94  TYR U CD1 1 
+ATOM   701  C CD2 . TYR A 1 87  ? 12.706  -27.402 -17.901 1.00 43.89  ? ? ? ? ? ? 94  TYR U CD2 1 
+ATOM   702  C CE1 . TYR A 1 87  ? 10.679  -29.114 -18.700 1.00 41.92  ? ? ? ? ? ? 94  TYR U CE1 1 
+ATOM   703  C CE2 . TYR A 1 87  ? 11.955  -27.101 -19.029 1.00 43.74  ? ? ? ? ? ? 94  TYR U CE2 1 
+ATOM   704  C CZ  . TYR A 1 87  ? 10.940  -27.954 -19.422 1.00 43.80  ? ? ? ? ? ? 94  TYR U CZ  1 
+ATOM   705  O OH  . TYR A 1 87  ? 10.192  -27.640 -20.533 1.00 41.36  ? ? ? ? ? ? 94  TYR U OH  1 
+ATOM   706  N N   . SER A 1 88  ? 14.657  -30.136 -18.558 1.00 45.91  ? ? ? ? ? ? 95  SER U N   1 
+ATOM   707  C CA  . SER A 1 88  ? 15.128  -29.774 -19.868 1.00 49.16  ? ? ? ? ? ? 95  SER U CA  1 
+ATOM   708  C C   . SER A 1 88  ? 14.104  -30.162 -20.929 1.00 49.81  ? ? ? ? ? ? 95  SER U C   1 
+ATOM   709  O O   . SER A 1 88  ? 13.450  -31.203 -20.822 1.00 52.32  ? ? ? ? ? ? 95  SER U O   1 
+ATOM   710  C CB  . SER A 1 88  ? 16.492  -30.431 -20.128 1.00 49.65  ? ? ? ? ? ? 95  SER U CB  1 
+ATOM   711  O OG  . SER A 1 88  ? 16.605  -30.878 -21.476 1.00 49.70  ? ? ? ? ? ? 95  SER U OG  1 
+ATOM   712  N N   . ALA A 1 89  ? 13.977  -29.344 -21.954 1.00 51.29  ? ? ? ? ? ? 96  ALA U N   1 
+ATOM   713  C CA  . ALA A 1 89  ? 13.162  -29.685 -23.092 1.00 53.47  ? ? ? ? ? ? 96  ALA U CA  1 
+ATOM   714  C C   . ALA A 1 89  ? 14.043  -30.041 -24.304 1.00 59.81  ? ? ? ? ? ? 96  ALA U C   1 
+ATOM   715  O O   . ALA A 1 89  ? 14.726  -29.205 -24.849 1.00 63.47  ? ? ? ? ? ? 96  ALA U O   1 
+ATOM   716  C CB  . ALA A 1 89  ? 12.261  -28.521 -23.421 1.00 51.23  ? ? ? ? ? ? 96  ALA U CB  1 
+ATOM   717  N N   . ASP A 1 90  ? 13.967  -31.272 -24.750 1.00 62.35  ? ? ? ? ? ? 97  ASP U N   1 
+ATOM   718  C CA  . ASP A 1 90  ? 14.792  -31.776 -25.816 1.00 64.26  ? ? ? ? ? ? 97  ASP U CA  1 
+ATOM   719  C C   . ASP A 1 90  ? 14.113  -31.368 -27.064 1.00 62.67  ? ? ? ? ? ? 97  ASP U C   1 
+ATOM   720  O O   . ASP A 1 90  ? 13.238  -30.563 -27.031 1.00 63.69  ? ? ? ? ? ? 97  ASP U O   1 
+ATOM   721  C CB  . ASP A 1 90  ? 14.800  -33.280 -25.813 1.00 67.23  ? ? ? ? ? ? 97  ASP U CB  1 
+ATOM   722  C CG  . ASP A 1 90  ? 15.959  -33.857 -25.056 1.00 73.18  ? ? ? ? ? ? 97  ASP U CG  1 
+ATOM   723  O OD1 . ASP A 1 90  ? 16.492  -33.125 -24.206 1.00 75.86  ? ? ? ? ? ? 97  ASP U OD1 1 
+ATOM   724  O OD2 . ASP A 1 90  ? 16.318  -35.047 -25.304 1.00 72.97  ? ? ? ? ? ? 97  ASP U OD2 1 
+ATOM   725  N N   . THR A 1 91  A 14.577  -31.894 -28.181 1.00 65.59  ? ? ? ? ? ? 97  THR U N   1 
+ATOM   726  C CA  . THR A 1 91  A 13.938  -31.721 -29.457 1.00 68.22  ? ? ? ? ? ? 97  THR U CA  1 
+ATOM   727  C C   . THR A 1 91  A 12.538  -32.337 -29.570 1.00 66.38  ? ? ? ? ? ? 97  THR U C   1 
+ATOM   728  O O   . THR A 1 91  A 11.665  -31.741 -30.183 1.00 64.95  ? ? ? ? ? ? 97  THR U O   1 
+ATOM   729  C CB  . THR A 1 91  A 14.855  -32.232 -30.591 1.00 70.20  ? ? ? ? ? ? 97  THR U CB  1 
+ATOM   730  O OG1 . THR A 1 91  A 16.047  -32.792 -30.035 1.00 66.08  ? ? ? ? ? ? 97  THR U OG1 1 
+ATOM   731  C CG2 . THR A 1 91  A 15.238  -31.101 -31.496 1.00 70.18  ? ? ? ? ? ? 97  THR U CG2 1 
+ATOM   732  N N   . LEU A 1 92  B 12.341  -33.541 -29.022 1.00 60.49  ? ? ? ? ? ? 97  LEU U N   1 
+ATOM   733  C CA  . LEU A 1 92  B 11.023  -34.184 -28.990 1.00 59.93  ? ? ? ? ? ? 97  LEU U CA  1 
+ATOM   734  C C   . LEU A 1 92  B 10.319  -34.329 -27.628 1.00 57.24  ? ? ? ? ? ? 97  LEU U C   1 
+ATOM   735  O O   . LEU A 1 92  B 9.122   -34.345 -27.583 1.00 58.01  ? ? ? ? ? ? 97  LEU U O   1 
+ATOM   736  C CB  . LEU A 1 92  B 11.098  -35.577 -29.596 1.00 63.22  ? ? ? ? ? ? 97  LEU U CB  1 
+ATOM   737  C CG  . LEU A 1 92  B 11.110  -35.808 -31.098 1.00 64.15  ? ? ? ? ? ? 97  LEU U CG  1 
+ATOM   738  C CD1 . LEU A 1 92  B 12.069  -34.844 -31.749 1.00 64.12  ? ? ? ? ? ? 97  LEU U CD1 1 
+ATOM   739  C CD2 . LEU A 1 92  B 11.498  -37.230 -31.394 1.00 63.60  ? ? ? ? ? ? 97  LEU U CD2 1 
+ATOM   740  N N   . ALA A 1 93  ? 11.066  -34.430 -26.542 1.00 55.59  ? ? ? ? ? ? 98  ALA U N   1 
+ATOM   741  C CA  . ALA A 1 93  ? 10.526  -34.770 -25.235 1.00 54.33  ? ? ? ? ? ? 98  ALA U CA  1 
+ATOM   742  C C   . ALA A 1 93  ? 11.067  -33.878 -24.135 1.00 51.73  ? ? ? ? ? ? 98  ALA U C   1 
+ATOM   743  O O   . ALA A 1 93  ? 11.925  -33.087 -24.383 1.00 51.68  ? ? ? ? ? ? 98  ALA U O   1 
+ATOM   744  C CB  . ALA A 1 93  ? 10.810  -36.207 -24.908 1.00 54.68  ? ? ? ? ? ? 98  ALA U CB  1 
+ATOM   745  N N   . TYR A 1 94  ? 10.515  -33.989 -22.938 1.00 45.95  ? ? ? ? ? ? 99  TYR U N   1 
+ATOM   746  C CA  . TYR A 1 94  ? 10.949  -33.235 -21.790 1.00 43.29  ? ? ? ? ? ? 99  TYR U CA  1 
+ATOM   747  C C   . TYR A 1 94  ? 11.557  -34.180 -20.793 1.00 43.60  ? ? ? ? ? ? 99  TYR U C   1 
+ATOM   748  O O   . TYR A 1 94  ? 11.166  -35.298 -20.710 1.00 47.06  ? ? ? ? ? ? 99  TYR U O   1 
+ATOM   749  C CB  . TYR A 1 94  ? 9.773   -32.598 -21.077 1.00 42.19  ? ? ? ? ? ? 99  TYR U CB  1 
+ATOM   750  C CG  . TYR A 1 94  ? 8.923   -31.612 -21.801 1.00 39.46  ? ? ? ? ? ? 99  TYR U CG  1 
+ATOM   751  C CD1 . TYR A 1 94  ? 9.287   -30.286 -21.880 1.00 40.17  ? ? ? ? ? ? 99  TYR U CD1 1 
+ATOM   752  C CD2 . TYR A 1 94  ? 7.718   -31.977 -22.329 1.00 37.96  ? ? ? ? ? ? 99  TYR U CD2 1 
+ATOM   753  C CE1 . TYR A 1 94  ? 8.493   -29.374 -22.498 1.00 38.31  ? ? ? ? ? ? 99  TYR U CE1 1 
+ATOM   754  C CE2 . TYR A 1 94  ? 6.924   -31.061 -22.945 1.00 37.18  ? ? ? ? ? ? 99  TYR U CE2 1 
+ATOM   755  C CZ  . TYR A 1 94  ? 7.326   -29.763 -23.029 1.00 35.69  ? ? ? ? ? ? 99  TYR U CZ  1 
+ATOM   756  O OH  . TYR A 1 94  ? 6.547   -28.861 -23.635 1.00 35.58  ? ? ? ? ? ? 99  TYR U OH  1 
+ATOM   757  N N   . HIS A 1 95  ? 12.514  -33.720 -20.026 1.00 42.03  ? ? ? ? ? ? 100 HIS U N   1 
+ATOM   758  C CA  . HIS A 1 95  ? 13.197  -34.522 -19.021 1.00 44.27  ? ? ? ? ? ? 100 HIS U CA  1 
+ATOM   759  C C   . HIS A 1 95  ? 13.026  -33.868 -17.666 1.00 40.35  ? ? ? ? ? ? 100 HIS U C   1 
+ATOM   760  O O   . HIS A 1 95  ? 13.025  -32.641 -17.565 1.00 43.35  ? ? ? ? ? ? 100 HIS U O   1 
+ATOM   761  C CB  . HIS A 1 95  ? 14.690  -34.650 -19.374 1.00 45.01  ? ? ? ? ? ? 100 HIS U CB  1 
+ATOM   762  C CG  . HIS A 1 95  ? 14.941  -35.508 -20.570 1.00 46.12  ? ? ? ? ? ? 100 HIS U CG  1 
+ATOM   763  N ND1 . HIS A 1 95  ? 14.810  -35.042 -21.862 1.00 50.92  ? ? ? ? ? ? 100 HIS U ND1 1 
+ATOM   764  C CD2 . HIS A 1 95  ? 15.270  -36.817 -20.672 1.00 46.22  ? ? ? ? ? ? 100 HIS U CD2 1 
+ATOM   765  C CE1 . HIS A 1 95  ? 15.051  -36.028 -22.709 1.00 49.73  ? ? ? ? ? ? 100 HIS U CE1 1 
+ATOM   766  N NE2 . HIS A 1 95  ? 15.329  -37.116 -22.011 1.00 48.60  ? ? ? ? ? ? 100 HIS U NE2 1 
+ATOM   767  N N   . ASN A 1 96  ? 12.920  -34.690 -16.632 1.00 38.56  ? ? ? ? ? ? 101 ASN U N   1 
+ATOM   768  C CA  . ASN A 1 96  ? 12.687  -34.223 -15.257 1.00 41.04  ? ? ? ? ? ? 101 ASN U CA  1 
+ATOM   769  C C   . ASN A 1 96  ? 11.533  -33.227 -15.208 1.00 38.44  ? ? ? ? ? ? 101 ASN U C   1 
+ATOM   770  O O   . ASN A 1 96  ? 11.626  -32.158 -14.590 1.00 39.59  ? ? ? ? ? ? 101 ASN U O   1 
+ATOM   771  C CB  . ASN A 1 96  ? 13.956  -33.632 -14.653 1.00 42.57  ? ? ? ? ? ? 101 ASN U CB  1 
+ATOM   772  C CG  . ASN A 1 96  ? 15.093  -34.638 -14.616 1.00 46.09  ? ? ? ? ? ? 101 ASN U CG  1 
+ATOM   773  O OD1 . ASN A 1 96  ? 15.080  -35.593 -13.824 1.00 44.95  ? ? ? ? ? ? 101 ASN U OD1 1 
+ATOM   774  N ND2 . ASN A 1 96  ? 16.073  -34.442 -15.487 1.00 43.89  ? ? ? ? ? ? 101 ASN U ND2 1 
+ATOM   775  N N   . ASP A 1 97  ? 10.442  -33.624 -15.859 1.00 36.78  ? ? ? ? ? ? 102 ASP U N   1 
+ATOM   776  C CA  . ASP A 1 97  ? 9.273   -32.777 -16.036 1.00 35.83  ? ? ? ? ? ? 102 ASP U CA  1 
+ATOM   777  C C   . ASP A 1 97  ? 8.369   -32.879 -14.817 1.00 33.87  ? ? ? ? ? ? 102 ASP U C   1 
+ATOM   778  O O   . ASP A 1 97  ? 7.360   -33.588 -14.814 1.00 34.77  ? ? ? ? ? ? 102 ASP U O   1 
+ATOM   779  C CB  . ASP A 1 97  ? 8.551   -33.169 -17.314 1.00 33.31  ? ? ? ? ? ? 102 ASP U CB  1 
+ATOM   780  C CG  . ASP A 1 97  ? 7.626   -32.075 -17.818 1.00 31.82  ? ? ? ? ? ? 102 ASP U CG  1 
+ATOM   781  O OD1 . ASP A 1 97  ? 7.766   -30.907 -17.397 1.00 31.87  ? ? ? ? ? ? 102 ASP U OD1 1 
+ATOM   782  O OD2 . ASP A 1 97  ? 6.754   -32.382 -18.651 1.00 32.25  ? ? ? ? ? ? 102 ASP U OD2 1 
+ATOM   783  N N   . ILE A 1 98  ? 8.774   -32.198 -13.759 1.00 35.25  ? ? ? ? ? ? 103 ILE U N   1 
+ATOM   784  C CA  . ILE A 1 98  ? 8.109   -32.308 -12.468 1.00 35.32  ? ? ? ? ? ? 103 ILE U CA  1 
+ATOM   785  C C   . ILE A 1 98  ? 8.231   -30.995 -11.732 1.00 36.54  ? ? ? ? ? ? 103 ILE U C   1 
+ATOM   786  O O   . ILE A 1 98  ? 9.278   -30.333 -11.793 1.00 37.38  ? ? ? ? ? ? 103 ILE U O   1 
+ATOM   787  C CB  . ILE A 1 98  ? 8.696   -33.460 -11.623 1.00 38.65  ? ? ? ? ? ? 103 ILE U CB  1 
+ATOM   788  C CG1 . ILE A 1 98  ? 7.863   -33.665 -10.355 1.00 39.84  ? ? ? ? ? ? 103 ILE U CG1 1 
+ATOM   789  C CG2 . ILE A 1 98  ? 10.177  -33.221 -11.295 1.00 38.32  ? ? ? ? ? ? 103 ILE U CG2 1 
+ATOM   790  C CD1 . ILE A 1 98  ? 7.957   -35.071 -9.775  1.00 40.87  ? ? ? ? ? ? 103 ILE U CD1 1 
+ATOM   791  N N   . ALA A 1 99  ? 7.151   -30.617 -11.056 1.00 32.80  ? ? ? ? ? ? 104 ALA U N   1 
+ATOM   792  C CA  . ALA A 1 99  ? 7.053   -29.314 -10.418 1.00 34.70  ? ? ? ? ? ? 104 ALA U CA  1 
+ATOM   793  C C   . ALA A 1 99  ? 6.174   -29.404 -9.189  1.00 33.92  ? ? ? ? ? ? 104 ALA U C   1 
+ATOM   794  O O   . ALA A 1 99  ? 5.314   -30.289 -9.084  1.00 34.98  ? ? ? ? ? ? 104 ALA U O   1 
+ATOM   795  C CB  . ALA A 1 99  ? 6.504   -28.279 -11.398 1.00 33.23  ? ? ? ? ? ? 104 ALA U CB  1 
+ATOM   796  N N   . LEU A 1 100 ? 6.403   -28.500 -8.246  1.00 38.17  ? ? ? ? ? ? 105 LEU U N   1 
+ATOM   797  C CA  . LEU A 1 100 ? 5.616   -28.459 -7.025  1.00 37.80  ? ? ? ? ? ? 105 LEU U CA  1 
+ATOM   798  C C   . LEU A 1 100 ? 5.140   -27.052 -6.735  1.00 38.70  ? ? ? ? ? ? 105 LEU U C   1 
+ATOM   799  O O   . LEU A 1 100 ? 5.868   -26.086 -6.935  1.00 40.69  ? ? ? ? ? ? 105 LEU U O   1 
+ATOM   800  C CB  . LEU A 1 100 ? 6.439   -28.988 -5.849  1.00 42.51  ? ? ? ? ? ? 105 LEU U CB  1 
+ATOM   801  C CG  . LEU A 1 100 ? 6.599   -30.504 -5.721  1.00 41.01  ? ? ? ? ? ? 105 LEU U CG  1 
+ATOM   802  C CD1 . LEU A 1 100 ? 7.816   -30.848 -4.856  1.00 43.64  ? ? ? ? ? ? 105 LEU U CD1 1 
+ATOM   803  C CD2 . LEU A 1 100 ? 5.342   -31.102 -5.119  1.00 42.10  ? ? ? ? ? ? 105 LEU U CD2 1 
+ATOM   804  N N   . LEU A 1 101 ? 3.891   -26.954 -6.285  1.00 37.26  ? ? ? ? ? ? 106 LEU U N   1 
+ATOM   805  C CA  . LEU A 1 101 ? 3.260   -25.704 -5.925  1.00 37.27  ? ? ? ? ? ? 106 LEU U CA  1 
+ATOM   806  C C   . LEU A 1 101 ? 2.765   -25.848 -4.500  1.00 37.35  ? ? ? ? ? ? 106 LEU U C   1 
+ATOM   807  O O   . LEU A 1 101 ? 2.016   -26.787 -4.200  1.00 37.50  ? ? ? ? ? ? 106 LEU U O   1 
+ATOM   808  C CB  . LEU A 1 101 ? 2.055   -25.438 -6.825  1.00 39.35  ? ? ? ? ? ? 106 LEU U CB  1 
+ATOM   809  C CG  . LEU A 1 101 ? 2.334   -25.167 -8.303  1.00 40.80  ? ? ? ? ? ? 106 LEU U CG  1 
+ATOM   810  C CD1 . LEU A 1 101 ? 2.681   -26.449 -9.053  1.00 42.83  ? ? ? ? ? ? 106 LEU U CD1 1 
+ATOM   811  C CD2 . LEU A 1 101 ? 1.158   -24.446 -8.956  1.00 40.66  ? ? ? ? ? ? 106 LEU U CD2 1 
+ATOM   812  N N   . LYS A 1 102 ? 3.196   -24.954 -3.615  1.00 39.53  ? ? ? ? ? ? 107 LYS U N   1 
+ATOM   813  C CA  . LYS A 1 102 ? 2.574   -24.858 -2.298  1.00 39.22  ? ? ? ? ? ? 107 LYS U CA  1 
+ATOM   814  C C   . LYS A 1 102 ? 1.446   -23.865 -2.381  1.00 34.16  ? ? ? ? ? ? 107 LYS U C   1 
+ATOM   815  O O   . LYS A 1 102 ? 1.655   -22.710 -2.689  1.00 36.60  ? ? ? ? ? ? 107 LYS U O   1 
+ATOM   816  C CB  . LYS A 1 102 ? 3.547   -24.455 -1.173  1.00 43.78  ? ? ? ? ? ? 107 LYS U CB  1 
+ATOM   817  C CG  . LYS A 1 102 ? 2.807   -24.134 0.133   1.00 49.60  ? ? ? ? ? ? 107 LYS U CG  1 
+ATOM   818  C CD  . LYS A 1 102 ? 3.360   -24.814 1.382   1.00 55.05  ? ? ? ? ? ? 107 LYS U CD  1 
+ATOM   819  C CE  . LYS A 1 102 ? 4.298   -23.917 2.166   1.00 58.30  ? ? ? ? ? ? 107 LYS U CE  1 
+ATOM   820  N NZ  . LYS A 1 102 ? 5.565   -23.677 1.415   1.00 61.03  ? ? ? ? ? ? 107 LYS U NZ  1 
+ATOM   821  N N   . ILE A 1 103 ? 0.257   -24.291 -2.044  1.00 34.15  ? ? ? ? ? ? 108 ILE U N   1 
+ATOM   822  C CA  . ILE A 1 103 ? -0.923  -23.490 -2.220  1.00 33.50  ? ? ? ? ? ? 108 ILE U CA  1 
+ATOM   823  C C   . ILE A 1 103 ? -1.353  -22.832 -0.927  1.00 38.14  ? ? ? ? ? ? 108 ILE U C   1 
+ATOM   824  O O   . ILE A 1 103 ? -1.304  -23.437 0.112   1.00 32.11  ? ? ? ? ? ? 108 ILE U O   1 
+ATOM   825  C CB  . ILE A 1 103 ? -2.077  -24.346 -2.746  1.00 34.30  ? ? ? ? ? ? 108 ILE U CB  1 
+ATOM   826  C CG1 . ILE A 1 103 ? -2.150  -25.657 -1.997  1.00 33.11  ? ? ? ? ? ? 108 ILE U CG1 1 
+ATOM   827  C CG2 . ILE A 1 103 ? -1.862  -24.655 -4.202  1.00 34.18  ? ? ? ? ? ? 108 ILE U CG2 1 
+ATOM   828  C CD1 . ILE A 1 103 ? -3.354  -26.472 -2.330  1.00 32.69  ? ? ? ? ? ? 108 ILE U CD1 1 
+ATOM   829  N N   . ARG A 1 104 ? -1.773  -21.583 -1.028  1.00 39.72  ? ? ? ? ? ? 109 ARG U N   1 
+ATOM   830  C CA  . ARG A 1 104 ? -2.349  -20.873 0.099   1.00 45.65  ? ? ? ? ? ? 109 ARG U CA  1 
+ATOM   831  C C   . ARG A 1 104 ? -3.528  -20.074 -0.321  1.00 44.91  ? ? ? ? ? ? 109 ARG U C   1 
+ATOM   832  O O   . ARG A 1 104 ? -3.409  -19.369 -1.338  1.00 44.66  ? ? ? ? ? ? 109 ARG U O   1 
+ATOM   833  C CB  . ARG A 1 104 ? -1.339  -19.967 0.773   1.00 52.08  ? ? ? ? ? ? 109 ARG U CB  1 
+ATOM   834  C CG  . ARG A 1 104 ? -1.688  -19.628 2.208   1.00 57.31  ? ? ? ? ? ? 109 ARG U CG  1 
+ATOM   835  C CD  . ARG A 1 104 ? -0.587  -18.799 2.848   1.00 63.92  ? ? ? ? ? ? 109 ARG U CD  1 
+ATOM   836  N NE  . ARG A 1 104 ? -0.500  -17.474 2.254   1.00 69.45  ? ? ? ? ? ? 109 ARG U NE  1 
+ATOM   837  C CZ  . ARG A 1 104 ? -0.767  -16.350 2.907   1.00 72.43  ? ? ? ? ? ? 109 ARG U CZ  1 
+ATOM   838  N NH1 . ARG A 1 104 ? -0.671  -15.200 2.289   1.00 73.28  ? ? ? ? ? ? 109 ARG U NH1 1 
+ATOM   839  N NH2 . ARG A 1 104 ? -1.127  -16.376 4.179   1.00 75.36  ? ? ? ? ? ? 109 ARG U NH2 1 
+ATOM   840  N N   . SER A 1 105 ? -4.600  -20.131 0.398   1.00 44.19  ? ? ? ? ? ? 110 SER U N   1 
+ATOM   841  C CA  . SER A 1 105 ? -5.735  -19.279 0.098   1.00 45.45  ? ? ? ? ? ? 110 SER U CA  1 
+ATOM   842  C C   . SER A 1 105 ? -5.322  -17.893 0.624   1.00 46.32  ? ? ? ? ? ? 110 SER U C   1 
+ATOM   843  O O   . SER A 1 105 ? -4.457  -17.654 1.261   1.00 44.74  ? ? ? ? ? ? 110 SER U O   1 
+ATOM   844  C CB  . SER A 1 105 ? -6.987  -19.805 0.685   1.00 46.82  ? ? ? ? ? ? 110 SER U CB  1 
+ATOM   845  O OG  . SER A 1 105 ? -7.254  -19.269 1.959   1.00 54.54  ? ? ? ? ? ? 110 SER U OG  1 
+ATOM   846  N N   . LYS A 1 106 A -6.105  -16.908 0.066   1.00 89.78  ? ? ? ? ? ? 110 LYS U N   1 
+ATOM   847  C CA  . LYS A 1 106 A -5.841  -15.529 0.418   1.00 92.42  ? ? ? ? ? ? 110 LYS U CA  1 
+ATOM   848  C C   . LYS A 1 106 A -6.042  -15.382 1.927   1.00 89.21  ? ? ? ? ? ? 110 LYS U C   1 
+ATOM   849  O O   . LYS A 1 106 A -5.626  -14.419 2.514   1.00 81.99  ? ? ? ? ? ? 110 LYS U O   1 
+ATOM   850  C CB  . LYS A 1 106 A -6.710  -14.578 -0.390  1.00 97.09  ? ? ? ? ? ? 110 LYS U CB  1 
+ATOM   851  C CG  . LYS A 1 106 A -6.506  -13.120 -0.036  1.00 102.34 ? ? ? ? ? ? 110 LYS U CG  1 
+ATOM   852  C CD  . LYS A 1 106 A -7.568  -12.636 0.947   1.00 105.75 ? ? ? ? ? ? 110 LYS U CD  1 
+ATOM   853  C CE  . LYS A 1 106 A -7.026  -11.669 1.992   1.00 107.37 ? ? ? ? ? ? 110 LYS U CE  1 
+ATOM   854  N NZ  . LYS A 1 106 A -8.077  -11.132 2.901   1.00 107.46 ? ? ? ? ? ? 110 LYS U NZ  1 
+ATOM   855  N N   . GLU A 1 107 B -6.722  -16.350 2.525   1.00 76.77  ? ? ? ? ? ? 110 GLU U N   1 
+ATOM   856  C CA  . GLU A 1 107 B -7.006  -16.386 3.948   1.00 75.88  ? ? ? ? ? ? 110 GLU U CA  1 
+ATOM   857  C C   . GLU A 1 107 B -6.050  -17.310 4.703   1.00 71.68  ? ? ? ? ? ? 110 GLU U C   1 
+ATOM   858  O O   . GLU A 1 107 B -6.278  -17.641 5.838   1.00 63.34  ? ? ? ? ? ? 110 GLU U O   1 
+ATOM   859  C CB  . GLU A 1 107 B -8.446  -16.812 4.174   1.00 78.12  ? ? ? ? ? ? 110 GLU U CB  1 
+ATOM   860  C CG  . GLU A 1 107 B -9.484  -15.827 3.669   1.00 82.45  ? ? ? ? ? ? 110 GLU U CG  1 
+ATOM   861  C CD  . GLU A 1 107 B -9.214  -15.301 2.265   1.00 88.20  ? ? ? ? ? ? 110 GLU U CD  1 
+ATOM   862  O OE1 . GLU A 1 107 B -8.621  -16.027 1.447   1.00 92.57  ? ? ? ? ? ? 110 GLU U OE1 1 
+ATOM   863  O OE2 . GLU A 1 107 B -9.558  -14.148 1.973   1.00 89.97  ? ? ? ? ? ? 110 GLU U OE2 1 
+ATOM   864  N N   . GLY A 1 108 C -5.005  -17.756 4.030   1.00 54.37  ? ? ? ? ? ? 110 GLY U N   1 
+ATOM   865  C CA  . GLY A 1 108 C -3.915  -18.483 4.640   1.00 52.53  ? ? ? ? ? ? 110 GLY U CA  1 
+ATOM   866  C C   . GLY A 1 108 C -4.029  -19.969 4.824   1.00 51.96  ? ? ? ? ? ? 110 GLY U C   1 
+ATOM   867  O O   . GLY A 1 108 C -3.116  -20.556 5.349   1.00 58.53  ? ? ? ? ? ? 110 GLY U O   1 
+ATOM   868  N N   . ARG A 1 109 D -5.128  -20.575 4.384   1.00 53.73  ? ? ? ? ? ? 110 ARG U N   1 
+ATOM   869  C CA  . ARG A 1 109 D -5.346  -22.017 4.488   1.00 53.51  ? ? ? ? ? ? 110 ARG U CA  1 
+ATOM   870  C C   . ARG A 1 109 D -4.819  -22.900 3.358   1.00 50.04  ? ? ? ? ? ? 110 ARG U C   1 
+ATOM   871  O O   . ARG A 1 109 D -4.765  -22.484 2.237   1.00 50.74  ? ? ? ? ? ? 110 ARG U O   1 
+ATOM   872  C CB  . ARG A 1 109 D -6.823  -22.302 4.642   1.00 54.66  ? ? ? ? ? ? 110 ARG U CB  1 
+ATOM   873  C CG  . ARG A 1 109 D -7.459  -21.576 5.807   1.00 59.75  ? ? ? ? ? ? 110 ARG U CG  1 
+ATOM   874  C CD  . ARG A 1 109 D -8.787  -22.170 6.189   1.00 61.17  ? ? ? ? ? ? 110 ARG U CD  1 
+ATOM   875  N NE  . ARG A 1 109 D -9.724  -21.103 6.434   1.00 68.01  ? ? ? ? ? ? 110 ARG U NE  1 
+ATOM   876  C CZ  . ARG A 1 109 D -10.771 -21.206 7.226   1.00 74.09  ? ? ? ? ? ? 110 ARG U CZ  1 
+ATOM   877  N NH1 . ARG A 1 109 D -11.018 -22.348 7.852   1.00 73.66  ? ? ? ? ? ? 110 ARG U NH1 1 
+ATOM   878  N NH2 . ARG A 1 109 D -11.571 -20.166 7.393   1.00 75.12  ? ? ? ? ? ? 110 ARG U NH2 1 
+ATOM   879  N N   . CYS A 1 110 ? -4.439  -24.120 3.702   1.00 48.67  ? ? ? ? ? ? 111 CYS U N   1 
+ATOM   880  C CA  . CYS A 1 110 ? -4.184  -25.211 2.778   1.00 49.10  ? ? ? ? ? ? 111 CYS U CA  1 
+ATOM   881  C C   . CYS A 1 110 ? -5.576  -25.824 2.413   1.00 48.69  ? ? ? ? ? ? 111 CYS U C   1 
+ATOM   882  O O   . CYS A 1 110 ? -6.564  -25.384 2.915   1.00 49.90  ? ? ? ? ? ? 111 CYS U O   1 
+ATOM   883  C CB  . CYS A 1 110 ? -3.221  -26.246 3.399   1.00 45.21  ? ? ? ? ? ? 111 CYS U CB  1 
+ATOM   884  S SG  . CYS A 1 110 ? -1.460  -25.845 3.581   1.00 49.12  ? ? ? ? ? ? 111 CYS U SG  1 
+ATOM   885  N N   . ALA A 1 111 ? -5.648  -26.817 1.546   1.00 47.44  ? ? ? ? ? ? 112 ALA U N   1 
+ATOM   886  C CA  . ALA A 1 111 ? -6.923  -27.402 1.122   1.00 46.29  ? ? ? ? ? ? 112 ALA U CA  1 
+ATOM   887  C C   . ALA A 1 111 ? -7.590  -28.190 2.251   1.00 48.29  ? ? ? ? ? ? 112 ALA U C   1 
+ATOM   888  O O   . ALA A 1 111 ? -6.906  -28.792 3.087   1.00 50.61  ? ? ? ? ? ? 112 ALA U O   1 
+ATOM   889  C CB  . ALA A 1 111 ? -6.720  -28.294 -0.087  1.00 43.90  ? ? ? ? ? ? 112 ALA U CB  1 
+ATOM   890  N N   . GLN A 1 112 ? -8.922  -28.186 2.256   1.00 47.86  ? ? ? ? ? ? 113 GLN U N   1 
+ATOM   891  C CA  . GLN A 1 112 ? -9.702  -28.987 3.184   1.00 49.20  ? ? ? ? ? ? 113 GLN U CA  1 
+ATOM   892  C C   . GLN A 1 112 ? -10.562 -30.035 2.470   1.00 46.19  ? ? ? ? ? ? 113 GLN U C   1 
+ATOM   893  O O   . GLN A 1 112 ? -11.475 -29.693 1.721   1.00 47.37  ? ? ? ? ? ? 113 GLN U O   1 
+ATOM   894  C CB  . GLN A 1 112 ? -10.570 -28.076 4.032   1.00 52.82  ? ? ? ? ? ? 113 GLN U CB  1 
+ATOM   895  C CG  . GLN A 1 112 ? -9.764  -27.359 5.105   1.00 55.88  ? ? ? ? ? ? 113 GLN U CG  1 
+ATOM   896  C CD  . GLN A 1 112 ? -10.651 -26.663 6.105   1.00 59.56  ? ? ? ? ? ? 113 GLN U CD  1 
+ATOM   897  O OE1 . GLN A 1 112 ? -10.892 -27.177 7.198   1.00 68.83  ? ? ? ? ? ? 113 GLN U OE1 1 
+ATOM   898  N NE2 . GLN A 1 112 ? -11.164 -25.499 5.728   1.00 61.37  ? ? ? ? ? ? 113 GLN U NE2 1 
+ATOM   899  N N   . PRO A 1 113 ? -10.284 -31.323 2.716   1.00 43.15  ? ? ? ? ? ? 114 PRO U N   1 
+ATOM   900  C CA  . PRO A 1 113 ? -11.062 -32.379 2.080   1.00 42.41  ? ? ? ? ? ? 114 PRO U CA  1 
+ATOM   901  C C   . PRO A 1 113 ? -12.573 -32.171 2.179   1.00 42.46  ? ? ? ? ? ? 114 PRO U C   1 
+ATOM   902  O O   . PRO A 1 113 ? -13.075 -31.708 3.203   1.00 42.37  ? ? ? ? ? ? 114 PRO U O   1 
+ATOM   903  C CB  . PRO A 1 113 ? -10.657 -33.624 2.849   1.00 44.15  ? ? ? ? ? ? 114 PRO U CB  1 
+ATOM   904  C CG  . PRO A 1 113 ? -9.266  -33.333 3.295   1.00 46.46  ? ? ? ? ? ? 114 PRO U CG  1 
+ATOM   905  C CD  . PRO A 1 113 ? -9.217  -31.863 3.571   1.00 44.30  ? ? ? ? ? ? 114 PRO U CD  1 
+ATOM   906  N N   . SER A 1 114 ? -13.267 -32.507 1.100   1.00 37.53  ? ? ? ? ? ? 115 SER U N   1 
+ATOM   907  C CA  . SER A 1 114 ? -14.705 -32.430 1.012   1.00 35.28  ? ? ? ? ? ? 115 SER U CA  1 
+ATOM   908  C C   . SER A 1 114 ? -15.146 -33.489 0.014   1.00 34.98  ? ? ? ? ? ? 115 SER U C   1 
+ATOM   909  O O   . SER A 1 114 ? -14.323 -34.224 -0.533  1.00 37.22  ? ? ? ? ? ? 115 SER U O   1 
+ATOM   910  C CB  . SER A 1 114 ? -15.131 -31.052 0.519   1.00 38.27  ? ? ? ? ? ? 115 SER U CB  1 
+ATOM   911  O OG  . SER A 1 114 ? -14.835 -30.871 -0.876  1.00 36.14  ? ? ? ? ? ? 115 SER U OG  1 
+ATOM   912  N N   . ARG A 1 115 ? -16.443 -33.545 -0.236  1.00 36.90  ? ? ? ? ? ? 116 ARG U N   1 
+ATOM   913  C CA  . ARG A 1 115 ? -17.000 -34.380 -1.282  1.00 37.14  ? ? ? ? ? ? 116 ARG U CA  1 
+ATOM   914  C C   . ARG A 1 115 ? -16.252 -34.194 -2.618  1.00 34.90  ? ? ? ? ? ? 116 ARG U C   1 
+ATOM   915  O O   . ARG A 1 115 ? -16.205 -35.108 -3.429  1.00 36.84  ? ? ? ? ? ? 116 ARG U O   1 
+ATOM   916  C CB  . ARG A 1 115 ? -18.471 -34.046 -1.474  1.00 40.66  ? ? ? ? ? ? 116 ARG U CB  1 
+ATOM   917  C CG  . ARG A 1 115 ? -19.251 -35.089 -2.251  1.00 44.49  ? ? ? ? ? ? 116 ARG U CG  1 
+ATOM   918  C CD  . ARG A 1 115 ? -20.644 -34.609 -2.585  1.00 45.81  ? ? ? ? ? ? 116 ARG U CD  1 
+ATOM   919  N NE  . ARG A 1 115 ? -20.641 -33.617 -3.664  1.00 49.41  ? ? ? ? ? ? 116 ARG U NE  1 
+ATOM   920  C CZ  . ARG A 1 115 ? -20.834 -32.307 -3.505  1.00 52.03  ? ? ? ? ? ? 116 ARG U CZ  1 
+ATOM   921  N NH1 . ARG A 1 115 ? -21.061 -31.773 -2.296  1.00 52.16  ? ? ? ? ? ? 116 ARG U NH1 1 
+ATOM   922  N NH2 . ARG A 1 115 ? -20.805 -31.516 -4.567  1.00 50.43  ? ? ? ? ? ? 116 ARG U NH2 1 
+ATOM   923  N N   . THR A 1 116 ? -15.681 -33.014 -2.842  1.00 32.67  ? ? ? ? ? ? 117 THR U N   1 
+ATOM   924  C CA  . THR A 1 116 ? -15.079 -32.699 -4.145  1.00 33.76  ? ? ? ? ? ? 117 THR U CA  1 
+ATOM   925  C C   . THR A 1 116 ? -13.568 -32.491 -4.093  1.00 33.28  ? ? ? ? ? ? 117 THR U C   1 
+ATOM   926  O O   . THR A 1 116 ? -12.976 -32.239 -5.119  1.00 30.65  ? ? ? ? ? ? 117 THR U O   1 
+ATOM   927  C CB  . THR A 1 116 ? -15.712 -31.449 -4.778  1.00 32.59  ? ? ? ? ? ? 117 THR U CB  1 
+ATOM   928  O OG1 . THR A 1 116 ? -15.670 -30.358 -3.857  1.00 32.18  ? ? ? ? ? ? 117 THR U OG1 1 
+ATOM   929  C CG2 . THR A 1 116 ? -17.100 -31.707 -5.147  1.00 34.11  ? ? ? ? ? ? 117 THR U CG2 1 
+ATOM   930  N N   . ILE A 1 117 ? -12.948 -32.598 -2.908  1.00 34.66  ? ? ? ? ? ? 118 ILE U N   1 
+ATOM   931  C CA  . ILE A 1 117 ? -11.502 -32.387 -2.764  1.00 33.34  ? ? ? ? ? ? 118 ILE U CA  1 
+ATOM   932  C C   . ILE A 1 117 ? -10.874 -33.441 -1.846  1.00 36.94  ? ? ? ? ? ? 118 ILE U C   1 
+ATOM   933  O O   . ILE A 1 117 ? -11.227 -33.553 -0.662  1.00 35.59  ? ? ? ? ? ? 118 ILE U O   1 
+ATOM   934  C CB  . ILE A 1 117 ? -11.206 -30.985 -2.221  1.00 33.91  ? ? ? ? ? ? 118 ILE U CB  1 
+ATOM   935  C CG1 . ILE A 1 117 ? -11.819 -29.898 -3.137  1.00 34.55  ? ? ? ? ? ? 118 ILE U CG1 1 
+ATOM   936  C CG2 . ILE A 1 117 ? -9.707  -30.795 -2.074  1.00 33.91  ? ? ? ? ? ? 118 ILE U CG2 1 
+ATOM   937  C CD1 . ILE A 1 117 ? -11.694 -28.499 -2.577  1.00 35.64  ? ? ? ? ? ? 118 ILE U CD1 1 
+ATOM   938  N N   . GLN A 1 118 ? -9.957  -34.231 -2.393  1.00 36.92  ? ? ? ? ? ? 119 GLN U N   1 
+ATOM   939  C CA  . GLN A 1 118 ? -9.206  -35.220 -1.597  1.00 35.98  ? ? ? ? ? ? 119 GLN U CA  1 
+ATOM   940  C C   . GLN A 1 118 ? -7.772  -35.274 -2.117  1.00 36.81  ? ? ? ? ? ? 119 GLN U C   1 
+ATOM   941  O O   . GLN A 1 118 ? -7.523  -34.927 -3.287  1.00 35.36  ? ? ? ? ? ? 119 GLN U O   1 
+ATOM   942  C CB  . GLN A 1 118 ? -9.823  -36.604 -1.752  1.00 38.21  ? ? ? ? ? ? 119 GLN U CB  1 
+ATOM   943  C CG  . GLN A 1 118 ? -11.275 -36.726 -1.306  1.00 41.27  ? ? ? ? ? ? 119 GLN U CG  1 
+ATOM   944  C CD  . GLN A 1 118 ? -11.405 -36.753 0.209   1.00 43.52  ? ? ? ? ? ? 119 GLN U CD  1 
+ATOM   945  O OE1 . GLN A 1 118 ? -10.549 -37.306 0.890   1.00 47.59  ? ? ? ? ? ? 119 GLN U OE1 1 
+ATOM   946  N NE2 . GLN A 1 118 ? -12.463 -36.147 0.739   1.00 42.20  ? ? ? ? ? ? 119 GLN U NE2 1 
+ATOM   947  N N   . THR A 1 119 ? -6.845  -35.759 -1.286  1.00 32.40  ? ? ? ? ? ? 120 THR U N   1 
+ATOM   948  C CA  . THR A 1 119 ? -5.441  -35.930 -1.689  1.00 31.34  ? ? ? ? ? ? 120 THR U CA  1 
+ATOM   949  C C   . THR A 1 119 ? -5.283  -37.273 -2.365  1.00 31.53  ? ? ? ? ? ? 120 THR U C   1 
+ATOM   950  O O   . THR A 1 119 ? -6.145  -38.111 -2.235  1.00 28.81  ? ? ? ? ? ? 120 THR U O   1 
+ATOM   951  C CB  . THR A 1 119 ? -4.483  -35.857 -0.472  1.00 33.91  ? ? ? ? ? ? 120 THR U CB  1 
+ATOM   952  O OG1 . THR A 1 119 ? -4.841  -36.860 0.491   1.00 32.72  ? ? ? ? ? ? 120 THR U OG1 1 
+ATOM   953  C CG2 . THR A 1 119 ? -4.560  -34.515 0.178   1.00 34.42  ? ? ? ? ? ? 120 THR U CG2 1 
+ATOM   954  N N   . ILE A 1 120 ? -4.210  -37.441 -3.146  1.00 33.28  ? ? ? ? ? ? 121 ILE U N   1 
+ATOM   955  C CA  . ILE A 1 120 ? -3.874  -38.703 -3.793  1.00 34.08  ? ? ? ? ? ? 121 ILE U CA  1 
+ATOM   956  C C   . ILE A 1 120 ? -2.496  -39.127 -3.254  1.00 36.42  ? ? ? ? ? ? 121 ILE U C   1 
+ATOM   957  O O   . ILE A 1 120 ? -1.558  -38.320 -3.208  1.00 33.43  ? ? ? ? ? ? 121 ILE U O   1 
+ATOM   958  C CB  . ILE A 1 120 ? -3.890  -38.594 -5.358  1.00 34.18  ? ? ? ? ? ? 121 ILE U CB  1 
+ATOM   959  C CG1 . ILE A 1 120 ? -3.658  -39.941 -6.037  1.00 36.25  ? ? ? ? ? ? 121 ILE U CG1 1 
+ATOM   960  C CG2 . ILE A 1 120 ? -2.861  -37.605 -5.887  1.00 34.96  ? ? ? ? ? ? 121 ILE U CG2 1 
+ATOM   961  C CD1 . ILE A 1 120 ? -4.834  -40.896 -5.990  1.00 37.76  ? ? ? ? ? ? 121 ILE U CD1 1 
+ATOM   962  N N   . ALA A 1 121 ? -2.391  -40.383 -2.831  1.00 38.89  ? ? ? ? ? ? 122 ALA U N   1 
+ATOM   963  C CA  . ALA A 1 121 ? -1.148  -40.898 -2.272  1.00 38.91  ? ? ? ? ? ? 122 ALA U CA  1 
+ATOM   964  C C   . ALA A 1 121 ? -0.021  -40.991 -3.319  1.00 40.95  ? ? ? ? ? ? 122 ALA U C   1 
+ATOM   965  O O   . ALA A 1 121 ? -0.244  -41.316 -4.492  1.00 34.23  ? ? ? ? ? ? 122 ALA U O   1 
+ATOM   966  C CB  . ALA A 1 121 ? -1.391  -42.262 -1.643  1.00 40.29  ? ? ? ? ? ? 122 ALA U CB  1 
+ATOM   967  N N   . LEU A 1 122 ? 1.195   -40.696 -2.874  1.00 41.47  ? ? ? ? ? ? 123 LEU U N   1 
+ATOM   968  C CA  . LEU A 1 122 ? 2.397   -40.983 -3.656  1.00 42.77  ? ? ? ? ? ? 123 LEU U CA  1 
+ATOM   969  C C   . LEU A 1 122 ? 2.752   -42.453 -3.565  1.00 43.82  ? ? ? ? ? ? 123 LEU U C   1 
+ATOM   970  O O   . LEU A 1 122 ? 2.408   -43.110 -2.574  1.00 41.98  ? ? ? ? ? ? 123 LEU U O   1 
+ATOM   971  C CB  . LEU A 1 122 ? 3.573   -40.161 -3.140  1.00 43.24  ? ? ? ? ? ? 123 LEU U CB  1 
+ATOM   972  C CG  . LEU A 1 122 ? 3.431   -38.657 -3.375  1.00 44.31  ? ? ? ? ? ? 123 LEU U CG  1 
+ATOM   973  C CD1 . LEU A 1 122 ? 4.494   -37.904 -2.586  1.00 42.49  ? ? ? ? ? ? 123 LEU U CD1 1 
+ATOM   974  C CD2 . LEU A 1 122 ? 3.514   -38.368 -4.870  1.00 44.61  ? ? ? ? ? ? 123 LEU U CD2 1 
+ATOM   975  N N   . PRO A 1 123 ? 3.454   -42.983 -4.587  1.00 45.13  ? ? ? ? ? ? 124 PRO U N   1 
+ATOM   976  C CA  . PRO A 1 123 ? 3.901   -44.360 -4.487  1.00 45.17  ? ? ? ? ? ? 124 PRO U CA  1 
+ATOM   977  C C   . PRO A 1 123 ? 5.124   -44.465 -3.589  1.00 45.56  ? ? ? ? ? ? 124 PRO U C   1 
+ATOM   978  O O   . PRO A 1 123 ? 5.662   -43.441 -3.138  1.00 41.08  ? ? ? ? ? ? 124 PRO U O   1 
+ATOM   979  C CB  . PRO A 1 123 ? 4.263   -44.721 -5.933  1.00 46.78  ? ? ? ? ? ? 124 PRO U CB  1 
+ATOM   980  C CG  . PRO A 1 123 ? 4.560   -43.421 -6.609  1.00 46.27  ? ? ? ? ? ? 124 PRO U CG  1 
+ATOM   981  C CD  . PRO A 1 123 ? 4.068   -42.299 -5.739  1.00 45.71  ? ? ? ? ? ? 124 PRO U CD  1 
+ATOM   982  N N   . SER A 1 124 ? 5.528   -45.701 -3.309  1.00 47.71  ? ? ? ? ? ? 125 SER U N   1 
+ATOM   983  C CA  . SER A 1 124 ? 6.776   -45.977 -2.609  1.00 47.99  ? ? ? ? ? ? 125 SER U CA  1 
+ATOM   984  C C   . SER A 1 124 ? 7.873   -45.813 -3.621  1.00 47.23  ? ? ? ? ? ? 125 SER U C   1 
+ATOM   985  O O   . SER A 1 124 ? 7.652   -46.039 -4.823  1.00 46.52  ? ? ? ? ? ? 125 SER U O   1 
+ATOM   986  C CB  . SER A 1 124 ? 6.813   -47.414 -2.084  1.00 51.90  ? ? ? ? ? ? 125 SER U CB  1 
+ATOM   987  O OG  . SER A 1 124 ? 5.652   -47.711 -1.352  1.00 55.23  ? ? ? ? ? ? 125 SER U OG  1 
+ATOM   988  N N   . MET A 1 125 ? 9.062   -45.445 -3.154  1.00 49.17  ? ? ? ? ? ? 126 MET U N   1 
+ATOM   989  C CA  . MET A 1 125 ? 10.140  -45.136 -4.085  1.00 52.50  ? ? ? ? ? ? 126 MET U CA  1 
+ATOM   990  C C   . MET A 1 125 ? 10.486  -46.343 -4.966  1.00 48.07  ? ? ? ? ? ? 126 MET U C   1 
+ATOM   991  O O   . MET A 1 125 ? 10.701  -47.459 -4.485  1.00 46.18  ? ? ? ? ? ? 126 MET U O   1 
+ATOM   992  C CB  . MET A 1 125 ? 11.385  -44.511 -3.405  1.00 59.78  ? ? ? ? ? ? 126 MET U CB  1 
+ATOM   993  C CG  . MET A 1 125 ? 11.881  -45.124 -2.097  1.00 64.92  ? ? ? ? ? ? 126 MET U CG  1 
+ATOM   994  S SD  . MET A 1 125 ? 13.181  -44.078 -1.384  1.00 73.02  ? ? ? ? ? ? 126 MET U SD  1 
+ATOM   995  C CE  . MET A 1 125 ? 13.436  -44.872 0.204   1.00 74.42  ? ? ? ? ? ? 126 MET U CE  1 
+ATOM   996  N N   . TYR A 1 126 ? 10.469  -46.093 -6.271  1.00 48.96  ? ? ? ? ? ? 127 TYR U N   1 
+ATOM   997  C CA  . TYR A 1 126 ? 10.782  -47.078 -7.310  1.00 48.36  ? ? ? ? ? ? 127 TYR U CA  1 
+ATOM   998  C C   . TYR A 1 126 ? 9.753   -48.197 -7.455  1.00 50.29  ? ? ? ? ? ? 127 TYR U C   1 
+ATOM   999  O O   . TYR A 1 126 ? 9.926   -49.091 -8.284  1.00 49.66  ? ? ? ? ? ? 127 TYR U O   1 
+ATOM   1000 C CB  . TYR A 1 126 ? 12.219  -47.584 -7.129  1.00 48.62  ? ? ? ? ? ? 127 TYR U CB  1 
+ATOM   1001 C CG  . TYR A 1 126 ? 13.164  -46.413 -7.226  1.00 48.15  ? ? ? ? ? ? 127 TYR U CG  1 
+ATOM   1002 C CD1 . TYR A 1 126 ? 13.590  -45.952 -8.461  1.00 49.24  ? ? ? ? ? ? 127 TYR U CD1 1 
+ATOM   1003 C CD2 . TYR A 1 126 ? 13.547  -45.702 -6.097  1.00 47.88  ? ? ? ? ? ? 127 TYR U CD2 1 
+ATOM   1004 C CE1 . TYR A 1 126 ? 14.415  -44.849 -8.569  1.00 48.28  ? ? ? ? ? ? 127 TYR U CE1 1 
+ATOM   1005 C CE2 . TYR A 1 126 ? 14.371  -44.593 -6.190  1.00 46.74  ? ? ? ? ? ? 127 TYR U CE2 1 
+ATOM   1006 C CZ  . TYR A 1 126 ? 14.797  -44.169 -7.428  1.00 48.57  ? ? ? ? ? ? 127 TYR U CZ  1 
+ATOM   1007 O OH  . TYR A 1 126 ? 15.614  -43.076 -7.534  1.00 47.59  ? ? ? ? ? ? 127 TYR U OH  1 
+ATOM   1008 N N   . ASN A 1 127 ? 8.663   -48.113 -6.689  1.00 51.91  ? ? ? ? ? ? 128 ASN U N   1 
+ATOM   1009 C CA  . ASN A 1 127 ? 7.573   -49.087 -6.771  1.00 54.32  ? ? ? ? ? ? 128 ASN U CA  1 
+ATOM   1010 C C   . ASN A 1 127 ? 6.525   -48.643 -7.799  1.00 53.23  ? ? ? ? ? ? 128 ASN U C   1 
+ATOM   1011 O O   . ASN A 1 127 ? 5.829   -47.651 -7.582  1.00 51.51  ? ? ? ? ? ? 128 ASN U O   1 
+ATOM   1012 C CB  . ASN A 1 127 ? 6.919   -49.264 -5.392  1.00 54.56  ? ? ? ? ? ? 128 ASN U CB  1 
+ATOM   1013 C CG  . ASN A 1 127 ? 5.952   -50.438 -5.336  1.00 55.60  ? ? ? ? ? ? 128 ASN U CG  1 
+ATOM   1014 O OD1 . ASN A 1 127 ? 5.079   -50.493 -4.464  1.00 56.96  ? ? ? ? ? ? 128 ASN U OD1 1 
+ATOM   1015 N ND2 . ASN A 1 127 ? 6.107   -51.386 -6.254  1.00 54.55  ? ? ? ? ? ? 128 ASN U ND2 1 
+ATOM   1016 N N   . ASP A 1 128 ? 6.452   -49.366 -8.916  1.00 53.13  ? ? ? ? ? ? 129 ASP U N   1 
+ATOM   1017 C CA  . ASP A 1 128 ? 5.469   -49.123 -9.982  1.00 56.26  ? ? ? ? ? ? 129 ASP U CA  1 
+ATOM   1018 C C   . ASP A 1 128 ? 4.711   -50.419 -10.285 1.00 58.41  ? ? ? ? ? ? 129 ASP U C   1 
+ATOM   1019 O O   . ASP A 1 128 ? 5.258   -51.510 -10.107 1.00 58.63  ? ? ? ? ? ? 129 ASP U O   1 
+ATOM   1020 C CB  . ASP A 1 128 ? 6.160   -48.666 -11.266 1.00 57.76  ? ? ? ? ? ? 129 ASP U CB  1 
+ATOM   1021 C CG  . ASP A 1 128 ? 6.842   -47.316 -11.123 1.00 61.96  ? ? ? ? ? ? 129 ASP U CG  1 
+ATOM   1022 O OD1 . ASP A 1 128 ? 7.707   -47.159 -10.233 1.00 63.92  ? ? ? ? ? ? 129 ASP U OD1 1 
+ATOM   1023 O OD2 . ASP A 1 128 ? 6.527   -46.416 -11.926 1.00 59.48  ? ? ? ? ? ? 129 ASP U OD2 1 
+ATOM   1024 N N   . PRO A 1 129 ? 3.469   -50.310 -10.789 1.00 57.92  ? ? ? ? ? ? 130 PRO U N   1 
+ATOM   1025 C CA  . PRO A 1 129 ? 2.702   -51.533 -11.011 1.00 57.29  ? ? ? ? ? ? 130 PRO U CA  1 
+ATOM   1026 C C   . PRO A 1 129 ? 3.104   -52.221 -12.304 1.00 55.31  ? ? ? ? ? ? 130 PRO U C   1 
+ATOM   1027 O O   . PRO A 1 129 ? 3.913   -51.691 -13.064 1.00 50.38  ? ? ? ? ? ? 130 PRO U O   1 
+ATOM   1028 C CB  . PRO A 1 129 ? 1.257   -51.033 -11.082 1.00 58.05  ? ? ? ? ? ? 130 PRO U CB  1 
+ATOM   1029 C CG  . PRO A 1 129 ? 1.364   -49.631 -11.576 1.00 57.70  ? ? ? ? ? ? 130 PRO U CG  1 
+ATOM   1030 C CD  . PRO A 1 129 ? 2.757   -49.118 -11.288 1.00 56.11  ? ? ? ? ? ? 130 PRO U CD  1 
+ATOM   1031 N N   . GLN A 1 130 ? 2.529   -53.393 -12.544 1.00 56.32  ? ? ? ? ? ? 131 GLN U N   1 
+ATOM   1032 C CA  . GLN A 1 130 ? 2.867   -54.194 -13.708 1.00 59.22  ? ? ? ? ? ? 131 GLN U CA  1 
+ATOM   1033 C C   . GLN A 1 130 ? 2.515   -53.464 -14.971 1.00 56.80  ? ? ? ? ? ? 131 GLN U C   1 
+ATOM   1034 O O   . GLN A 1 130 ? 1.588   -52.671 -15.005 1.00 55.40  ? ? ? ? ? ? 131 GLN U O   1 
+ATOM   1035 C CB  . GLN A 1 130 ? 2.107   -55.535 -13.720 1.00 65.76  ? ? ? ? ? ? 131 GLN U CB  1 
+ATOM   1036 C CG  . GLN A 1 130 ? 2.916   -56.744 -13.269 1.00 71.02  ? ? ? ? ? ? 131 GLN U CG  1 
+ATOM   1037 C CD  . GLN A 1 130 ? 3.115   -56.821 -11.765 1.00 73.46  ? ? ? ? ? ? 131 GLN U CD  1 
+ATOM   1038 O OE1 . GLN A 1 130 ? 3.912   -57.627 -11.283 1.00 77.35  ? ? ? ? ? ? 131 GLN U OE1 1 
+ATOM   1039 N NE2 . GLN A 1 130 ? 2.391   -55.996 -11.016 1.00 73.36  ? ? ? ? ? ? 131 GLN U NE2 1 
+ATOM   1040 N N   . PHE A 1 131 ? 3.241   -53.786 -16.027 1.00 60.06  ? ? ? ? ? ? 132 PHE U N   1 
+ATOM   1041 C CA  . PHE A 1 131 ? 2.891   -53.347 -17.356 1.00 60.41  ? ? ? ? ? ? 132 PHE U CA  1 
+ATOM   1042 C C   . PHE A 1 131 ? 1.614   -54.094 -17.746 1.00 58.35  ? ? ? ? ? ? 132 PHE U C   1 
+ATOM   1043 O O   . PHE A 1 131 ? 1.399   -55.228 -17.308 1.00 54.52  ? ? ? ? ? ? 132 PHE U O   1 
+ATOM   1044 C CB  . PHE A 1 131 ? 4.056   -53.621 -18.297 1.00 67.26  ? ? ? ? ? ? 132 PHE U CB  1 
+ATOM   1045 C CG  . PHE A 1 131 ? 5.382   -53.169 -17.738 1.00 73.40  ? ? ? ? ? ? 132 PHE U CG  1 
+ATOM   1046 C CD1 . PHE A 1 131 ? 5.692   -51.811 -17.665 1.00 78.42  ? ? ? ? ? ? 132 PHE U CD1 1 
+ATOM   1047 C CD2 . PHE A 1 131 ? 6.295   -54.089 -17.233 1.00 76.81  ? ? ? ? ? ? 132 PHE U CD2 1 
+ATOM   1048 C CE1 . PHE A 1 131 ? 6.904   -51.385 -17.140 1.00 79.27  ? ? ? ? ? ? 132 PHE U CE1 1 
+ATOM   1049 C CE2 . PHE A 1 131 ? 7.507   -53.669 -16.702 1.00 79.43  ? ? ? ? ? ? 132 PHE U CE2 1 
+ATOM   1050 C CZ  . PHE A 1 131 ? 7.812   -52.316 -16.657 1.00 80.91  ? ? ? ? ? ? 132 PHE U CZ  1 
+ATOM   1051 N N   . GLY A 1 132 ? 0.751   -53.429 -18.516 1.00 53.51  ? ? ? ? ? ? 133 GLY U N   1 
+ATOM   1052 C CA  . GLY A 1 132 ? -0.601  -53.924 -18.792 1.00 51.27  ? ? ? ? ? ? 133 GLY U CA  1 
+ATOM   1053 C C   . GLY A 1 132 ? -1.640  -53.588 -17.718 1.00 48.81  ? ? ? ? ? ? 133 GLY U C   1 
+ATOM   1054 O O   . GLY A 1 132 ? -2.820  -53.910 -17.885 1.00 50.33  ? ? ? ? ? ? 133 GLY U O   1 
+ATOM   1055 N N   . THR A 1 133 ? -1.225  -52.959 -16.616 1.00 44.34  ? ? ? ? ? ? 134 THR U N   1 
+ATOM   1056 C CA  . THR A 1 133 ? -2.180  -52.468 -15.607 1.00 42.75  ? ? ? ? ? ? 134 THR U CA  1 
+ATOM   1057 C C   . THR A 1 133 ? -2.981  -51.274 -16.153 1.00 40.10  ? ? ? ? ? ? 134 THR U C   1 
+ATOM   1058 O O   . THR A 1 133 ? -2.464  -50.463 -16.895 1.00 37.40  ? ? ? ? ? ? 134 THR U O   1 
+ATOM   1059 C CB  . THR A 1 133 ? -1.475  -52.025 -14.320 1.00 41.47  ? ? ? ? ? ? 134 THR U CB  1 
+ATOM   1060 O OG1 . THR A 1 133 ? -0.651  -53.085 -13.832 1.00 41.60  ? ? ? ? ? ? 134 THR U OG1 1 
+ATOM   1061 C CG2 . THR A 1 133 ? -2.488  -51.624 -13.240 1.00 42.05  ? ? ? ? ? ? 134 THR U CG2 1 
+ATOM   1062 N N   . SER A 1 134 ? -4.249  -51.192 -15.777 1.00 41.37  ? ? ? ? ? ? 135 SER U N   1 
+ATOM   1063 C CA  . SER A 1 134 ? -5.136  -50.117 -16.225 1.00 41.39  ? ? ? ? ? ? 135 SER U CA  1 
+ATOM   1064 C C   . SER A 1 134 ? -5.102  -48.944 -15.251 1.00 40.48  ? ? ? ? ? ? 135 SER U C   1 
+ATOM   1065 O O   . SER A 1 134 ? -5.324  -49.124 -14.059 1.00 41.40  ? ? ? ? ? ? 135 SER U O   1 
+ATOM   1066 C CB  . SER A 1 134 ? -6.571  -50.637 -16.347 1.00 40.39  ? ? ? ? ? ? 135 SER U CB  1 
+ATOM   1067 O OG  . SER A 1 134 ? -6.740  -51.324 -17.558 1.00 41.15  ? ? ? ? ? ? 135 SER U OG  1 
+ATOM   1068 N N   . CYS A 1 135 ? -4.846  -47.748 -15.774 1.00 39.96  ? ? ? ? ? ? 136 CYS U N   1 
+ATOM   1069 C CA  . CYS A 1 135 ? -4.759  -46.524 -14.970 1.00 39.34  ? ? ? ? ? ? 136 CYS U CA  1 
+ATOM   1070 C C   . CYS A 1 135 ? -5.729  -45.459 -15.487 1.00 36.62  ? ? ? ? ? ? 136 CYS U C   1 
+ATOM   1071 O O   . CYS A 1 135 ? -6.227  -45.565 -16.606 1.00 35.21  ? ? ? ? ? ? 136 CYS U O   1 
+ATOM   1072 C CB  . CYS A 1 135 ? -3.327  -45.991 -15.025 1.00 43.13  ? ? ? ? ? ? 136 CYS U CB  1 
+ATOM   1073 S SG  . CYS A 1 135 ? -2.110  -47.225 -14.492 1.00 45.64  ? ? ? ? ? ? 136 CYS U SG  1 
+ATOM   1074 N N   . GLU A 1 136 ? -5.978  -44.440 -14.669 1.00 34.81  ? ? ? ? ? ? 137 GLU U N   1 
+ATOM   1075 C CA  . GLU A 1 136 ? -6.893  -43.350 -15.004 1.00 36.61  ? ? ? ? ? ? 137 GLU U CA  1 
+ATOM   1076 C C   . GLU A 1 136 ? -6.136  -42.057 -15.245 1.00 33.80  ? ? ? ? ? ? 137 GLU U C   1 
+ATOM   1077 O O   . GLU A 1 136 ? -5.212  -41.732 -14.500 1.00 33.23  ? ? ? ? ? ? 137 GLU U O   1 
+ATOM   1078 C CB  . GLU A 1 136 ? -7.848  -43.089 -13.839 1.00 43.81  ? ? ? ? ? ? 137 GLU U CB  1 
+ATOM   1079 C CG  . GLU A 1 136 ? -8.914  -44.150 -13.652 1.00 51.99  ? ? ? ? ? ? 137 GLU U CG  1 
+ATOM   1080 C CD  . GLU A 1 136 ? -9.692  -43.966 -12.366 1.00 58.74  ? ? ? ? ? ? 137 GLU U CD  1 
+ATOM   1081 O OE1 . GLU A 1 136 ? -9.218  -43.209 -11.478 1.00 57.19  ? ? ? ? ? ? 137 GLU U OE1 1 
+ATOM   1082 O OE2 . GLU A 1 136 ? -10.785 -44.577 -12.255 1.00 63.33  ? ? ? ? ? ? 137 GLU U OE2 1 
+ATOM   1083 N N   . ILE A 1 137 ? -6.551  -41.319 -16.270 1.00 30.78  ? ? ? ? ? ? 138 ILE U N   1 
+ATOM   1084 C CA  . ILE A 1 137 ? -6.050  -39.956 -16.533 1.00 30.68  ? ? ? ? ? ? 138 ILE U CA  1 
+ATOM   1085 C C   . ILE A 1 137 ? -7.248  -39.028 -16.607 1.00 29.77  ? ? ? ? ? ? 138 ILE U C   1 
+ATOM   1086 O O   . ILE A 1 137 ? -8.356  -39.479 -16.876 1.00 30.43  ? ? ? ? ? ? 138 ILE U O   1 
+ATOM   1087 C CB  . ILE A 1 137 ? -5.240  -39.850 -17.847 1.00 32.00  ? ? ? ? ? ? 138 ILE U CB  1 
+ATOM   1088 C CG1 . ILE A 1 137 ? -5.977  -40.566 -18.988 1.00 33.11  ? ? ? ? ? ? 138 ILE U CG1 1 
+ATOM   1089 C CG2 . ILE A 1 137 ? -3.839  -40.415 -17.634 1.00 33.32  ? ? ? ? ? ? 138 ILE U CG2 1 
+ATOM   1090 C CD1 . ILE A 1 137 ? -5.402  -40.353 -20.382 1.00 34.56  ? ? ? ? ? ? 138 ILE U CD1 1 
+ATOM   1091 N N   . THR A 1 138 ? -7.018  -37.737 -16.371 1.00 29.88  ? ? ? ? ? ? 139 THR U N   1 
+ATOM   1092 C CA  . THR A 1 138 ? -8.084  -36.767 -16.256 1.00 31.21  ? ? ? ? ? ? 139 THR U CA  1 
+ATOM   1093 C C   . THR A 1 138 ? -7.570  -35.452 -16.751 1.00 28.96  ? ? ? ? ? ? 139 THR U C   1 
+ATOM   1094 O O   . THR A 1 138 ? -6.395  -35.145 -16.553 1.00 29.81  ? ? ? ? ? ? 139 THR U O   1 
+ATOM   1095 C CB  . THR A 1 138 ? -8.500  -36.529 -14.780 1.00 36.17  ? ? ? ? ? ? 139 THR U CB  1 
+ATOM   1096 O OG1 . THR A 1 138 ? -8.443  -37.755 -14.048 1.00 44.21  ? ? ? ? ? ? 139 THR U OG1 1 
+ATOM   1097 C CG2 . THR A 1 138 ? -9.907  -35.980 -14.701 1.00 35.56  ? ? ? ? ? ? 139 THR U CG2 1 
+ATOM   1098 N N   . GLY A 1 139 ? -8.446  -34.633 -17.324 1.00 25.83  ? ? ? ? ? ? 140 GLY U N   1 
+ATOM   1099 C CA  . GLY A 1 139 ? -8.043  -33.269 -17.685 1.00 25.07  ? ? ? ? ? ? 140 GLY U CA  1 
+ATOM   1100 C C   . GLY A 1 139 ? -9.023  -32.482 -18.542 1.00 25.81  ? ? ? ? ? ? 140 GLY U C   1 
+ATOM   1101 O O   . GLY A 1 139 ? -10.085 -32.991 -18.916 1.00 26.46  ? ? ? ? ? ? 140 GLY U O   1 
+ATOM   1102 N N   . PHE A 1 140 ? -8.638  -31.232 -18.838 1.00 25.60  ? ? ? ? ? ? 141 PHE U N   1 
+ATOM   1103 C CA  . PHE A 1 140 ? -9.413  -30.307 -19.655 1.00 26.36  ? ? ? ? ? ? 141 PHE U CA  1 
+ATOM   1104 C C   . PHE A 1 140 ? -8.749  -30.174 -21.032 1.00 26.09  ? ? ? ? ? ? 141 PHE U C   1 
+ATOM   1105 O O   . PHE A 1 140 ? -8.950  -29.189 -21.742 1.00 26.20  ? ? ? ? ? ? 141 PHE U O   1 
+ATOM   1106 C CB  . PHE A 1 140 ? -9.504  -28.923 -18.973 1.00 27.59  ? ? ? ? ? ? 141 PHE U CB  1 
+ATOM   1107 C CG  . PHE A 1 140 ? -10.409 -28.865 -17.730 1.00 24.53  ? ? ? ? ? ? 141 PHE U CG  1 
+ATOM   1108 C CD1 . PHE A 1 140 ? -11.780 -29.018 -17.842 1.00 25.90  ? ? ? ? ? ? 141 PHE U CD1 1 
+ATOM   1109 C CD2 . PHE A 1 140 ? -9.891  -28.551 -16.482 1.00 27.11  ? ? ? ? ? ? 141 PHE U CD2 1 
+ATOM   1110 C CE1 . PHE A 1 140 ? -12.613 -28.906 -16.733 1.00 24.91  ? ? ? ? ? ? 141 PHE U CE1 1 
+ATOM   1111 C CE2 . PHE A 1 140 ? -10.717 -28.452 -15.356 1.00 25.48  ? ? ? ? ? ? 141 PHE U CE2 1 
+ATOM   1112 C CZ  . PHE A 1 140 ? -12.078 -28.625 -15.484 1.00 24.44  ? ? ? ? ? ? 141 PHE U CZ  1 
+ATOM   1113 N N   . GLY A 1 141 ? -7.996  -31.195 -21.423 1.00 27.17  ? ? ? ? ? ? 142 GLY U N   1 
+ATOM   1114 C CA  . GLY A 1 141 ? -7.219  -31.141 -22.644 1.00 28.31  ? ? ? ? ? ? 142 GLY U CA  1 
+ATOM   1115 C C   . GLY A 1 141 ? -8.114  -31.366 -23.857 1.00 30.98  ? ? ? ? ? ? 142 GLY U C   1 
+ATOM   1116 O O   . GLY A 1 141 ? -9.283  -31.751 -23.740 1.00 26.18  ? ? ? ? ? ? 142 GLY U O   1 
+ATOM   1117 N N   . LYS A 1 142 ? -7.545  -31.163 -25.038 1.00 33.16  ? ? ? ? ? ? 143 LYS U N   1 
+ATOM   1118 C CA  . LYS A 1 142 ? -8.315  -31.304 -26.269 1.00 32.87  ? ? ? ? ? ? 143 LYS U CA  1 
+ATOM   1119 C C   . LYS A 1 142 ? -8.930  -32.665 -26.414 1.00 29.68  ? ? ? ? ? ? 143 LYS U C   1 
+ATOM   1120 O O   . LYS A 1 142 ? -8.353  -33.660 -26.004 1.00 31.54  ? ? ? ? ? ? 143 LYS U O   1 
+ATOM   1121 C CB  . LYS A 1 142 ? -7.442  -31.000 -27.495 1.00 36.13  ? ? ? ? ? ? 143 LYS U CB  1 
+ATOM   1122 C CG  . LYS A 1 142 ? -6.960  -29.562 -27.538 1.00 38.57  ? ? ? ? ? ? 143 LYS U CG  1 
+ATOM   1123 C CD  . LYS A 1 142 ? -6.288  -29.225 -28.869 1.00 40.16  ? ? ? ? ? ? 143 LYS U CD  1 
+ATOM   1124 C CE  . LYS A 1 142 ? -5.867  -27.765 -28.898 1.00 42.91  ? ? ? ? ? ? 143 LYS U CE  1 
+ATOM   1125 N NZ  . LYS A 1 142 ? -4.817  -27.493 -29.925 1.00 45.54  ? ? ? ? ? ? 143 LYS U NZ  1 
+ATOM   1126 N N   . GLU A 1 143 ? -10.112 -32.711 -27.021 1.00 31.87  ? ? ? ? ? ? 144 GLU U N   1 
+ATOM   1127 C CA  . GLU A 1 143 ? -10.760 -33.980 -27.344 1.00 34.55  ? ? ? ? ? ? 144 GLU U CA  1 
+ATOM   1128 C C   . GLU A 1 143 ? -10.281 -34.575 -28.683 1.00 35.87  ? ? ? ? ? ? 144 GLU U C   1 
+ATOM   1129 O O   . GLU A 1 143 ? -10.622 -35.702 -28.988 1.00 36.47  ? ? ? ? ? ? 144 GLU U O   1 
+ATOM   1130 C CB  . GLU A 1 143 ? -12.277 -33.807 -27.395 1.00 35.10  ? ? ? ? ? ? 144 GLU U CB  1 
+ATOM   1131 C CG  . GLU A 1 143 ? -12.880 -33.408 -26.059 1.00 35.77  ? ? ? ? ? ? 144 GLU U CG  1 
+ATOM   1132 C CD  . GLU A 1 143 ? -14.387 -33.415 -26.105 1.00 35.11  ? ? ? ? ? ? 144 GLU U CD  1 
+ATOM   1133 O OE1 . GLU A 1 143 ? -14.967 -32.328 -26.238 1.00 34.58  ? ? ? ? ? ? 144 GLU U OE1 1 
+ATOM   1134 O OE2 . GLU A 1 143 ? -14.975 -34.510 -26.019 1.00 38.13  ? ? ? ? ? ? 144 GLU U OE2 1 
+ATOM   1135 N N   . GLN A 1 144 ? -9.551  -33.789 -29.472 1.00 39.16  ? ? ? ? ? ? 145 GLN U N   1 
+ATOM   1136 C CA  . GLN A 1 144 ? -8.894  -34.240 -30.711 1.00 38.26  ? ? ? ? ? ? 145 GLN U CA  1 
+ATOM   1137 C C   . GLN A 1 144 ? -7.651  -33.392 -30.947 1.00 35.78  ? ? ? ? ? ? 145 GLN U C   1 
+ATOM   1138 O O   . GLN A 1 144 ? -7.672  -32.161 -30.774 1.00 33.16  ? ? ? ? ? ? 145 GLN U O   1 
+ATOM   1139 C CB  . GLN A 1 144 ? -9.828  -34.084 -31.913 1.00 42.28  ? ? ? ? ? ? 145 GLN U CB  1 
+ATOM   1140 C CG  . GLN A 1 144 ? -11.065 -34.971 -31.872 1.00 45.91  ? ? ? ? ? ? 145 GLN U CG  1 
+ATOM   1141 C CD  . GLN A 1 144 ? -12.206 -34.458 -32.735 1.00 56.27  ? ? ? ? ? ? 145 GLN U CD  1 
+ATOM   1142 O OE1 . GLN A 1 144 ? -12.346 -33.248 -32.962 1.00 62.36  ? ? ? ? ? ? 145 GLN U OE1 1 
+ATOM   1143 N NE2 . GLN A 1 144 ? -13.058 -35.377 -33.189 1.00 59.10  ? ? ? ? ? ? 145 GLN U NE2 1 
+ATOM   1144 N N   . SER A 1 145 ? -6.566  -34.038 -31.359 1.00 38.86  ? ? ? ? ? ? 146 SER U N   1 
+ATOM   1145 C CA  . SER A 1 145 ? -5.338  -33.323 -31.762 1.00 44.56  ? ? ? ? ? ? 146 SER U CA  1 
+ATOM   1146 C C   . SER A 1 145 ? -5.593  -32.069 -32.574 1.00 44.02  ? ? ? ? ? ? 146 SER U C   1 
+ATOM   1147 O O   . SER A 1 145 ? -4.931  -31.046 -32.378 1.00 46.62  ? ? ? ? ? ? 146 SER U O   1 
+ATOM   1148 C CB  . SER A 1 145 ? -4.428  -34.236 -32.594 1.00 45.08  ? ? ? ? ? ? 146 SER U CB  1 
+ATOM   1149 O OG  . SER A 1 145 ? -3.396  -34.742 -31.793 1.00 50.15  ? ? ? ? ? ? 146 SER U OG  1 
+ATOM   1150 N N   . THR A 1 146 ? -6.536  -32.168 -33.505 1.00 43.87  ? ? ? ? ? ? 147 THR U N   1 
+ATOM   1151 C CA  . THR A 1 146 ? -6.772  -31.121 -34.489 1.00 44.09  ? ? ? ? ? ? 147 THR U CA  1 
+ATOM   1152 C C   . THR A 1 146 ? -7.676  -29.990 -34.011 1.00 45.34  ? ? ? ? ? ? 147 THR U C   1 
+ATOM   1153 O O   . THR A 1 146 ? -7.843  -28.993 -34.724 1.00 44.92  ? ? ? ? ? ? 147 THR U O   1 
+ATOM   1154 C CB  . THR A 1 146 ? -7.387  -31.740 -35.758 1.00 45.09  ? ? ? ? ? ? 147 THR U CB  1 
+ATOM   1155 O OG1 . THR A 1 146 ? -8.587  -32.444 -35.420 1.00 41.63  ? ? ? ? ? ? 147 THR U OG1 1 
+ATOM   1156 C CG2 . THR A 1 146 ? -6.400  -32.734 -36.381 1.00 46.78  ? ? ? ? ? ? 147 THR U CG2 1 
+ATOM   1157 N N   . ASP A 1 147 ? -8.264  -30.134 -32.821 1.00 42.74  ? ? ? ? ? ? 148 ASP U N   1 
+ATOM   1158 C CA  . ASP A 1 147 ? -9.169  -29.118 -32.290 1.00 42.79  ? ? ? ? ? ? 148 ASP U CA  1 
+ATOM   1159 C C   . ASP A 1 147 ? -8.409  -27.841 -31.951 1.00 41.48  ? ? ? ? ? ? 148 ASP U C   1 
+ATOM   1160 O O   . ASP A 1 147 ? -7.205  -27.856 -31.708 1.00 38.50  ? ? ? ? ? ? 148 ASP U O   1 
+ATOM   1161 C CB  . ASP A 1 147 ? -9.913  -29.620 -31.026 1.00 44.26  ? ? ? ? ? ? 148 ASP U CB  1 
+ATOM   1162 C CG  . ASP A 1 147 ? -10.984 -30.665 -31.332 1.00 43.73  ? ? ? ? ? ? 148 ASP U CG  1 
+ATOM   1163 O OD1 . ASP A 1 147 ? -11.376 -30.808 -32.504 1.00 39.98  ? ? ? ? ? ? 148 ASP U OD1 1 
+ATOM   1164 O OD2 . ASP A 1 147 ? -11.450 -31.348 -30.390 1.00 41.35  ? ? ? ? ? ? 148 ASP U OD2 1 
+ATOM   1165 N N   . TYR A 1 148 ? -9.145  -26.741 -31.956 1.00 42.35  ? ? ? ? ? ? 149 TYR U N   1 
+ATOM   1166 C CA  . TYR A 1 148 ? -8.658  -25.431 -31.555 1.00 48.15  ? ? ? ? ? ? 149 TYR U CA  1 
+ATOM   1167 C C   . TYR A 1 148 ? -9.212  -25.090 -30.167 1.00 44.82  ? ? ? ? ? ? 149 TYR U C   1 
+ATOM   1168 O O   . TYR A 1 148 ? -8.550  -24.421 -29.381 1.00 45.92  ? ? ? ? ? ? 149 TYR U O   1 
+ATOM   1169 C CB  . TYR A 1 148 ? -9.115  -24.393 -32.581 1.00 53.99  ? ? ? ? ? ? 149 TYR U CB  1 
+ATOM   1170 C CG  . TYR A 1 148 ? -8.594  -22.988 -32.363 1.00 67.30  ? ? ? ? ? ? 149 TYR U CG  1 
+ATOM   1171 C CD1 . TYR A 1 148 ? -7.296  -22.632 -32.743 1.00 72.59  ? ? ? ? ? ? 149 TYR U CD1 1 
+ATOM   1172 C CD2 . TYR A 1 148 ? -9.411  -21.997 -31.805 1.00 75.78  ? ? ? ? ? ? 149 TYR U CD2 1 
+ATOM   1173 C CE1 . TYR A 1 148 ? -6.821  -21.337 -32.553 1.00 78.23  ? ? ? ? ? ? 149 TYR U CE1 1 
+ATOM   1174 C CE2 . TYR A 1 148 ? -8.947  -20.698 -31.614 1.00 78.28  ? ? ? ? ? ? 149 TYR U CE2 1 
+ATOM   1175 C CZ  . TYR A 1 148 ? -7.652  -20.372 -31.987 1.00 78.98  ? ? ? ? ? ? 149 TYR U CZ  1 
+ATOM   1176 O OH  . TYR A 1 148 ? -7.189  -19.088 -31.797 1.00 79.95  ? ? ? ? ? ? 149 TYR U OH  1 
+ATOM   1177 N N   . LEU A 1 149 ? -10.422 -25.560 -29.870 1.00 43.17  ? ? ? ? ? ? 150 LEU U N   1 
+ATOM   1178 C CA  . LEU A 1 149 ? -11.048 -25.318 -28.573 1.00 42.24  ? ? ? ? ? ? 150 LEU U CA  1 
+ATOM   1179 C C   . LEU A 1 149 ? -10.788 -26.442 -27.563 1.00 40.09  ? ? ? ? ? ? 150 LEU U C   1 
+ATOM   1180 O O   . LEU A 1 149 ? -10.355 -27.558 -27.922 1.00 34.76  ? ? ? ? ? ? 150 LEU U O   1 
+ATOM   1181 C CB  . LEU A 1 149 ? -12.546 -25.108 -28.762 1.00 47.45  ? ? ? ? ? ? 150 LEU U CB  1 
+ATOM   1182 C CG  . LEU A 1 149 ? -12.935 -23.958 -29.703 1.00 49.63  ? ? ? ? ? ? 150 LEU U CG  1 
+ATOM   1183 C CD1 . LEU A 1 149 ? -14.432 -23.957 -29.964 1.00 52.12  ? ? ? ? ? ? 150 LEU U CD1 1 
+ATOM   1184 C CD2 . LEU A 1 149 ? -12.495 -22.620 -29.124 1.00 52.03  ? ? ? ? ? ? 150 LEU U CD2 1 
+ATOM   1185 N N   . TYR A 1 150 ? -11.037 -26.130 -26.291 1.00 34.40  ? ? ? ? ? ? 151 TYR U N   1 
+ATOM   1186 C CA  . TYR A 1 150 ? -10.911 -27.100 -25.211 1.00 35.83  ? ? ? ? ? ? 151 TYR U CA  1 
+ATOM   1187 C C   . TYR A 1 150 ? -12.288 -27.368 -24.627 1.00 32.78  ? ? ? ? ? ? 151 TYR U C   1 
+ATOM   1188 O O   . TYR A 1 150 ? -13.167 -26.504 -24.696 1.00 30.24  ? ? ? ? ? ? 151 TYR U O   1 
+ATOM   1189 C CB  . TYR A 1 150 ? -9.958  -26.586 -24.133 1.00 35.82  ? ? ? ? ? ? 151 TYR U CB  1 
+ATOM   1190 C CG  . TYR A 1 150 ? -8.550  -26.444 -24.649 1.00 37.87  ? ? ? ? ? ? 151 TYR U CG  1 
+ATOM   1191 C CD1 . TYR A 1 150 ? -8.174  -25.324 -25.378 1.00 39.44  ? ? ? ? ? ? 151 TYR U CD1 1 
+ATOM   1192 C CD2 . TYR A 1 150 ? -7.592  -27.418 -24.406 1.00 38.18  ? ? ? ? ? ? 151 TYR U CD2 1 
+ATOM   1193 C CE1 . TYR A 1 150 ? -6.882  -25.178 -25.859 1.00 40.53  ? ? ? ? ? ? 151 TYR U CE1 1 
+ATOM   1194 C CE2 . TYR A 1 150 ? -6.299  -27.287 -24.882 1.00 37.97  ? ? ? ? ? ? 151 TYR U CE2 1 
+ATOM   1195 C CZ  . TYR A 1 150 ? -5.949  -26.165 -25.609 1.00 41.92  ? ? ? ? ? ? 151 TYR U CZ  1 
+ATOM   1196 O OH  . TYR A 1 150 ? -4.672  -26.020 -26.095 1.00 40.49  ? ? ? ? ? ? 151 TYR U OH  1 
+ATOM   1197 N N   . PRO A 1 151 ? -12.501 -28.589 -24.105 1.00 30.49  ? ? ? ? ? ? 152 PRO U N   1 
+ATOM   1198 C CA  . PRO A 1 151 ? -13.820 -28.907 -23.600 1.00 31.36  ? ? ? ? ? ? 152 PRO U CA  1 
+ATOM   1199 C C   . PRO A 1 151 ? -14.097 -28.097 -22.340 1.00 30.71  ? ? ? ? ? ? 152 PRO U C   1 
+ATOM   1200 O O   . PRO A 1 151 ? -13.140 -27.703 -21.644 1.00 28.82  ? ? ? ? ? ? 152 PRO U O   1 
+ATOM   1201 C CB  . PRO A 1 151 ? -13.747 -30.410 -23.309 1.00 31.11  ? ? ? ? ? ? 152 PRO U CB  1 
+ATOM   1202 C CG  . PRO A 1 151 ? -12.294 -30.703 -23.147 1.00 32.17  ? ? ? ? ? ? 152 PRO U CG  1 
+ATOM   1203 C CD  . PRO A 1 151 ? -11.618 -29.763 -24.104 1.00 32.55  ? ? ? ? ? ? 152 PRO U CD  1 
+ATOM   1204 N N   . GLU A 1 152 ? -15.372 -27.810 -22.086 1.00 29.22  ? ? ? ? ? ? 153 GLU U N   1 
+ATOM   1205 C CA  . GLU A 1 152 ? -15.755 -27.014 -20.908 1.00 32.47  ? ? ? ? ? ? 153 GLU U CA  1 
+ATOM   1206 C C   . GLU A 1 152 ? -15.900 -27.901 -19.671 1.00 28.25  ? ? ? ? ? ? 153 GLU U C   1 
+ATOM   1207 O O   . GLU A 1 152 ? -15.680 -27.454 -18.553 1.00 31.16  ? ? ? ? ? ? 153 GLU U O   1 
+ATOM   1208 C CB  . GLU A 1 152 ? -17.035 -26.212 -21.172 1.00 35.26  ? ? ? ? ? ? 153 GLU U CB  1 
+ATOM   1209 C CG  . GLU A 1 152 ? -16.861 -24.702 -20.960 1.00 45.25  ? ? ? ? ? ? 153 GLU U CG  1 
+ATOM   1210 C CD  . GLU A 1 152 ? -17.149 -24.235 -19.528 1.00 49.45  ? ? ? ? ? ? 153 GLU U CD  1 
+ATOM   1211 O OE1 . GLU A 1 152 ? -16.209 -23.983 -18.755 1.00 49.01  ? ? ? ? ? ? 153 GLU U OE1 1 
+ATOM   1212 O OE2 . GLU A 1 152 ? -18.337 -24.098 -19.181 1.00 59.35  ? ? ? ? ? ? 153 GLU U OE2 1 
+ATOM   1213 N N   . GLN A 1 153 ? -16.218 -29.165 -19.889 1.00 28.98  ? ? ? ? ? ? 154 GLN U N   1 
+ATOM   1214 C CA  . GLN A 1 153 ? -16.411 -30.134 -18.818 1.00 30.62  ? ? ? ? ? ? 154 GLN U CA  1 
+ATOM   1215 C C   . GLN A 1 153 ? -15.181 -31.040 -18.694 1.00 30.90  ? ? ? ? ? ? 154 GLN U C   1 
+ATOM   1216 O O   . GLN A 1 153 ? -14.579 -31.455 -19.684 1.00 27.99  ? ? ? ? ? ? 154 GLN U O   1 
+ATOM   1217 C CB  . GLN A 1 153 ? -17.671 -30.965 -19.112 1.00 28.97  ? ? ? ? ? ? 154 GLN U CB  1 
+ATOM   1218 C CG  . GLN A 1 153 ? -18.155 -31.875 -17.990 1.00 30.36  ? ? ? ? ? ? 154 GLN U CG  1 
+ATOM   1219 C CD  . GLN A 1 153 ? -18.622 -31.139 -16.751 1.00 30.09  ? ? ? ? ? ? 154 GLN U CD  1 
+ATOM   1220 O OE1 . GLN A 1 153 ? -19.099 -30.004 -16.827 1.00 28.33  ? ? ? ? ? ? 154 GLN U OE1 1 
+ATOM   1221 N NE2 . GLN A 1 153 ? -18.465 -31.781 -15.581 1.00 29.02  ? ? ? ? ? ? 154 GLN U NE2 1 
+ATOM   1222 N N   . LEU A 1 154 ? -14.831 -31.361 -17.460 1.00 29.24  ? ? ? ? ? ? 155 LEU U N   1 
+ATOM   1223 C CA  . LEU A 1 154 ? -13.744 -32.290 -17.198 1.00 27.12  ? ? ? ? ? ? 155 LEU U CA  1 
+ATOM   1224 C C   . LEU A 1 154 ? -13.997 -33.645 -17.822 1.00 26.10  ? ? ? ? ? ? 155 LEU U C   1 
+ATOM   1225 O O   . LEU A 1 154 ? -15.111 -34.165 -17.770 1.00 29.26  ? ? ? ? ? ? 155 LEU U O   1 
+ATOM   1226 C CB  . LEU A 1 154 ? -13.591 -32.455 -15.682 1.00 27.00  ? ? ? ? ? ? 155 LEU U CB  1 
+ATOM   1227 C CG  . LEU A 1 154 ? -12.393 -33.237 -15.159 1.00 25.93  ? ? ? ? ? ? 155 LEU U CG  1 
+ATOM   1228 C CD1 . LEU A 1 154 ? -11.061 -32.552 -15.495 1.00 24.96  ? ? ? ? ? ? 155 LEU U CD1 1 
+ATOM   1229 C CD2 . LEU A 1 154 ? -12.602 -33.370 -13.649 1.00 28.19  ? ? ? ? ? ? 155 LEU U CD2 1 
+ATOM   1230 N N   . LYS A 1 155 ? -12.944 -34.259 -18.339 1.00 25.91  ? ? ? ? ? ? 156 LYS U N   1 
+ATOM   1231 C CA  . LYS A 1 155 ? -13.004 -35.638 -18.793 1.00 25.80  ? ? ? ? ? ? 156 LYS U CA  1 
+ATOM   1232 C C   . LYS A 1 155 ? -11.982 -36.564 -18.131 1.00 24.15  ? ? ? ? ? ? 156 LYS U C   1 
+ATOM   1233 O O   . LYS A 1 155 ? -10.993 -36.117 -17.571 1.00 24.53  ? ? ? ? ? ? 156 LYS U O   1 
+ATOM   1234 C CB  . LYS A 1 155 ? -12.719 -35.686 -20.296 1.00 26.48  ? ? ? ? ? ? 156 LYS U CB  1 
+ATOM   1235 C CG  . LYS A 1 155 ? -13.726 -34.952 -21.174 1.00 28.36  ? ? ? ? ? ? 156 LYS U CG  1 
+ATOM   1236 C CD  . LYS A 1 155 ? -13.372 -35.098 -22.654 1.00 29.65  ? ? ? ? ? ? 156 LYS U CD  1 
+ATOM   1237 C CE  . LYS A 1 155 ? -13.901 -36.391 -23.269 1.00 31.11  ? ? ? ? ? ? 156 LYS U CE  1 
+ATOM   1238 N NZ  . LYS A 1 155 ? -13.273 -36.663 -24.607 1.00 34.73  ? ? ? ? ? ? 156 LYS U NZ  1 
+ATOM   1239 N N   . MET A 1 156 ? -12.216 -37.858 -18.260 1.00 27.40  ? ? ? ? ? ? 157 MET U N   1 
+ATOM   1240 C CA  . MET A 1 156 ? -11.259 -38.894 -17.867 1.00 31.33  ? ? ? ? ? ? 157 MET U CA  1 
+ATOM   1241 C C   . MET A 1 156 ? -11.330 -40.085 -18.813 1.00 31.61  ? ? ? ? ? ? 157 MET U C   1 
+ATOM   1242 O O   . MET A 1 156 ? -12.314 -40.265 -19.532 1.00 32.01  ? ? ? ? ? ? 157 MET U O   1 
+ATOM   1243 C CB  . MET A 1 156 ? -11.523 -39.375 -16.438 1.00 35.87  ? ? ? ? ? ? 157 MET U CB  1 
+ATOM   1244 C CG  . MET A 1 156 ? -12.868 -40.041 -16.216 1.00 44.32  ? ? ? ? ? ? 157 MET U CG  1 
+ATOM   1245 S SD  . MET A 1 156 ? -13.024 -40.704 -14.528 1.00 60.90  ? ? ? ? ? ? 157 MET U SD  1 
+ATOM   1246 C CE  . MET A 1 156 ? -11.749 -41.966 -14.561 1.00 56.17  ? ? ? ? ? ? 157 MET U CE  1 
+ATOM   1247 N N   . THR A 1 157 ? -10.312 -40.930 -18.765 1.00 31.24  ? ? ? ? ? ? 158 THR U N   1 
+ATOM   1248 C CA  . THR A 1 157 ? -10.303 -42.146 -19.561 1.00 33.50  ? ? ? ? ? ? 158 THR U CA  1 
+ATOM   1249 C C   . THR A 1 157 ? -9.359  -43.146 -18.917 1.00 35.48  ? ? ? ? ? ? 158 THR U C   1 
+ATOM   1250 O O   . THR A 1 157 ? -8.731  -42.835 -17.923 1.00 32.95  ? ? ? ? ? ? 158 THR U O   1 
+ATOM   1251 C CB  . THR A 1 157 ? -9.911  -41.861 -21.032 1.00 34.92  ? ? ? ? ? ? 158 THR U CB  1 
+ATOM   1252 O OG1 . THR A 1 157 ? -10.122 -43.034 -21.832 1.00 35.42  ? ? ? ? ? ? 158 THR U OG1 1 
+ATOM   1253 C CG2 . THR A 1 157 ? -8.465  -41.384 -21.143 1.00 35.28  ? ? ? ? ? ? 158 THR U CG2 1 
+ATOM   1254 N N   . VAL A 1 158 ? -9.290  -44.351 -19.477 1.00 36.79  ? ? ? ? ? ? 159 VAL U N   1 
+ATOM   1255 C CA  . VAL A 1 158 ? -8.449  -45.403 -18.942 1.00 38.37  ? ? ? ? ? ? 159 VAL U CA  1 
+ATOM   1256 C C   . VAL A 1 158 ? -7.417  -45.802 -20.002 1.00 39.28  ? ? ? ? ? ? 159 VAL U C   1 
+ATOM   1257 O O   . VAL A 1 158 ? -7.732  -45.920 -21.193 1.00 34.81  ? ? ? ? ? ? 159 VAL U O   1 
+ATOM   1258 C CB  . VAL A 1 158 ? -9.295  -46.618 -18.446 1.00 39.88  ? ? ? ? ? ? 159 VAL U CB  1 
+ATOM   1259 C CG1 . VAL A 1 158 ? -8.405  -47.798 -18.034 1.00 38.15  ? ? ? ? ? ? 159 VAL U CG1 1 
+ATOM   1260 C CG2 . VAL A 1 158 ? -10.162 -46.184 -17.269 1.00 39.06  ? ? ? ? ? ? 159 VAL U CG2 1 
+ATOM   1261 N N   . VAL A 1 159 ? -6.175  -45.953 -19.546 1.00 42.59  ? ? ? ? ? ? 160 VAL U N   1 
+ATOM   1262 C CA  . VAL A 1 159 ? -5.066  -46.410 -20.378 1.00 41.57  ? ? ? ? ? ? 160 VAL U CA  1 
+ATOM   1263 C C   . VAL A 1 159 ? -4.286  -47.465 -19.602 1.00 43.83  ? ? ? ? ? ? 160 VAL U C   1 
+ATOM   1264 O O   . VAL A 1 159 ? -4.388  -47.544 -18.371 1.00 41.98  ? ? ? ? ? ? 160 VAL U O   1 
+ATOM   1265 C CB  . VAL A 1 159 ? -4.122  -45.259 -20.782 1.00 41.45  ? ? ? ? ? ? 160 VAL U CB  1 
+ATOM   1266 C CG1 . VAL A 1 159 ? -4.735  -44.459 -21.924 1.00 39.83  ? ? ? ? ? ? 160 VAL U CG1 1 
+ATOM   1267 C CG2 . VAL A 1 159 ? -3.797  -44.373 -19.586 1.00 41.56  ? ? ? ? ? ? 160 VAL U CG2 1 
+ATOM   1268 N N   . LYS A 1 160 ? -3.519  -48.275 -20.329 1.00 41.48  ? ? ? ? ? ? 161 LYS U N   1 
+ATOM   1269 C CA  . LYS A 1 160 ? -2.715  -49.308 -19.719 1.00 43.76  ? ? ? ? ? ? 161 LYS U CA  1 
+ATOM   1270 C C   . LYS A 1 160 ? -1.236  -48.964 -19.824 1.00 44.55  ? ? ? ? ? ? 161 LYS U C   1 
+ATOM   1271 O O   . LYS A 1 160 ? -0.768  -48.522 -20.881 1.00 44.98  ? ? ? ? ? ? 161 LYS U O   1 
+ATOM   1272 C CB  . LYS A 1 160 ? -2.991  -50.659 -20.382 1.00 48.39  ? ? ? ? ? ? 161 LYS U CB  1 
+ATOM   1273 C CG  . LYS A 1 160 ? -4.424  -51.145 -20.186 1.00 50.56  ? ? ? ? ? ? 161 LYS U CG  1 
+ATOM   1274 C CD  . LYS A 1 160 ? -4.584  -52.622 -20.529 1.00 52.38  ? ? ? ? ? ? 161 LYS U CD  1 
+ATOM   1275 C CE  . LYS A 1 160 ? -5.994  -53.123 -20.235 1.00 52.81  ? ? ? ? ? ? 161 LYS U CE  1 
+ATOM   1276 N NZ  . LYS A 1 160 ? -6.096  -54.614 -20.250 1.00 52.79  ? ? ? ? ? ? 161 LYS U NZ  1 
+ATOM   1277 N N   . LEU A 1 161 ? -0.519  -49.158 -18.719 1.00 40.16  ? ? ? ? ? ? 162 LEU U N   1 
+ATOM   1278 C CA  . LEU A 1 161 ? 0.927   -49.008 -18.685 1.00 45.27  ? ? ? ? ? ? 162 LEU U CA  1 
+ATOM   1279 C C   . LEU A 1 161 ? 1.568   -49.926 -19.704 1.00 47.69  ? ? ? ? ? ? 162 LEU U C   1 
+ATOM   1280 O O   . LEU A 1 161 ? 1.138   -51.077 -19.888 1.00 42.70  ? ? ? ? ? ? 162 LEU U O   1 
+ATOM   1281 C CB  . LEU A 1 161 ? 1.475   -49.340 -17.292 1.00 46.19  ? ? ? ? ? ? 162 LEU U CB  1 
+ATOM   1282 C CG  . LEU A 1 161 ? 1.567   -48.207 -16.258 1.00 48.56  ? ? ? ? ? ? 162 LEU U CG  1 
+ATOM   1283 C CD1 . LEU A 1 161 ? 0.644   -47.030 -16.548 1.00 50.54  ? ? ? ? ? ? 162 LEU U CD1 1 
+ATOM   1284 C CD2 . LEU A 1 161 ? 1.318   -48.749 -14.863 1.00 47.68  ? ? ? ? ? ? 162 LEU U CD2 1 
+ATOM   1285 N N   . ILE A 1 162 ? 2.592   -49.400 -20.367 1.00 50.90  ? ? ? ? ? ? 163 ILE U N   1 
+ATOM   1286 C CA  . ILE A 1 162 ? 3.314   -50.127 -21.401 1.00 53.56  ? ? ? ? ? ? 163 ILE U CA  1 
+ATOM   1287 C C   . ILE A 1 162 ? 4.718   -50.419 -20.880 1.00 53.46  ? ? ? ? ? ? 163 ILE U C   1 
+ATOM   1288 O O   . ILE A 1 162 ? 5.267   -49.659 -20.071 1.00 54.05  ? ? ? ? ? ? 163 ILE U O   1 
+ATOM   1289 C CB  . ILE A 1 162 ? 3.349   -49.324 -22.724 1.00 55.10  ? ? ? ? ? ? 163 ILE U CB  1 
+ATOM   1290 C CG1 . ILE A 1 162 ? 1.933   -49.203 -23.291 1.00 57.74  ? ? ? ? ? ? 163 ILE U CG1 1 
+ATOM   1291 C CG2 . ILE A 1 162 ? 4.247   -50.000 -23.754 1.00 55.65  ? ? ? ? ? ? 163 ILE U CG2 1 
+ATOM   1292 C CD1 . ILE A 1 162 ? 1.751   -48.063 -24.272 1.00 59.80  ? ? ? ? ? ? 163 ILE U CD1 1 
+ATOM   1293 N N   . SER A 1 163 ? 5.283   -51.539 -21.321 1.00 51.93  ? ? ? ? ? ? 164 SER U N   1 
+ATOM   1294 C CA  . SER A 1 163 ? 6.615   -51.939 -20.881 1.00 54.46  ? ? ? ? ? ? 164 SER U CA  1 
+ATOM   1295 C C   . SER A 1 163 ? 7.647   -50.967 -21.428 1.00 52.15  ? ? ? ? ? ? 164 SER U C   1 
+ATOM   1296 O O   . SER A 1 163 ? 7.493   -50.410 -22.518 1.00 54.35  ? ? ? ? ? ? 164 SER U O   1 
+ATOM   1297 C CB  . SER A 1 163 ? 6.940   -53.368 -21.324 1.00 50.19  ? ? ? ? ? ? 164 SER U CB  1 
+ATOM   1298 O OG  . SER A 1 163 ? 7.176   -53.418 -22.724 1.00 49.75  ? ? ? ? ? ? 164 SER U OG  1 
+ATOM   1299 N N   . HIS A 1 164 ? 8.735   -50.793 -20.740 1.00 56.45  ? ? ? ? ? ? 165 HIS U N   1 
+ATOM   1300 C CA  . HIS A 1 164 ? 9.777   -49.985 -21.314 1.00 67.84  ? ? ? ? ? ? 165 HIS U CA  1 
+ATOM   1301 C C   . HIS A 1 164 ? 10.307  -50.540 -22.621 1.00 68.26  ? ? ? ? ? ? 165 HIS U C   1 
+ATOM   1302 O O   . HIS A 1 164 ? 10.509  -49.827 -23.574 1.00 68.60  ? ? ? ? ? ? 165 HIS U O   1 
+ATOM   1303 C CB  . HIS A 1 164 ? 10.908  -49.848 -20.325 1.00 70.86  ? ? ? ? ? ? 165 HIS U CB  1 
+ATOM   1304 C CG  . HIS A 1 164 ? 10.794  -48.616 -19.519 1.00 80.16  ? ? ? ? ? ? 165 HIS U CG  1 
+ATOM   1305 N ND1 . HIS A 1 164 ? 10.983  -47.367 -20.061 1.00 85.61  ? ? ? ? ? ? 165 HIS U ND1 1 
+ATOM   1306 C CD2 . HIS A 1 164 ? 10.415  -48.420 -18.239 1.00 84.15  ? ? ? ? ? ? 165 HIS U CD2 1 
+ATOM   1307 C CE1 . HIS A 1 164 ? 10.752  -46.455 -19.137 1.00 88.15  ? ? ? ? ? ? 165 HIS U CE1 1 
+ATOM   1308 N NE2 . HIS A 1 164 ? 10.415  -47.067 -18.021 1.00 89.48  ? ? ? ? ? ? 165 HIS U NE2 1 
+ATOM   1309 N N   . ARG A 1 165 ? 10.468  -51.837 -22.669 1.00 69.35  ? ? ? ? ? ? 166 ARG U N   1 
+ATOM   1310 C CA  . ARG A 1 165 ? 10.995  -52.476 -23.833 1.00 71.94  ? ? ? ? ? ? 166 ARG U CA  1 
+ATOM   1311 C C   . ARG A 1 165 ? 10.086  -52.206 -24.980 1.00 69.85  ? ? ? ? ? ? 166 ARG U C   1 
+ATOM   1312 O O   . ARG A 1 165 ? 10.549  -51.964 -26.081 1.00 71.36  ? ? ? ? ? ? 166 ARG U O   1 
+ATOM   1313 C CB  . ARG A 1 165 ? 11.059  -53.988 -23.603 1.00 74.16  ? ? ? ? ? ? 166 ARG U CB  1 
+ATOM   1314 C CG  . ARG A 1 165 ? 11.232  -54.808 -24.858 1.00 78.74  ? ? ? ? ? ? 166 ARG U CG  1 
+ATOM   1315 C CD  . ARG A 1 165 ? 10.086  -55.770 -25.053 1.00 81.19  ? ? ? ? ? ? 166 ARG U CD  1 
+ATOM   1316 N NE  . ARG A 1 165 ? 10.458  -57.081 -24.569 1.00 84.43  ? ? ? ? ? ? 166 ARG U NE  1 
+ATOM   1317 C CZ  . ARG A 1 165 ? 10.372  -58.196 -25.278 1.00 89.36  ? ? ? ? ? ? 166 ARG U CZ  1 
+ATOM   1318 N NH1 . ARG A 1 165 ? 9.900   -58.157 -26.513 1.00 91.97  ? ? ? ? ? ? 166 ARG U NH1 1 
+ATOM   1319 N NH2 . ARG A 1 165 ? 10.740  -59.351 -24.742 1.00 89.42  ? ? ? ? ? ? 166 ARG U NH2 1 
+ATOM   1320 N N   . GLU A 1 166 ? 8.801   -52.309 -24.748 1.00 67.25  ? ? ? ? ? ? 167 GLU U N   1 
+ATOM   1321 C CA  . GLU A 1 166 ? 7.935   -52.032 -25.823 1.00 63.86  ? ? ? ? ? ? 167 GLU U CA  1 
+ATOM   1322 C C   . GLU A 1 166 ? 8.146   -50.590 -26.153 1.00 63.77  ? ? ? ? ? ? 167 GLU U C   1 
+ATOM   1323 O O   . GLU A 1 166 ? 8.217   -50.225 -27.292 1.00 64.60  ? ? ? ? ? ? 167 GLU U O   1 
+ATOM   1324 C CB  . GLU A 1 166 ? 6.496   -52.282 -25.463 1.00 65.00  ? ? ? ? ? ? 167 GLU U CB  1 
+ATOM   1325 C CG  . GLU A 1 166 ? 5.627   -52.295 -26.701 1.00 67.37  ? ? ? ? ? ? 167 GLU U CG  1 
+ATOM   1326 C CD  . GLU A 1 166 ? 4.155   -52.382 -26.426 1.00 71.06  ? ? ? ? ? ? 167 GLU U CD  1 
+ATOM   1327 O OE1 . GLU A 1 166 ? 3.774   -52.966 -25.403 1.00 71.41  ? ? ? ? ? ? 167 GLU U OE1 1 
+ATOM   1328 O OE2 . GLU A 1 166 ? 3.377   -51.860 -27.246 1.00 74.75  ? ? ? ? ? ? 167 GLU U OE2 1 
+ATOM   1329 N N   . CYS A 1 167 ? 8.234   -49.759 -25.138 1.00 62.45  ? ? ? ? ? ? 168 CYS U N   1 
+ATOM   1330 C CA  . CYS A 1 167 ? 8.249   -48.335 -25.364 1.00 62.23  ? ? ? ? ? ? 168 CYS U CA  1 
+ATOM   1331 C C   . CYS A 1 167 ? 9.440   -47.851 -26.140 1.00 61.76  ? ? ? ? ? ? 168 CYS U C   1 
+ATOM   1332 O O   . CYS A 1 167 ? 9.331   -46.913 -26.882 1.00 57.53  ? ? ? ? ? ? 168 CYS U O   1 
+ATOM   1333 C CB  . CYS A 1 167 ? 8.050   -47.535 -24.093 1.00 59.94  ? ? ? ? ? ? 168 CYS U CB  1 
+ATOM   1334 S SG  . CYS A 1 167 ? 7.015   -46.133 -24.463 1.00 62.94  ? ? ? ? ? ? 168 CYS U SG  1 
+ATOM   1335 N N   . GLN A 1 168 ? 10.564  -48.512 -25.946 1.00 63.74  ? ? ? ? ? ? 169 GLN U N   1 
+ATOM   1336 C CA  . GLN A 1 168 ? 11.835  -48.156 -26.553 1.00 66.75  ? ? ? ? ? ? 169 GLN U CA  1 
+ATOM   1337 C C   . GLN A 1 168 ? 11.978  -48.704 -27.962 1.00 69.12  ? ? ? ? ? ? 169 GLN U C   1 
+ATOM   1338 O O   . GLN A 1 168 ? 13.020  -48.608 -28.551 1.00 66.73  ? ? ? ? ? ? 169 GLN U O   1 
+ATOM   1339 C CB  . GLN A 1 168 ? 13.000  -48.633 -25.710 1.00 68.82  ? ? ? ? ? ? 169 GLN U CB  1 
+ATOM   1340 C CG  . GLN A 1 168 ? 13.274  -47.776 -24.498 1.00 74.80  ? ? ? ? ? ? 169 GLN U CG  1 
+ATOM   1341 C CD  . GLN A 1 168 ? 13.157  -48.537 -23.185 1.00 79.76  ? ? ? ? ? ? 169 GLN U CD  1 
+ATOM   1342 O OE1 . GLN A 1 168 ? 12.889  -49.736 -23.160 1.00 81.85  ? ? ? ? ? ? 169 GLN U OE1 1 
+ATOM   1343 N NE2 . GLN A 1 168 ? 13.368  -47.835 -22.085 1.00 81.00  ? ? ? ? ? ? 169 GLN U NE2 1 
+ATOM   1344 N N   . GLN A 1 169 ? 10.941  -49.334 -28.468 1.00 72.71  ? ? ? ? ? ? 170 GLN U N   1 
+ATOM   1345 C CA  . GLN A 1 169 ? 10.926  -49.774 -29.839 1.00 72.39  ? ? ? ? ? ? 170 GLN U CA  1 
+ATOM   1346 C C   . GLN A 1 169 ? 10.944  -48.635 -30.826 1.00 72.96  ? ? ? ? ? ? 170 GLN U C   1 
+ATOM   1347 O O   . GLN A 1 169 ? 10.348  -47.605 -30.616 1.00 72.34  ? ? ? ? ? ? 170 GLN U O   1 
+ATOM   1348 C CB  . GLN A 1 169 ? 9.694   -50.586 -30.109 1.00 75.02  ? ? ? ? ? ? 170 GLN U CB  1 
+ATOM   1349 C CG  . GLN A 1 169 ? 9.710   -51.986 -29.560 1.00 76.78  ? ? ? ? ? ? 170 GLN U CG  1 
+ATOM   1350 C CD  . GLN A 1 169 ? 8.570   -52.791 -30.111 1.00 76.82  ? ? ? ? ? ? 170 GLN U CD  1 
+ATOM   1351 O OE1 . GLN A 1 169 ? 7.503   -52.266 -30.384 1.00 79.40  ? ? ? ? ? ? 170 GLN U OE1 1 
+ATOM   1352 N NE2 . GLN A 1 169 ? 8.783   -54.073 -30.258 1.00 76.80  ? ? ? ? ? ? 170 GLN U NE2 1 
+ATOM   1353 N N   . PRO A 1 170 A 11.682  -48.888 -31.988 1.00 93.83  ? ? ? ? ? ? 170 PRO U N   1 
+ATOM   1354 C CA  . PRO A 1 170 A 11.828  -47.726 -32.870 1.00 97.45  ? ? ? ? ? ? 170 PRO U CA  1 
+ATOM   1355 C C   . PRO A 1 170 A 10.541  -47.213 -33.469 1.00 97.89  ? ? ? ? ? ? 170 PRO U C   1 
+ATOM   1356 O O   . PRO A 1 170 A 10.437  -46.039 -33.668 1.00 106.92 ? ? ? ? ? ? 170 PRO U O   1 
+ATOM   1357 C CB  . PRO A 1 170 A 12.800  -48.208 -33.955 1.00 97.74  ? ? ? ? ? ? 170 PRO U CB  1 
+ATOM   1358 C CG  . PRO A 1 170 A 13.569  -49.297 -33.326 1.00 96.65  ? ? ? ? ? ? 170 PRO U CG  1 
+ATOM   1359 C CD  . PRO A 1 170 A 12.466  -50.027 -32.674 1.00 94.64  ? ? ? ? ? ? 170 PRO U CD  1 
+ATOM   1360 N N   . HIS A 1 171 B 9.612   -48.075 -33.809 1.00 55.07  ? ? ? ? ? ? 170 HIS U N   1 
+ATOM   1361 C CA  . HIS A 1 171 B 8.349   -47.606 -34.315 1.00 62.48  ? ? ? ? ? ? 170 HIS U CA  1 
+ATOM   1362 C C   . HIS A 1 171 B 7.492   -46.961 -33.178 1.00 59.71  ? ? ? ? ? ? 170 HIS U C   1 
+ATOM   1363 O O   . HIS A 1 171 B 6.510   -46.296 -33.442 1.00 55.93  ? ? ? ? ? ? 170 HIS U O   1 
+ATOM   1364 C CB  . HIS A 1 171 B 7.664   -48.664 -35.174 1.00 62.91  ? ? ? ? ? ? 170 HIS U CB  1 
+ATOM   1365 C CG  . HIS A 1 171 B 7.301   -49.896 -34.431 1.00 67.40  ? ? ? ? ? ? 170 HIS U CG  1 
+ATOM   1366 N ND1 . HIS A 1 171 B 8.127   -50.461 -33.495 1.00 67.21  ? ? ? ? ? ? 170 HIS U ND1 1 
+ATOM   1367 C CD2 . HIS A 1 171 B 6.193   -50.663 -34.474 1.00 69.20  ? ? ? ? ? ? 170 HIS U CD2 1 
+ATOM   1368 C CE1 . HIS A 1 171 B 7.539   -51.521 -32.983 1.00 69.84  ? ? ? ? ? ? 170 HIS U CE1 1 
+ATOM   1369 N NE2 . HIS A 1 171 B 6.368   -51.669 -33.566 1.00 71.65  ? ? ? ? ? ? 170 HIS U NE2 1 
+ATOM   1370 N N   . TYR A 1 172 ? 7.894   -47.157 -31.928 1.00 57.86  ? ? ? ? ? ? 171 TYR U N   1 
+ATOM   1371 C CA  . TYR A 1 172 ? 7.379   -46.346 -30.833 1.00 57.93  ? ? ? ? ? ? 171 TYR U CA  1 
+ATOM   1372 C C   . TYR A 1 172 ? 8.220   -45.097 -30.624 1.00 57.29  ? ? ? ? ? ? 171 TYR U C   1 
+ATOM   1373 O O   . TYR A 1 172 ? 8.215   -44.222 -31.447 1.00 57.94  ? ? ? ? ? ? 171 TYR U O   1 
+ATOM   1374 C CB  . TYR A 1 172 ? 7.322   -47.114 -29.543 1.00 57.50  ? ? ? ? ? ? 171 TYR U CB  1 
+ATOM   1375 C CG  . TYR A 1 172 ? 6.188   -48.080 -29.478 1.00 57.71  ? ? ? ? ? ? 171 TYR U CG  1 
+ATOM   1376 C CD1 . TYR A 1 172 ? 5.706   -48.674 -30.612 1.00 60.53  ? ? ? ? ? ? 171 TYR U CD1 1 
+ATOM   1377 C CD2 . TYR A 1 172 ? 5.607   -48.400 -28.284 1.00 58.76  ? ? ? ? ? ? 171 TYR U CD2 1 
+ATOM   1378 C CE1 . TYR A 1 172 ? 4.667   -49.566 -30.563 1.00 61.80  ? ? ? ? ? ? 171 TYR U CE1 1 
+ATOM   1379 C CE2 . TYR A 1 172 ? 4.571   -49.292 -28.213 1.00 60.92  ? ? ? ? ? ? 171 TYR U CE2 1 
+ATOM   1380 C CZ  . TYR A 1 172 ? 4.101   -49.876 -29.358 1.00 61.96  ? ? ? ? ? ? 171 TYR U CZ  1 
+ATOM   1381 O OH  . TYR A 1 172 ? 3.068   -50.757 -29.297 1.00 60.68  ? ? ? ? ? ? 171 TYR U OH  1 
+ATOM   1382 N N   . TYR A 1 173 ? 8.940   -45.009 -29.528 1.00 54.61  ? ? ? ? ? ? 172 TYR U N   1 
+ATOM   1383 C CA  . TYR A 1 173 ? 9.700   -43.803 -29.285 1.00 57.83  ? ? ? ? ? ? 172 TYR U CA  1 
+ATOM   1384 C C   . TYR A 1 173 ? 11.201  -43.994 -29.329 1.00 59.42  ? ? ? ? ? ? 172 TYR U C   1 
+ATOM   1385 O O   . TYR A 1 173 ? 11.952  -43.037 -29.301 1.00 57.54  ? ? ? ? ? ? 172 TYR U O   1 
+ATOM   1386 C CB  . TYR A 1 173 ? 9.245   -43.095 -27.996 1.00 56.24  ? ? ? ? ? ? 172 TYR U CB  1 
+ATOM   1387 C CG  . TYR A 1 173 ? 7.881   -42.418 -28.096 1.00 54.14  ? ? ? ? ? ? 172 TYR U CG  1 
+ATOM   1388 C CD1 . TYR A 1 173 ? 7.584   -41.577 -29.132 1.00 49.98  ? ? ? ? ? ? 172 TYR U CD1 1 
+ATOM   1389 C CD2 . TYR A 1 173 ? 6.894   -42.643 -27.145 1.00 52.48  ? ? ? ? ? ? 172 TYR U CD2 1 
+ATOM   1390 C CE1 . TYR A 1 173 ? 6.357   -40.995 -29.241 1.00 49.41  ? ? ? ? ? ? 172 TYR U CE1 1 
+ATOM   1391 C CE2 . TYR A 1 173 ? 5.665   -42.047 -27.248 1.00 49.49  ? ? ? ? ? ? 172 TYR U CE2 1 
+ATOM   1392 C CZ  . TYR A 1 173 ? 5.413   -41.225 -28.298 1.00 48.49  ? ? ? ? ? ? 172 TYR U CZ  1 
+ATOM   1393 O OH  . TYR A 1 173 ? 4.220   -40.631 -28.411 1.00 46.09  ? ? ? ? ? ? 172 TYR U OH  1 
+ATOM   1394 N N   . GLY A 1 174 ? 11.627  -45.236 -29.422 1.00 63.31  ? ? ? ? ? ? 173 GLY U N   1 
+ATOM   1395 C CA  . GLY A 1 174 ? 13.031  -45.563 -29.427 1.00 62.34  ? ? ? ? ? ? 173 GLY U CA  1 
+ATOM   1396 C C   . GLY A 1 174 ? 13.750  -45.093 -28.193 1.00 64.74  ? ? ? ? ? ? 173 GLY U C   1 
+ATOM   1397 O O   . GLY A 1 174 ? 13.325  -45.321 -27.086 1.00 68.20  ? ? ? ? ? ? 173 GLY U O   1 
+ATOM   1398 N N   . SER A 1 175 ? 14.860  -44.419 -28.404 1.00 60.88  ? ? ? ? ? ? 174 SER U N   1 
+ATOM   1399 C CA  . SER A 1 175 ? 15.762  -43.988 -27.354 1.00 65.81  ? ? ? ? ? ? 174 SER U CA  1 
+ATOM   1400 C C   . SER A 1 175 ? 15.238  -42.930 -26.393 1.00 65.08  ? ? ? ? ? ? 174 SER U C   1 
+ATOM   1401 O O   . SER A 1 175 ? 15.772  -42.724 -25.333 1.00 65.60  ? ? ? ? ? ? 174 SER U O   1 
+ATOM   1402 C CB  . SER A 1 175 ? 17.076  -43.498 -27.972 1.00 65.66  ? ? ? ? ? ? 174 SER U CB  1 
+ATOM   1403 O OG  . SER A 1 175 ? 16.872  -42.362 -28.799 1.00 65.97  ? ? ? ? ? ? 174 SER U OG  1 
+ATOM   1404 N N   . GLU A 1 176 ? 14.236  -42.211 -26.836 1.00 69.07  ? ? ? ? ? ? 175 GLU U N   1 
+ATOM   1405 C CA  . GLU A 1 176 ? 13.773  -40.968 -26.228 1.00 70.69  ? ? ? ? ? ? 175 GLU U CA  1 
+ATOM   1406 C C   . GLU A 1 176 ? 12.981  -41.186 -24.937 1.00 70.43  ? ? ? ? ? ? 175 GLU U C   1 
+ATOM   1407 O O   . GLU A 1 176 ? 12.889  -40.274 -24.117 1.00 66.66  ? ? ? ? ? ? 175 GLU U O   1 
+ATOM   1408 C CB  . GLU A 1 176 ? 12.968  -40.145 -27.238 1.00 73.46  ? ? ? ? ? ? 175 GLU U CB  1 
+ATOM   1409 C CG  . GLU A 1 176 ? 12.710  -38.695 -26.833 1.00 78.38  ? ? ? ? ? ? 175 GLU U CG  1 
+ATOM   1410 C CD  . GLU A 1 176 ? 13.970  -37.914 -26.478 1.00 79.70  ? ? ? ? ? ? 175 GLU U CD  1 
+ATOM   1411 O OE1 . GLU A 1 176 ? 14.459  -37.163 -27.349 1.00 81.17  ? ? ? ? ? ? 175 GLU U OE1 1 
+ATOM   1412 O OE2 . GLU A 1 176 ? 14.460  -38.039 -25.330 1.00 76.34  ? ? ? ? ? ? 175 GLU U OE2 1 
+ATOM   1413 N N   . VAL A 1 177 ? 12.419  -42.383 -24.761 1.00 69.71  ? ? ? ? ? ? 176 VAL U N   1 
+ATOM   1414 C CA  . VAL A 1 177 ? 11.818  -42.772 -23.477 1.00 68.88  ? ? ? ? ? ? 176 VAL U CA  1 
+ATOM   1415 C C   . VAL A 1 177 ? 12.883  -43.342 -22.557 1.00 65.06  ? ? ? ? ? ? 176 VAL U C   1 
+ATOM   1416 O O   . VAL A 1 177 ? 13.503  -44.358 -22.866 1.00 66.46  ? ? ? ? ? ? 176 VAL U O   1 
+ATOM   1417 C CB  . VAL A 1 177 ? 10.670  -43.808 -23.607 1.00 73.97  ? ? ? ? ? ? 176 VAL U CB  1 
+ATOM   1418 C CG1 . VAL A 1 177 ? 9.360   -43.111 -23.936 1.00 75.83  ? ? ? ? ? ? 176 VAL U CG1 1 
+ATOM   1419 C CG2 . VAL A 1 177 ? 10.989  -44.892 -24.632 1.00 77.21  ? ? ? ? ? ? 176 VAL U CG2 1 
+ATOM   1420 N N   . THR A 1 178 ? 13.084  -42.682 -21.425 1.00 59.45  ? ? ? ? ? ? 177 THR U N   1 
+ATOM   1421 C CA  . THR A 1 178 ? 14.099  -43.080 -20.458 1.00 56.02  ? ? ? ? ? ? 177 THR U CA  1 
+ATOM   1422 C C   . THR A 1 178 ? 13.421  -43.722 -19.264 1.00 56.63  ? ? ? ? ? ? 177 THR U C   1 
+ATOM   1423 O O   . THR A 1 178 ? 12.185  -43.708 -19.163 1.00 53.04  ? ? ? ? ? ? 177 THR U O   1 
+ATOM   1424 C CB  . THR A 1 178 ? 14.882  -41.857 -19.969 1.00 53.96  ? ? ? ? ? ? 177 THR U CB  1 
+ATOM   1425 O OG1 . THR A 1 178 ? 13.996  -41.001 -19.247 1.00 52.42  ? ? ? ? ? ? 177 THR U OG1 1 
+ATOM   1426 C CG2 . THR A 1 178 ? 15.466  -41.091 -21.149 1.00 51.27  ? ? ? ? ? ? 177 THR U CG2 1 
+ATOM   1427 N N   . THR A 1 179 ? 14.221  -44.267 -18.349 1.00 55.06  ? ? ? ? ? ? 178 THR U N   1 
+ATOM   1428 C CA  . THR A 1 179 ? 13.682  -45.035 -17.223 1.00 55.23  ? ? ? ? ? ? 178 THR U CA  1 
+ATOM   1429 C C   . THR A 1 179 ? 12.968  -44.162 -16.197 1.00 52.69  ? ? ? ? ? ? 178 THR U C   1 
+ATOM   1430 O O   . THR A 1 179 ? 12.304  -44.685 -15.311 1.00 53.39  ? ? ? ? ? ? 178 THR U O   1 
+ATOM   1431 C CB  . THR A 1 179 ? 14.750  -45.883 -16.486 1.00 53.89  ? ? ? ? ? ? 178 THR U CB  1 
+ATOM   1432 O OG1 . THR A 1 179 ? 15.524  -45.050 -15.611 1.00 56.93  ? ? ? ? ? ? 178 THR U OG1 1 
+ATOM   1433 C CG2 . THR A 1 179 ? 15.658  -46.601 -17.464 1.00 52.89  ? ? ? ? ? ? 178 THR U CG2 1 
+ATOM   1434 N N   . LYS A 1 180 ? 13.117  -42.843 -16.305 1.00 54.11  ? ? ? ? ? ? 179 LYS U N   1 
+ATOM   1435 C CA  . LYS A 1 180 ? 12.454  -41.911 -15.389 1.00 53.34  ? ? ? ? ? ? 179 LYS U CA  1 
+ATOM   1436 C C   . LYS A 1 180 ? 11.087  -41.497 -15.932 1.00 49.26  ? ? ? ? ? ? 179 LYS U C   1 
+ATOM   1437 O O   . LYS A 1 180 ? 10.417  -40.661 -15.344 1.00 47.30  ? ? ? ? ? ? 179 LYS U O   1 
+ATOM   1438 C CB  . LYS A 1 180 ? 13.339  -40.689 -15.127 1.00 58.24  ? ? ? ? ? ? 179 LYS U CB  1 
+ATOM   1439 C CG  . LYS A 1 180 ? 14.779  -41.052 -14.775 1.00 61.76  ? ? ? ? ? ? 179 LYS U CG  1 
+ATOM   1440 C CD  . LYS A 1 180 ? 15.522  -39.948 -14.036 1.00 64.91  ? ? ? ? ? ? 179 LYS U CD  1 
+ATOM   1441 C CE  . LYS A 1 180 ? 15.629  -40.242 -12.546 1.00 70.20  ? ? ? ? ? ? 179 LYS U CE  1 
+ATOM   1442 N NZ  . LYS A 1 180 ? 16.137  -39.070 -11.781 1.00 72.99  ? ? ? ? ? ? 179 LYS U NZ  1 
+ATOM   1443 N N   . MET A 1 181 ? 10.691  -42.112 -17.044 1.00 43.39  ? ? ? ? ? ? 180 MET U N   1 
+ATOM   1444 C CA  . MET A 1 181 ? 9.407   -41.898 -17.671 1.00 43.19  ? ? ? ? ? ? 180 MET U CA  1 
+ATOM   1445 C C   . MET A 1 181 ? 8.599   -43.210 -17.691 1.00 42.59  ? ? ? ? ? ? 180 MET U C   1 
+ATOM   1446 O O   . MET A 1 181 ? 9.163   -44.292 -17.613 1.00 40.59  ? ? ? ? ? ? 180 MET U O   1 
+ATOM   1447 C CB  . MET A 1 181 ? 9.631   -41.403 -19.102 1.00 42.10  ? ? ? ? ? ? 180 MET U CB  1 
+ATOM   1448 C CG  . MET A 1 181 ? 10.667  -40.291 -19.238 1.00 41.20  ? ? ? ? ? ? 180 MET U CG  1 
+ATOM   1449 S SD  . MET A 1 181 ? 10.990  -39.856 -20.953 1.00 46.55  ? ? ? ? ? ? 180 MET U SD  1 
+ATOM   1450 C CE  . MET A 1 181 ? 12.109  -38.474 -20.732 1.00 47.02  ? ? ? ? ? ? 180 MET U CE  1 
+ATOM   1451 N N   . LEU A 1 182 ? 7.278   -43.101 -17.793 1.00 43.69  ? ? ? ? ? ? 181 LEU U N   1 
+ATOM   1452 C CA  . LEU A 1 182 ? 6.405   -44.252 -18.082 1.00 43.09  ? ? ? ? ? ? 181 LEU U CA  1 
+ATOM   1453 C C   . LEU A 1 182 ? 5.593   -43.990 -19.331 1.00 39.21  ? ? ? ? ? ? 181 LEU U C   1 
+ATOM   1454 O O   . LEU A 1 182 ? 5.142   -42.865 -19.547 1.00 37.78  ? ? ? ? ? ? 181 LEU U O   1 
+ATOM   1455 C CB  . LEU A 1 182 ? 5.412   -44.494 -16.945 1.00 45.14  ? ? ? ? ? ? 181 LEU U CB  1 
+ATOM   1456 C CG  . LEU A 1 182 ? 5.935   -44.862 -15.559 1.00 49.66  ? ? ? ? ? ? 181 LEU U CG  1 
+ATOM   1457 C CD1 . LEU A 1 182 ? 4.756   -44.904 -14.597 1.00 50.05  ? ? ? ? ? ? 181 LEU U CD1 1 
+ATOM   1458 C CD2 . LEU A 1 182 ? 6.683   -46.191 -15.552 1.00 51.13  ? ? ? ? ? ? 181 LEU U CD2 1 
+ATOM   1459 N N   . CYS A 1 183 ? 5.375   -45.026 -20.134 1.00 37.68  ? ? ? ? ? ? 182 CYS U N   1 
+ATOM   1460 C CA  . CYS A 1 183 ? 4.428   -44.949 -21.237 1.00 39.57  ? ? ? ? ? ? 182 CYS U CA  1 
+ATOM   1461 C C   . CYS A 1 183 ? 3.118   -45.603 -20.834 1.00 38.53  ? ? ? ? ? ? 182 CYS U C   1 
+ATOM   1462 O O   . CYS A 1 183 ? 3.094   -46.527 -20.024 1.00 39.35  ? ? ? ? ? ? 182 CYS U O   1 
+ATOM   1463 C CB  . CYS A 1 183 ? 4.961   -45.629 -22.503 1.00 43.27  ? ? ? ? ? ? 182 CYS U CB  1 
+ATOM   1464 S SG  . CYS A 1 183 ? 6.421   -44.838 -23.190 1.00 51.84  ? ? ? ? ? ? 182 CYS U SG  1 
+ATOM   1465 N N   . ALA A 1 184 ? 2.035   -45.106 -21.414 1.00 37.74  ? ? ? ? ? ? 183 ALA U N   1 
+ATOM   1466 C CA  . ALA A 1 184 ? 0.698   -45.648 -21.189 1.00 38.15  ? ? ? ? ? ? 183 ALA U CA  1 
+ATOM   1467 C C   . ALA A 1 184 ? -0.122  -45.287 -22.415 1.00 38.02  ? ? ? ? ? ? 183 ALA U C   1 
+ATOM   1468 O O   . ALA A 1 184 ? 0.034   -44.202 -22.993 1.00 43.62  ? ? ? ? ? ? 183 ALA U O   1 
+ATOM   1469 C CB  . ALA A 1 184 ? 0.082   -45.080 -19.920 1.00 37.07  ? ? ? ? ? ? 183 ALA U CB  1 
+ATOM   1470 N N   . ALA A 1 185 ? -0.942  -46.210 -22.861 1.00 38.98  ? ? ? ? ? ? 184 ALA U N   1 
+ATOM   1471 C CA  . ALA A 1 185 ? -1.736  -46.005 -24.046 1.00 40.30  ? ? ? ? ? ? 184 ALA U CA  1 
+ATOM   1472 C C   . ALA A 1 185 ? -2.864  -46.960 -24.041 1.00 40.89  ? ? ? ? ? ? 184 ALA U C   1 
+ATOM   1473 O O   . ALA A 1 185 ? -2.907  -47.850 -23.247 1.00 37.18  ? ? ? ? ? ? 184 ALA U O   1 
+ATOM   1474 C CB  . ALA A 1 185 ? -0.929  -46.206 -25.296 1.00 40.64  ? ? ? ? ? ? 184 ALA U CB  1 
+ATOM   1475 N N   . ASP A 1 186 ? -3.782  -46.730 -24.953 1.00 43.12  ? ? ? ? ? ? 185 ASP U N   1 
+ATOM   1476 C CA  . ASP A 1 186 ? -4.851  -47.652 -25.242 1.00 46.58  ? ? ? ? ? ? 185 ASP U CA  1 
+ATOM   1477 C C   . ASP A 1 186 ? -4.410  -48.825 -26.098 1.00 47.01  ? ? ? ? ? ? 185 ASP U C   1 
+ATOM   1478 O O   . ASP A 1 186 ? -3.766  -48.664 -27.111 1.00 48.86  ? ? ? ? ? ? 185 ASP U O   1 
+ATOM   1479 C CB  . ASP A 1 186 ? -5.982  -46.936 -25.930 1.00 48.86  ? ? ? ? ? ? 185 ASP U CB  1 
+ATOM   1480 C CG  . ASP A 1 186 ? -7.122  -47.823 -26.197 1.00 51.39  ? ? ? ? ? ? 185 ASP U CG  1 
+ATOM   1481 O OD1 . ASP A 1 186 ? -7.570  -48.474 -25.281 1.00 50.11  ? ? ? ? ? ? 185 ASP U OD1 1 
+ATOM   1482 O OD2 . ASP A 1 186 ? -7.568  -47.908 -27.325 1.00 56.77  ? ? ? ? ? ? 185 ASP U OD2 1 
+ATOM   1483 N N   . PRO A 1 187 A -4.870  -50.066 -25.641 1.00 64.68  ? ? ? ? ? ? 185 PRO U N   1 
+ATOM   1484 C CA  . PRO A 1 187 A -4.486  -51.188 -26.511 1.00 63.75  ? ? ? ? ? ? 185 PRO U CA  1 
+ATOM   1485 C C   . PRO A 1 187 A -4.944  -51.011 -27.953 1.00 59.62  ? ? ? ? ? ? 185 PRO U C   1 
+ATOM   1486 O O   . PRO A 1 187 A -4.174  -51.325 -28.792 1.00 57.77  ? ? ? ? ? ? 185 PRO U O   1 
+ATOM   1487 C CB  . PRO A 1 187 A -5.187  -52.353 -25.884 1.00 64.93  ? ? ? ? ? ? 185 PRO U CB  1 
+ATOM   1488 C CG  . PRO A 1 187 A -5.052  -52.105 -24.451 1.00 65.30  ? ? ? ? ? ? 185 PRO U CG  1 
+ATOM   1489 C CD  . PRO A 1 187 A -5.498  -50.698 -24.377 1.00 64.44  ? ? ? ? ? ? 185 PRO U CD  1 
+ATOM   1490 N N   . GLN A 1 188 B -6.113  -50.475 -28.245 1.00 63.47  ? ? ? ? ? ? 185 GLN U N   1 
+ATOM   1491 C CA  . GLN A 1 188 B -6.463  -50.169 -29.641 1.00 61.25  ? ? ? ? ? ? 185 GLN U CA  1 
+ATOM   1492 C C   . GLN A 1 188 B -6.157  -48.747 -30.114 1.00 55.76  ? ? ? ? ? ? 185 GLN U C   1 
+ATOM   1493 O O   . GLN A 1 188 B -6.580  -48.346 -31.168 1.00 53.02  ? ? ? ? ? ? 185 GLN U O   1 
+ATOM   1494 C CB  . GLN A 1 188 B -7.904  -50.500 -29.938 1.00 66.74  ? ? ? ? ? ? 185 GLN U CB  1 
+ATOM   1495 C CG  . GLN A 1 188 B -8.110  -51.814 -30.661 1.00 69.79  ? ? ? ? ? ? 185 GLN U CG  1 
+ATOM   1496 C CD  . GLN A 1 188 B -9.568  -52.210 -30.706 1.00 74.72  ? ? ? ? ? ? 185 GLN U CD  1 
+ATOM   1497 O OE1 . GLN A 1 188 B -10.452 -51.360 -30.693 1.00 76.45  ? ? ? ? ? ? 185 GLN U OE1 1 
+ATOM   1498 N NE2 . GLN A 1 188 B -9.823  -53.497 -30.770 1.00 78.62  ? ? ? ? ? ? 185 GLN U NE2 1 
+ATOM   1499 N N   . TRP A 1 189 ? -5.458  -47.980 -29.300 1.00 52.78  ? ? ? ? ? ? 186 TRP U N   1 
+ATOM   1500 C CA  . TRP A 1 189 ? -5.049  -46.633 -29.674 1.00 52.58  ? ? ? ? ? ? 186 TRP U CA  1 
+ATOM   1501 C C   . TRP A 1 189 ? -6.176  -45.625 -29.877 1.00 49.69  ? ? ? ? ? ? 186 TRP U C   1 
+ATOM   1502 O O   . TRP A 1 189 ? -6.065  -44.702 -30.638 1.00 50.81  ? ? ? ? ? ? 186 TRP U O   1 
+ATOM   1503 C CB  . TRP A 1 189 ? -4.196  -46.701 -30.917 1.00 58.56  ? ? ? ? ? ? 186 TRP U CB  1 
+ATOM   1504 C CG  . TRP A 1 189 ? -3.066  -47.578 -30.758 1.00 60.67  ? ? ? ? ? ? 186 TRP U CG  1 
+ATOM   1505 C CD1 . TRP A 1 189 ? -2.968  -48.848 -31.172 1.00 63.63  ? ? ? ? ? ? 186 TRP U CD1 1 
+ATOM   1506 C CD2 . TRP A 1 189 ? -1.854  -47.267 -30.114 1.00 61.62  ? ? ? ? ? ? 186 TRP U CD2 1 
+ATOM   1507 N NE1 . TRP A 1 189 ? -1.764  -49.363 -30.826 1.00 63.38  ? ? ? ? ? ? 186 TRP U NE1 1 
+ATOM   1508 C CE2 . TRP A 1 189 ? -1.058  -48.405 -30.165 1.00 63.67  ? ? ? ? ? ? 186 TRP U CE2 1 
+ATOM   1509 C CE3 . TRP A 1 189 ? -1.370  -46.139 -29.482 1.00 61.67  ? ? ? ? ? ? 186 TRP U CE3 1 
+ATOM   1510 C CZ2 . TRP A 1 189 ? 0.194   -48.450 -29.620 1.00 65.30  ? ? ? ? ? ? 186 TRP U CZ2 1 
+ATOM   1511 C CZ3 . TRP A 1 189 ? -0.144  -46.179 -28.945 1.00 64.20  ? ? ? ? ? ? 186 TRP U CZ3 1 
+ATOM   1512 C CH2 . TRP A 1 189 ? 0.637   -47.323 -29.008 1.00 67.05  ? ? ? ? ? ? 186 TRP U CH2 1 
+ATOM   1513 N N   . LYS A 1 190 ? -7.263  -45.811 -29.172 1.00 47.28  ? ? ? ? ? ? 187 LYS U N   1 
+ATOM   1514 C CA  . LYS A 1 190 ? -8.457  -44.978 -29.327 1.00 50.42  ? ? ? ? ? ? 187 LYS U CA  1 
+ATOM   1515 C C   . LYS A 1 190 ? -8.585  -43.886 -28.255 1.00 47.92  ? ? ? ? ? ? 187 LYS U C   1 
+ATOM   1516 O O   . LYS A 1 190 ? -9.296  -42.905 -28.464 1.00 50.43  ? ? ? ? ? ? 187 LYS U O   1 
+ATOM   1517 C CB  . LYS A 1 190 ? -9.737  -45.839 -29.341 1.00 51.31  ? ? ? ? ? ? 187 LYS U CB  1 
+ATOM   1518 C CG  . LYS A 1 190 ? -9.758  -46.968 -30.366 1.00 55.05  ? ? ? ? ? ? 187 LYS U CG  1 
+ATOM   1519 C CD  . LYS A 1 190 ? -9.491  -46.476 -31.786 1.00 56.67  ? ? ? ? ? ? 187 LYS U CD  1 
+ATOM   1520 C CE  . LYS A 1 190 ? -9.931  -47.501 -32.818 1.00 57.61  ? ? ? ? ? ? 187 LYS U CE  1 
+ATOM   1521 N NZ  . LYS A 1 190 ? -9.640  -47.075 -34.212 1.00 60.50  ? ? ? ? ? ? 187 LYS U NZ  1 
+ATOM   1522 N N   . THR A 1 191 ? -7.916  -44.047 -27.119 1.00 44.56  ? ? ? ? ? ? 188 THR U N   1 
+ATOM   1523 C CA  . THR A 1 191 ? -7.965  -43.026 -26.077 1.00 43.01  ? ? ? ? ? ? 188 THR U CA  1 
+ATOM   1524 C C   . THR A 1 191 ? -6.565  -42.614 -25.597 1.00 41.31  ? ? ? ? ? ? 188 THR U C   1 
+ATOM   1525 O O   . THR A 1 191 ? -5.628  -43.398 -25.674 1.00 41.99  ? ? ? ? ? ? 188 THR U O   1 
+ATOM   1526 C CB  . THR A 1 191 ? -8.870  -43.473 -24.918 1.00 41.89  ? ? ? ? ? ? 188 THR U CB  1 
+ATOM   1527 O OG1 . THR A 1 191 ? -9.215  -42.338 -24.113 1.00 43.76  ? ? ? ? ? ? 188 THR U OG1 1 
+ATOM   1528 C CG2 . THR A 1 191 ? -8.215  -44.588 -24.077 1.00 42.33  ? ? ? ? ? ? 188 THR U CG2 1 
+ATOM   1529 N N   . ASP A 1 192 ? -6.442  -41.367 -25.131 1.00 37.61  ? ? ? ? ? ? 189 ASP U N   1 
+ATOM   1530 C CA  . ASP A 1 192 ? -5.155  -40.773 -24.775 1.00 36.60  ? ? ? ? ? ? 189 ASP U CA  1 
+ATOM   1531 C C   . ASP A 1 192 ? -5.379  -39.453 -24.016 1.00 37.72  ? ? ? ? ? ? 189 ASP U C   1 
+ATOM   1532 O O   . ASP A 1 192 ? -6.487  -38.892 -24.002 1.00 38.44  ? ? ? ? ? ? 189 ASP U O   1 
+ATOM   1533 C CB  . ASP A 1 192 ? -4.331  -40.516 -26.070 1.00 36.16  ? ? ? ? ? ? 189 ASP U CB  1 
+ATOM   1534 C CG  . ASP A 1 192 ? -2.809  -40.513 -25.854 1.00 36.91  ? ? ? ? ? ? 189 ASP U CG  1 
+ATOM   1535 O OD1 . ASP A 1 192 ? -2.322  -40.682 -24.710 1.00 34.76  ? ? ? ? ? ? 189 ASP U OD1 1 
+ATOM   1536 O OD2 . ASP A 1 192 ? -2.071  -40.315 -26.868 1.00 37.12  ? ? ? ? ? ? 189 ASP U OD2 1 
+ATOM   1537 N N   . SER A 1 193 ? -4.317  -38.963 -23.391 1.00 38.86  ? ? ? ? ? ? 190 SER U N   1 
+ATOM   1538 C CA  . SER A 1 193 ? -4.228  -37.544 -23.023 1.00 38.26  ? ? ? ? ? ? 190 SER U CA  1 
+ATOM   1539 C C   . SER A 1 193 ? -3.850  -36.756 -24.277 1.00 39.64  ? ? ? ? ? ? 190 SER U C   1 
+ATOM   1540 O O   . SER A 1 193 ? -3.350  -37.349 -25.236 1.00 40.42  ? ? ? ? ? ? 190 SER U O   1 
+ATOM   1541 C CB  . SER A 1 193 ? -3.187  -37.333 -21.926 1.00 36.73  ? ? ? ? ? ? 190 SER U CB  1 
+ATOM   1542 O OG  . SER A 1 193 ? -1.979  -38.034 -22.198 1.00 33.10  ? ? ? ? ? ? 190 SER U OG  1 
+ATOM   1543 N N   . CYS A 1 194 ? -4.084  -35.442 -24.260 1.00 37.87  ? ? ? ? ? ? 191 CYS U N   1 
+ATOM   1544 C CA  . CYS A 1 194 ? -3.764  -34.571 -25.391 1.00 38.48  ? ? ? ? ? ? 191 CYS U CA  1 
+ATOM   1545 C C   . CYS A 1 194 ? -3.278  -33.199 -24.921 1.00 37.99  ? ? ? ? ? ? 191 CYS U C   1 
+ATOM   1546 O O   . CYS A 1 194 ? -2.972  -33.006 -23.734 1.00 35.65  ? ? ? ? ? ? 191 CYS U O   1 
+ATOM   1547 C CB  . CYS A 1 194 ? -4.980  -34.427 -26.307 1.00 42.36  ? ? ? ? ? ? 191 CYS U CB  1 
+ATOM   1548 S SG  . CYS A 1 194 ? -4.577  -33.967 -28.019 1.00 44.62  ? ? ? ? ? ? 191 CYS U SG  1 
+ATOM   1549 N N   . GLN A 1 195 ? -3.177  -32.249 -25.850 1.00 37.81  ? ? ? ? ? ? 192 GLN U N   1 
+ATOM   1550 C CA  . GLN A 1 195 ? -2.729  -30.904 -25.517 1.00 35.44  ? ? ? ? ? ? 192 GLN U CA  1 
+ATOM   1551 C C   . GLN A 1 195 ? -3.666  -30.313 -24.465 1.00 32.10  ? ? ? ? ? ? 192 GLN U C   1 
+ATOM   1552 O O   . GLN A 1 195 ? -4.870  -30.379 -24.616 1.00 28.26  ? ? ? ? ? ? 192 GLN U O   1 
+ATOM   1553 C CB  . GLN A 1 195 ? -2.682  -29.996 -26.756 1.00 37.78  ? ? ? ? ? ? 192 GLN U CB  1 
+ATOM   1554 C CG  . GLN A 1 195 ? -1.965  -28.664 -26.502 1.00 39.00  ? ? ? ? ? ? 192 GLN U CG  1 
+ATOM   1555 C CD  . GLN A 1 195 ? -1.872  -27.764 -27.732 1.00 40.61  ? ? ? ? ? ? 192 GLN U CD  1 
+ATOM   1556 O OE1 . GLN A 1 195 ? -2.552  -27.981 -28.726 1.00 41.94  ? ? ? ? ? ? 192 GLN U OE1 1 
+ATOM   1557 N NE2 . GLN A 1 195 ? -1.043  -26.739 -27.651 1.00 42.25  ? ? ? ? ? ? 192 GLN U NE2 1 
+ATOM   1558 N N   . GLY A 1 196 ? -3.099  -29.750 -23.401 1.00 32.37  ? ? ? ? ? ? 193 GLY U N   1 
+ATOM   1559 C CA  . GLY A 1 196 ? -3.899  -29.209 -22.309 1.00 31.88  ? ? ? ? ? ? 193 GLY U CA  1 
+ATOM   1560 C C   . GLY A 1 196 ? -4.193  -30.167 -21.156 1.00 30.98  ? ? ? ? ? ? 193 GLY U C   1 
+ATOM   1561 O O   . GLY A 1 196 ? -4.797  -29.765 -20.169 1.00 31.47  ? ? ? ? ? ? 193 GLY U O   1 
+ATOM   1562 N N   . ASP A 1 197 ? -3.783  -31.420 -21.262 1.00 31.38  ? ? ? ? ? ? 194 ASP U N   1 
+ATOM   1563 C CA  . ASP A 1 197 ? -3.872  -32.359 -20.126 1.00 32.73  ? ? ? ? ? ? 194 ASP U CA  1 
+ATOM   1564 C C   . ASP A 1 197 ? -2.562  -32.413 -19.325 1.00 33.97  ? ? ? ? ? ? 194 ASP U C   1 
+ATOM   1565 O O   . ASP A 1 197 ? -2.484  -33.082 -18.263 1.00 31.27  ? ? ? ? ? ? 194 ASP U O   1 
+ATOM   1566 C CB  . ASP A 1 197 ? -4.245  -33.765 -20.623 1.00 30.57  ? ? ? ? ? ? 194 ASP U CB  1 
+ATOM   1567 C CG  . ASP A 1 197 ? -5.683  -33.853 -21.118 1.00 32.29  ? ? ? ? ? ? 194 ASP U CG  1 
+ATOM   1568 O OD1 . ASP A 1 197 ? -6.603  -33.255 -20.486 1.00 30.97  ? ? ? ? ? ? 194 ASP U OD1 1 
+ATOM   1569 O OD2 . ASP A 1 197 ? -5.918  -34.546 -22.139 1.00 31.06  ? ? ? ? ? ? 194 ASP U OD2 1 
+ATOM   1570 N N   . SER A 1 198 ? -1.545  -31.741 -19.878 1.00 33.35  ? ? ? ? ? ? 195 SER U N   1 
+ATOM   1571 C CA  . SER A 1 198 ? -0.218  -31.608 -19.313 1.00 33.59  ? ? ? ? ? ? 195 SER U CA  1 
+ATOM   1572 C C   . SER A 1 198 ? -0.248  -31.274 -17.850 1.00 30.30  ? ? ? ? ? ? 195 SER U C   1 
+ATOM   1573 O O   . SER A 1 198 ? -1.061  -30.464 -17.438 1.00 25.80  ? ? ? ? ? ? 195 SER U O   1 
+ATOM   1574 C CB  . SER A 1 198 ? 0.524   -30.444 -19.988 1.00 35.48  ? ? ? ? ? ? 195 SER U CB  1 
+ATOM   1575 O OG  . SER A 1 198 ? 1.157   -30.886 -21.155 1.00 46.17  ? ? ? ? ? ? 195 SER U OG  1 
+ATOM   1576 N N   . GLY A 1 199 ? 0.699   -31.838 -17.099 1.00 28.28  ? ? ? ? ? ? 196 GLY U N   1 
+ATOM   1577 C CA  . GLY A 1 199 ? 0.749   -31.697 -15.635 1.00 28.43  ? ? ? ? ? ? 196 GLY U CA  1 
+ATOM   1578 C C   . GLY A 1 199 ? -0.239  -32.553 -14.855 1.00 28.94  ? ? ? ? ? ? 196 GLY U C   1 
+ATOM   1579 O O   . GLY A 1 199 ? -0.094  -32.729 -13.634 1.00 26.93  ? ? ? ? ? ? 196 GLY U O   1 
+ATOM   1580 N N   . GLY A 1 200 ? -1.227  -33.116 -15.552 1.00 29.36  ? ? ? ? ? ? 197 GLY U N   1 
+ATOM   1581 C CA  . GLY A 1 200 ? -2.287  -33.906 -14.943 1.00 26.55  ? ? ? ? ? ? 197 GLY U CA  1 
+ATOM   1582 C C   . GLY A 1 200 ? -1.865  -35.279 -14.419 1.00 28.76  ? ? ? ? ? ? 197 GLY U C   1 
+ATOM   1583 O O   . GLY A 1 200 ? -0.766  -35.735 -14.706 1.00 29.09  ? ? ? ? ? ? 197 GLY U O   1 
+ATOM   1584 N N   . PRO A 1 201 ? -2.750  -35.944 -13.629 1.00 26.42  ? ? ? ? ? ? 198 PRO U N   1 
+ATOM   1585 C CA  . PRO A 1 201 ? -2.457  -37.233 -13.024 1.00 27.70  ? ? ? ? ? ? 198 PRO U CA  1 
+ATOM   1586 C C   . PRO A 1 201 ? -2.650  -38.463 -13.905 1.00 29.98  ? ? ? ? ? ? 198 PRO U C   1 
+ATOM   1587 O O   . PRO A 1 201 ? -3.587  -38.549 -14.707 1.00 29.89  ? ? ? ? ? ? 198 PRO U O   1 
+ATOM   1588 C CB  . PRO A 1 201 ? -3.435  -37.310 -11.840 1.00 28.60  ? ? ? ? ? ? 198 PRO U CB  1 
+ATOM   1589 C CG  . PRO A 1 201 ? -4.571  -36.415 -12.201 1.00 27.79  ? ? ? ? ? ? 198 PRO U CG  1 
+ATOM   1590 C CD  . PRO A 1 201 ? -4.031  -35.386 -13.162 1.00 27.37  ? ? ? ? ? ? 198 PRO U CD  1 
+ATOM   1591 N N   . LEU A 1 202 ? -1.745  -39.413 -13.719 1.00 30.93  ? ? ? ? ? ? 199 LEU U N   1 
+ATOM   1592 C CA  . LEU A 1 202 ? -1.978  -40.797 -14.083 1.00 33.41  ? ? ? ? ? ? 199 LEU U CA  1 
+ATOM   1593 C C   . LEU A 1 202 ? -2.093  -41.553 -12.771 1.00 32.35  ? ? ? ? ? ? 199 LEU U C   1 
+ATOM   1594 O O   . LEU A 1 202 ? -1.135  -41.592 -12.004 1.00 31.95  ? ? ? ? ? ? 199 LEU U O   1 
+ATOM   1595 C CB  . LEU A 1 202 ? -0.808  -41.338 -14.884 1.00 34.21  ? ? ? ? ? ? 199 LEU U CB  1 
+ATOM   1596 C CG  . LEU A 1 202 ? -0.803  -42.818 -15.277 1.00 36.81  ? ? ? ? ? ? 199 LEU U CG  1 
+ATOM   1597 C CD1 . LEU A 1 202 ? -1.926  -43.130 -16.246 1.00 35.91  ? ? ? ? ? ? 199 LEU U CD1 1 
+ATOM   1598 C CD2 . LEU A 1 202 ? 0.538   -43.127 -15.924 1.00 38.99  ? ? ? ? ? ? 199 LEU U CD2 1 
+ATOM   1599 N N   . VAL A 1 203 ? -3.246  -42.175 -12.542 1.00 31.59  ? ? ? ? ? ? 200 VAL U N   1 
+ATOM   1600 C CA  . VAL A 1 203 ? -3.545  -42.822 -11.265 1.00 34.91  ? ? ? ? ? ? 200 VAL U CA  1 
+ATOM   1601 C C   . VAL A 1 203 ? -3.796  -44.306 -11.481 1.00 36.76  ? ? ? ? ? ? 200 VAL U C   1 
+ATOM   1602 O O   . VAL A 1 203 ? -4.608  -44.686 -12.317 1.00 34.12  ? ? ? ? ? ? 200 VAL U O   1 
+ATOM   1603 C CB  . VAL A 1 203 ? -4.796  -42.216 -10.606 1.00 36.21  ? ? ? ? ? ? 200 VAL U CB  1 
+ATOM   1604 C CG1 . VAL A 1 203 ? -5.182  -42.999 -9.346  1.00 38.54  ? ? ? ? ? ? 200 VAL U CG1 1 
+ATOM   1605 C CG2 . VAL A 1 203 ? -4.559  -40.756 -10.280 1.00 34.67  ? ? ? ? ? ? 200 VAL U CG2 1 
+ATOM   1606 N N   . CYS A 1 204 ? -3.096  -45.106 -10.718 1.00 41.79  ? ? ? ? ? ? 201 CYS U N   1 
+ATOM   1607 C CA  . CYS A 1 204 ? -3.132  -46.522 -10.871 1.00 44.77  ? ? ? ? ? ? 201 CYS U CA  1 
+ATOM   1608 C C   . CYS A 1 204 ? -3.378  -47.091 -9.499  1.00 44.92  ? ? ? ? ? ? 201 CYS U C   1 
+ATOM   1609 O O   . CYS A 1 204 ? -3.065  -46.475 -8.526  1.00 44.47  ? ? ? ? ? ? 201 CYS U O   1 
+ATOM   1610 C CB  . CYS A 1 204 ? -1.808  -47.000 -11.451 1.00 45.70  ? ? ? ? ? ? 201 CYS U CB  1 
+ATOM   1611 S SG  . CYS A 1 204 ? -1.498  -46.727 -13.199 1.00 49.59  ? ? ? ? ? ? 201 CYS U SG  1 
+ATOM   1612 N N   . SER A 1 205 ? -3.971  -48.264 -9.424  1.00 46.61  ? ? ? ? ? ? 202 SER U N   1 
+ATOM   1613 C CA  . SER A 1 205 ? -4.148  -48.923 -8.152  1.00 50.62  ? ? ? ? ? ? 202 SER U CA  1 
+ATOM   1614 C C   . SER A 1 205 ? -2.915  -49.743 -7.855  1.00 52.40  ? ? ? ? ? ? 202 SER U C   1 
+ATOM   1615 O O   . SER A 1 205 ? -2.580  -50.594 -8.616  1.00 51.03  ? ? ? ? ? ? 202 SER U O   1 
+ATOM   1616 C CB  . SER A 1 205 ? -5.352  -49.835 -8.209  1.00 52.93  ? ? ? ? ? ? 202 SER U CB  1 
+ATOM   1617 O OG  . SER A 1 205 ? -5.326  -50.750 -7.164  1.00 56.77  ? ? ? ? ? ? 202 SER U OG  1 
+ATOM   1618 N N   . LEU A 1 206 ? -2.229  -49.464 -6.762  1.00 54.91  ? ? ? ? ? ? 203 LEU U N   1 
+ATOM   1619 C CA  . LEU A 1 206 ? -0.997  -50.170 -6.468  1.00 61.90  ? ? ? ? ? ? 203 LEU U CA  1 
+ATOM   1620 C C   . LEU A 1 206 ? -1.038  -50.732 -5.077  1.00 66.73  ? ? ? ? ? ? 203 LEU U C   1 
+ATOM   1621 O O   . LEU A 1 206 ? -1.319  -50.011 -4.153  1.00 67.42  ? ? ? ? ? ? 203 LEU U O   1 
+ATOM   1622 C CB  . LEU A 1 206 ? 0.180   -49.229 -6.607  1.00 64.95  ? ? ? ? ? ? 203 LEU U CB  1 
+ATOM   1623 C CG  . LEU A 1 206 ? 1.587   -49.821 -6.532  1.00 66.70  ? ? ? ? ? ? 203 LEU U CG  1 
+ATOM   1624 C CD1 . LEU A 1 206 ? 1.687   -51.081 -7.354  1.00 65.54  ? ? ? ? ? ? 203 LEU U CD1 1 
+ATOM   1625 C CD2 . LEU A 1 206 ? 2.612   -48.795 -6.988  1.00 67.60  ? ? ? ? ? ? 203 LEU U CD2 1 
+ATOM   1626 N N   . GLN A 1 207 ? -0.768  -52.025 -4.906  1.00 71.72  ? ? ? ? ? ? 204 GLN U N   1 
+ATOM   1627 C CA  . GLN A 1 207 ? -1.258  -52.750 -3.738  1.00 73.13  ? ? ? ? ? ? 204 GLN U CA  1 
+ATOM   1628 C C   . GLN A 1 207 ? -2.773  -52.596 -3.814  1.00 71.45  ? ? ? ? ? ? 204 GLN U C   1 
+ATOM   1629 O O   . GLN A 1 207 ? -3.356  -52.876 -4.846  1.00 71.67  ? ? ? ? ? ? 204 GLN U O   1 
+ATOM   1630 C CB  . GLN A 1 207 ? -0.709  -52.229 -2.403  1.00 75.96  ? ? ? ? ? ? 204 GLN U CB  1 
+ATOM   1631 C CG  . GLN A 1 207 ? 0.647   -51.540 -2.430  1.00 77.21  ? ? ? ? ? ? 204 GLN U CG  1 
+ATOM   1632 C CD  . GLN A 1 207 ? 1.819   -52.489 -2.314  1.00 80.39  ? ? ? ? ? ? 204 GLN U CD  1 
+ATOM   1633 O OE1 . GLN A 1 207 ? 2.218   -52.887 -1.204  1.00 78.50  ? ? ? ? ? ? 204 GLN U OE1 1 
+ATOM   1634 N NE2 . GLN A 1 207 ? 2.391   -52.857 -3.462  1.00 78.97  ? ? ? ? ? ? 204 GLN U NE2 1 
+ATOM   1635 N N   . GLY A 1 208 ? -3.406  -52.112 -2.756  1.00 71.52  ? ? ? ? ? ? 205 GLY U N   1 
+ATOM   1636 C CA  . GLY A 1 208 ? -4.802  -51.729 -2.849  1.00 66.27  ? ? ? ? ? ? 205 GLY U CA  1 
+ATOM   1637 C C   . GLY A 1 208 ? -5.060  -50.256 -3.024  1.00 62.24  ? ? ? ? ? ? 205 GLY U C   1 
+ATOM   1638 O O   . GLY A 1 208 ? -6.193  -49.826 -3.065  1.00 64.37  ? ? ? ? ? ? 205 GLY U O   1 
+ATOM   1639 N N   . ARG A 1 209 ? -4.008  -49.469 -3.117  1.00 59.60  ? ? ? ? ? ? 206 ARG U N   1 
+ATOM   1640 C CA  . ARG A 1 209 ? -4.159  -48.044 -3.011  1.00 60.84  ? ? ? ? ? ? 206 ARG U CA  1 
+ATOM   1641 C C   . ARG A 1 209 ? -4.182  -47.400 -4.381  1.00 56.25  ? ? ? ? ? ? 206 ARG U C   1 
+ATOM   1642 O O   . ARG A 1 209 ? -3.475  -47.810 -5.277  1.00 55.48  ? ? ? ? ? ? 206 ARG U O   1 
+ATOM   1643 C CB  . ARG A 1 209 ? -3.082  -47.447 -2.086  1.00 63.47  ? ? ? ? ? ? 206 ARG U CB  1 
+ATOM   1644 C CG  . ARG A 1 209 ? -3.437  -46.111 -1.429  1.00 68.29  ? ? ? ? ? ? 206 ARG U CG  1 
+ATOM   1645 C CD  . ARG A 1 209 ? -4.587  -46.227 -0.425  1.00 73.26  ? ? ? ? ? ? 206 ARG U CD  1 
+ATOM   1646 N NE  . ARG A 1 209 ? -5.446  -45.038 -0.311  1.00 78.15  ? ? ? ? ? ? 206 ARG U NE  1 
+ATOM   1647 C CZ  . ARG A 1 209 ? -6.485  -44.913 0.516   1.00 80.70  ? ? ? ? ? ? 206 ARG U CZ  1 
+ATOM   1648 N NH1 . ARG A 1 209 ? -6.828  -45.882 1.339   1.00 79.08  ? ? ? ? ? ? 206 ARG U NH1 1 
+ATOM   1649 N NH2 . ARG A 1 209 ? -7.181  -43.802 0.532   1.00 80.94  ? ? ? ? ? ? 206 ARG U NH2 1 
+ATOM   1650 N N   . MET A 1 210 ? -5.039  -46.408 -4.519  1.00 52.64  ? ? ? ? ? ? 207 MET U N   1 
+ATOM   1651 C CA  . MET A 1 210 ? -5.034  -45.548 -5.670  1.00 52.89  ? ? ? ? ? ? 207 MET U CA  1 
+ATOM   1652 C C   . MET A 1 210 ? -3.833  -44.680 -5.495  1.00 46.69  ? ? ? ? ? ? 207 MET U C   1 
+ATOM   1653 O O   . MET A 1 210 ? -3.756  -43.968 -4.560  1.00 44.64  ? ? ? ? ? ? 207 MET U O   1 
+ATOM   1654 C CB  . MET A 1 210 ? -6.232  -44.616 -5.623  1.00 55.01  ? ? ? ? ? ? 207 MET U CB  1 
+ATOM   1655 C CG  . MET A 1 210 ? -7.573  -45.250 -5.934  1.00 61.05  ? ? ? ? ? ? 207 MET U CG  1 
+ATOM   1656 S SD  . MET A 1 210 ? -7.664  -45.756 -7.631  1.00 64.56  ? ? ? ? ? ? 207 MET U SD  1 
+ATOM   1657 C CE  . MET A 1 210 ? -9.068  -44.822 -8.174  1.00 65.38  ? ? ? ? ? ? 207 MET U CE  1 
+ATOM   1658 N N   . THR A 1 211 ? -2.933  -44.692 -6.450  1.00 45.14  ? ? ? ? ? ? 208 THR U N   1 
+ATOM   1659 C CA  . THR A 1 211 ? -1.677  -43.984 -6.309  1.00 43.27  ? ? ? ? ? ? 208 THR U CA  1 
+ATOM   1660 C C   . THR A 1 211 ? -1.342  -43.115 -7.526  1.00 38.29  ? ? ? ? ? ? 208 THR U C   1 
+ATOM   1661 O O   . THR A 1 211 ? -1.716  -43.444 -8.607  1.00 36.05  ? ? ? ? ? ? 208 THR U O   1 
+ATOM   1662 C CB  . THR A 1 211 ? -0.553  -44.987 -6.012  1.00 46.68  ? ? ? ? ? ? 208 THR U CB  1 
+ATOM   1663 O OG1 . THR A 1 211 ? -0.787  -45.582 -4.744  1.00 49.73  ? ? ? ? ? ? 208 THR U OG1 1 
+ATOM   1664 C CG2 . THR A 1 211 ? 0.758   -44.327 -5.955  1.00 47.15  ? ? ? ? ? ? 208 THR U CG2 1 
+ATOM   1665 N N   . LEU A 1 212 ? -0.622  -42.018 -7.315  1.00 36.25  ? ? ? ? ? ? 209 LEU U N   1 
+ATOM   1666 C CA  . LEU A 1 212 ? -0.178  -41.144 -8.415  1.00 33.00  ? ? ? ? ? ? 209 LEU U CA  1 
+ATOM   1667 C C   . LEU A 1 212 ? 1.089   -41.709 -9.023  1.00 35.05  ? ? ? ? ? ? 209 LEU U C   1 
+ATOM   1668 O O   . LEU A 1 212 ? 2.193   -41.539 -8.490  1.00 36.32  ? ? ? ? ? ? 209 LEU U O   1 
+ATOM   1669 C CB  . LEU A 1 212 ? 0.089   -39.728 -7.921  1.00 32.58  ? ? ? ? ? ? 209 LEU U CB  1 
+ATOM   1670 C CG  . LEU A 1 212 ? 0.559   -38.756 -9.012  1.00 32.68  ? ? ? ? ? ? 209 LEU U CG  1 
+ATOM   1671 C CD1 . LEU A 1 212 ? -0.548  -38.483 -10.015 1.00 31.98  ? ? ? ? ? ? 209 LEU U CD1 1 
+ATOM   1672 C CD2 . LEU A 1 212 ? 1.053   -37.472 -8.387  1.00 34.41  ? ? ? ? ? ? 209 LEU U CD2 1 
+ATOM   1673 N N   . THR A 1 213 ? 0.920   -42.384 -10.141 1.00 32.61  ? ? ? ? ? ? 210 THR U N   1 
+ATOM   1674 C CA  . THR A 1 213 ? 1.993   -43.107 -10.777 1.00 37.14  ? ? ? ? ? ? 210 THR U CA  1 
+ATOM   1675 C C   . THR A 1 213 ? 2.679   -42.274 -11.871 1.00 37.48  ? ? ? ? ? ? 210 THR U C   1 
+ATOM   1676 O O   . THR A 1 213 ? 3.867   -42.451 -12.136 1.00 41.48  ? ? ? ? ? ? 210 THR U O   1 
+ATOM   1677 C CB  . THR A 1 213 ? 1.434   -44.435 -11.335 1.00 36.82  ? ? ? ? ? ? 210 THR U CB  1 
+ATOM   1678 O OG1 . THR A 1 213 ? 1.043   -45.261 -10.229 1.00 39.44  ? ? ? ? ? ? 210 THR U OG1 1 
+ATOM   1679 C CG2 . THR A 1 213 ? 2.462   -45.170 -12.177 1.00 39.37  ? ? ? ? ? ? 210 THR U CG2 1 
+ATOM   1680 N N   . GLY A 1 214 ? 1.941   -41.362 -12.490 1.00 36.47  ? ? ? ? ? ? 211 GLY U N   1 
+ATOM   1681 C CA  . GLY A 1 214 ? 2.501   -40.503 -13.514 1.00 34.57  ? ? ? ? ? ? 211 GLY U CA  1 
+ATOM   1682 C C   . GLY A 1 214 ? 1.964   -39.087 -13.537 1.00 35.05  ? ? ? ? ? ? 211 GLY U C   1 
+ATOM   1683 O O   . GLY A 1 214 ? 0.944   -38.777 -12.905 1.00 32.72  ? ? ? ? ? ? 211 GLY U O   1 
+ATOM   1684 N N   . ILE A 1 215 ? 2.664   -38.249 -14.307 1.00 33.48  ? ? ? ? ? ? 212 ILE U N   1 
+ATOM   1685 C CA  . ILE A 1 215 ? 2.300   -36.859 -14.562 1.00 31.96  ? ? ? ? ? ? 212 ILE U CA  1 
+ATOM   1686 C C   . ILE A 1 215 ? 2.384   -36.665 -16.067 1.00 34.00  ? ? ? ? ? ? 212 ILE U C   1 
+ATOM   1687 O O   . ILE A 1 215 ? 3.418   -36.949 -16.677 1.00 34.87  ? ? ? ? ? ? 212 ILE U O   1 
+ATOM   1688 C CB  . ILE A 1 215 ? 3.293   -35.887 -13.912 1.00 32.01  ? ? ? ? ? ? 212 ILE U CB  1 
+ATOM   1689 C CG1 . ILE A 1 215 ? 3.278   -36.058 -12.403 1.00 30.50  ? ? ? ? ? ? 212 ILE U CG1 1 
+ATOM   1690 C CG2 . ILE A 1 215 ? 2.998   -34.443 -14.314 1.00 33.11  ? ? ? ? ? ? 212 ILE U CG2 1 
+ATOM   1691 C CD1 . ILE A 1 215 ? 4.270   -35.204 -11.671 1.00 33.53  ? ? ? ? ? ? 212 ILE U CD1 1 
+ATOM   1692 N N   . VAL A 1 216 ? 1.316   -36.171 -16.671 1.00 31.64  ? ? ? ? ? ? 213 VAL U N   1 
+ATOM   1693 C CA  . VAL A 1 216 ? 1.283   -36.094 -18.119 1.00 31.03  ? ? ? ? ? ? 213 VAL U CA  1 
+ATOM   1694 C C   . VAL A 1 216 ? 2.412   -35.199 -18.595 1.00 32.27  ? ? ? ? ? ? 213 VAL U C   1 
+ATOM   1695 O O   . VAL A 1 216 ? 2.483   -34.032 -18.194 1.00 31.82  ? ? ? ? ? ? 213 VAL U O   1 
+ATOM   1696 C CB  . VAL A 1 216 ? -0.041  -35.511 -18.646 1.00 30.18  ? ? ? ? ? ? 213 VAL U CB  1 
+ATOM   1697 C CG1 . VAL A 1 216 ? -0.008  -35.433 -20.161 1.00 29.51  ? ? ? ? ? ? 213 VAL U CG1 1 
+ATOM   1698 C CG2 . VAL A 1 216 ? -1.226  -36.336 -18.191 1.00 29.61  ? ? ? ? ? ? 213 VAL U CG2 1 
+ATOM   1699 N N   . SER A 1 217 ? 3.272   -35.745 -19.464 1.00 33.07  ? ? ? ? ? ? 214 SER U N   1 
+ATOM   1700 C CA  . SER A 1 217 ? 4.489   -35.062 -19.924 1.00 34.62  ? ? ? ? ? ? 214 SER U CA  1 
+ATOM   1701 C C   . SER A 1 217 ? 4.544   -34.792 -21.426 1.00 34.86  ? ? ? ? ? ? 214 SER U C   1 
+ATOM   1702 O O   . SER A 1 217 ? 4.639   -33.639 -21.827 1.00 36.34  ? ? ? ? ? ? 214 SER U O   1 
+ATOM   1703 C CB  . SER A 1 217 ? 5.740   -35.823 -19.496 1.00 35.04  ? ? ? ? ? ? 214 SER U CB  1 
+ATOM   1704 O OG  . SER A 1 217 ? 6.889   -35.004 -19.589 1.00 36.50  ? ? ? ? ? ? 214 SER U OG  1 
+ATOM   1705 N N   . TRP A 1 218 ? 4.497   -35.830 -22.254 1.00 35.10  ? ? ? ? ? ? 215 TRP U N   1 
+ATOM   1706 C CA  . TRP A 1 218 ? 4.537   -35.642 -23.714 1.00 37.36  ? ? ? ? ? ? 215 TRP U CA  1 
+ATOM   1707 C C   . TRP A 1 218 ? 4.014   -36.859 -24.467 1.00 39.07  ? ? ? ? ? ? 215 TRP U C   1 
+ATOM   1708 O O   . TRP A 1 218 ? 3.683   -37.876 -23.876 1.00 43.28  ? ? ? ? ? ? 215 TRP U O   1 
+ATOM   1709 C CB  . TRP A 1 218 ? 5.967   -35.280 -24.190 1.00 36.21  ? ? ? ? ? ? 215 TRP U CB  1 
+ATOM   1710 C CG  . TRP A 1 218 ? 7.010   -36.276 -23.766 1.00 35.22  ? ? ? ? ? ? 215 TRP U CG  1 
+ATOM   1711 C CD1 . TRP A 1 218 ? 7.639   -36.322 -22.568 1.00 36.43  ? ? ? ? ? ? 215 TRP U CD1 1 
+ATOM   1712 C CD2 . TRP A 1 218 ? 7.515   -37.376 -24.526 1.00 34.94  ? ? ? ? ? ? 215 TRP U CD2 1 
+ATOM   1713 N NE1 . TRP A 1 218 ? 8.516   -37.379 -22.527 1.00 37.30  ? ? ? ? ? ? 215 TRP U NE1 1 
+ATOM   1714 C CE2 . TRP A 1 218 ? 8.451   -38.049 -23.715 1.00 37.37  ? ? ? ? ? ? 215 TRP U CE2 1 
+ATOM   1715 C CE3 . TRP A 1 218 ? 7.262   -37.864 -25.810 1.00 36.87  ? ? ? ? ? ? 215 TRP U CE3 1 
+ATOM   1716 C CZ2 . TRP A 1 218 ? 9.150   -39.184 -24.153 1.00 39.28  ? ? ? ? ? ? 215 TRP U CZ2 1 
+ATOM   1717 C CZ3 . TRP A 1 218 ? 7.949   -38.989 -26.244 1.00 39.13  ? ? ? ? ? ? 215 TRP U CZ3 1 
+ATOM   1718 C CH2 . TRP A 1 218 ? 8.885   -39.634 -25.420 1.00 40.07  ? ? ? ? ? ? 215 TRP U CH2 1 
+ATOM   1719 N N   . GLY A 1 219 ? 3.696   -36.679 -25.731 1.00 45.63  ? ? ? ? ? ? 216 GLY U N   1 
+ATOM   1720 C CA  . GLY A 1 219 ? 3.445   -37.747 -26.654 1.00 46.54  ? ? ? ? ? ? 216 GLY U CA  1 
+ATOM   1721 C C   . GLY A 1 219 ? 3.290   -37.148 -28.022 1.00 49.46  ? ? ? ? ? ? 216 GLY U C   1 
+ATOM   1722 O O   . GLY A 1 219 ? 2.894   -36.020 -28.108 1.00 50.30  ? ? ? ? ? ? 216 GLY U O   1 
+ATOM   1723 N N   . ARG A 1 220 ? 3.498   -37.919 -29.077 1.00 51.54  ? ? ? ? ? ? 217 ARG U N   1 
+ATOM   1724 C CA  . ARG A 1 220 ? 3.329   -37.424 -30.431 1.00 54.70  ? ? ? ? ? ? 217 ARG U CA  1 
+ATOM   1725 C C   . ARG A 1 220 ? 1.896   -37.587 -30.821 1.00 53.58  ? ? ? ? ? ? 217 ARG U C   1 
+ATOM   1726 O O   . ARG A 1 220 ? 1.357   -38.661 -30.731 1.00 60.05  ? ? ? ? ? ? 217 ARG U O   1 
+ATOM   1727 C CB  . ARG A 1 220 ? 4.238   -38.193 -31.391 1.00 57.79  ? ? ? ? ? ? 217 ARG U CB  1 
+ATOM   1728 C CG  . ARG A 1 220 ? 4.113   -37.823 -32.849 1.00 62.46  ? ? ? ? ? ? 217 ARG U CG  1 
+ATOM   1729 C CD  . ARG A 1 220 ? 4.793   -38.829 -33.763 1.00 66.41  ? ? ? ? ? ? 217 ARG U CD  1 
+ATOM   1730 N NE  . ARG A 1 220 ? 5.322   -38.196 -34.958 1.00 72.21  ? ? ? ? ? ? 217 ARG U NE  1 
+ATOM   1731 C CZ  . ARG A 1 220 ? 6.238   -38.722 -35.773 1.00 72.39  ? ? ? ? ? ? 217 ARG U CZ  1 
+ATOM   1732 N NH1 . ARG A 1 220 ? 6.649   -38.039 -36.837 1.00 69.34  ? ? ? ? ? ? 217 ARG U NH1 1 
+ATOM   1733 N NH2 . ARG A 1 220 ? 6.749   -39.913 -35.526 1.00 69.94  ? ? ? ? ? ? 217 ARG U NH2 1 
+ATOM   1734 N N   . GLY A 1 221 ? 1.256   -36.499 -31.194 1.00 48.92  ? ? ? ? ? ? 219 GLY U N   1 
+ATOM   1735 C CA  . GLY A 1 221 ? -0.142  -36.526 -31.539 1.00 49.62  ? ? ? ? ? ? 219 GLY U CA  1 
+ATOM   1736 C C   . GLY A 1 221 ? -0.922  -36.891 -30.297 1.00 50.98  ? ? ? ? ? ? 219 GLY U C   1 
+ATOM   1737 O O   . GLY A 1 221 ? -0.387  -36.877 -29.226 1.00 47.58  ? ? ? ? ? ? 219 GLY U O   1 
+ATOM   1738 N N   . CYS A 1 222 ? -2.170  -37.283 -30.463 1.00 48.73  ? ? ? ? ? ? 220 CYS U N   1 
+ATOM   1739 C CA  . CYS A 1 222 ? -2.923  -37.905 -29.401 1.00 47.18  ? ? ? ? ? ? 220 CYS U CA  1 
+ATOM   1740 C C   . CYS A 1 222 ? -3.684  -39.155 -29.906 1.00 44.32  ? ? ? ? ? ? 220 CYS U C   1 
+ATOM   1741 O O   . CYS A 1 222 ? -4.292  -39.111 -30.947 1.00 43.12  ? ? ? ? ? ? 220 CYS U O   1 
+ATOM   1742 C CB  . CYS A 1 222 ? -3.882  -36.885 -28.811 1.00 44.97  ? ? ? ? ? ? 220 CYS U CB  1 
+ATOM   1743 S SG  . CYS A 1 222 ? -3.268  -35.222 -28.499 1.00 49.80  ? ? ? ? ? ? 220 CYS U SG  1 
+ATOM   1744 N N   . ALA A 1 223 ? -3.657  -40.246 -29.159 1.00 44.24  ? ? ? ? ? ? 221 ALA U N   1 
+ATOM   1745 C CA  . ALA A 1 223 ? -4.330  -41.476 -29.565 1.00 45.80  ? ? ? ? ? ? 221 ALA U CA  1 
+ATOM   1746 C C   . ALA A 1 223 ? -3.866  -41.835 -30.984 1.00 50.44  ? ? ? ? ? ? 221 ALA U C   1 
+ATOM   1747 O O   . ALA A 1 223 ? -4.676  -42.175 -31.853 1.00 49.11  ? ? ? ? ? ? 221 ALA U O   1 
+ATOM   1748 C CB  . ALA A 1 223 ? -5.839  -41.299 -29.521 1.00 46.47  ? ? ? ? ? ? 221 ALA U CB  1 
+ATOM   1749 N N   . LEU A 1 224 ? -2.570  -41.765 -31.213 1.00 49.32  ? ? ? ? ? ? 222 LEU U N   1 
+ATOM   1750 C CA  . LEU A 1 224 ? -2.002  -42.001 -32.530 1.00 52.04  ? ? ? ? ? ? 222 LEU U CA  1 
+ATOM   1751 C C   . LEU A 1 224 ? -1.284  -43.303 -32.507 1.00 53.42  ? ? ? ? ? ? 222 LEU U C   1 
+ATOM   1752 O O   . LEU A 1 224 ? -0.503  -43.550 -31.620 1.00 50.49  ? ? ? ? ? ? 222 LEU U O   1 
+ATOM   1753 C CB  . LEU A 1 224 ? -1.033  -40.898 -32.866 1.00 51.24  ? ? ? ? ? ? 222 LEU U CB  1 
+ATOM   1754 C CG  . LEU A 1 224 ? -0.116  -40.939 -34.060 1.00 53.84  ? ? ? ? ? ? 222 LEU U CG  1 
+ATOM   1755 C CD1 . LEU A 1 224 ? -0.901  -40.974 -35.341 1.00 51.26  ? ? ? ? ? ? 222 LEU U CD1 1 
+ATOM   1756 C CD2 . LEU A 1 224 ? 0.716   -39.694 -34.004 1.00 54.48  ? ? ? ? ? ? 222 LEU U CD2 1 
+ATOM   1757 N N   . LYS A 1 225 ? -1.520  -44.140 -33.502 1.00 57.73  ? ? ? ? ? ? 223 LYS U N   1 
+ATOM   1758 C CA  . LYS A 1 225 ? -0.990  -45.489 -33.450 1.00 60.71  ? ? ? ? ? ? 223 LYS U CA  1 
+ATOM   1759 C C   . LYS A 1 225 ? 0.510   -45.464 -33.242 1.00 55.18  ? ? ? ? ? ? 223 LYS U C   1 
+ATOM   1760 O O   . LYS A 1 225 ? 1.217   -44.686 -33.851 1.00 51.83  ? ? ? ? ? ? 223 LYS U O   1 
+ATOM   1761 C CB  . LYS A 1 225 ? -1.328  -46.232 -34.727 1.00 64.23  ? ? ? ? ? ? 223 LYS U CB  1 
+ATOM   1762 C CG  . LYS A 1 225 ? -0.926  -47.685 -34.752 1.00 67.90  ? ? ? ? ? ? 223 LYS U CG  1 
+ATOM   1763 C CD  . LYS A 1 225 ? -1.805  -48.460 -35.721 1.00 69.21  ? ? ? ? ? ? 223 LYS U CD  1 
+ATOM   1764 C CE  . LYS A 1 225 ? -1.316  -49.874 -35.899 1.00 72.31  ? ? ? ? ? ? 223 LYS U CE  1 
+ATOM   1765 N NZ  . LYS A 1 225 ? -2.415  -50.762 -36.351 1.00 77.12  ? ? ? ? ? ? 223 LYS U NZ  1 
+ATOM   1766 N N   . ASP A 1 226 A 0.919   -46.288 -32.285 1.00 56.35  ? ? ? ? ? ? 223 ASP U N   1 
+ATOM   1767 C CA  . ASP A 1 226 A 2.280   -46.618 -31.905 1.00 62.68  ? ? ? ? ? ? 223 ASP U CA  1 
+ATOM   1768 C C   . ASP A 1 226 A 3.054   -45.554 -31.179 1.00 63.30  ? ? ? ? ? ? 223 ASP U C   1 
+ATOM   1769 O O   . ASP A 1 226 A 4.232   -45.717 -30.885 1.00 60.77  ? ? ? ? ? ? 223 ASP U O   1 
+ATOM   1770 C CB  . ASP A 1 226 A 3.078   -47.256 -33.026 1.00 65.15  ? ? ? ? ? ? 223 ASP U CB  1 
+ATOM   1771 C CG  . ASP A 1 226 A 2.598   -48.652 -33.348 1.00 67.13  ? ? ? ? ? ? 223 ASP U CG  1 
+ATOM   1772 O OD1 . ASP A 1 226 A 2.947   -49.597 -32.629 1.00 64.06  ? ? ? ? ? ? 223 ASP U OD1 1 
+ATOM   1773 O OD2 . ASP A 1 226 A 1.855   -48.786 -34.323 1.00 69.76  ? ? ? ? ? ? 223 ASP U OD2 1 
+ATOM   1774 N N   . LYS A 1 227 ? 2.336   -44.510 -30.805 1.00 62.31  ? ? ? ? ? ? 224 LYS U N   1 
+ATOM   1775 C CA  . LYS A 1 227 ? 2.873   -43.401 -30.059 1.00 57.48  ? ? ? ? ? ? 224 LYS U CA  1 
+ATOM   1776 C C   . LYS A 1 227 ? 2.133   -43.180 -28.728 1.00 54.69  ? ? ? ? ? ? 224 LYS U C   1 
+ATOM   1777 O O   . LYS A 1 227 ? 1.234   -42.392 -28.659 1.00 51.72  ? ? ? ? ? ? 224 LYS U O   1 
+ATOM   1778 C CB  . LYS A 1 227 ? 2.772   -42.161 -30.920 1.00 58.65  ? ? ? ? ? ? 224 LYS U CB  1 
+ATOM   1779 C CG  . LYS A 1 227 ? 3.322   -42.339 -32.319 1.00 59.63  ? ? ? ? ? ? 224 LYS U CG  1 
+ATOM   1780 C CD  . LYS A 1 227 ? 4.827   -42.407 -32.260 1.00 61.08  ? ? ? ? ? ? 224 LYS U CD  1 
+ATOM   1781 C CE  . LYS A 1 227 ? 5.471   -42.839 -33.569 1.00 60.77  ? ? ? ? ? ? 224 LYS U CE  1 
+ATOM   1782 N NZ  . LYS A 1 227 ? 6.875   -43.215 -33.286 1.00 62.48  ? ? ? ? ? ? 224 LYS U NZ  1 
+ATOM   1783 N N   . PRO A 1 228 ? 2.540   -43.875 -27.685 1.00 50.53  ? ? ? ? ? ? 225 PRO U N   1 
+ATOM   1784 C CA  . PRO A 1 228 ? 1.904   -43.742 -26.365 1.00 50.03  ? ? ? ? ? ? 225 PRO U CA  1 
+ATOM   1785 C C   . PRO A 1 228 ? 2.020   -42.358 -25.721 1.00 48.14  ? ? ? ? ? ? 225 PRO U C   1 
+ATOM   1786 O O   . PRO A 1 228 ? 2.935   -41.589 -26.022 1.00 46.97  ? ? ? ? ? ? 225 PRO U O   1 
+ATOM   1787 C CB  . PRO A 1 228 ? 2.679   -44.738 -25.489 1.00 49.76  ? ? ? ? ? ? 225 PRO U CB  1 
+ATOM   1788 C CG  . PRO A 1 228 ? 3.371   -45.656 -26.429 1.00 51.71  ? ? ? ? ? ? 225 PRO U CG  1 
+ATOM   1789 C CD  . PRO A 1 228 ? 3.621   -44.877 -27.677 1.00 50.15  ? ? ? ? ? ? 225 PRO U CD  1 
+ATOM   1790 N N   . GLY A 1 229 ? 1.108   -42.054 -24.806 1.00 43.25  ? ? ? ? ? ? 226 GLY U N   1 
+ATOM   1791 C CA  . GLY A 1 229 ? 1.327   -40.940 -23.911 1.00 39.06  ? ? ? ? ? ? 226 GLY U CA  1 
+ATOM   1792 C C   . GLY A 1 229 ? 2.515   -41.255 -23.020 1.00 39.11  ? ? ? ? ? ? 226 GLY U C   1 
+ATOM   1793 O O   . GLY A 1 229 ? 2.734   -42.411 -22.636 1.00 37.46  ? ? ? ? ? ? 226 GLY U O   1 
+ATOM   1794 N N   . VAL A 1 230 ? 3.292   -40.231 -22.690 1.00 37.48  ? ? ? ? ? ? 227 VAL U N   1 
+ATOM   1795 C CA  . VAL A 1 230 ? 4.478   -40.416 -21.862 1.00 36.17  ? ? ? ? ? ? 227 VAL U CA  1 
+ATOM   1796 C C   . VAL A 1 230 ? 4.338   -39.581 -20.617 1.00 35.81  ? ? ? ? ? ? 227 VAL U C   1 
+ATOM   1797 O O   . VAL A 1 230 ? 3.863   -38.432 -20.665 1.00 35.45  ? ? ? ? ? ? 227 VAL U O   1 
+ATOM   1798 C CB  . VAL A 1 230 ? 5.754   -40.005 -22.629 1.00 36.64  ? ? ? ? ? ? 227 VAL U CB  1 
+ATOM   1799 C CG1 . VAL A 1 230 ? 6.998   -40.399 -21.849 1.00 33.70  ? ? ? ? ? ? 227 VAL U CG1 1 
+ATOM   1800 C CG2 . VAL A 1 230 ? 5.726   -40.612 -24.029 1.00 35.76  ? ? ? ? ? ? 227 VAL U CG2 1 
+ATOM   1801 N N   . TYR A 1 231 ? 4.755   -40.158 -19.497 1.00 37.81  ? ? ? ? ? ? 228 TYR U N   1 
+ATOM   1802 C CA  . TYR A 1 231 ? 4.490   -39.580 -18.194 1.00 36.72  ? ? ? ? ? ? 228 TYR U CA  1 
+ATOM   1803 C C   . TYR A 1 231 ? 5.725   -39.596 -17.337 1.00 37.14  ? ? ? ? ? ? 228 TYR U C   1 
+ATOM   1804 O O   . TYR A 1 231 ? 6.563   -40.485 -17.457 1.00 38.67  ? ? ? ? ? ? 228 TYR U O   1 
+ATOM   1805 C CB  . TYR A 1 231 ? 3.393   -40.374 -17.490 1.00 38.41  ? ? ? ? ? ? 228 TYR U CB  1 
+ATOM   1806 C CG  . TYR A 1 231 ? 2.079   -40.394 -18.235 1.00 37.85  ? ? ? ? ? ? 228 TYR U CG  1 
+ATOM   1807 C CD1 . TYR A 1 231 ? 1.918   -41.145 -19.414 1.00 39.30  ? ? ? ? ? ? 228 TYR U CD1 1 
+ATOM   1808 C CD2 . TYR A 1 231 ? 0.987   -39.682 -17.760 1.00 35.57  ? ? ? ? ? ? 228 TYR U CD2 1 
+ATOM   1809 C CE1 . TYR A 1 231 ? 0.711   -41.156 -20.093 1.00 36.43  ? ? ? ? ? ? 228 TYR U CE1 1 
+ATOM   1810 C CE2 . TYR A 1 231 ? -0.214  -39.697 -18.421 1.00 35.14  ? ? ? ? ? ? 228 TYR U CE2 1 
+ATOM   1811 C CZ  . TYR A 1 231 ? -0.353  -40.418 -19.586 1.00 35.67  ? ? ? ? ? ? 228 TYR U CZ  1 
+ATOM   1812 O OH  . TYR A 1 231 ? -1.564  -40.394 -20.221 1.00 35.42  ? ? ? ? ? ? 228 TYR U OH  1 
+ATOM   1813 N N   . THR A 1 232 ? 5.828   -38.609 -16.456 1.00 35.74  ? ? ? ? ? ? 229 THR U N   1 
+ATOM   1814 C CA  . THR A 1 232 ? 6.923   -38.536 -15.524 1.00 35.58  ? ? ? ? ? ? 229 THR U CA  1 
+ATOM   1815 C C   . THR A 1 232 ? 6.692   -39.617 -14.450 1.00 38.63  ? ? ? ? ? ? 229 THR U C   1 
+ATOM   1816 O O   . THR A 1 232 ? 5.630   -39.677 -13.845 1.00 37.06  ? ? ? ? ? ? 229 THR U O   1 
+ATOM   1817 C CB  . THR A 1 232 ? 7.004   -37.139 -14.900 1.00 34.20  ? ? ? ? ? ? 229 THR U CB  1 
+ATOM   1818 O OG1 . THR A 1 232 ? 7.151   -36.150 -15.947 1.00 31.37  ? ? ? ? ? ? 229 THR U OG1 1 
+ATOM   1819 C CG2 . THR A 1 232 ? 8.171   -37.060 -13.904 1.00 33.68  ? ? ? ? ? ? 229 THR U CG2 1 
+ATOM   1820 N N   . ARG A 1 233 ? 7.688   -40.477 -14.252 1.00 40.72  ? ? ? ? ? ? 230 ARG U N   1 
+ATOM   1821 C CA  . ARG A 1 233 ? 7.579   -41.624 -13.357 1.00 42.16  ? ? ? ? ? ? 230 ARG U CA  1 
+ATOM   1822 C C   . ARG A 1 233 ? 7.738   -41.170 -11.918 1.00 44.96  ? ? ? ? ? ? 230 ARG U C   1 
+ATOM   1823 O O   . ARG A 1 233 ? 8.854   -41.074 -11.388 1.00 45.97  ? ? ? ? ? ? 230 ARG U O   1 
+ATOM   1824 C CB  . ARG A 1 233 ? 8.639   -42.663 -13.741 1.00 45.67  ? ? ? ? ? ? 230 ARG U CB  1 
+ATOM   1825 C CG  . ARG A 1 233 ? 8.507   -44.023 -13.091 1.00 47.78  ? ? ? ? ? ? 230 ARG U CG  1 
+ATOM   1826 C CD  . ARG A 1 233 ? 9.439   -45.014 -13.780 1.00 51.35  ? ? ? ? ? ? 230 ARG U CD  1 
+ATOM   1827 N NE  . ARG A 1 233 ? 9.548   -46.265 -13.033 1.00 55.93  ? ? ? ? ? ? 230 ARG U NE  1 
+ATOM   1828 C CZ  . ARG A 1 233 ? 10.170  -47.366 -13.457 1.00 60.26  ? ? ? ? ? ? 230 ARG U CZ  1 
+ATOM   1829 N NH1 . ARG A 1 233 ? 10.766  -47.404 -14.648 1.00 57.22  ? ? ? ? ? ? 230 ARG U NH1 1 
+ATOM   1830 N NH2 . ARG A 1 233 ? 10.198  -48.445 -12.672 1.00 61.39  ? ? ? ? ? ? 230 ARG U NH2 1 
+ATOM   1831 N N   . VAL A 1 234 ? 6.606   -40.899 -11.277 1.00 45.54  ? ? ? ? ? ? 231 VAL U N   1 
+ATOM   1832 C CA  . VAL A 1 234 ? 6.594   -40.278 -9.949  1.00 45.01  ? ? ? ? ? ? 231 VAL U CA  1 
+ATOM   1833 C C   . VAL A 1 234 ? 7.427   -41.016 -8.905  1.00 47.58  ? ? ? ? ? ? 231 VAL U C   1 
+ATOM   1834 O O   . VAL A 1 234 ? 8.110   -40.379 -8.085  1.00 42.07  ? ? ? ? ? ? 231 VAL U O   1 
+ATOM   1835 C CB  . VAL A 1 234 ? 5.144   -40.114 -9.427  1.00 45.33  ? ? ? ? ? ? 231 VAL U CB  1 
+ATOM   1836 C CG1 . VAL A 1 234 ? 5.133   -39.662 -7.976  1.00 45.23  ? ? ? ? ? ? 231 VAL U CG1 1 
+ATOM   1837 C CG2 . VAL A 1 234 ? 4.384   -39.119 -10.304 1.00 44.83  ? ? ? ? ? ? 231 VAL U CG2 1 
+ATOM   1838 N N   . SER A 1 235 ? 7.349   -42.349 -8.922  1.00 48.57  ? ? ? ? ? ? 232 SER U N   1 
+ATOM   1839 C CA  . SER A 1 235 ? 8.043   -43.188 -7.929  1.00 48.44  ? ? ? ? ? ? 232 SER U CA  1 
+ATOM   1840 C C   . SER A 1 235 ? 9.542   -42.916 -7.887  1.00 50.78  ? ? ? ? ? ? 232 SER U C   1 
+ATOM   1841 O O   . SER A 1 235 ? 10.191  -43.190 -6.878  1.00 57.08  ? ? ? ? ? ? 232 SER U O   1 
+ATOM   1842 C CB  . SER A 1 235 ? 7.797   -44.683 -8.208  1.00 46.09  ? ? ? ? ? ? 232 SER U CB  1 
+ATOM   1843 O OG  . SER A 1 235 ? 8.405   -45.089 -9.425  1.00 44.08  ? ? ? ? ? ? 232 SER U OG  1 
+ATOM   1844 N N   . HIS A 1 236 ? 10.089  -42.388 -8.979  1.00 52.99  ? ? ? ? ? ? 233 HIS U N   1 
+ATOM   1845 C CA  . HIS A 1 236 ? 11.504  -42.015 -9.041  1.00 56.17  ? ? ? ? ? ? 233 HIS U CA  1 
+ATOM   1846 C C   . HIS A 1 236 ? 11.853  -40.654 -8.433  1.00 54.08  ? ? ? ? ? ? 233 HIS U C   1 
+ATOM   1847 O O   . HIS A 1 236 ? 13.029  -40.302 -8.392  1.00 53.58  ? ? ? ? ? ? 233 HIS U O   1 
+ATOM   1848 C CB  . HIS A 1 236 ? 12.002  -42.048 -10.493 1.00 57.52  ? ? ? ? ? ? 233 HIS U CB  1 
+ATOM   1849 C CG  . HIS A 1 236 ? 12.248  -43.428 -11.014 1.00 61.06  ? ? ? ? ? ? 233 HIS U CG  1 
+ATOM   1850 N ND1 . HIS A 1 236 ? 11.473  -44.511 -10.655 1.00 63.63  ? ? ? ? ? ? 233 HIS U ND1 1 
+ATOM   1851 C CD2 . HIS A 1 236 ? 13.178  -43.900 -11.879 1.00 61.75  ? ? ? ? ? ? 233 HIS U CD2 1 
+ATOM   1852 C CE1 . HIS A 1 236 ? 11.920  -45.593 -11.270 1.00 64.37  ? ? ? ? ? ? 233 HIS U CE1 1 
+ATOM   1853 N NE2 . HIS A 1 236 ? 12.952  -45.249 -12.020 1.00 64.01  ? ? ? ? ? ? 233 HIS U NE2 1 
+ATOM   1854 N N   . PHE A 1 237 ? 10.867  -39.896 -7.950  1.00 50.30  ? ? ? ? ? ? 234 PHE U N   1 
+ATOM   1855 C CA  . PHE A 1 237 ? 11.132  -38.524 -7.481  1.00 47.22  ? ? ? ? ? ? 234 PHE U CA  1 
+ATOM   1856 C C   . PHE A 1 237 ? 10.774  -38.263 -6.017  1.00 48.08  ? ? ? ? ? ? 234 PHE U C   1 
+ATOM   1857 O O   . PHE A 1 237 ? 10.667  -37.109 -5.588  1.00 46.92  ? ? ? ? ? ? 234 PHE U O   1 
+ATOM   1858 C CB  . PHE A 1 237 ? 10.444  -37.512 -8.398  1.00 45.49  ? ? ? ? ? ? 234 PHE U CB  1 
+ATOM   1859 C CG  . PHE A 1 237 ? 11.076  -37.402 -9.757  1.00 44.01  ? ? ? ? ? ? 234 PHE U CG  1 
+ATOM   1860 C CD1 . PHE A 1 237 ? 10.712  -38.258 -10.770 1.00 41.82  ? ? ? ? ? ? 234 PHE U CD1 1 
+ATOM   1861 C CD2 . PHE A 1 237 ? 12.046  -36.428 -10.022 1.00 44.60  ? ? ? ? ? ? 234 PHE U CD2 1 
+ATOM   1862 C CE1 . PHE A 1 237 ? 11.285  -38.160 -12.029 1.00 41.43  ? ? ? ? ? ? 234 PHE U CE1 1 
+ATOM   1863 C CE2 . PHE A 1 237 ? 12.621  -36.320 -11.278 1.00 42.69  ? ? ? ? ? ? 234 PHE U CE2 1 
+ATOM   1864 C CZ  . PHE A 1 237 ? 12.243  -37.194 -12.282 1.00 42.40  ? ? ? ? ? ? 234 PHE U CZ  1 
+ATOM   1865 N N   . LEU A 1 238 ? 10.639  -39.328 -5.233  1.00 48.13  ? ? ? ? ? ? 235 LEU U N   1 
+ATOM   1866 C CA  . LEU A 1 238 ? 10.296  -39.175 -3.821  1.00 48.67  ? ? ? ? ? ? 235 LEU U CA  1 
+ATOM   1867 C C   . LEU A 1 238 ? 11.294  -38.372 -2.992  1.00 51.19  ? ? ? ? ? ? 235 LEU U C   1 
+ATOM   1868 O O   . LEU A 1 238 ? 10.882  -37.681 -2.050  1.00 52.19  ? ? ? ? ? ? 235 LEU U O   1 
+ATOM   1869 C CB  . LEU A 1 238 ? 10.067  -40.537 -3.166  1.00 51.02  ? ? ? ? ? ? 235 LEU U CB  1 
+ATOM   1870 C CG  . LEU A 1 238 ? 8.768   -41.257 -3.542  1.00 52.30  ? ? ? ? ? ? 235 LEU U CG  1 
+ATOM   1871 C CD1 . LEU A 1 238 ? 8.558   -42.458 -2.634  1.00 54.66  ? ? ? ? ? ? 235 LEU U CD1 1 
+ATOM   1872 C CD2 . LEU A 1 238 ? 7.556   -40.339 -3.453  1.00 52.39  ? ? ? ? ? ? 235 LEU U CD2 1 
+ATOM   1873 N N   . PRO A 1 239 ? 12.603  -38.468 -3.308  1.00 51.45  ? ? ? ? ? ? 236 PRO U N   1 
+ATOM   1874 C CA  . PRO A 1 239 ? 13.566  -37.645 -2.563  1.00 50.77  ? ? ? ? ? ? 236 PRO U CA  1 
+ATOM   1875 C C   . PRO A 1 239 ? 13.427  -36.174 -2.928  1.00 49.84  ? ? ? ? ? ? 236 PRO U C   1 
+ATOM   1876 O O   . PRO A 1 239 ? 13.297  -35.315 -2.035  1.00 48.94  ? ? ? ? ? ? 236 PRO U O   1 
+ATOM   1877 C CB  . PRO A 1 239 ? 14.927  -38.196 -3.008  1.00 49.68  ? ? ? ? ? ? 236 PRO U CB  1 
+ATOM   1878 C CG  . PRO A 1 239 ? 14.634  -39.550 -3.558  1.00 49.22  ? ? ? ? ? ? 236 PRO U CG  1 
+ATOM   1879 C CD  . PRO A 1 239 ? 13.284  -39.447 -4.173  1.00 47.92  ? ? ? ? ? ? 236 PRO U CD  1 
+ATOM   1880 N N   . TRP A 1 240 ? 13.439  -35.896 -4.233  1.00 46.84  ? ? ? ? ? ? 237 TRP U N   1 
+ATOM   1881 C CA  . TRP A 1 240 ? 13.155  -34.555 -4.733  1.00 44.84  ? ? ? ? ? ? 237 TRP U CA  1 
+ATOM   1882 C C   . TRP A 1 240 ? 11.886  -34.033 -4.064  1.00 43.35  ? ? ? ? ? ? 237 TRP U C   1 
+ATOM   1883 O O   . TRP A 1 240 ? 11.890  -32.969 -3.442  1.00 44.13  ? ? ? ? ? ? 237 TRP U O   1 
+ATOM   1884 C CB  . TRP A 1 240 ? 13.038  -34.564 -6.268  1.00 47.28  ? ? ? ? ? ? 237 TRP U CB  1 
+ATOM   1885 C CG  . TRP A 1 240 ? 12.903  -33.186 -6.849  1.00 48.30  ? ? ? ? ? ? 237 TRP U CG  1 
+ATOM   1886 C CD1 . TRP A 1 240 ? 13.913  -32.310 -7.159  1.00 51.54  ? ? ? ? ? ? 237 TRP U CD1 1 
+ATOM   1887 C CD2 . TRP A 1 240 ? 11.681  -32.505 -7.140  1.00 47.99  ? ? ? ? ? ? 237 TRP U CD2 1 
+ATOM   1888 N NE1 . TRP A 1 240 ? 13.387  -31.130 -7.636  1.00 50.76  ? ? ? ? ? ? 237 TRP U NE1 1 
+ATOM   1889 C CE2 . TRP A 1 240 ? 12.019  -31.226 -7.637  1.00 49.18  ? ? ? ? ? ? 237 TRP U CE2 1 
+ATOM   1890 C CE3 . TRP A 1 240 ? 10.330  -32.855 -7.035  1.00 45.30  ? ? ? ? ? ? 237 TRP U CE3 1 
+ATOM   1891 C CZ2 . TRP A 1 240 ? 11.051  -30.303 -8.044  1.00 49.49  ? ? ? ? ? ? 237 TRP U CZ2 1 
+ATOM   1892 C CZ3 . TRP A 1 240 ? 9.372   -31.938 -7.436  1.00 44.24  ? ? ? ? ? ? 237 TRP U CZ3 1 
+ATOM   1893 C CH2 . TRP A 1 240 ? 9.737   -30.680 -7.932  1.00 49.14  ? ? ? ? ? ? 237 TRP U CH2 1 
+ATOM   1894 N N   . ILE A 1 241 ? 10.816  -34.822 -4.107  1.00 46.25  ? ? ? ? ? ? 238 ILE U N   1 
+ATOM   1895 C CA  . ILE A 1 241 ? 9.541   -34.403 -3.505  1.00 45.62  ? ? ? ? ? ? 238 ILE U CA  1 
+ATOM   1896 C C   . ILE A 1 241 ? 9.613   -34.198 -1.988  1.00 49.87  ? ? ? ? ? ? 238 ILE U C   1 
+ATOM   1897 O O   . ILE A 1 241 ? 9.224   -33.138 -1.474  1.00 45.97  ? ? ? ? ? ? 238 ILE U O   1 
+ATOM   1898 C CB  . ILE A 1 241 ? 8.415   -35.414 -3.809  1.00 43.33  ? ? ? ? ? ? 238 ILE U CB  1 
+ATOM   1899 C CG1 . ILE A 1 241 ? 8.057   -35.364 -5.304  1.00 42.43  ? ? ? ? ? ? 238 ILE U CG1 1 
+ATOM   1900 C CG2 . ILE A 1 241 ? 7.190   -35.130 -2.944  1.00 41.30  ? ? ? ? ? ? 238 ILE U CG2 1 
+ATOM   1901 C CD1 . ILE A 1 241 ? 7.360   -36.600 -5.819  1.00 42.78  ? ? ? ? ? ? 238 ILE U CD1 1 
+ATOM   1902 N N   . ARG A 1 242 ? 10.081  -35.213 -1.265  1.00 54.45  ? ? ? ? ? ? 239 ARG U N   1 
+ATOM   1903 C CA  . ARG A 1 242 ? 10.082  -35.132 0.198   1.00 57.40  ? ? ? ? ? ? 239 ARG U CA  1 
+ATOM   1904 C C   . ARG A 1 242 ? 10.957  -33.987 0.705   1.00 55.46  ? ? ? ? ? ? 239 ARG U C   1 
+ATOM   1905 O O   . ARG A 1 242 ? 10.628  -33.378 1.720   1.00 56.87  ? ? ? ? ? ? 239 ARG U O   1 
+ATOM   1906 C CB  . ARG A 1 242 ? 10.462  -36.464 0.854   1.00 60.79  ? ? ? ? ? ? 239 ARG U CB  1 
+ATOM   1907 C CG  . ARG A 1 242 ? 9.311   -37.467 0.920   1.00 65.69  ? ? ? ? ? ? 239 ARG U CG  1 
+ATOM   1908 C CD  . ARG A 1 242 ? 9.643   -38.679 1.783   1.00 70.68  ? ? ? ? ? ? 239 ARG U CD  1 
+ATOM   1909 N NE  . ARG A 1 242 ? 10.797  -39.432 1.268   1.00 78.05  ? ? ? ? ? ? 239 ARG U NE  1 
+ATOM   1910 C CZ  . ARG A 1 242 ? 10.759  -40.653 0.723   1.00 80.90  ? ? ? ? ? ? 239 ARG U CZ  1 
+ATOM   1911 N NH1 . ARG A 1 242 ? 9.615   -41.329 0.607   1.00 82.51  ? ? ? ? ? ? 239 ARG U NH1 1 
+ATOM   1912 N NH2 . ARG A 1 242 ? 11.888  -41.213 0.292   1.00 80.69  ? ? ? ? ? ? 239 ARG U NH2 1 
+ATOM   1913 N N   . SER A 1 243 ? 12.028  -33.657 -0.018  1.00 57.94  ? ? ? ? ? ? 240 SER U N   1 
+ATOM   1914 C CA  . SER A 1 243 ? 12.914  -32.557 0.391   1.00 61.03  ? ? ? ? ? ? 240 SER U CA  1 
+ATOM   1915 C C   . SER A 1 243 ? 12.149  -31.244 0.470   1.00 68.10  ? ? ? ? ? ? 240 SER U C   1 
+ATOM   1916 O O   . SER A 1 243 ? 12.332  -30.462 1.409   1.00 69.05  ? ? ? ? ? ? 240 SER U O   1 
+ATOM   1917 C CB  . SER A 1 243 ? 14.083  -32.381 -0.577  1.00 63.36  ? ? ? ? ? ? 240 SER U CB  1 
+ATOM   1918 O OG  . SER A 1 243 ? 13.763  -31.458 -1.613  1.00 62.27  ? ? ? ? ? ? 240 SER U OG  1 
+ATOM   1919 N N   . HIS A 1 244 ? 11.293  -31.007 -0.523  1.00 68.42  ? ? ? ? ? ? 241 HIS U N   1 
+ATOM   1920 C CA  . HIS A 1 244 ? 10.561  -29.750 -0.617  1.00 68.66  ? ? ? ? ? ? 241 HIS U CA  1 
+ATOM   1921 C C   . HIS A 1 244 ? 9.303   -29.761 0.238   1.00 70.80  ? ? ? ? ? ? 241 HIS U C   1 
+ATOM   1922 O O   . HIS A 1 244 ? 8.932   -28.728 0.794   1.00 69.14  ? ? ? ? ? ? 241 HIS U O   1 
+ATOM   1923 C CB  . HIS A 1 244 ? 10.214  -29.438 -2.071  1.00 68.22  ? ? ? ? ? ? 241 HIS U CB  1 
+ATOM   1924 C CG  . HIS A 1 244 ? 11.414  -29.281 -2.954  1.00 67.18  ? ? ? ? ? ? 241 HIS U CG  1 
+ATOM   1925 N ND1 . HIS A 1 244 ? 11.873  -30.291 -3.770  1.00 68.96  ? ? ? ? ? ? 241 HIS U ND1 1 
+ATOM   1926 C CD2 . HIS A 1 244 ? 12.257  -28.237 -3.137  1.00 68.26  ? ? ? ? ? ? 241 HIS U CD2 1 
+ATOM   1927 C CE1 . HIS A 1 244 ? 12.944  -29.877 -4.422  1.00 68.81  ? ? ? ? ? ? 241 HIS U CE1 1 
+ATOM   1928 N NE2 . HIS A 1 244 ? 13.196  -28.632 -4.059  1.00 67.66  ? ? ? ? ? ? 241 HIS U NE2 1 
+ATOM   1929 N N   . THR A 1 245 ? 8.653   -30.920 0.349   1.00 74.54  ? ? ? ? ? ? 242 THR U N   1 
+ATOM   1930 C CA  . THR A 1 245 ? 7.409   -31.026 1.116   1.00 76.27  ? ? ? ? ? ? 242 THR U CA  1 
+ATOM   1931 C C   . THR A 1 245 ? 7.657   -30.922 2.624   1.00 80.13  ? ? ? ? ? ? 242 THR U C   1 
+ATOM   1932 O O   . THR A 1 245 ? 6.841   -30.341 3.339   1.00 77.76  ? ? ? ? ? ? 242 THR U O   1 
+ATOM   1933 C CB  . THR A 1 245 ? 6.622   -32.311 0.793   1.00 75.34  ? ? ? ? ? ? 242 THR U CB  1 
+ATOM   1934 O OG1 . THR A 1 245 ? 7.438   -33.459 1.042   1.00 76.53  ? ? ? ? ? ? 242 THR U OG1 1 
+ATOM   1935 C CG2 . THR A 1 245 ? 6.172   -32.307 -0.662  1.00 73.83  ? ? ? ? ? ? 242 THR U CG2 1 
+ATOM   1936 N N   . LYS A 1 246 ? 8.768   -31.478 3.107   1.00 86.92  ? ? ? ? ? ? 243 LYS U N   1 
+ATOM   1937 C CA  . LYS A 1 246 ? 9.235   -31.152 4.455   1.00 95.27  ? ? ? ? ? ? 243 LYS U CA  1 
+ATOM   1938 C C   . LYS A 1 246 ? 9.786   -29.722 4.400   1.00 99.56  ? ? ? ? ? ? 243 LYS U C   1 
+ATOM   1939 O O   . LYS A 1 246 ? 10.753  -29.448 3.687   1.00 101.05 ? ? ? ? ? ? 243 LYS U O   1 
+ATOM   1940 C CB  . LYS A 1 246 ? 10.276  -32.164 4.974   1.00 98.66  ? ? ? ? ? ? 243 LYS U CB  1 
+ATOM   1941 C CG  . LYS A 1 246 ? 11.615  -32.201 4.237   1.00 100.95 ? ? ? ? ? ? 243 LYS U CG  1 
+ATOM   1942 C CD  . LYS A 1 246 ? 12.713  -31.416 4.945   1.00 100.55 ? ? ? ? ? ? 243 LYS U CD  1 
+ATOM   1943 C CE  . LYS A 1 246 ? 13.340  -32.222 6.070   1.00 102.22 ? ? ? ? ? ? 243 LYS U CE  1 
+ATOM   1944 N NZ  . LYS A 1 246 ? 14.226  -31.372 6.911   1.00 103.94 ? ? ? ? ? ? 243 LYS U NZ  1 
+ATOM   1945 N N   . GLU A 1 247 ? 9.131   -28.808 5.114   1.00 106.90 ? ? ? ? ? ? 244 GLU U N   1 
+ATOM   1946 C CA  . GLU A 1 247 ? 9.487   -27.380 5.089   1.00 110.20 ? ? ? ? ? ? 244 GLU U CA  1 
+ATOM   1947 C C   . GLU A 1 247 ? 9.927   -26.901 6.471   1.00 111.64 ? ? ? ? ? ? 244 GLU U C   1 
+ATOM   1948 O O   . GLU A 1 247 ? 10.291  -25.738 6.651   1.00 112.43 ? ? ? ? ? ? 244 GLU U O   1 
+ATOM   1949 C CB  . GLU A 1 247 ? 8.299   -26.537 4.604   1.00 108.39 ? ? ? ? ? ? 244 GLU U CB  1 
+ATOM   1950 C CG  . GLU A 1 247 ? 7.955   -26.707 3.131   1.00 104.51 ? ? ? ? ? ? 244 GLU U CG  1 
+ATOM   1951 C CD  . GLU A 1 247 ? 8.855   -25.899 2.210   1.00 101.07 ? ? ? ? ? ? 244 GLU U CD  1 
+ATOM   1952 O OE1 . GLU A 1 247 ? 8.407   -24.836 1.730   1.00 95.77  ? ? ? ? ? ? 244 GLU U OE1 1 
+ATOM   1953 O OE2 . GLU A 1 247 ? 10.008  -26.319 1.965   1.00 97.75  ? ? ? ? ? ? 244 GLU U OE2 1 
+ATOM   1954 N N   . CYS B 2 1   ? 9.932   -25.495 -26.711 1.00 61.14  ? ? ? ? ? ? 1   CYS P N   1 
+ATOM   1955 C CA  . CYS B 2 1   ? 8.757   -26.333 -27.107 1.00 61.95  ? ? ? ? ? ? 1   CYS P CA  1 
+ATOM   1956 C C   . CYS B 2 1   ? 9.165   -27.548 -27.937 1.00 64.13  ? ? ? ? ? ? 1   CYS P C   1 
+ATOM   1957 O O   . CYS B 2 1   ? 9.308   -27.445 -29.164 1.00 66.79  ? ? ? ? ? ? 1   CYS P O   1 
+ATOM   1958 C CB  . CYS B 2 1   ? 7.718   -25.518 -27.887 1.00 61.72  ? ? ? ? ? ? 1   CYS P CB  1 
+ATOM   1959 S SG  . CYS B 2 1   ? 6.591   -24.549 -26.861 1.00 64.20  ? ? ? ? ? ? 1   CYS P SG  1 
+ATOM   1960 N N   . PRO B 2 2   ? 9.343   -28.707 -27.271 1.00 64.07  ? ? ? ? ? ? 2   PRO P N   1 
+ATOM   1961 C CA  . PRO B 2 2   ? 9.575   -29.985 -27.948 1.00 61.08  ? ? ? ? ? ? 2   PRO P CA  1 
+ATOM   1962 C C   . PRO B 2 2   ? 8.482   -30.299 -28.942 1.00 57.86  ? ? ? ? ? ? 2   PRO P C   1 
+ATOM   1963 O O   . PRO B 2 2   ? 7.348   -29.842 -28.777 1.00 58.73  ? ? ? ? ? ? 2   PRO P O   1 
+ATOM   1964 C CB  . PRO B 2 2   ? 9.556   -30.998 -26.801 1.00 58.76  ? ? ? ? ? ? 2   PRO P CB  1 
+ATOM   1965 C CG  . PRO B 2 2   ? 9.989   -30.222 -25.616 1.00 59.79  ? ? ? ? ? ? 2   PRO P CG  1 
+ATOM   1966 C CD  . PRO B 2 2   ? 9.423   -28.847 -25.805 1.00 61.94  ? ? ? ? ? ? 2   PRO P CD  1 
+ATOM   1967 N N   . ALA B 2 3   ? 8.811   -31.082 -29.963 1.00 57.28  ? ? ? ? ? ? 3   ALA P N   1 
+ATOM   1968 C CA  . ALA B 2 3   ? 7.864   -31.357 -31.039 1.00 55.53  ? ? ? ? ? ? 3   ALA P CA  1 
+ATOM   1969 C C   . ALA B 2 3   ? 6.631   -32.123 -30.551 1.00 52.32  ? ? ? ? ? ? 3   ALA P C   1 
+ATOM   1970 O O   . ALA B 2 3   ? 5.561   -32.012 -31.156 1.00 49.22  ? ? ? ? ? ? 3   ALA P O   1 
+ATOM   1971 C CB  . ALA B 2 3   ? 8.543   -32.118 -32.173 1.00 54.04  ? ? ? ? ? ? 3   ALA P CB  1 
+ATOM   1972 N N   . TYR B 2 4   ? 6.793   -32.899 -29.476 1.00 50.93  ? ? ? ? ? ? 4   TYR P N   1 
+ATOM   1973 C CA  . TYR B 2 4   ? 5.711   -33.732 -28.926 1.00 52.40  ? ? ? ? ? ? 4   TYR P CA  1 
+ATOM   1974 C C   . TYR B 2 4   ? 5.147   -33.219 -27.583 1.00 48.85  ? ? ? ? ? ? 4   TYR P C   1 
+ATOM   1975 O O   . TYR B 2 4   ? 4.501   -33.970 -26.851 1.00 47.07  ? ? ? ? ? ? 4   TYR P O   1 
+ATOM   1976 C CB  . TYR B 2 4   ? 6.188   -35.179 -28.742 1.00 55.28  ? ? ? ? ? ? 4   TYR P CB  1 
+ATOM   1977 C CG  . TYR B 2 4   ? 6.721   -35.888 -29.979 1.00 59.55  ? ? ? ? ? ? 4   TYR P CG  1 
+ATOM   1978 C CD1 . TYR B 2 4   ? 6.363   -35.487 -31.271 1.00 60.19  ? ? ? ? ? ? 4   TYR P CD1 1 
+ATOM   1979 C CD2 . TYR B 2 4   ? 7.539   -37.009 -29.846 1.00 63.16  ? ? ? ? ? ? 4   TYR P CD2 1 
+ATOM   1980 C CE1 . TYR B 2 4   ? 6.841   -36.154 -32.387 1.00 63.36  ? ? ? ? ? ? 4   TYR P CE1 1 
+ATOM   1981 C CE2 . TYR B 2 4   ? 8.018   -37.687 -30.959 1.00 67.13  ? ? ? ? ? ? 4   TYR P CE2 1 
+ATOM   1982 C CZ  . TYR B 2 4   ? 7.665   -37.256 -32.228 1.00 66.50  ? ? ? ? ? ? 4   TYR P CZ  1 
+ATOM   1983 O OH  . TYR B 2 4   ? 8.132   -37.931 -33.337 1.00 70.97  ? ? ? ? ? ? 4   TYR P OH  1 
+ATOM   1984 N N   . SER B 2 5   ? 5.378   -31.950 -27.262 1.00 48.66  ? ? ? ? ? ? 5   SER P N   1 
+ATOM   1985 C CA  . SER B 2 5   ? 4.739   -31.325 -26.099 1.00 45.45  ? ? ? ? ? ? 5   SER P CA  1 
+ATOM   1986 C C   . SER B 2 5   ? 3.224   -31.525 -26.132 1.00 45.97  ? ? ? ? ? ? 5   SER P C   1 
+ATOM   1987 O O   . SER B 2 5   ? 2.621   -31.604 -27.206 1.00 45.14  ? ? ? ? ? ? 5   SER P O   1 
+ATOM   1988 C CB  . SER B 2 5   ? 5.039   -29.823 -26.059 1.00 46.03  ? ? ? ? ? ? 5   SER P CB  1 
+ATOM   1989 O OG  . SER B 2 5   ? 4.527   -29.220 -24.880 1.00 43.06  ? ? ? ? ? ? 5   SER P OG  1 
+ATOM   1990 N N   . ARG B 2 6   ? 2.614   -31.621 -24.952 1.00 44.10  ? ? ? ? ? ? 6   ARG P N   1 
+ATOM   1991 C CA  . ARG B 2 6   ? 1.151   -31.611 -24.841 1.00 43.41  ? ? ? ? ? ? 6   ARG P CA  1 
+ATOM   1992 C C   . ARG B 2 6   ? 0.726   -30.410 -23.993 1.00 42.93  ? ? ? ? ? ? 6   ARG P C   1 
+ATOM   1993 O O   . ARG B 2 6   ? -0.314  -30.420 -23.330 1.00 36.73  ? ? ? ? ? ? 6   ARG P O   1 
+ATOM   1994 C CB  . ARG B 2 6   ? 0.657   -32.947 -24.274 1.00 42.63  ? ? ? ? ? ? 6   ARG P CB  1 
+ATOM   1995 C CG  . ARG B 2 6   ? 0.742   -34.062 -25.308 1.00 43.41  ? ? ? ? ? ? 6   ARG P CG  1 
+ATOM   1996 C CD  . ARG B 2 6   ? 0.207   -35.400 -24.813 1.00 43.13  ? ? ? ? ? ? 6   ARG P CD  1 
+ATOM   1997 N NE  . ARG B 2 6   ? 0.206   -36.377 -25.899 1.00 41.21  ? ? ? ? ? ? 6   ARG P NE  1 
+ATOM   1998 C CZ  . ARG B 2 6   ? -0.245  -37.622 -25.809 1.00 43.13  ? ? ? ? ? ? 6   ARG P CZ  1 
+ATOM   1999 N NH1 . ARG B 2 6   ? -0.750  -38.091 -24.669 1.00 41.09  ? ? ? ? ? ? 6   ARG P NH1 1 
+ATOM   2000 N NH2 . ARG B 2 6   ? -0.203  -38.406 -26.879 1.00 41.66  ? ? ? ? ? ? 6   ARG P NH2 1 
+ATOM   2001 N N   . TYR B 2 7   ? 1.559   -29.373 -24.043 1.00 43.50  ? ? ? ? ? ? 7   TYR P N   1 
+ATOM   2002 C CA  . TYR B 2 7   ? 1.356   -28.150 -23.282 1.00 43.68  ? ? ? ? ? ? 7   TYR P CA  1 
+ATOM   2003 C C   . TYR B 2 7   ? 0.548   -27.174 -24.105 1.00 46.17  ? ? ? ? ? ? 7   TYR P C   1 
+ATOM   2004 O O   . TYR B 2 7   ? 0.818   -26.984 -25.307 1.00 38.22  ? ? ? ? ? ? 7   TYR P O   1 
+ATOM   2005 C CB  . TYR B 2 7   ? 2.710   -27.538 -22.937 1.00 45.68  ? ? ? ? ? ? 7   TYR P CB  1 
+ATOM   2006 C CG  . TYR B 2 7   ? 2.675   -26.207 -22.189 1.00 46.46  ? ? ? ? ? ? 7   TYR P CG  1 
+ATOM   2007 C CD1 . TYR B 2 7   ? 1.857   -26.018 -21.078 1.00 45.08  ? ? ? ? ? ? 7   TYR P CD1 1 
+ATOM   2008 C CD2 . TYR B 2 7   ? 3.494   -25.149 -22.588 1.00 46.18  ? ? ? ? ? ? 7   TYR P CD2 1 
+ATOM   2009 C CE1 . TYR B 2 7   ? 1.843   -24.809 -20.402 1.00 44.62  ? ? ? ? ? ? 7   TYR P CE1 1 
+ATOM   2010 C CE2 . TYR B 2 7   ? 3.497   -23.944 -21.911 1.00 45.96  ? ? ? ? ? ? 7   TYR P CE2 1 
+ATOM   2011 C CZ  . TYR B 2 7   ? 2.666   -23.772 -20.824 1.00 45.64  ? ? ? ? ? ? 7   TYR P CZ  1 
+ATOM   2012 O OH  . TYR B 2 7   ? 2.673   -22.560 -20.175 1.00 45.91  ? ? ? ? ? ? 7   TYR P OH  1 
+ATOM   2013 N N   . ILE B 2 8   ? -0.455  -26.572 -23.459 1.00 45.83  ? ? ? ? ? ? 8   ILE P N   1 
+ATOM   2014 C CA  . ILE B 2 8   ? -1.235  -25.479 -24.052 1.00 49.63  ? ? ? ? ? ? 8   ILE P CA  1 
+ATOM   2015 C C   . ILE B 2 8   ? -0.358  -24.407 -24.699 1.00 50.24  ? ? ? ? ? ? 8   ILE P C   1 
+ATOM   2016 O O   . ILE B 2 8   ? -0.668  -23.921 -25.791 1.00 55.47  ? ? ? ? ? ? 8   ILE P O   1 
+ATOM   2017 C CB  . ILE B 2 8   ? -2.126  -24.788 -22.994 1.00 50.22  ? ? ? ? ? ? 8   ILE P CB  1 
+ATOM   2018 C CG1 . ILE B 2 8   ? -3.401  -25.587 -22.810 1.00 51.23  ? ? ? ? ? ? 8   ILE P CG1 1 
+ATOM   2019 C CG2 . ILE B 2 8   ? -2.497  -23.365 -23.414 1.00 50.36  ? ? ? ? ? ? 8   ILE P CG2 1 
+ATOM   2020 C CD1 . ILE B 2 8   ? -4.261  -25.116 -21.663 1.00 53.34  ? ? ? ? ? ? 8   ILE P CD1 1 
+ATOM   2021 N N   . GLY B 2 9   ? 0.720   -24.029 -24.014 1.00 50.02  ? ? ? ? ? ? 9   GLY P N   1 
+ATOM   2022 C CA  . GLY B 2 9   ? 1.612   -22.976 -24.504 1.00 52.80  ? ? ? ? ? ? 9   GLY P CA  1 
+ATOM   2023 C C   . GLY B 2 9   ? 2.339   -23.284 -25.806 1.00 51.95  ? ? ? ? ? ? 9   GLY P C   1 
+ATOM   2024 O O   . GLY B 2 9   ? 2.823   -22.366 -26.468 1.00 54.32  ? ? ? ? ? ? 9   GLY P O   1 
+ATOM   2025 N N   . CYS B 2 10  ? 2.437   -24.565 -26.160 1.00 50.43  ? ? ? ? ? ? 10  CYS P N   1 
+ATOM   2026 C CA  . CYS B 2 10  ? 3.038   -24.985 -27.428 1.00 52.19  ? ? ? ? ? ? 10  CYS P CA  1 
+ATOM   2027 C C   . CYS B 2 10  ? 1.953   -25.392 -28.415 1.00 52.70  ? ? ? ? ? ? 10  CYS P C   1 
+ATOM   2028 O O   . CYS B 2 10  ? 2.051   -25.095 -29.606 1.00 51.82  ? ? ? ? ? ? 10  CYS P O   1 
+ATOM   2029 C CB  . CYS B 2 10  ? 4.008   -26.160 -27.221 1.00 54.24  ? ? ? ? ? ? 10  CYS P CB  1 
+ATOM   2030 S SG  . CYS B 2 10  ? 5.315   -25.901 -25.989 1.00 54.32  ? ? ? ? ? ? 10  CYS P SG  1 
+HETATM 2031 O O   . HOH C 3 .   ? -15.205 -28.509 -1.223  1.00 29.09  ? ? ? ? ? ? 301 HOH U O   1 
+HETATM 2032 O O   . HOH C 3 .   ? -10.582 -31.047 -35.113 1.00 46.52  ? ? ? ? ? ? 302 HOH U O   1 
+HETATM 2033 O O   . HOH C 3 .   ? 7.248   -26.510 -23.512 1.00 44.78  ? ? ? ? ? ? 303 HOH U O   1 
+HETATM 2034 O O   . HOH C 3 .   ? -3.871  -24.043 -27.314 1.00 44.49  ? ? ? ? ? ? 304 HOH U O   1 
+HETATM 2035 O O   . HOH C 3 .   ? -6.024  -30.401 -17.786 1.00 24.98  ? ? ? ? ? ? 305 HOH U O   1 
+HETATM 2036 O O   . HOH C 3 .   ? 3.279   -31.441 -22.381 1.00 38.18  ? ? ? ? ? ? 306 HOH U O   1 
+HETATM 2037 O O   . HOH C 3 .   ? -2.060  -42.028 -22.501 1.00 40.45  ? ? ? ? ? ? 307 HOH U O   1 
+HETATM 2038 O O   . HOH C 3 .   ? -6.449  -39.304 -12.768 1.00 43.65  ? ? ? ? ? ? 308 HOH U O   1 
+HETATM 2039 O O   . HOH C 3 .   ? -11.732 -26.754 -32.343 1.00 47.16  ? ? ? ? ? ? 309 HOH U O   1 
+HETATM 2040 O O   . HOH C 3 .   ? 8.854   -36.746 -19.881 1.00 44.47  ? ? ? ? ? ? 310 HOH U O   1 
+HETATM 2041 O O   . HOH C 3 .   ? -21.614 -29.382 -16.299 1.00 40.58  ? ? ? ? ? ? 311 HOH U O   1 
+HETATM 2042 O O   . HOH C 3 .   ? -13.578 -24.026 -23.861 1.00 39.67  ? ? ? ? ? ? 312 HOH U O   1 
+HETATM 2043 O O   . HOH C 3 .   ? -12.634 -31.149 5.764   1.00 43.05  ? ? ? ? ? ? 313 HOH U O   1 
+HETATM 2044 O O   . HOH C 3 .   ? -4.729  -33.001 -16.829 1.00 27.82  ? ? ? ? ? ? 314 HOH U O   1 
+HETATM 2045 O O   . HOH C 3 .   ? -9.351  -49.347 -35.580 1.00 58.54  ? ? ? ? ? ? 315 HOH U O   1 
+HETATM 2046 O O   . HOH C 3 .   ? -9.715  -31.118 -6.760  1.00 30.03  ? ? ? ? ? ? 316 HOH U O   1 
+HETATM 2047 O O   . HOH C 3 .   ? -10.004 -28.922 -9.641  1.00 27.62  ? ? ? ? ? ? 317 HOH U O   1 
+HETATM 2048 O O   . HOH C 3 .   ? -2.748  -38.564 0.587   1.00 67.64  ? ? ? ? ? ? 318 HOH U O   1 
+HETATM 2049 O O   . HOH C 3 .   ? -1.231  -27.629 -20.903 1.00 30.15  ? ? ? ? ? ? 319 HOH U O   1 
+HETATM 2050 O O   . HOH C 3 .   ? -0.170  -17.083 -6.735  1.00 38.06  ? ? ? ? ? ? 320 HOH U O   1 
+HETATM 2051 O O   . HOH C 3 .   ? -7.957  -36.517 1.153   1.00 39.86  ? ? ? ? ? ? 321 HOH U O   1 
+HETATM 2052 O O   . HOH C 3 .   ? -17.811 -29.994 -2.210  1.00 40.20  ? ? ? ? ? ? 322 HOH U O   1 
+HETATM 2053 O O   . HOH C 3 .   ? 5.563   -42.564 -0.545  1.00 42.63  ? ? ? ? ? ? 323 HOH U O   1 
+HETATM 2054 O O   . HOH C 3 .   ? 0.081   -34.268 -11.373 1.00 25.90  ? ? ? ? ? ? 324 HOH U O   1 
+HETATM 2055 O O   . HOH C 3 .   ? -5.368  -52.995 -14.023 1.00 54.06  ? ? ? ? ? ? 325 HOH U O   1 
+HETATM 2056 O O   . HOH C 3 .   ? -5.478  -54.040 -17.122 1.00 50.78  ? ? ? ? ? ? 326 HOH U O   1 
+HETATM 2057 O O   . HOH C 3 .   ? -10.145 -33.448 -21.719 1.00 22.58  ? ? ? ? ? ? 327 HOH U O   1 
+HETATM 2058 O O   . HOH C 3 .   ? -11.202 -30.221 -27.821 1.00 25.86  ? ? ? ? ? ? 328 HOH U O   1 
+HETATM 2059 O O   . HOH C 3 .   ? -19.287 -38.659 -21.394 1.00 42.56  ? ? ? ? ? ? 329 HOH U O   1 
+HETATM 2060 O O   . HOH C 3 .   ? 6.284   -48.673 1.226   1.00 51.54  ? ? ? ? ? ? 330 HOH U O   1 
+HETATM 2061 O O   . HOH C 3 .   ? 5.772   -43.998 -10.624 1.00 31.93  ? ? ? ? ? ? 331 HOH U O   1 
+HETATM 2062 O O   . HOH C 3 .   ? -13.193 -17.030 -9.501  1.00 49.17  ? ? ? ? ? ? 332 HOH U O   1 
+HETATM 2063 O O   . HOH C 3 .   ? -5.134  -42.308 -2.689  1.00 46.68  ? ? ? ? ? ? 333 HOH U O   1 
+HETATM 2064 O O   . HOH C 3 .   ? -18.403 -32.212 1.364   1.00 48.20  ? ? ? ? ? ? 334 HOH U O   1 
+HETATM 2065 O O   . HOH C 3 .   ? 0.333   -52.903 -21.942 1.00 55.02  ? ? ? ? ? ? 335 HOH U O   1 
+HETATM 2066 O O   . HOH C 3 .   ? 11.524  -22.951 -8.970  1.00 41.19  ? ? ? ? ? ? 336 HOH U O   1 
+HETATM 2067 O O   . HOH C 3 .   ? -15.285 -25.840 -26.512 1.00 41.37  ? ? ? ? ? ? 337 HOH U O   1 
+HETATM 2068 O O   . HOH C 3 .   ? 15.757  -35.672 -9.161  1.00 52.36  ? ? ? ? ? ? 338 HOH U O   1 
+HETATM 2069 O O   . HOH C 3 .   ? -13.785 -29.782 -26.953 1.00 36.62  ? ? ? ? ? ? 339 HOH U O   1 
+HETATM 2070 O O   . HOH C 3 .   ? 17.277  -38.677 -25.037 1.00 61.25  ? ? ? ? ? ? 340 HOH U O   1 
+HETATM 2071 O O   . HOH C 3 .   ? 7.298   -24.742 -21.763 1.00 37.07  ? ? ? ? ? ? 341 HOH U O   1 
+HETATM 2072 O O   . HOH C 3 .   ? 8.838   -20.977 1.814   1.00 53.21  ? ? ? ? ? ? 342 HOH U O   1 
+HETATM 2073 O O   . HOH C 3 .   ? -8.819  -14.105 -10.535 1.00 69.60  ? ? ? ? ? ? 343 HOH U O   1 
+HETATM 2074 O O   . HOH C 3 .   ? -2.071  -20.104 -20.584 1.00 36.73  ? ? ? ? ? ? 344 HOH U O   1 
+HETATM 2075 O O   . HOH C 3 .   ? 8.785   -19.682 -5.908  1.00 43.33  ? ? ? ? ? ? 345 HOH U O   1 
+HETATM 2076 O O   . HOH C 3 .   ? -4.607  -24.422 -29.749 1.00 50.81  ? ? ? ? ? ? 346 HOH U O   1 
+HETATM 2077 O O   . HOH C 3 .   ? 0.634   -19.498 -22.707 1.00 59.12  ? ? ? ? ? ? 347 HOH U O   1 
+HETATM 2078 O O   . HOH C 3 .   ? 14.723  -37.697 -7.396  1.00 54.19  ? ? ? ? ? ? 348 HOH U O   1 
+HETATM 2079 O O   . HOH D 3 .   ? 2.769   -21.902 -31.481 1.00 49.70  ? ? ? ? ? ? 101 HOH P O   1 
+HETATM 2080 O O   . HOH D 3 .   ? 6.454   -23.113 -29.929 1.00 45.25  ? ? ? ? ? ? 102 HOH P O   1 
+# 
+loop_
+_pdbx_poly_seq_scheme.asym_id 
+_pdbx_poly_seq_scheme.entity_id 
+_pdbx_poly_seq_scheme.seq_id 
+_pdbx_poly_seq_scheme.mon_id 
+_pdbx_poly_seq_scheme.ndb_seq_num 
+_pdbx_poly_seq_scheme.pdb_seq_num 
+_pdbx_poly_seq_scheme.auth_seq_num 
+_pdbx_poly_seq_scheme.pdb_mon_id 
+_pdbx_poly_seq_scheme.auth_mon_id 
+_pdbx_poly_seq_scheme.pdb_strand_id 
+_pdbx_poly_seq_scheme.pdb_ins_code 
+_pdbx_poly_seq_scheme.hetero 
+A 1 1   ILE 1   16  16  ILE ILE U . n 
+A 1 2   ILE 2   17  17  ILE ILE U . n 
+A 1 3   GLY 3   18  18  GLY GLY U . n 
+A 1 4   GLY 4   19  19  GLY GLY U . n 
+A 1 5   GLU 5   20  20  GLU GLU U . n 
+A 1 6   PHE 6   21  21  PHE PHE U . n 
+A 1 7   THR 7   22  22  THR THR U . n 
+A 1 8   THR 8   23  23  THR THR U . n 
+A 1 9   ILE 9   24  24  ILE ILE U . n 
+A 1 10  GLU 10  25  25  GLU GLU U . n 
+A 1 11  ASN 11  26  26  ASN ASN U . n 
+A 1 12  GLN 12  27  27  GLN GLN U . n 
+A 1 13  PRO 13  28  28  PRO PRO U . n 
+A 1 14  TRP 14  29  29  TRP TRP U . n 
+A 1 15  PHE 15  30  30  PHE PHE U . n 
+A 1 16  ALA 16  31  31  ALA ALA U . n 
+A 1 17  ALA 17  32  32  ALA ALA U . n 
+A 1 18  ILE 18  33  33  ILE ILE U . n 
+A 1 19  TYR 19  34  34  TYR TYR U . n 
+A 1 20  ARG 20  35  35  ARG ARG U . n 
+A 1 21  ARG 21  36  36  ARG ARG U . n 
+A 1 22  HIS 22  37  37  HIS HIS U . n 
+A 1 23  ARG 23  37  37  ARG ARG U A n 
+A 1 24  GLY 24  37  37  GLY GLY U B n 
+A 1 25  GLY 25  37  37  GLY GLY U C n 
+A 1 26  SER 26  37  37  SER SER U D n 
+A 1 27  VAL 27  38  38  VAL VAL U . n 
+A 1 28  THR 28  39  39  THR THR U . n 
+A 1 29  TYR 29  40  40  TYR TYR U . n 
+A 1 30  VAL 30  41  41  VAL VAL U . n 
+A 1 31  CYS 31  42  42  CYS CYS U . n 
+A 1 32  GLY 32  43  43  GLY GLY U . n 
+A 1 33  GLY 33  44  44  GLY GLY U . n 
+A 1 34  SER 34  45  45  SER SER U . n 
+A 1 35  LEU 35  46  46  LEU LEU U . n 
+A 1 36  ILE 36  47  47  ILE ILE U . n 
+A 1 37  SER 37  48  48  SER SER U . n 
+A 1 38  PRO 38  49  49  PRO PRO U . n 
+A 1 39  CYS 39  50  50  CYS CYS U . n 
+A 1 40  TRP 40  51  51  TRP TRP U . n 
+A 1 41  VAL 41  52  52  VAL VAL U . n 
+A 1 42  ILE 42  53  53  ILE ILE U . n 
+A 1 43  SER 43  54  54  SER SER U . n 
+A 1 44  ALA 44  55  55  ALA ALA U . n 
+A 1 45  THR 45  56  56  THR THR U . n 
+A 1 46  HIS 46  57  57  HIS HIS U . n 
+A 1 47  CYS 47  58  58  CYS CYS U . n 
+A 1 48  PHE 48  59  59  PHE PHE U . n 
+A 1 49  ILE 49  60  60  ILE ILE U . n 
+A 1 50  ASP 50  60  60  ASP ASP U A n 
+A 1 51  TYR 51  60  60  TYR TYR U B n 
+A 1 52  PRO 52  60  60  PRO PRO U C n 
+A 1 53  LYS 53  61  61  LYS LYS U . n 
+A 1 54  LYS 54  62  62  LYS LYS U . n 
+A 1 55  GLU 55  62  62  GLU GLU U A n 
+A 1 56  ASP 56  63  63  ASP ASP U . n 
+A 1 57  TYR 57  64  64  TYR TYR U . n 
+A 1 58  ILE 58  65  65  ILE ILE U . n 
+A 1 59  VAL 59  66  66  VAL VAL U . n 
+A 1 60  TYR 60  67  67  TYR TYR U . n 
+A 1 61  LEU 61  68  68  LEU LEU U . n 
+A 1 62  GLY 62  69  69  GLY GLY U . n 
+A 1 63  ARG 63  70  70  ARG ARG U . n 
+A 1 64  SER 64  71  71  SER SER U . n 
+A 1 65  ARG 65  72  72  ARG ARG U . n 
+A 1 66  LEU 66  73  73  LEU LEU U . n 
+A 1 67  ASN 67  74  74  ASN ASN U . n 
+A 1 68  SER 68  75  75  SER SER U . n 
+A 1 69  ASN 69  76  76  ASN ASN U . n 
+A 1 70  THR 70  77  77  THR THR U . n 
+A 1 71  GLN 71  78  78  GLN GLN U . n 
+A 1 72  GLY 72  79  79  GLY GLY U . n 
+A 1 73  GLU 73  80  80  GLU GLU U . n 
+A 1 74  MET 74  81  81  MET MET U . n 
+A 1 75  LYS 75  82  82  LYS LYS U . n 
+A 1 76  PHE 76  83  83  PHE PHE U . n 
+A 1 77  GLU 77  84  84  GLU GLU U . n 
+A 1 78  VAL 78  85  85  VAL VAL U . n 
+A 1 79  GLU 79  86  86  GLU GLU U . n 
+A 1 80  ASN 80  87  87  ASN ASN U . n 
+A 1 81  LEU 81  88  88  LEU LEU U . n 
+A 1 82  ILE 82  89  89  ILE ILE U . n 
+A 1 83  LEU 83  90  90  LEU LEU U . n 
+A 1 84  HIS 84  91  91  HIS HIS U . n 
+A 1 85  LYS 85  92  92  LYS LYS U . n 
+A 1 86  ASP 86  93  93  ASP ASP U . n 
+A 1 87  TYR 87  94  94  TYR TYR U . n 
+A 1 88  SER 88  95  95  SER SER U . n 
+A 1 89  ALA 89  96  96  ALA ALA U . n 
+A 1 90  ASP 90  97  97  ASP ASP U . n 
+A 1 91  THR 91  97  97  THR THR U A n 
+A 1 92  LEU 92  97  97  LEU LEU U B n 
+A 1 93  ALA 93  98  98  ALA ALA U . n 
+A 1 94  TYR 94  99  99  TYR TYR U . n 
+A 1 95  HIS 95  100 100 HIS HIS U . n 
+A 1 96  ASN 96  101 101 ASN ASN U . n 
+A 1 97  ASP 97  102 102 ASP ASP U . n 
+A 1 98  ILE 98  103 103 ILE ILE U . n 
+A 1 99  ALA 99  104 104 ALA ALA U . n 
+A 1 100 LEU 100 105 105 LEU LEU U . n 
+A 1 101 LEU 101 106 106 LEU LEU U . n 
+A 1 102 LYS 102 107 107 LYS LYS U . n 
+A 1 103 ILE 103 108 108 ILE ILE U . n 
+A 1 104 ARG 104 109 109 ARG ARG U . n 
+A 1 105 SER 105 110 110 SER SER U . n 
+A 1 106 LYS 106 110 110 LYS LYS U A n 
+A 1 107 GLU 107 110 110 GLU GLU U B n 
+A 1 108 GLY 108 110 110 GLY GLY U C n 
+A 1 109 ARG 109 110 110 ARG ARG U D n 
+A 1 110 CYS 110 111 111 CYS CYS U . n 
+A 1 111 ALA 111 112 112 ALA ALA U . n 
+A 1 112 GLN 112 113 113 GLN GLN U . n 
+A 1 113 PRO 113 114 114 PRO PRO U . n 
+A 1 114 SER 114 115 115 SER SER U . n 
+A 1 115 ARG 115 116 116 ARG ARG U . n 
+A 1 116 THR 116 117 117 THR THR U . n 
+A 1 117 ILE 117 118 118 ILE ILE U . n 
+A 1 118 GLN 118 119 119 GLN GLN U . n 
+A 1 119 THR 119 120 120 THR THR U . n 
+A 1 120 ILE 120 121 121 ILE ILE U . n 
+A 1 121 ALA 121 122 122 ALA ALA U . n 
+A 1 122 LEU 122 123 123 LEU LEU U . n 
+A 1 123 PRO 123 124 124 PRO PRO U . n 
+A 1 124 SER 124 125 125 SER SER U . n 
+A 1 125 MET 125 126 126 MET MET U . n 
+A 1 126 TYR 126 127 127 TYR TYR U . n 
+A 1 127 ASN 127 128 128 ASN ASN U . n 
+A 1 128 ASP 128 129 129 ASP ASP U . n 
+A 1 129 PRO 129 130 130 PRO PRO U . n 
+A 1 130 GLN 130 131 131 GLN GLN U . n 
+A 1 131 PHE 131 132 132 PHE PHE U . n 
+A 1 132 GLY 132 133 133 GLY GLY U . n 
+A 1 133 THR 133 134 134 THR THR U . n 
+A 1 134 SER 134 135 135 SER SER U . n 
+A 1 135 CYS 135 136 136 CYS CYS U . n 
+A 1 136 GLU 136 137 137 GLU GLU U . n 
+A 1 137 ILE 137 138 138 ILE ILE U . n 
+A 1 138 THR 138 139 139 THR THR U . n 
+A 1 139 GLY 139 140 140 GLY GLY U . n 
+A 1 140 PHE 140 141 141 PHE PHE U . n 
+A 1 141 GLY 141 142 142 GLY GLY U . n 
+A 1 142 LYS 142 143 143 LYS LYS U . n 
+A 1 143 GLU 143 144 144 GLU GLU U . n 
+A 1 144 GLN 144 145 145 GLN GLN U . n 
+A 1 145 SER 145 146 146 SER SER U . n 
+A 1 146 THR 146 147 147 THR THR U . n 
+A 1 147 ASP 147 148 148 ASP ASP U . n 
+A 1 148 TYR 148 149 149 TYR TYR U . n 
+A 1 149 LEU 149 150 150 LEU LEU U . n 
+A 1 150 TYR 150 151 151 TYR TYR U . n 
+A 1 151 PRO 151 152 152 PRO PRO U . n 
+A 1 152 GLU 152 153 153 GLU GLU U . n 
+A 1 153 GLN 153 154 154 GLN GLN U . n 
+A 1 154 LEU 154 155 155 LEU LEU U . n 
+A 1 155 LYS 155 156 156 LYS LYS U . n 
+A 1 156 MET 156 157 157 MET MET U . n 
+A 1 157 THR 157 158 158 THR THR U . n 
+A 1 158 VAL 158 159 159 VAL VAL U . n 
+A 1 159 VAL 159 160 160 VAL VAL U . n 
+A 1 160 LYS 160 161 161 LYS LYS U . n 
+A 1 161 LEU 161 162 162 LEU LEU U . n 
+A 1 162 ILE 162 163 163 ILE ILE U . n 
+A 1 163 SER 163 164 164 SER SER U . n 
+A 1 164 HIS 164 165 165 HIS HIS U . n 
+A 1 165 ARG 165 166 166 ARG ARG U . n 
+A 1 166 GLU 166 167 167 GLU GLU U . n 
+A 1 167 CYS 167 168 168 CYS CYS U . n 
+A 1 168 GLN 168 169 169 GLN GLN U . n 
+A 1 169 GLN 169 170 170 GLN GLN U . n 
+A 1 170 PRO 170 170 170 PRO PRO U A n 
+A 1 171 HIS 171 170 170 HIS HIS U B n 
+A 1 172 TYR 172 171 171 TYR TYR U . n 
+A 1 173 TYR 173 172 172 TYR TYR U . n 
+A 1 174 GLY 174 173 173 GLY GLY U . n 
+A 1 175 SER 175 174 174 SER SER U . n 
+A 1 176 GLU 176 175 175 GLU GLU U . n 
+A 1 177 VAL 177 176 176 VAL VAL U . n 
+A 1 178 THR 178 177 177 THR THR U . n 
+A 1 179 THR 179 178 178 THR THR U . n 
+A 1 180 LYS 180 179 179 LYS LYS U . n 
+A 1 181 MET 181 180 180 MET MET U . n 
+A 1 182 LEU 182 181 181 LEU LEU U . n 
+A 1 183 CYS 183 182 182 CYS CYS U . n 
+A 1 184 ALA 184 183 183 ALA ALA U . n 
+A 1 185 ALA 185 184 184 ALA ALA U . n 
+A 1 186 ASP 186 185 185 ASP ASP U . n 
+A 1 187 PRO 187 185 185 PRO PRO U A n 
+A 1 188 GLN 188 185 185 GLN GLN U B n 
+A 1 189 TRP 189 186 186 TRP TRP U . n 
+A 1 190 LYS 190 187 187 LYS LYS U . n 
+A 1 191 THR 191 188 188 THR THR U . n 
+A 1 192 ASP 192 189 189 ASP ASP U . n 
+A 1 193 SER 193 190 190 SER SER U . n 
+A 1 194 CYS 194 191 191 CYS CYS U . n 
+A 1 195 GLN 195 192 192 GLN GLN U . n 
+A 1 196 GLY 196 193 193 GLY GLY U . n 
+A 1 197 ASP 197 194 194 ASP ASP U . n 
+A 1 198 SER 198 195 195 SER SER U . n 
+A 1 199 GLY 199 196 196 GLY GLY U . n 
+A 1 200 GLY 200 197 197 GLY GLY U . n 
+A 1 201 PRO 201 198 198 PRO PRO U . n 
+A 1 202 LEU 202 199 199 LEU LEU U . n 
+A 1 203 VAL 203 200 200 VAL VAL U . n 
+A 1 204 CYS 204 201 201 CYS CYS U . n 
+A 1 205 SER 205 202 202 SER SER U . n 
+A 1 206 LEU 206 203 203 LEU LEU U . n 
+A 1 207 GLN 207 204 204 GLN GLN U . n 
+A 1 208 GLY 208 205 205 GLY GLY U . n 
+A 1 209 ARG 209 206 206 ARG ARG U . n 
+A 1 210 MET 210 207 207 MET MET U . n 
+A 1 211 THR 211 208 208 THR THR U . n 
+A 1 212 LEU 212 209 209 LEU LEU U . n 
+A 1 213 THR 213 210 210 THR THR U . n 
+A 1 214 GLY 214 211 211 GLY GLY U . n 
+A 1 215 ILE 215 212 212 ILE ILE U . n 
+A 1 216 VAL 216 213 213 VAL VAL U . n 
+A 1 217 SER 217 214 214 SER SER U . n 
+A 1 218 TRP 218 215 215 TRP TRP U . n 
+A 1 219 GLY 219 216 216 GLY GLY U . n 
+A 1 220 ARG 220 217 217 ARG ARG U . n 
+A 1 221 GLY 221 219 219 GLY GLY U . n 
+A 1 222 CYS 222 220 220 CYS CYS U . n 
+A 1 223 ALA 223 221 221 ALA ALA U . n 
+A 1 224 LEU 224 222 222 LEU LEU U . n 
+A 1 225 LYS 225 223 223 LYS LYS U . n 
+A 1 226 ASP 226 223 223 ASP ASP U A n 
+A 1 227 LYS 227 224 224 LYS LYS U . n 
+A 1 228 PRO 228 225 225 PRO PRO U . n 
+A 1 229 GLY 229 226 226 GLY GLY U . n 
+A 1 230 VAL 230 227 227 VAL VAL U . n 
+A 1 231 TYR 231 228 228 TYR TYR U . n 
+A 1 232 THR 232 229 229 THR THR U . n 
+A 1 233 ARG 233 230 230 ARG ARG U . n 
+A 1 234 VAL 234 231 231 VAL VAL U . n 
+A 1 235 SER 235 232 232 SER SER U . n 
+A 1 236 HIS 236 233 233 HIS HIS U . n 
+A 1 237 PHE 237 234 234 PHE PHE U . n 
+A 1 238 LEU 238 235 235 LEU LEU U . n 
+A 1 239 PRO 239 236 236 PRO PRO U . n 
+A 1 240 TRP 240 237 237 TRP TRP U . n 
+A 1 241 ILE 241 238 238 ILE ILE U . n 
+A 1 242 ARG 242 239 239 ARG ARG U . n 
+A 1 243 SER 243 240 240 SER SER U . n 
+A 1 244 HIS 244 241 241 HIS HIS U . n 
+A 1 245 THR 245 242 242 THR THR U . n 
+A 1 246 LYS 246 243 243 LYS LYS U . n 
+A 1 247 GLU 247 244 244 GLU GLU U . n 
+B 2 1   CYS 1   1   1   CYS CYS P . n 
+B 2 2   PRO 2   2   2   PRO PRO P . n 
+B 2 3   ALA 3   3   3   ALA ALA P . n 
+B 2 4   TYR 4   4   4   TYR TYR P . n 
+B 2 5   SER 5   5   5   SER SER P . n 
+B 2 6   ARG 6   6   6   ARG ARG P . n 
+B 2 7   TYR 7   7   7   TYR TYR P . n 
+B 2 8   ILE 8   8   8   ILE ILE P . n 
+B 2 9   GLY 9   9   9   GLY GLY P . n 
+B 2 10  CYS 10  10  10  CYS CYS P . n 
+# 
+loop_
+_pdbx_nonpoly_scheme.asym_id 
+_pdbx_nonpoly_scheme.entity_id 
+_pdbx_nonpoly_scheme.mon_id 
+_pdbx_nonpoly_scheme.ndb_seq_num 
+_pdbx_nonpoly_scheme.pdb_seq_num 
+_pdbx_nonpoly_scheme.auth_seq_num 
+_pdbx_nonpoly_scheme.pdb_mon_id 
+_pdbx_nonpoly_scheme.auth_mon_id 
+_pdbx_nonpoly_scheme.pdb_strand_id 
+_pdbx_nonpoly_scheme.pdb_ins_code 
+C 3 HOH 1  301 6  HOH HOH U . 
+C 3 HOH 2  302 19 HOH HOH U . 
+C 3 HOH 3  303 4  HOH HOH U . 
+C 3 HOH 4  304 33 HOH HOH U . 
+C 3 HOH 5  305 9  HOH HOH U . 
+C 3 HOH 6  306 50 HOH HOH U . 
+C 3 HOH 7  307 46 HOH HOH U . 
+C 3 HOH 8  308 35 HOH HOH U . 
+C 3 HOH 9  309 12 HOH HOH U . 
+C 3 HOH 10 310 40 HOH HOH U . 
+C 3 HOH 11 311 44 HOH HOH U . 
+C 3 HOH 12 312 45 HOH HOH U . 
+C 3 HOH 13 313 39 HOH HOH U . 
+C 3 HOH 14 314 5  HOH HOH U . 
+C 3 HOH 15 315 43 HOH HOH U . 
+C 3 HOH 16 316 21 HOH HOH U . 
+C 3 HOH 17 317 7  HOH HOH U . 
+C 3 HOH 18 318 8  HOH HOH U . 
+C 3 HOH 19 319 30 HOH HOH U . 
+C 3 HOH 20 320 1  HOH HOH U . 
+C 3 HOH 21 321 22 HOH HOH U . 
+C 3 HOH 22 322 23 HOH HOH U . 
+C 3 HOH 23 323 17 HOH HOH U . 
+C 3 HOH 24 324 11 HOH HOH U . 
+C 3 HOH 25 325 47 HOH HOH U . 
+C 3 HOH 26 326 31 HOH HOH U . 
+C 3 HOH 27 327 18 HOH HOH U . 
+C 3 HOH 28 328 10 HOH HOH U . 
+C 3 HOH 29 329 34 HOH HOH U . 
+C 3 HOH 30 330 16 HOH HOH U . 
+C 3 HOH 31 331 3  HOH HOH U . 
+C 3 HOH 32 332 41 HOH HOH U . 
+C 3 HOH 33 333 24 HOH HOH U . 
+C 3 HOH 34 334 13 HOH HOH U . 
+C 3 HOH 35 335 36 HOH HOH U . 
+C 3 HOH 36 336 20 HOH HOH U . 
+C 3 HOH 37 337 2  HOH HOH U . 
+C 3 HOH 38 338 25 HOH HOH U . 
+C 3 HOH 39 339 29 HOH HOH U . 
+C 3 HOH 40 340 28 HOH HOH U . 
+C 3 HOH 41 341 42 HOH HOH U . 
+C 3 HOH 42 342 37 HOH HOH U . 
+C 3 HOH 43 343 26 HOH HOH U . 
+C 3 HOH 44 344 14 HOH HOH U . 
+C 3 HOH 45 345 32 HOH HOH U . 
+C 3 HOH 46 346 15 HOH HOH U . 
+C 3 HOH 47 347 38 HOH HOH U . 
+C 3 HOH 48 348 27 HOH HOH U . 
+D 3 HOH 1  101 48 HOH HOH P . 
+D 3 HOH 2  102 49 HOH HOH P . 
+# 
+_pdbx_struct_assembly.id                   1 
+_pdbx_struct_assembly.details              author_and_software_defined_assembly 
+_pdbx_struct_assembly.method_details       PISA 
+_pdbx_struct_assembly.oligomeric_details   dimeric 
+_pdbx_struct_assembly.oligomeric_count     2 
+# 
+_pdbx_struct_assembly_gen.assembly_id       1 
+_pdbx_struct_assembly_gen.oper_expression   1 
+_pdbx_struct_assembly_gen.asym_id_list      A,B,C,D 
+# 
+loop_
+_pdbx_struct_assembly_prop.biol_id 
+_pdbx_struct_assembly_prop.type 
+_pdbx_struct_assembly_prop.value 
+_pdbx_struct_assembly_prop.details 
+1 'ABSA (A^2)' 1460  ? 
+1 MORE         -7    ? 
+1 'SSA (A^2)'  11300 ? 
+# 
+_pdbx_struct_oper_list.id                   1 
+_pdbx_struct_oper_list.type                 'identity operation' 
+_pdbx_struct_oper_list.name                 1_555 
+_pdbx_struct_oper_list.symmetry_operation   ? 
+_pdbx_struct_oper_list.matrix[1][1]         1.0000000000 
+_pdbx_struct_oper_list.matrix[1][2]         0.0000000000 
+_pdbx_struct_oper_list.matrix[1][3]         0.0000000000 
+_pdbx_struct_oper_list.vector[1]            0.0000000000 
+_pdbx_struct_oper_list.matrix[2][1]         0.0000000000 
+_pdbx_struct_oper_list.matrix[2][2]         1.0000000000 
+_pdbx_struct_oper_list.matrix[2][3]         0.0000000000 
+_pdbx_struct_oper_list.vector[2]            0.0000000000 
+_pdbx_struct_oper_list.matrix[3][1]         0.0000000000 
+_pdbx_struct_oper_list.matrix[3][2]         0.0000000000 
+_pdbx_struct_oper_list.matrix[3][3]         1.0000000000 
+_pdbx_struct_oper_list.vector[3]            0.0000000000 
+# 
+loop_
+_pdbx_version.entry_id 
+_pdbx_version.revision_date 
+_pdbx_version.major_version 
+_pdbx_version.minor_version 
+_pdbx_version.revision_type 
+_pdbx_version.details 
+4ZHL 2015-09-16 4 0000 'Initial release' 'Initial release' 
+4ZHL 2015-10-14 4 0001 Citation          Citation          
+# 
+loop_
+_software.citation_id 
+_software.classification 
+_software.compiler_name 
+_software.compiler_version 
+_software.contact_author 
+_software.contact_author_email 
+_software.date 
+_software.description 
+_software.dependencies 
+_software.hardware 
+_software.language 
+_software.location 
+_software.mods 
+_software.name 
+_software.os 
+_software.os_version 
+_software.type 
+_software.version 
+_software.pdbx_ordinal 
+? refinement        ? ? ? ? ? ? ? ? ? ? ? REFMAC   ? ? ? 5.7.0029 1 
+? 'model building'  ? ? ? ? ? ? ? ? ? ? ? Coot     ? ? ? .        2 
+? 'data scaling'    ? ? ? ? ? ? ? ? ? ? ? HKL-2000 ? ? ? .        3 
+? 'data processing' ? ? ? ? ? ? ? ? ? ? ? HKL-2000 ? ? ? .        4 
+# 
+loop_
+_pdbx_validate_close_contact.id 
+_pdbx_validate_close_contact.PDB_model_num 
+_pdbx_validate_close_contact.auth_atom_id_1 
+_pdbx_validate_close_contact.auth_asym_id_1 
+_pdbx_validate_close_contact.auth_comp_id_1 
+_pdbx_validate_close_contact.auth_seq_id_1 
+_pdbx_validate_close_contact.PDB_ins_code_1 
+_pdbx_validate_close_contact.label_alt_id_1 
+_pdbx_validate_close_contact.auth_atom_id_2 
+_pdbx_validate_close_contact.auth_asym_id_2 
+_pdbx_validate_close_contact.auth_comp_id_2 
+_pdbx_validate_close_contact.auth_seq_id_2 
+_pdbx_validate_close_contact.PDB_ins_code_2 
+_pdbx_validate_close_contact.label_alt_id_2 
+_pdbx_validate_close_contact.dist 
+1 1 SG  U CYS 136 ? ? SG U CYS 201 ? ? 1.51 
+2 1 SG  U CYS 50  ? ? SG U CYS 111 ? ? 1.64 
+3 1 NH2 U ARG 36  ? ? O  U GLU 62  A ? 2.17 
+# 
+_pdbx_validate_symm_contact.id                1 
+_pdbx_validate_symm_contact.PDB_model_num     1 
+_pdbx_validate_symm_contact.auth_atom_id_1    OD1 
+_pdbx_validate_symm_contact.auth_asym_id_1    U 
+_pdbx_validate_symm_contact.auth_comp_id_1    ASN 
+_pdbx_validate_symm_contact.auth_seq_id_1     76 
+_pdbx_validate_symm_contact.PDB_ins_code_1    ? 
+_pdbx_validate_symm_contact.label_alt_id_1    ? 
+_pdbx_validate_symm_contact.site_symmetry_1   1_555 
+_pdbx_validate_symm_contact.auth_atom_id_2    NH1 
+_pdbx_validate_symm_contact.auth_asym_id_2    U 
+_pdbx_validate_symm_contact.auth_comp_id_2    ARG 
+_pdbx_validate_symm_contact.auth_seq_id_2     206 
+_pdbx_validate_symm_contact.PDB_ins_code_2    ? 
+_pdbx_validate_symm_contact.label_alt_id_2    ? 
+_pdbx_validate_symm_contact.site_symmetry_2   8_444 
+_pdbx_validate_symm_contact.dist              1.87 
+# 
+_pdbx_validate_rmsd_bond.id                        1 
+_pdbx_validate_rmsd_bond.PDB_model_num             1 
+_pdbx_validate_rmsd_bond.auth_atom_id_1            C 
+_pdbx_validate_rmsd_bond.auth_asym_id_1            U 
+_pdbx_validate_rmsd_bond.auth_comp_id_1            SER 
+_pdbx_validate_rmsd_bond.auth_seq_id_1             110 
+_pdbx_validate_rmsd_bond.PDB_ins_code_1            ? 
+_pdbx_validate_rmsd_bond.label_alt_id_1            ? 
+_pdbx_validate_rmsd_bond.auth_atom_id_2            O 
+_pdbx_validate_rmsd_bond.auth_asym_id_2            U 
+_pdbx_validate_rmsd_bond.auth_comp_id_2            SER 
+_pdbx_validate_rmsd_bond.auth_seq_id_2             110 
+_pdbx_validate_rmsd_bond.PDB_ins_code_2            ? 
+_pdbx_validate_rmsd_bond.label_alt_id_2            ? 
+_pdbx_validate_rmsd_bond.bond_value                1.101 
+_pdbx_validate_rmsd_bond.bond_target_value         1.229 
+_pdbx_validate_rmsd_bond.bond_deviation            -0.128 
+_pdbx_validate_rmsd_bond.bond_standard_deviation   0.019 
+_pdbx_validate_rmsd_bond.linker_flag               N 
+# 
+loop_
+_pdbx_validate_torsion.id 
+_pdbx_validate_torsion.PDB_model_num 
+_pdbx_validate_torsion.auth_comp_id 
+_pdbx_validate_torsion.auth_asym_id 
+_pdbx_validate_torsion.auth_seq_id 
+_pdbx_validate_torsion.PDB_ins_code 
+_pdbx_validate_torsion.label_alt_id 
+_pdbx_validate_torsion.phi 
+_pdbx_validate_torsion.psi 
+1 1 GLN U 27  ? ? -157.33 54.96   
+2 1 SER U 54  ? ? -148.41 -157.39 
+3 1 TYR U 171 ? ? -89.01  -111.29 
+4 1 GLN U 204 ? ? 58.66   -126.06 
+# 
+_pdbx_entity_nonpoly.entity_id   3 
+_pdbx_entity_nonpoly.name        water 
+_pdbx_entity_nonpoly.comp_id     HOH 
+# 
diff --git a/Tests/PDB/a_structure.pdb b/Tests/PDB/a_structure.pdb
index ca1e3eb..3845eb8 100644
--- a/Tests/PDB/a_structure.pdb
+++ b/Tests/PDB/a_structure.pdb
@@ -1,4 +1,4 @@
-REMARK INTRODUCED BUGS IN RESIDUES 2,3,4 AND WAT 67 
+REMARK INTRODUCED BUGS IN RESIDUES 2,3,4 AND WAT 67
 REMARK 2 DUPLICATE N
 REMARK 3 DISORDERED ATOM WITH BLANC ALTLOC
 REMARK 4 CHANGE IN RESNAME
@@ -6,826 +6,826 @@ REMARK WAT 67 IS PRESENT TWICE
 REMARK RESIDUE 0 SHOULD GENERATE A POLYPEPTIDE WARNING (DUPLICATED C ATOM)
 REMARK MISSING ELEMENTS IN THE FIRST RESIDUE
 MODEL
-HETATM    1  N   PCA A   1       0.525   2.690  13.317  1.00 20.26             
-HETATM    2  CA  PCA A   1      -0.993   2.924  13.160  1.00 21.23             
-HETATM    3  CB  PCA A   1      -1.783   1.879  14.043  1.00 24.73           C  
-HETATM    4  CG  PCA A   1      -0.609   1.208  14.621  1.00 22.31           C  
-HETATM    5  CD  PCA A   1       0.869   1.560  14.229  1.00 23.27           C  
-HETATM    6  OE  PCA A   1       2.053   1.323  14.293  1.00 26.24           O  
-HETATM    7  C   PCA A   1      -1.375   4.270  13.719  1.00 19.21           C  
-HETATM    8  O   PCA A   1      -0.389   5.021  14.230  1.00 18.05           O  
-HETATM    8 CA   PCA A   1      -0.189   5.321  14.250  1.00 18.06             
+HETATM    1  N   PCA A   1       0.525   2.690  13.317  1.00 20.26      A
+HETATM    2  CA  PCA A   1      -0.993   2.924  13.160  1.00 21.23      A
+HETATM    3  CB  PCA A   1      -1.783   1.879  14.043  1.00 24.73           C
+HETATM    4  CG  PCA A   1      -0.609   1.208  14.621  1.00 22.31           C
+HETATM    5  CD  PCA A   1       0.869   1.560  14.229  1.00 23.27           C
+HETATM    6  OE  PCA A   1       2.053   1.323  14.293  1.00 26.24           O
+HETATM    7  C   PCA A   1      -1.375   4.270  13.719  1.00 19.21           C
+HETATM    8  O   PCA A   1      -0.389   5.021  14.230  1.00 18.05           O
+HETATM    8 CA   PCA A   1      -0.189   5.321  14.250  1.00 18.06
 ENDMDL
-ATOM     22  C   CYS A   0      -3.637   9.026  17.672  1.00 16.22           C  
-ATOM      9  N   ARG A   2      -2.607   4.673  13.504  1.00 20.57           N  
-ATOM      9  N   ARG A   2      -2.607   4.673  13.504  1.00 20.57           N  
-ATOM     10  CA  ARG A   2      -3.091   6.010  13.918  1.00 20.82           C  
-ATOM     11  C   ARG A   2      -3.361   6.101  15.403  1.00 19.55           C  
-ATOM     12  O   ARG A   2      -3.742   5.121  16.050  1.00 19.99           O  
-ATOM     13  CB  ARG A   2      -4.348   6.395  13.124  1.00 25.30           C  
-ATOM     14  CG  ARG A   2      -4.110   6.337  11.619  1.00 30.98           C  
-ATOM     15  CD  ARG A   2      -4.277   7.652  10.942  1.00 31.79           C  
-ATOM     16  NE  ARG A   2      -3.441   8.730  11.457  1.00 31.91           N  
-ATOM     17  CZ  ARG A   2      -3.550   9.979  10.980  1.00 31.95           C  
-ATOM     18  NH1 ARG A   2      -4.444  10.212  10.022  1.00 33.34           N  
-ATOM     19  NH2 ARG A   2      -2.804  10.949  11.444  1.00 29.59           N  
-ATOM     20  N   CYS A   3      -3.248   7.317  15.948  1.00 15.71           N  
-ATOM     20  N  ACYS A   3      -3.248   7.317  15.948  1.00 15.71           N  
-ATOM     21  CA  CYS A   3      -3.419   7.555  17.361  1.00 14.13           C  
-ATOM     22  C   CYS A   3      -3.637   9.026  17.672  1.00 16.22           C  
-ATOM     23  O   CYS A   3      -3.406   9.901  16.828  1.00 15.46           O  
-ATOM     24  CB  CYS A   3      -2.127   7.069  18.110  1.00 13.74           C  
-ATOM     25  SG  CYS A   3      -0.659   7.917  17.525  1.00 11.51           S  
-ATOM     26  N   GLY A   4      -4.122   9.328  18.863  1.00 15.45           N  
-ATOM     27  CA  GLY A   4      -4.129  10.656  19.402  1.00 17.05           C  
-ATOM     28  C   GLY A   4      -5.029  11.674  18.735  1.00 16.11           C  
-ATOM     29  O   GLY A   4      -6.039  11.345  18.125  1.00 17.88           O  
-ATOM     29  O   SER A   4      -6.039  11.345  18.125  1.00 17.88           O  
-ATOM     30  N   SER A   5      -4.651  12.958  18.899  1.00 17.09           N  
-ATOM     31  CA  SER A   5      -5.559  14.034  18.479  1.00 19.08           C  
-ATOM     32  C   SER A   5      -5.752  14.104  16.987  1.00 21.30           C  
-ATOM     33  O   SER A   5      -6.858  14.468  16.528  1.00 22.84           O  
-ATOM     34  CB  SER A   5      -5.107  15.371  19.047  1.00 19.54           C  
-ATOM     35  OG  SER A   5      -3.926  15.835  18.448  1.00 21.28           O  
-ATOM     36  N   GLN A   6      -4.747  13.742  16.200  1.00 20.14           N  
-ATOM     37  CA  GLN A   6      -4.860  13.800  14.746  1.00 22.55           C  
-ATOM     38  C   GLN A   6      -5.298  12.480  14.152  1.00 23.94           C  
-ATOM     39  O   GLN A   6      -5.600  12.407  12.952  1.00 26.03           O  
-ATOM     40  CB  GLN A   6      -3.493  14.198  14.155  1.00 22.92           C  
-ATOM     41  CG  GLN A   6      -3.158  15.672  14.364  1.00 26.86           C  
-ATOM     42  CD  GLN A   6      -4.060  16.549  13.492  1.00 30.27           C  
-ATOM     43  OE1 GLN A   6      -4.888  17.280  14.006  1.00 32.91           O  
-ATOM     44  NE2 GLN A   6      -3.896  16.416  12.182  1.00 32.97           N  
-ATOM     45  N   GLY A   7      -5.311  11.413  14.956  1.00 21.65           N  
-ATOM     46  CA  GLY A   7      -5.574  10.092  14.428  1.00 23.37           C  
-ATOM     47  C   GLY A   7      -6.707   9.337  15.036  1.00 24.14           C  
-ATOM     48  O   GLY A   7      -6.704   8.084  15.015  1.00 27.63           O  
-ATOM     49  N   GLY A   8      -7.727  10.003  15.582  1.00 24.63           N  
-ATOM     50  CA  GLY A   8      -8.913   9.333  16.063  1.00 25.88           C  
-ATOM     51  C   GLY A   8      -8.994   9.133  17.546  1.00 26.42           C  
-ATOM     52  O   GLY A   8     -10.027   8.635  18.054  1.00 27.64           O  
-ATOM     53  N   GLY A   9      -7.952   9.460  18.305  1.00 24.34           N  
-ATOM     54  CA  GLY A   9      -7.970   9.377  19.738  1.00 25.05           C  
-ATOM     55  C   GLY A   9      -7.440   8.111  20.351  1.00 24.44           C  
-ATOM     56  O   GLY A   9      -7.431   8.002  21.594  1.00 26.91           O  
-ATOM     57  N  AGLY A  10      -6.973   7.149  19.576  0.67 23.87           N  
-ATOM     58  CA AGLY A  10      -6.498   5.882  20.072  0.67 23.51           C  
-ATOM     59  C  AGLY A  10      -5.157   5.939  20.783  0.67 20.73           C  
-ATOM     60  O  AGLY A  10      -4.407   6.920  20.735  0.67 18.63           O  
-ATOM     61  N  BSER A  10      -6.973   7.149  19.576  0.33 23.87           N  
-ATOM     62  CA BSER A  10      -6.498   5.882  20.072  0.33 23.51           C  
-ATOM     63  C  BSER A  10      -5.157   5.939  20.783  0.33 20.73           C  
-ATOM     64  O  BSER A  10      -4.407   6.920  20.735  0.33 18.63           O  
-ATOM     65  CB BSER A  10      -6.423   4.843  18.935  0.33 25.06           C  
-ATOM     66  OG BSER A  10      -5.085   4.685  18.488  0.33 26.99           O  
-ATOM     67  N   THR A  11      -4.827   4.849  21.451  1.00 18.73           N  
-ATOM     68  CA  THR A  11      -3.558   4.608  22.100  1.00 18.41           C  
-ATOM     69  C   THR A  11      -2.789   3.579  21.270  1.00 17.02           C  
-ATOM     70  O   THR A  11      -3.445   2.660  20.722  1.00 19.87           O  
-ATOM     71  CB  THR A  11      -3.766   4.031  23.526  1.00 21.54           C  
-ATOM     72  OG1 THR A  11      -4.454   5.039  24.302  1.00 24.31           O  
-ATOM     73  CG2 THR A  11      -2.424   3.745  24.174  1.00 22.15           C  
-ATOM     74  N   CYS A  12      -1.513   3.704  21.104  1.00 14.16           N  
-ATOM     75  CA  CYS A  12      -0.739   2.807  20.240  1.00 13.37           C  
-ATOM     76  C   CYS A  12      -0.634   1.400  20.784  1.00 14.05           C  
-ATOM     77  O   CYS A  12      -0.393   1.199  21.977  1.00 14.75           O  
-ATOM     78  CB  CYS A  12       0.693   3.394  20.115  1.00 11.29           C  
-ATOM     79  SG  CYS A  12       0.658   5.001  19.231  1.00 10.97           S  
-ATOM     80  N   PRO A  13      -0.523   0.411  19.889  1.00 13.45           N  
-ATOM     81  CA  PRO A  13      -0.169  -0.942  20.296  1.00 14.93           C  
-ATOM     82  C   PRO A  13       1.156  -0.911  21.027  1.00 14.45           C  
-ATOM     83  O   PRO A  13       2.082  -0.150  20.677  1.00 14.74           O  
-ATOM     84  CB  PRO A  13      -0.029  -1.675  18.958  1.00 15.07           C  
-ATOM     85  CG  PRO A  13      -0.978  -0.948  18.064  1.00 15.93           C  
-ATOM     86  CD  PRO A  13      -0.772   0.535  18.443  1.00 14.96           C  
-ATOM     87  N  AALA A  14       1.268  -1.692  22.089  0.56 14.65           N  
-ATOM     88  CA AALA A  14       2.445  -1.814  22.901  0.56 16.97           C  
-ATOM     89  C  AALA A  14       2.754  -0.531  23.694  0.56 14.77           C  
-ATOM     90  O  AALA A  14       3.849  -0.405  24.231  0.56 17.39           O  
-ATOM     91  CB AALA A  14       3.645  -2.269  22.108  0.56 18.86           C  
-ATOM     92  N  BGLY A  14       1.268  -1.692  22.089  0.44 14.65           N  
-ATOM     93  CA BGLY A  14       2.445  -1.814  22.901  0.44 16.97           C  
-ATOM     94  C  BGLY A  14       2.754  -0.531  23.694  0.44 14.77           C  
-ATOM     95  O  BGLY A  14       3.849  -0.405  24.231  0.44 17.39           O  
-ATOM     96  N   LEU A  15       1.754   0.327  23.778  1.00 13.72           N  
-ATOM     97  CA  LEU A  15       1.873   1.601  24.487  1.00 13.52           C  
-ATOM     98  C   LEU A  15       3.033   2.427  23.993  1.00 13.54           C  
-ATOM     99  O   LEU A  15       3.727   3.109  24.761  1.00 13.53           O  
-ATOM    100  CB  LEU A  15       1.831   1.406  25.994  1.00 16.99           C  
-ATOM    101  CG ALEU A  15       0.485   1.257  26.672  0.50 18.92           C  
-ATOM    102  CG BLEU A  15       0.561   0.693  26.511  0.50 18.16           C  
-ATOM    103  CD1ALEU A  15      -0.272   2.579  26.709  0.50 19.64           C  
-ATOM    104  CD1BLEU A  15       0.675   0.390  27.988  0.50 20.69           C  
-ATOM    105  CD2ALEU A  15      -0.364   0.182  26.012  0.50 21.59           C  
-ATOM    106  CD2BLEU A  15      -0.677   1.532  26.219  0.50 20.18           C  
-ATOM    107  N  ATRP A  16       3.298   2.388  22.684  0.56 11.29           N  
-ATOM    108  CA ATRP A  16       4.273   3.211  22.015  0.56 11.25           C  
-ATOM    109  C  ATRP A  16       3.838   4.674  21.911  0.56  9.81           C  
-ATOM    110  O  ATRP A  16       2.731   5.041  22.272  0.56 10.80           O  
-ATOM    111  CB ATRP A  16       4.530   2.673  20.587  0.56 15.06           C  
-ATOM    112  CG ATRP A  16       5.500   1.531  20.589  0.56 22.22           C  
-ATOM    113  CD1ATRP A  16       5.344   0.300  20.028  0.56 22.99           C  
-ATOM    114  CD2ATRP A  16       6.804   1.549  21.180  0.56 23.43           C  
-ATOM    115  NE1ATRP A  16       6.469  -0.459  20.244  0.56 25.66           N  
-ATOM    116  CE2ATRP A  16       7.380   0.282  20.950  0.56 25.18           C  
-ATOM    117  CE3ATRP A  16       7.532   2.508  21.894  0.56 26.28           C  
-ATOM    118  CZ2ATRP A  16       8.652  -0.045  21.409  0.56 25.91           C  
-ATOM    119  CZ3ATRP A  16       8.793   2.178  22.349  0.56 26.49           C  
-ATOM    120  CH2ATRP A  16       9.341   0.908  22.104  0.56 25.88           C  
-ATOM    121  N  BARG A  16       3.259   2.392  22.674  0.44 13.28           N  
-ATOM    122  CA BARG A  16       4.280   3.162  22.014  0.44 13.26           C  
-ATOM    123  C  BARG A  16       3.896   4.656  21.956  0.44 11.99           C  
-ATOM    124  O  BARG A  16       2.800   5.040  22.308  0.44 12.53           O  
-ATOM    125  CB BARG A  16       4.656   2.661  20.638  0.22 13.35           C  
-ATOM    126  CB CARG A  16       4.437   2.680  20.555  0.22 16.20           C  
-ATOM    127  CG BARG A  16       5.385   1.331  20.601  0.22 17.64           C  
-ATOM    128  CG CARG A  16       5.639   1.870  20.229  0.22 21.14           C  
-ATOM    129  CD BARG A  16       6.160   1.040  21.834  0.22 19.84           C  
-ATOM    130  CD CARG A  16       5.796   0.630  21.081  0.22 21.56           C  
-ATOM    131  NE BARG A  16       7.593   1.203  21.768  0.22 21.21           N  
-ATOM    132  NE CARG A  16       7.173   0.375  21.465  0.22 24.22           N  
-ATOM    133  CZ BARG A  16       8.455   0.224  21.501  0.22 22.06           C  
-ATOM    134  CZ CARG A  16       8.046   1.259  21.909  0.22 25.11           C  
-ATOM    135  NH1BARG A  16       8.004  -0.997  21.239  0.22 23.25           N  
-ATOM    136  NH1CARG A  16       7.750   2.548  22.045  0.22 26.05           N  
-ATOM    137  NH2BARG A  16       9.757   0.463  21.492  0.22 23.17           N  
-ATOM    138  NH2CARG A  16       9.277   0.859  22.235  0.22 24.63           N  
-ATOM    139  N   CYS A  17       4.860   5.440  21.477  1.00 11.64           N  
-ATOM    140  CA  CYS A  17       4.659   6.900  21.426  1.00 10.21           C  
-ATOM    141  C   CYS A  17       3.731   7.303  20.294  1.00 10.66           C  
-ATOM    142  O   CYS A  17       3.858   6.790  19.169  1.00 12.03           O  
-ATOM    143  CB  CYS A  17       5.991   7.617  21.171  1.00 10.96           C  
-ATOM    144  SG  CYS A  17       7.304   7.032  22.268  1.00 11.64           S  
-ATOM    145  N   CYS A  18       2.863   8.268  20.538  1.00 10.28           N  
-ATOM    146  CA  CYS A  18       2.045   8.868  19.490  1.00 11.27           C  
-ATOM    147  C   CYS A  18       2.592  10.233  19.140  1.00 10.85           C  
-ATOM    148  O   CYS A  18       2.716  11.073  20.064  1.00 11.54           O  
-ATOM    149  CB  CYS A  18       0.606   9.001  20.013  1.00 11.58           C  
-ATOM    150  SG  CYS A  18      -0.516   9.593  18.698  1.00 11.18           S  
-ATOM    151  N   SER A  19       2.983  10.469  17.906  1.00 10.63           N  
-ATOM    152  CA  SER A  19       3.522  11.773  17.506  1.00 10.33           C  
-ATOM    153  C   SER A  19       2.395  12.812  17.355  1.00 10.06           C  
-ATOM    154  O   SER A  19       1.239  12.483  17.289  1.00 10.50           O  
-ATOM    155  CB  SER A  19       4.213  11.607  16.137  1.00 12.53           C  
-ATOM    156  OG  SER A  19       3.218  11.521  15.119  1.00 11.20           O  
-ATOM    157  N   ILE A  20       2.843  14.085  17.220  1.00 10.99           N  
-ATOM    158  CA  ILE A  20       1.854  15.159  17.009  1.00 11.86           C  
-ATOM    159  C   ILE A  20       1.162  15.020  15.673  1.00 13.07           C  
-ATOM    160  O   ILE A  20       0.135  15.665  15.448  1.00 13.75           O  
-ATOM    161  CB  ILE A  20       2.505  16.546  17.133  1.00 12.91           C  
-ATOM    162  CG1 ILE A  20       3.608  16.733  16.099  1.00 13.68           C  
-ATOM    163  CG2 ILE A  20       3.006  16.773  18.552  1.00 14.60           C  
-ATOM    164  CD1 ILE A  20       4.127  18.178  16.031  1.00 15.84           C  
-ATOM    165  N   TRP A  21       1.662  14.176  14.766  1.00 11.46           N  
-ATOM    166  CA  TRP A  21       1.006  13.921  13.502  1.00 11.50           C  
-ATOM    167  C   TRP A  21       0.012  12.789  13.564  1.00 12.13           C  
-ATOM    168  O   TRP A  21      -0.698  12.510  12.602  1.00 14.34           O  
-ATOM    169  CB  TRP A  21       2.031  13.690  12.369  1.00 12.06           C  
-ATOM    170  CG  TRP A  21       2.940  14.872  12.185  1.00 13.41           C  
-ATOM    171  CD1 TRP A  21       2.610  16.023  11.471  1.00 14.87           C  
-ATOM    172  CD2 TRP A  21       4.240  15.070  12.680  1.00 13.19           C  
-ATOM    173  NE1 TRP A  21       3.663  16.890  11.537  1.00 14.69           N  
-ATOM    174  CE2 TRP A  21       4.693  16.334  12.246  1.00 14.96           C  
-ATOM    175  CE3 TRP A  21       5.105  14.273  13.462  1.00 14.98           C  
-ATOM    176  CZ2 TRP A  21       5.949  16.830  12.589  1.00 16.73           C  
-ATOM    177  CZ3 TRP A  21       6.354  14.760  13.773  1.00 17.06           C  
-ATOM    178  CH2 TRP A  21       6.760  16.040  13.348  1.00 18.02           C  
-ATOM    179  N   GLY A  22      -0.131  12.116  14.717  1.00 11.06           N  
-ATOM    180  CA  GLY A  22      -1.145  11.092  14.893  1.00 11.02           C  
-ATOM    181  C   GLY A  22      -0.683   9.708  14.431  1.00  9.47           C  
-ATOM    182  O   GLY A  22      -1.567   8.923  14.000  1.00 11.91           O  
-ATOM    183  N   TRP A  23       0.567   9.408  14.566  1.00 10.13           N  
-ATOM    184  CA  TRP A  23       1.085   8.080  14.169  1.00 11.05           C  
-ATOM    185  C   TRP A  23       1.906   7.506  15.308  1.00 11.89           C  
-ATOM    186  O   TRP A  23       2.573   8.227  16.048  1.00 11.18           O  
-ATOM    187  CB  TRP A  23       1.938   8.195  12.906  1.00 12.91           C  
-ATOM    188  CG  TRP A  23       1.141   8.597  11.694  1.00 14.32           C  
-ATOM    189  CD1 TRP A  23       1.043   9.842  11.148  1.00 15.32           C  
-ATOM    190  CD2 TRP A  23       0.335   7.731  10.891  1.00 13.59           C  
-ATOM    191  NE1 TRP A  23       0.231   9.802  10.044  1.00 16.07           N  
-ATOM    192  CE2 TRP A  23      -0.257   8.535   9.883  1.00 15.22           C  
-ATOM    193  CE3 TRP A  23       0.013   6.374  10.946  1.00 15.09           C  
-ATOM    194  CZ2 TRP A  23      -1.085   7.991   8.898  1.00 17.75           C  
-ATOM    195  CZ3 TRP A  23      -0.823   5.844   9.974  1.00 19.21           C  
-ATOM    196  CH2 TRP A  23      -1.358   6.660   8.971  1.00 20.75           C  
-ATOM    197  N   CYS A  24       1.986   6.179  15.351  1.00 11.52           N  
-ATOM    198  CA  CYS A  24       2.689   5.457  16.386  1.00 12.25           C  
-ATOM    199  C   CYS A  24       4.094   5.076  16.014  1.00 11.84           C  
-ATOM    200  O   CYS A  24       4.313   4.643  14.856  1.00 13.73           O  
-ATOM    201  CB  CYS A  24       1.937   4.110  16.664  1.00 12.40           C  
-ATOM    202  SG  CYS A  24       0.275   4.401  17.331  1.00 11.68           S  
-ATOM    203  N   GLY A  25       5.037   5.149  16.934  1.00 12.17           N  
-ATOM    204  CA  GLY A  25       6.397   4.709  16.654  1.00 12.62           C  
-ATOM    205  C   GLY A  25       7.268   4.734  17.898  1.00 13.04           C  
-ATOM    206  O   GLY A  25       6.806   4.993  19.009  1.00 13.30           O  
-ATOM    207  N   ASP A  26       8.546   4.411  17.713  1.00 15.66           N  
-ATOM    208  CA  ASP A  26       9.453   4.213  18.836  1.00 16.94           C  
-ATOM    209  C   ASP A  26      10.742   4.984  18.704  1.00 19.41           C  
-ATOM    210  O   ASP A  26      11.679   4.757  19.495  1.00 22.27           O  
-ATOM    211  CB  ASP A  26       9.733   2.705  19.003  1.00 22.37           C  
-ATOM    212  CG  ASP A  26      10.242   2.048  17.750  1.00 27.17           C  
-ATOM    213  OD1 ASP A  26      10.821   2.716  16.876  1.00 30.74           O  
-ATOM    214  OD2 ASP A  26      10.050   0.806  17.615  1.00 33.27           O  
-ATOM    215  N   SER A  27      10.836   5.914  17.772  1.00 16.57           N  
-ATOM    216  CA  SER A  27      12.025   6.714  17.541  1.00 18.12           C  
-ATOM    217  C   SER A  27      11.784   8.177  17.845  1.00 16.83           C  
-ATOM    218  O   SER A  27      10.654   8.569  18.171  1.00 15.78           O  
-ATOM    219  CB  SER A  27      12.504   6.526  16.094  1.00 19.54           C  
-ATOM    220  OG  SER A  27      11.449   6.922  15.205  1.00 21.34           O  
-ATOM    221  N   GLU A  28      12.795   9.020  17.760  1.00 17.82           N  
-ATOM    222  CA  GLU A  28      12.747  10.415  18.151  1.00 18.18           C  
-ATOM    223  C   GLU A  28      11.580  11.219  17.642  1.00 16.44           C  
-ATOM    224  O   GLU A  28      10.943  11.965  18.410  1.00 15.84           O  
-ATOM    225  CB AGLU A  28      14.087  11.070  17.738  0.50 20.93           C  
-ATOM    226  CB BGLU A  28      14.080  11.099  17.807  0.50 21.15           C  
-ATOM    227  CG AGLU A  28      14.061  12.574  17.631  0.50 22.90           C  
-ATOM    228  CG BGLU A  28      14.458  12.231  18.733  0.50 23.48           C  
-ATOM    229  CD AGLU A  28      13.788  13.255  18.959  0.50 23.94           C  
-ATOM    230  CD BGLU A  28      13.705  13.513  18.493  0.50 25.49           C  
-ATOM    231  OE1AGLU A  28      14.121  12.673  20.008  0.50 25.78           O  
-ATOM    232  OE1BGLU A  28      13.293  13.783  17.345  0.50 26.68           O  
-ATOM    233  OE2AGLU A  28      13.210  14.367  18.948  0.50 25.81           O  
-ATOM    234  OE2BGLU A  28      13.513  14.286  19.469  0.50 28.16           O  
-ATOM    235  N   PRO A  29      11.257  11.149  16.353  1.00 16.44           N  
-ATOM    236  CA  PRO A  29      10.144  11.917  15.806  1.00 17.18           C  
-ATOM    237  C   PRO A  29       8.823  11.616  16.471  1.00 14.68           C  
-ATOM    238  O   PRO A  29       7.892  12.446  16.505  1.00 15.00           O  
-ATOM    239  CB  PRO A  29      10.136  11.541  14.327  1.00 19.03           C  
-ATOM    240  CG  PRO A  29      11.501  11.035  14.037  1.00 20.48           C  
-ATOM    241  CD  PRO A  29      12.006  10.414  15.326  1.00 18.12           C  
-ATOM    242  N   TYR A  30       8.646  10.397  16.996  1.00 13.61           N  
-ATOM    243  CA  TYR A  30       7.443   9.994  17.663  1.00 12.47           C  
-ATOM    244  C   TYR A  30       7.479  10.211  19.186  1.00 12.14           C  
-ATOM    245  O   TYR A  30       6.458  10.498  19.778  1.00 12.08           O  
-ATOM    246  CB  TYR A  30       7.181   8.480  17.444  1.00 12.23           C  
-ATOM    247  CG  TYR A  30       7.057   8.081  15.992  1.00 12.06           C  
-ATOM    248  CD1 TYR A  30       8.213   7.750  15.277  1.00 13.86           C  
-ATOM    249  CD2 TYR A  30       5.842   8.011  15.344  1.00 11.48           C  
-ATOM    250  CE1 TYR A  30       8.133   7.383  13.943  1.00 14.64           C  
-ATOM    251  CE2 TYR A  30       5.756   7.627  14.005  1.00 12.08           C  
-ATOM    252  CZ  TYR A  30       6.908   7.328  13.334  1.00 13.37           C  
-ATOM    253  OH  TYR A  30       6.830   6.950  11.998  1.00 15.25           O  
-ATOM    254  N   CYS A  31       8.670  10.046  19.759  1.00 13.67           N  
-ATOM    255  CA  CYS A  31       8.831   9.940  21.199  1.00 12.61           C  
-ATOM    256  C   CYS A  31       9.529  11.123  21.850  1.00 14.04           C  
-ATOM    257  O   CYS A  31       9.506  11.227  23.089  1.00 14.90           O  
-ATOM    258  CB  CYS A  31       9.719   8.692  21.503  1.00 13.30           C  
-ATOM    259  SG  CYS A  31       8.953   7.136  21.032  1.00 12.81           S  
-ATOM    260  N   GLY A  32      10.191  11.953  21.069  1.00 14.92           N  
-ATOM    261  CA  GLY A  32      10.970  13.072  21.570  1.00 16.76           C  
-ATOM    262  C   GLY A  32      10.183  14.376  21.576  1.00 16.37           C  
-ATOM    263  O   GLY A  32       9.089  14.508  22.067  1.00 17.28           O  
-ATOM    264  N   ARG A  33      10.805  15.400  20.943  1.00 18.68           N  
-ATOM    265  CA  ARG A  33      10.253  16.736  20.909  1.00 20.22           C  
-ATOM    266  C   ARG A  33       8.893  16.835  20.258  1.00 19.35           C  
-ATOM    267  O   ARG A  33       8.053  17.653  20.668  1.00 21.59           O  
-ATOM    268  CB  ARG A  33      11.250  17.687  20.210  1.00 23.28           C  
-ATOM    269  CG  ARG A  33      12.707  17.055  20.301  0.00 20.00           C  
-ATOM    270  CD  ARG A  33      13.726  18.003  19.669  0.00 20.00           C  
-ATOM    271  NE  ARG A  33      13.518  18.180  18.226  0.00 20.00           N  
-ATOM    272  CZ  ARG A  33      12.493  18.858  17.694  0.00 20.00           C  
-ATOM    273  NH1 ARG A  33      11.568  19.434  18.472  0.00 20.00           N  
-ATOM    274  NH2 ARG A  33      12.305  19.018  16.376  0.00 20.00           N  
-ATOM    275  N   THR A  34       8.629  15.996  19.260  1.00 17.05           N  
-ATOM    276  CA  THR A  34       7.367  16.075  18.514  1.00 15.55           C  
-ATOM    277  C   THR A  34       6.416  14.947  18.911  1.00 13.76           C  
-ATOM    278  O   THR A  34       5.571  14.521  18.145  1.00 14.63           O  
-ATOM    279  CB  THR A  34       7.620  16.068  17.006  1.00 15.68           C  
-ATOM    280  OG1 THR A  34       8.589  15.063  16.678  1.00 18.28           O  
-ATOM    281  CG2 THR A  34       8.192  17.420  16.539  1.00 17.63           C  
-ATOM    282  N   CYS A  35       6.527  14.537  20.184  1.00 12.85           N  
-ATOM    283  CA  CYS A  35       5.619  13.529  20.733  1.00 11.99           C  
-ATOM    284  C   CYS A  35       4.388  14.118  21.364  1.00 12.75           C  
-ATOM    285  O   CYS A  35       4.491  15.127  22.102  1.00 15.67           O  
-ATOM    286  CB  CYS A  35       6.432  12.713  21.770  1.00 11.83           C  
-ATOM    287  SG  CYS A  35       5.470  11.406  22.561  1.00 10.89           S  
-ATOM    288  N   GLU A  36       3.235  13.515  21.205  1.00 10.58           N  
-ATOM    289  CA  GLU A  36       2.004  13.930  21.842  1.00 12.03           C  
-ATOM    290  C   GLU A  36       1.683  13.107  23.086  1.00 13.55           C  
-ATOM    291  O   GLU A  36       1.129  13.669  24.043  1.00 13.26           O  
-ATOM    292  CB  GLU A  36       0.819  13.833  20.876  1.00 12.59           C  
-ATOM    293  CG  GLU A  36      -0.497  14.343  21.457  1.00 16.20           C  
-ATOM    294  CD  GLU A  36      -1.609  14.316  20.436  1.00 18.54           C  
-ATOM    295  OE1 GLU A  36      -2.248  13.270  20.239  1.00 18.98           O  
-ATOM    296  OE2 GLU A  36      -1.858  15.380  19.821  1.00 23.44           O  
-ATOM    297  N   ASN A  37       1.897  11.793  23.068  1.00 12.95           N  
-ATOM    298  CA  ASN A  37       1.497  10.964  24.209  1.00 12.65           C  
-ATOM    299  C   ASN A  37       2.446   9.775  24.337  1.00 12.21           C  
-ATOM    300  O   ASN A  37       3.003   9.296  23.346  1.00 12.29           O  
-ATOM    301  CB  ASN A  37       0.071  10.481  24.067  1.00 15.42           C  
-ATOM    302  CG  ASN A  37      -0.529   9.956  25.368  1.00 18.91           C  
-ATOM    303  OD1 ASN A  37      -0.166  10.434  26.429  1.00 20.42           O  
-ATOM    304  ND2 ASN A  37      -1.426   8.985  25.231  1.00 21.73           N  
-ATOM    305  N   LYS A  38       2.547   9.285  25.569  1.00 12.38           N  
-ATOM    306  CA  LYS A  38       3.418   8.157  25.928  1.00 11.24           C  
-ATOM    307  C   LYS A  38       4.814   8.422  25.439  1.00 11.57           C  
-ATOM    308  O   LYS A  38       5.475   7.649  24.737  1.00 11.73           O  
-ATOM    309  CB  LYS A  38       2.872   6.835  25.390  1.00 12.50           C  
-ATOM    310  CG  LYS A  38       1.463   6.489  25.778  1.00 13.84           C  
-ATOM    311  CD  LYS A  38       1.233   6.470  27.289  1.00 16.31           C  
-ATOM    312  CE  LYS A  38      -0.159   5.917  27.584  1.00 17.80           C  
-ATOM    313  NZ  LYS A  38      -0.465   5.932  29.040  1.00 20.45           N  
-ATOM    314  N   CYS A  39       5.378   9.556  25.880  1.00 12.02           N  
-ATOM    315  CA  CYS A  39       6.632  10.077  25.433  1.00 11.58           C  
-ATOM    316  C   CYS A  39       7.809   9.718  26.328  1.00 12.51           C  
-ATOM    317  O   CYS A  39       7.574   9.420  27.513  1.00 13.84           O  
-ATOM    318  CB  CYS A  39       6.512  11.641  25.373  1.00 13.49           C  
-ATOM    319  SG  CYS A  39       5.076  12.190  24.393  1.00 12.58           S  
-ATOM    320  N   TRP A  40       9.011   9.764  25.795  1.00 11.50           N  
-ATOM    321  CA  TRP A  40      10.193   9.395  26.592  1.00 14.12           C  
-ATOM    322  C   TRP A  40      10.259  10.234  27.861  1.00 15.88           C  
-ATOM    323  O   TRP A  40      10.656   9.728  28.912  1.00 17.55           O  
-ATOM    324  CB  TRP A  40      11.457   9.577  25.773  1.00 14.30           C  
-ATOM    325  CG  TRP A  40      11.683   8.533  24.724  1.00 15.08           C  
-ATOM    326  CD1 TRP A  40      11.189   7.256  24.703  1.00 14.60           C  
-ATOM    327  CD2 TRP A  40      12.438   8.680  23.518  1.00 15.20           C  
-ATOM    328  NE1 TRP A  40      11.642   6.583  23.594  1.00 16.14           N  
-ATOM    329  CE2 TRP A  40      12.406   7.451  22.843  1.00 15.90           C  
-ATOM    330  CE3 TRP A  40      13.169   9.739  22.959  1.00 18.61           C  
-ATOM    331  CZ2 TRP A  40      13.047   7.240  21.623  1.00 17.72           C  
-ATOM    332  CZ3 TRP A  40      13.814   9.530  21.756  1.00 18.97           C  
-ATOM    333  CH2 TRP A  40      13.746   8.296  21.099  1.00 19.39           C  
-ATOM    334  N   SER A  41       9.951  11.518  27.737  1.00 15.53           N  
-ATOM    335  CA  SER A  41      10.078  12.431  28.878  1.00 17.72           C  
-ATOM    336  C   SER A  41       9.043  12.192  29.935  1.00 16.70           C  
-ATOM    337  O   SER A  41       9.104  12.813  31.031  1.00 20.51           O  
-ATOM    338  CB  SER A  41       9.984  13.886  28.348  1.00 19.25           C  
-ATOM    339  OG  SER A  41       8.682  14.140  27.877  1.00 25.31           O  
-ATOM    340  N   GLY A  42       8.063  11.327  29.727  1.00 14.48           N  
-ATOM    341  CA  GLY A  42       7.007  11.031  30.616  1.00 14.53           C  
-ATOM    342  C   GLY A  42       7.286   9.912  31.616  1.00 13.87           C  
-ATOM    343  O   GLY A  42       6.373   9.593  32.395  1.00 16.81           O  
-ATOM    344  N   GLU A  43       8.465   9.322  31.606  1.00 13.36           N  
-ATOM    345  CA  GLU A  43       8.767   8.271  32.589  1.00 13.28           C  
-ATOM    346  C   GLU A  43       8.673   8.877  33.995  1.00 13.80           C  
-ATOM    347  O   GLU A  43       9.223   9.950  34.249  1.00 15.48           O  
-ATOM    348  CB  GLU A  43      10.134   7.656  32.361  1.00 11.95           C  
-ATOM    349  CG  GLU A  43      10.372   6.481  33.343  1.00 13.25           C  
-ATOM    350  CD  GLU A  43      11.587   5.683  33.100  1.00 13.51           C  
-ATOM    351  OE1 GLU A  43      12.044   5.460  31.959  1.00 13.57           O  
-ATOM    352  OE2 GLU A  43      12.195   5.150  34.093  1.00 15.32           O  
-ATOM    353  N   ARG A  44       8.071   8.145  34.910  1.00 13.63           N  
-ATOM    354  CA  ARG A  44       7.983   8.564  36.308  1.00 14.52           C  
-ATOM    355  C   ARG A  44       9.362   8.626  36.944  1.00 14.63           C  
-ATOM    356  O   ARG A  44      10.261   7.845  36.633  1.00 13.65           O  
-ATOM    357  CB  ARG A  44       7.130   7.565  37.097  1.00 14.38           C  
-ATOM    358  CG  ARG A  44       5.662   7.552  36.738  1.00 16.88           C  
-ATOM    359  CD  ARG A  44       4.918   6.451  37.452  1.00 18.14           C  
-ATOM    360  NE  ARG A  44       5.070   6.493  38.896  1.00 18.73           N  
-ATOM    361  CZ  ARG A  44       4.153   6.921  39.751  1.00 23.67           C  
-ATOM    362  NH1 ARG A  44       2.982   7.369  39.322  1.00 26.62           N  
-ATOM    363  NH2 ARG A  44       4.406   6.879  41.057  1.00 24.53           N  
-ATOM    364  N   SER A  45       9.537   9.569  37.886  1.00 16.58           N  
-ATOM    365  CA  SER A  45      10.814   9.666  38.587  1.00 17.75           C  
-ATOM    366  C   SER A  45      11.163   8.400  39.353  1.00 16.53           C  
-ATOM    367  O   SER A  45      12.346   8.077  39.486  1.00 16.92           O  
-ATOM    368  CB  SER A  45      10.784  10.870  39.555  1.00 19.53           C  
-ATOM    369  OG ASER A  45       9.839  10.598  40.594  0.50 22.46           O  
-ATOM    370  OG BSER A  45      10.680  12.068  38.784  0.50 23.41           O  
-ATOM    371  N   ASP A  46      10.154   7.656  39.787  1.00 16.16           N  
-ATOM    372  CA  ASP A  46      10.357   6.414  40.526  1.00 15.58           C  
-ATOM    373  C   ASP A  46      10.486   5.209  39.608  1.00 16.25           C  
-ATOM    374  O   ASP A  46      10.715   4.082  40.082  1.00 17.85           O  
-ATOM    375  CB  ASP A  46       9.273   6.208  41.568  1.00 18.42           C  
-ATOM    376  CG  ASP A  46       7.890   5.991  41.051  1.00 18.67           C  
-ATOM    377  OD1 ASP A  46       7.678   5.851  39.823  1.00 17.94           O  
-ATOM    378  OD2 ASP A  46       6.922   5.928  41.861  1.00 23.44           O  
-ATOM    379  N   HIS A  47      10.390   5.412  38.301  1.00 13.79           N  
-ATOM    380  CA  HIS A  47      10.535   4.366  37.316  1.00 13.07           C  
-ATOM    381  C   HIS A  47       9.438   3.321  37.347  1.00 13.66           C  
-ATOM    382  O   HIS A  47       9.591   2.308  36.646  1.00 14.37           O  
-ATOM    383  CB  HIS A  47      11.904   3.663  37.393  1.00 13.90           C  
-ATOM    384  CG  HIS A  47      13.049   4.620  37.468  1.00 14.22           C  
-ATOM    385  ND1 HIS A  47      13.258   5.596  36.524  1.00 15.45           N  
-ATOM    386  CD2 HIS A  47      14.039   4.749  38.378  1.00 14.58           C  
-ATOM    387  CE1 HIS A  47      14.336   6.300  36.845  1.00 16.14           C  
-ATOM    388  NE2 HIS A  47      14.821   5.798  37.978  1.00 16.13           N  
-ATOM    389  N   ARG A  48       8.356   3.510  38.057  1.00 13.49           N  
-ATOM    390  CA  ARG A  48       7.271   2.527  38.068  1.00 14.17           C  
-ATOM    391  C   ARG A  48       6.513   2.568  36.750  1.00 15.86           C  
-ATOM    392  O   ARG A  48       6.416   3.632  36.116  1.00 15.15           O  
-ATOM    393  CB  ARG A  48       6.310   2.789  39.231  1.00 16.19           C  
-ATOM    394  CG  ARG A  48       6.953   2.481  40.594  1.00 21.74           C  
-ATOM    395  CD  ARG A  48       5.897   2.677  41.696  1.00 27.15           C  
-ATOM    396  NE  ARG A  48       4.979   1.552  41.722  1.00 33.45           N  
-ATOM    397  CZ  ARG A  48       3.811   1.502  42.336  1.00 35.53           C  
-ATOM    398  NH1 ARG A  48       3.350   2.536  43.022  1.00 37.75           N  
-ATOM    399  NH2 ARG A  48       3.078   0.387  42.267  1.00 37.26           N  
-ATOM    400  N   CYS A  49       5.942   1.451  36.352  1.00 14.67           N  
-ATOM    401  CA  CYS A  49       5.096   1.418  35.149  1.00 14.02           C  
-ATOM    402  C   CYS A  49       4.011   0.378  35.343  1.00 16.59           C  
-ATOM    403  O   CYS A  49       3.937  -0.224  36.444  1.00 17.70           O  
-ATOM    404  CB  CYS A  49       5.922   1.172  33.902  1.00 14.60           C  
-ATOM    405  SG  CYS A  49       6.790  -0.418  33.889  1.00 13.58           S  
-ATOM    406  N   GLY A  50       3.193   0.151  34.344  1.00 16.32           N  
-ATOM    407  CA  GLY A  50       2.143  -0.866  34.446  1.00 18.12           C  
-ATOM    408  C   GLY A  50       0.822  -0.238  34.870  1.00 20.85           C  
-ATOM    409  O   GLY A  50       0.703   0.947  35.143  1.00 21.47           O  
-ATOM    410  N   ALA A  51      -0.178  -1.110  35.040  1.00 23.34           N  
-ATOM    411  CA  ALA A  51      -1.543  -0.713  35.271  1.00 26.04           C  
-ATOM    412  C   ALA A  51      -1.795   0.012  36.564  1.00 27.16           C  
-ATOM    413  O   ALA A  51      -2.665   0.906  36.617  1.00 29.51           O  
-ATOM    414  CB  ALA A  51      -2.454  -1.955  35.167  1.00 27.39           C  
-ATOM    415  N   ALA A  52      -1.074  -0.305  37.627  1.00 29.21           N  
-ATOM    416  CA  ALA A  52      -1.299   0.301  38.927  1.00 30.20           C  
-ATOM    417  C   ALA A  52      -0.994   1.784  38.962  1.00 30.84           C  
-ATOM    418  O   ALA A  52      -1.494   2.501  39.844  1.00 32.25           O  
-ATOM    419  CB  ALA A  52      -0.515  -0.431  40.004  1.00 33.09           C  
-ATOM    420  N   VAL A  53      -0.141   2.261  38.056  1.00 28.71           N  
-ATOM    421  CA  VAL A  53       0.194   3.685  38.018  1.00 26.18           C  
-ATOM    422  C   VAL A  53      -0.261   4.318  36.712  1.00 25.00           C  
-ATOM    423  O   VAL A  53       0.267   5.337  36.264  1.00 24.56           O  
-ATOM    424  CB  VAL A  53       1.673   3.942  38.268  1.00 25.15           C  
-ATOM    425  CG1 VAL A  53       2.041   3.676  39.733  1.00 26.52           C  
-ATOM    426  CG2 VAL A  53       2.533   3.055  37.362  1.00 22.44           C  
-ATOM    427  N   GLY A  54      -1.239   3.695  36.058  1.00 23.89           N  
-ATOM    428  CA  GLY A  54      -1.868   4.224  34.880  1.00 23.95           C  
-ATOM    429  C   GLY A  54      -1.116   4.076  33.585  1.00 23.40           C  
-ATOM    430  O   GLY A  54      -1.266   4.908  32.665  1.00 23.79           O  
-ATOM    431  N   ASN A  55      -0.333   3.021  33.429  1.00 21.36           N  
-ATOM    432  CA  ASN A  55       0.391   2.709  32.223  1.00 19.27           C  
-ATOM    433  C   ASN A  55       1.196   3.856  31.645  1.00 18.15           C  
-ATOM    434  O   ASN A  55       1.105   4.166  30.442  1.00 18.32           O  
-ATOM    435  CB  ASN A  55      -0.582   2.178  31.147  1.00 21.82           C  
-ATOM    436  CG  ASN A  55      -1.151   0.821  31.511  1.00 26.16           C  
-ATOM    437  OD1 ASN A  55      -0.501  -0.011  32.131  1.00 26.45           O  
-ATOM    438  ND2 ASN A  55      -2.397   0.598  31.091  1.00 29.80           N  
-ATOM    439  N   PRO A  56       2.101   4.416  32.416  1.00 14.99           N  
-ATOM    440  CA  PRO A  56       2.969   5.491  31.927  1.00 13.73           C  
-ATOM    441  C   PRO A  56       4.009   4.955  30.985  1.00 14.07           C  
-ATOM    442  O   PRO A  56       4.346   3.755  30.986  1.00 15.11           O  
-ATOM    443  CB  PRO A  56       3.585   6.035  33.205  1.00 14.47           C  
-ATOM    444  CG  PRO A  56       3.701   4.793  34.077  1.00 15.83           C  
-ATOM    445  CD  PRO A  56       2.407   4.054  33.822  1.00 15.95           C  
-ATOM    446  N   PRO A  57       4.649   5.806  30.194  1.00 11.98           N  
-ATOM    447  CA  PRO A  57       5.722   5.426  29.322  1.00 12.65           C  
-ATOM    448  C   PRO A  57       7.036   5.165  30.051  1.00 12.11           C  
-ATOM    449  O   PRO A  57       7.157   5.472  31.250  1.00 13.60           O  
-ATOM    450  CB  PRO A  57       5.895   6.659  28.401  1.00 12.88           C  
-ATOM    451  CG  PRO A  57       5.466   7.798  29.280  1.00 12.77           C  
-ATOM    452  CD  PRO A  57       4.319   7.262  30.094  1.00 14.56           C  
-ATOM    453  N   CYS A  58       8.020   4.682  29.324  1.00 11.80           N  
-ATOM    454  CA  CYS A  58       9.395   4.596  29.803  1.00 12.36           C  
-ATOM    455  C   CYS A  58      10.282   5.443  28.876  1.00 13.05           C  
-ATOM    456  O   CYS A  58       9.824   5.791  27.764  1.00 12.47           O  
-ATOM    457  CB  CYS A  58       9.923   3.175  29.845  1.00 12.38           C  
-ATOM    458  SG  CYS A  58       8.999   2.008  30.881  1.00 12.44           S  
-ATOM    459  N   GLY A  59      11.462   5.793  29.318  1.00 13.40           N  
-ATOM    460  CA  GLY A  59      12.370   6.599  28.530  1.00 13.37           C  
-ATOM    461  C   GLY A  59      12.983   5.848  27.371  1.00 15.29           C  
-ATOM    462  O   GLY A  59      12.636   4.695  27.085  1.00 15.41           O  
-ATOM    463  N   GLN A  60      13.926   6.483  26.680  1.00 15.49           N  
-ATOM    464  CA  GLN A  60      14.578   5.933  25.521  1.00 16.65           C  
-ATOM    465  C   GLN A  60      15.322   4.640  25.808  1.00 17.24           C  
-ATOM    466  O   GLN A  60      16.084   4.583  26.784  1.00 17.93           O  
-ATOM    467  CB  GLN A  60      15.560   6.976  24.932  1.00 21.03           C  
-ATOM    468  CG  GLN A  60      16.137   6.596  23.600  1.00 24.12           C  
-ATOM    469  CD  GLN A  60      16.930   7.708  22.933  1.00 26.24           C  
-ATOM    470  OE1 GLN A  60      17.102   8.793  23.476  1.00 28.53           O  
-ATOM    471  NE2 GLN A  60      17.396   7.430  21.717  1.00 30.08           N  
-ATOM    472  N   ASP A  61      15.074   3.617  25.016  1.00 17.93           N  
-ATOM    473  CA  ASP A  61      15.745   2.336  25.132  1.00 20.04           C  
-ATOM    474  C   ASP A  61      15.467   1.648  26.455  1.00 20.73           C  
-ATOM    475  O   ASP A  61      16.257   0.866  26.990  1.00 21.72           O  
-ATOM    476  CB  ASP A  61      17.234   2.449  24.844  1.00 24.31           C  
-ATOM    477  CG  ASP A  61      17.506   2.718  23.360  1.00 27.72           C  
-ATOM    478  OD1 ASP A  61      16.939   1.999  22.517  1.00 33.16           O  
-ATOM    479  OD2 ASP A  61      18.293   3.639  23.088  1.00 32.19           O  
-ATOM    480  N   ARG A  62      14.303   1.961  27.014  1.00 18.60           N  
-ATOM    481  CA  ARG A  62      13.834   1.325  28.244  1.00 16.85           C  
-ATOM    482  C   ARG A  62      12.481   0.710  28.021  1.00 15.70           C  
-ATOM    483  O   ARG A  62      11.700   1.178  27.159  1.00 17.28           O  
-ATOM    484  CB  ARG A  62      13.808   2.372  29.372  1.00 15.52           C  
-ATOM    485  CG  ARG A  62      15.215   2.900  29.694  1.00 16.78           C  
-ATOM    486  CD  ARG A  62      15.199   4.169  30.493  1.00 17.30           C  
-ATOM    487  NE  ARG A  62      14.592   4.088  31.791  1.00 15.85           N  
-ATOM    488  CZ  ARG A  62      15.169   3.631  32.910  1.00 16.22           C  
-ATOM    489  NH1 ARG A  62      16.419   3.181  32.862  1.00 19.33           N  
-ATOM    490  NH2 ARG A  62      14.505   3.658  34.049  1.00 17.20           N  
-ATOM    491  N   CYS A  63      12.133  -0.348  28.724  1.00 14.67           N  
-ATOM    492  CA  CYS A  63      10.895  -1.061  28.575  1.00 14.75           C  
-ATOM    493  C   CYS A  63      10.220  -1.258  29.921  1.00 12.67           C  
-ATOM    494  O   CYS A  63      10.923  -1.181  30.961  1.00 13.29           O  
-ATOM    495  CB  CYS A  63      11.141  -2.476  27.981  1.00 16.49           C  
-ATOM    496  SG  CYS A  63      12.260  -2.460  26.567  1.00 16.96           S  
-ATOM    497  N   CYS A  64       8.953  -1.528  29.925  1.00 12.88           N  
-ATOM    498  CA  CYS A  64       8.206  -1.780  31.148  1.00 12.83           C  
-ATOM    499  C   CYS A  64       8.146  -3.285  31.429  1.00 15.34           C  
-ATOM    500  O   CYS A  64       7.524  -4.027  30.660  1.00 15.86           O  
-ATOM    501  CB  CYS A  64       6.781  -1.219  30.990  1.00 14.85           C  
-ATOM    502  SG  CYS A  64       5.806  -1.504  32.499  1.00 13.42           S  
-ATOM    503  N   SER A  65       8.826  -3.717  32.477  1.00 14.67           N  
-ATOM    504  CA  SER A  65       8.872  -5.161  32.798  1.00 16.00           C  
-ATOM    505  C   SER A  65       7.551  -5.636  33.353  1.00 16.63           C  
-ATOM    506  O   SER A  65       6.710  -4.894  33.815  1.00 15.54           O  
-ATOM    507  CB  SER A  65       9.998  -5.411  33.827  1.00 16.86           C  
-ATOM    508  OG  SER A  65       9.484  -5.147  35.124  1.00 17.06           O  
-ATOM    509  N   VAL A  66       7.392  -6.999  33.348  1.00 18.72           N  
-ATOM    510  CA  VAL A  66       6.204  -7.591  33.922  1.00 19.62           C  
-ATOM    511  C   VAL A  66       6.103  -7.376  35.428  1.00 19.10           C  
-ATOM    512  O   VAL A  66       5.023  -7.531  35.993  1.00 22.45           O  
-ATOM    513  CB  VAL A  66       6.081  -9.099  33.636  1.00 18.76           C  
-ATOM    514  CG1 VAL A  66       5.849  -9.334  32.154  1.00 20.22           C  
-ATOM    515  CG2 VAL A  66       7.308  -9.852  34.123  1.00 20.88           C  
-ATOM    516  N   HIS A  67       7.205  -6.932  36.043  1.00 17.59           N  
-ATOM    517  CA  HIS A  67       7.183  -6.665  37.480  1.00 19.57           C  
-ATOM    518  C   HIS A  67       6.787  -5.235  37.796  1.00 20.01           C  
-ATOM    519  O   HIS A  67       6.668  -4.859  38.965  1.00 21.59           O  
-ATOM    520  CB  HIS A  67       8.525  -7.022  38.123  1.00 19.55           C  
-ATOM    521  CG  HIS A  67       8.951  -8.426  37.769  1.00 19.66           C  
-ATOM    522  ND1 HIS A  67       8.317  -9.542  38.278  1.00 24.79           N  
-ATOM    523  CD2 HIS A  67       9.897  -8.863  36.924  1.00 20.78           C  
-ATOM    524  CE1 HIS A  67       8.889 -10.621  37.766  1.00 21.81           C  
-ATOM    525  NE2 HIS A  67       9.854 -10.250  36.958  1.00 23.01           N  
-ATOM    526  N   GLY A  68       6.635  -4.403  36.763  1.00 18.21           N  
-ATOM    527  CA  GLY A  68       6.124  -3.053  36.937  1.00 17.97           C  
-ATOM    528  C   GLY A  68       7.194  -1.980  37.055  1.00 13.37           C  
-ATOM    529  O   GLY A  68       6.922  -0.965  37.742  1.00 15.56           O  
-ATOM    530  N   TRP A  69       8.337  -2.157  36.449  1.00 12.34           N  
-ATOM    531  CA  TRP A  69       9.382  -1.130  36.486  1.00 12.62           C  
-ATOM    532  C   TRP A  69       9.962  -0.917  35.099  1.00 13.75           C  
-ATOM    533  O   TRP A  69      10.077  -1.829  34.287  1.00 13.58           O  
-ATOM    534  CB  TRP A  69      10.515  -1.554  37.436  1.00 16.11           C  
-ATOM    535  CG  TRP A  69      10.061  -1.845  38.837  1.00 18.96           C  
-ATOM    536  CD1 TRP A  69       9.648  -3.068  39.323  1.00 18.72           C  
-ATOM    537  CD2 TRP A  69       9.952  -0.930  39.923  1.00 20.84           C  
-ATOM    538  NE1 TRP A  69       9.303  -2.949  40.636  1.00 21.95           N  
-ATOM    539  CE2 TRP A  69       9.480  -1.643  41.038  1.00 19.50           C  
-ATOM    540  CE3 TRP A  69      10.212   0.438  40.056  1.00 20.51           C  
-ATOM    541  CZ2 TRP A  69       9.255  -1.048  42.273  1.00 22.79           C  
-ATOM    542  CZ3 TRP A  69       9.979   1.038  41.281  1.00 22.68           C  
-ATOM    543  CH2 TRP A  69       9.512   0.293  42.378  1.00 23.67           C  
-ATOM    544  N   CYS A  70      10.307   0.345  34.795  1.00 13.10           N  
-ATOM    545  CA  CYS A  70      11.002   0.669  33.563  1.00 13.78           C  
-ATOM    546  C   CYS A  70      12.499   0.383  33.729  1.00 13.92           C  
-ATOM    547  O   CYS A  70      13.047   0.691  34.814  1.00 15.28           O  
-ATOM    548  CB  CYS A  70      10.857   2.178  33.254  1.00 14.26           C  
-ATOM    549  SG  CYS A  70       9.243   2.751  32.767  1.00 12.08           S  
-ATOM    550  N   GLY A  71      13.177  -0.024  32.685  1.00 14.37           N  
-ATOM    551  CA  GLY A  71      14.628  -0.187  32.729  1.00 15.37           C  
-ATOM    552  C   GLY A  71      15.130  -0.683  31.374  1.00 18.20           C  
-ATOM    553  O   GLY A  71      14.340  -1.016  30.505  1.00 16.44           O  
-ATOM    554  N   GLY A  72      16.455  -0.770  31.259  1.00 19.67           N  
-ATOM    555  CA  GLY A  72      17.033  -1.225  29.985  1.00 22.41           C  
-ATOM    556  C   GLY A  72      17.590  -2.635  30.117  1.00 24.30           C  
-ATOM    557  O   GLY A  72      17.886  -3.101  31.216  1.00 26.21           O  
-ATOM    558  N   GLY A  73      17.694  -3.324  28.982  1.00 25.36           N  
-ATOM    559  CA  GLY A  73      18.291  -4.645  28.942  1.00 26.44           C  
-ATOM    560  C   GLY A  73      17.283  -5.775  28.973  1.00 27.24           C  
-ATOM    561  O   GLY A  73      16.086  -5.595  29.157  1.00 26.15           O  
-ATOM    562  N   ASN A  74      17.798  -7.004  28.869  1.00 27.53           N  
-ATOM    563  CA  ASN A  74      17.013  -8.206  28.788  1.00 27.66           C  
-ATOM    564  C   ASN A  74      16.001  -8.395  29.892  1.00 25.55           C  
-ATOM    565  O   ASN A  74      14.915  -8.955  29.662  1.00 26.18           O  
-ATOM    566  CB  ASN A  74      17.981  -9.421  28.763  1.00 32.27           C  
-ATOM    567  CG AASN A  74      17.279 -10.749  28.825  0.50 33.40           C  
-ATOM    568  CG BASN A  74      18.917  -9.356  27.574  0.50 34.22           C  
-ATOM    569  OD1AASN A  74      17.230 -11.395  29.878  0.50 35.56           O  
-ATOM    570  OD1BASN A  74      18.479  -9.218  26.432  0.50 36.46           O  
-ATOM    571  ND2AASN A  74      16.736 -11.203  27.699  0.50 34.86           N  
-ATOM    572  ND2BASN A  74      20.215  -9.465  27.831  0.50 36.27           N  
-ATOM    573  N   ASP A  75      16.355  -8.036  31.129  1.00 25.87           N  
-ATOM    574  CA  ASP A  75      15.472  -8.252  32.256  1.00 25.73           C  
-ATOM    575  C   ASP A  75      14.200  -7.431  32.203  1.00 23.25           C  
-ATOM    576  O   ASP A  75      13.194  -7.782  32.826  1.00 22.95           O  
-ATOM    577  CB  ASP A  75      16.209  -8.005  33.580  1.00 31.03           C  
-ATOM    578  CG  ASP A  75      16.090  -9.241  34.475  1.00 37.03           C  
-ATOM    579  OD1 ASP A  75      16.882 -10.182  34.258  1.00 40.33           O  
-ATOM    580  OD2 ASP A  75      15.200  -9.254  35.339  1.00 39.88           O  
-ATOM    581  N   TYR A  76      14.205  -6.354  31.409  1.00 21.83           N  
-ATOM    582  CA  TYR A  76      13.025  -5.520  31.259  1.00 20.29           C  
-ATOM    583  C   TYR A  76      12.385  -5.662  29.875  1.00 20.48           C  
-ATOM    584  O   TYR A  76      11.191  -5.458  29.724  1.00 20.48           O  
-ATOM    585  CB  TYR A  76      13.423  -4.033  31.431  1.00 18.44           C  
-ATOM    586  CG  TYR A  76      13.911  -3.669  32.810  1.00 15.85           C  
-ATOM    587  CD1 TYR A  76      15.235  -3.876  33.168  1.00 16.59           C  
-ATOM    588  CD2 TYR A  76      13.050  -3.117  33.750  1.00 14.97           C  
-ATOM    589  CE1 TYR A  76      15.689  -3.555  34.435  1.00 16.24           C  
-ATOM    590  CE2 TYR A  76      13.502  -2.778  35.017  1.00 14.15           C  
-ATOM    591  CZ  TYR A  76      14.809  -3.032  35.355  1.00 15.60           C  
-ATOM    592  OH  TYR A  76      15.277  -2.679  36.604  1.00 17.09           O  
-ATOM    593  N   CYS A  77      13.208  -5.961  28.876  1.00 22.81           N  
-ATOM    594  CA  CYS A  77      12.814  -5.846  27.491  1.00 23.23           C  
-ATOM    595  C   CYS A  77      12.573  -7.119  26.727  1.00 27.50           C  
-ATOM    596  O   CYS A  77      11.961  -7.054  25.632  1.00 27.57           O  
-ATOM    597  CB  CYS A  77      13.893  -5.011  26.744  1.00 21.70           C  
-ATOM    598  SG  CYS A  77      13.970  -3.285  27.311  1.00 19.69           S  
-ATOM    599  N   SER A  78      13.001  -8.275  27.211  1.00 29.47           N  
-ATOM    600  CA  SER A  78      12.928  -9.511  26.448  1.00 32.50           C  
-ATOM    601  C   SER A  78      11.665 -10.314  26.666  1.00 33.17           C  
-ATOM    602  O   SER A  78      11.178 -10.437  27.814  1.00 35.05           O  
-ATOM    603  CB  SER A  78      14.183 -10.362  26.725  1.00 35.08           C  
-ATOM    604  OG  SER A  78      15.357  -9.643  26.373  1.00 38.49           O  
-ATOM    605  N  AGLY A  79      11.026 -10.753  25.586  0.56 32.73           N  
-ATOM    606  N  BGLY A  79      11.268 -11.086  25.671  0.44 33.24           N  
-ATOM    607  CA AGLY A  79       9.821 -11.505  25.585  0.56 32.16           C  
-ATOM    608  CA BGLY A  79      10.123 -11.952  25.726  0.44 32.77           C  
-ATOM    609  C  AGLY A  79       9.052 -11.575  26.887  0.56 32.04           C  
-ATOM    610  C  BGLY A  79       9.866 -12.542  27.101  0.44 32.18           C  
-ATOM    611  O  AGLY A  79       8.312 -10.654  27.235  0.56 32.01           O  
-ATOM    612  O  BGLY A  79      10.734 -13.176  27.699  0.44 32.81           O  
-ATOM    613  N  ASER A  80       9.205 -12.682  27.605  0.56 31.46           N  
-ATOM    614  CA ASER A  80       8.473 -12.954  28.816  0.56 31.14           C  
-ATOM    615  C  ASER A  80       8.880 -12.084  29.993  0.56 30.28           C  
-ATOM    616  O  ASER A  80       8.341 -12.253  31.098  0.56 30.92           O  
-ATOM    617  CB ASER A  80       8.629 -14.441  29.205  0.56 32.24           C  
-ATOM    618  OG ASER A  80       9.999 -14.810  29.194  0.56 33.96           O  
-ATOM    619  N  BGLY A  80       8.644 -12.355  27.599  0.44 31.90           N  
-ATOM    620  CA BGLY A  80       8.263 -12.856  28.907  0.44 30.90           C  
-ATOM    621  C  BGLY A  80       8.635 -11.879  30.017  0.44 29.74           C  
-ATOM    622  O  BGLY A  80       7.877 -11.725  30.978  0.44 31.09           O  
-ATOM    623  N  ALYS A  81       9.846 -11.202  29.794  0.56 29.23           N  
-ATOM    624  CA ALYS A  81      10.291 -10.308  30.869  0.56 28.16           C  
-ATOM    625  C  ALYS A  81       9.575  -8.959  30.759  0.56 26.65           C  
-ATOM    626  O  ALYS A  81       9.248  -8.335  31.767  0.56 24.90           O  
-ATOM    627  CB ALYS A  81      11.795 -10.109  30.820  0.56 29.85           C  
-ATOM    628  CG ALYS A  81      12.626 -11.336  31.168  0.56 32.07           C  
-ATOM    629  CD ALYS A  81      14.097 -11.094  30.837  0.56 33.72           C  
-ATOM    630  CE ALYS A  81      14.890 -12.388  30.924  0.56 35.00           C  
-ATOM    631  NZ ALYS A  81      16.264 -12.237  30.375  0.56 35.39           N  
-ATOM    632  N  BASN A  81       9.796 -11.250  29.900  0.44 28.30           N  
-ATOM    633  CA BASN A  81      10.254 -10.303  30.910  0.44 26.39           C  
-ATOM    634  C  BASN A  81       9.566  -8.946  30.756  0.44 24.18           C  
-ATOM    635  O  BASN A  81       9.603  -8.128  31.673  0.44 23.07           O  
-ATOM    636  CB BASN A  81      11.773 -10.095  30.784  0.44 27.87           C  
-ATOM    637  CG BASN A  81      12.573 -11.218  31.412  0.44 29.21           C  
-ATOM    638  OD1BASN A  81      12.064 -11.941  32.265  0.44 28.74           O  
-ATOM    639  ND2BASN A  81      13.822 -11.353  30.992  0.44 30.49           N  
-ATOM    640  N   CYS A  82       8.950  -8.719  29.607  1.00 22.45           N  
-ATOM    641  CA  CYS A  82       8.458  -7.382  29.254  1.00 21.45           C  
-ATOM    642  C   CYS A  82       6.972  -7.304  29.058  1.00 22.09           C  
-ATOM    643  O   CYS A  82       6.378  -8.160  28.372  1.00 22.87           O  
-ATOM    644  CB  CYS A  82       9.172  -6.970  27.942  1.00 18.44           C  
-ATOM    645  SG  CYS A  82       8.735  -5.286  27.433  1.00 17.39           S  
-ATOM    646  N   GLN A  83       6.311  -6.265  29.586  1.00 20.84           N  
-ATOM    647  CA  GLN A  83       4.892  -6.092  29.419  1.00 20.12           C  
-ATOM    648  C   GLN A  83       4.524  -5.110  28.319  1.00 20.04           C  
-ATOM    649  O   GLN A  83       3.521  -5.334  27.618  1.00 21.43           O  
-ATOM    650  CB  GLN A  83       4.123  -5.836  30.680  1.00 22.18           C  
-ATOM    651  CG  GLN A  83       4.345  -4.549  31.422  1.00 23.67           C  
-ATOM    652  CD  GLN A  83       3.365  -4.361  32.567  1.00 22.91           C  
-ATOM    653  OE1 GLN A  83       2.153  -4.246  32.353  1.00 26.53           O  
-ATOM    654  NE2 GLN A  83       3.857  -4.338  33.797  1.00 21.79           N  
-ATOM    655  N   TYR A  84       5.264  -4.025  28.158  1.00 17.66           N  
-ATOM    656  CA  TYR A  84       5.002  -3.076  27.074  1.00 16.39           C  
-ATOM    657  C   TYR A  84       6.245  -2.265  26.742  1.00 15.08           C  
-ATOM    658  O   TYR A  84       7.234  -2.242  27.458  1.00 15.01           O  
-ATOM    659  CB  TYR A  84       3.797  -2.233  27.255  1.00 15.78           C  
-ATOM    660  CG  TYR A  84       3.715  -1.293  28.430  1.00 14.92           C  
-ATOM    661  CD1 TYR A  84       4.476  -0.110  28.447  1.00 15.62           C  
-ATOM    662  CD2 TYR A  84       2.839  -1.516  29.471  1.00 16.69           C  
-ATOM    663  CE1 TYR A  84       4.346   0.776  29.503  1.00 15.02           C  
-ATOM    664  CE2 TYR A  84       2.695  -0.634  30.519  1.00 15.77           C  
-ATOM    665  CZ  TYR A  84       3.480   0.519  30.526  1.00 14.45           C  
-ATOM    666  OH  TYR A  84       3.339   1.400  31.579  1.00 14.64           O  
-ATOM    667  N   ARG A  85       6.205  -1.609  25.564  1.00 15.20           N  
-ATOM    668  CA  ARG A  85       7.334  -0.893  25.027  1.00 14.16           C  
-ATOM    669  C   ARG A  85       8.578  -1.769  24.953  1.00 15.58           C  
-ATOM    670  O   ARG A  85       9.673  -1.417  25.337  1.00 18.52           O  
-ATOM    671  CB  ARG A  85       7.617   0.425  25.764  1.00 15.31           C  
-ATOM    672  CG  ARG A  85       6.496   1.448  25.540  1.00 12.89           C  
-ATOM    673  CD  ARG A  85       6.532   2.564  26.562  1.00 13.54           C  
-ATOM    674  NE  ARG A  85       7.452   3.638  26.250  1.00 12.05           N  
-ATOM    675  CZ  ARG A  85       7.087   4.731  25.517  1.00 10.84           C  
-ATOM    676  NH1 ARG A  85       5.893   4.849  25.020  1.00 12.54           N  
-ATOM    677  NH2 ARG A  85       8.007   5.678  25.397  1.00 12.10           N  
-ATOM    678  N   CYS A  86       8.355  -2.983  24.413  1.00 19.00           N  
-ATOM    679  CA  CYS A  86       9.369  -4.015  24.374  1.00 21.08           C  
-ATOM    680  C   CYS A  86      10.260  -3.991  23.160  1.00 23.22           C  
-ATOM    681  O   CYS A  86       9.846  -3.464  22.105  1.00 25.66           O  
-ATOM    682  CB  CYS A  86       8.644  -5.399  24.430  1.00 21.37           C  
-ATOM    683  SG  CYS A  86       7.527  -5.528  25.832  1.00 19.98           S  
-TER     684      CYS A  86                                                      
-HETATM  685  C1  NAG B   1       2.845   5.730   8.410  1.00 13.00           C  
-HETATM  686  C2  NAG B   1       2.980   4.600   7.346  1.00 14.42           C  
-HETATM  687  C3  NAG B   1       2.175   3.411   7.793  1.00 14.65           C  
-HETATM  688  C4  NAG B   1       2.774   2.928   9.166  1.00 14.34           C  
-HETATM  689  C5  NAG B   1       2.670   4.122  10.167  1.00 14.77           C  
-HETATM  690  C6  NAG B   1       3.374   3.715  11.471  1.00 14.26           C  
-HETATM  691  C7  NAG B   1       3.348   5.467   5.049  1.00 14.97           C  
-HETATM  692  C8  NAG B   1       2.707   6.219   3.930  1.00 15.87           C  
-HETATM  693  N2  NAG B   1       2.477   5.116   6.044  1.00 15.79           N  
-HETATM  694  O3  NAG B   1       2.404   2.342   6.849  1.00 16.72           O  
-HETATM  695  O4  NAG B   1       1.906   1.888   9.675  1.00 16.83           O  
-HETATM  696  O5  NAG B   1       3.422   5.196   9.636  1.00 14.49           O  
-HETATM  697  O6  NAG B   1       2.814   4.521  12.515  1.00 17.10           O  
-HETATM  698  O7  NAG B   1       4.476   5.236   5.105  1.00 15.81           O  
-HETATM  699  C1  NAG B   2       4.454  10.706   8.957  1.00 15.08           C  
-HETATM  700  C2  NAG B   2       4.460   9.780  10.201  1.00 13.08           C  
-HETATM  701  C3  NAG B   2       4.585   8.348   9.693  1.00 14.61           C  
-HETATM  702  C4  NAG B   2       3.438   8.059   8.721  1.00 14.25           C  
-HETATM  703  C5  NAG B   2       3.377   9.108   7.581  1.00 15.30           C  
-HETATM  704  C6  NAG B   2       2.128   8.919   6.764  1.00 17.09           C  
-HETATM  705  C7  NAG B   2       5.438  10.602  12.343  1.00 12.64           C  
-HETATM  706  C8  NAG B   2       6.705  10.853  13.053  1.00 14.46           C  
-HETATM  707  N2  NAG B   2       5.562  10.111  11.086  1.00 14.12           N  
-HETATM  708  O3  NAG B   2       4.506   7.454  10.798  1.00 14.67           O  
-HETATM  709  O4  NAG B   2       3.695   6.769   8.064  1.00 13.33           O  
-HETATM  710  O5  NAG B   2       3.294  10.396   8.215  1.00 16.15           O  
-HETATM  711  O6  NAG B   2       2.067   9.786   5.669  1.00 21.57           O  
-HETATM  712  O7  NAG B   2       4.383  10.784  12.792  1.00 13.02           O  
-HETATM  713  C1  NAG B   3       4.558  15.870   8.029  1.00 22.68           C  
-HETATM  714  C2  NAG B   3       3.781  14.939   7.134  1.00 23.40           C  
-HETATM  715  C3  NAG B   3       3.385  13.670   7.891  1.00 21.15           C  
-HETATM  716  C4  NAG B   3       4.638  13.082   8.560  1.00 19.08           C  
-HETATM  717  C5  NAG B   3       5.387  14.135   9.404  1.00 20.09           C  
-HETATM  718  C6  NAG B   3       6.662  13.594   9.979  1.00 20.74           C  
-HETATM  719  C7  NAG B   3       2.333  15.941   5.366  1.00 30.04           C  
-HETATM  720  C8  NAG B   3       0.935  16.346   5.056  1.00 30.86           C  
-HETATM  721  N2  NAG B   3       2.536  15.585   6.659  1.00 26.09           N  
-HETATM  722  O3  NAG B   3       2.831  12.779   6.934  1.00 22.73           O  
-HETATM  723  O4  NAG B   3       4.253  12.032   9.493  1.00 16.48           O  
-HETATM  724  O5  NAG B   3       5.732  15.223   8.531  1.00 21.12           O  
-HETATM  725  O6  NAG B   3       7.565  13.140   9.015  1.00 23.92           O  
-HETATM  726  O7  NAG B   3       3.172  15.935   4.593  1.00 31.92           O  
-HETATM  727  C1  NAG B   4       6.424  20.761   7.837  1.00 35.33           C  
-HETATM  728  C2  NAG B   4       7.333  19.637   8.339  1.00 35.66           C  
-HETATM  729  C3  NAG B   4       6.907  18.331   7.686  1.00 35.20           C  
-HETATM  730  C4  NAG B   4       5.422  18.072   8.014  1.00 33.37           C  
-HETATM  731  C5  NAG B   4       4.527  19.299   7.696  1.00 33.27           C  
-HETATM  732  C6  NAG B   4       3.173  19.102   8.406  1.00 32.57           C  
-HETATM  733  C7  NAG B   4       9.448  20.235   9.622  0.00 40.22           C  
-HETATM  734  C8  NAG B   4      10.921  20.272   9.570  0.00 40.07           C  
-HETATM  735  N2  NAG B   4       8.744  19.920   8.013  1.00 37.41           N  
-HETATM  736  O3  NAG B   4       7.711  17.283   8.235  1.00 36.78           O  
-HETATM  737  O4  NAG B   4       4.923  16.987   7.202  1.00 29.35           O  
-HETATM  738  O5  NAG B   4       5.078  20.464   8.245  1.00 34.32           O  
-HETATM  739  O6  NAG B   4       3.314  19.212   9.787  1.00 31.85           O  
-HETATM  740  O7  NAG B   4       8.849  20.404  10.603  0.00 43.75           O  
-HETATM  741  O   HOH     1       6.997   5.537  34.149  1.00 15.09           O  
-HETATM  742  O   HOH     2      10.295   3.302  26.095  1.00 16.76           O  
-HETATM  743  O   HOH     3      10.019  12.949  25.133  1.00 20.11           O  
-HETATM  744  O   HOH     4      -0.006   5.891  22.509  1.00 18.64           O  
-HETATM  745  O   HOH     5       3.935  11.236  27.997  1.00 20.18           O  
-HETATM  746  O   HOH     6      -0.274  -4.153  23.054  1.00 19.37           O  
-HETATM  747  O   HOH     7      13.817   8.788  41.782  1.00 19.15           O  
-HETATM  748  O   HOH     8      -1.891  13.459  17.137  1.00 19.03           O  
-HETATM  749  O   HOH     9      -2.083   8.214  21.987  1.00 20.14           O  
-HETATM  750  O   HOH    10       2.797   0.928  18.102  1.00 27.68           O  
-HETATM  751  O   HOH    11      12.691   9.974  30.666  1.00 27.47           O  
-HETATM  752  O   HOH    12       9.666   4.203  14.797  1.00 27.80           O  
-HETATM  753  O   HOH    13       1.809  -0.509   7.352  1.00 30.54           O  
-HETATM  754  O   HOH    14      -1.004  17.744  16.794  1.00 27.36           O  
-HETATM  755  O   HOH    15       8.061   9.329   9.741  1.00 31.81           O  
-HETATM  756  O   HOH    16       7.423   9.060  40.549  1.00 26.40           O  
-HETATM  757  O   HOH    17      -0.420   5.798   5.808  1.00 27.21           O  
-HETATM  758  O   HOH    18       2.796  -0.282  11.074  1.00 26.18           O  
-HETATM  759  O   HOH    19       6.627   3.510  13.365  1.00 29.55           O  
-HETATM  760  O   HOH    20       7.716  11.785  37.937  1.00 33.90           O  
-HETATM  761  O   HOH    21      13.017   2.743  41.126  1.00 29.39           O  
-HETATM  762  O   HOH    22      12.586   8.818  35.183  1.00 31.81           O  
-HETATM  763  O   HOH    23      -0.672  17.945  19.813  1.00 29.17           O  
-HETATM  764  O   HOH    24      15.414   7.885  17.622  1.00 34.99           O  
-HETATM  765  O   HOH    25      15.120   1.027  36.513  1.00 34.01           O  
-HETATM  766  O   HOH    26      12.255   0.609  24.383  1.00 37.64           O  
-HETATM  767  O   HOH    27      15.910  10.470  25.375  1.00 39.63           O  
-HETATM  768  O   HOH    28       2.672  -4.881  24.854  1.00 38.54           O  
-HETATM  769  O   HOH    29       5.651  -3.894  23.305  1.00 37.18           O  
-HETATM  770  O   HOH    30       1.757   7.661  36.606  1.00 29.03           O  
-HETATM  771  O   HOH    31       0.331  -3.984  34.349  1.00 30.94           O  
-HETATM  772  O   HOH    32      14.751   9.106  27.605  1.00 26.56           O  
-HETATM  773  O   HOH    33      -1.326  14.783  10.991  1.00 28.85           O  
-HETATM  774  O   HOH    34      14.132   7.665  31.734  1.00 39.44           O  
-HETATM  775  O   HOH    35       8.520  15.382  25.290  1.00 37.93           O  
-HETATM  776  O   HOH    36       4.900   1.282  16.585  1.00 39.95           O  
-HETATM  777  O   HOH    37      16.453   3.015  36.120  1.00 36.31           O  
-HETATM  778  O   HOH    38      18.443   2.687  30.904  1.00 37.22           O  
-HETATM  779  O   HOH    39      18.669   1.609  28.224  1.00 39.90           O  
-HETATM  780  O   HOH    40       9.694  13.193  10.908  1.00 64.04           O  
-HETATM  781  O   HOH    41      17.631   6.215  28.193  1.00 37.41           O  
-HETATM  782  O   HOH    42       0.923   7.940  41.082  1.00 47.91           O  
-HETATM  783  O   HOH    43      -3.992   5.719  31.647  1.00 53.05           O  
-HETATM  784  O   HOH    44      -0.321  12.255   8.483  1.00 35.86           O  
-HETATM  785  O   HOH    45       7.615   4.350  11.184  1.00 31.62           O  
-HETATM  786  O   HOH    46      -3.066   4.753  29.076  1.00 40.05           O  
-HETATM  787  O   HOH    47      -0.945   1.949   9.712  1.00 36.88           O  
-HETATM  788  O   HOH    48      -7.059   6.398  16.836  1.00 36.00           O  
-HETATM  789  O   HOH    49      17.449  -1.699  26.400  1.00 44.33           O  
-HETATM  790  O   HOH    50      -6.832   2.828  21.276  1.00 40.92           O  
-HETATM  791  O   HOH    51      12.986   3.279  23.159  1.00 32.76           O  
-HETATM  792  O   HOH    52      17.990  -0.372  33.679  1.00 38.43           O  
-HETATM  793  O   HOH    53       2.479  -4.119  36.286  1.00 36.51           O  
-HETATM  794  O   HOH    54      -2.996   7.951  27.405  1.00 35.06           O  
-HETATM  795  O   HOH    55      -4.774   1.179  18.535  1.00 46.84           O  
-HETATM  796  O   HOH    56       8.407  14.447   7.084  1.00 49.80           O  
-HETATM  797  O   HOH    57      -8.668  12.634  15.670  1.00 40.31           O  
-HETATM  798  O   HOH    58       6.835  -3.272  20.532  1.00 54.97           O  
-HETATM  799  O   HOH    59      17.790 -12.129  35.546  1.00 46.47           O  
-HETATM  800  O   HOH    60       2.036  -1.933  37.909  1.00 45.42           O  
-HETATM  801  O   HOH    61      10.515   4.141  23.049  1.00 39.36           O  
-HETATM  802  O   HOH    62      13.853  11.829  27.829  1.00 48.06           O  
-HETATM  803  O   HOH    63       6.585  -6.548  41.143  1.00 45.67           O  
-HETATM  804  O   HOH    64       4.117  17.977  21.677  1.00 40.12           O  
-HETATM  805  O   HOH    65       4.885 -10.712  28.859  1.00 54.15           O  
-HETATM  806  O   HOH    66      -4.389   3.044  11.770  1.00 43.85           O  
-HETATM  807  O   HOH    67      12.708  -9.701  34.933  1.00 34.56           O  
-HETATM  807  O   HOH    67      12.708  -9.701  34.933  1.00 34.56           O  
-HETATM  808  O   HOH    68      12.853  13.538  25.505  1.00 43.43           O  
-HETATM  809  O   HOH    69       6.223  15.935  24.195  1.00 56.63           O  
-HETATM  810  O   HOH    70      -6.184   3.543  26.723  1.00 45.90           O  
-HETATM  811  O   HOH    71       5.679  -0.794  40.337  1.00 42.73           O  
-HETATM  812  O   HOH    72       9.327  16.970  10.364  1.00 44.27           O  
-HETATM  813  O   HOH    73       8.994  19.338  12.497  1.00 57.40           O  
-HETATM  814  O   HOH    74       0.017   8.087  30.620  1.00 43.92           O  
-HETATM  815  O   HOH    75      -0.903   8.986  28.883  1.00 45.39           O  
-HETATM  816  O   HOH    77       0.315  -4.363  30.096  1.00 43.95           O  
+ATOM     22  C   CYS A   0      -3.637   9.026  17.672  1.00 16.22           C
+ATOM      9  N   ARG A   2      -2.607   4.673  13.504  1.00 20.57           N
+ATOM      9  N   ARG A   2      -2.607   4.673  13.504  1.00 20.57           N
+ATOM     10  CA  ARG A   2      -3.091   6.010  13.918  1.00 20.82           C
+ATOM     11  C   ARG A   2      -3.361   6.101  15.403  1.00 19.55           C
+ATOM     12  O   ARG A   2      -3.742   5.121  16.050  1.00 19.99           O
+ATOM     13  CB  ARG A   2      -4.348   6.395  13.124  1.00 25.30           C
+ATOM     14  CG  ARG A   2      -4.110   6.337  11.619  1.00 30.98           C
+ATOM     15  CD  ARG A   2      -4.277   7.652  10.942  1.00 31.79           C
+ATOM     16  NE  ARG A   2      -3.441   8.730  11.457  1.00 31.91           N
+ATOM     17  CZ  ARG A   2      -3.550   9.979  10.980  1.00 31.95           C
+ATOM     18  NH1 ARG A   2      -4.444  10.212  10.022  1.00 33.34           N
+ATOM     19  NH2 ARG A   2      -2.804  10.949  11.444  1.00 29.59           N
+ATOM     20  N   CYS A   3      -3.248   7.317  15.948  1.00 15.71           N
+ATOM     20  N  ACYS A   3      -3.248   7.317  15.948  1.00 15.71           N
+ATOM     21  CA  CYS A   3      -3.419   7.555  17.361  1.00 14.13           C
+ATOM     22  C   CYS A   3      -3.637   9.026  17.672  1.00 16.22           C
+ATOM     23  O   CYS A   3      -3.406   9.901  16.828  1.00 15.46           O
+ATOM     24  CB  CYS A   3      -2.127   7.069  18.110  1.00 13.74           C
+ATOM     25  SG  CYS A   3      -0.659   7.917  17.525  1.00 11.51           S
+ATOM     26  N   GLY A   4      -4.122   9.328  18.863  1.00 15.45           N
+ATOM     27  CA  GLY A   4      -4.129  10.656  19.402  1.00 17.05           C
+ATOM     28  C   GLY A   4      -5.029  11.674  18.735  1.00 16.11           C
+ATOM     29  O   GLY A   4      -6.039  11.345  18.125  1.00 17.88           O
+ATOM     29  O   SER A   4      -6.039  11.345  18.125  1.00 17.88           O
+ATOM     30  N   SER A   5      -4.651  12.958  18.899  1.00 17.09           N
+ATOM     31  CA  SER A   5      -5.559  14.034  18.479  1.00 19.08           C
+ATOM     32  C   SER A   5      -5.752  14.104  16.987  1.00 21.30           C
+ATOM     33  O   SER A   5      -6.858  14.468  16.528  1.00 22.84           O
+ATOM     34  CB  SER A   5      -5.107  15.371  19.047  1.00 19.54           C
+ATOM     35  OG  SER A   5      -3.926  15.835  18.448  1.00 21.28           O
+ATOM     36  N   GLN A   6      -4.747  13.742  16.200  1.00 20.14           N
+ATOM     37  CA  GLN A   6      -4.860  13.800  14.746  1.00 22.55           C
+ATOM     38  C   GLN A   6      -5.298  12.480  14.152  1.00 23.94           C
+ATOM     39  O   GLN A   6      -5.600  12.407  12.952  1.00 26.03           O
+ATOM     40  CB  GLN A   6      -3.493  14.198  14.155  1.00 22.92           C
+ATOM     41  CG  GLN A   6      -3.158  15.672  14.364  1.00 26.86           C
+ATOM     42  CD  GLN A   6      -4.060  16.549  13.492  1.00 30.27           C
+ATOM     43  OE1 GLN A   6      -4.888  17.280  14.006  1.00 32.91           O
+ATOM     44  NE2 GLN A   6      -3.896  16.416  12.182  1.00 32.97           N
+ATOM     45  N   GLY A   7      -5.311  11.413  14.956  1.00 21.65           N
+ATOM     46  CA  GLY A   7      -5.574  10.092  14.428  1.00 23.37           C
+ATOM     47  C   GLY A   7      -6.707   9.337  15.036  1.00 24.14           C
+ATOM     48  O   GLY A   7      -6.704   8.084  15.015  1.00 27.63           O
+ATOM     49  N   GLY A   8      -7.727  10.003  15.582  1.00 24.63           N
+ATOM     50  CA  GLY A   8      -8.913   9.333  16.063  1.00 25.88           C
+ATOM     51  C   GLY A   8      -8.994   9.133  17.546  1.00 26.42           C
+ATOM     52  O   GLY A   8     -10.027   8.635  18.054  1.00 27.64           O
+ATOM     53  N   GLY A   9      -7.952   9.460  18.305  1.00 24.34           N
+ATOM     54  CA  GLY A   9      -7.970   9.377  19.738  1.00 25.05           C
+ATOM     55  C   GLY A   9      -7.440   8.111  20.351  1.00 24.44           C
+ATOM     56  O   GLY A   9      -7.431   8.002  21.594  1.00 26.91           O
+ATOM     57  N  AGLY A  10      -6.973   7.149  19.576  0.67 23.87           N
+ATOM     58  CA AGLY A  10      -6.498   5.882  20.072  0.67 23.51           C
+ATOM     59  C  AGLY A  10      -5.157   5.939  20.783  0.67 20.73           C
+ATOM     60  O  AGLY A  10      -4.407   6.920  20.735  0.67 18.63           O
+ATOM     61  N  BSER A  10      -6.973   7.149  19.576  0.33 23.87           N
+ATOM     62  CA BSER A  10      -6.498   5.882  20.072  0.33 23.51           C
+ATOM     63  C  BSER A  10      -5.157   5.939  20.783  0.33 20.73           C
+ATOM     64  O  BSER A  10      -4.407   6.920  20.735  0.33 18.63           O
+ATOM     65  CB BSER A  10      -6.423   4.843  18.935  0.33 25.06           C
+ATOM     66  OG BSER A  10      -5.085   4.685  18.488  0.33 26.99           O
+ATOM     67  N   THR A  11      -4.827   4.849  21.451  1.00 18.73           N
+ATOM     68  CA  THR A  11      -3.558   4.608  22.100  1.00 18.41           C
+ATOM     69  C   THR A  11      -2.789   3.579  21.270  1.00 17.02           C
+ATOM     70  O   THR A  11      -3.445   2.660  20.722  1.00 19.87           O
+ATOM     71  CB  THR A  11      -3.766   4.031  23.526  1.00 21.54           C
+ATOM     72  OG1 THR A  11      -4.454   5.039  24.302  1.00 24.31           O
+ATOM     73  CG2 THR A  11      -2.424   3.745  24.174  1.00 22.15           C
+ATOM     74  N   CYS A  12      -1.513   3.704  21.104  1.00 14.16           N
+ATOM     75  CA  CYS A  12      -0.739   2.807  20.240  1.00 13.37           C
+ATOM     76  C   CYS A  12      -0.634   1.400  20.784  1.00 14.05           C
+ATOM     77  O   CYS A  12      -0.393   1.199  21.977  1.00 14.75           O
+ATOM     78  CB  CYS A  12       0.693   3.394  20.115  1.00 11.29           C
+ATOM     79  SG  CYS A  12       0.658   5.001  19.231  1.00 10.97           S
+ATOM     80  N   PRO A  13      -0.523   0.411  19.889  1.00 13.45           N
+ATOM     81  CA  PRO A  13      -0.169  -0.942  20.296  1.00 14.93           C
+ATOM     82  C   PRO A  13       1.156  -0.911  21.027  1.00 14.45           C
+ATOM     83  O   PRO A  13       2.082  -0.150  20.677  1.00 14.74           O
+ATOM     84  CB  PRO A  13      -0.029  -1.675  18.958  1.00 15.07           C
+ATOM     85  CG  PRO A  13      -0.978  -0.948  18.064  1.00 15.93           C
+ATOM     86  CD  PRO A  13      -0.772   0.535  18.443  1.00 14.96           C
+ATOM     87  N  AALA A  14       1.268  -1.692  22.089  0.56 14.65           N
+ATOM     88  CA AALA A  14       2.445  -1.814  22.901  0.56 16.97           C
+ATOM     89  C  AALA A  14       2.754  -0.531  23.694  0.56 14.77           C
+ATOM     90  O  AALA A  14       3.849  -0.405  24.231  0.56 17.39           O
+ATOM     91  CB AALA A  14       3.645  -2.269  22.108  0.56 18.86           C
+ATOM     92  N  BGLY A  14       1.268  -1.692  22.089  0.44 14.65           N
+ATOM     93  CA BGLY A  14       2.445  -1.814  22.901  0.44 16.97           C
+ATOM     94  C  BGLY A  14       2.754  -0.531  23.694  0.44 14.77           C
+ATOM     95  O  BGLY A  14       3.849  -0.405  24.231  0.44 17.39           O
+ATOM     96  N   LEU A  15       1.754   0.327  23.778  1.00 13.72           N
+ATOM     97  CA  LEU A  15       1.873   1.601  24.487  1.00 13.52           C
+ATOM     98  C   LEU A  15       3.033   2.427  23.993  1.00 13.54           C
+ATOM     99  O   LEU A  15       3.727   3.109  24.761  1.00 13.53           O
+ATOM    100  CB  LEU A  15       1.831   1.406  25.994  1.00 16.99           C
+ATOM    101  CG ALEU A  15       0.485   1.257  26.672  0.50 18.92           C
+ATOM    102  CG BLEU A  15       0.561   0.693  26.511  0.50 18.16           C
+ATOM    103  CD1ALEU A  15      -0.272   2.579  26.709  0.50 19.64           C
+ATOM    104  CD1BLEU A  15       0.675   0.390  27.988  0.50 20.69           C
+ATOM    105  CD2ALEU A  15      -0.364   0.182  26.012  0.50 21.59           C
+ATOM    106  CD2BLEU A  15      -0.677   1.532  26.219  0.50 20.18           C
+ATOM    107  N  ATRP A  16       3.298   2.388  22.684  0.56 11.29           N
+ATOM    108  CA ATRP A  16       4.273   3.211  22.015  0.56 11.25           C
+ATOM    109  C  ATRP A  16       3.838   4.674  21.911  0.56  9.81           C
+ATOM    110  O  ATRP A  16       2.731   5.041  22.272  0.56 10.80           O
+ATOM    111  CB ATRP A  16       4.530   2.673  20.587  0.56 15.06           C
+ATOM    112  CG ATRP A  16       5.500   1.531  20.589  0.56 22.22           C
+ATOM    113  CD1ATRP A  16       5.344   0.300  20.028  0.56 22.99           C
+ATOM    114  CD2ATRP A  16       6.804   1.549  21.180  0.56 23.43           C
+ATOM    115  NE1ATRP A  16       6.469  -0.459  20.244  0.56 25.66           N
+ATOM    116  CE2ATRP A  16       7.380   0.282  20.950  0.56 25.18           C
+ATOM    117  CE3ATRP A  16       7.532   2.508  21.894  0.56 26.28           C
+ATOM    118  CZ2ATRP A  16       8.652  -0.045  21.409  0.56 25.91           C
+ATOM    119  CZ3ATRP A  16       8.793   2.178  22.349  0.56 26.49           C
+ATOM    120  CH2ATRP A  16       9.341   0.908  22.104  0.56 25.88           C
+ATOM    121  N  BARG A  16       3.259   2.392  22.674  0.44 13.28           N
+ATOM    122  CA BARG A  16       4.280   3.162  22.014  0.44 13.26           C
+ATOM    123  C  BARG A  16       3.896   4.656  21.956  0.44 11.99           C
+ATOM    124  O  BARG A  16       2.800   5.040  22.308  0.44 12.53           O
+ATOM    125  CB BARG A  16       4.656   2.661  20.638  0.22 13.35           C
+ATOM    126  CB CARG A  16       4.437   2.680  20.555  0.22 16.20           C
+ATOM    127  CG BARG A  16       5.385   1.331  20.601  0.22 17.64           C
+ATOM    128  CG CARG A  16       5.639   1.870  20.229  0.22 21.14           C
+ATOM    129  CD BARG A  16       6.160   1.040  21.834  0.22 19.84           C
+ATOM    130  CD CARG A  16       5.796   0.630  21.081  0.22 21.56           C
+ATOM    131  NE BARG A  16       7.593   1.203  21.768  0.22 21.21           N
+ATOM    132  NE CARG A  16       7.173   0.375  21.465  0.22 24.22           N
+ATOM    133  CZ BARG A  16       8.455   0.224  21.501  0.22 22.06           C
+ATOM    134  CZ CARG A  16       8.046   1.259  21.909  0.22 25.11           C
+ATOM    135  NH1BARG A  16       8.004  -0.997  21.239  0.22 23.25           N
+ATOM    136  NH1CARG A  16       7.750   2.548  22.045  0.22 26.05           N
+ATOM    137  NH2BARG A  16       9.757   0.463  21.492  0.22 23.17           N
+ATOM    138  NH2CARG A  16       9.277   0.859  22.235  0.22 24.63           N
+ATOM    139  N   CYS A  17       4.860   5.440  21.477  1.00 11.64           N
+ATOM    140  CA  CYS A  17       4.659   6.900  21.426  1.00 10.21           C
+ATOM    141  C   CYS A  17       3.731   7.303  20.294  1.00 10.66           C
+ATOM    142  O   CYS A  17       3.858   6.790  19.169  1.00 12.03           O
+ATOM    143  CB  CYS A  17       5.991   7.617  21.171  1.00 10.96           C
+ATOM    144  SG  CYS A  17       7.304   7.032  22.268  1.00 11.64           S
+ATOM    145  N   CYS A  18       2.863   8.268  20.538  1.00 10.28           N
+ATOM    146  CA  CYS A  18       2.045   8.868  19.490  1.00 11.27           C
+ATOM    147  C   CYS A  18       2.592  10.233  19.140  1.00 10.85           C
+ATOM    148  O   CYS A  18       2.716  11.073  20.064  1.00 11.54           O
+ATOM    149  CB  CYS A  18       0.606   9.001  20.013  1.00 11.58           C
+ATOM    150  SG  CYS A  18      -0.516   9.593  18.698  1.00 11.18           S
+ATOM    151  N   SER A  19       2.983  10.469  17.906  1.00 10.63           N
+ATOM    152  CA  SER A  19       3.522  11.773  17.506  1.00 10.33           C
+ATOM    153  C   SER A  19       2.395  12.812  17.355  1.00 10.06           C
+ATOM    154  O   SER A  19       1.239  12.483  17.289  1.00 10.50           O
+ATOM    155  CB  SER A  19       4.213  11.607  16.137  1.00 12.53           C
+ATOM    156  OG  SER A  19       3.218  11.521  15.119  1.00 11.20           O
+ATOM    157  N   ILE A  20       2.843  14.085  17.220  1.00 10.99           N
+ATOM    158  CA  ILE A  20       1.854  15.159  17.009  1.00 11.86           C
+ATOM    159  C   ILE A  20       1.162  15.020  15.673  1.00 13.07           C
+ATOM    160  O   ILE A  20       0.135  15.665  15.448  1.00 13.75           O
+ATOM    161  CB  ILE A  20       2.505  16.546  17.133  1.00 12.91           C
+ATOM    162  CG1 ILE A  20       3.608  16.733  16.099  1.00 13.68           C
+ATOM    163  CG2 ILE A  20       3.006  16.773  18.552  1.00 14.60           C
+ATOM    164  CD1 ILE A  20       4.127  18.178  16.031  1.00 15.84           C
+ATOM    165  N   TRP A  21       1.662  14.176  14.766  1.00 11.46           N
+ATOM    166  CA  TRP A  21       1.006  13.921  13.502  1.00 11.50           C
+ATOM    167  C   TRP A  21       0.012  12.789  13.564  1.00 12.13           C
+ATOM    168  O   TRP A  21      -0.698  12.510  12.602  1.00 14.34           O
+ATOM    169  CB  TRP A  21       2.031  13.690  12.369  1.00 12.06           C
+ATOM    170  CG  TRP A  21       2.940  14.872  12.185  1.00 13.41           C
+ATOM    171  CD1 TRP A  21       2.610  16.023  11.471  1.00 14.87           C
+ATOM    172  CD2 TRP A  21       4.240  15.070  12.680  1.00 13.19           C
+ATOM    173  NE1 TRP A  21       3.663  16.890  11.537  1.00 14.69           N
+ATOM    174  CE2 TRP A  21       4.693  16.334  12.246  1.00 14.96           C
+ATOM    175  CE3 TRP A  21       5.105  14.273  13.462  1.00 14.98           C
+ATOM    176  CZ2 TRP A  21       5.949  16.830  12.589  1.00 16.73           C
+ATOM    177  CZ3 TRP A  21       6.354  14.760  13.773  1.00 17.06           C
+ATOM    178  CH2 TRP A  21       6.760  16.040  13.348  1.00 18.02           C
+ATOM    179  N   GLY A  22      -0.131  12.116  14.717  1.00 11.06           N
+ATOM    180  CA  GLY A  22      -1.145  11.092  14.893  1.00 11.02           C
+ATOM    181  C   GLY A  22      -0.683   9.708  14.431  1.00  9.47           C
+ATOM    182  O   GLY A  22      -1.567   8.923  14.000  1.00 11.91           O
+ATOM    183  N   TRP A  23       0.567   9.408  14.566  1.00 10.13           N
+ATOM    184  CA  TRP A  23       1.085   8.080  14.169  1.00 11.05           C
+ATOM    185  C   TRP A  23       1.906   7.506  15.308  1.00 11.89           C
+ATOM    186  O   TRP A  23       2.573   8.227  16.048  1.00 11.18           O
+ATOM    187  CB  TRP A  23       1.938   8.195  12.906  1.00 12.91           C
+ATOM    188  CG  TRP A  23       1.141   8.597  11.694  1.00 14.32           C
+ATOM    189  CD1 TRP A  23       1.043   9.842  11.148  1.00 15.32           C
+ATOM    190  CD2 TRP A  23       0.335   7.731  10.891  1.00 13.59           C
+ATOM    191  NE1 TRP A  23       0.231   9.802  10.044  1.00 16.07           N
+ATOM    192  CE2 TRP A  23      -0.257   8.535   9.883  1.00 15.22           C
+ATOM    193  CE3 TRP A  23       0.013   6.374  10.946  1.00 15.09           C
+ATOM    194  CZ2 TRP A  23      -1.085   7.991   8.898  1.00 17.75           C
+ATOM    195  CZ3 TRP A  23      -0.823   5.844   9.974  1.00 19.21           C
+ATOM    196  CH2 TRP A  23      -1.358   6.660   8.971  1.00 20.75           C
+ATOM    197  N   CYS A  24       1.986   6.179  15.351  1.00 11.52           N
+ATOM    198  CA  CYS A  24       2.689   5.457  16.386  1.00 12.25           C
+ATOM    199  C   CYS A  24       4.094   5.076  16.014  1.00 11.84           C
+ATOM    200  O   CYS A  24       4.313   4.643  14.856  1.00 13.73           O
+ATOM    201  CB  CYS A  24       1.937   4.110  16.664  1.00 12.40           C
+ATOM    202  SG  CYS A  24       0.275   4.401  17.331  1.00 11.68           S
+ATOM    203  N   GLY A  25       5.037   5.149  16.934  1.00 12.17           N
+ATOM    204  CA  GLY A  25       6.397   4.709  16.654  1.00 12.62           C
+ATOM    205  C   GLY A  25       7.268   4.734  17.898  1.00 13.04           C
+ATOM    206  O   GLY A  25       6.806   4.993  19.009  1.00 13.30           O
+ATOM    207  N   ASP A  26       8.546   4.411  17.713  1.00 15.66           N
+ATOM    208  CA  ASP A  26       9.453   4.213  18.836  1.00 16.94           C
+ATOM    209  C   ASP A  26      10.742   4.984  18.704  1.00 19.41           C
+ATOM    210  O   ASP A  26      11.679   4.757  19.495  1.00 22.27           O
+ATOM    211  CB  ASP A  26       9.733   2.705  19.003  1.00 22.37           C
+ATOM    212  CG  ASP A  26      10.242   2.048  17.750  1.00 27.17           C
+ATOM    213  OD1 ASP A  26      10.821   2.716  16.876  1.00 30.74           O
+ATOM    214  OD2 ASP A  26      10.050   0.806  17.615  1.00 33.27           O
+ATOM    215  N   SER A  27      10.836   5.914  17.772  1.00 16.57           N
+ATOM    216  CA  SER A  27      12.025   6.714  17.541  1.00 18.12           C
+ATOM    217  C   SER A  27      11.784   8.177  17.845  1.00 16.83           C
+ATOM    218  O   SER A  27      10.654   8.569  18.171  1.00 15.78           O
+ATOM    219  CB  SER A  27      12.504   6.526  16.094  1.00 19.54           C
+ATOM    220  OG  SER A  27      11.449   6.922  15.205  1.00 21.34           O
+ATOM    221  N   GLU A  28      12.795   9.020  17.760  1.00 17.82           N
+ATOM    222  CA  GLU A  28      12.747  10.415  18.151  1.00 18.18           C
+ATOM    223  C   GLU A  28      11.580  11.219  17.642  1.00 16.44           C
+ATOM    224  O   GLU A  28      10.943  11.965  18.410  1.00 15.84           O
+ATOM    225  CB AGLU A  28      14.087  11.070  17.738  0.50 20.93           C
+ATOM    226  CB BGLU A  28      14.080  11.099  17.807  0.50 21.15           C
+ATOM    227  CG AGLU A  28      14.061  12.574  17.631  0.50 22.90           C
+ATOM    228  CG BGLU A  28      14.458  12.231  18.733  0.50 23.48           C
+ATOM    229  CD AGLU A  28      13.788  13.255  18.959  0.50 23.94           C
+ATOM    230  CD BGLU A  28      13.705  13.513  18.493  0.50 25.49           C
+ATOM    231  OE1AGLU A  28      14.121  12.673  20.008  0.50 25.78           O
+ATOM    232  OE1BGLU A  28      13.293  13.783  17.345  0.50 26.68           O
+ATOM    233  OE2AGLU A  28      13.210  14.367  18.948  0.50 25.81           O
+ATOM    234  OE2BGLU A  28      13.513  14.286  19.469  0.50 28.16           O
+ATOM    235  N   PRO A  29      11.257  11.149  16.353  1.00 16.44           N
+ATOM    236  CA  PRO A  29      10.144  11.917  15.806  1.00 17.18           C
+ATOM    237  C   PRO A  29       8.823  11.616  16.471  1.00 14.68           C
+ATOM    238  O   PRO A  29       7.892  12.446  16.505  1.00 15.00           O
+ATOM    239  CB  PRO A  29      10.136  11.541  14.327  1.00 19.03           C
+ATOM    240  CG  PRO A  29      11.501  11.035  14.037  1.00 20.48           C
+ATOM    241  CD  PRO A  29      12.006  10.414  15.326  1.00 18.12           C
+ATOM    242  N   TYR A  30       8.646  10.397  16.996  1.00 13.61           N
+ATOM    243  CA  TYR A  30       7.443   9.994  17.663  1.00 12.47           C
+ATOM    244  C   TYR A  30       7.479  10.211  19.186  1.00 12.14           C
+ATOM    245  O   TYR A  30       6.458  10.498  19.778  1.00 12.08           O
+ATOM    246  CB  TYR A  30       7.181   8.480  17.444  1.00 12.23           C
+ATOM    247  CG  TYR A  30       7.057   8.081  15.992  1.00 12.06           C
+ATOM    248  CD1 TYR A  30       8.213   7.750  15.277  1.00 13.86           C
+ATOM    249  CD2 TYR A  30       5.842   8.011  15.344  1.00 11.48           C
+ATOM    250  CE1 TYR A  30       8.133   7.383  13.943  1.00 14.64           C
+ATOM    251  CE2 TYR A  30       5.756   7.627  14.005  1.00 12.08           C
+ATOM    252  CZ  TYR A  30       6.908   7.328  13.334  1.00 13.37           C
+ATOM    253  OH  TYR A  30       6.830   6.950  11.998  1.00 15.25           O
+ATOM    254  N   CYS A  31       8.670  10.046  19.759  1.00 13.67           N
+ATOM    255  CA  CYS A  31       8.831   9.940  21.199  1.00 12.61           C
+ATOM    256  C   CYS A  31       9.529  11.123  21.850  1.00 14.04           C
+ATOM    257  O   CYS A  31       9.506  11.227  23.089  1.00 14.90           O
+ATOM    258  CB  CYS A  31       9.719   8.692  21.503  1.00 13.30           C
+ATOM    259  SG  CYS A  31       8.953   7.136  21.032  1.00 12.81           S
+ATOM    260  N   GLY A  32      10.191  11.953  21.069  1.00 14.92           N
+ATOM    261  CA  GLY A  32      10.970  13.072  21.570  1.00 16.76           C
+ATOM    262  C   GLY A  32      10.183  14.376  21.576  1.00 16.37           C
+ATOM    263  O   GLY A  32       9.089  14.508  22.067  1.00 17.28           O
+ATOM    264  N   ARG A  33      10.805  15.400  20.943  1.00 18.68           N
+ATOM    265  CA  ARG A  33      10.253  16.736  20.909  1.00 20.22           C
+ATOM    266  C   ARG A  33       8.893  16.835  20.258  1.00 19.35           C
+ATOM    267  O   ARG A  33       8.053  17.653  20.668  1.00 21.59           O
+ATOM    268  CB  ARG A  33      11.250  17.687  20.210  1.00 23.28           C
+ATOM    269  CG  ARG A  33      12.707  17.055  20.301  0.00 20.00           C
+ATOM    270  CD  ARG A  33      13.726  18.003  19.669  0.00 20.00           C
+ATOM    271  NE  ARG A  33      13.518  18.180  18.226  0.00 20.00           N
+ATOM    272  CZ  ARG A  33      12.493  18.858  17.694  0.00 20.00           C
+ATOM    273  NH1 ARG A  33      11.568  19.434  18.472  0.00 20.00           N
+ATOM    274  NH2 ARG A  33      12.305  19.018  16.376  0.00 20.00           N
+ATOM    275  N   THR A  34       8.629  15.996  19.260  1.00 17.05           N
+ATOM    276  CA  THR A  34       7.367  16.075  18.514  1.00 15.55           C
+ATOM    277  C   THR A  34       6.416  14.947  18.911  1.00 13.76           C
+ATOM    278  O   THR A  34       5.571  14.521  18.145  1.00 14.63           O
+ATOM    279  CB  THR A  34       7.620  16.068  17.006  1.00 15.68           C
+ATOM    280  OG1 THR A  34       8.589  15.063  16.678  1.00 18.28           O
+ATOM    281  CG2 THR A  34       8.192  17.420  16.539  1.00 17.63           C
+ATOM    282  N   CYS A  35       6.527  14.537  20.184  1.00 12.85           N
+ATOM    283  CA  CYS A  35       5.619  13.529  20.733  1.00 11.99           C
+ATOM    284  C   CYS A  35       4.388  14.118  21.364  1.00 12.75           C
+ATOM    285  O   CYS A  35       4.491  15.127  22.102  1.00 15.67           O
+ATOM    286  CB  CYS A  35       6.432  12.713  21.770  1.00 11.83           C
+ATOM    287  SG  CYS A  35       5.470  11.406  22.561  1.00 10.89           S
+ATOM    288  N   GLU A  36       3.235  13.515  21.205  1.00 10.58           N
+ATOM    289  CA  GLU A  36       2.004  13.930  21.842  1.00 12.03           C
+ATOM    290  C   GLU A  36       1.683  13.107  23.086  1.00 13.55           C
+ATOM    291  O   GLU A  36       1.129  13.669  24.043  1.00 13.26           O
+ATOM    292  CB  GLU A  36       0.819  13.833  20.876  1.00 12.59           C
+ATOM    293  CG  GLU A  36      -0.497  14.343  21.457  1.00 16.20           C
+ATOM    294  CD  GLU A  36      -1.609  14.316  20.436  1.00 18.54           C
+ATOM    295  OE1 GLU A  36      -2.248  13.270  20.239  1.00 18.98           O
+ATOM    296  OE2 GLU A  36      -1.858  15.380  19.821  1.00 23.44           O
+ATOM    297  N   ASN A  37       1.897  11.793  23.068  1.00 12.95           N
+ATOM    298  CA  ASN A  37       1.497  10.964  24.209  1.00 12.65           C
+ATOM    299  C   ASN A  37       2.446   9.775  24.337  1.00 12.21           C
+ATOM    300  O   ASN A  37       3.003   9.296  23.346  1.00 12.29           O
+ATOM    301  CB  ASN A  37       0.071  10.481  24.067  1.00 15.42           C
+ATOM    302  CG  ASN A  37      -0.529   9.956  25.368  1.00 18.91           C
+ATOM    303  OD1 ASN A  37      -0.166  10.434  26.429  1.00 20.42           O
+ATOM    304  ND2 ASN A  37      -1.426   8.985  25.231  1.00 21.73           N
+ATOM    305  N   LYS A  38       2.547   9.285  25.569  1.00 12.38           N
+ATOM    306  CA  LYS A  38       3.418   8.157  25.928  1.00 11.24           C
+ATOM    307  C   LYS A  38       4.814   8.422  25.439  1.00 11.57           C
+ATOM    308  O   LYS A  38       5.475   7.649  24.737  1.00 11.73           O
+ATOM    309  CB  LYS A  38       2.872   6.835  25.390  1.00 12.50           C
+ATOM    310  CG  LYS A  38       1.463   6.489  25.778  1.00 13.84           C
+ATOM    311  CD  LYS A  38       1.233   6.470  27.289  1.00 16.31           C
+ATOM    312  CE  LYS A  38      -0.159   5.917  27.584  1.00 17.80           C
+ATOM    313  NZ  LYS A  38      -0.465   5.932  29.040  1.00 20.45           N
+ATOM    314  N   CYS A  39       5.378   9.556  25.880  1.00 12.02           N
+ATOM    315  CA  CYS A  39       6.632  10.077  25.433  1.00 11.58           C
+ATOM    316  C   CYS A  39       7.809   9.718  26.328  1.00 12.51           C
+ATOM    317  O   CYS A  39       7.574   9.420  27.513  1.00 13.84           O
+ATOM    318  CB  CYS A  39       6.512  11.641  25.373  1.00 13.49           C
+ATOM    319  SG  CYS A  39       5.076  12.190  24.393  1.00 12.58           S
+ATOM    320  N   TRP A  40       9.011   9.764  25.795  1.00 11.50           N
+ATOM    321  CA  TRP A  40      10.193   9.395  26.592  1.00 14.12           C
+ATOM    322  C   TRP A  40      10.259  10.234  27.861  1.00 15.88           C
+ATOM    323  O   TRP A  40      10.656   9.728  28.912  1.00 17.55           O
+ATOM    324  CB  TRP A  40      11.457   9.577  25.773  1.00 14.30           C
+ATOM    325  CG  TRP A  40      11.683   8.533  24.724  1.00 15.08           C
+ATOM    326  CD1 TRP A  40      11.189   7.256  24.703  1.00 14.60           C
+ATOM    327  CD2 TRP A  40      12.438   8.680  23.518  1.00 15.20           C
+ATOM    328  NE1 TRP A  40      11.642   6.583  23.594  1.00 16.14           N
+ATOM    329  CE2 TRP A  40      12.406   7.451  22.843  1.00 15.90           C
+ATOM    330  CE3 TRP A  40      13.169   9.739  22.959  1.00 18.61           C
+ATOM    331  CZ2 TRP A  40      13.047   7.240  21.623  1.00 17.72           C
+ATOM    332  CZ3 TRP A  40      13.814   9.530  21.756  1.00 18.97           C
+ATOM    333  CH2 TRP A  40      13.746   8.296  21.099  1.00 19.39           C
+ATOM    334  N   SER A  41       9.951  11.518  27.737  1.00 15.53           N
+ATOM    335  CA  SER A  41      10.078  12.431  28.878  1.00 17.72           C
+ATOM    336  C   SER A  41       9.043  12.192  29.935  1.00 16.70           C
+ATOM    337  O   SER A  41       9.104  12.813  31.031  1.00 20.51           O
+ATOM    338  CB  SER A  41       9.984  13.886  28.348  1.00 19.25           C
+ATOM    339  OG  SER A  41       8.682  14.140  27.877  1.00 25.31           O
+ATOM    340  N   GLY A  42       8.063  11.327  29.727  1.00 14.48           N
+ATOM    341  CA  GLY A  42       7.007  11.031  30.616  1.00 14.53           C
+ATOM    342  C   GLY A  42       7.286   9.912  31.616  1.00 13.87           C
+ATOM    343  O   GLY A  42       6.373   9.593  32.395  1.00 16.81           O
+ATOM    344  N   GLU A  43       8.465   9.322  31.606  1.00 13.36           N
+ATOM    345  CA  GLU A  43       8.767   8.271  32.589  1.00 13.28           C
+ATOM    346  C   GLU A  43       8.673   8.877  33.995  1.00 13.80           C
+ATOM    347  O   GLU A  43       9.223   9.950  34.249  1.00 15.48           O
+ATOM    348  CB  GLU A  43      10.134   7.656  32.361  1.00 11.95           C
+ATOM    349  CG  GLU A  43      10.372   6.481  33.343  1.00 13.25           C
+ATOM    350  CD  GLU A  43      11.587   5.683  33.100  1.00 13.51           C
+ATOM    351  OE1 GLU A  43      12.044   5.460  31.959  1.00 13.57           O
+ATOM    352  OE2 GLU A  43      12.195   5.150  34.093  1.00 15.32           O
+ATOM    353  N   ARG A  44       8.071   8.145  34.910  1.00 13.63           N
+ATOM    354  CA  ARG A  44       7.983   8.564  36.308  1.00 14.52           C
+ATOM    355  C   ARG A  44       9.362   8.626  36.944  1.00 14.63           C
+ATOM    356  O   ARG A  44      10.261   7.845  36.633  1.00 13.65           O
+ATOM    357  CB  ARG A  44       7.130   7.565  37.097  1.00 14.38           C
+ATOM    358  CG  ARG A  44       5.662   7.552  36.738  1.00 16.88           C
+ATOM    359  CD  ARG A  44       4.918   6.451  37.452  1.00 18.14           C
+ATOM    360  NE  ARG A  44       5.070   6.493  38.896  1.00 18.73           N
+ATOM    361  CZ  ARG A  44       4.153   6.921  39.751  1.00 23.67           C
+ATOM    362  NH1 ARG A  44       2.982   7.369  39.322  1.00 26.62           N
+ATOM    363  NH2 ARG A  44       4.406   6.879  41.057  1.00 24.53           N
+ATOM    364  N   SER A  45       9.537   9.569  37.886  1.00 16.58           N
+ATOM    365  CA  SER A  45      10.814   9.666  38.587  1.00 17.75           C
+ATOM    366  C   SER A  45      11.163   8.400  39.353  1.00 16.53           C
+ATOM    367  O   SER A  45      12.346   8.077  39.486  1.00 16.92           O
+ATOM    368  CB  SER A  45      10.784  10.870  39.555  1.00 19.53           C
+ATOM    369  OG ASER A  45       9.839  10.598  40.594  0.50 22.46           O
+ATOM    370  OG BSER A  45      10.680  12.068  38.784  0.50 23.41           O
+ATOM    371  N   ASP A  46      10.154   7.656  39.787  1.00 16.16           N
+ATOM    372  CA  ASP A  46      10.357   6.414  40.526  1.00 15.58           C
+ATOM    373  C   ASP A  46      10.486   5.209  39.608  1.00 16.25           C
+ATOM    374  O   ASP A  46      10.715   4.082  40.082  1.00 17.85           O
+ATOM    375  CB  ASP A  46       9.273   6.208  41.568  1.00 18.42           C
+ATOM    376  CG  ASP A  46       7.890   5.991  41.051  1.00 18.67           C
+ATOM    377  OD1 ASP A  46       7.678   5.851  39.823  1.00 17.94           O
+ATOM    378  OD2 ASP A  46       6.922   5.928  41.861  1.00 23.44           O
+ATOM    379  N   HIS A  47      10.390   5.412  38.301  1.00 13.79           N
+ATOM    380  CA  HIS A  47      10.535   4.366  37.316  1.00 13.07           C
+ATOM    381  C   HIS A  47       9.438   3.321  37.347  1.00 13.66           C
+ATOM    382  O   HIS A  47       9.591   2.308  36.646  1.00 14.37           O
+ATOM    383  CB  HIS A  47      11.904   3.663  37.393  1.00 13.90           C
+ATOM    384  CG  HIS A  47      13.049   4.620  37.468  1.00 14.22           C
+ATOM    385  ND1 HIS A  47      13.258   5.596  36.524  1.00 15.45           N
+ATOM    386  CD2 HIS A  47      14.039   4.749  38.378  1.00 14.58           C
+ATOM    387  CE1 HIS A  47      14.336   6.300  36.845  1.00 16.14           C
+ATOM    388  NE2 HIS A  47      14.821   5.798  37.978  1.00 16.13           N
+ATOM    389  N   ARG A  48       8.356   3.510  38.057  1.00 13.49           N
+ATOM    390  CA  ARG A  48       7.271   2.527  38.068  1.00 14.17           C
+ATOM    391  C   ARG A  48       6.513   2.568  36.750  1.00 15.86           C
+ATOM    392  O   ARG A  48       6.416   3.632  36.116  1.00 15.15           O
+ATOM    393  CB  ARG A  48       6.310   2.789  39.231  1.00 16.19           C
+ATOM    394  CG  ARG A  48       6.953   2.481  40.594  1.00 21.74           C
+ATOM    395  CD  ARG A  48       5.897   2.677  41.696  1.00 27.15           C
+ATOM    396  NE  ARG A  48       4.979   1.552  41.722  1.00 33.45           N
+ATOM    397  CZ  ARG A  48       3.811   1.502  42.336  1.00 35.53           C
+ATOM    398  NH1 ARG A  48       3.350   2.536  43.022  1.00 37.75           N
+ATOM    399  NH2 ARG A  48       3.078   0.387  42.267  1.00 37.26           N
+ATOM    400  N   CYS A  49       5.942   1.451  36.352  1.00 14.67           N
+ATOM    401  CA  CYS A  49       5.096   1.418  35.149  1.00 14.02           C
+ATOM    402  C   CYS A  49       4.011   0.378  35.343  1.00 16.59           C
+ATOM    403  O   CYS A  49       3.937  -0.224  36.444  1.00 17.70           O
+ATOM    404  CB  CYS A  49       5.922   1.172  33.902  1.00 14.60           C
+ATOM    405  SG  CYS A  49       6.790  -0.418  33.889  1.00 13.58           S
+ATOM    406  N   GLY A  50       3.193   0.151  34.344  1.00 16.32           N
+ATOM    407  CA  GLY A  50       2.143  -0.866  34.446  1.00 18.12           C
+ATOM    408  C   GLY A  50       0.822  -0.238  34.870  1.00 20.85           C
+ATOM    409  O   GLY A  50       0.703   0.947  35.143  1.00 21.47           O
+ATOM    410  N   ALA A  51      -0.178  -1.110  35.040  1.00 23.34           N
+ATOM    411  CA  ALA A  51      -1.543  -0.713  35.271  1.00 26.04           C
+ATOM    412  C   ALA A  51      -1.795   0.012  36.564  1.00 27.16           C
+ATOM    413  O   ALA A  51      -2.665   0.906  36.617  1.00 29.51           O
+ATOM    414  CB  ALA A  51      -2.454  -1.955  35.167  1.00 27.39           C
+ATOM    415  N   ALA A  52      -1.074  -0.305  37.627  1.00 29.21           N
+ATOM    416  CA  ALA A  52      -1.299   0.301  38.927  1.00 30.20           C
+ATOM    417  C   ALA A  52      -0.994   1.784  38.962  1.00 30.84           C
+ATOM    418  O   ALA A  52      -1.494   2.501  39.844  1.00 32.25           O
+ATOM    419  CB  ALA A  52      -0.515  -0.431  40.004  1.00 33.09           C
+ATOM    420  N   VAL A  53      -0.141   2.261  38.056  1.00 28.71           N
+ATOM    421  CA  VAL A  53       0.194   3.685  38.018  1.00 26.18           C
+ATOM    422  C   VAL A  53      -0.261   4.318  36.712  1.00 25.00           C
+ATOM    423  O   VAL A  53       0.267   5.337  36.264  1.00 24.56           O
+ATOM    424  CB  VAL A  53       1.673   3.942  38.268  1.00 25.15           C
+ATOM    425  CG1 VAL A  53       2.041   3.676  39.733  1.00 26.52           C
+ATOM    426  CG2 VAL A  53       2.533   3.055  37.362  1.00 22.44           C
+ATOM    427  N   GLY A  54      -1.239   3.695  36.058  1.00 23.89           N
+ATOM    428  CA  GLY A  54      -1.868   4.224  34.880  1.00 23.95           C
+ATOM    429  C   GLY A  54      -1.116   4.076  33.585  1.00 23.40           C
+ATOM    430  O   GLY A  54      -1.266   4.908  32.665  1.00 23.79           O
+ATOM    431  N   ASN A  55      -0.333   3.021  33.429  1.00 21.36           N
+ATOM    432  CA  ASN A  55       0.391   2.709  32.223  1.00 19.27           C
+ATOM    433  C   ASN A  55       1.196   3.856  31.645  1.00 18.15           C
+ATOM    434  O   ASN A  55       1.105   4.166  30.442  1.00 18.32           O
+ATOM    435  CB  ASN A  55      -0.582   2.178  31.147  1.00 21.82           C
+ATOM    436  CG  ASN A  55      -1.151   0.821  31.511  1.00 26.16           C
+ATOM    437  OD1 ASN A  55      -0.501  -0.011  32.131  1.00 26.45           O
+ATOM    438  ND2 ASN A  55      -2.397   0.598  31.091  1.00 29.80           N
+ATOM    439  N   PRO A  56       2.101   4.416  32.416  1.00 14.99           N
+ATOM    440  CA  PRO A  56       2.969   5.491  31.927  1.00 13.73           C
+ATOM    441  C   PRO A  56       4.009   4.955  30.985  1.00 14.07           C
+ATOM    442  O   PRO A  56       4.346   3.755  30.986  1.00 15.11           O
+ATOM    443  CB  PRO A  56       3.585   6.035  33.205  1.00 14.47           C
+ATOM    444  CG  PRO A  56       3.701   4.793  34.077  1.00 15.83           C
+ATOM    445  CD  PRO A  56       2.407   4.054  33.822  1.00 15.95           C
+ATOM    446  N   PRO A  57       4.649   5.806  30.194  1.00 11.98           N
+ATOM    447  CA  PRO A  57       5.722   5.426  29.322  1.00 12.65           C
+ATOM    448  C   PRO A  57       7.036   5.165  30.051  1.00 12.11           C
+ATOM    449  O   PRO A  57       7.157   5.472  31.250  1.00 13.60           O
+ATOM    450  CB  PRO A  57       5.895   6.659  28.401  1.00 12.88           C
+ATOM    451  CG  PRO A  57       5.466   7.798  29.280  1.00 12.77           C
+ATOM    452  CD  PRO A  57       4.319   7.262  30.094  1.00 14.56           C
+ATOM    453  N   CYS A  58       8.020   4.682  29.324  1.00 11.80           N
+ATOM    454  CA  CYS A  58       9.395   4.596  29.803  1.00 12.36           C
+ATOM    455  C   CYS A  58      10.282   5.443  28.876  1.00 13.05           C
+ATOM    456  O   CYS A  58       9.824   5.791  27.764  1.00 12.47           O
+ATOM    457  CB  CYS A  58       9.923   3.175  29.845  1.00 12.38           C
+ATOM    458  SG  CYS A  58       8.999   2.008  30.881  1.00 12.44           S
+ATOM    459  N   GLY A  59      11.462   5.793  29.318  1.00 13.40           N
+ATOM    460  CA  GLY A  59      12.370   6.599  28.530  1.00 13.37           C
+ATOM    461  C   GLY A  59      12.983   5.848  27.371  1.00 15.29           C
+ATOM    462  O   GLY A  59      12.636   4.695  27.085  1.00 15.41           O
+ATOM    463  N   GLN A  60      13.926   6.483  26.680  1.00 15.49           N
+ATOM    464  CA  GLN A  60      14.578   5.933  25.521  1.00 16.65           C
+ATOM    465  C   GLN A  60      15.322   4.640  25.808  1.00 17.24           C
+ATOM    466  O   GLN A  60      16.084   4.583  26.784  1.00 17.93           O
+ATOM    467  CB  GLN A  60      15.560   6.976  24.932  1.00 21.03           C
+ATOM    468  CG  GLN A  60      16.137   6.596  23.600  1.00 24.12           C
+ATOM    469  CD  GLN A  60      16.930   7.708  22.933  1.00 26.24           C
+ATOM    470  OE1 GLN A  60      17.102   8.793  23.476  1.00 28.53           O
+ATOM    471  NE2 GLN A  60      17.396   7.430  21.717  1.00 30.08           N
+ATOM    472  N   ASP A  61      15.074   3.617  25.016  1.00 17.93           N
+ATOM    473  CA  ASP A  61      15.745   2.336  25.132  1.00 20.04           C
+ATOM    474  C   ASP A  61      15.467   1.648  26.455  1.00 20.73           C
+ATOM    475  O   ASP A  61      16.257   0.866  26.990  1.00 21.72           O
+ATOM    476  CB  ASP A  61      17.234   2.449  24.844  1.00 24.31           C
+ATOM    477  CG  ASP A  61      17.506   2.718  23.360  1.00 27.72           C
+ATOM    478  OD1 ASP A  61      16.939   1.999  22.517  1.00 33.16           O
+ATOM    479  OD2 ASP A  61      18.293   3.639  23.088  1.00 32.19           O
+ATOM    480  N   ARG A  62      14.303   1.961  27.014  1.00 18.60           N
+ATOM    481  CA  ARG A  62      13.834   1.325  28.244  1.00 16.85           C
+ATOM    482  C   ARG A  62      12.481   0.710  28.021  1.00 15.70           C
+ATOM    483  O   ARG A  62      11.700   1.178  27.159  1.00 17.28           O
+ATOM    484  CB  ARG A  62      13.808   2.372  29.372  1.00 15.52           C
+ATOM    485  CG  ARG A  62      15.215   2.900  29.694  1.00 16.78           C
+ATOM    486  CD  ARG A  62      15.199   4.169  30.493  1.00 17.30           C
+ATOM    487  NE  ARG A  62      14.592   4.088  31.791  1.00 15.85           N
+ATOM    488  CZ  ARG A  62      15.169   3.631  32.910  1.00 16.22           C
+ATOM    489  NH1 ARG A  62      16.419   3.181  32.862  1.00 19.33           N
+ATOM    490  NH2 ARG A  62      14.505   3.658  34.049  1.00 17.20           N
+ATOM    491  N   CYS A  63      12.133  -0.348  28.724  1.00 14.67           N
+ATOM    492  CA  CYS A  63      10.895  -1.061  28.575  1.00 14.75           C
+ATOM    493  C   CYS A  63      10.220  -1.258  29.921  1.00 12.67           C
+ATOM    494  O   CYS A  63      10.923  -1.181  30.961  1.00 13.29           O
+ATOM    495  CB  CYS A  63      11.141  -2.476  27.981  1.00 16.49           C
+ATOM    496  SG  CYS A  63      12.260  -2.460  26.567  1.00 16.96           S
+ATOM    497  N   CYS A  64       8.953  -1.528  29.925  1.00 12.88           N
+ATOM    498  CA  CYS A  64       8.206  -1.780  31.148  1.00 12.83           C
+ATOM    499  C   CYS A  64       8.146  -3.285  31.429  1.00 15.34           C
+ATOM    500  O   CYS A  64       7.524  -4.027  30.660  1.00 15.86           O
+ATOM    501  CB  CYS A  64       6.781  -1.219  30.990  1.00 14.85           C
+ATOM    502  SG  CYS A  64       5.806  -1.504  32.499  1.00 13.42           S
+ATOM    503  N   SER A  65       8.826  -3.717  32.477  1.00 14.67           N
+ATOM    504  CA  SER A  65       8.872  -5.161  32.798  1.00 16.00           C
+ATOM    505  C   SER A  65       7.551  -5.636  33.353  1.00 16.63           C
+ATOM    506  O   SER A  65       6.710  -4.894  33.815  1.00 15.54           O
+ATOM    507  CB  SER A  65       9.998  -5.411  33.827  1.00 16.86           C
+ATOM    508  OG  SER A  65       9.484  -5.147  35.124  1.00 17.06           O
+ATOM    509  N   VAL A  66       7.392  -6.999  33.348  1.00 18.72           N
+ATOM    510  CA  VAL A  66       6.204  -7.591  33.922  1.00 19.62           C
+ATOM    511  C   VAL A  66       6.103  -7.376  35.428  1.00 19.10           C
+ATOM    512  O   VAL A  66       5.023  -7.531  35.993  1.00 22.45           O
+ATOM    513  CB  VAL A  66       6.081  -9.099  33.636  1.00 18.76           C
+ATOM    514  CG1 VAL A  66       5.849  -9.334  32.154  1.00 20.22           C
+ATOM    515  CG2 VAL A  66       7.308  -9.852  34.123  1.00 20.88           C
+ATOM    516  N   HIS A  67       7.205  -6.932  36.043  1.00 17.59           N
+ATOM    517  CA  HIS A  67       7.183  -6.665  37.480  1.00 19.57           C
+ATOM    518  C   HIS A  67       6.787  -5.235  37.796  1.00 20.01           C
+ATOM    519  O   HIS A  67       6.668  -4.859  38.965  1.00 21.59           O
+ATOM    520  CB  HIS A  67       8.525  -7.022  38.123  1.00 19.55           C
+ATOM    521  CG  HIS A  67       8.951  -8.426  37.769  1.00 19.66           C
+ATOM    522  ND1 HIS A  67       8.317  -9.542  38.278  1.00 24.79           N
+ATOM    523  CD2 HIS A  67       9.897  -8.863  36.924  1.00 20.78           C
+ATOM    524  CE1 HIS A  67       8.889 -10.621  37.766  1.00 21.81           C
+ATOM    525  NE2 HIS A  67       9.854 -10.250  36.958  1.00 23.01           N
+ATOM    526  N   GLY A  68       6.635  -4.403  36.763  1.00 18.21           N
+ATOM    527  CA  GLY A  68       6.124  -3.053  36.937  1.00 17.97           C
+ATOM    528  C   GLY A  68       7.194  -1.980  37.055  1.00 13.37           C
+ATOM    529  O   GLY A  68       6.922  -0.965  37.742  1.00 15.56           O
+ATOM    530  N   TRP A  69       8.337  -2.157  36.449  1.00 12.34           N
+ATOM    531  CA  TRP A  69       9.382  -1.130  36.486  1.00 12.62           C
+ATOM    532  C   TRP A  69       9.962  -0.917  35.099  1.00 13.75           C
+ATOM    533  O   TRP A  69      10.077  -1.829  34.287  1.00 13.58           O
+ATOM    534  CB  TRP A  69      10.515  -1.554  37.436  1.00 16.11           C
+ATOM    535  CG  TRP A  69      10.061  -1.845  38.837  1.00 18.96           C
+ATOM    536  CD1 TRP A  69       9.648  -3.068  39.323  1.00 18.72           C
+ATOM    537  CD2 TRP A  69       9.952  -0.930  39.923  1.00 20.84           C
+ATOM    538  NE1 TRP A  69       9.303  -2.949  40.636  1.00 21.95           N
+ATOM    539  CE2 TRP A  69       9.480  -1.643  41.038  1.00 19.50           C
+ATOM    540  CE3 TRP A  69      10.212   0.438  40.056  1.00 20.51           C
+ATOM    541  CZ2 TRP A  69       9.255  -1.048  42.273  1.00 22.79           C
+ATOM    542  CZ3 TRP A  69       9.979   1.038  41.281  1.00 22.68           C
+ATOM    543  CH2 TRP A  69       9.512   0.293  42.378  1.00 23.67           C
+ATOM    544  N   CYS A  70      10.307   0.345  34.795  1.00 13.10           N
+ATOM    545  CA  CYS A  70      11.002   0.669  33.563  1.00 13.78           C
+ATOM    546  C   CYS A  70      12.499   0.383  33.729  1.00 13.92           C
+ATOM    547  O   CYS A  70      13.047   0.691  34.814  1.00 15.28           O
+ATOM    548  CB  CYS A  70      10.857   2.178  33.254  1.00 14.26           C
+ATOM    549  SG  CYS A  70       9.243   2.751  32.767  1.00 12.08           S
+ATOM    550  N   GLY A  71      13.177  -0.024  32.685  1.00 14.37           N
+ATOM    551  CA  GLY A  71      14.628  -0.187  32.729  1.00 15.37           C
+ATOM    552  C   GLY A  71      15.130  -0.683  31.374  1.00 18.20           C
+ATOM    553  O   GLY A  71      14.340  -1.016  30.505  1.00 16.44           O
+ATOM    554  N   GLY A  72      16.455  -0.770  31.259  1.00 19.67           N
+ATOM    555  CA  GLY A  72      17.033  -1.225  29.985  1.00 22.41           C
+ATOM    556  C   GLY A  72      17.590  -2.635  30.117  1.00 24.30           C
+ATOM    557  O   GLY A  72      17.886  -3.101  31.216  1.00 26.21           O
+ATOM    558  N   GLY A  73      17.694  -3.324  28.982  1.00 25.36           N
+ATOM    559  CA  GLY A  73      18.291  -4.645  28.942  1.00 26.44           C
+ATOM    560  C   GLY A  73      17.283  -5.775  28.973  1.00 27.24           C
+ATOM    561  O   GLY A  73      16.086  -5.595  29.157  1.00 26.15           O
+ATOM    562  N   ASN A  74      17.798  -7.004  28.869  1.00 27.53           N
+ATOM    563  CA  ASN A  74      17.013  -8.206  28.788  1.00 27.66           C
+ATOM    564  C   ASN A  74      16.001  -8.395  29.892  1.00 25.55           C
+ATOM    565  O   ASN A  74      14.915  -8.955  29.662  1.00 26.18           O
+ATOM    566  CB  ASN A  74      17.981  -9.421  28.763  1.00 32.27           C
+ATOM    567  CG AASN A  74      17.279 -10.749  28.825  0.50 33.40           C
+ATOM    568  CG BASN A  74      18.917  -9.356  27.574  0.50 34.22           C
+ATOM    569  OD1AASN A  74      17.230 -11.395  29.878  0.50 35.56           O
+ATOM    570  OD1BASN A  74      18.479  -9.218  26.432  0.50 36.46           O
+ATOM    571  ND2AASN A  74      16.736 -11.203  27.699  0.50 34.86           N
+ATOM    572  ND2BASN A  74      20.215  -9.465  27.831  0.50 36.27           N
+ATOM    573  N   ASP A  75      16.355  -8.036  31.129  1.00 25.87           N
+ATOM    574  CA  ASP A  75      15.472  -8.252  32.256  1.00 25.73           C
+ATOM    575  C   ASP A  75      14.200  -7.431  32.203  1.00 23.25           C
+ATOM    576  O   ASP A  75      13.194  -7.782  32.826  1.00 22.95           O
+ATOM    577  CB  ASP A  75      16.209  -8.005  33.580  1.00 31.03           C
+ATOM    578  CG  ASP A  75      16.090  -9.241  34.475  1.00 37.03           C
+ATOM    579  OD1 ASP A  75      16.882 -10.182  34.258  1.00 40.33           O
+ATOM    580  OD2 ASP A  75      15.200  -9.254  35.339  1.00 39.88           O
+ATOM    581  N   TYR A  76      14.205  -6.354  31.409  1.00 21.83           N
+ATOM    582  CA  TYR A  76      13.025  -5.520  31.259  1.00 20.29           C
+ATOM    583  C   TYR A  76      12.385  -5.662  29.875  1.00 20.48           C
+ATOM    584  O   TYR A  76      11.191  -5.458  29.724  1.00 20.48           O
+ATOM    585  CB  TYR A  76      13.423  -4.033  31.431  1.00 18.44           C
+ATOM    586  CG  TYR A  76      13.911  -3.669  32.810  1.00 15.85           C
+ATOM    587  CD1 TYR A  76      15.235  -3.876  33.168  1.00 16.59           C
+ATOM    588  CD2 TYR A  76      13.050  -3.117  33.750  1.00 14.97           C
+ATOM    589  CE1 TYR A  76      15.689  -3.555  34.435  1.00 16.24           C
+ATOM    590  CE2 TYR A  76      13.502  -2.778  35.017  1.00 14.15           C
+ATOM    591  CZ  TYR A  76      14.809  -3.032  35.355  1.00 15.60           C
+ATOM    592  OH  TYR A  76      15.277  -2.679  36.604  1.00 17.09           O
+ATOM    593  N   CYS A  77      13.208  -5.961  28.876  1.00 22.81           N
+ATOM    594  CA  CYS A  77      12.814  -5.846  27.491  1.00 23.23           C
+ATOM    595  C   CYS A  77      12.573  -7.119  26.727  1.00 27.50           C
+ATOM    596  O   CYS A  77      11.961  -7.054  25.632  1.00 27.57           O
+ATOM    597  CB  CYS A  77      13.893  -5.011  26.744  1.00 21.70           C
+ATOM    598  SG  CYS A  77      13.970  -3.285  27.311  1.00 19.69           S
+ATOM    599  N   SER A  78      13.001  -8.275  27.211  1.00 29.47           N
+ATOM    600  CA  SER A  78      12.928  -9.511  26.448  1.00 32.50           C
+ATOM    601  C   SER A  78      11.665 -10.314  26.666  1.00 33.17           C
+ATOM    602  O   SER A  78      11.178 -10.437  27.814  1.00 35.05           O
+ATOM    603  CB  SER A  78      14.183 -10.362  26.725  1.00 35.08           C
+ATOM    604  OG  SER A  78      15.357  -9.643  26.373  1.00 38.49           O
+ATOM    605  N  AGLY A  79      11.026 -10.753  25.586  0.56 32.73           N
+ATOM    606  N  BGLY A  79      11.268 -11.086  25.671  0.44 33.24           N
+ATOM    607  CA AGLY A  79       9.821 -11.505  25.585  0.56 32.16           C
+ATOM    608  CA BGLY A  79      10.123 -11.952  25.726  0.44 32.77           C
+ATOM    609  C  AGLY A  79       9.052 -11.575  26.887  0.56 32.04           C
+ATOM    610  C  BGLY A  79       9.866 -12.542  27.101  0.44 32.18           C
+ATOM    611  O  AGLY A  79       8.312 -10.654  27.235  0.56 32.01           O
+ATOM    612  O  BGLY A  79      10.734 -13.176  27.699  0.44 32.81           O
+ATOM    613  N  ASER A  80       9.205 -12.682  27.605  0.56 31.46           N
+ATOM    614  CA ASER A  80       8.473 -12.954  28.816  0.56 31.14           C
+ATOM    615  C  ASER A  80       8.880 -12.084  29.993  0.56 30.28           C
+ATOM    616  O  ASER A  80       8.341 -12.253  31.098  0.56 30.92           O
+ATOM    617  CB ASER A  80       8.629 -14.441  29.205  0.56 32.24           C
+ATOM    618  OG ASER A  80       9.999 -14.810  29.194  0.56 33.96           O
+ATOM    619  N  BGLY A  80       8.644 -12.355  27.599  0.44 31.90           N
+ATOM    620  CA BGLY A  80       8.263 -12.856  28.907  0.44 30.90           C
+ATOM    621  C  BGLY A  80       8.635 -11.879  30.017  0.44 29.74           C
+ATOM    622  O  BGLY A  80       7.877 -11.725  30.978  0.44 31.09           O
+ATOM    623  N  ALYS A  81       9.846 -11.202  29.794  0.56 29.23           N
+ATOM    624  CA ALYS A  81      10.291 -10.308  30.869  0.56 28.16           C
+ATOM    625  C  ALYS A  81       9.575  -8.959  30.759  0.56 26.65           C
+ATOM    626  O  ALYS A  81       9.248  -8.335  31.767  0.56 24.90           O
+ATOM    627  CB ALYS A  81      11.795 -10.109  30.820  0.56 29.85           C
+ATOM    628  CG ALYS A  81      12.626 -11.336  31.168  0.56 32.07           C
+ATOM    629  CD ALYS A  81      14.097 -11.094  30.837  0.56 33.72           C
+ATOM    630  CE ALYS A  81      14.890 -12.388  30.924  0.56 35.00           C
+ATOM    631  NZ ALYS A  81      16.264 -12.237  30.375  0.56 35.39           N
+ATOM    632  N  BASN A  81       9.796 -11.250  29.900  0.44 28.30           N
+ATOM    633  CA BASN A  81      10.254 -10.303  30.910  0.44 26.39           C
+ATOM    634  C  BASN A  81       9.566  -8.946  30.756  0.44 24.18           C
+ATOM    635  O  BASN A  81       9.603  -8.128  31.673  0.44 23.07           O
+ATOM    636  CB BASN A  81      11.773 -10.095  30.784  0.44 27.87           C
+ATOM    637  CG BASN A  81      12.573 -11.218  31.412  0.44 29.21           C
+ATOM    638  OD1BASN A  81      12.064 -11.941  32.265  0.44 28.74           O
+ATOM    639  ND2BASN A  81      13.822 -11.353  30.992  0.44 30.49           N
+ATOM    640  N   CYS A  82       8.950  -8.719  29.607  1.00 22.45           N
+ATOM    641  CA  CYS A  82       8.458  -7.382  29.254  1.00 21.45           C
+ATOM    642  C   CYS A  82       6.972  -7.304  29.058  1.00 22.09           C
+ATOM    643  O   CYS A  82       6.378  -8.160  28.372  1.00 22.87           O
+ATOM    644  CB  CYS A  82       9.172  -6.970  27.942  1.00 18.44           C
+ATOM    645  SG  CYS A  82       8.735  -5.286  27.433  1.00 17.39           S
+ATOM    646  N   GLN A  83       6.311  -6.265  29.586  1.00 20.84           N
+ATOM    647  CA  GLN A  83       4.892  -6.092  29.419  1.00 20.12           C
+ATOM    648  C   GLN A  83       4.524  -5.110  28.319  1.00 20.04           C
+ATOM    649  O   GLN A  83       3.521  -5.334  27.618  1.00 21.43           O
+ATOM    650  CB  GLN A  83       4.123  -5.836  30.680  1.00 22.18           C
+ATOM    651  CG  GLN A  83       4.345  -4.549  31.422  1.00 23.67           C
+ATOM    652  CD  GLN A  83       3.365  -4.361  32.567  1.00 22.91           C
+ATOM    653  OE1 GLN A  83       2.153  -4.246  32.353  1.00 26.53           O
+ATOM    654  NE2 GLN A  83       3.857  -4.338  33.797  1.00 21.79           N
+ATOM    655  N   TYR A  84       5.264  -4.025  28.158  1.00 17.66           N
+ATOM    656  CA  TYR A  84       5.002  -3.076  27.074  1.00 16.39           C
+ATOM    657  C   TYR A  84       6.245  -2.265  26.742  1.00 15.08           C
+ATOM    658  O   TYR A  84       7.234  -2.242  27.458  1.00 15.01           O
+ATOM    659  CB  TYR A  84       3.797  -2.233  27.255  1.00 15.78           C
+ATOM    660  CG  TYR A  84       3.715  -1.293  28.430  1.00 14.92           C
+ATOM    661  CD1 TYR A  84       4.476  -0.110  28.447  1.00 15.62           C
+ATOM    662  CD2 TYR A  84       2.839  -1.516  29.471  1.00 16.69           C
+ATOM    663  CE1 TYR A  84       4.346   0.776  29.503  1.00 15.02           C
+ATOM    664  CE2 TYR A  84       2.695  -0.634  30.519  1.00 15.77           C
+ATOM    665  CZ  TYR A  84       3.480   0.519  30.526  1.00 14.45           C
+ATOM    666  OH  TYR A  84       3.339   1.400  31.579  1.00 14.64           O
+ATOM    667  N   ARG A  85       6.205  -1.609  25.564  1.00 15.20           N
+ATOM    668  CA  ARG A  85       7.334  -0.893  25.027  1.00 14.16           C
+ATOM    669  C   ARG A  85       8.578  -1.769  24.953  1.00 15.58           C
+ATOM    670  O   ARG A  85       9.673  -1.417  25.337  1.00 18.52           O
+ATOM    671  CB  ARG A  85       7.617   0.425  25.764  1.00 15.31           C
+ATOM    672  CG  ARG A  85       6.496   1.448  25.540  1.00 12.89           C
+ATOM    673  CD  ARG A  85       6.532   2.564  26.562  1.00 13.54           C
+ATOM    674  NE  ARG A  85       7.452   3.638  26.250  1.00 12.05           N
+ATOM    675  CZ  ARG A  85       7.087   4.731  25.517  1.00 10.84           C
+ATOM    676  NH1 ARG A  85       5.893   4.849  25.020  1.00 12.54           N
+ATOM    677  NH2 ARG A  85       8.007   5.678  25.397  1.00 12.10           N
+ATOM    678  N   CYS A  86       8.355  -2.983  24.413  1.00 19.00           N
+ATOM    679  CA  CYS A  86       9.369  -4.015  24.374  1.00 21.08           C
+ATOM    680  C   CYS A  86      10.260  -3.991  23.160  1.00 23.22           C
+ATOM    681  O   CYS A  86       9.846  -3.464  22.105  1.00 25.66           O
+ATOM    682  CB  CYS A  86       8.644  -5.399  24.430  1.00 21.37           C
+ATOM    683  SG  CYS A  86       7.527  -5.528  25.832  1.00 19.98           S
+TER     684      CYS A  86
+HETATM  685  C1  NAG B   1       2.845   5.730   8.410  1.00 13.00           C
+HETATM  686  C2  NAG B   1       2.980   4.600   7.346  1.00 14.42           C
+HETATM  687  C3  NAG B   1       2.175   3.411   7.793  1.00 14.65           C
+HETATM  688  C4  NAG B   1       2.774   2.928   9.166  1.00 14.34           C
+HETATM  689  C5  NAG B   1       2.670   4.122  10.167  1.00 14.77           C
+HETATM  690  C6  NAG B   1       3.374   3.715  11.471  1.00 14.26           C
+HETATM  691  C7  NAG B   1       3.348   5.467   5.049  1.00 14.97           C
+HETATM  692  C8  NAG B   1       2.707   6.219   3.930  1.00 15.87           C
+HETATM  693  N2  NAG B   1       2.477   5.116   6.044  1.00 15.79           N
+HETATM  694  O3  NAG B   1       2.404   2.342   6.849  1.00 16.72           O
+HETATM  695  O4  NAG B   1       1.906   1.888   9.675  1.00 16.83           O
+HETATM  696  O5  NAG B   1       3.422   5.196   9.636  1.00 14.49           O
+HETATM  697  O6  NAG B   1       2.814   4.521  12.515  1.00 17.10           O
+HETATM  698  O7  NAG B   1       4.476   5.236   5.105  1.00 15.81           O
+HETATM  699  C1  NAG B   2       4.454  10.706   8.957  1.00 15.08           C
+HETATM  700  C2  NAG B   2       4.460   9.780  10.201  1.00 13.08           C
+HETATM  701  C3  NAG B   2       4.585   8.348   9.693  1.00 14.61           C
+HETATM  702  C4  NAG B   2       3.438   8.059   8.721  1.00 14.25           C
+HETATM  703  C5  NAG B   2       3.377   9.108   7.581  1.00 15.30           C
+HETATM  704  C6  NAG B   2       2.128   8.919   6.764  1.00 17.09           C
+HETATM  705  C7  NAG B   2       5.438  10.602  12.343  1.00 12.64           C
+HETATM  706  C8  NAG B   2       6.705  10.853  13.053  1.00 14.46           C
+HETATM  707  N2  NAG B   2       5.562  10.111  11.086  1.00 14.12           N
+HETATM  708  O3  NAG B   2       4.506   7.454  10.798  1.00 14.67           O
+HETATM  709  O4  NAG B   2       3.695   6.769   8.064  1.00 13.33           O
+HETATM  710  O5  NAG B   2       3.294  10.396   8.215  1.00 16.15           O
+HETATM  711  O6  NAG B   2       2.067   9.786   5.669  1.00 21.57           O
+HETATM  712  O7  NAG B   2       4.383  10.784  12.792  1.00 13.02           O
+HETATM  713  C1  NAG B   3       4.558  15.870   8.029  1.00 22.68           C
+HETATM  714  C2  NAG B   3       3.781  14.939   7.134  1.00 23.40           C
+HETATM  715  C3  NAG B   3       3.385  13.670   7.891  1.00 21.15           C
+HETATM  716  C4  NAG B   3       4.638  13.082   8.560  1.00 19.08           C
+HETATM  717  C5  NAG B   3       5.387  14.135   9.404  1.00 20.09           C
+HETATM  718  C6  NAG B   3       6.662  13.594   9.979  1.00 20.74           C
+HETATM  719  C7  NAG B   3       2.333  15.941   5.366  1.00 30.04           C
+HETATM  720  C8  NAG B   3       0.935  16.346   5.056  1.00 30.86           C
+HETATM  721  N2  NAG B   3       2.536  15.585   6.659  1.00 26.09           N
+HETATM  722  O3  NAG B   3       2.831  12.779   6.934  1.00 22.73           O
+HETATM  723  O4  NAG B   3       4.253  12.032   9.493  1.00 16.48           O
+HETATM  724  O5  NAG B   3       5.732  15.223   8.531  1.00 21.12           O
+HETATM  725  O6  NAG B   3       7.565  13.140   9.015  1.00 23.92           O
+HETATM  726  O7  NAG B   3       3.172  15.935   4.593  1.00 31.92           O
+HETATM  727  C1  NAG B   4       6.424  20.761   7.837  1.00 35.33           C
+HETATM  728  C2  NAG B   4       7.333  19.637   8.339  1.00 35.66           C
+HETATM  729  C3  NAG B   4       6.907  18.331   7.686  1.00 35.20           C
+HETATM  730  C4  NAG B   4       5.422  18.072   8.014  1.00 33.37           C
+HETATM  731  C5  NAG B   4       4.527  19.299   7.696  1.00 33.27           C
+HETATM  732  C6  NAG B   4       3.173  19.102   8.406  1.00 32.57           C
+HETATM  733  C7  NAG B   4       9.448  20.235   9.622  0.00 40.22           C
+HETATM  734  C8  NAG B   4      10.921  20.272   9.570  0.00 40.07           C
+HETATM  735  N2  NAG B   4       8.744  19.920   8.013  1.00 37.41           N
+HETATM  736  O3  NAG B   4       7.711  17.283   8.235  1.00 36.78           O
+HETATM  737  O4  NAG B   4       4.923  16.987   7.202  1.00 29.35           O
+HETATM  738  O5  NAG B   4       5.078  20.464   8.245  1.00 34.32           O
+HETATM  739  O6  NAG B   4       3.314  19.212   9.787  1.00 31.85           O
+HETATM  740  O7  NAG B   4       8.849  20.404  10.603  0.00 43.75           O
+HETATM  741  O   HOH     1       6.997   5.537  34.149  1.00 15.09           O
+HETATM  742  O   HOH     2      10.295   3.302  26.095  1.00 16.76           O
+HETATM  743  O   HOH     3      10.019  12.949  25.133  1.00 20.11           O
+HETATM  744  O   HOH     4      -0.006   5.891  22.509  1.00 18.64           O
+HETATM  745  O   HOH     5       3.935  11.236  27.997  1.00 20.18           O
+HETATM  746  O   HOH     6      -0.274  -4.153  23.054  1.00 19.37           O
+HETATM  747  O   HOH     7      13.817   8.788  41.782  1.00 19.15           O
+HETATM  748  O   HOH     8      -1.891  13.459  17.137  1.00 19.03           O
+HETATM  749  O   HOH     9      -2.083   8.214  21.987  1.00 20.14           O
+HETATM  750  O   HOH    10       2.797   0.928  18.102  1.00 27.68           O
+HETATM  751  O   HOH    11      12.691   9.974  30.666  1.00 27.47           O
+HETATM  752  O   HOH    12       9.666   4.203  14.797  1.00 27.80           O
+HETATM  753  O   HOH    13       1.809  -0.509   7.352  1.00 30.54           O
+HETATM  754  O   HOH    14      -1.004  17.744  16.794  1.00 27.36           O
+HETATM  755  O   HOH    15       8.061   9.329   9.741  1.00 31.81           O
+HETATM  756  O   HOH    16       7.423   9.060  40.549  1.00 26.40           O
+HETATM  757  O   HOH    17      -0.420   5.798   5.808  1.00 27.21           O
+HETATM  758  O   HOH    18       2.796  -0.282  11.074  1.00 26.18           O
+HETATM  759  O   HOH    19       6.627   3.510  13.365  1.00 29.55           O
+HETATM  760  O   HOH    20       7.716  11.785  37.937  1.00 33.90           O
+HETATM  761  O   HOH    21      13.017   2.743  41.126  1.00 29.39           O
+HETATM  762  O   HOH    22      12.586   8.818  35.183  1.00 31.81           O
+HETATM  763  O   HOH    23      -0.672  17.945  19.813  1.00 29.17           O
+HETATM  764  O   HOH    24      15.414   7.885  17.622  1.00 34.99           O
+HETATM  765  O   HOH    25      15.120   1.027  36.513  1.00 34.01           O
+HETATM  766  O   HOH    26      12.255   0.609  24.383  1.00 37.64           O
+HETATM  767  O   HOH    27      15.910  10.470  25.375  1.00 39.63           O
+HETATM  768  O   HOH    28       2.672  -4.881  24.854  1.00 38.54           O
+HETATM  769  O   HOH    29       5.651  -3.894  23.305  1.00 37.18           O
+HETATM  770  O   HOH    30       1.757   7.661  36.606  1.00 29.03           O
+HETATM  771  O   HOH    31       0.331  -3.984  34.349  1.00 30.94           O
+HETATM  772  O   HOH    32      14.751   9.106  27.605  1.00 26.56           O
+HETATM  773  O   HOH    33      -1.326  14.783  10.991  1.00 28.85           O
+HETATM  774  O   HOH    34      14.132   7.665  31.734  1.00 39.44           O
+HETATM  775  O   HOH    35       8.520  15.382  25.290  1.00 37.93           O
+HETATM  776  O   HOH    36       4.900   1.282  16.585  1.00 39.95           O
+HETATM  777  O   HOH    37      16.453   3.015  36.120  1.00 36.31           O
+HETATM  778  O   HOH    38      18.443   2.687  30.904  1.00 37.22           O
+HETATM  779  O   HOH    39      18.669   1.609  28.224  1.00 39.90           O
+HETATM  780  O   HOH    40       9.694  13.193  10.908  1.00 64.04           O
+HETATM  781  O   HOH    41      17.631   6.215  28.193  1.00 37.41           O
+HETATM  782  O   HOH    42       0.923   7.940  41.082  1.00 47.91           O
+HETATM  783  O   HOH    43      -3.992   5.719  31.647  1.00 53.05           O
+HETATM  784  O   HOH    44      -0.321  12.255   8.483  1.00 35.86           O
+HETATM  785  O   HOH    45       7.615   4.350  11.184  1.00 31.62           O
+HETATM  786  O   HOH    46      -3.066   4.753  29.076  1.00 40.05           O
+HETATM  787  O   HOH    47      -0.945   1.949   9.712  1.00 36.88           O
+HETATM  788  O   HOH    48      -7.059   6.398  16.836  1.00 36.00           O
+HETATM  789  O   HOH    49      17.449  -1.699  26.400  1.00 44.33           O
+HETATM  790  O   HOH    50      -6.832   2.828  21.276  1.00 40.92           O
+HETATM  791  O   HOH    51      12.986   3.279  23.159  1.00 32.76           O
+HETATM  792  O   HOH    52      17.990  -0.372  33.679  1.00 38.43           O
+HETATM  793  O   HOH    53       2.479  -4.119  36.286  1.00 36.51           O
+HETATM  794  O   HOH    54      -2.996   7.951  27.405  1.00 35.06           O
+HETATM  795  O   HOH    55      -4.774   1.179  18.535  1.00 46.84           O
+HETATM  796  O   HOH    56       8.407  14.447   7.084  1.00 49.80           O
+HETATM  797  O   HOH    57      -8.668  12.634  15.670  1.00 40.31           O
+HETATM  798  O   HOH    58       6.835  -3.272  20.532  1.00 54.97           O
+HETATM  799  O   HOH    59      17.790 -12.129  35.546  1.00 46.47           O
+HETATM  800  O   HOH    60       2.036  -1.933  37.909  1.00 45.42           O
+HETATM  801  O   HOH    61      10.515   4.141  23.049  1.00 39.36           O
+HETATM  802  O   HOH    62      13.853  11.829  27.829  1.00 48.06           O
+HETATM  803  O   HOH    63       6.585  -6.548  41.143  1.00 45.67           O
+HETATM  804  O   HOH    64       4.117  17.977  21.677  1.00 40.12           O
+HETATM  805  O   HOH    65       4.885 -10.712  28.859  1.00 54.15           O
+HETATM  806  O   HOH    66      -4.389   3.044  11.770  1.00 43.85           O
+HETATM  807  O   HOH    67      12.708  -9.701  34.933  1.00 34.56           O
+HETATM  807  O   HOH    67      12.708  -9.701  34.933  1.00 34.56           O
+HETATM  808  O   HOH    68      12.853  13.538  25.505  1.00 43.43           O
+HETATM  809  O   HOH    69       6.223  15.935  24.195  1.00 56.63           O
+HETATM  810  O   HOH    70      -6.184   3.543  26.723  1.00 45.90           O
+HETATM  811  O   HOH    71       5.679  -0.794  40.337  1.00 42.73           O
+HETATM  812  O   HOH    72       9.327  16.970  10.364  1.00 44.27           O
+HETATM  813  O   HOH    73       8.994  19.338  12.497  1.00 57.40           O
+HETATM  814  O   HOH    74       0.017   8.087  30.620  1.00 43.92           O
+HETATM  815  O   HOH    75      -0.903   8.986  28.883  1.00 45.39           O
+HETATM  816  O   HOH    77       0.315  -4.363  30.096  1.00 43.95           O
diff --git a/Tests/biosql.ini.sample b/Tests/biosql.ini.sample
new file mode 100644
index 0000000..3f25c0e
--- /dev/null
+++ b/Tests/biosql.ini.sample
@@ -0,0 +1,25 @@
+# We provide biosql.ini.sample as an example
+# (preconfigured to work on the virtual machines
+# used for automated TravisCI testing)
+#
+# Please copy this to biosql.ini and edit it
+# match your local system if you want to run
+# the Biopython BioSQL tests locally yourself.
+#
+# To disable testing against any specific back-end,
+# set dbuser to an empty string. There is no
+# configuration entry needed for sqlite3.
+
+[mysql]
+# Covers DBDRIVER="MySQLdb" and "mysql.connector" etc
+dbhost=localhost
+dbuser=root
+dbpasswd=
+testdb=biosql_test
+
+[pg]
+# Covers DBDRIVER="psycopg2" etc
+dbhost=localhost
+dbuser=postgres
+dbpasswd=
+testdb=biosql_test
diff --git a/Tests/common_BioSQL.py b/Tests/common_BioSQL.py
index baeec63..5cc00eb 100644
--- a/Tests/common_BioSQL.py
+++ b/Tests/common_BioSQL.py
@@ -10,6 +10,10 @@ import platform
 import unittest
 import tempfile
 import time
+try:
+    import configparser  # Python 3
+except ImportError:
+    import ConfigParser as configparser  # Python 2
 
 from Bio._py3k import StringIO
 from Bio._py3k import zip
@@ -36,13 +40,30 @@ from seq_tests_common import compare_record, compare_records
 if __name__ == "__main__":
     raise RuntimeError("Call this via test_BioSQL_*.py not directly")
 
-global DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB, DBSCHEMA, SQL_FILE
-global SYSTEM
+# Exporting these to the test_BioSQL_XXX.py files which import this file:
+# DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB, DBSCHEMA, SQL_FILE, SYSTEM
 
 SYSTEM = platform.system()
 
 
+def load_biosql_ini(DBTYPE):
+    """Load the database settings from INI file."""
+    if not os.path.isfile("biosql.ini"):
+        raise MissingExternalDependencyError("BioSQL test configuration"
+                                             " file biosql.ini missing"
+                                             " (see biosql.ini.sample)")
+
+    config = configparser.ConfigParser()
+    config.read("biosql.ini")
+    DBHOST = config.get(DBTYPE, "dbhost")
+    DBUSER = config.get(DBTYPE, "dbuser")
+    DBPASSWD = config.get(DBTYPE, "dbpasswd")
+    TESTDB = config.get(DBTYPE, "testdb")
+    return DBHOST, DBUSER, DBPASSWD, TESTDB
+
+
 def temp_db_filename():
+    """Generate a temporary filename for SQLite database."""
     # In memory SQLite does not work with current test structure since the tests
     # expect databases to be retained between individual tests.
     # TESTDB = ':memory:'
@@ -57,6 +78,7 @@ def temp_db_filename():
 
 
 def check_config(dbdriver, dbtype, dbhost, dbuser, dbpasswd, testdb):
+    """Verify the database settings work for connecting."""
     global DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB, DBSCHEMA
     global SYSTEM, SQL_FILE
     DBDRIVER = dbdriver
@@ -66,6 +88,10 @@ def check_config(dbdriver, dbtype, dbhost, dbuser, dbpasswd, testdb):
     DBPASSWD = dbpasswd
     TESTDB = testdb
 
+    if not DBDRIVER or not DBTYPE or not DBUSER:
+        # No point going any further...
+        raise MissingExternalDependencyError("Incomplete BioSQL test settings")
+
     # Check the database driver is installed:
     if SYSTEM == "Java":
         try:
@@ -148,7 +174,7 @@ def _do_db_create():
 
 
 def create_database():
-    """Delete any existing BioSQL test database, then (re)create an empty BioSQL database."""
+    """Delete any existing BioSQL test DB, then (re)create an empty BioSQL DB."""
     if DBDRIVER in ["sqlite3"]:
         global TESTDB
         if os.path.exists(TESTDB):
@@ -158,7 +184,7 @@ def create_database():
                 time.sleep(1)
                 try:
                     os.remove(TESTDB)
-                except:
+                except Exception:
                     # Seen this with PyPy 2.1 (and older) on Windows -
                     # which suggests an open handle still exists?
                     print("Could not remove %r" % TESTDB)
@@ -213,6 +239,115 @@ def load_database(gb_filename_or_handle):
     return count
 
 
+def load_multi_database(gb_filename_or_handle, gb_filename_or_handle2):
+    """Load two GenBank files into a new BioSQL database as different subdatabases.
+
+    This is useful for running tests against a newly created database.
+    """
+
+    create_database()
+    # now open a connection to load the database
+    db_name = "biosql-test"
+    db_name2 = "biosql-test2"
+    server = BioSeqDatabase.open_database(driver=DBDRIVER,
+                                          user=DBUSER, passwd=DBPASSWD,
+                                          host=DBHOST, db=TESTDB)
+    db = server.new_database(db_name)
+
+    # get the GenBank file we are going to put into it
+    iterator = SeqIO.parse(gb_filename_or_handle, "gb")
+    count = db.load(iterator)
+
+    db = server.new_database(db_name2)
+
+    # get the GenBank file we are going to put into it
+    iterator = SeqIO.parse(gb_filename_or_handle2, "gb")
+    # finally put it in the database
+    count2 = db.load(iterator)
+    server.commit()
+
+    server.close()
+    return count + count2
+
+
+class MultiReadTest(unittest.TestCase):
+    """Test reading a database with multiple namespaces."""
+
+    loaded_db = 0
+
+    def setUp(self):
+        """Connect to and load up the database.
+        """
+        load_multi_database("GenBank/cor6_6.gb", "GenBank/NC_000932.gb")
+
+        self.server = BioSeqDatabase.open_database(driver=DBDRIVER,
+                                                   user=DBUSER,
+                                                   passwd=DBPASSWD,
+                                                   host=DBHOST,
+                                                   db=TESTDB)
+
+        self.db = self.server["biosql-test"]
+        self.db2 = self.server['biosql-test2']
+
+    def tearDown(self):
+        self.server.close()
+        destroy_database()
+        del self.db
+        del self.db2
+        del self.server
+
+    def test_server(self):
+        """Check BioSeqDatabase methods"""
+        server = self.server
+        self.assertTrue("biosql-test" in server)
+        self.assertTrue("biosql-test2" in server)
+        self.assertEqual(2, len(server))
+        self.assertEqual(["biosql-test", 'biosql-test2'], list(server.keys()))
+        # Check we can delete the namespace...
+        del server["biosql-test"]
+        del server["biosql-test2"]
+        self.assertEqual(0, len(server))
+        try:
+            del server["non-existant-name"]
+            assert False, "Should have raised KeyError"
+        except KeyError:
+            pass
+
+    def test_get_db_items(self):
+        """Check list, keys, length etc"""
+        db = self.db
+        items = list(db.values())
+        keys = list(db)
+        l = len(items)
+        self.assertEqual(l, len(db))
+        self.assertEqual(l, len(list(db.items())))
+        self.assertEqual(l, len(list(db)))
+        self.assertEqual(l, len(list(db.values())))
+        for (k1, r1), (k2, r2) in zip(zip(keys, items), db.items()):
+            self.assertEqual(k1, k2)
+            self.assertEqual(r1.id, r2.id)
+        for k in keys:
+            del db[k]
+        self.assertEqual(0, len(db))
+        try:
+            del db["non-existant-name"]
+            assert False, "Should have raised KeyError"
+        except KeyError:
+            pass
+
+    def test_cross_retrieval_of_items(self):
+        """Test that valid ids can't be retrieved between namespaces.
+        """
+        db = self.db
+        db2 = self.db2
+        for db2_id in db2.keys():
+            try:
+                rec = db[db2_id]
+                assert False, "Should have raised KeyError"
+            except KeyError:
+                pass
+
+
 class ReadTest(unittest.TestCase):
     """Test reading a database from an already built database."""
 
@@ -475,6 +610,120 @@ class LoaderTest(unittest.TestCase):
                                     'M81224.1', 'X55053.1', 'X62281.1'])
 
 
+class TaxonomyTest(unittest.TestCase):
+    """Test proper insertion and retrieval of taxonomy data
+    """
+    def setUp(self):
+        from Bio import Entrez
+        Entrez.email = "biopython-dev at biopython.org"
+        # create TESTDB
+        create_database()
+
+        # load the database
+        db_name = "biosql-test"
+        self.server = BioSeqDatabase.open_database(driver=DBDRIVER,
+                                                   user=DBUSER, passwd=DBPASSWD,
+                                                   host=DBHOST, db=TESTDB)
+
+        # remove the database if it already exists
+        try:
+            self.server[db_name]
+            self.server.remove_database(db_name)
+        except KeyError:
+            pass
+
+        self.db = self.server.new_database(db_name)
+
+        # get the GenBank file we are going to put into it
+        self.iterator = SeqIO.parse("GenBank/cor6_6.gb", "gb")
+
+    def tearDown(self):
+        self.server.close()
+        destroy_database()
+        del self.db
+        del self.server
+
+    def test_taxon_left_right_values(self):
+        self.db.load(self.iterator, True)
+        sql = """SELECT DISTINCT include.ncbi_taxon_id FROM taxon
+                  INNER JOIN taxon AS include ON
+                      (include.left_value BETWEEN taxon.left_value
+                                  AND taxon.right_value)
+                  WHERE taxon.taxon_id IN
+                      (SELECT taxon_id FROM taxon_name
+                                  WHERE name = 'Brassicales')
+                      AND include.right_value - include.left_value = 1"""
+
+        rows = self.db.adaptor.execute_and_fetchall(sql)
+        self.assertEqual(4, len(rows))
+        values = set()
+        for row in rows:
+            values.add(row[0])
+        self.assertEqual(set([3704, 3711, 3708, 3702]), set(values))
+
+
+class DeleteTest(unittest.TestCase):
+    """Test proper deletion of entries from a database."""
+
+    loaded_db = 0
+
+    def setUp(self):
+        """Connect to and load up the database.
+        """
+        load_database("GenBank/cor6_6.gb")
+
+        self.server = BioSeqDatabase.open_database(driver=DBDRIVER,
+                                                   user=DBUSER,
+                                                   passwd=DBPASSWD,
+                                                   host=DBHOST,
+                                                   db=TESTDB)
+
+        self.db = self.server["biosql-test"]
+
+    def tearDown(self):
+        self.server.close()
+        destroy_database()
+        del self.db
+        del self.server
+
+    def test_server(self):
+        """Check BioSeqDatabase methods"""
+        server = self.server
+        self.assertTrue("biosql-test" in server)
+        self.assertEqual(1, len(server))
+        self.assertEqual(["biosql-test"], list(server.keys()))
+        # Check we can delete the namespace...
+        del server["biosql-test"]
+        self.assertEqual(0, len(server))
+        try:
+            del server["non-existant-name"]
+            assert False, "Should have raised KeyError"
+        except KeyError:
+            pass
+
+    def test_del_db_items(self):
+        """Check all associated data is deleted from an item"""
+        db = self.db
+        items = list(db.values())
+        keys = list(db)
+        l = len(items)
+
+        for seq_id in self.db.keys():
+            sql = "SELECT seqfeature_id from seqfeature where bioentry_id = '%s'"
+            # get the original number of seqfeatures associated with the bioentry
+            seqfeatures = self.db.adaptor.execute_and_fetchall(sql % (seq_id))
+
+            del db[seq_id]
+            # check to see that the entry in the bioentry table is removed
+            self.assertEqual(seq_id in db, False)
+
+            # no need to check seqfeature presence if it had none to begin with
+            if len(seqfeatures):
+                rows_d = self.db.adaptor.execute_and_fetchall(sql % (seq_id))
+                # check to see that associated data is removed
+                self.assertEqual(len(rows_d), 0)
+
+
 class DupLoadTest(unittest.TestCase):
     """Check a few duplicate conditions fail."""
 
@@ -553,7 +802,10 @@ class ClosedLoopTest(unittest.TestCase):
 
     def test_NC_005816(self):
         """GenBank file to BioSQL and back to a GenBank file, NC_005816."""
-        self.loop("GenBank/NC_005816.gb", "gb")
+        with warnings.catch_warnings():
+            # BiopythonWarning: order location operators are not fully supported
+            warnings.simplefilter('ignore', BiopythonWarning)
+            self.loop("GenBank/NC_005816.gb", "gb")
 
     def test_NC_000932(self):
         """GenBank file to BioSQL and back to a GenBank file, NC_000932."""
@@ -565,7 +817,10 @@ class ClosedLoopTest(unittest.TestCase):
 
     def test_protein_refseq2(self):
         """GenBank file to BioSQL and back to a GenBank file, protein_refseq2."""
-        self.loop("GenBank/protein_refseq2.gb", "gb")
+        with warnings.catch_warnings():
+            # BiopythonWarning: order location operators are not fully supported
+            warnings.simplefilter('ignore', BiopythonWarning)
+            self.loop("GenBank/protein_refseq2.gb", "gb")
 
     def test_no_ref(self):
         """GenBank file to BioSQL and back to a GenBank file, noref."""
@@ -629,7 +884,10 @@ class TransferTest(unittest.TestCase):
 
     def test_NC_005816(self):
         """GenBank file to BioSQL, then again to a new namespace, NC_005816."""
-        self.trans("GenBank/NC_005816.gb", "gb")
+        with warnings.catch_warnings():
+            # BiopythonWarning: order location operators are not fully supported
+            warnings.simplefilter('ignore', BiopythonWarning)
+            self.trans("GenBank/NC_005816.gb", "gb")
 
     def test_NC_000932(self):
         """GenBank file to BioSQL, then again to a new namespace, NC_000932."""
@@ -641,7 +899,10 @@ class TransferTest(unittest.TestCase):
 
     def test_protein_refseq2(self):
         """GenBank file to BioSQL, then again to a new namespace, protein_refseq2."""
-        self.trans("GenBank/protein_refseq2.gb", "gb")
+        with warnings.catch_warnings():
+            # BiopythonWarning: order location operators are not fully supported
+            warnings.simplefilter('ignore', BiopythonWarning)
+            self.trans("GenBank/protein_refseq2.gb", "gb")
 
     def test_no_ref(self):
         """GenBank file to BioSQL, then again to a new namespace, noref."""
@@ -935,7 +1196,9 @@ class AutoSeqIOTests(unittest.TestCase):
             # BiopythonWarning: bond location operators are not fully supported
             warnings.simplefilter("ignore", BiopythonWarning)
             self.check('genbank', 'GenBank/dbsource_wrap.gb')
-        self.check('genbank', 'GenBank/NC_005816.gb')
+            # BiopythonWarning: order location operators are not fully
+            # supported
+            self.check('genbank', 'GenBank/NC_005816.gb')
         self.check('genbank', 'GenBank/gbvrl1_start.seq', 3)
         self.check('genbank', 'GFF/NC_001422.gbk')
         self.check('embl', 'EMBL/TRBG361.embl')
diff --git a/Tests/output/test_KEGG b/Tests/output/test_KEGG
index 2f372e6..fc1cc98 100644
--- a/Tests/output/test_KEGG
+++ b/Tests/output/test_KEGG
@@ -507,6 +507,100 @@ DBLINKS     ExplorEnz - The Enzyme Database: 6.2.1.25
             BRENDA, the Enzyme Database: 6.2.1.25
             CAS: 95329-17-2
 ///
+ENTRY       EC 6.2.1.25
+NAME        benzoate---CoA ligase
+            benzoate---coenzyme A ligase
+            benzoyl-coenzyme A synthetase
+            benzoyl CoA synthetase (AMP forming)
+CLASS       Ligases;
+            Forming carbon-sulfur bonds;
+            Acid-thiol ligases
+SYSNAME     benzoate:CoA ligase (AMP-forming)
+REACTION    ATP + benzoate + CoA = AMP + diphosphate + benzoyl-CoA [RN:R01422]
+SUBSTRATE   ATP [CPD:C00002]
+            benzoate [CPD:C00180]
+            CoA [CPD:C00010]
+PRODUCT     AMP [CPD:C00020]
+            diphosphate [CPD:C00013]
+            benzoyl-CoA [CPD:C00512]
+COMMENT     Also acts on 2-, 3- and 4-fluorobenzoate, but only very slowly on
+            the corresponding chlorobenzoates.
+PATHWAY     PATH: ec00362  Benzoate degradation
+            PATH: ec00627  Aminobenzoate degradation
+            PATH: ec01100  Metabolic pathways
+            PATH: ec01120  Microbial metabolism in diverse environments
+GENES       REU: Reut_A1327
+            REH: H16_A1412 H16_B1918
+            RME: Rmet_1224
+            CTI: RALTA_A1325 RALTA_B1617
+            CNC: CNE_1c14400 CNE_2c18780
+            BXE: Bxe_A1419 Bxe_C0896
+            BPH: Bphy_1543
+            BPY: Bphyt_2700
+            BGL: bglu_2g11460
+            BUG: BC1001_2395
+            BGE: BC1002_1980
+            BGF: BC1003_1063
+            BRH: RBRH_00494
+            BPT: Bpet3574
+            AXY: AXYL_05035
+            AKA: TKWG_07570
+            RFR: Rfer_0216
+            POL: Bpro_1624 Bpro_2983
+            PNA: Pnap_2948
+            VEI: Veis_0730
+            DAC: Daci_0076
+            DEL: DelCs14_0077
+            VAP: Vapar_0089
+            VPE: Varpa_0102
+            CTT: CtCNB1_0097
+            RTA: Rta_22340
+            LCH: Lcho_3655
+            EBA: ebA2757 ebA5301
+            AZO: azo3052
+            TMZ: Tmz1t_3136
+            GME: Gmet_2143
+            GLO: Glov_2397
+            GEO: Geob_0200
+            GEM: GM21_2825
+            DMA: DMR_19390
+            DAS: Daes_3238
+            DPR: Despr_3172
+            DTI: Desti_5323
+            BJA: blr1077
+            BRA: BRADO6791
+            BBT: BBta_0747 BBta_6637
+            RPA: RPA0661 RPA0669
+            RPB: RPB_4656
+            RPC: RPC_1016 RPC_1025
+            RPD: RPD_1526 RPD_1534
+            RPE: RPE_0592 RPE_0604
+            RPT: Rpal_0728 Rpal_0736
+            RPX: Rpdx1_4117 Rpdx1_4125
+            RVA: Rvan_0044
+            SIL: SPO3697
+            JAN: Jann_0669
+            SWI: Swit_0829
+            RPM: RSPPHO_01133
+            MAG: amb2869
+            GYC: GYMC61_2828
+            GYA: GYMC52_1957
+            GCT: GC56T3_1519
+            SCB: SCAB_8391
+            SCT: SCAT_5491
+            SCY: SCATT_54890
+            BSD: BLASA_2555
+            AMD: AMED_8609
+            AMN: RAM_35745 RAM_44185
+            AMM: AMES_6861 AMES_8478
+            PDX: Psed_1491
+DBLINKS     ExplorEnz - The Enzyme Database: 6.2.1.25
+            IUBMB Enzyme Nomenclature: 6.2.1.25
+            ExPASy - ENZYME nomenclature database: 6.2.1.25
+            UM-BBD (Biocatalysis/Biodegradation Database): 6.2.1.25
+            BRENDA, the Enzyme Database: 6.2.1.25
+            CAS: 95329-17-2
+///
 
 
 Testing Bio.KEGG.Compound on compound.sample
diff --git a/Tests/output/test_SeqIO b/Tests/output/test_SeqIO
index eb1ddaa..55b6136 100644
--- a/Tests/output/test_SeqIO
+++ b/Tests/output/test_SeqIO
@@ -2309,6 +2309,22 @@ Testing reading genbank format file GenBank/1MRR_A.gp
  Checking can write/read as 'tab' format
  Checking can write/read as 'nexus' format
  Checking can write/read as 'phylip-sequential' format
+Testing reading genbank format file GenBank/DS830848.gb
+ ID = 'DS830848.1', Name='DS830848',
+ Seq='NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNN', length=1311
+ Checking can write/read as 'embl' format
+ Checking can write/read as 'genbank' format
+ Checking can write/read as 'imgt' format
+ Checking can write/read as 'qual' format
+ Failed: No suitable quality scores found in letter_annotations of SeqRecord (id=DS830848.1).
+Testing reading embl format file EMBL/DS830848.embl
+ ID = 'DS830848.1', Name='DS830848',
+ Seq='NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN...NNNNNNN', length=1311
+ Checking can write/read as 'embl' format
+ Checking can write/read as 'genbank' format
+ Checking can write/read as 'imgt' format
+ Checking can write/read as 'qual' format
+ Failed: No suitable quality scores found in letter_annotations of SeqRecord (id=DS830848.1).
 Testing reading embl format file EMBL/epo_prt_selection.embl
  ID = 'A00022.1', Name='A00022',
  Seq='CLARIIRYFYNAKA', length=14
diff --git a/Tests/phenotype/BadPlate.json b/Tests/phenotype/BadPlate.json
new file mode 100644
index 0000000..0a21823
--- /dev/null
+++ b/Tests/phenotype/BadPlate.json
@@ -0,0 +1 @@
+{"aggr_settings": {"software": "opm", "options": {"Dummy": "Dummy"}, "method": "opm"}, "measurements": {"H09": [13.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 12.0, 11.0, 11.0, 11.0, 11.0, 11.0, 14.0, 14.0, 14.0, 14.0, 17.0, 20.0, 19.0, 19.0, 20.0, 20.0, 21.0, 22.0, 24.0, 23.0, 25.0, 20.0, 22.0, 20.0, 20.0, 19.0, 25.0, 26.0, 22.0, 26.0, 22.0, 23.0, 27.0, 27.0, 26.0, 24.0, 23.0, 35.0, 27.0, 28.0, 26.0, 28.0, 27.0, 30.0, 33.0, 31.0, 32.0, 32.0, 33.0, 38.0, 39.0, 38.0, 35 [...]
\ No newline at end of file
diff --git a/Tests/phenotype/Plate.json b/Tests/phenotype/Plate.json
new file mode 100644
index 0000000..9a52387
--- /dev/null
+++ b/Tests/phenotype/Plate.json
@@ -0,0 +1 @@
+{"aggr_settings": {"options": {"Dummy": "Dummy"}, "method": "opm", "software": "opm"}, "aggregated": {"H09": {"A": 69.0, "lambda CI95 low": ".na.real", "AUC": 4989.75, "lambda CI95 high": ".na.real", "A CI95 high": ".na.real", "AUC CI95 high": ".na.real", "A CI95 low": ".na.real", "mu": ".na.real", "mu CI95 high": ".na.real", "AUC CI95 low": ".na.real", "mu CI95 low": ".na.real", "lambda": ".na.real"}, "H08": {"A": 282.0, "lambda CI95 low": ".na.real", "AUC": 20859.88, "lambda CI95 high" [...]
\ No newline at end of file
diff --git a/Tests/phenotype/Plate_2.json b/Tests/phenotype/Plate_2.json
new file mode 100644
index 0000000..e7add44
--- /dev/null
+++ b/Tests/phenotype/Plate_2.json
@@ -0,0 +1 @@
+{"metadata": [], "measurements": {"H09": [1.0, 1.0, 1.0, 5.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0 [...]
\ No newline at end of file
diff --git a/Tests/phenotype/Plates.csv b/Tests/phenotype/Plates.csv
new file mode 100644
index 0000000..be0dd29
--- /dev/null
+++ b/Tests/phenotype/Plates.csv
@@ -0,0 +1,602 @@
+"Data File    "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___1_30_IDS_207_Octx01x2007_A_25A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time  ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position     ","25-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type   ","PM 1-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type  ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name  ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other        ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Hour","  A01","  A02","  A03","  A04","  A05","  A06","  A07","  A08","  A09","  A10","  A11","  A12","  B01","  B02","  B03","  B04","  B05","  B06","  B07","  B08","  B09","  B10","  B11","  B12","  C01","  C02","  C03","  C04","  C05","  C06","  C07","  C08","  C09","  C10","  C11","  C12","  D01","  D02","  D03","  D04","  D05","  D06","  D07","  D08","  D09","  D10","  D11","  D12","  E01","  E02","  E03","  E04","  E05","  E06","  E07","  E08","  E09","  E10","  E11","  E12","  F0 [...]
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Data File    "," C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___9_30_IDS_207_Octx01x2007_A_23A.oka",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Set up Time  ","Oct 01 2007 3:12 PM",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Position     ","23-A",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Plate Type   ","PM 9-",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Type  ","NOT APPLICABLE",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Sample Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Name  ","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Strain Number","Rm1021",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"Other        ",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+1.75,2,52,0,17,1,5,18,1,12,11,30,31,24,8,15,22,0,6,5,3,6,8,8,27,6,2,12,11,15,14,9,5,11,7,13,15,8,1,7,26,0,4,0,17,0,0,0,0,35,6,17,35,23,19,0,0,0,0,0,2,28,0,1,17,5,0,11,16,2,0,7,30,3,21,40,31,0,0,3,10,0,0,23,34,0,7,9,14,0,21,17,1,14,17,6,23
+2,2,52,0,17,1,7,18,1,12,13,31,31,24,8,15,22,0,6,7,7,9,8,8,27,11,2,17,11,15,19,11,5,11,7,16,15,8,1,7,26,0,13,4,17,0,0,0,0,35,6,17,35,23,19,0,0,0,0,0,3,29,1,1,17,9,0,11,16,2,0,7,31,3,21,40,31,0,0,3,10,0,0,24,34,0,7,9,14,0,21,17,1,14,17,7,25
+2.25,6,52,3,17,1,13,18,1,12,13,33,31,24,10,15,22,0,9,7,7,9,8,12,27,11,2,17,17,15,19,11,5,11,7,16,15,8,1,7,26,0,13,4,17,0,0,0,0,35,6,17,35,25,19,0,0,0,0,0,7,32,1,1,17,9,0,11,16,2,0,9,35,3,21,40,31,0,0,3,10,0,6,28,42,0,7,9,14,0,23,17,1,14,17,7,29
+2.5,6,52,3,17,1,13,18,1,12,16,38,31,24,10,16,22,0,9,7,7,9,8,14,27,22,2,17,17,15,19,13,5,11,7,16,21,8,1,7,26,0,13,4,17,0,0,0,0,37,6,17,35,25,19,0,0,0,0,0,7,34,1,1,17,9,0,11,16,2,0,9,38,3,21,40,31,0,0,3,10,0,6,28,42,0,7,9,14,0,24,17,1,14,17,7,29
+2.75,9,52,3,17,4,13,18,1,12,16,38,31,24,10,17,22,0,15,7,7,9,8,14,27,22,2,17,17,15,22,13,5,11,7,16,21,8,1,7,26,0,13,4,17,0,0,0,0,39,6,17,35,25,19,0,0,0,0,0,7,34,1,1,17,9,0,11,24,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,42,0,7,9,14,0,32,17,1,14,17,7,29
+3,9,52,3,17,4,13,18,1,13,16,38,31,24,10,17,22,0,15,7,7,12,8,17,27,23,2,18,17,15,22,13,5,12,7,16,22,9,1,7,26,0,17,4,17,0,0,0,0,39,6,17,35,26,19,0,0,0,0,0,7,34,1,1,17,9,0,11,24,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,42,0,7,11,14,0,32,17,1,14,17,7,30
+3.25,9,52,3,17,4,13,18,1,14,16,38,31,24,10,17,22,0,15,7,10,12,8,17,30,25,2,18,17,15,25,13,5,14,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,26,19,0,0,0,0,0,7,34,1,1,17,12,0,11,24,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,42,0,7,11,14,0,32,17,1,14,17,7,31
+3.5,15,54,3,17,4,13,18,1,20,16,38,31,24,10,24,22,0,15,8,10,12,8,17,37,25,2,18,17,15,25,13,5,15,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,26,19,0,0,0,0,0,7,34,1,1,17,13,0,11,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,43,0,7,11,14,0,35,17,1,16,17,7,31
+3.75,15,54,5,17,4,13,18,1,20,16,40,31,24,12,24,22,0,16,8,10,17,8,19,37,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,28,19,0,0,0,0,0,7,34,1,1,17,13,0,17,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,43,0,7,11,14,0,35,17,1,16,17,7,34
+4,15,54,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,21,8,10,17,8,19,37,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,28,19,0,0,0,0,0,7,34,1,1,17,13,0,22,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,43,0,7,11,14,0,35,17,1,16,17,7,34
+4.25,15,54,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,24,8,10,17,14,23,37,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,63,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,17,1,16,17,7,34
+4.5,15,55,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,27,8,10,17,14,23,38,25,2,18,20,15,25,13,5,20,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,63,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,17,1,16,17,7,34
+4.75,15,55,5,17,8,13,18,1,20,16,40,31,24,16,24,22,0,28,8,10,17,14,23,38,25,2,18,20,15,25,13,5,23,7,16,22,9,1,7,26,0,17,9,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,65,25,12,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,18,1,16,17,7,37
+5,15,55,5,17,8,13,18,1,21,18,40,31,24,16,24,22,0,32,9,10,17,15,23,38,26,2,18,20,15,25,13,5,23,7,16,22,9,1,7,26,0,17,11,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,0,66,25,13,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,18,1,16,17,7,37
+5.25,15,55,5,17,8,13,18,1,21,18,40,34,24,18,24,22,0,32,9,10,17,15,23,38,26,2,18,20,15,25,13,5,23,7,16,22,12,1,7,26,0,17,11,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,25,13,0,9,38,6,21,40,31,0,0,3,13,0,6,28,48,0,7,11,14,0,35,18,1,16,17,7,37
+5.5,15,55,5,17,8,13,18,2,23,18,40,34,24,18,24,22,0,32,9,10,17,15,25,38,26,2,18,20,15,25,13,5,23,7,16,22,12,1,7,26,0,17,12,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,26,13,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,1,16,17,7,37
+5.75,15,55,5,17,8,13,18,3,24,18,42,34,24,18,24,22,0,33,9,10,17,15,25,38,26,2,18,25,15,25,13,5,23,7,16,28,12,1,7,26,0,17,12,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,26,21,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,2,16,17,7,37
+6,15,55,5,17,8,13,18,3,24,21,44,34,24,18,28,22,0,33,9,10,17,15,25,42,26,2,18,25,15,28,15,5,23,7,16,29,12,2,7,26,0,17,13,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,1,66,26,21,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,2,16,17,7,37
+6.25,15,56,5,17,8,13,18,3,24,21,44,34,24,18,28,22,0,33,9,10,17,17,25,42,26,2,18,25,15,28,15,5,23,7,16,29,12,2,7,26,0,17,13,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,6,66,26,21,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,0,7,11,14,0,35,18,2,16,17,7,37
+6.5,15,56,5,17,8,13,18,3,24,21,44,34,24,18,28,22,0,33,9,10,17,17,25,42,26,2,18,25,15,29,16,5,23,7,16,29,12,2,7,26,0,17,15,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,11,14,0,35,18,2,16,17,7,37
+6.75,15,56,6,17,8,13,18,3,24,23,44,34,24,18,28,22,0,33,9,10,19,17,25,42,26,2,18,25,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,7,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,11,14,0,35,18,2,16,17,7,37
+7,15,56,6,17,8,13,18,3,24,23,44,37,24,18,28,22,0,33,9,10,19,18,25,42,26,3,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,11,14,0,35,18,2,16,17,7,37
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+8,15,56,6,17,8,15,18,4,25,23,44,37,24,18,28,22,0,33,9,10,21,18,25,42,27,4,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+8.25,15,58,6,17,8,15,18,4,25,23,44,38,24,18,28,22,0,33,9,10,21,18,25,42,27,4,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
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+9,15,59,6,17,8,15,18,6,25,23,44,42,24,18,28,22,0,33,9,10,21,18,25,42,27,5,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,18,2,17,17,7,37
+9.25,15,62,6,17,8,15,18,6,25,23,44,42,24,18,28,22,0,33,9,10,21,18,25,42,27,6,18,26,15,32,16,5,23,7,16,29,12,2,7,26,0,17,17,17,0,0,0,0,39,6,17,35,30,19,0,0,0,0,0,10,34,1,1,17,13,6,66,26,22,0,9,38,6,21,40,31,0,0,3,13,0,6,28,49,2,7,12,14,0,35,19,2,17,17,7,37
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+70.75,404,364,10,17,12,15,18,21,41,23,44,50,24,18,28,22,0,33,9,10,38,18,33,42,31,6,19,26,15,43,22,8,23,7,16,36,34,2,17,26,361,230,17,17,411,397,403,417,39,6,17,37,31,21,301,36,0,0,0,11,34,3,9,18,13,6,66,26,22,0,9,38,47,21,40,31,0,0,5,14,252,280,89,49,435,439,443,439,426,448,209,45,18,22,30,37
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+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/Tests/phenotype/SmallPlate.json b/Tests/phenotype/SmallPlate.json
new file mode 100644
index 0000000..d332398
--- /dev/null
+++ b/Tests/phenotype/SmallPlate.json
@@ -0,0 +1 @@
+{"aggr_settings": {"software": "opm", "options": {"Dummy": "Dummy"}, "method": "opm"}, "measurements": {"Hour": [0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16. [...]
\ No newline at end of file
diff --git a/Tests/phenotype/SmallPlate_2.json b/Tests/phenotype/SmallPlate_2.json
new file mode 100644
index 0000000..8c1d1f6
--- /dev/null
+++ b/Tests/phenotype/SmallPlate_2.json
@@ -0,0 +1 @@
+{"measurements": {"Hour": [0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.25, 5.5, 5.75, 6.0, 6.25, 6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0, 11.25, 11.5, 11.75, 12.0, 12.25, 12.5, 12.75, 13.0, 13.25, 13.5, 13.75, 14.0, 14.25, 14.5, 14.75, 15.0, 15.25, 15.5, 15.75, 16.0, 16.25, 16.5, 16.75, 17.0, 17.25, 17.5, 17.75, 18.0, 18.25, 18.5, 18.75, 19.0, 19.25 [...]
\ No newline at end of file
diff --git a/Tests/phenotype/SmallPlates.csv b/Tests/phenotype/SmallPlates.csv
new file mode 100644
index 0000000..240f29e
--- /dev/null
+++ b/Tests/phenotype/SmallPlates.csv
@@ -0,0 +1,600 @@
+Data File    , C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___1_30_IDS_207_Octx01x2007_A_25A.oka,,
+Set up Time  ,Oct 01 2007 3:12 PM,,
+Position     ,25-A,,
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+Strain Name  ,Rm1021,,
+Strain Number,Rm1021,,
+Other        ,,,
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+,,,
+Data File    , C:\Documents and Settings\Standard\Desktop\esperimenti e lavori in corso\Phenotype MicroArray\dati PM\2007\10\smel_Rm1021___9_30_IDS_207_Octx01x2007_A_23A.oka,,
+Set up Time  ,Oct 01 2007 3:12 PM,,
+Position     ,23-A,,
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+Strain Type  ,NOT APPLICABLE,,
+Sample Number,Rm1021,,
+Strain Name  ,Rm1021,,
+Strain Number,Rm1021,,
+Other        ,,,
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diff --git a/Tests/run_tests.py b/Tests/run_tests.py
index 12c126a..31382ed 100644
--- a/Tests/run_tests.py
+++ b/Tests/run_tests.py
@@ -20,14 +20,12 @@ Command line options:
 <test_name>   -- supply the name of one (or more) tests to be run.
                  The .py file extension is optional.
 doctest       -- run the docstring tests.
+
 By default, all tests are run.
 """
 
-# The default verbosity (not verbose)
 from __future__ import print_function
 
-VERBOSITY = 0
-
 # standard modules
 import sys
 import os
@@ -71,6 +69,9 @@ def is_numpy():
     except ImportError:
         return False
 
+# The default verbosity (not verbose)
+VERBOSITY = 0
+
 # This is the list of modules containing docstring tests.
 # If you develop docstring tests for other modules, please add
 # those modules here. Please sort names alphabetically.
@@ -79,6 +80,7 @@ DOCTEST_MODULES = [
     "Bio.Align.Generic",
     "Bio.Align.Applications._Clustalw",
     "Bio.Align.Applications._ClustalOmega",
+    "Bio.Align.Applications._Dialign",
     "Bio.Align.Applications._MSAProbs",
     "Bio.Align.Applications._Mafft",
     "Bio.Align.Applications._Muscle",
@@ -91,18 +93,21 @@ DOCTEST_MODULES = [
     "Bio.Application",
     "Bio.bgzf",
     "Bio.codonalign",
+    "Bio.codonalign.codonalignment",
+    "Bio.codonalign.codonalphabet",
+    "Bio.codonalign.codonseq",
     "Bio.Blast.Applications",
     "Bio.Emboss.Applications",
     "Bio.GenBank",
     "Bio.KEGG.Compound",
     "Bio.KEGG.Enzyme",
     "Bio.NMR.xpktools",
-    "Bio.Motif",
     "Bio.motifs",
-    "Bio.motifs.applications._alignace",
     "Bio.motifs.applications._xxmotif",
     "Bio.pairwise2",
     "Bio.Phylo.Applications._Raxml",
+    "Bio.Phylo.Consensus",
+    "Bio.Phylo.BaseTree",
     "Bio.SearchIO",
     "Bio.SearchIO._model",
     "Bio.SearchIO._model.query",
@@ -115,14 +120,19 @@ DOCTEST_MODULES = [
     "Bio.SearchIO.ExonerateIO",
     "Bio.Seq",
     "Bio.SeqIO",
-    "Bio.SeqIO.FastaIO",
     "Bio.SeqIO.AceIO",
+    "Bio.SeqIO.FastaIO",
+    "Bio.SeqIO.IgIO",
+    "Bio.SeqIO.InsdcIO",
     "Bio.SeqIO.PhdIO",
+    "Bio.SeqIO.PirIO",
     "Bio.SeqIO.QualityIO",
     "Bio.SeqIO.SffIO",
+    "Bio.SeqIO.TabIO",
     "Bio.SeqFeature",
     "Bio.SeqRecord",
     "Bio.SeqUtils",
+    "Bio.SeqUtils.CheckSum",
     "Bio.SeqUtils.MeltingTemp",
     "Bio.Sequencing.Applications._Novoalign",
     "Bio.Sequencing.Applications._bwa",
@@ -135,7 +145,8 @@ if is_numpy():
     DOCTEST_MODULES.extend(["Bio.Affy.CelFile",
                             "Bio.Statistics.lowess",
                             "Bio.PDB.Polypeptide",
-                            "Bio.PDB.Selection"
+                            "Bio.PDB.Selection",
+                            "Bio.SeqIO.PdbIO",
                             ])
 
 
@@ -367,9 +378,9 @@ class TestRunner(unittest.TextTestRunner):
         file = sys.argv[0]
     else:
         file = __file__
-    testdir = os.path.dirname(file) or os.curdir
+    testdir = os.path.abspath(os.path.dirname(file) or os.curdir)
 
-    def __init__(self, tests=[], verbosity=0):
+    def __init__(self, tests=(), verbosity=0):
         # if no tests were specified to run, we run them all
         # including the doctests
         self.tests = tests
@@ -396,8 +407,9 @@ class TestRunner(unittest.TextTestRunner):
         # test changed this, e.g. to help with detecting command line tools)
         global system_lang
         os.environ['LANG'] = system_lang
-        # Note the current directory:
-        cur_dir = os.path.abspath(".")
+        # Always run tests from the Tests/ folder where run_tests.py
+        # should be located (as we assume this with relative paths etc)
+        os.chdir(self.testdir)
         try:
             stdout = sys.stdout
             sys.stdout = output
@@ -430,21 +442,20 @@ class TestRunner(unittest.TextTestRunner):
             else:
                 # It's a doc test
                 sys.stderr.write("%s docstring test ... " % name)
-                # Can't use fromlist=name.split(".") until python 2.5+
-                module = __import__(name, None, None, name.split("."))
+                module = __import__(name, fromlist=name.split("."))
                 suite = doctest.DocTestSuite(module,
                                              optionflags=doctest.ELLIPSIS)
                 del module
             suite.run(result)
-            if cur_dir != os.path.abspath("."):
+            if self.testdir != os.path.abspath("."):
                 sys.stderr.write("FAIL\n")
                 result.stream.write(result.separator1 + "\n")
                 result.stream.write("ERROR: %s\n" % name)
                 result.stream.write(result.separator2 + "\n")
                 result.stream.write("Current directory changed\n")
-                result.stream.write("Was: %s\n" % cur_dir)
+                result.stream.write("Was: %s\n" % self.testdir)
                 result.stream.write("Now: %s\n" % os.path.abspath("."))
-                os.chdir(cur_dir)
+                os.chdir(self.testdir)
                 if not result.wasSuccessful():
                     result.printErrors()
                 return False
diff --git a/Tests/search_tests_common.py b/Tests/search_tests_common.py
index fb671b8..9e9bbe1 100644
--- a/Tests/search_tests_common.py
+++ b/Tests/search_tests_common.py
@@ -1,5 +1,5 @@
 # Copyright 2012 by Wibowo Arindrarto. All rights reserved.
-# Revisions Copyright 2012 by Peter Cock. All rights reserved.
+# Revisions Copyright 2012-2015 by Peter Cock. All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -35,15 +35,21 @@ class CheckRaw(unittest.TestCase):
         raw = _as_bytes(raw)
         # Anticipate cases where the raw string and/or file uses different
         # newline characters ~ we set everything to \n.
+        new = idx.get_raw(id)
+        self.assertTrue(isinstance(new, bytes),
+                        "Didn't get bytes from %s get_raw" % self.fmt)
         self.assertEqual(raw.replace(b'\r\n', b'\n'),
-                idx.get_raw(id).replace(b'\r\n', b'\n'))
+                         new.replace(b'\r\n', b'\n'))
         idx.close()
 
         # Now again, but using SQLite backend
         if sqlite3:
             idx = SearchIO.index_db(":memory:", filename, self.fmt, **kwargs)
+            new = idx.get_raw(id)
+            self.assertTrue(isinstance(new, bytes),
+                            "Didn't get bytes from %s get_raw" % self.fmt)
             self.assertEqual(raw.replace(b'\r\n', b'\n'),
-                    idx.get_raw(id).replace(b'\r\n', b'\n'))
+                             new.replace(b'\r\n', b'\n'))
             idx.close()
 
         if os.path.isfile(filename + ".bgz"):
diff --git a/Tests/seq_tests_common.py b/Tests/seq_tests_common.py
index f51d556..79276b7 100644
--- a/Tests/seq_tests_common.py
+++ b/Tests/seq_tests_common.py
@@ -188,7 +188,7 @@ def compare_feature(old_f, new_f):
                         % (repr(old_f.qualifiers[key]),
                            repr(new_f.qualifiers[key]))
             else:
-                assert False, "Problem with feature's '%s' qualifier" & key
+                assert False, "Problem with feature's '%s' qualifier" % key
         else:
             # Should both be lists of strings...
             assert old_f.qualifiers[key] == new_f.qualifiers[key], \
@@ -291,12 +291,13 @@ def compare_record(old, new):
     # TODO - address these, see Bug 2681?
     new_keys = set(new.annotations).difference(old.annotations)
     new_keys = new_keys.difference(['cross_references', 'date',
-                                    'data_file_division', 'ncbi_taxid', 'gi'])
+                                    'data_file_division', 'ncbi_taxid', 
+                                    'gi'])
     assert not new_keys, "Unexpected new annotation keys: %s" \
            % ", ".join(new_keys)
     missing_keys = set(old.annotations).difference(new.annotations)
     missing_keys = missing_keys.difference(['ncbi_taxid',  # Can't store chimeras
-                                            ])
+                                            'structured_comment'])
     assert not missing_keys, "Unexpectedly missing annotation keys: %s" \
            % ", ".join(missing_keys)
 
diff --git a/Tests/test_AlignIO.py b/Tests/test_AlignIO.py
index fe983e0..9eade9a 100644
--- a/Tests/test_AlignIO.py
+++ b/Tests/test_AlignIO.py
@@ -91,14 +91,14 @@ def alignment_summary(alignment, index="  ", vertical_threshold=5):
     else:
         # Show each sequence row vertically
         for i in range(min(5, alignment_len)):
-            answer.append(index + str_summary(alignment[:, i])
-                                + " alignment column %i" % i)
+            answer.append(index + str_summary(alignment[:, i]) +
+                          " alignment column %i" % i)
         if alignment_len > 5:
             i = alignment_len - 1
-            answer.append(index + str_summary("|" * rec_count)
-                                + " ...")
-            answer.append(index + str_summary(alignment[:, i])
-                                + " alignment column %i" % i)
+            answer.append(index + str_summary("|" * rec_count) +
+                          " ...")
+            answer.append(index + str_summary(alignment[:, i]) +
+                          " alignment column %i" % i)
     return "\n".join(answer)
 
 
diff --git a/Tests/test_AlignInfo.py b/Tests/test_AlignInfo.py
new file mode 100644
index 0000000..2934828
--- /dev/null
+++ b/Tests/test_AlignInfo.py
@@ -0,0 +1,119 @@
+# Copyright 2016 by Peter Cock.  All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+
+"""Bio.Align.AlignInfo related tests."""
+import unittest
+
+from Bio.Alphabet import DNAAlphabet, generic_protein
+from Bio.Alphabet import HasStopCodon, Gapped
+from Bio.Alphabet.IUPAC import unambiguous_dna
+from Bio.Align import MultipleSeqAlignment
+from Bio.Seq import Seq
+from Bio.SeqRecord import SeqRecord
+from Bio import AlignIO
+from Bio.SubsMat.FreqTable import FreqTable, FREQ
+from Bio.Align.AlignInfo import SummaryInfo
+
+
+class AlignInfoTests(unittest.TestCase):
+    """Test basic usage."""
+
+    def test_nucleotides(self):
+        filename = "GFF/multi.fna"
+        format = "fasta"
+        alignment = AlignIO.read(filename, format, alphabet=unambiguous_dna)
+        summary = SummaryInfo(alignment)
+
+        c = summary.dumb_consensus(ambiguous="N")
+        self.assertEqual(str(c), 'NNNNNNNN')
+        self.assertNotEqual(c.alphabet, unambiguous_dna)
+        self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
+
+        c = summary.gap_consensus(ambiguous="N")
+        self.assertEqual(str(c), 'NNNNNNNN')
+        self.assertNotEqual(c.alphabet, unambiguous_dna)
+        self.assertTrue(isinstance(c.alphabet, DNAAlphabet))
+
+        expected = FreqTable({"A": 0.25, "G": 0.25, "T": 0.25, "C": 0.25},
+                             FREQ, unambiguous_dna)
+
+        m = summary.pos_specific_score_matrix(chars_to_ignore=['-'],
+                                              axis_seq=c)
+        self.assertEqual(str(m), """    A   C   G   T
+N  2.0 0.0 1.0 0.0
+N  1.0 1.0 1.0 0.0
+N  1.0 0.0 2.0 0.0
+N  0.0 1.0 1.0 1.0
+N  1.0 2.0 0.0 0.0
+N  0.0 2.0 1.0 0.0
+N  1.0 2.0 0.0 0.0
+N  0.0 2.0 1.0 0.0
+""")
+
+        # Have a generic alphabet, without a declared gap char, so must tell
+        # provide the frequencies and chars to ignore explicitly.
+        ic = summary.information_content(e_freq_table=expected,
+                                         chars_to_ignore=['-'])
+        self.assertAlmostEqual(ic, 7.32029999423075, places=6)
+
+    def test_proteins(self):
+        alpha = HasStopCodon(Gapped(generic_protein, "-"), "*")
+        a = MultipleSeqAlignment([
+                SeqRecord(Seq("MHQAIFIYQIGYP*LKSGYIQSIRSPEYDNW-", alpha), id="ID001"),
+                SeqRecord(Seq("MH--IFIYQIGYAYLKSGYIQSIRSPEY-NW*", alpha), id="ID002"),
+                SeqRecord(Seq("MHQAIFIYQIGYPYLKSGYIQSIRSPEYDNW*", alpha), id="ID003")])
+        self.assertEqual(32, a.get_alignment_length())
+
+        s = SummaryInfo(a)
+
+        c = s.dumb_consensus(ambiguous="X")
+        self.assertEqual(str(c), "MHQAIFIYQIGYXXLKSGYIQSIRSPEYDNW*")
+
+        c = s.gap_consensus(ambiguous="X")
+        self.assertEqual(str(c), "MHXXIFIYQIGYXXLKSGYIQSIRSPEYXNWX")
+
+        m = s.pos_specific_score_matrix(chars_to_ignore=['-', '*'], axis_seq=c)
+        self.assertEqual(str(m), """    A   D   E   F   G   H   I   K   L   M   N   P   Q   R   S   W   Y
+M  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+H  0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+X  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0
+X  2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+I  0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+F  0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+I  0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+Q  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0
+I  0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+G  0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+X  1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0
+X  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0
+L  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+K  0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+S  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
+G  0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+I  0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Q  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0
+S  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
+I  0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+R  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0
+S  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0
+P  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0
+E  0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+Y  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
+X  0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+N  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0
+W  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0 0.0
+X  0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+""")
+
+        ic = s.information_content(chars_to_ignore=['-', '*'])
+        self.assertAlmostEqual(ic, 133.061475107, places=6)
+
+
+if __name__ == "__main__":
+    runner = unittest.TextTestRunner(verbosity=2)
+    unittest.main(testRunner=runner)
diff --git a/Tests/test_BioSQL_MySQLdb.py b/Tests/test_BioSQL_MySQLdb.py
index 5e21d1c..2996e0d 100644
--- a/Tests/test_BioSQL_MySQLdb.py
+++ b/Tests/test_BioSQL_MySQLdb.py
@@ -9,23 +9,11 @@ from BioSQL import BioSeqDatabase
 
 from common_BioSQL import *
 
-##################################
-# Start of user-editable section #
-##################################
-
-# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'root'
-DBPASSWD = ''
-TESTDB = 'biosql_test'
-
-################################
-# End of user-editable section #
-################################
-
 DBDRIVER = 'MySQLdb'
 DBTYPE = 'mysql'
 
+DBHOST, DBUSER, DBPASSWD, TESTDB = load_biosql_ini(DBTYPE)
+
 # This will abort if driver not installed etc:
 check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
 
diff --git a/Tests/test_BioSQL_mysql_connector.py b/Tests/test_BioSQL_mysql_connector.py
index 5a43e28..fa1185d 100644
--- a/Tests/test_BioSQL_mysql_connector.py
+++ b/Tests/test_BioSQL_mysql_connector.py
@@ -9,23 +9,11 @@ from BioSQL import BioSeqDatabase
 
 from common_BioSQL import *
 
-##################################
-# Start of user-editable section #
-##################################
-
-# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'root'
-DBPASSWD = ''
-TESTDB = 'biosql_test'
-
-################################
-# End of user-editable section #
-################################
-
 DBDRIVER = 'mysql.connector'
 DBTYPE = 'mysql'
 
+DBHOST, DBUSER, DBPASSWD, TESTDB = load_biosql_ini(DBTYPE)
+
 # This will abort if driver not installed etc:
 check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
 
diff --git a/Tests/test_BioSQL_psycopg2.py b/Tests/test_BioSQL_psycopg2.py
index 51908b4..bc14248 100644
--- a/Tests/test_BioSQL_psycopg2.py
+++ b/Tests/test_BioSQL_psycopg2.py
@@ -9,22 +9,9 @@ from BioSQL import BioSeqDatabase
 
 from common_BioSQL import *
 
-##################################
-# Start of user-editable section #
-##################################
-
-# Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'postgres'
-DBPASSWD = ''
-TESTDB = 'biosql_test'
-
-################################
-# End of user-editable section #
-################################
-
 DBDRIVER = 'psycopg2'
 DBTYPE = 'pg'
+DBHOST, DBUSER, DBPASSWD, TESTDB = load_biosql_ini(DBTYPE)
 
 # This will abort if driver not installed etc:
 check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
diff --git a/Tests/test_BioSQL_sqlite3.py b/Tests/test_BioSQL_sqlite3.py
index 4be7831..0b03c91 100644
--- a/Tests/test_BioSQL_sqlite3.py
+++ b/Tests/test_BioSQL_sqlite3.py
@@ -13,16 +13,14 @@ from BioSQL import BioSeqDatabase
 from common_BioSQL import *
 
 # Constants for the database driver
-DBHOST = 'localhost'
-DBUSER = 'root'
-DBPASSWD = ''
-
 DBDRIVER = 'sqlite3'
 DBTYPE = 'sqlite'
 
+DBHOST = None
+DBUSER = 'root'
+DBPASSWD = None
 TESTDB = temp_db_filename()
 
-
 # This will abort if driver not installed etc:
 check_config(DBDRIVER, DBTYPE, DBHOST, DBUSER, DBPASSWD, TESTDB)
 
@@ -66,6 +64,7 @@ class BackwardsCompatibilityTest(unittest.TestCase):
                           for rec in original_records]
         # And check they agree
         self.assertTrue(compare_records(original_records, biosql_records))
+        server.close()
 
 if __name__ == "__main__":
     # Run the test cases
diff --git a/Tests/test_Chi2.py b/Tests/test_Chi2.py
index 6eb9dee..ef3a28f 100644
--- a/Tests/test_Chi2.py
+++ b/Tests/test_Chi2.py
@@ -9,18 +9,18 @@ from Bio.Phylo.PAML import chi2
 
 class ModTest(unittest.TestCase):
 
-    def testCdfChi2(self):
+    def test_cdf_chi2(self):
         self.assertRaises(ValueError, chi2.cdf_chi2, df=0, stat=3.84)
         self.assertRaises(ValueError, chi2.cdf_chi2, df=1, stat=-3.84)
         self.assertRaises(TypeError, chi2.cdf_chi2, df="d", stat="stat")
         self.assertAlmostEqual(chi2.cdf_chi2(2, 3.84), 0.1466070, places=5)
 
-    def testLnGamma(self):
+    def test_ln_gamma(self):
         self.assertRaises(ValueError, chi2._ln_gamma_function, -1)
         self.assertAlmostEqual(chi2._ln_gamma_function(10), 12.80183,
                                places=5)
 
-    def testIncompleteGamma(self):
+    def test_incomplete_gamma(self):
         self.assertRaises(ValueError, chi2._incomplete_gamma, x=0.5,
                           alpha=-1)
         self.assertAlmostEqual(chi2._incomplete_gamma(0.5, 0.5), 0.6826895,
diff --git a/Tests/test_Cluster.py b/Tests/test_Cluster.py
index ae923c0..235ac7c 100644
--- a/Tests/test_Cluster.py
+++ b/Tests/test_Cluster.py
@@ -101,12 +101,12 @@ class TestCluster(unittest.TestCase):
 
         try:
             treecluster(data1)
-        except:
+        except Exception:  # TODO - Which exceptions?
             self.fail("treecluster failed to accept matrix data1")
 
         try:
             treecluster(data2)
-        except:
+        except Exception:  # TODO - Which exceptions?
             self.fail("treecluster failed to accept matrix data2")
 
         self.assertRaises(TypeError, lambda: treecluster(data3))
diff --git a/Tests/test_Crystal.py b/Tests/test_Crystal.py
index 1d0b079..2bddc75 100644
--- a/Tests/test_Crystal.py
+++ b/Tests/test_Crystal.py
@@ -463,10 +463,6 @@ class CrystalTestCase(unittest.TestCase):
         self.assertEqual(list(self.crystal.items()),
                          list(self.crystal.data.items()))
 
-    def testKeys(self):
-        self.assertEqual(list(self.crystal.keys()),
-                         list(self.crystal.data.keys()))
-
     def testHasKey(self):
         self.assertTrue('b' in self.crystal)
         self.assertTrue('c' in self.crystal)
diff --git a/Tests/test_Emboss.py b/Tests/test_Emboss.py
index 03020d6..3c6a7c7 100644
--- a/Tests/test_Emboss.py
+++ b/Tests/test_Emboss.py
@@ -225,7 +225,7 @@ def compare_records(old_list, new_list):
         if old.features and new.features \
         and len(old.features) != len(new.features):
             raise ValueError("%i vs %i features"
-                             % (len(old.features, len(new.features))))
+                             % (len(old.features), len(new.features)))
         # TODO - check annotation
     return True
 
@@ -249,7 +249,7 @@ class SeqRetSeqIOTests(unittest.TestCase):
     def tearDown(self):
         clean_up()
 
-    def check_SeqIO_to_EMBOSS(self, in_filename, in_format, skip_formats=[],
+    def check_SeqIO_to_EMBOSS(self, in_filename, in_format, skip_formats=(),
                               alphabet=None):
         """Can Bio.SeqIO write files seqret can read back?"""
         if alphabet:
@@ -267,7 +267,7 @@ class SeqRetSeqIOTests(unittest.TestCase):
                                  % (in_format, in_filename, temp_format, err))
 
     def check_EMBOSS_to_SeqIO(self, filename, old_format,
-                              skip_formats=[]):
+                              skip_formats=()):
         """Can Bio.SeqIO read seqret's conversion of the file?"""
         # TODO: Why can't we read EMBOSS's swiss output?
         self.assertTrue(os.path.isfile(filename))
@@ -284,7 +284,7 @@ class SeqRetSeqIOTests(unittest.TestCase):
                 raise ValueError("Disagree on %s file %s in %s format: %s"
                                  % (old_format, filename, new_format, err))
 
-    def check_SeqIO_with_EMBOSS(self, filename, old_format, skip_formats=[],
+    def check_SeqIO_with_EMBOSS(self, filename, old_format, skip_formats=(),
                                 alphabet=None):
         # Check EMBOSS can read Bio.SeqIO output...
         self.check_SeqIO_to_EMBOSS(filename, old_format, skip_formats,
@@ -361,7 +361,7 @@ class SeqRetAlignIOTests(unittest.TestCase):
         clean_up()
 
     def check_EMBOSS_to_AlignIO(self, filename, old_format,
-                              skip_formats=[]):
+                              skip_formats=()):
         """Can AlignIO read seqret's conversion of the file?"""
         self.assertTrue(os.path.isfile(filename), filename)
         old_aligns = list(AlignIO.parse(filename, old_format))
@@ -374,7 +374,7 @@ class SeqRetAlignIOTests(unittest.TestCase):
             handle = emboss_convert(filename, old_format, new_format)
             try:
                 new_aligns = list(AlignIO.parse(handle, new_format))
-            except:
+            except Exception:  # TODO - Which exceptions?
                 handle.close()
                 raise ValueError("Can't parse %s file %s in %s format."
                                  % (old_format, filename, new_format))
@@ -385,7 +385,7 @@ class SeqRetAlignIOTests(unittest.TestCase):
                 raise ValueError("Disagree on %s file %s in %s format: %s"
                                  % (old_format, filename, new_format, err))
 
-    def check_AlignIO_to_EMBOSS(self, in_filename, in_format, skip_formats=[],
+    def check_AlignIO_to_EMBOSS(self, in_filename, in_format, skip_formats=(),
                                 alphabet=None):
         """Can Bio.AlignIO write files seqret can read back?"""
         if alphabet:
@@ -415,11 +415,11 @@ class SeqRetAlignIOTests(unittest.TestCase):
                 raise ValueError("Disagree on file %s %s in %s format: %s"
                                  % (in_format, in_filename, temp_format, err))
 
-    def check_AlignIO_with_EMBOSS(self, filename, old_format, skip_formats=[],
+    def check_AlignIO_with_EMBOSS(self, filename, old_format, skip_formats=(),
                                   alphabet=None):
         # Check EMBOSS can read Bio.AlignIO output...
         self.check_AlignIO_to_EMBOSS(filename, old_format, skip_formats,
-                                   alphabet)
+                                     alphabet)
         # Check Bio.AlignIO can read EMBOSS seqret output...
         self.check_EMBOSS_to_AlignIO(filename, old_format, skip_formats)
 
@@ -520,10 +520,10 @@ class PairwiseAlignmentTests(unittest.TestCase):
                                  gapextend=0.5,
                                  auto=True, filter=True)
         self.assertEqual(str(cline),
-                         exes["water"] + " -auto -filter"
-                         + " -asequence=asis:ACCCGGGCGCGGT"
-                         + " -bsequence=asis:ACCCGAGCGCGGT"
-                         + " -gapopen=10 -gapextend=0.5")
+                         exes["water"] + " -auto -filter" +
+                         " -asequence=asis:ACCCGGGCGCGGT" +
+                         " -bsequence=asis:ACCCGAGCGCGGT" +
+                         " -gapopen=10 -gapextend=0.5")
         # Run the tool,
         child = subprocess.Popen(str(cline),
                                  stdin=subprocess.PIPE,
@@ -580,10 +580,10 @@ class PairwiseAlignmentTests(unittest.TestCase):
                                  gapextend=0.5,
                                  auto=True, filter=True)
         self.assertEqual(str(cline),
-                         exes["needle"] + " -auto -filter"
-                         + " -asequence=asis:ACCCGGGCGCGGT"
-                         + " -bsequence=asis:ACCCGAGCGCGGT"
-                         + " -gapopen=10 -gapextend=0.5")
+                         exes["needle"] + " -auto -filter" +
+                         " -asequence=asis:ACCCGGGCGCGGT" +
+                         " -bsequence=asis:ACCCGAGCGCGGT" +
+                         " -gapopen=10 -gapextend=0.5")
         # Run the tool,
         child = subprocess.Popen(str(cline),
                                  stdin=subprocess.PIPE,
@@ -750,9 +750,9 @@ class PairwiseAlignmentTests(unittest.TestCase):
                                        aformat="pair", wordsize=9,
                                        auto=True, stdout=True)
         self.assertEqual(str(cline),
-                         exes["seqmatchall"] + " -auto -stdout"
-                         + " -sequence=Fasta/f002"
-                         + " -wordsize=9 -aformat=pair")
+                         exes["seqmatchall"] + " -auto -stdout" +
+                         " -sequence=Fasta/f002" +
+                         " -wordsize=9 -aformat=pair")
         # Run the tool,
         child = subprocess.Popen(str(cline),
                                  stdin=subprocess.PIPE,
@@ -933,7 +933,7 @@ def clean_up():
         if filename.startswith("temp_"):
             try:
                 os.remove(filename)
-            except:
+            except Exception:  # TODO - Which exceptions?
                 pass
 
 if __name__ == "__main__":
diff --git a/Tests/test_Entrez.py b/Tests/test_Entrez.py
index 0a28f6a..6025420 100644
--- a/Tests/test_Entrez.py
+++ b/Tests/test_Entrez.py
@@ -1,5 +1,5 @@
 # Copyright 2008-2010 by Michiel de Hoon.  All rights reserved.
-# Revisions copyright 2009-2013 by Peter Cock. All rights reserved.
+# Revisions copyright 2009-2016 by Peter Cock. All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -7,7 +7,7 @@
 '''
 
 import unittest
-
+import sys
 import os
 if os.name == 'java':
     try:
@@ -18,7 +18,8 @@ if os.name == 'java':
         raise MissingPythonDependencyError("The Bio.Entrez XML parser fails on "
                                   "Jython, see http://bugs.jython.org/issue1447")
 
-
+from io import BytesIO
+from Bio._py3k import StringIO
 from Bio import Entrez
 
 
@@ -31,6 +32,36 @@ class GeneralTests(unittest.TestCase):
         handle.close()
         self.assertRaises(IOError, Entrez.read, handle)
 
+    def test_bytes_handle(self):
+        """Test parsing a handle opened in binary mode."""
+        with open("Entrez/einfo1.xml", "rb") as handle:
+            record = Entrez.read(handle)
+        self.assertTrue("DbList" in record)
+
+    def test_text_handle(self):
+        """Test parsing a handle opened in text mode."""
+        with open("Entrez/einfo1.xml", "rt") as handle:
+            record = Entrez.read(handle)
+        self.assertTrue("DbList" in record)
+
+    def test_BytesIO(self):
+        """Test parsing a BytesIO handle (bytes not unicode)."""
+        with open("Entrez/einfo1.xml", "rb") as in_handle:
+            data = in_handle.read()
+        handle = BytesIO(data)
+        record = Entrez.read(handle)
+        self.assertTrue("DbList" in record)
+        handle.close()
+
+    def test_StringIO(self):
+        """Test parsing a StringIO handle (unicode not bytes)."""
+        with open("Entrez/einfo1.xml", "rt") as in_handle:
+            data = in_handle.read()
+        handle = StringIO(data)
+        record = Entrez.read(handle)
+        self.assertTrue("DbList" in record)
+        handle.close()
+
 
 class EInfoTest(unittest.TestCase):
     '''Tests for parsing XML output returned by EInfo
@@ -40,9 +71,8 @@ class EInfoTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.einfo()
-        handle = open('Entrez/einfo1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/einfo1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["DbList"], ['pubmed',
                                             'protein',
                                             'nucleotide',
@@ -87,9 +117,8 @@ class EInfoTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.einfo(db="pubmed")
-        handle = open('Entrez/einfo2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/einfo2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["DbInfo"]['DbName'], 'pubmed')
         self.assertEqual(record["DbInfo"]['MenuName'], 'PubMed')
         self.assertEqual(record["DbInfo"]['Description'], 'PubMed bibliographic record')
@@ -123,9 +152,8 @@ class EInfoTest(unittest.TestCase):
         # Starting some time in 2010, the results returned by Bio.Entrez
         # included some tags that are not part of the corresponding DTD.
         from Bio.Entrez import Parser
-        handle = open('Entrez/einfo3.xml', "rb")
-        self.assertRaises(Parser.ValidationError, Entrez.read, handle)
-        handle.close()
+        with open('Entrez/einfo3.xml', "rb") as handle:
+            self.assertRaises(Parser.ValidationError, Entrez.read, handle)
 
     def test_pubmed3(self):
         '''Test non-validating parser on XML with an inconsistent DTD
@@ -134,9 +162,8 @@ class EInfoTest(unittest.TestCase):
         # >>> Bio.Entrez.einfo(db="pubmed")
         # Starting some time in 2010, the results returned by Bio.Entrez
         # included some tags that are not part of the corresponding DTD.
-        handle = open('Entrez/einfo3.xml', "rb")
-        record = Entrez.read(handle, validate=False)
-        handle.close()
+        with open('Entrez/einfo3.xml', "rb") as handle:
+            record = Entrez.read(handle, validate=False)
         self.assertEqual(record["DbInfo"]['DbName'], 'pubmed')
         self.assertEqual(record["DbInfo"]['MenuName'], 'PubMed')
         self.assertEqual(record["DbInfo"]['Description'], 'PubMed bibliographic record')
@@ -786,9 +813,8 @@ class EInfoTest(unittest.TestCase):
         # >>> Bio.Entrez.einfo()
         # and manually delete the last couple of lines
         from Bio.Entrez import Parser
-        handle = open('Entrez/einfo4.xml', "rb")
-        self.assertRaises(Parser.CorruptedXMLError, Entrez.read, handle)
-        handle.close()
+        with open('Entrez/einfo4.xml', "rb") as handle:
+            self.assertRaises(Parser.CorruptedXMLError, Entrez.read, handle)
 
 
 class ESearchTest(unittest.TestCase):
@@ -799,9 +825,8 @@ class ESearchTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="pubmed", term="biopython")
-        handle = open('Entrez/esearch1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], '5')
         self.assertEqual(record['RetMax'], '5')
         self.assertEqual(record['RetStart'], '0')
@@ -829,9 +854,8 @@ class ESearchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="pubmed", term="cancer", reldate=60,
         #                        datetype="edat", retmax=100, usehistory="y")
-        handle = open('Entrez/esearch2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], "10238")
         self.assertEqual(record['RetMax'], "100")
         self.assertEqual(record['RetStart'], "0")
@@ -983,9 +1007,8 @@ class ESearchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="pubmed", term="PNAS[ta] AND 97[vi]",
         #                        retstart=6, retmax=6)
-        handle = open('Entrez/esearch3.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch3.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], '2652')
         self.assertEqual(record['RetMax'], '6')
         self.assertEqual(record['RetStart'], '6')
@@ -1017,9 +1040,8 @@ class ESearchTest(unittest.TestCase):
         # Search in Journals for the term obstetrics.
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="journals", term="obstetrics")
-        handle = open('Entrez/esearch4.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch4.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], '177')
         self.assertEqual(record['RetMax'], '20')
         self.assertEqual(record['RetStart'], '0')
@@ -1062,9 +1084,8 @@ class ESearchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="pmc",
         #                        term="stem cells AND free fulltext[filter]")
-        handle = open('Entrez/esearch5.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch5.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], '23492')
         self.assertEqual(record['RetMax'], '20')
         self.assertEqual(record['RetStart'], '0')
@@ -1141,9 +1162,8 @@ class ESearchTest(unittest.TestCase):
         # Search in Nucleotide for a property of the sequence,
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="nucleotide", term="biomol trna[prop]")
-        handle = open('Entrez/esearch6.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch6.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], "699")
         self.assertEqual(record['RetMax'], "20")
         self.assertEqual(record['RetStart'], "0")
@@ -1177,9 +1197,8 @@ class ESearchTest(unittest.TestCase):
         # Search in Protein for a molecular weight
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="protein", term="200020[molecular weight]")
-        handle = open('Entrez/esearch7.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch7.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], '3')
         self.assertEqual(record['RetMax'], '3')
         self.assertEqual(record['RetStart'], '0')
@@ -1201,33 +1220,8 @@ class ESearchTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.esearch(db="protein", term="abcXYZ")
-        handle = open('Entrez/esearch8.xml')
-        record = Entrez.read(handle)
-        handle.close()
-        self.assertEqual(record['Count'], '3')
-        self.assertEqual(record['RetMax'], '3')
-        self.assertEqual(record['RetStart'], '0')
-        self.assertEqual(len(record['IdList']), 3)
-        self.assertEqual(record['IdList'][0], '16766766')
-        self.assertEqual(record['IdList'][1], '16422035')
-        self.assertEqual(record['IdList'][2], '4104812')
-        self.assertEqual(len(record['TranslationSet']), 0)
-        self.assertEqual(len(record['TranslationStack']), 2)
-        self.assertEqual(record['TranslationStack'][0]['Term'], '000200020[molecular weight]')
-        self.assertEqual(record['TranslationStack'][0]['Field'], 'molecular weight')
-        self.assertEqual(record['TranslationStack'][0]['Count'], '3')
-        self.assertEqual(record['TranslationStack'][0]['Explode'], 'Y')
-        self.assertEqual(record['TranslationStack'][1], 'GROUP')
-        self.assertEqual(record['QueryTranslation'], '000200020[molecular weight]')
-
-    def test_notfound(self):
-        '''Test parsing XML returned by ESearch when no items were found
-        '''
-        # To create the XML file, use
-        # >>> Bio.Entrez.esearch(db="protein", term="abcXYZ")
-        handle = open('Entrez/esearch8.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esearch8.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record['Count'], "0")
         self.assertEqual(record['RetMax'], "0")
         self.assertEqual(record['RetStart'], "0")
@@ -1260,9 +1254,8 @@ class EPostTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.epost(db="pubmed", id="11237011")
-        handle = open('Entrez/epost1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/epost1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["QueryKey"], '1')
         self.assertEqual(record["WebEnv"], '0zYsuLk3zG_lRMkblPBEqnT8nIENUGw4HAy8xXChTnoVm7GEnWY71jv3nz at 1FC077F3806DE010_0042SID')
 
@@ -1271,18 +1264,16 @@ class EPostTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.epost(db="nothing")
-        handle = open('Entrez/epost2.xml', "rb")
-        self.assertRaises(RuntimeError, Entrez.read, handle)
-        handle.close()
+        with open('Entrez/epost2.xml', "rb") as handle:
+            self.assertRaises(RuntimeError, Entrez.read, handle)
 
     def test_invalid(self):
         '''Test parsing XML returned by EPost with an invalid id (overflow tag)
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.epost(db="pubmed", id=99999999999999999999999999999999)
-        handle = open('Entrez/epost3.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/epost3.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["InvalidIdList"], ["-1"])
         self.assertEqual(record["QueryKey"], "1")
         self.assertEqual(record["WebEnv"], "08AIUeBsfIk6BfdzKnd3GM2RtCudczC9jm5aeb4US0o7azCTQCeCsr-xg0 at 1EDE54E680D03C40_0011SID")
@@ -1303,9 +1294,8 @@ class ESummaryTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="pubmed", id=["11850928","11482001"],
         #                         retmode="xml")
-        handle = open('Entrez/esummary1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "11850928")
         self.assertEqual(record[0]["PubDate"], "1965 Aug")
         self.assertEqual(record[0]["EPubDate"], "")
@@ -1381,9 +1371,8 @@ class ESummaryTest(unittest.TestCase):
         # In Journals display records for journal IDs 27731,439,735,905
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="journals", id="27731,439,735,905")
-        handle = open('Entrez/esummary2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "27731")
         self.assertEqual(record[0]["Title"], "The American journal of obstetrics and diseases of women and children")
         self.assertEqual(record[0]["MedAbbr"], "Am J Obstet Dis Women Child")
@@ -1456,9 +1445,8 @@ class ESummaryTest(unittest.TestCase):
         # In Protein display records for GIs 28800982 and 28628843 in xml retrieval mode
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="protein", id="28800982,28628843", retmode="xml")
-        handle = open('Entrez/esummary3.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary3.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "28800982")
         self.assertEqual(record[0]["Caption"], "AAO47091")
         self.assertEqual(record[0]["Title"], "hemochromatosis [Homo sapiens]")
@@ -1495,9 +1483,8 @@ class ESummaryTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="nucleotide", id="28864546,28800981",
         #                         retmode="xml")
-        handle = open('Entrez/esummary4.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary4.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "28864546")
         self.assertEqual(record[0]["Caption"], "AY207443")
         self.assertEqual(record[0]["Title"], "Homo sapiens alpha hemoglobin (HBZP) pseudogene 3' UTR/AluJo repeat breakpoint junction")
@@ -1534,9 +1521,8 @@ class ESummaryTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="structure", id=["19923","12120"],
         #                         retmode="xml")
-        handle = open('Entrez/esummary5.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary5.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "19923")
         self.assertEqual(record[0]["PdbAcc"], "1L5J")
         self.assertEqual(record[0]["PdbDescr"], "Crystal Structure Of E. Coli Aconitase B")
@@ -1585,9 +1571,8 @@ class ESummaryTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="taxonomy", id=["9913","30521"],
         #                         retmode="xml")
-        handle = open('Entrez/esummary6.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary6.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "9913")
         self.assertEqual(record[0]["Rank"], "species")
         self.assertEqual(record[0]["Division"], "even-toed ungulates")
@@ -1626,9 +1611,8 @@ class ESummaryTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.esummary(db="unists", id=["254085","254086"],
         #                         retmode="xml")
-        handle = open('Entrez/esummary7.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/esummary7.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["Id"], "254085")
         self.assertEqual(record[0]["Marker_Name"], "SE234324")
         self.assertEqual(len(record[0]["Map_Gene_Summary_List"]), 1)
@@ -1652,9 +1636,8 @@ class ESummaryTest(unittest.TestCase):
         '''
         # To create the XML file, use
         # >>> Bio.Entrez.esummary()
-        handle = open('Entrez/esummary8.xml', "rb")
-        self.assertRaises(RuntimeError, Entrez.read, handle)
-        handle.close()
+        with open('Entrez/esummary8.xml', "rb") as handle:
+            self.assertRaises(RuntimeError, Entrez.read, handle)
 
 
 class ELinkTest(unittest.TestCase):
@@ -1666,9 +1649,8 @@ class ELinkTest(unittest.TestCase):
         # Retrieve IDs from PubMed for PMID 9298984 to the PubMed database
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="9298984", cmd="neighbor")
-        handle = open('Entrez/elink1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(len(record[0]), 5)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
@@ -1877,9 +1859,8 @@ class ELinkTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="nucleotide", db="protein",
         #                      id="48819,7140345")
-        handle = open('Entrez/elink2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(len(record[0]), 5)
         self.assertEqual(record[0]["DbFrom"], "nuccore")
@@ -1918,9 +1899,8 @@ class ELinkTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="11812492,11774222",
         #                      db="pubmed", mindate="1995", datetype="pdat")
-        handle = open('Entrez/elink3.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink3.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
         self.assertEqual(len(record[0]['IdList']), 2)
@@ -2298,9 +2278,8 @@ class ELinkTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="12242737", db="pubmed",
         #                      term="medline[sb]")
-        handle = open('Entrez/elink4.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink4.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
         self.assertEqual(record[0]["IdList"], ["12242737"])
@@ -2532,9 +2511,8 @@ class ELinkTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="10611131", cmd="prlinks")
 
-        handle = open('Entrez/elink5.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink5.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(len(record[0]), 5)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
@@ -2566,9 +2544,8 @@ class ELinkTest(unittest.TestCase):
         # PMIDs 12085856 and 12085853
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="12085856,12085853", cmd="llinks")
-        handle = open('Entrez/elink6.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink6.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
         self.assertEqual(len(record[0]["IdUrlList"]), 2)
         self.assertEqual(record[0]["IdUrlList"]["IdUrlSet"][0]["Id"], "12085856")
@@ -2633,9 +2610,8 @@ class ELinkTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="12169658,11748140",
         #                      cmd="acheck")
-        handle = open('Entrez/elink7.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink7.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
         self.assertEqual(len(record[0]["IdCheckList"]), 2)
@@ -2856,9 +2832,8 @@ class ELinkTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.elink(dbfrom="pubmed", id="12068369", cmd="ncheck")
 
-        handle = open('Entrez/elink8.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/elink8.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(record[0]["DbFrom"], "pubmed")
         self.assertEqual(len(record[0]["IdCheckList"]), 2)
@@ -2878,11 +2853,9 @@ class EGQueryTest(unittest.TestCase):
         # Display counts in XML for stem cells in each Entrez database
         # To create the XML file, use
         # >>> Bio.Entrez.egquery(term="stem cells")
-        handle = open('Entrez/egquery1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/egquery1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["Term"], "stem cells")
-
         self.assertEqual(record["eGQueryResult"][0]["DbName"], "pubmed")
         self.assertEqual(record["eGQueryResult"][0]["MenuName"], "PubMed")
         self.assertEqual(record["eGQueryResult"][0]["Count"], "392")
@@ -3030,11 +3003,9 @@ class EGQueryTest(unittest.TestCase):
         # Display counts in XML for brca1 or brca2 for each Entrez database
         # To create the XML file, use
         # >>> Bio.Entrez.egquery(term="brca1 OR brca2")
-        handle = open('Entrez/egquery2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/egquery2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["Term"], "brca1 OR brca2")
-
         self.assertEqual(record["eGQueryResult"][0]["DbName"], "pubmed")
         self.assertEqual(record["eGQueryResult"][0]["MenuName"], "PubMed")
         self.assertEqual(record["eGQueryResult"][0]["Count"], "0")
@@ -3186,9 +3157,8 @@ class ESpellTest(unittest.TestCase):
         # Request suggestions for the PubMed search biopythooon
         # To create the XML file, use
         # >>> Bio.Entrez.espell(db="pubmed", term="biopythooon")
-        handle = open('Entrez/espell.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/espell.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record["Database"], "pubmed")
         self.assertEqual(record["Query"], "biopythooon")
         self.assertEqual(record["CorrectedQuery"], "biopython")
@@ -3208,9 +3178,8 @@ class EFetchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db='pubmed', id='12091962,9997',
         #                       retmode='xml', rettype='abstract')
-        handle = open('Entrez/pubmed1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/pubmed1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["MedlineCitation"].attributes["Owner"], "KIE")
         self.assertEqual(record[0]["MedlineCitation"].attributes["Status"], "MEDLINE")
         self.assertEqual(record[0]["MedlineCitation"]["PMID"], "12091962")
@@ -3410,9 +3379,8 @@ class EFetchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db='pubmed', id="11748933,11700088",
         #                       retmode="xml")
-        handle = open('Entrez/pubmed2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/pubmed2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["MedlineCitation"].attributes["Owner"], "NLM")
         self.assertEqual(record[0]["MedlineCitation"].attributes["Status"], "MEDLINE")
         self.assertEqual(record[0]["MedlineCitation"]["PMID"], "11748933")
@@ -3624,9 +3592,8 @@ class EFetchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db="omim", id="601100", retmode='xml',
         #                       rettype='full')
-        handle = open('Entrez/ncbi_mim.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/ncbi_mim.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(record[0]["Mim-entry_mimNumber"], "601100")
         self.assertEqual(record[0]["Mim-entry_mimType"], "1")
@@ -3801,9 +3768,8 @@ class EFetchTest(unittest.TestCase):
         # Access the Taxonomy database using efetch.
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db="taxonomy", id="9685", retmode="xml")
-        handle = open('Entrez/taxonomy.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/taxonomy.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(len(record), 1)
         self.assertEqual(record[0]["TaxId"], "9685")
         self.assertEqual(record[0]["ScientificName"], "Felis catus")
@@ -3910,9 +3876,8 @@ class EFetchTest(unittest.TestCase):
         # Access the nucleotide database using efetch.
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db='nucleotide', id=5, retmode='xml')
-        handle = open('Entrez/nucleotide1.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/nucleotide1.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["GBSeq_locus"], "X60065")
         self.assertEqual(record[0]["GBSeq_length"], "1136")
         self.assertEqual(record[0]["GBSeq_strandedness"], "single")
@@ -4034,9 +3999,8 @@ class EFetchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db='nucleotide', id=5,
         #                       rettype='fasta', complexity=0, retmode='xml')
-        handle = open('Entrez/nucleotide2.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/nucleotide2.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["TSeq_seqtype"], "")
         self.assertEqual(record[0]["TSeq_seqtype"].attributes["value"], "nucleotide")
         self.assertEqual(record[0]["TSeq_gi"], "5")
@@ -4056,15 +4020,14 @@ class EFetchTest(unittest.TestCase):
         self.assertEqual(record[1]["TSeq_length"], "342")
         self.assertEqual(record[1]["TSeq_sequence"], "PALVLLLGFLCHVAIAGRTCPKPDELPFSTVVPLKRTYEPGEQIVFSCQPGYVSRGGIRRFTCPLTGLWPINTLKCMPRVCPFAGILENGTVRYTTFEYPNTISFSCHTGFYLKGASSAKCTEEGKWSPDLPVCAPITCPPPPIPKFASLSVYKPLAGNNSFYGSKAVFKCLPHHAMFGNDTVTCTEHGNWTQLPECREVRCPFPSRPDNGFVNHPANPVLYYKDTATFGCHETYSLDGPEEVECSKFGNWSAQPSCKASCKLSIKRATVIYEGERVAIQNKFKNGMLHGQKVSFFCKHKEKKCSYTEDAQCIDGTIEIPKCFKEHSSLAFWKTDASDVKPC")
 
-    def test_nucleotide2(self):
+    def test_protein(self):
         '''Test parsing XML returned by EFetch, Protein database
         '''
         # Access the protein database using efetch.
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db='protein', id=8, rettype='gp', retmode='xml')
-        handle = open('Entrez/protein.xml', "rb")
-        record = Entrez.read(handle)
-        handle.close()
+        with open('Entrez/protein.xml', "rb") as handle:
+            record = Entrez.read(handle)
         self.assertEqual(record[0]["GBSeq_locus"], "CAA35997")
         self.assertEqual(record[0]["GBSeq_length"], "100")
         self.assertEqual(record[0]["GBSeq_moltype"], "AA")
@@ -4156,25 +4119,21 @@ class EFetchTest(unittest.TestCase):
         # To create the GenBank file, use
         # >>> Bio.Entrez.efetch(db='nucleotide', id='NT_019265', rettype='gb')
         from Bio.Entrez import Parser
-        handle = open('GenBank/NT_019265.gb', "rb")
-        self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
-        handle.close()
-        handle = open('GenBank/NT_019265.gb', "rb")
-        iterator = Entrez.parse(handle)
-        self.assertRaises(Parser.NotXMLError, next, iterator)
-        handle.close()
+        with open('GenBank/NT_019265.gb', "rb") as handle:
+            self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
+        with open('GenBank/NT_019265.gb', "rb") as handle:
+            iterator = Entrez.parse(handle)
+            self.assertRaises(Parser.NotXMLError, next, iterator)
 
     def test_fasta(self):
         '''Test error handling when presented with Fasta non-XML data
         '''
         from Bio.Entrez import Parser
-        handle = open('Fasta/wisteria.nu', "rb")
-        self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
-        handle.close()
-        handle = open('Fasta/wisteria.nu', "rb")
-        iterator = Entrez.parse(handle)
-        self.assertRaises(Parser.NotXMLError, next, iterator)
-        handle.close()
+        with open('Fasta/wisteria.nu', "rb") as handle:
+            self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
+        with open('Fasta/wisteria.nu', "rb") as handle:
+            iterator = Entrez.parse(handle)
+            self.assertRaises(Parser.NotXMLError, next, iterator)
 
     def test_pubmed_html(self):
         '''Test error handling when presented with HTML (so XML-like) data
@@ -4182,14 +4141,12 @@ class EFetchTest(unittest.TestCase):
         # To create the HTML file, use
         # >>> Bio.Entrez.efetch(db="pubmed", id="19304878")
         from Bio.Entrez import Parser
-        handle = open('Entrez/pubmed3.html', "rb")
-        self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
-        handle.close()
+        with open('Entrez/pubmed3.html', "rb") as handle:
+            self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
         # Test if the error is also raised with Entrez.parse
-        handle = open('Entrez/pubmed3.html', "rb")
-        records = Entrez.parse(handle)
-        self.assertRaises(Parser.NotXMLError, next, records)
-        handle.close()
+        with open('Entrez/pubmed3.html', "rb") as handle:
+            records = Entrez.parse(handle)
+            self.assertRaises(Parser.NotXMLError, next, records)
 
     def test_xml_without_declaration(self):
         '''Test error handling for a missing XML declaration
@@ -4197,14 +4154,12 @@ class EFetchTest(unittest.TestCase):
         # To create the XML file, use
         # >>> Bio.Entrez.efetch(db="journals",id="2830,6011,7473",retmode='xml')
         from Bio.Entrez import Parser
-        handle = open('Entrez/journals.xml', "rb")
-        self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
-        handle.close()
+        with open('Entrez/journals.xml', "rb") as handle:
+            self.assertRaises(Parser.NotXMLError, Entrez.read, handle)
         # Test if the error is also raised with Entrez.parse
-        handle = open('Entrez/journals.xml', "rb")
-        records = Entrez.parse(handle)
-        self.assertRaises(Parser.NotXMLError, next, records)
-        handle.close()
+        with open('Entrez/journals.xml', "rb") as handle:
+            records = Entrez.parse(handle)
+            self.assertRaises(Parser.NotXMLError, next, records)
 
     def test_truncated_xml(self):
         """Test error handling for a truncated XML declaration"""
diff --git a/Tests/test_Entrez_online.py b/Tests/test_Entrez_online.py
index 307d4c5..b4bbdca 100644
--- a/Tests/test_Entrez_online.py
+++ b/Tests/test_Entrez_online.py
@@ -131,6 +131,15 @@ class EntrezOnlineCase(unittest.TestCase):
         self.assertEqual(len(records), 1)
         self.assertEqual(records[0]['System_sysid']['Sys-id']['Sys-id_bsid'], '1134002')
 
+    def test_efetch_taxonomy_xml(self):
+        """Test Entrez using a integer id - like a taxon id
+        """
+        handle = Entrez.efetch( db="taxonomy", id=3702, retmode="XML")
+        taxon_record = Entrez.read(handle)
+        self.assertTrue(1, len(taxon_record))
+        self.assertTrue('TaxId' in taxon_record[0])
+        self.assertTrue('3702', taxon_record[0]['TaxId'])
+
     def test_elink(self):
         # Commas: Link from protein to gene
         handle = Entrez.elink(db="gene", dbfrom="protein",
diff --git a/Tests/test_GAOrganism.py b/Tests/test_GAOrganism.py
index abb416c..9ab9d28 100644
--- a/Tests/test_GAOrganism.py
+++ b/Tests/test_GAOrganism.py
@@ -85,13 +85,13 @@ class CreatePopulationTest(unittest.TestCase):
     def test_random_population_types(self):
         """Creating a random population with different types of alphabets.
         """
-        class DoubleAlphabet:
+        class DoubleAlphabet(object):
             letters = [1.0, 2.0]
 
-        class CharacterAlphabet:
+        class CharacterAlphabet(object):
             letters = ["a", "b"]
 
-        class IntegerAlphabet:
+        class IntegerAlphabet(object):
             letters = [1, 2]
 
         def test_fitness(genome):
diff --git a/Tests/test_GenBank_unittest.py b/Tests/test_GenBank_unittest.py
index 9344402..d23f4d9 100644
--- a/Tests/test_GenBank_unittest.py
+++ b/Tests/test_GenBank_unittest.py
@@ -1,4 +1,5 @@
 # Copyright 2013 by Kai Blin.
+# Revisions copyright 2015 by Peter Cock.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -84,6 +85,65 @@ class GenBankTests(unittest.TestCase):
         self.assertEqual(rec.annotations["organism"], ".")
         self.assertEqual(rec.annotations["taxonomy"], [])
 
+    def test_dblink(self):
+        """GenBank record with old DBLINK project entry."""
+        record = SeqIO.read("GenBank/NC_005816.gb", "gb")
+        self.assertEqual(record.dbxrefs, ["Project:58037"])
+        embl = record.format("embl")
+        self.assertTrue("XX\nPR   Project:58037;\nXX\n" in embl, embl)
+
+    def test_dblink_two(self):
+        """GenBank record with old and new DBLINK project entries."""
+        record = SeqIO.read("GenBank/NP_416719.gbwithparts", "gb")
+        self.assertEqual(record.dbxrefs,
+                         ["Project:57779", "BioProject:PRJNA57779"])
+        embl = record.format("embl")
+        self.assertTrue("XX\nPR   Project:PRJNA57779;\nXX\n" in embl, embl)
+
+    def test_dbline_gb_embl(self):
+        """GenBank / EMBL paired records with PR project entry: GenBank"""
+        record = SeqIO.read("GenBank/DS830848.gb", "gb")
+        self.assertTrue("BioProject:PRJNA16232" in record.dbxrefs, record.dbxrefs)
+        gb = record.format("gb")
+        self.assertTrue("\nDBLINK      BioProject:PRJNA16232\n" in gb, gb)
+        # Also check EMBL output
+        embl = record.format("embl")
+        self.assertTrue("XX\nPR   Project:PRJNA16232;\nXX\n" in embl, embl)
+
+    def test_dbline_embl_gb(self):
+        """GenBank / EMBL paired records with PR project entry: EMBL"""
+        record = SeqIO.read("EMBL/DS830848.embl", "embl")
+        # TODO: Should we map this to BioProject:PRJNA16232
+        self.assertTrue("Project:PRJNA16232" in record.dbxrefs, record.dbxrefs)
+        gb = record.format("gb")
+        self.assertTrue("\nDBLINK      Project:PRJNA16232\n" in gb, gb)
+        embl = record.format("embl")
+        self.assertTrue("XX\nPR   Project:PRJNA16232;\nXX\n" in embl, embl)
+
+    def test_structured_comment_parsing(self):
+        # GISAID_EpiFlu(TM)Data, HM138502.gbk has both 'comment' and 'structured_comment'
+        record = SeqIO.read(path.join('GenBank', 'HM138502.gbk'), 'genbank')
+        self.assertEqual(record.annotations['comment'],
+            "Swine influenza A (H1N1) virus isolated during human swine flu\noutbreak of 2009.")
+        self.assertEqual(record.annotations['structured_comment']['GISAID_EpiFlu(TM)Data']['Lineage'], 'swl')
+        self.assertEqual(len(record.annotations['structured_comment']['GISAID_EpiFlu(TM)Data']), 3)
+        # FluData structured comment
+        record = SeqIO.read(path.join('GenBank', 'EU851978.gbk'), 'genbank')
+        self.assertEqual(record.annotations['structured_comment']['FluData']['LabID'], '2008704957')
+        self.assertEqual(len(record.annotations['structured_comment']['FluData']), 5)
+        # Assembly-Data structured comment
+        record = SeqIO.read(path.join('GenBank', 'KF527485.gbk'), 'genbank')
+        self.assertEqual(record.annotations['structured_comment']['Assembly-Data']['Assembly Method'], 'Lasergene v. 10')
+        self.assertEqual(len(record.annotations['structured_comment']['Assembly-Data']), 2)
+        # No structured comment in NC_000932.gb, just a regular comment
+        record = SeqIO.read(path.join('GenBank', 'NC_000932.gb'), 'genbank')
+        self.assertFalse("structured_comment" in record.annotations)
+        self.assertEqual(record.annotations['comment'],
+                         'REVIEWED REFSEQ: This record has been curated by NCBI staff. The\n'
+                         'reference sequence was derived from AP000423.\n'
+                         'COMPLETENESS: full length.')
+
+
 if __name__ == "__main__":
     runner = unittest.TextTestRunner(verbosity=2)
     unittest.main(testRunner=runner)
diff --git a/Tests/test_GenomeDiagram.py b/Tests/test_GenomeDiagram.py
index 4a3a089..e8bf35e 100755
--- a/Tests/test_GenomeDiagram.py
+++ b/Tests/test_GenomeDiagram.py
@@ -24,7 +24,7 @@ try:
     from reportlab.lib.units import cm
 except ImportError:
     raise MissingPythonDependencyError(
-            "Install reportlab if you want to use Bio.Graphics.")
+        "Install reportlab if you want to use Bio.Graphics.")
 
 try:
     # The preferred PIL import has changed over time...
@@ -39,17 +39,11 @@ except ImportError:
     # bitmap format is attempted.
     renderPM = None
 
-# Biopython core
 from Bio import SeqIO
 from Bio.SeqFeature import SeqFeature, FeatureLocation
-from Bio import SeqUtils
 
-# Bio.Graphics.GenomeDiagram
 from Bio.Graphics.GenomeDiagram import FeatureSet, GraphSet, Track, Diagram
 from Bio.Graphics.GenomeDiagram import CrossLink
-# from Bio.Graphics.GenomeDiagram.Utilities import *
-
-# Currently private, but we test them here:
 from Bio.Graphics.GenomeDiagram._Graph import GraphData
 from Bio.Graphics.GenomeDiagram._Colors import ColorTranslator
 
@@ -70,21 +64,20 @@ def fill_and_border(base_color, alpha=0.5):
 
 
 def apply_to_window(sequence, window_size, function, step=None):
-    """ apply_to_window(sequence, window_size, function) -> [(int, float),(int, float),...]
-
-        o sequence      Bio.Seq.Seq object
+    """Returns a list of (position, value) tuples for fragments of the passed
+    sequence of length window_size (stepped by step), calculated by the passed
+    function.  Returned positions are the midpoint of each window.
 
-        o window_size   Int describing the length of sequence to consider
+    Arguments:
+        - sequence      - Bio.Seq.Seq object.
+        - window_size   - an integer describing the length of sequence to consider.
+        - step          - an integer describing the step to take between windows
+          (default = window_size//2).
 
-        o step          Int describing the step to take between windows
-                        (default = window_size//2)
+        - function      - Method or function that accepts a Bio.Seq.Seq object
+          as its sole argument and returns a single value.
 
-        o function      Method or function that accepts a Bio.Seq.Seq object
-                        as its sole argument and returns a single value
-
-        Returns a list of (position, value) tuples for fragments of the passed
-        sequence of length window_size (stepped by step), calculated by the
-        passed function.  Returned positions are the midpoint of each window.
+    apply_to_window(sequence, window_size, function) -> [(int, float),(int, float),...]
     """
     seqlen = len(sequence)      # Total length of sequence to be used
     if step is None:    # No step specified, so use half window-width or 1 if larger
@@ -118,18 +111,16 @@ def apply_to_window(sequence, window_size, function, step=None):
         value = function(fragment)
         results.append((middle, value))  # Add results to list
 
-    # Check on last sequence
-    # print(fragment)
-    # print(seq[-100:])
     return results      # Return the list of (position, value) results
 
 
 def calc_gc_content(sequence):
-    """ calc_gc_content(sequence)
+    """Returns the % G+C content in a passed sequence.
 
-        o sequence  A Bio.Seq.Seq object
+    Arguments:
+        - sequence  - a Bio.Seq.Seq object.
 
-        Returns the % G+C content in a passed sequence
+    calc_gc_content(sequence)
     """
     d = {}
     for nt in ['A', 'T', 'G', 'C']:
@@ -143,11 +134,12 @@ def calc_gc_content(sequence):
 
 
 def calc_at_content(sequence):
-    """ calc_at_content(sequence)
+    """Returns the % A+T content in a passed sequence.
 
-        o sequence  A Bio.Seq.Seq object
+    Arguments:
+        - sequence  - a Bio.Seq.Seq object.
 
-        Returns the % A+T content in a passed sequence
+    calc_at_content(sequence)
     """
     d = {}
     for nt in ['A', 'T', 'G', 'C']:
@@ -160,11 +152,12 @@ def calc_at_content(sequence):
 
 
 def calc_gc_skew(sequence):
-    """ calc_gc_skew(sequence)
+    """Returns the (G-C)/(G+C) GC skew in a passed sequence.
 
-        o sequence   A Bio.Seq.Seq object
+    Arguments:
+        - sequence   - a Bio.Seq.Seq object.
 
-        Returns the (G-C)/(G+C) GC skew in a passed sequence
+    calc_gc_skew(sequence)
     """
     g = sequence.count('G') + sequence.count('g')
     c = sequence.count('C') + sequence.count('c')
@@ -175,11 +168,12 @@ def calc_gc_skew(sequence):
 
 
 def calc_at_skew(sequence):
-    """ calc_at_skew(sequence)
+    """Returns the (A-T)/(A+T) AT skew in a passed sequence.
 
-        o sequence   A Bio.Seq.Seq object
+    Arguments:
+        - sequence   - a Bio.Seq.Seq object.
 
-        Returns the (A-T)/(A+T) AT skew in a passed sequence
+    calc_at_skew(sequence)
     """
     a = sequence.count('A') + sequence.count('a')
     t = sequence.count('T') + sequence.count('t')
@@ -274,7 +268,7 @@ class GraphTest(unittest.TestCase):
         gd.add_point((10, 20))
 
         assert gd[4:16] == [(5, 15), (10, 20)], \
-                "Unable to insert and retrieve points correctly"
+            "Unable to insert and retrieve points correctly"
 
 
 class LabelTest(unittest.TestCase):
@@ -287,7 +281,7 @@ class LabelTest(unittest.TestCase):
     def finish(self, name, circular=True):
         # And draw it...
         tracks = len(self.gdd.tracks)
-        # Work arround the page orientation code being too clever
+        # Work around the page orientation code being too clever
         # and flipping the h & w round:
         if tracks <= 3:
             orient = "landscape"
@@ -307,7 +301,8 @@ class LabelTest(unittest.TestCase):
             except renderPM.RenderPMError:
                 # Probably a font problem, e.g.
                 # RenderPMError: Can't setFont(Times-Roman) missing the T1 files?
-                # Originally <type 'exceptions.TypeError'>: makeT1Font() argument 2 must be string, not None
+                # Originally <type 'exceptions.TypeError'>: makeT1Font() argument 2
+                # must be string, not None
                 renderPM = None
             except IOError:
                 # Probably a library problem, e.g.
@@ -378,7 +373,7 @@ class SigilsTest(unittest.TestCase):
     def finish(self, name, circular=True):
         # And draw it...
         tracks = len(self.gdd.tracks)
-        # Work arround the page orientation code being too clever
+        # Work around the page orientation code being too clever
         # and flipping the h & w round:
         if tracks <= 3:
             orient = "landscape"
@@ -556,8 +551,8 @@ class SigilsTest(unittest.TestCase):
         """Feature ARROW sigil heads within bounding box."""
         self.long_sigils("ARROW")
 
-    def test_long_arrow_heads(self):
-        """Feature ARROW sigil heads within bounding box."""
+    def test_long_bigarrow_heads(self):
+        """Feature BIGARROW sigil heads within bounding box."""
         self.long_sigils("BIGARROW")
 
     def test_long_octo_heads(self):
@@ -577,6 +572,66 @@ class DiagramTest(unittest.TestCase):
         self.record = SeqIO.read(handle, "genbank")
         handle.close()
 
+        self.gdd = Diagram('Test Diagram')
+        # Add a track of features,
+        self.gdd.new_track(1, greytrack=True, name="CDS Features",
+                           greytrack_labels=0, height=0.5)
+
+    def tearDown(self):
+        del self.gdd
+
+    def test_str(self):
+        """Test diagram's info as string."""
+        expected = "\n<<class 'Bio.Graphics.GenomeDiagram._Diagram.Diagram'>: Test Diagram>" \
+                   "\n1 tracks" \
+                   "\nTrack 1: " \
+                   "\n<<class 'Bio.Graphics.GenomeDiagram._Track.Track'>: CDS Features>" \
+                   "\n0 sets" \
+                   "\n"
+        self.assertEqual(expected, str(self.gdd))
+
+    def test_add_track(self):
+        track = Track(name="Annotated Features")
+        self.gdd.add_track(track, 2)
+        self.assertEqual(2, len(self.gdd.get_tracks()))
+
+    def test_add_track_to_occupied_level(self):
+        new_track = self.gdd.get_tracks()[0]
+        self.gdd.add_track(new_track, 1)
+        self.assertEqual(2, len(self.gdd.get_tracks()))
+
+    def test_add_track_error(self):
+        """Test adding unspecified track."""
+        self.assertRaises(ValueError, self.gdd.add_track, None, 1)
+
+    def test_del_tracks(self):
+        self.gdd.del_track(1)
+        self.assertEqual(0, len(self.gdd.get_tracks()))
+
+    def test_get_tracks(self):
+        self.assertEqual(1, len(self.gdd.get_tracks()))
+
+    def test_move_track(self):
+        self.gdd.move_track(1, 2)
+        expected = "\n<<class 'Bio.Graphics.GenomeDiagram._Diagram.Diagram'>: Test Diagram>" \
+                   "\n1 tracks" \
+                   "\nTrack 2: " \
+                   "\n<<class 'Bio.Graphics.GenomeDiagram._Track.Track'>: CDS Features>" \
+                   "\n0 sets" \
+                   "\n"
+        self.assertEqual(expected, str(self.gdd))
+
+    def test_renumber(self):
+        """Test renumbering tracks."""
+        self.gdd.renumber_tracks(0)
+        expected = "\n<<class 'Bio.Graphics.GenomeDiagram._Diagram.Diagram'>: Test Diagram>" \
+                   "\n1 tracks" \
+                   "\nTrack 0: " \
+                   "\n<<class 'Bio.Graphics.GenomeDiagram._Track.Track'>: CDS Features>" \
+                   "\n0 sets" \
+                   "\n"
+        self.assertEqual(expected, str(self.gdd))
+
     def test_write_arguments(self):
         """Check how the write methods respond to output format arguments."""
         gdd = Diagram('Test Diagram')
@@ -587,15 +642,15 @@ class DiagramTest(unittest.TestCase):
             try:
                 gdd.write(filename, output)
                 assert False, \
-                       "Should have rejected %s as an output format" % output
-            except ValueError as e:
+                    "Should have rejected %s as an output format" % output
+            except ValueError:
                 # Good!
                 pass
             try:
                 gdd.write_to_string(output)
                 assert False, \
-                       "Should have rejected %s as an output format" % output
-            except ValueError as e:
+                    "Should have rejected %s as an output format" % output
+            except ValueError:
                 # Good!
                 pass
 
@@ -697,13 +752,13 @@ class DiagramTest(unittest.TestCase):
                 else:
                     color = "lightblue"
                 gds_features.add_feature(feature, color=color,
-                                            # label_position="middle",
-                                            # label_position="end",
-                                            label_position="start",
-                                            label_size=11,
-                                            # label_angle=90,
-                                            sigil="ARROW",
-                                            label=True)
+                                         # label_position="middle",
+                                         # label_position="end",
+                                         label_position="start",
+                                         label_size=11,
+                                         # label_angle=90,
+                                         sigil="ARROW",
+                                         label=True)
 
         # I want to include some strandless features, so for an example
         # will use EcoRI recognition sites etc.
@@ -745,17 +800,17 @@ class DiagramTest(unittest.TestCase):
 
         step = len(genbank_entry) // 200
         gds_at_gc.new_graph(apply_to_window(genbank_entry.seq, step, calc_gc_content, step),
-                        'GC content', style='line',
-                        color=colors.lightgreen,
-                        altcolor=colors.darkseagreen)
+                            'GC content', style='line',
+                            color=colors.lightgreen,
+                            altcolor=colors.darkseagreen)
         gds_at_gc.new_graph(apply_to_window(genbank_entry.seq, step, calc_at_content, step),
-                        'AT content', style='line',
-                        color=colors.orange,
-                        altcolor=colors.red)
+                            'AT content', style='line',
+                            color=colors.orange,
+                            altcolor=colors.red)
 
         # Finally draw it in both formats,
-        gdd.draw(format='linear', orientation='landscape',
-             tracklines=0, pagesize='A4', fragments=3)
+        gdd.draw(format='linear', orientation='landscape', tracklines=0,
+                 pagesize='A4', fragments=3)
         output_filename = os.path.join('Graphics', 'GD_by_meth_linear.pdf')
         gdd.write(output_filename, 'PDF')
 
@@ -775,8 +830,7 @@ class DiagramTest(unittest.TestCase):
                      greytrack_labels=10,
                      greytrack_font_color="red",
                      scale_format="SInt")
-        gdt2 = Track('gene features', greytrack=1,
-                   scale_largetick_interval=1e4)
+        gdt2 = Track('gene features', greytrack=1, scale_largetick_interval=1e4)
 
         # First add some feature sets:
         gdfsA = FeatureSet(name='CDS backgrounds')
@@ -800,7 +854,7 @@ class DiagramTest(unittest.TestCase):
                         dark, light = colors.burlywood, colors.bisque
                     # Background for CDS,
                     a = gdfsA.add_feature(SeqFeature(FeatureLocation(feature.location.start, feature.location.end, strand=0)),
-                                         color=dark)
+                                          color=dark)
                     # Background for gene,
                     b = gdfsB.add_feature(SeqFeature(FeatureLocation(prev_gene.location.start, prev_gene.location.end, strand=0)),
                                           color=dark)
@@ -894,7 +948,7 @@ class DiagramTest(unittest.TestCase):
         gdt2.add_set(gdfs2)
 
         gdt3 = Track('misc features and repeats', greytrack=1,
-                   scale_largetick_interval=1e4)
+                     scale_largetick_interval=1e4)
         gdt3.add_set(gdfs3)
         gdt3.add_set(gdfs4)
 
@@ -907,30 +961,23 @@ class DiagramTest(unittest.TestCase):
 
         graphdata1 = apply_to_window(genbank_entry.seq, step, calc_gc_skew, step)
         gdgs1.new_graph(graphdata1, 'GC Skew', style='bar',
-                color=colors.violet,
-                altcolor=colors.purple)
+                        color=colors.violet, altcolor=colors.purple)
 
-        gdt4 = Track(
-                'GC Skew (bar)',
-                height=1.94, greytrack=1,
-                scale_largetick_interval=1e4)
+        gdt4 = Track('GC Skew (bar)', height=1.94, greytrack=1,
+                     scale_largetick_interval=1e4)
         gdt4.add_set(gdgs1)
 
         gdgs2 = GraphSet('GC and AT Content')
         gdgs2.new_graph(apply_to_window(genbank_entry.seq, step, calc_gc_content, step),
-                        'GC content', style='line',
-                        color=colors.lightgreen,
+                        'GC content', style='line', color=colors.lightgreen,
                         altcolor=colors.darkseagreen)
 
         gdgs2.new_graph(apply_to_window(genbank_entry.seq, step, calc_at_content, step),
-                        'AT content', style='line',
-                        color=colors.orange,
+                        'AT content', style='line', color=colors.orange,
                         altcolor=colors.red)
 
-        gdt5 = Track(
-                'GC Content(green line), AT Content(red line)',
-                height=1.94, greytrack=1,
-                scale_largetick_interval=1e4)
+        gdt5 = Track('GC Content(green line), AT Content(red line)',
+                     height=1.94, greytrack=1, scale_largetick_interval=1e4)
         gdt5.add_set(gdgs2)
 
         gdgs3 = GraphSet('Di-nucleotide count')
@@ -952,25 +999,25 @@ class DiagramTest(unittest.TestCase):
 
         # Finally draw it in both formats, and full view and partial
         gdd.draw(format='circular', orientation='landscape',
-             tracklines=0, pagesize='A0')
+                 tracklines=0, pagesize='A0')
         output_filename = os.path.join('Graphics', 'GD_by_obj_circular.pdf')
         gdd.write(output_filename, 'PDF')
 
         gdd.circular = False
         gdd.draw(format='circular', orientation='landscape',
-             tracklines=0, pagesize='A0', start=3000, end=6300)
+                 tracklines=0, pagesize='A0', start=3000, end=6300)
         output_filename = os.path.join('Graphics', 'GD_by_obj_frag_circular.pdf')
         gdd.write(output_filename, 'PDF')
 
         gdd.draw(format='linear', orientation='landscape',
-             tracklines=0, pagesize='A0', fragments=3)
+                 tracklines=0, pagesize='A0', fragments=3)
         output_filename = os.path.join('Graphics', 'GD_by_obj_linear.pdf')
         gdd.write(output_filename, 'PDF')
 
         gdd.set_all_tracks("greytrack_labels", 2)
         gdd.draw(format='linear', orientation='landscape',
-             tracklines=0, pagesize=(30 * cm, 10 * cm), fragments=1,
-             start=3000, end=6300)
+                 tracklines=0, pagesize=(30 * cm, 10 * cm), fragments=1,
+                 start=3000, end=6300)
         output_filename = os.path.join('Graphics', 'GD_by_obj_frag_linear.pdf')
         gdd.write(output_filename, 'PDF')
 
diff --git a/Tests/test_KEGG.py b/Tests/test_KEGG.py
index 12e877f..9426b11 100644
--- a/Tests/test_KEGG.py
+++ b/Tests/test_KEGG.py
@@ -27,8 +27,19 @@ def t_KEGG_Enzyme(testfiles):
         fh = open(os.path.join("KEGG", file))
         print("Testing Bio.KEGG.Enzyme on " + file + "\n\n")
         records = Enzyme.parse(fh)
-        for record in records:
+        for i, record in enumerate(records):
             print(record)
+
+        fh.seek(0)
+        if i == 0:
+            print(Enzyme.read(fh))
+        else:
+            try:
+                print(Enzyme.read(fh))
+                assert False
+            except ValueError as e:
+                assert str(e) == 'More than one record found in handle'
+
         print("\n")
         fh.close()
 
diff --git a/Tests/test_KEGG_online.py b/Tests/test_KEGG_online.py
index da135e0..65b115b 100644
--- a/Tests/test_KEGG_online.py
+++ b/Tests/test_KEGG_online.py
@@ -15,11 +15,11 @@ import tempfile
 import requires_internet
 requires_internet.check()
 
-from Bio._py3k import _as_string
-
 from Bio.KEGG.KGML import KGML_parser
 from Bio.KEGG.REST import *
 
+from Bio import SeqIO
+
 # TODO - revert to using with statements once we drop
 # Python 2.6 and 2.7, see http://bugs.python.org/issue12487
 
@@ -120,25 +120,29 @@ class KEGGTests(unittest.TestCase):
     def test_find_compound_C7H10O5_formula(self):
         h = kegg_find("compound", "C7H10O5", "formula")
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/C7H10O5/formula")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/find/compound/C7H10O5/formula")
         h.close()
 
     def test_find_compound_O5C7_formula(self):
         h = kegg_find("compound", "O5C7", "formula")
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/O5C7/formula")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/find/compound/O5C7/formula")
         h.close()
 
     def test_find_compound_exact_mass(self):
         h = kegg_find("compound", "174.05", "exact_mass")
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/174.05/exact_mass")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/find/compound/174.05/exact_mass")
         h.close()
 
     def test_find_compound_weight(self):
         h = kegg_find("compound", "300-310", "mol_weight")
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/find/compound/300-310/mol_weight")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/find/compound/300-310/mol_weight")
         h.close()
 
     def test_get_cpd_C01290_plus_gl_G00092(self):
@@ -179,15 +183,34 @@ class KEGGTests(unittest.TestCase):
 
     def test_get_hsa_10458_plus_ece_Z5100_as_aaseq(self):
         h = kegg_get("hsa:10458+ece:Z5100", "aaseq")
-        h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+        data = SeqIO.parse(h, 'fasta')
+        self.assertEqual(len(list(data)), 2)
         h.close()
 
     def test_get_hsa_10458_list_ece_Z5100_as_aaseq(self):
         h = kegg_get(["hsa:10458", "ece:Z5100"], "aaseq")
-        data = _as_string(h.read())
-        self.assertEqual(data.count(">"), 2)
-        self.assertEqual(h.url, "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/aaseq")
+        data = SeqIO.parse(h, 'fasta')
+        self.assertEqual(len(list(data)), 2)
+        h.close()
+
+    def test_get_hsa_10458_plus_ece_Z5100_as_ntseq(self):
+        h = kegg_get("hsa:10458+ece:Z5100", "ntseq")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/ntseq")
+        data = SeqIO.parse(h, 'fasta')
+        self.assertEqual(len(list(data)), 2)
+        h.close()
+
+    def test_get_hsa_10458_list_ece_Z5100_as_ntseq(self):
+        h = kegg_get(["hsa:10458", "ece:Z5100"], "ntseq")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/get/hsa:10458+ece:Z5100/ntseq")
+        data = SeqIO.parse(h, 'fasta')
+        self.assertEqual(len(list(data)), 2)
         h.close()
 
     def test_get_hsa05130_image(self):
@@ -212,13 +235,15 @@ class KEGGTests(unittest.TestCase):
     def test_conv_ncbi_gi_hsa_10458_plus_ece_Z5100(self):
         h = kegg_conv("ncbi-gi", "hsa:10458+ece:Z5100")
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
         h.close()
 
     def test_conv_ncbi_gi_hsa_10458_list_ece_Z5100(self):
         h = kegg_conv("ncbi-gi", ["hsa:10458", "ece:Z5100"])
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/conv/ncbi-gi/hsa:10458+ece:Z5100")
         h.close()
 
     def test_link_pathway_hsa(self):
@@ -236,13 +261,15 @@ class KEGGTests(unittest.TestCase):
     def test_pathway_hsa_10458_plus_ece_Z5100(self):
         h = kegg_link("pathway", "hsa:10458+ece:Z5100")
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
         h.close()
 
     def test_pathway_hsa_10458_list_ece_Z5100(self):
         h = kegg_link("pathway", ["hsa:10458", "ece:Z5100"])
         h.read()
-        self.assertEqual(h.url, "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
+        self.assertEqual(h.url,
+                         "http://rest.kegg.jp/link/pathway/hsa:10458+ece:Z5100")
         h.close()
 
 
@@ -250,8 +277,7 @@ class KGMLPathwayTests(unittest.TestCase):
     """Tests with metabolic maps."""
 
     def test_parse_remote_pathway(self):
-        """Download a KEGG pathway from the KEGG server and write KGML."""
-        # Download the KEGG ko03070 pathway as a filehandle
+        """Download a KEGG pathway from the KEGG server and parse KGML."""
         h = kegg_get("ko03070", "kgml")
         pathway = KGML_parser.read(h)
         self.assertEqual(pathway.name, "path:ko03070")
diff --git a/Tests/test_KGML_graphics.py b/Tests/test_KGML_graphics.py
index f894387..50b68d7 100644
--- a/Tests/test_KGML_graphics.py
+++ b/Tests/test_KGML_graphics.py
@@ -18,6 +18,7 @@ from Bio.Graphics.ColorSpiral import ColorSpiral
 # Do we have ReportLab?  Raise error if not present.
 from Bio import MissingExternalDependencyError
 try:
+    # Not actually using these imports directly:
     from reportlab.pdfgen.canvas import Canvas
     from reportlab.lib.pagesizes import A4
     from reportlab.lib.colors import HexColor
@@ -26,7 +27,7 @@ except ImportError:
         "Install reportlab if you want to use Bio.Graphics.")
 
 try:
-   c = HexColor('#8080F780')
+    c = HexColor('#8080F780')
 except TypeError:
     # Known to fail under ReportLab 2.6 with:
     # unsupported operand type(s) for &: 'int' and 'float'
@@ -66,8 +67,8 @@ class PathwayData(object):
 class KGMLPathwayTest(unittest.TestCase):
     """Import XML file and write KGML
 
-    Import the ko01100 metabolic map from a local .xml KGML file, 
-    and write valid KGML output for each
+    Import the ko01100 metabolic map from a local .xml KGML file,
+    and write valid KGML output for each.
     """
     def setUp(self):
         # Does our output directory exist?  If not, create it
@@ -182,7 +183,6 @@ class KGMLPathwayTest(unittest.TestCase):
             kgml_map.draw(p[1].output_stem + '_transparency.pdf')
 
 
-
 if __name__ == '__main__':
     runner = unittest.TextTestRunner(verbosity=2)
     unittest.main(testRunner=runner)
diff --git a/Tests/test_MMCIF.py b/Tests/test_MMCIF.py
index f447acf..a062be9 100644
--- a/Tests/test_MMCIF.py
+++ b/Tests/test_MMCIF.py
@@ -10,6 +10,7 @@
 """Unit tests for the MMCIF portion of the Bio.PDB module."""
 
 import unittest
+import warnings
 
 try:
     import numpy
@@ -27,41 +28,67 @@ from Bio.Alphabet import generic_protein
 from Bio.PDB.PDBExceptions import PDBConstructionException, PDBConstructionWarning
 
 from Bio.PDB import PPBuilder, CaPPBuilder
-from Bio.PDB.MMCIFParser import MMCIFParser
+from Bio.PDB.MMCIFParser import MMCIFParser, FastMMCIFParser
 
 
 class ParseReal(unittest.TestCase):
     """Testing with real CIF file(s)."""
 
-    def test_parser(self):
+    def test_parsers(self):
         """Extract polypeptides from 1A80."""
+
         parser = MMCIFParser()
+        fast_parser = FastMMCIFParser()
+
         structure = parser.get_structure("example", "PDB/1A8O.cif")
+        f_structure = fast_parser.get_structure("example", "PDB/1A8O.cif")
+
         self.assertEqual(len(structure), 1)
+        self.assertEqual(len(f_structure), 1)
+
         for ppbuild in [PPBuilder(), CaPPBuilder()]:
             # ==========================================================
             # Check that serial_num (model column) is stored properly
             self.assertEqual(structure[0].serial_num, 1)
+            self.assertEqual(f_structure[0].serial_num, structure[0].serial_num)
+
             # First try allowing non-standard amino acids,
             polypeptides = ppbuild.build_peptides(structure[0], False)
+            f_polypeptides = ppbuild.build_peptides(f_structure[0], False)
+
             self.assertEqual(len(polypeptides), 1)
+            self.assertEqual(len(f_polypeptides), 1)
+
             pp = polypeptides[0]
+            f_pp = f_polypeptides[0]
+
             # Check the start and end positions
             self.assertEqual(pp[0].get_id()[1], 151)
             self.assertEqual(pp[-1].get_id()[1], 220)
+
+            self.assertEqual(f_pp[0].get_id()[1], 151)
+            self.assertEqual(f_pp[-1].get_id()[1], 220)
+
             # Check the sequence
             s = pp.get_sequence()
+            f_s = f_pp.get_sequence()
+
+            self.assertEqual(s, f_s)  # enough to test this
+
             self.assertTrue(isinstance(s, Seq))
             self.assertEqual(s.alphabet, generic_protein)
+
             # Here non-standard MSE are shown as M
             self.assertEqual("MDIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNWMTETLLVQ"
                              "NANPDCKTILKALGPGATLEEMMTACQG", str(s))
+
             # ==========================================================
             # Now try strict version with only standard amino acids
             # Should ignore MSE 151 at start, and then break the chain
             # at MSE 185, and MSE 214,215
             polypeptides = ppbuild.build_peptides(structure[0], True)
             self.assertEqual(len(polypeptides), 3)
+
             # First fragment
             pp = polypeptides[0]
             self.assertEqual(pp[0].get_id()[1], 152)
@@ -70,6 +97,7 @@ class ParseReal(unittest.TestCase):
             self.assertTrue(isinstance(s, Seq))
             self.assertEqual(s.alphabet, generic_protein)
             self.assertEqual("DIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNW", str(s))
+
             # Second fragment
             pp = polypeptides[1]
             self.assertEqual(pp[0].get_id()[1], 186)
@@ -78,6 +106,7 @@ class ParseReal(unittest.TestCase):
             self.assertTrue(isinstance(s, Seq))
             self.assertEqual(s.alphabet, generic_protein)
             self.assertEqual("TETLLVQNANPDCKTILKALGPGATLEE", str(s))
+
             # Third fragment
             pp = polypeptides[2]
             self.assertEqual(pp[0].get_id()[1], 216)
@@ -89,50 +118,82 @@ class ParseReal(unittest.TestCase):
 
     def testModels(self):
         """Test file with multiple models"""
-        parser = MMCIFParser()
-        structure = parser.get_structure("example", "PDB/1LCD.cif")
+
+        parser = MMCIFParser(QUIET=1)
+        f_parser = FastMMCIFParser(QUIET=1)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', PDBConstructionWarning)
+            structure = parser.get_structure("example", "PDB/1LCD.cif")
+            f_structure = f_parser.get_structure("example", "PDB/1LCD.cif")
+
         self.assertEqual(len(structure), 3)
+        self.assertEqual(len(f_structure), 3)
+
         for ppbuild in [PPBuilder(), CaPPBuilder()]:
-                # ==========================================================
-                # Check that serial_num (model column) is stored properly
-                self.assertEqual(structure[0].serial_num, 1)
-                self.assertEqual(structure[1].serial_num, 2)
-                self.assertEqual(structure[2].serial_num, 3)
-                # First try allowing non-standard amino acids,
-                polypeptides = ppbuild.build_peptides(structure[0], False)
-                self.assertEqual(len(polypeptides), 1)
-                pp = polypeptides[0]
-                # Check the start and end positions
-                self.assertEqual(pp[0].get_id()[1], 1)
-                self.assertEqual(pp[-1].get_id()[1], 51)
-                # Check the sequence
-                s = pp.get_sequence()
-                self.assertTrue(isinstance(s, Seq))
-                self.assertEqual(s.alphabet, generic_protein)
-                # Here non-standard MSE are shown as M
-                self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
-                                 str(s))
-                # ==========================================================
-                # Now try strict version with only standard amino acids
-                polypeptides = ppbuild.build_peptides(structure[0], True)
-                self.assertEqual(len(polypeptides), 1)
-                pp = polypeptides[0]
-                # Check the start and end positions
-                self.assertEqual(pp[0].get_id()[1], 1)
-                self.assertEqual(pp[-1].get_id()[1], 51)
-                # Check the sequence
-                s = pp.get_sequence()
-                self.assertTrue(isinstance(s, Seq))
-                self.assertEqual(s.alphabet, generic_protein)
-                self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
-                                 str(s))
+            # ==========================================================
+            # Check that serial_num (model column) is stored properly
+            self.assertEqual(structure[0].serial_num, 1)
+            self.assertEqual(structure[1].serial_num, 2)
+            self.assertEqual(structure[2].serial_num, 3)
+            # First try allowing non-standard amino acids,
+            polypeptides = ppbuild.build_peptides(structure[0], False)
+            self.assertEqual(len(polypeptides), 1)
+            pp = polypeptides[0]
+            # Check the start and end positions
+            self.assertEqual(pp[0].get_id()[1], 1)
+            self.assertEqual(pp[-1].get_id()[1], 51)
+            # Check the sequence
+            s = pp.get_sequence()
+            self.assertTrue(isinstance(s, Seq))
+            self.assertEqual(s.alphabet, generic_protein)
+            # Here non-standard MSE are shown as M
+            self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
+                             str(s))
+            # ==========================================================
+            # Now try strict version with only standard amino acids
+            polypeptides = ppbuild.build_peptides(structure[0], True)
+            self.assertEqual(len(polypeptides), 1)
+            pp = polypeptides[0]
+            # Check the start and end positions
+            self.assertEqual(pp[0].get_id()[1], 1)
+            self.assertEqual(pp[-1].get_id()[1], 51)
+            # Check the sequence
+            s = pp.get_sequence()
+            self.assertTrue(isinstance(s, Seq))
+            self.assertEqual(s.alphabet, generic_protein)
+            self.assertEqual("MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR",
+                             str(s))
 
-        parser = MMCIFParser()
         # This structure contains several models with multiple lengths.
         # The tests were failing.
         structure = parser.get_structure("example", "PDB/2OFG.cif")
         self.assertEqual(len(structure), 3)
 
+    def test_insertions(self):
+        """Test file with residue insertion codes"""
+        parser = MMCIFParser(QUIET=1)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', PDBConstructionWarning)
+            structure = parser.get_structure("example", "PDB/4zhl.cif")
+        for ppbuild in [PPBuilder(), CaPPBuilder()]:
+            # First try allowing non-standard amino acids,
+            polypeptides = ppbuild.build_peptides(structure[0], False)
+            self.assertEqual(len(polypeptides), 2)
+            pp = polypeptides[0]
+            # Check the start and end positions (first segment only)
+            self.assertEqual(pp[0].get_id()[1], 16)
+            self.assertEqual(pp[-1].get_id()[1], 244)
+            # Check the sequence
+            refseq = "IIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGR" \
+                     "SRLNSNTQGEMKFEVENLILHKDYSADTLAYHNDIALLKIRSKEGRCAQPSRTIQTIALPSMY" \
+                     "NDPQFGTSCEITGFGKEQSTDYLYPEQLKMTVVKLISHRECQQPHYYGSEVTTKMLCAADPQW" \
+                     "KTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPWIRSHTKE"
+
+            s = pp.get_sequence()
+            self.assertTrue(isinstance(s, Seq))
+            self.assertEqual(s.alphabet, generic_protein)
+            self.assertEqual(refseq, str(s))
+
     def test_filehandle(self):
         """Test if the parser can handle file handle as well as filename"""
         parser = MMCIFParser()
diff --git a/Tests/test_Mafft_tool.py b/Tests/test_Mafft_tool.py
index 251c70d..a591ffd 100644
--- a/Tests/test_Mafft_tool.py
+++ b/Tests/test_Mafft_tool.py
@@ -163,11 +163,11 @@ class MafftApplication(unittest.TestCase):
         cmdline.set_parameter("--treeout", True)
         cmdline.set_parameter("nuc", True)
         self.assertEqual(str(eval(repr(cmdline))), str(cmdline))
-        self.assertEqual(str(cmdline), mafft_exe
-                         + " --localpair --weighti 4.2 --retree 5 "
-                         + "--maxiterate 200 --nofft --op 2.04 --ep 0.51"
-                         + " --lop 0.233 --lep 0.2 --reorder --treeout"
-                         + " --nuc Fasta/f002")
+        self.assertEqual(str(cmdline), mafft_exe +
+                         " --localpair --weighti 4.2 --retree 5 " +
+                         "--maxiterate 200 --nofft --op 2.04 --ep 0.51" +
+                         " --lop 0.233 --lep 0.2 --reorder --treeout" +
+                         " --nuc Fasta/f002")
         stdoutdata, stderrdata = cmdline()
         self.assertTrue(stdoutdata.startswith(">gi|1348912|gb|G26680|G26680"))
         self.assertTrue("$#=0" not in stderrdata)
diff --git a/Tests/test_Motif.py b/Tests/test_Motif.py
deleted file mode 100644
index cb532c4..0000000
--- a/Tests/test_Motif.py
+++ /dev/null
@@ -1,1536 +0,0 @@
-# Copyright 2008 by Bartek Wilczynski.  All rights reserved.
-# Adapted from test_Mymodule.py by Jeff Chang
-# This code is part of the Biopython distribution and governed by its
-# license.  Please see the LICENSE file that should have been included
-# as part of this package.
-
-import os
-import unittest
-import warnings
-
-from Bio import BiopythonDeprecationWarning
-with warnings.catch_warnings():
-    warnings.simplefilter("ignore", BiopythonDeprecationWarning)
-    from Bio import Motif
-
-from Bio.Alphabet import IUPAC
-from Bio.Seq import Seq
-
-
-class MotifTestsBasic(unittest.TestCase):
-    def setUp(self):
-        self.PFMin = open("Motif/SRF.pfm")
-        self.SITESin = open("Motif/Arnt.sites")
-        self.TFout = "Motif/tf.out"
-        self.FAout = "Motif/fa.out"
-        self.PFMout = "Motif/fa.out"
-        self.m = Motif.Motif()
-        self.m.add_instance(Seq("ATATA", self.m.alphabet))
-
-    def tearDown(self):
-        self.PFMin.close()
-        self.SITESin.close()
-        if os.path.exists(self.TFout):
-            os.remove(self.TFout)
-        if os.path.exists(self.FAout):
-            os.remove(self.FAout)
-
-    def test_alignace_parsing(self):
-        """Test if Motif can parse AlignAce output files.
-        """
-        from Bio.Motif.Parsers import AlignAce
-        handle = open("Motif/alignace.out")
-        record = AlignAce.read(handle)
-        handle.close()
-        self.assertEqual(record.ver, "AlignACE 4.0 05/13/04\n")
-        self.assertEqual(record.cmd_line, "./AlignACE -i test.fa \n")
-        self.assertEqual(len(record.param_dict), 7)
-        self.assertEqual(record.param_dict['expect'], "10")
-        self.assertEqual(record.param_dict['gcback'], "0.38")
-        self.assertEqual(record.param_dict['minpass'], "200")
-        self.assertEqual(record.param_dict['seed'], "1227623309")
-        self.assertEqual(record.param_dict['numcols'], "10")
-        self.assertEqual(record.param_dict['undersample'], "1")
-        self.assertEqual(record.param_dict['oversample'], "1")
-        self.assertEqual(len(record.seq_dict), 10)
-        self.assertEqual(record.seq_dict[0], "SEQ1; M: CTCAATCGTAGA at 52\n")
-        self.assertEqual(record.seq_dict[1], "SEQ2; M: CTCAATCGTAGA at 172\n")
-        self.assertEqual(record.seq_dict[2], "SEQ3; M: CTCAATCGTAGA at 112\n")
-        self.assertEqual(record.seq_dict[3], "SEQ4; M: CTCAATCGTAGA at 173\n")
-        self.assertEqual(record.seq_dict[4], "SEQ5; M: CTCAATCGTAGA at 185\n")
-        self.assertEqual(record.seq_dict[5], "SEQ6; M: CTCAATCGTAGA at 105\n")
-        self.assertEqual(record.seq_dict[6], "SEQ7; M: CTCAATCGTAGA at 177\n")
-        self.assertEqual(record.seq_dict[7], "SEQ8; M: CTCAATCGTAGA at 172\n")
-        self.assertEqual(record.seq_dict[8], "SEQ9; M: CTCAATCGTAGA at 93\n")
-        self.assertEqual(record.seq_dict[9], "SEQ10; M: CTCAATCGTAGA at 3\n")
-        self.assertEqual(len(record.motifs), 16)
-        self.assertEqual(record.motifs[0].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[0].instances), 11)
-        self.assertEqual(str(record.motifs[0].instances[0]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[1]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[2]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[3]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[4]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[5]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[6]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[7]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[8]), "TCTACGATTGAG")
-        self.assertEqual(str(record.motifs[0].instances[9]), "TCAAAGATAGAG")
-        self.assertEqual(str(record.motifs[0].instances[10]), "TCTACGATTGAG")
-        self.assertEqual(record.motifs[0].mask, [1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1])
-        self.assertAlmostEqual(record.motifs[0].score, 57.9079)
-        self.assertEqual(record.motifs[1].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[1].instances), 22)
-        self.assertEqual(str(record.motifs[1].instances[0]), "GCGAAGGAAGCAGCGCGTGTG")
-        self.assertEqual(str(record.motifs[1].instances[1]), "GGCACCGCCTCTACGATTGAG")
-        self.assertEqual(str(record.motifs[1].instances[2]), "CAGAGCTTAGCATTGAACGCG")
-        self.assertEqual(str(record.motifs[1].instances[3]), "CTAATGAAAGCAATGAGAGTG")
-        self.assertEqual(str(record.motifs[1].instances[4]), "CTTGTGCCCTCTAAGCGTCCG")
-        self.assertEqual(str(record.motifs[1].instances[5]), "GAGCACGACGCTTTGTACCTG")
-        self.assertEqual(str(record.motifs[1].instances[6]), "CGGCACTTAGCAGCGTATCGT")
-        self.assertEqual(str(record.motifs[1].instances[7]), "CTGGTTTCATCTACGATTGAG")
-        self.assertEqual(str(record.motifs[1].instances[8]), "GGGCCAATAGCGGCGCCGGAG")
-        self.assertEqual(str(record.motifs[1].instances[9]), "GTGGAGTTATCTTAGTGCGCG")
-        self.assertEqual(str(record.motifs[1].instances[10]), "GAGAGGTTATCTACGATTGAG")
-        self.assertEqual(str(record.motifs[1].instances[11]), "CTGCTCCCCGCATACAGCGCG")
-        self.assertEqual(str(record.motifs[1].instances[12]), "CAGAACCGAGGTCCGGTACGG")
-        self.assertEqual(str(record.motifs[1].instances[13]), "GTGCCCCAAGCTTACCCAGGG")
-        self.assertEqual(str(record.motifs[1].instances[14]), "CGCCTCTGATCTACGATTGAG")
-        self.assertEqual(str(record.motifs[1].instances[15]), "GTGCTCATAGGGACGTCGCGG")
-        self.assertEqual(str(record.motifs[1].instances[16]), "CTGCCCCCCGCATAGTAGGGG")
-        self.assertEqual(str(record.motifs[1].instances[17]), "GTAAAGAAATCGATGTGCCAG")
-        self.assertEqual(str(record.motifs[1].instances[18]), "CACCTGCAATTGCTGGCAGCG")
-        self.assertEqual(str(record.motifs[1].instances[19]), "GGCGGGCCATCCCTGTATGAA")
-        self.assertEqual(str(record.motifs[1].instances[20]), "CTCCAGGTCGCATGGAGAGAG")
-        self.assertEqual(str(record.motifs[1].instances[21]), "CCTCGGATCGCTTGGGAAGAG")
-        self.assertEqual(record.motifs[1].mask, [1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1])
-        self.assertAlmostEqual(record.motifs[1].score, 19.6235)
-
-        self.assertEqual(record.motifs[2].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[2].instances), 18)
-        self.assertEqual(str(record.motifs[2].instances[0]), "GTGCGCGAAGGAAGCAGCGCG")
-        self.assertEqual(str(record.motifs[2].instances[1]), "CAGAGCTTAGCATTGAACGCG")
-        self.assertEqual(str(record.motifs[2].instances[2]), "GTGCCCGATGACCACCCGTCG")
-        self.assertEqual(str(record.motifs[2].instances[3]), "GCCCTCTAAGCGTCCGCGGAT")
-        self.assertEqual(str(record.motifs[2].instances[4]), "GAGCACGACGCTTTGTACCTG")
-        self.assertEqual(str(record.motifs[2].instances[5]), "CGGCACTTAGCAGCGTATCGT")
-        self.assertEqual(str(record.motifs[2].instances[6]), "GGGCCAATAGCGGCGCCGGAG")
-        self.assertEqual(str(record.motifs[2].instances[7]), "GCGCACTAAGATAACTCCACG")
-        self.assertEqual(str(record.motifs[2].instances[8]), "CGGCCCGTTGTCCAGCAGACG")
-        self.assertEqual(str(record.motifs[2].instances[9]), "CTGCTCCCCGCATACAGCGCG")
-        self.assertEqual(str(record.motifs[2].instances[10]), "GTGCCCCAAGCTTACCCAGGG")
-        self.assertEqual(str(record.motifs[2].instances[11]), "GTGCTCATAGGGACGTCGCGG")
-        self.assertEqual(str(record.motifs[2].instances[12]), "CTGCCCCCCGCATAGTAGGGG")
-        self.assertEqual(str(record.motifs[2].instances[13]), "CGCCGCCATGCGACGCAGAGG")
-        self.assertEqual(str(record.motifs[2].instances[14]), "AACCTCTAAGCATACTCTACG")
-        self.assertEqual(str(record.motifs[2].instances[15]), "GACCTGGAGGCTTAGACTTGG")
-        self.assertEqual(str(record.motifs[2].instances[16]), "GCGCTCTTCCCAAGCGATCCG")
-        self.assertEqual(str(record.motifs[2].instances[17]), "GGGCCGTCAGCTCTCAAGTCT")
-        self.assertEqual(record.motifs[2].mask, [1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[2].score, 19.1804)
-
-        self.assertEqual(record.motifs[3].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[3].instances), 16)
-        self.assertEqual(str(record.motifs[3].instances[0]), "GCCCCAAGCTTACCCAGGGAC")
-        self.assertEqual(str(record.motifs[3].instances[1]), "GCCGTCTGCTGGACAACGGGC")
-        self.assertEqual(str(record.motifs[3].instances[2]), "GCCGACGGGTGGTCATCGGGC")
-        self.assertEqual(str(record.motifs[3].instances[3]), "GCCAATAGCGGCGCCGGAGTC")
-        self.assertEqual(str(record.motifs[3].instances[4]), "GCCCCCCGCATAGTAGGGGGA")
-        self.assertEqual(str(record.motifs[3].instances[5]), "GCCCGTACCGGACCTCGGTTC")
-        self.assertEqual(str(record.motifs[3].instances[6]), "GCCTCATGTACCGGAAGGGAC")
-        self.assertEqual(str(record.motifs[3].instances[7]), "GACACGCGCCTGGGAGGGTTC")
-        self.assertEqual(str(record.motifs[3].instances[8]), "GCCTTTGGCCTTGGATGAGAA")
-        self.assertEqual(str(record.motifs[3].instances[9]), "GGCCCTCGGATCGCTTGGGAA")
-        self.assertEqual(str(record.motifs[3].instances[10]), "GCATGTTGGGAATCCGCGGAC")
-        self.assertEqual(str(record.motifs[3].instances[11]), "GACACGCGCTGTATGCGGGGA")
-        self.assertEqual(str(record.motifs[3].instances[12]), "GCCAGGTACAAAGCGTCGTGC")
-        self.assertEqual(str(record.motifs[3].instances[13]), "GCGATCAGCTTGTGGGCGTGC")
-        self.assertEqual(str(record.motifs[3].instances[14]), "GACAAATCGGATACTGGGGCA")
-        self.assertEqual(str(record.motifs[3].instances[15]), "GCACTTAGCAGCGTATCGTTA")
-        self.assertEqual(record.motifs[3].mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1])
-        self.assertAlmostEqual(record.motifs[3].score, 18.0097)
-        self.assertEqual(record.motifs[4].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[4].instances), 15)
-        self.assertEqual(str(record.motifs[4].instances[0]), "CGGCACAGAGCTT")
-        self.assertEqual(str(record.motifs[4].instances[1]), "ATCCGCGGACGCT")
-        self.assertEqual(str(record.motifs[4].instances[2]), "CGCCTGGGAGGGT")
-        self.assertEqual(str(record.motifs[4].instances[3]), "CGGAAGGGACGTT")
-        self.assertEqual(str(record.motifs[4].instances[4]), "ACACACAGACGGT")
-        self.assertEqual(str(record.motifs[4].instances[5]), "TGCCAGAGAGGTT")
-        self.assertEqual(str(record.motifs[4].instances[6]), "AGACTGAGACGTT")
-        self.assertEqual(str(record.motifs[4].instances[7]), "AATCGTAGAGGAT")
-        self.assertEqual(str(record.motifs[4].instances[8]), "CGTCTCGTAGGGT")
-        self.assertEqual(str(record.motifs[4].instances[9]), "CGTCGCGGAGGAT")
-        self.assertEqual(str(record.motifs[4].instances[10]), "CTTCTTAGACGCT")
-        self.assertEqual(str(record.motifs[4].instances[11]), "CGACGCAGAGGAT")
-        self.assertEqual(str(record.motifs[4].instances[12]), "ATGCTTAGAGGTT")
-        self.assertEqual(str(record.motifs[4].instances[13]), "AGACTTGGGCGAT")
-        self.assertEqual(str(record.motifs[4].instances[14]), "CGACCTGGAGGCT")
-        self.assertEqual(record.motifs[4].mask, [1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1])
-        self.assertAlmostEqual(record.motifs[4].score, 16.8287)
-        self.assertEqual(record.motifs[5].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[5].instances), 18)
-        self.assertEqual(str(record.motifs[5].instances[0]), "GTGCGCGAAGGAAGCAGCGCGTG")
-        self.assertEqual(str(record.motifs[5].instances[1]), "TTGAGCCGAGTAAAGGGCTGGTG")
-        self.assertEqual(str(record.motifs[5].instances[2]), "CAATGCTAAGCTCTGTGCCGACG")
-        self.assertEqual(str(record.motifs[5].instances[3]), "CAACTCTCTATGTAGTGCCCGAG")
-        self.assertEqual(str(record.motifs[5].instances[4]), "CGACGCTTTGTACCTGGCTTGCG")
-        self.assertEqual(str(record.motifs[5].instances[5]), "CGAGTCAATGACACGCGCCTGGG")
-        self.assertEqual(str(record.motifs[5].instances[6]), "CGATACGCTGCTAAGTGCCGTCC")
-        self.assertEqual(str(record.motifs[5].instances[7]), "CCGGGCCAATAGCGGCGCCGGAG")
-        self.assertEqual(str(record.motifs[5].instances[8]), "CCACGCTTCGACACGTGGTATAG")
-        self.assertEqual(str(record.motifs[5].instances[9]), "CCGAGCCTCATGTACCGGAAGGG")
-        self.assertEqual(str(record.motifs[5].instances[10]), "CTGCTCCCCGCATACAGCGCGTG")
-        self.assertEqual(str(record.motifs[5].instances[11]), "CCGAGGTCCGGTACGGGCAAGCC")
-        self.assertEqual(str(record.motifs[5].instances[12]), "GTGCTCATAGGGACGTCGCGGAG")
-        self.assertEqual(str(record.motifs[5].instances[13]), "CCCTACTATGCGGGGGGCAGGTC")
-        self.assertEqual(str(record.motifs[5].instances[14]), "GCCAGCAATTGCAGGTGGTCGTG")
-        self.assertEqual(str(record.motifs[5].instances[15]), "CTCTGCGTCGCATGGCGGCGTGG")
-        self.assertEqual(str(record.motifs[5].instances[16]), "GGAGGCTTAGACTTGGGCGATAC")
-        self.assertEqual(str(record.motifs[5].instances[17]), "GCATGGAGAGAGATCCGGAGGAG")
-        self.assertEqual(record.motifs[5].mask, [1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1])
-        self.assertAlmostEqual(record.motifs[5].score, 15.0441)
-        self.assertEqual(record.motifs[6].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[6].instances), 20)
-        self.assertEqual(str(record.motifs[6].instances[0]), "GCGCGTGTGTGTAAC")
-        self.assertEqual(str(record.motifs[6].instances[1]), "GCACAGAGCTTAGCA")
-        self.assertEqual(str(record.motifs[6].instances[2]), "GGTGGTCATCGGGCA")
-        self.assertEqual(str(record.motifs[6].instances[3]), "GCGCGTGTCATTGAC")
-        self.assertEqual(str(record.motifs[6].instances[4]), "GGACGGCACTTAGCA")
-        self.assertEqual(str(record.motifs[6].instances[5]), "GCGCGTCCCGGGCCA")
-        self.assertEqual(str(record.motifs[6].instances[6]), "GCTCGGCCCGTTGTC")
-        self.assertEqual(str(record.motifs[6].instances[7]), "GCGCGTGTCCTTTAA")
-        self.assertEqual(str(record.motifs[6].instances[8]), "GCTGATCGCTGCTCC")
-        self.assertEqual(str(record.motifs[6].instances[9]), "GCCCGTACCGGACCT")
-        self.assertEqual(str(record.motifs[6].instances[10]), "GGACGTCGCGGAGGA")
-        self.assertEqual(str(record.motifs[6].instances[11]), "GCGGGGGGCAGGTCA")
-        self.assertEqual(str(record.motifs[6].instances[12]), "GGACGTACTGGCACA")
-        self.assertEqual(str(record.motifs[6].instances[13]), "GCAGGTGGTCGTGCA")
-        self.assertEqual(str(record.motifs[6].instances[14]), "GCGCATACCTTAACA")
-        self.assertEqual(str(record.motifs[6].instances[15]), "GCACGGGACTTCAAC")
-        self.assertEqual(str(record.motifs[6].instances[16]), "GCACGTAGCTGGTAA")
-        self.assertEqual(str(record.motifs[6].instances[17]), "GCTCGTCTATGGTCA")
-        self.assertEqual(str(record.motifs[6].instances[18]), "GCGCATGCTGGATCC")
-        self.assertEqual(str(record.motifs[6].instances[19]), "GGCCGTCAGCTCTCA")
-        self.assertEqual(record.motifs[6].mask, [1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[6].score, 13.3145)
-        self.assertEqual(record.motifs[7].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[7].instances), 20)
-        self.assertEqual(str(record.motifs[7].instances[0]), "GAACCGAGGTCCGGTACGGGC")
-        self.assertEqual(str(record.motifs[7].instances[1]), "GCCCCCCGCATAGTAGGGGGA")
-        self.assertEqual(str(record.motifs[7].instances[2]), "GTCCCTGGGTAAGCTTGGGGC")
-        self.assertEqual(str(record.motifs[7].instances[3]), "ACTCCACGCTTCGACACGTGG")
-        self.assertEqual(str(record.motifs[7].instances[4]), "ATCCTCTGCGTCGCATGGCGG")
-        self.assertEqual(str(record.motifs[7].instances[5]), "GTTCAATGCTAAGCTCTGTGC")
-        self.assertEqual(str(record.motifs[7].instances[6]), "GCTCATAGGGACGTCGCGGAG")
-        self.assertEqual(str(record.motifs[7].instances[7]), "GTCCCGGGCCAATAGCGGCGC")
-        self.assertEqual(str(record.motifs[7].instances[8]), "GCACTTAGCAGCGTATCGTTA")
-        self.assertEqual(str(record.motifs[7].instances[9]), "GGCCCTCGGATCGCTTGGGAA")
-        self.assertEqual(str(record.motifs[7].instances[10]), "CTGCTGGACAACGGGCCGAGC")
-        self.assertEqual(str(record.motifs[7].instances[11]), "GGGCACTACATAGAGAGTTGC")
-        self.assertEqual(str(record.motifs[7].instances[12]), "AGCCTCCAGGTCGCATGGAGA")
-        self.assertEqual(str(record.motifs[7].instances[13]), "AATCGTAGATCAGAGGCGAGA")
-        self.assertEqual(str(record.motifs[7].instances[14]), "GAACTCCACTAAGACTTGAGA")
-        self.assertEqual(str(record.motifs[7].instances[15]), "GAGCAGCGATCAGCTTGTGGG")
-        self.assertEqual(str(record.motifs[7].instances[16]), "GCCAGGTACAAAGCGTCGTGC")
-        self.assertEqual(str(record.motifs[7].instances[17]), "AGTCAATGACACGCGCCTGGG")
-        self.assertEqual(str(record.motifs[7].instances[18]), "GGTCATGGAATCTTATGTAGC")
-        self.assertEqual(str(record.motifs[7].instances[19]), "GTAGATAACAGAGGTCGGGGG")
-        self.assertEqual(record.motifs[7].mask, [1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[7].score, 11.6098)
-        self.assertEqual(record.motifs[8].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[8].instances), 14)
-        self.assertEqual(str(record.motifs[8].instances[0]), "CCGAGTAAAGGGCTG")
-        self.assertEqual(str(record.motifs[8].instances[1]), "GTGGTCATCGGGCAC")
-        self.assertEqual(str(record.motifs[8].instances[2]), "GATAACAGAGGTCGG")
-        self.assertEqual(str(record.motifs[8].instances[3]), "CGGCGCCGGAGTCTG")
-        self.assertEqual(str(record.motifs[8].instances[4]), "GCGCGTCCCGGGCCA")
-        self.assertEqual(str(record.motifs[8].instances[5]), "CTGGACAACGGGCCG")
-        self.assertEqual(str(record.motifs[8].instances[6]), "CGGATACTGGGGCAG")
-        self.assertEqual(str(record.motifs[8].instances[7]), "GGGAGCAGCGATCAG")
-        self.assertEqual(str(record.motifs[8].instances[8]), "CAGAACCGAGGTCCG")
-        self.assertEqual(str(record.motifs[8].instances[9]), "GGGTCCCTGGGTAAG")
-        self.assertEqual(str(record.motifs[8].instances[10]), "GTGCTCATAGGGACG")
-        self.assertEqual(str(record.motifs[8].instances[11]), "GAGATCCGGAGGAGG")
-        self.assertEqual(str(record.motifs[8].instances[12]), "GCGATCCGAGGGCCG")
-        self.assertEqual(str(record.motifs[8].instances[13]), "GAGTTCACATGGCTG")
-        self.assertEqual(record.motifs[8].mask, [1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1])
-        self.assertAlmostEqual(record.motifs[8].score, 11.2943)
-        self.assertEqual(record.motifs[9].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[9].instances), 18)
-        self.assertEqual(str(record.motifs[9].instances[0]), "TAGAGGCGGTG")
-        self.assertEqual(str(record.motifs[9].instances[1]), "GCTAAGCTCTG")
-        self.assertEqual(str(record.motifs[9].instances[2]), "TGGAAGCAGTG")
-        self.assertEqual(str(record.motifs[9].instances[3]), "GCGAGGCTGTG")
-        self.assertEqual(str(record.motifs[9].instances[4]), "ACGACGCTTTG")
-        self.assertEqual(str(record.motifs[9].instances[5]), "GGGACGCGCAC")
-        self.assertEqual(str(record.motifs[9].instances[6]), "TCGAAGCGTGG")
-        self.assertEqual(str(record.motifs[9].instances[7]), "TGTATGCGGGG")
-        self.assertEqual(str(record.motifs[9].instances[8]), "GGTAAGCTTGG")
-        self.assertEqual(str(record.motifs[9].instances[9]), "TGTACGCTGGG")
-        self.assertEqual(str(record.motifs[9].instances[10]), "ACTATGCGGGG")
-        self.assertEqual(str(record.motifs[9].instances[11]), "GGTATGCGCTG")
-        self.assertEqual(str(record.motifs[9].instances[12]), "GGTACCCGGAG")
-        self.assertEqual(str(record.motifs[9].instances[13]), "GCGACGCAGAG")
-        self.assertEqual(str(record.motifs[9].instances[14]), "TGGCGGCGTGG")
-        self.assertEqual(str(record.motifs[9].instances[15]), "TCTAGGCGGGC")
-        self.assertEqual(str(record.motifs[9].instances[16]), "AGTATGCTTAG")
-        self.assertEqual(str(record.motifs[9].instances[17]), "TGGAGGCTTAG")
-        self.assertEqual(record.motifs[9].mask, [1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1])
-        self.assertAlmostEqual(record.motifs[9].score, 9.7924)
-        self.assertEqual(record.motifs[10].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[10].instances), 13)
-        self.assertEqual(str(record.motifs[10].instances[0]), "GCACAGAGCTTAGCATTGAAC")
-        self.assertEqual(str(record.motifs[10].instances[1]), "GTCCGCGGATTCCCAACATGC")
-        self.assertEqual(str(record.motifs[10].instances[2]), "ATACACAGCCTCGCAAGCCAG")
-        self.assertEqual(str(record.motifs[10].instances[3]), "GGCCCGGGACGCGCACTAAGA")
-        self.assertEqual(str(record.motifs[10].instances[4]), "GCCCGTTGTCCAGCAGACGGC")
-        self.assertEqual(str(record.motifs[10].instances[5]), "GAGCAGCGATCAGCTTGTGGG")
-        self.assertEqual(str(record.motifs[10].instances[6]), "GAACCGAGGTCCGGTACGGGC")
-        self.assertEqual(str(record.motifs[10].instances[7]), "GTCCCTGGGTAAGCTTGGGGC")
-        self.assertEqual(str(record.motifs[10].instances[8]), "GACCTGCCCCCCGCATAGTAG")
-        self.assertEqual(str(record.motifs[10].instances[9]), "AACCAGCGCATACCTTAACAG")
-        self.assertEqual(str(record.motifs[10].instances[10]), "ATCCTCTGCGTCGCATGGCGG")
-        self.assertEqual(str(record.motifs[10].instances[11]), "GACCATAGACGAGCATCAAAG")
-        self.assertEqual(str(record.motifs[10].instances[12]), "GGCCCTCGGATCGCTTGGGAA")
-        self.assertEqual(record.motifs[10].mask, [1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[10].score, 9.01393)
-        self.assertEqual(record.motifs[11].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[11].instances), 16)
-        self.assertEqual(str(record.motifs[11].instances[0]), "GCCGTCCGTC")
-        self.assertEqual(str(record.motifs[11].instances[1]), "GGCGTGCGCG")
-        self.assertEqual(str(record.motifs[11].instances[2]), "GGCGCGTGTC")
-        self.assertEqual(str(record.motifs[11].instances[3]), "AGCGCGTGTG")
-        self.assertEqual(str(record.motifs[11].instances[4]), "GCGGTGCGTG")
-        self.assertEqual(str(record.motifs[11].instances[5]), "AGCGCGTGTC")
-        self.assertEqual(str(record.motifs[11].instances[6]), "AGCGTCCGCG")
-        self.assertEqual(str(record.motifs[11].instances[7]), "ACCGTCTGTG")
-        self.assertEqual(str(record.motifs[11].instances[8]), "GCCATGCGAC")
-        self.assertEqual(str(record.motifs[11].instances[9]), "ACCACCCGTC")
-        self.assertEqual(str(record.motifs[11].instances[10]), "GGCGCCGGAG")
-        self.assertEqual(str(record.motifs[11].instances[11]), "ACCACGTGTC")
-        self.assertEqual(str(record.motifs[11].instances[12]), "GGCTTGCGAG")
-        self.assertEqual(str(record.motifs[11].instances[13]), "GCGATCCGAG")
-        self.assertEqual(str(record.motifs[11].instances[14]), "AGTGCGCGTC")
-        self.assertEqual(str(record.motifs[11].instances[15]), "AGTGCCCGAG")
-        self.assertEqual(record.motifs[11].mask, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
-        self.assertAlmostEqual(record.motifs[11].score, 7.51121)
-        self.assertEqual(record.motifs[12].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[12].instances), 16)
-        self.assertEqual(str(record.motifs[12].instances[0]), "GCCGACGGGTGGTCATCGGG")
-        self.assertEqual(str(record.motifs[12].instances[1]), "GCACGACGCTTTGTACCTGG")
-        self.assertEqual(str(record.motifs[12].instances[2]), "CCTGGGAGGGTTCAATAACG")
-        self.assertEqual(str(record.motifs[12].instances[3]), "GCGCGTCCCGGGCCAATAGC")
-        self.assertEqual(str(record.motifs[12].instances[4]), "GCCGTCTGCTGGACAACGGG")
-        self.assertEqual(str(record.motifs[12].instances[5]), "GTCCCTTCCGGTACATGAGG")
-        self.assertEqual(str(record.motifs[12].instances[6]), "GCTGCTCCCCGCATACAGCG")
-        self.assertEqual(str(record.motifs[12].instances[7]), "GCCCCAAGCTTACCCAGGGA")
-        self.assertEqual(str(record.motifs[12].instances[8]), "ACCGGCTGACGCTAATACGG")
-        self.assertEqual(str(record.motifs[12].instances[9]), "GCGGGGGGCAGGTCATTACA")
-        self.assertEqual(str(record.motifs[12].instances[10]), "GCTGGCAGCGTCTAAGAAGG")
-        self.assertEqual(str(record.motifs[12].instances[11]), "GCAGGTGGTCGTGCAATACG")
-        self.assertEqual(str(record.motifs[12].instances[12]), "GCTGGTTGAAGTCCCGTGCG")
-        self.assertEqual(str(record.motifs[12].instances[13]), "GCACGTAGCTGGTAAATAGG")
-        self.assertEqual(str(record.motifs[12].instances[14]), "GCGGCGTGGATTTCATACAG")
-        self.assertEqual(str(record.motifs[12].instances[15]), "CCTGGAGGCTTAGACTTGGG")
-        self.assertEqual(record.motifs[12].mask, [1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[12].score, 5.63667)
-        self.assertEqual(record.motifs[13].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[13].instances), 15)
-        self.assertEqual(str(record.motifs[13].instances[0]), "GCCGACGGGTGGTCATCGGG")
-        self.assertEqual(str(record.motifs[13].instances[1]), "ATCCGCGGACGCTTAGAGGG")
-        self.assertEqual(str(record.motifs[13].instances[2]), "ACGCTTTGTACCTGGCTTGC")
-        self.assertEqual(str(record.motifs[13].instances[3]), "ACGGACGGCACTTAGCAGCG")
-        self.assertEqual(str(record.motifs[13].instances[4]), "GCCGTCTGCTGGACAACGGG")
-        self.assertEqual(str(record.motifs[13].instances[5]), "ACACACAGACGGTTGAAAGG")
-        self.assertEqual(str(record.motifs[13].instances[6]), "GCCGATAGTGCTTAAGTTCG")
-        self.assertEqual(str(record.motifs[13].instances[7]), "CTTGCCCGTACCGGACCTCG")
-        self.assertEqual(str(record.motifs[13].instances[8]), "ACCGGCTGACGCTAATACGG")
-        self.assertEqual(str(record.motifs[13].instances[9]), "GCCCCCCGCATAGTAGGGGG")
-        self.assertEqual(str(record.motifs[13].instances[10]), "GCTGGCAGCGTCTAAGAAGG")
-        self.assertEqual(str(record.motifs[13].instances[11]), "GCAGGTGGTCGTGCAATACG")
-        self.assertEqual(str(record.motifs[13].instances[12]), "ACGCACGGGACTTCAACCAG")
-        self.assertEqual(str(record.motifs[13].instances[13]), "GCACGTAGCTGGTAAATAGG")
-        self.assertEqual(str(record.motifs[13].instances[14]), "ATCCTCTGCGTCGCATGGCG")
-        self.assertEqual(record.motifs[13].mask, [1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[13].score, 3.89842)
-        self.assertEqual(record.motifs[14].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[14].instances), 14)
-        self.assertEqual(str(record.motifs[14].instances[0]), "GAGGCTGTGTAT")
-        self.assertEqual(str(record.motifs[14].instances[1]), "GAGGTCGGGGGT")
-        self.assertEqual(str(record.motifs[14].instances[2]), "GACGGACGGCAC")
-        self.assertEqual(str(record.motifs[14].instances[3]), "TTGGCCCGGGAC")
-        self.assertEqual(str(record.motifs[14].instances[4]), "GAGGCTCGGCCC")
-        self.assertEqual(str(record.motifs[14].instances[5]), "CACGCGCTGTAT")
-        self.assertEqual(str(record.motifs[14].instances[6]), "TAGGCCAGGTAT")
-        self.assertEqual(str(record.motifs[14].instances[7]), "GAGGTCCGGTAC")
-        self.assertEqual(str(record.motifs[14].instances[8]), "TACGCTGGGGAT")
-        self.assertEqual(str(record.motifs[14].instances[9]), "GTCGCGGAGGAT")
-        self.assertEqual(str(record.motifs[14].instances[10]), "TACGCACGGGAC")
-        self.assertEqual(str(record.motifs[14].instances[11]), "TACTCCGGGTAC")
-        self.assertEqual(str(record.motifs[14].instances[12]), "GACGCAGAGGAT")
-        self.assertEqual(str(record.motifs[14].instances[13]), "TAGGCGGGCCAT")
-        self.assertEqual(record.motifs[14].mask, [1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[14].score, 3.33444)
-        self.assertEqual(record.motifs[15].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs[15].instances), 21)
-        self.assertEqual(str(record.motifs[15].instances[0]), "CGGCTCAATCGTAGAGGC")
-        self.assertEqual(str(record.motifs[15].instances[1]), "CGACGGGTGGTCATCGGG")
-        self.assertEqual(str(record.motifs[15].instances[2]), "CGCTTAGAGGGCACAAGC")
-        self.assertEqual(str(record.motifs[15].instances[3]), "TGACACGCGCCTGGGAGG")
-        self.assertEqual(str(record.motifs[15].instances[4]), "CGATACGCTGCTAAGTGC")
-        self.assertEqual(str(record.motifs[15].instances[5]), "CGTCCCGGGCCAATAGCG")
-        self.assertEqual(str(record.motifs[15].instances[6]), "CCACGCTTCGACACGTGG")
-        self.assertEqual(str(record.motifs[15].instances[7]), "CGTCTGCTGGACAACGGG")
-        self.assertEqual(str(record.motifs[15].instances[8]), "ACACAGACGGTTGAAAGG")
-        self.assertEqual(str(record.motifs[15].instances[9]), "TGCTCCCCGCATACAGCG")
-        self.assertEqual(str(record.motifs[15].instances[10]), "TGAGGCTTGCCCGTACCG")
-        self.assertEqual(str(record.motifs[15].instances[11]), "TGCCCCAAGCTTACCCAG")
-        self.assertEqual(str(record.motifs[15].instances[12]), "CGGCTGACGCTAATACGG")
-        self.assertEqual(str(record.motifs[15].instances[13]), "CGCGACGTCCCTATGAGC")
-        self.assertEqual(str(record.motifs[15].instances[14]), "TGCCCCCCGCATAGTAGG")
-        self.assertEqual(str(record.motifs[15].instances[15]), "CGTTGCCTTCTTAGACGC")
-        self.assertEqual(str(record.motifs[15].instances[16]), "TGACTCAATCGTAGACCC")
-        self.assertEqual(str(record.motifs[15].instances[17]), "AGTCCCGTGCGTATGTGG")
-        self.assertEqual(str(record.motifs[15].instances[18]), "AGGCTCGCACGTAGCTGG")
-        self.assertEqual(str(record.motifs[15].instances[19]), "CCACGCCGCCATGCGACG")
-        self.assertEqual(str(record.motifs[15].instances[20]), "AGCCTCCAGGTCGCATGG")
-        self.assertEqual(record.motifs[15].mask, [1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1])
-        self.assertAlmostEqual(record.motifs[15].score, 1.0395)
-
-    def test_pfm_parsing(self):
-        """Test to be sure that Motif can parse pfm  files.
-        """
-        motif = Motif.read(self.PFMin, "jaspar-pfm")
-        self.assertEqual(motif.length, 12)
-
-    def test_sites_parsing(self):
-        """Test to be sure that Motif can parse sites files.
-        """
-        motif = Motif.read(self.SITESin, "jaspar-sites")
-        self.assertEqual(motif.length, 6)
-
-    def test_FAoutput(self):
-        """Ensure that we can write proper FASTA output files.
-        """
-        output_handle = open(self.FAout, "w")
-        output_handle.write(self.m.format("fasta"))
-        output_handle.close()
-
-    def test_TFoutput(self):
-        """Ensure that we can write proper TransFac output files.
-        """
-        output_handle = open(self.TFout, "w")
-        output_handle.write(self.m.format("transfac"))
-        output_handle.close()
-
-    def test_pfm_output(self):
-        """Ensure that we can write proper pfm output files.
-        """
-        output_handle = open(self.PFMout, "w")
-        output_handle.write(self.m.format("jaspar-pfm"))
-        output_handle.close()
-
-
-class TestMEME(unittest.TestCase):
-
-    def test_meme_parser_1(self):
-        """Test if Motif can parse MEME output files (first test)
-        """
-        from Bio.Motif.Parsers import MEME
-        handle = open("Motif/meme.out")
-        record = MEME.read(handle)
-        self.assertEqual(record.version, '3.5.7')
-        self.assertEqual(record.datafile, 'test.fa')
-        self.assertEqual(record.alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.sequence_names), 10)
-        self.assertEqual(record.sequence_names[0], 'SEQ1;')
-        self.assertEqual(record.sequence_names[1], 'SEQ2;')
-        self.assertEqual(record.sequence_names[2], 'SEQ3;')
-        self.assertEqual(record.sequence_names[3], 'SEQ4;')
-        self.assertEqual(record.sequence_names[4], 'SEQ5;')
-        self.assertEqual(record.sequence_names[5], 'SEQ6;')
-        self.assertEqual(record.sequence_names[6], 'SEQ7;')
-        self.assertEqual(record.sequence_names[7], 'SEQ8;')
-        self.assertEqual(record.sequence_names[8], 'SEQ9;')
-        self.assertEqual(record.sequence_names[9], 'SEQ10;')
-        self.assertEqual(record.command, 'meme test.fa -dna -w 10 -dir /home/bartek/MetaMotif/meme')
-        self.assertEqual(len(record.motifs), 1)
-        motif = record.motifs[0]
-        self.assertEqual(motif.num_occurrences, 10)
-        self.assertAlmostEqual(motif.evalue, 1.1e-22)
-        self.assertEqual(motif.alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.name, "Motif 1")
-        self.assertEqual(len(motif.instances), 10)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[7].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[8].pvalue, 8.71e-07)
-        self.assertAlmostEqual(motif.instances[9].pvalue, 8.71e-07)
-        self.assertEqual(motif.instances[0].sequence_name, 'SEQ10;')
-        self.assertEqual(motif.instances[1].sequence_name, 'SEQ9;')
-        self.assertEqual(motif.instances[2].sequence_name, 'SEQ8;')
-        self.assertEqual(motif.instances[3].sequence_name, 'SEQ7;')
-        self.assertEqual(motif.instances[4].sequence_name, 'SEQ6;')
-        self.assertEqual(motif.instances[5].sequence_name, 'SEQ5;')
-        self.assertEqual(motif.instances[6].sequence_name, 'SEQ4;')
-        self.assertEqual(motif.instances[7].sequence_name, 'SEQ3;')
-        self.assertEqual(motif.instances[8].sequence_name, 'SEQ2;')
-        self.assertEqual(motif.instances[9].sequence_name, 'SEQ1;')
-        self.assertEqual(motif.instances[0].start, 3)
-        self.assertEqual(motif.instances[1].start, 93)
-        self.assertEqual(motif.instances[2].start, 172)
-        self.assertEqual(motif.instances[3].start, 177)
-        self.assertEqual(motif.instances[4].start, 105)
-        self.assertEqual(motif.instances[5].start, 185)
-        self.assertEqual(motif.instances[6].start, 173)
-        self.assertEqual(motif.instances[7].start, 112)
-        self.assertEqual(motif.instances[8].start, 172)
-        self.assertEqual(motif.instances[9].start, 52)
-        self.assertEqual(motif.instances[0].strand, '+')
-        self.assertEqual(motif.instances[1].strand, '+')
-        self.assertEqual(motif.instances[2].strand, '+')
-        self.assertEqual(motif.instances[3].strand, '+')
-        self.assertEqual(motif.instances[4].strand, '+')
-        self.assertEqual(motif.instances[5].strand, '+')
-        self.assertEqual(motif.instances[6].strand, '+')
-        self.assertEqual(motif.instances[7].strand, '+')
-        self.assertEqual(motif.instances[8].strand, '+')
-        self.assertEqual(motif.instances[9].strand, '+')
-        self.assertEqual(motif.instances[0].length, 10)
-        self.assertEqual(motif.instances[1].length, 10)
-        self.assertEqual(motif.instances[2].length, 10)
-        self.assertEqual(motif.instances[3].length, 10)
-        self.assertEqual(motif.instances[4].length, 10)
-        self.assertEqual(motif.instances[5].length, 10)
-        self.assertEqual(motif.instances[6].length, 10)
-        self.assertEqual(motif.instances[7].length, 10)
-        self.assertEqual(motif.instances[8].length, 10)
-        self.assertEqual(motif.instances[9].length, 10)
-        self.assertEqual(motif.instances[0].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[1].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[2].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[3].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[4].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[5].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[6].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[7].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[8].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[9].motif_name, 'Motif 1')
-        self.assertEqual(motif.instances[0].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[1].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[2].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[3].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[4].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[5].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[6].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[7].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[8].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.instances[9].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(str(motif.instances[0]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[1]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[2]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[3]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[4]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[5]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[6]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[7]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[8]), "CTCAATCGTA")
-        self.assertEqual(str(motif.instances[9]), "CTCAATCGTA")
-        handle.close()
-
-    def test_meme_parser_2(self):
-        """Test if Motif can parse MEME output files (second test)
-        """
-        from Bio.Motif.Parsers import MEME
-        handle = open("Motif/meme.dna.oops.txt")
-        record = MEME.read(handle)
-        self.assertEqual(record.version, '3.0')
-        self.assertEqual(record.datafile, 'INO_up800.s')
-        self.assertEqual(record.alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.sequence_names), 7)
-        self.assertEqual(record.sequence_names[0], 'CHO1')
-        self.assertEqual(record.sequence_names[1], 'CHO2')
-        self.assertEqual(record.sequence_names[2], 'FAS1')
-        self.assertEqual(record.sequence_names[3], 'FAS2')
-        self.assertEqual(record.sequence_names[4], 'ACC1')
-        self.assertEqual(record.sequence_names[5], 'INO1')
-        self.assertEqual(record.sequence_names[6], 'OPI3')
-        self.assertEqual(record.command, 'meme -mod oops -dna -revcomp -nmotifs 2 -bfile yeast.nc.6.freq INO_up800.s')
-        self.assertEqual(len(record.motifs), 2)
-        motif = record.motifs[0]
-        self.assertEqual(motif.num_occurrences, 7)
-        self.assertAlmostEqual(motif.evalue, 0.2)
-        self.assertEqual(motif.alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.name, "Motif 1")
-        self.assertEqual(len(motif.instances), 7)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 1.85e-08)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 1.85e-08)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 1.52e-07)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 2.52e-07)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 4.23e-07)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 9.43e-07)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 3.32e-06)
-        self.assertEqual(motif.instances[0].sequence_name, 'INO1')
-        self.assertEqual(motif.instances[1].sequence_name, 'FAS1')
-        self.assertEqual(motif.instances[2].sequence_name, 'ACC1')
-        self.assertEqual(motif.instances[3].sequence_name, 'CHO2')
-        self.assertEqual(motif.instances[4].sequence_name, 'CHO1')
-        self.assertEqual(motif.instances[5].sequence_name, 'FAS2')
-        self.assertEqual(motif.instances[6].sequence_name, 'OPI3')
-        self.assertEqual(motif.instances[0].strand, '-')
-        self.assertEqual(motif.instances[1].strand, '+')
-        self.assertEqual(motif.instances[2].strand, '+')
-        self.assertEqual(motif.instances[3].strand, '+')
-        self.assertEqual(motif.instances[4].strand, '+')
-        self.assertEqual(motif.instances[5].strand, '+')
-        self.assertEqual(motif.instances[6].strand, '+')
-        self.assertEqual(motif.instances[0].length, 12)
-        self.assertEqual(motif.instances[1].length, 12)
-        self.assertEqual(motif.instances[2].length, 12)
-        self.assertEqual(motif.instances[3].length, 12)
-        self.assertEqual(motif.instances[4].length, 12)
-        self.assertEqual(motif.instances[5].length, 12)
-        self.assertEqual(motif.instances[6].length, 12)
-        self.assertEqual(motif.instances[0].start, 620)
-        self.assertEqual(motif.instances[1].start, 95)
-        self.assertEqual(motif.instances[2].start, 83)
-        self.assertEqual(motif.instances[3].start, 354)
-        self.assertEqual(motif.instances[4].start, 611)
-        self.assertEqual(motif.instances[5].start, 567)
-        self.assertEqual(motif.instances[6].start, 340)
-        self.assertEqual(str(motif.instances[0]), "TTCACATGCCGC")
-        self.assertEqual(str(motif.instances[1]), "TTCACATGCCGC")
-        self.assertEqual(str(motif.instances[2]), "TTCACATGGCCC")
-        self.assertEqual(str(motif.instances[3]), "TTCTCATGCCGC")
-        self.assertEqual(str(motif.instances[4]), "TTCACACGGCAC")
-        self.assertEqual(str(motif.instances[5]), "TTCACATGCTAC")
-        self.assertEqual(str(motif.instances[6]), "TTCAGATCGCTC")
-        motif = record.motifs[1]
-        self.assertEqual(motif.num_occurrences, 7)
-        self.assertAlmostEqual(motif.evalue, 110)
-        self.assertEqual(motif.alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(motif.name, "Motif 2")
-        self.assertEqual(len(motif.instances), 7)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 3.24e-07)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 3.24e-07)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 3.24e-07)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 5.29e-06)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 6.25e-06)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 8.48e-06)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 8.48e-06)
-        self.assertEqual(motif.instances[0].sequence_name, 'OPI3')
-        self.assertEqual(motif.instances[1].sequence_name, 'ACC1')
-        self.assertEqual(motif.instances[2].sequence_name, 'CHO1')
-        self.assertEqual(motif.instances[3].sequence_name, 'INO1')
-        self.assertEqual(motif.instances[4].sequence_name, 'FAS1')
-        self.assertEqual(motif.instances[5].sequence_name, 'FAS2')
-        self.assertEqual(motif.instances[6].sequence_name, 'CHO2')
-        self.assertEqual(motif.instances[0].strand, '-')
-        self.assertEqual(motif.instances[1].strand, '+')
-        self.assertEqual(motif.instances[2].strand, '-')
-        self.assertEqual(motif.instances[3].strand, '-')
-        self.assertEqual(motif.instances[4].strand, '+')
-        self.assertEqual(motif.instances[5].strand, '-')
-        self.assertEqual(motif.instances[6].strand, '-')
-        self.assertEqual(motif.instances[0].length, 10)
-        self.assertEqual(motif.instances[1].length, 10)
-        self.assertEqual(motif.instances[2].length, 10)
-        self.assertEqual(motif.instances[3].length, 10)
-        self.assertEqual(motif.instances[4].length, 10)
-        self.assertEqual(motif.instances[5].length, 10)
-        self.assertEqual(motif.instances[6].length, 10)
-        self.assertEqual(motif.instances[0].start, 186)
-        self.assertEqual(motif.instances[1].start, 232)
-        self.assertEqual(motif.instances[2].start, 559)
-        self.assertEqual(motif.instances[3].start, 283)
-        self.assertEqual(motif.instances[4].start, 44)
-        self.assertEqual(motif.instances[5].start, 185)
-        self.assertEqual(motif.instances[6].start, 413)
-        self.assertEqual(str(motif.instances[0]), "TCTGGCACAG")
-        self.assertEqual(str(motif.instances[1]), "TCTGGCACAG")
-        self.assertEqual(str(motif.instances[2]), "TCTGGCACAG")
-        self.assertEqual(str(motif.instances[3]), "GCGGGCGCAG")
-        self.assertEqual(str(motif.instances[4]), "GCAGGCACGG")
-        self.assertEqual(str(motif.instances[5]), "TCTGGCACTC")
-        self.assertEqual(str(motif.instances[6]), "TCTGGCATCG")
-        handle.close()
-
-    def test_meme_parser_3(self):
-        """Test if Motif can parse MEME output files (third test)
-        """
-        from Bio.Motif.Parsers import MEME
-        handle = open("Motif/meme.protein.oops.txt")
-        record = MEME.read(handle)
-        self.assertEqual(record.version, '3.0')
-        self.assertEqual(record.datafile, 'adh.s')
-        self.assertEqual(record.alphabet, IUPAC.protein)
-        self.assertEqual(len(record.sequence_names), 33)
-        self.assertEqual(record.sequence_names[0], "2BHD_STREX")
-        self.assertEqual(record.sequence_names[1], "3BHD_COMTE")
-        self.assertEqual(record.sequence_names[2], "ADH_DROME")
-        self.assertEqual(record.sequence_names[3], "AP27_MOUSE")
-        self.assertEqual(record.sequence_names[4], "BA72_EUBSP")
-        self.assertEqual(record.sequence_names[5], "BDH_HUMAN")
-        self.assertEqual(record.sequence_names[6], "BPHB_PSEPS")
-        self.assertEqual(record.sequence_names[7], "BUDC_KLETE")
-        self.assertEqual(record.sequence_names[8], "DHES_HUMAN")
-        self.assertEqual(record.sequence_names[9], "DHGB_BACME")
-        self.assertEqual(record.sequence_names[10], "DHII_HUMAN")
-        self.assertEqual(record.sequence_names[11], "DHMA_FLAS1")
-        self.assertEqual(record.sequence_names[12], "ENTA_ECOLI")
-        self.assertEqual(record.sequence_names[13], "FIXR_BRAJA")
-        self.assertEqual(record.sequence_names[14], "GUTD_ECOLI")
-        self.assertEqual(record.sequence_names[15], "HDE_CANTR")
-        self.assertEqual(record.sequence_names[16], "HDHA_ECOLI")
-        self.assertEqual(record.sequence_names[17], "LIGD_PSEPA")
-        self.assertEqual(record.sequence_names[18], "NODG_RHIME")
-        self.assertEqual(record.sequence_names[19], "RIDH_KLEAE")
-        self.assertEqual(record.sequence_names[20], "YINL_LISMO")
-        self.assertEqual(record.sequence_names[21], "YRTP_BACSU")
-        self.assertEqual(record.sequence_names[22], "CSGA_MYXXA")
-        self.assertEqual(record.sequence_names[23], "DHB2_HUMAN")
-        self.assertEqual(record.sequence_names[24], "DHB3_HUMAN")
-        self.assertEqual(record.sequence_names[25], "DHCA_HUMAN")
-        self.assertEqual(record.sequence_names[26], "FABI_ECOLI")
-        self.assertEqual(record.sequence_names[27], "FVT1_HUMAN")
-        self.assertEqual(record.sequence_names[28], "HMTR_LEIMA")
-        self.assertEqual(record.sequence_names[29], "MAS1_AGRRA")
-        self.assertEqual(record.sequence_names[30], "PCR_PEA")
-        self.assertEqual(record.sequence_names[31], "RFBB_NEIGO")
-        self.assertEqual(record.sequence_names[32], "YURA_MYXXA")
-        self.assertEqual(record.command, 'meme adh.s -mod oops -protein -nmotifs 2')
-        self.assertEqual(len(record.motifs), 2)
-        motif = record.motifs[0]
-        self.assertEqual(motif.num_occurrences, 33)
-        self.assertAlmostEqual(motif.evalue, 3.6e-165)
-        self.assertEqual(motif.alphabet, IUPAC.protein)
-        self.assertEqual(motif.name, "Motif 1")
-        self.assertEqual(len(motif.instances), 33)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 1.64e-22)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 6.32e-22)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 1.13e-21)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 4.04e-21)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 6.12e-21)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 7.52e-20)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 3.35e-19)
-        self.assertAlmostEqual(motif.instances[7].pvalue, 4.82e-19)
-        self.assertAlmostEqual(motif.instances[8].pvalue, 4.82e-19)
-        self.assertAlmostEqual(motif.instances[9].pvalue, 1.11e-18)
-        self.assertAlmostEqual(motif.instances[10].pvalue, 1.25e-18)
-        self.assertAlmostEqual(motif.instances[11].pvalue, 2.23e-18)
-        self.assertAlmostEqual(motif.instances[12].pvalue, 5.53e-18)
-        self.assertAlmostEqual(motif.instances[13].pvalue, 9.65e-18)
-        self.assertAlmostEqual(motif.instances[14].pvalue, 2.86e-17)
-        self.assertAlmostEqual(motif.instances[15].pvalue, 8.20e-17)
-        self.assertAlmostEqual(motif.instances[16].pvalue, 9.09e-17)
-        self.assertAlmostEqual(motif.instances[17].pvalue, 1.37e-16)
-        self.assertAlmostEqual(motif.instances[18].pvalue, 2.52e-16)
-        self.assertAlmostEqual(motif.instances[19].pvalue, 1.21e-15)
-        self.assertAlmostEqual(motif.instances[20].pvalue, 1.61e-15)
-        self.assertAlmostEqual(motif.instances[21].pvalue, 1.77e-15)
-        self.assertAlmostEqual(motif.instances[22].pvalue, 7.81e-15)
-        self.assertAlmostEqual(motif.instances[23].pvalue, 8.55e-15)
-        self.assertAlmostEqual(motif.instances[24].pvalue, 1.47e-14)
-        self.assertAlmostEqual(motif.instances[25].pvalue, 3.24e-14)
-        self.assertAlmostEqual(motif.instances[26].pvalue, 1.80e-12)
-        self.assertAlmostEqual(motif.instances[27].pvalue, 2.10e-12)
-        self.assertAlmostEqual(motif.instances[28].pvalue, 4.15e-12)
-        self.assertAlmostEqual(motif.instances[29].pvalue, 5.20e-12)
-        self.assertAlmostEqual(motif.instances[30].pvalue, 4.80e-10)
-        self.assertAlmostEqual(motif.instances[31].pvalue, 2.77e-08)
-        self.assertAlmostEqual(motif.instances[32].pvalue, 5.72e-08)
-        self.assertEqual(motif.instances[0].sequence_name, 'YRTP_BACSU')
-        self.assertEqual(motif.instances[1].sequence_name, 'AP27_MOUSE')
-        self.assertEqual(motif.instances[2].sequence_name, 'NODG_RHIME')
-        self.assertEqual(motif.instances[3].sequence_name, 'BUDC_KLETE')
-        self.assertEqual(motif.instances[4].sequence_name, 'FIXR_BRAJA')
-        self.assertEqual(motif.instances[5].sequence_name, 'DHGB_BACME')
-        self.assertEqual(motif.instances[6].sequence_name, 'HMTR_LEIMA')
-        self.assertEqual(motif.instances[7].sequence_name, 'YURA_MYXXA')
-        self.assertEqual(motif.instances[8].sequence_name, 'GUTD_ECOLI')
-        self.assertEqual(motif.instances[9].sequence_name, '2BHD_STREX')
-        self.assertEqual(motif.instances[10].sequence_name, 'HDHA_ECOLI')
-        self.assertEqual(motif.instances[11].sequence_name, 'DHB2_HUMAN')
-        self.assertEqual(motif.instances[12].sequence_name, 'DHMA_FLAS1')
-        self.assertEqual(motif.instances[13].sequence_name, 'HDE_CANTR')
-        self.assertEqual(motif.instances[14].sequence_name, 'FVT1_HUMAN')
-        self.assertEqual(motif.instances[15].sequence_name, 'BDH_HUMAN')
-        self.assertEqual(motif.instances[16].sequence_name, 'RIDH_KLEAE')
-        self.assertEqual(motif.instances[17].sequence_name, 'DHES_HUMAN')
-        self.assertEqual(motif.instances[18].sequence_name, 'BA72_EUBSP')
-        self.assertEqual(motif.instances[19].sequence_name, 'LIGD_PSEPA')
-        self.assertEqual(motif.instances[20].sequence_name, 'DHII_HUMAN')
-        self.assertEqual(motif.instances[21].sequence_name, 'ENTA_ECOLI')
-        self.assertEqual(motif.instances[22].sequence_name, '3BHD_COMTE')
-        self.assertEqual(motif.instances[23].sequence_name, 'DHB3_HUMAN')
-        self.assertEqual(motif.instances[24].sequence_name, 'RFBB_NEIGO')
-        self.assertEqual(motif.instances[25].sequence_name, 'YINL_LISMO')
-        self.assertEqual(motif.instances[26].sequence_name, 'BPHB_PSEPS')
-        self.assertEqual(motif.instances[27].sequence_name, 'CSGA_MYXXA')
-        self.assertEqual(motif.instances[28].sequence_name, 'FABI_ECOLI')
-        self.assertEqual(motif.instances[29].sequence_name, 'ADH_DROME')
-        self.assertEqual(motif.instances[30].sequence_name, 'DHCA_HUMAN')
-        self.assertEqual(motif.instances[31].sequence_name, 'PCR_PEA')
-        self.assertEqual(motif.instances[32].sequence_name, 'MAS1_AGRRA')
-        self.assertEqual(motif.instances[0].strand, '+')
-        self.assertEqual(motif.instances[1].strand, '+')
-        self.assertEqual(motif.instances[2].strand, '+')
-        self.assertEqual(motif.instances[3].strand, '+')
-        self.assertEqual(motif.instances[4].strand, '+')
-        self.assertEqual(motif.instances[5].strand, '+')
-        self.assertEqual(motif.instances[6].strand, '+')
-        self.assertEqual(motif.instances[7].strand, '+')
-        self.assertEqual(motif.instances[8].strand, '+')
-        self.assertEqual(motif.instances[9].strand, '+')
-        self.assertEqual(motif.instances[10].strand, '+')
-        self.assertEqual(motif.instances[11].strand, '+')
-        self.assertEqual(motif.instances[12].strand, '+')
-        self.assertEqual(motif.instances[13].strand, '+')
-        self.assertEqual(motif.instances[14].strand, '+')
-        self.assertEqual(motif.instances[15].strand, '+')
-        self.assertEqual(motif.instances[16].strand, '+')
-        self.assertEqual(motif.instances[17].strand, '+')
-        self.assertEqual(motif.instances[18].strand, '+')
-        self.assertEqual(motif.instances[19].strand, '+')
-        self.assertEqual(motif.instances[20].strand, '+')
-        self.assertEqual(motif.instances[21].strand, '+')
-        self.assertEqual(motif.instances[22].strand, '+')
-        self.assertEqual(motif.instances[23].strand, '+')
-        self.assertEqual(motif.instances[24].strand, '+')
-        self.assertEqual(motif.instances[25].strand, '+')
-        self.assertEqual(motif.instances[26].strand, '+')
-        self.assertEqual(motif.instances[27].strand, '+')
-        self.assertEqual(motif.instances[28].strand, '+')
-        self.assertEqual(motif.instances[29].strand, '+')
-        self.assertEqual(motif.instances[30].strand, '+')
-        self.assertEqual(motif.instances[31].strand, '+')
-        self.assertEqual(motif.instances[32].strand, '+')
-        self.assertEqual(motif.instances[0].length, 29)
-        self.assertEqual(motif.instances[1].length, 29)
-        self.assertEqual(motif.instances[2].length, 29)
-        self.assertEqual(motif.instances[3].length, 29)
-        self.assertEqual(motif.instances[4].length, 29)
-        self.assertEqual(motif.instances[5].length, 29)
-        self.assertEqual(motif.instances[6].length, 29)
-        self.assertEqual(motif.instances[7].length, 29)
-        self.assertEqual(motif.instances[8].length, 29)
-        self.assertEqual(motif.instances[9].length, 29)
-        self.assertEqual(motif.instances[10].length, 29)
-        self.assertEqual(motif.instances[11].length, 29)
-        self.assertEqual(motif.instances[12].length, 29)
-        self.assertEqual(motif.instances[13].length, 29)
-        self.assertEqual(motif.instances[14].length, 29)
-        self.assertEqual(motif.instances[15].length, 29)
-        self.assertEqual(motif.instances[16].length, 29)
-        self.assertEqual(motif.instances[17].length, 29)
-        self.assertEqual(motif.instances[18].length, 29)
-        self.assertEqual(motif.instances[19].length, 29)
-        self.assertEqual(motif.instances[20].length, 29)
-        self.assertEqual(motif.instances[21].length, 29)
-        self.assertEqual(motif.instances[22].length, 29)
-        self.assertEqual(motif.instances[23].length, 29)
-        self.assertEqual(motif.instances[24].length, 29)
-        self.assertEqual(motif.instances[25].length, 29)
-        self.assertEqual(motif.instances[26].length, 29)
-        self.assertEqual(motif.instances[27].length, 29)
-        self.assertEqual(motif.instances[28].length, 29)
-        self.assertEqual(motif.instances[29].length, 29)
-        self.assertEqual(motif.instances[30].length, 29)
-        self.assertEqual(motif.instances[31].length, 29)
-        self.assertEqual(motif.instances[32].length, 29)
-        self.assertEqual(motif.instances[0].start, 155)
-        self.assertEqual(motif.instances[1].start, 149)
-        self.assertEqual(motif.instances[2].start, 152)
-        self.assertEqual(motif.instances[3].start, 152)
-        self.assertEqual(motif.instances[4].start, 189)
-        self.assertEqual(motif.instances[5].start, 160)
-        self.assertEqual(motif.instances[6].start, 193)
-        self.assertEqual(motif.instances[7].start, 160)
-        self.assertEqual(motif.instances[8].start, 154)
-        self.assertEqual(motif.instances[9].start, 152)
-        self.assertEqual(motif.instances[10].start, 159)
-        self.assertEqual(motif.instances[11].start, 232)
-        self.assertEqual(motif.instances[12].start, 165)
-        self.assertEqual(motif.instances[13].start, 467)
-        self.assertEqual(motif.instances[14].start, 186)
-        self.assertEqual(motif.instances[15].start, 208)
-        self.assertEqual(motif.instances[16].start, 160)
-        self.assertEqual(motif.instances[17].start, 155)
-        self.assertEqual(motif.instances[18].start, 157)
-        self.assertEqual(motif.instances[19].start, 157)
-        self.assertEqual(motif.instances[20].start, 183)
-        self.assertEqual(motif.instances[21].start, 144)
-        self.assertEqual(motif.instances[22].start, 151)
-        self.assertEqual(motif.instances[23].start, 198)
-        self.assertEqual(motif.instances[24].start, 165)
-        self.assertEqual(motif.instances[25].start, 154)
-        self.assertEqual(motif.instances[26].start, 153)
-        self.assertEqual(motif.instances[27].start, 88)
-        self.assertEqual(motif.instances[28].start, 159)
-        self.assertEqual(motif.instances[29].start, 152)
-        self.assertEqual(motif.instances[30].start, 193)
-        self.assertEqual(motif.instances[31].start, 26)
-        self.assertEqual(motif.instances[32].start, 349)
-        self.assertEqual(str(motif.instances[0]), "YSASKFAVLGLTESLMQEVRKHNIRVSAL")
-        self.assertEqual(str(motif.instances[1]), "YSSTKGAMTMLTKAMAMELGPHKIRVNSV")
-        self.assertEqual(str(motif.instances[2]), "YCASKAGMIGFSKSLAQEIATRNITVNCV")
-        self.assertEqual(str(motif.instances[3]), "YSSSKFAVRGLTQTAARDLAPLGITVNGF")
-        self.assertEqual(str(motif.instances[4]), "YATSKAALASLTRELAHDYAPHGIRVNAI")
-        self.assertEqual(str(motif.instances[5]), "YAASKGGMKLMTETLALEYAPKGIRVNNI")
-        self.assertEqual(str(motif.instances[6]), "YTMAKGALEGLTRSAALELAPLQIRVNGV")
-        self.assertEqual(str(motif.instances[7]), "YSASKAFLSTFMESLRVDLRGTGVRVTCI")
-        self.assertEqual(str(motif.instances[8]), "YSAAKFGGVGLTQSLALDLAEYGITVHSL")
-        self.assertEqual(str(motif.instances[9]), "YGASKWGVRGLSKLAAVELGTDRIRVNSV")
-        self.assertEqual(str(motif.instances[10]), "YASSKAAASHLVRNMAFDLGEKNIRVNGI")
-        self.assertEqual(str(motif.instances[11]), "YGSSKAAVTMFSSVMRLELSKWGIKVASI")
-        self.assertEqual(str(motif.instances[12]), "YVAAKGGVAMLTRAMAVDLARHGILVNMI")
-        self.assertEqual(str(motif.instances[13]), "YSSSKAGILGLSKTMAIEGAKNNIKVNIV")
-        self.assertEqual(str(motif.instances[14]), "YSASKFAIRGLAEALQMEVKPYNVYITVA")
-        self.assertEqual(str(motif.instances[15]), "YCITKFGVEAFSDCLRYEMYPLGVKVSVV")
-        self.assertEqual(str(motif.instances[16]), "YTASKFAVQAFVHTTRRQVAQYGVRVGAV")
-        self.assertEqual(str(motif.instances[17]), "YCASKFALEGLCESLAVLLLPFGVHLSLI")
-        self.assertEqual(str(motif.instances[18]), "YPASKASVIGLTHGLGREIIRKNIRVVGV")
-        self.assertEqual(str(motif.instances[19]), "YSAAKAASINLMEGYRQGLEKYGIGVSVC")
-        self.assertEqual(str(motif.instances[20]), "YSASKFALDGFFSSIRKEYSVSRVNVSIT")
-        self.assertEqual(str(motif.instances[21]), "YGASKAALKSLALSVGLELAGSGVRCNVV")
-        self.assertEqual(str(motif.instances[22]), "YSASKAAVSALTRAAALSCRKQGYAIRVN")
-        self.assertEqual(str(motif.instances[23]), "YSASKAFVCAFSKALQEEYKAKEVIIQVL")
-        self.assertEqual(str(motif.instances[24]), "YSASKAAADHLVRAWQRTYRLPSIVSNCS")
-        self.assertEqual(str(motif.instances[25]), "YGATKWAVRDLMEVLRMESAQEGTNIRTA")
-        self.assertEqual(str(motif.instances[26]), "YTAAKQAIVGLVRELAFELAPYVRVNGVG")
-        self.assertEqual(str(motif.instances[27]), "YRMSKAALNMAVRSMSTDLRPEGFVTVLL")
-        self.assertEqual(str(motif.instances[28]), "MGLAKASLEANVRYMANAMGPEGVRVNAI")
-        self.assertEqual(str(motif.instances[29]), "YSGTKAAVVNFTSSLAKLAPITGVTAYTV")
-        self.assertEqual(str(motif.instances[30]), "YGVTKIGVTVLSRIHARKLSEQRKGDKIL")
-        self.assertEqual(str(motif.instances[31]), "KDSTLFGVSSLSDSLKGDFTSSALRCKEL")
-        self.assertEqual(str(motif.instances[32]), "YINCVAPLRMTELCLPHLYETGSGRIVNI")
-        motif = record.motifs[1]
-        self.assertEqual(motif.num_occurrences, 33)
-        self.assertAlmostEqual(motif.evalue, 2.3e-159)
-        self.assertEqual(motif.alphabet, IUPAC.protein)
-        self.assertEqual(motif.name, "Motif 2")
-        self.assertEqual(len(motif.instances), 33)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 2.44e-23)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 5.50e-23)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 5.38e-22)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 5.65e-20)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 1.17e-19)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 1.17e-19)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 4.74e-19)
-        self.assertAlmostEqual(motif.instances[7].pvalue, 9.31e-19)
-        self.assertAlmostEqual(motif.instances[8].pvalue, 2.50e-18)
-        self.assertAlmostEqual(motif.instances[9].pvalue, 3.45e-18)
-        self.assertAlmostEqual(motif.instances[10].pvalue, 5.86e-18)
-        self.assertAlmostEqual(motif.instances[11].pvalue, 9.86e-18)
-        self.assertAlmostEqual(motif.instances[12].pvalue, 2.47e-17)
-        self.assertAlmostEqual(motif.instances[13].pvalue, 3.01e-17)
-        self.assertAlmostEqual(motif.instances[14].pvalue, 3.33e-17)
-        self.assertAlmostEqual(motif.instances[15].pvalue, 4.06e-17)
-        self.assertAlmostEqual(motif.instances[16].pvalue, 4.06e-17)
-        self.assertAlmostEqual(motif.instances[17].pvalue, 8.05e-17)
-        self.assertAlmostEqual(motif.instances[18].pvalue, 1.90e-16)
-        self.assertAlmostEqual(motif.instances[19].pvalue, 2.77e-16)
-        self.assertAlmostEqual(motif.instances[20].pvalue, 3.65e-16)
-        self.assertAlmostEqual(motif.instances[21].pvalue, 8.31e-16)
-        self.assertAlmostEqual(motif.instances[22].pvalue, 4.05e-15)
-        self.assertAlmostEqual(motif.instances[23].pvalue, 5.24e-15)
-        self.assertAlmostEqual(motif.instances[24].pvalue, 3.00e-14)
-        self.assertAlmostEqual(motif.instances[25].pvalue, 8.47e-14)
-        self.assertAlmostEqual(motif.instances[26].pvalue, 1.46e-13)
-        self.assertAlmostEqual(motif.instances[27].pvalue, 1.46e-13)
-        self.assertAlmostEqual(motif.instances[28].pvalue, 1.59e-12)
-        self.assertAlmostEqual(motif.instances[29].pvalue, 6.97e-10)
-        self.assertAlmostEqual(motif.instances[30].pvalue, 3.15e-09)
-        self.assertAlmostEqual(motif.instances[31].pvalue, 2.77e-07)
-        self.assertAlmostEqual(motif.instances[32].pvalue, 4.24e-07)
-        self.assertEqual(motif.instances[0].sequence_name, 'HDE_CANTR')
-        self.assertEqual(motif.instances[1].sequence_name, 'DHII_HUMAN')
-        self.assertEqual(motif.instances[2].sequence_name, 'YINL_LISMO')
-        self.assertEqual(motif.instances[3].sequence_name, 'HDHA_ECOLI')
-        self.assertEqual(motif.instances[4].sequence_name, 'RIDH_KLEAE')
-        self.assertEqual(motif.instances[5].sequence_name, 'BUDC_KLETE')
-        self.assertEqual(motif.instances[6].sequence_name, 'ENTA_ECOLI')
-        self.assertEqual(motif.instances[7].sequence_name, 'AP27_MOUSE')
-        self.assertEqual(motif.instances[8].sequence_name, 'DHMA_FLAS1')
-        self.assertEqual(motif.instances[9].sequence_name, 'YRTP_BACSU')
-        self.assertEqual(motif.instances[10].sequence_name, 'DHGB_BACME')
-        self.assertEqual(motif.instances[11].sequence_name, 'DHB3_HUMAN')
-        self.assertEqual(motif.instances[12].sequence_name, 'PCR_PEA')
-        self.assertEqual(motif.instances[13].sequence_name, 'BDH_HUMAN')
-        self.assertEqual(motif.instances[14].sequence_name, 'BA72_EUBSP')
-        self.assertEqual(motif.instances[15].sequence_name, 'FIXR_BRAJA')
-        self.assertEqual(motif.instances[16].sequence_name, '3BHD_COMTE')
-        self.assertEqual(motif.instances[17].sequence_name, '2BHD_STREX')
-        self.assertEqual(motif.instances[18].sequence_name, 'HMTR_LEIMA')
-        self.assertEqual(motif.instances[19].sequence_name, 'FVT1_HUMAN')
-        self.assertEqual(motif.instances[20].sequence_name, 'DHB2_HUMAN')
-        self.assertEqual(motif.instances[21].sequence_name, 'LIGD_PSEPA')
-        self.assertEqual(motif.instances[22].sequence_name, 'NODG_RHIME')
-        self.assertEqual(motif.instances[23].sequence_name, 'DHCA_HUMAN')
-        self.assertEqual(motif.instances[24].sequence_name, 'MAS1_AGRRA')
-        self.assertEqual(motif.instances[25].sequence_name, 'BPHB_PSEPS')
-        self.assertEqual(motif.instances[26].sequence_name, 'GUTD_ECOLI')
-        self.assertEqual(motif.instances[27].sequence_name, 'DHES_HUMAN')
-        self.assertEqual(motif.instances[28].sequence_name, 'RFBB_NEIGO')
-        self.assertEqual(motif.instances[29].sequence_name, 'ADH_DROME')
-        self.assertEqual(motif.instances[30].sequence_name, 'FABI_ECOLI')
-        self.assertEqual(motif.instances[31].sequence_name, 'YURA_MYXXA')
-        self.assertEqual(motif.instances[32].sequence_name, 'CSGA_MYXXA')
-        self.assertEqual(motif.instances[0].start, 323)
-        self.assertEqual(motif.instances[1].start, 35)
-        self.assertEqual(motif.instances[2].start, 6)
-        self.assertEqual(motif.instances[3].start, 12)
-        self.assertEqual(motif.instances[4].start, 15)
-        self.assertEqual(motif.instances[5].start, 3)
-        self.assertEqual(motif.instances[6].start, 6)
-        self.assertEqual(motif.instances[7].start, 8)
-        self.assertEqual(motif.instances[8].start, 15)
-        self.assertEqual(motif.instances[9].start, 7)
-        self.assertEqual(motif.instances[10].start, 8)
-        self.assertEqual(motif.instances[11].start, 49)
-        self.assertEqual(motif.instances[12].start, 87)
-        self.assertEqual(motif.instances[13].start, 56)
-        self.assertEqual(motif.instances[14].start, 7)
-        self.assertEqual(motif.instances[15].start, 37)
-        self.assertEqual(motif.instances[16].start, 7)
-        self.assertEqual(motif.instances[17].start, 7)
-        self.assertEqual(motif.instances[18].start, 7)
-        self.assertEqual(motif.instances[19].start, 33)
-        self.assertEqual(motif.instances[20].start, 83)
-        self.assertEqual(motif.instances[21].start, 7)
-        self.assertEqual(motif.instances[22].start, 7)
-        self.assertEqual(motif.instances[23].start, 5)
-        self.assertEqual(motif.instances[24].start, 246)
-        self.assertEqual(motif.instances[25].start, 6)
-        self.assertEqual(motif.instances[26].start, 3)
-        self.assertEqual(motif.instances[27].start, 3)
-        self.assertEqual(motif.instances[28].start, 7)
-        self.assertEqual(motif.instances[29].start, 7)
-        self.assertEqual(motif.instances[30].start, 7)
-        self.assertEqual(motif.instances[31].start, 117)
-        self.assertEqual(motif.instances[32].start, 52)
-        self.assertEqual(str(motif.instances[0]), 'KVVLITGAGAGLGKEYAKWFAKYGAKVVV')
-        self.assertEqual(str(motif.instances[1]), 'KKVIVTGASKGIGREMAYHLAKMGAHVVV')
-        self.assertEqual(str(motif.instances[2]), 'KVIIITGASSGIGKATALLLAEKGAKLVL')
-        self.assertEqual(str(motif.instances[3]), 'KCAIITGAGAGIGKEIAITFATAGASVVV')
-        self.assertEqual(str(motif.instances[4]), 'KVAAITGAASGIGLECARTLLGAGAKVVL')
-        self.assertEqual(str(motif.instances[5]), 'KVALVTGAGQGIGKAIALRLVKDGFAVAI')
-        self.assertEqual(str(motif.instances[6]), 'KNVWVTGAGKGIGYATALAFVEAGAKVTG')
-        self.assertEqual(str(motif.instances[7]), 'LRALVTGAGKGIGRDTVKALHASGAKVVA')
-        self.assertEqual(str(motif.instances[8]), 'KAAIVTGAAGGIGRATVEAYLREGASVVA')
-        self.assertEqual(str(motif.instances[9]), 'KTALITGGGRGIGRATALALAKEGVNIGL')
-        self.assertEqual(str(motif.instances[10]), 'KVVVITGSSTGLGKSMAIRFATEKAKVVV')
-        self.assertEqual(str(motif.instances[11]), 'QWAVITGAGDGIGKAYSFELAKRGLNVVL')
-        self.assertEqual(str(motif.instances[12]), 'GNVVITGASSGLGLATAKALAESGKWHVI')
-        self.assertEqual(str(motif.instances[13]), 'KAVLVTGCDSGFGFSLAKHLHSKGFLVFA')
-        self.assertEqual(str(motif.instances[14]), 'KVTIITGGTRGIGFAAAKIFIDNGAKVSI')
-        self.assertEqual(str(motif.instances[15]), 'KVMLLTGASRGIGHATAKLFSEAGWRIIS')
-        self.assertEqual(str(motif.instances[16]), 'KVALVTGGASGVGLEVVKLLLGEGAKVAF')
-        self.assertEqual(str(motif.instances[17]), 'KTVIITGGARGLGAEAARQAVAAGARVVL')
-        self.assertEqual(str(motif.instances[18]), 'PVALVTGAAKRLGRSIAEGLHAEGYAVCL')
-        self.assertEqual(str(motif.instances[19]), 'AHVVVTGGSSGIGKCIAIECYKQGAFITL')
-        self.assertEqual(str(motif.instances[20]), 'KAVLVTGGDCGLGHALCKYLDELGFTVFA')
-        self.assertEqual(str(motif.instances[21]), 'QVAFITGGASGAGFGQAKVFGQAGAKIVV')
-        self.assertEqual(str(motif.instances[22]), 'RKALVTGASGAIGGAIARVLHAQGAIVGL')
-        self.assertEqual(str(motif.instances[23]), 'HVALVTGGNKGIGLAIVRDLCRLFSGDVV')
-        self.assertEqual(str(motif.instances[24]), 'PVILVSGSNRGVGKAIAEDLIAHGYRLSL')
-        self.assertEqual(str(motif.instances[25]), 'EAVLITGGASGLGRALVDRFVAEAKVAVL')
-        self.assertEqual(str(motif.instances[26]), 'QVAVVIGGGQTLGAFLCHGLAAEGYRVAV')
-        self.assertEqual(str(motif.instances[27]), 'TVVLITGCSSGIGLHLAVRLASDPSQSFK')
-        self.assertEqual(str(motif.instances[28]), 'KNILVTGGAGFIGSAVVRHIIQNTRDSVV')
-        self.assertEqual(str(motif.instances[29]), 'KNVIFVAGLGGIGLDTSKELLKRDLKNLV')
-        self.assertEqual(str(motif.instances[30]), 'KRILVTGVASKLSIAYGIAQAMHREGAEL')
-        self.assertEqual(str(motif.instances[31]), 'IDTNVTGAAATLSAVLPQMVERKRGHLVG')
-        self.assertEqual(str(motif.instances[32]), 'TSAMLPGLRQGALRRVAHVTSRMGSLAAN')
-        handle.close()
-
-    def test_meme_parser_4(self):
-        """Test if Motif can parse MEME output files (fourth test)
-        """
-        from Bio.Motif.Parsers import MEME
-        handle = open("Motif/meme.protein.tcm.txt")
-        record = MEME.read(handle)
-        self.assertEqual(record.version, '3.0')
-        self.assertEqual(record.datafile, 'farntrans5.s')
-        self.assertEqual(record.alphabet, IUPAC.protein)
-        self.assertEqual(len(record.sequence_names), 5)
-        self.assertEqual(record.sequence_names[0], "RAM1_YEAST")
-        self.assertEqual(record.sequence_names[1], "PFTB_RAT")
-        self.assertEqual(record.sequence_names[2], "BET2_YEAST")
-        self.assertEqual(record.sequence_names[3], "RATRABGERB")
-        self.assertEqual(record.sequence_names[4], "CAL1_YEAST")
-        self.assertEqual(record.command, 'meme farntrans5.s -mod tcm -protein -nmotifs 2')
-        self.assertEqual(len(record.motifs), 2)
-        motif = record.motifs[0]
-        self.assertEqual(motif.num_occurrences, 24)
-        self.assertAlmostEqual(motif.evalue, 2.2e-94)
-        self.assertEqual(motif.alphabet, IUPAC.protein)
-        self.assertEqual(motif.name, "Motif 1")
-        self.assertEqual(len(motif.instances), 24)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 7.28e-22)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 6.18e-21)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 9.17e-20)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 1.15e-19)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 4.30e-19)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 7.36e-19)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 8.19e-19)
-        self.assertAlmostEqual(motif.instances[7].pvalue, 2.10e-18)
-        self.assertAlmostEqual(motif.instances[8].pvalue, 1.43e-17)
-        self.assertAlmostEqual(motif.instances[9].pvalue, 3.41e-17)
-        self.assertAlmostEqual(motif.instances[10].pvalue, 5.00e-17)
-        self.assertAlmostEqual(motif.instances[11].pvalue, 6.64e-17)
-        self.assertAlmostEqual(motif.instances[12].pvalue, 1.27e-16)
-        self.assertAlmostEqual(motif.instances[13].pvalue, 3.17e-16)
-        self.assertAlmostEqual(motif.instances[14].pvalue, 3.47e-16)
-        self.assertAlmostEqual(motif.instances[15].pvalue, 4.30e-15)
-        self.assertAlmostEqual(motif.instances[16].pvalue, 2.40e-14)
-        self.assertAlmostEqual(motif.instances[17].pvalue, 2.81e-14)
-        self.assertAlmostEqual(motif.instances[18].pvalue, 7.78e-14)
-        self.assertAlmostEqual(motif.instances[19].pvalue, 1.14e-13)
-        self.assertAlmostEqual(motif.instances[20].pvalue, 1.33e-13)
-        self.assertAlmostEqual(motif.instances[21].pvalue, 3.52e-13)
-        self.assertAlmostEqual(motif.instances[22].pvalue, 5.47e-13)
-        self.assertAlmostEqual(motif.instances[23].pvalue, 3.11e-10)
-        self.assertEqual(motif.instances[0].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[1].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[2].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[3].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[4].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[5].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[6].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[7].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[8].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[9].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[10].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[11].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[12].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[13].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[14].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[15].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[16].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[17].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[18].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[19].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[20].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[21].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[22].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[23].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[0].strand, '+')
-        self.assertEqual(motif.instances[1].strand, '+')
-        self.assertEqual(motif.instances[2].strand, '+')
-        self.assertEqual(motif.instances[3].strand, '+')
-        self.assertEqual(motif.instances[4].strand, '+')
-        self.assertEqual(motif.instances[5].strand, '+')
-        self.assertEqual(motif.instances[6].strand, '+')
-        self.assertEqual(motif.instances[7].strand, '+')
-        self.assertEqual(motif.instances[8].strand, '+')
-        self.assertEqual(motif.instances[9].strand, '+')
-        self.assertEqual(motif.instances[10].strand, '+')
-        self.assertEqual(motif.instances[11].strand, '+')
-        self.assertEqual(motif.instances[12].strand, '+')
-        self.assertEqual(motif.instances[13].strand, '+')
-        self.assertEqual(motif.instances[14].strand, '+')
-        self.assertEqual(motif.instances[15].strand, '+')
-        self.assertEqual(motif.instances[16].strand, '+')
-        self.assertEqual(motif.instances[17].strand, '+')
-        self.assertEqual(motif.instances[18].strand, '+')
-        self.assertEqual(motif.instances[19].strand, '+')
-        self.assertEqual(motif.instances[20].strand, '+')
-        self.assertEqual(motif.instances[21].strand, '+')
-        self.assertEqual(motif.instances[22].strand, '+')
-        self.assertEqual(motif.instances[23].strand, '+')
-        self.assertEqual(motif.instances[0].length, 30)
-        self.assertEqual(motif.instances[1].length, 30)
-        self.assertEqual(motif.instances[2].length, 30)
-        self.assertEqual(motif.instances[3].length, 30)
-        self.assertEqual(motif.instances[4].length, 30)
-        self.assertEqual(motif.instances[5].length, 30)
-        self.assertEqual(motif.instances[6].length, 30)
-        self.assertEqual(motif.instances[7].length, 30)
-        self.assertEqual(motif.instances[8].length, 30)
-        self.assertEqual(motif.instances[9].length, 30)
-        self.assertEqual(motif.instances[10].length, 30)
-        self.assertEqual(motif.instances[11].length, 30)
-        self.assertEqual(motif.instances[12].length, 30)
-        self.assertEqual(motif.instances[13].length, 30)
-        self.assertEqual(motif.instances[14].length, 30)
-        self.assertEqual(motif.instances[15].length, 30)
-        self.assertEqual(motif.instances[16].length, 30)
-        self.assertEqual(motif.instances[17].length, 30)
-        self.assertEqual(motif.instances[18].length, 30)
-        self.assertEqual(motif.instances[19].length, 30)
-        self.assertEqual(motif.instances[20].length, 30)
-        self.assertEqual(motif.instances[21].length, 30)
-        self.assertEqual(motif.instances[22].length, 30)
-        self.assertEqual(motif.instances[23].length, 30)
-        self.assertEqual(motif.instances[0].start, 223)
-        self.assertEqual(motif.instances[1].start, 227)
-        self.assertEqual(motif.instances[2].start, 275)
-        self.assertEqual(motif.instances[3].start, 237)
-        self.assertEqual(motif.instances[4].start, 138)
-        self.assertEqual(motif.instances[5].start, 179)
-        self.assertEqual(motif.instances[6].start, 131)
-        self.assertEqual(motif.instances[7].start, 172)
-        self.assertEqual(motif.instances[8].start, 276)
-        self.assertEqual(motif.instances[9].start, 124)
-        self.assertEqual(motif.instances[10].start, 247)
-        self.assertEqual(motif.instances[11].start, 272)
-        self.assertEqual(motif.instances[12].start, 145)
-        self.assertEqual(motif.instances[13].start, 286)
-        self.assertEqual(motif.instances[14].start, 296)
-        self.assertEqual(motif.instances[15].start, 348)
-        self.assertEqual(motif.instances[16].start, 83)
-        self.assertEqual(motif.instances[17].start, 189)
-        self.assertEqual(motif.instances[18].start, 73)
-        self.assertEqual(motif.instances[19].start, 205)
-        self.assertEqual(motif.instances[20].start, 198)
-        self.assertEqual(motif.instances[21].start, 349)
-        self.assertEqual(motif.instances[22].start, 327)
-        self.assertEqual(motif.instances[23].start, 24)
-        self.assertEqual(str(motif.instances[0]), "GGLNGRPSKLPDVCYSWWVLSSLAIIGRLD")
-        self.assertEqual(str(motif.instances[1]), "GGLNGRPEKLPDVCYSWWVLASLKIIGRLH")
-        self.assertEqual(str(motif.instances[2]), "GGFQGRENKFADTCYAFWCLNSLHLLTKDW")
-        self.assertEqual(str(motif.instances[3]), "GGIGGVPGMEAHGGYTFCGLAALVILKKER")
-        self.assertEqual(str(motif.instances[4]), "GGFGGGPGQYPHLAPTYAAVNALCIIGTEE")
-        self.assertEqual(str(motif.instances[5]), "GGFGCRPGSESHAGQIYCCTGFLAITSQLH")
-        self.assertEqual(str(motif.instances[6]), "GSFAGDIWGEIDTRFSFCAVATLALLGKLD")
-        self.assertEqual(str(motif.instances[7]), "GGFGLCPNAESHAAQAFTCLGALAIANKLD")
-        self.assertEqual(str(motif.instances[8]), "GGFADRPGDMVDPFHTLFGIAGLSLLGEEQ")
-        self.assertEqual(str(motif.instances[9]), "GSFQGDRFGEVDTRFVYTALSALSILGELT")
-        self.assertEqual(str(motif.instances[10]), "GFGSCPHVDEAHGGYTFCATASLAILRSMD")
-        self.assertEqual(str(motif.instances[11]), "GGISDRPENEVDVFHTVFGVAGLSLMGYDN")
-        self.assertEqual(str(motif.instances[12]), "GPFGGGPGQLSHLASTYAAINALSLCDNID")
-        self.assertEqual(str(motif.instances[13]), "GGFQGRCNKLVDGCYSFWQAGLLPLLHRAL")
-        self.assertEqual(str(motif.instances[14]), "RGFCGRSNKLVDGCYSFWVGGSAAILEAFG")
-        self.assertEqual(str(motif.instances[15]), "GGLLDKPGKSRDFYHTCYCLSGLSIAQHFG")
-        self.assertEqual(str(motif.instances[16]), "GGVSASIGHDPHLLYTLSAVQILTLYDSIH")
-        self.assertEqual(str(motif.instances[17]), "GSFLMHVGGEVDVRSAYCAASVASLTNIIT")
-        self.assertEqual(str(motif.instances[18]), "GAFAPFPRHDAHLLTTLSAVQILATYDALD")
-        self.assertEqual(str(motif.instances[19]), "YNGAFGAHNEPHSGYTSCALSTLALLSSLE")
-        self.assertEqual(str(motif.instances[20]), "GFKTCLEVGEVDTRGIYCALSIATLLNILT")
-        self.assertEqual(str(motif.instances[21]), "PGLRDKPGAHSDFYHTNYCLLGLAVAESSY")
-        self.assertEqual(str(motif.instances[22]), "GGFSKNDEEDADLYHSCLGSAALALIEGKF")
-        self.assertEqual(str(motif.instances[23]), "HNFEYWLTEHLRLNGIYWGLTALCVLDSPE")
-        motif = record.motifs[1]
-        self.assertEqual(motif.num_occurrences, 21)
-        self.assertAlmostEqual(motif.evalue, 3.1e-19)
-        self.assertEqual(motif.alphabet, IUPAC.protein)
-        self.assertEqual(motif.name, "Motif 2")
-        self.assertEqual(len(motif.instances), 21)
-        self.assertAlmostEqual(motif.instances[0].pvalue, 2.24e-13)
-        self.assertAlmostEqual(motif.instances[1].pvalue, 1.30e-12)
-        self.assertAlmostEqual(motif.instances[2].pvalue, 4.20e-12)
-        self.assertAlmostEqual(motif.instances[3].pvalue, 9.60e-12)
-        self.assertAlmostEqual(motif.instances[4].pvalue, 5.08e-11)
-        self.assertAlmostEqual(motif.instances[5].pvalue, 5.01e-10)
-        self.assertAlmostEqual(motif.instances[6].pvalue, 6.90e-10)
-        self.assertAlmostEqual(motif.instances[7].pvalue, 1.57e-09)
-        self.assertAlmostEqual(motif.instances[8].pvalue, 2.34e-09)
-        self.assertAlmostEqual(motif.instances[9].pvalue, 4.59e-09)
-        self.assertAlmostEqual(motif.instances[10].pvalue, 1.65e-08)
-        self.assertAlmostEqual(motif.instances[11].pvalue, 1.65e-08)
-        self.assertAlmostEqual(motif.instances[12].pvalue, 1.65e-08)
-        self.assertAlmostEqual(motif.instances[13].pvalue, 2.54e-08)
-        self.assertAlmostEqual(motif.instances[14].pvalue, 4.58e-08)
-        self.assertAlmostEqual(motif.instances[15].pvalue, 5.86e-08)
-        self.assertAlmostEqual(motif.instances[16].pvalue, 1.52e-07)
-        self.assertAlmostEqual(motif.instances[17].pvalue, 1.91e-07)
-        self.assertAlmostEqual(motif.instances[18].pvalue, 4.34e-07)
-        self.assertAlmostEqual(motif.instances[19].pvalue, 5.01e-07)
-        self.assertAlmostEqual(motif.instances[20].pvalue, 5.78e-07)
-        self.assertEqual(motif.instances[0].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[1].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[2].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[3].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[4].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[5].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[6].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[7].sequence_name, "RATRABGERB")
-        self.assertEqual(motif.instances[8].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[9].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[10].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[11].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[12].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[13].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[14].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[15].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[16].sequence_name, "PFTB_RAT")
-        self.assertEqual(motif.instances[17].sequence_name, "CAL1_YEAST")
-        self.assertEqual(motif.instances[18].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[19].sequence_name, "BET2_YEAST")
-        self.assertEqual(motif.instances[20].sequence_name, "RAM1_YEAST")
-        self.assertEqual(motif.instances[0].strand, '+')
-        self.assertEqual(motif.instances[1].strand, '+')
-        self.assertEqual(motif.instances[2].strand, '+')
-        self.assertEqual(motif.instances[3].strand, '+')
-        self.assertEqual(motif.instances[4].strand, '+')
-        self.assertEqual(motif.instances[5].strand, '+')
-        self.assertEqual(motif.instances[6].strand, '+')
-        self.assertEqual(motif.instances[7].strand, '+')
-        self.assertEqual(motif.instances[8].strand, '+')
-        self.assertEqual(motif.instances[9].strand, '+')
-        self.assertEqual(motif.instances[10].strand, '+')
-        self.assertEqual(motif.instances[11].strand, '+')
-        self.assertEqual(motif.instances[12].strand, '+')
-        self.assertEqual(motif.instances[13].strand, '+')
-        self.assertEqual(motif.instances[14].strand, '+')
-        self.assertEqual(motif.instances[15].strand, '+')
-        self.assertEqual(motif.instances[16].strand, '+')
-        self.assertEqual(motif.instances[17].strand, '+')
-        self.assertEqual(motif.instances[18].strand, '+')
-        self.assertEqual(motif.instances[19].strand, '+')
-        self.assertEqual(motif.instances[20].strand, '+')
-        self.assertEqual(motif.instances[0].length, 14)
-        self.assertEqual(motif.instances[1].length, 14)
-        self.assertEqual(motif.instances[2].length, 14)
-        self.assertEqual(motif.instances[3].length, 14)
-        self.assertEqual(motif.instances[4].length, 14)
-        self.assertEqual(motif.instances[5].length, 14)
-        self.assertEqual(motif.instances[6].length, 14)
-        self.assertEqual(motif.instances[7].length, 14)
-        self.assertEqual(motif.instances[8].length, 14)
-        self.assertEqual(motif.instances[9].length, 14)
-        self.assertEqual(motif.instances[10].length, 14)
-        self.assertEqual(motif.instances[11].length, 14)
-        self.assertEqual(motif.instances[12].length, 14)
-        self.assertEqual(motif.instances[13].length, 14)
-        self.assertEqual(motif.instances[14].length, 14)
-        self.assertEqual(motif.instances[15].length, 14)
-        self.assertEqual(motif.instances[16].length, 14)
-        self.assertEqual(motif.instances[17].length, 14)
-        self.assertEqual(motif.instances[18].length, 14)
-        self.assertEqual(motif.instances[19].length, 14)
-        self.assertEqual(motif.instances[20].length, 14)
-        self.assertEqual(motif.instances[0].start, 254)
-        self.assertEqual(motif.instances[1].start, 258)
-        self.assertEqual(motif.instances[2].start, 162)
-        self.assertEqual(motif.instances[3].start, 66)
-        self.assertEqual(motif.instances[4].start, 278)
-        self.assertEqual(motif.instances[5].start, 190)
-        self.assertEqual(motif.instances[6].start, 55)
-        self.assertEqual(motif.instances[7].start, 114)
-        self.assertEqual(motif.instances[8].start, 172)
-        self.assertEqual(motif.instances[9].start, 330)
-        self.assertEqual(motif.instances[10].start, 126)
-        self.assertEqual(motif.instances[11].start, 268)
-        self.assertEqual(motif.instances[12].start, 220)
-        self.assertEqual(motif.instances[13].start, 229)
-        self.assertEqual(motif.instances[14].start, 330)
-        self.assertEqual(motif.instances[15].start, 239)
-        self.assertEqual(motif.instances[16].start, 121)
-        self.assertEqual(motif.instances[17].start, 362)
-        self.assertEqual(motif.instances[18].start, 107)
-        self.assertEqual(motif.instances[19].start, 155)
-        self.assertEqual(motif.instances[20].start, 180)
-        self.assertEqual(str(motif.instances[0]), "INYEKLTEFILKCQ")
-        self.assertEqual(str(motif.instances[1]), "IDREKLRSFILACQ")
-        self.assertEqual(str(motif.instances[2]), "INVEKAIEFVLSCM")
-        self.assertEqual(str(motif.instances[3]), "MNKEEILVFIKSCQ")
-        self.assertEqual(str(motif.instances[4]), "INVEKLLEWSSARQ")
-        self.assertEqual(str(motif.instances[5]), "IDTEKLLGYIMSQQ")
-        self.assertEqual(str(motif.instances[6]), "FVKEEVISFVLSCW")
-        self.assertEqual(str(motif.instances[7]), "INVDKVVAYVQSLQ")
-        self.assertEqual(str(motif.instances[8]), "INREKLLQYLYSLK")
-        self.assertEqual(str(motif.instances[9]), "FNKHALRDYILYCC")
-        self.assertEqual(str(motif.instances[10]), "LDKRSLARFVSKCQ")
-        self.assertEqual(str(motif.instances[11]), "LNLKSLLQWVTSRQ")
-        self.assertEqual(str(motif.instances[12]), "DLFEGTAEWIARCQ")
-        self.assertEqual(str(motif.instances[13]), "ELTEGVLNYLKNCQ")
-        self.assertEqual(str(motif.instances[14]), "FHQQALQEYILMCC")
-        self.assertEqual(str(motif.instances[15]), "KFKEDTITWLLHRQ")
-        self.assertEqual(str(motif.instances[16]), "IVATDVCQFLELCQ")
-        self.assertEqual(str(motif.instances[17]), "IPQEIFNDFSKRCC")
-        self.assertEqual(str(motif.instances[18]), "DRKVRLISFIRGNQ")
-        self.assertEqual(str(motif.instances[19]), "EVVDPAVDFVLKCY")
-        self.assertEqual(str(motif.instances[20]), "IDRKGIYQWLISLK")
-        handle.close()
-
-
-class TestMAST(unittest.TestCase):
-
-    def test_mast_parser_1(self):
-        """Test if Motif can parse MAST output files (first test)
-        """
-        from Bio.Motif.Parsers import MAST
-        handle = open("Motif/mast.dna.oops.txt")
-        record = MAST.read(handle)
-        self.assertEqual(record.version, "3.0")
-        self.assertEqual(record.database, "INO_up800.s")
-        self.assertEqual(record.alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(len(record.motifs), 2)
-        self.assertEqual(record.motifs[0].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(record.motifs[0].length, 12)
-        self.assertEqual(record.motifs[0].name, "1")
-        self.assertEqual(record.motifs[1].alphabet, IUPAC.unambiguous_dna)
-        self.assertEqual(record.motifs[1].length, 10)
-        self.assertEqual(record.motifs[1].name, "2")
-        self.assertEqual(len(record.sequences), 7)
-        self.assertEqual(record.sequences[0], "ACC1")
-        self.assertEqual(record.sequences[1], "CHO1")
-        self.assertEqual(record.sequences[2], "INO1")
-        self.assertEqual(record.sequences[3], "FAS1")
-        self.assertEqual(record.sequences[4], "OPI3")
-        self.assertEqual(record.sequences[5], "CHO2")
-        self.assertEqual(record.sequences[6], "FAS2")
-        self.assertEqual(record.diagrams["ACC1"], "82_[+1]_137_[+2]_559")
-        self.assertEqual(record.diagrams["CHO1"], "152_[+2]_396_[-2]_42_[+1]_17_[+1]_149")
-        self.assertEqual(record.diagrams["INO1"], "282_[-2]_327_[-1]_55_[+1]_102")
-        self.assertEqual(record.diagrams["FAS1"], "43_[+2]_41_[+1]_694")
-        self.assertEqual(record.diagrams["OPI3"], "185_[-2]_144_[+1]_449")
-        self.assertEqual(record.diagrams["CHO2"], "353_[+1]_47_[-2]_378")
-        self.assertEqual(record.diagrams["FAS2"], "184_[-2]_372_[+1]_222")
-        handle.close()
-
-    def test_mast_parser_2(self):
-        """Test if Motif can parse MAST output files (second test)
-        """
-        from Bio.Motif.Parsers import MAST
-        handle = open("Motif/mast.protein.oops.txt")
-        record = MAST.read(handle)
-        self.assertEqual(record.version, "3.0")
-        self.assertEqual(record.database, "adh.s")
-        self.assertEqual(record.alphabet, IUPAC.protein)
-        self.assertEqual(len(record.motifs), 2)
-        self.assertEqual(record.motifs[0].alphabet, IUPAC.protein)
-        self.assertEqual(record.motifs[0].length, 29)
-        self.assertEqual(record.motifs[0].name, "1")
-        self.assertEqual(record.motifs[1].alphabet, IUPAC.protein)
-        self.assertEqual(record.motifs[1].length, 29)
-        self.assertEqual(record.motifs[1].name, "2")
-        self.assertEqual(len(record.sequences), 33)
-        self.assertEqual(record.sequences[0], "BUDC_KLETE")
-        self.assertEqual(record.sequences[1], "YRTP_BACSU")
-        self.assertEqual(record.sequences[2], "AP27_MOUSE")
-        self.assertEqual(record.sequences[3], "HDE_CANTR")
-        self.assertEqual(record.sequences[4], "HDHA_ECOLI")
-        self.assertEqual(record.sequences[5], "DHII_HUMAN")
-        self.assertEqual(record.sequences[6], "FIXR_BRAJA")
-        self.assertEqual(record.sequences[7], "DHGB_BACME")
-        self.assertEqual(record.sequences[8], "NODG_RHIME")
-        self.assertEqual(record.sequences[9], "RIDH_KLEAE")
-        self.assertEqual(record.sequences[10], "YINL_LISMO")
-        self.assertEqual(record.sequences[11], "DHMA_FLAS1")
-        self.assertEqual(record.sequences[12], "HMTR_LEIMA")
-        self.assertEqual(record.sequences[13], "2BHD_STREX")
-        self.assertEqual(record.sequences[14], "ENTA_ECOLI")
-        self.assertEqual(record.sequences[15], "DHB2_HUMAN")
-        self.assertEqual(record.sequences[16], "BDH_HUMAN")
-        self.assertEqual(record.sequences[17], "BA72_EUBSP")
-        self.assertEqual(record.sequences[18], "FVT1_HUMAN")
-        self.assertEqual(record.sequences[19], "GUTD_ECOLI")
-        self.assertEqual(record.sequences[20], "DHB3_HUMAN")
-        self.assertEqual(record.sequences[21], "3BHD_COMTE")
-        self.assertEqual(record.sequences[22], "LIGD_PSEPA")
-        self.assertEqual(record.sequences[23], "DHES_HUMAN")
-        self.assertEqual(record.sequences[24], "RFBB_NEIGO")
-        self.assertEqual(record.sequences[25], "BPHB_PSEPS")
-        self.assertEqual(record.sequences[26], "YURA_MYXXA")
-        self.assertEqual(record.sequences[27], "PCR_PEA")
-        self.assertEqual(record.sequences[28], "DHCA_HUMAN")
-        self.assertEqual(record.sequences[29], "ADH_DROME")
-        self.assertEqual(record.sequences[30], "MAS1_AGRRA")
-        self.assertEqual(record.sequences[31], "FABI_ECOLI")
-        self.assertEqual(record.sequences[32], "CSGA_MYXXA")
-        self.assertEqual(record.diagrams["BUDC_KLETE"], "2_[2]_120_[1]_61")
-        self.assertEqual(record.diagrams["YRTP_BACSU"], "6_[2]_119_[1]_55")
-        self.assertEqual(record.diagrams["AP27_MOUSE"], "7_[2]_112_[1]_67")
-        self.assertEqual(record.diagrams["HDE_CANTR"], "8_[2]_125_[1]_131_[2]_115_[1]_411")
-        self.assertEqual(record.diagrams["HDHA_ECOLI"], "11_[2]_74_[1]_15_[1]_68")
-        self.assertEqual(record.diagrams["DHII_HUMAN"], "34_[2]_119_[1]_81")
-        self.assertEqual(record.diagrams["FIXR_BRAJA"], "36_[2]_123_[1]_61")
-        self.assertEqual(record.diagrams["DHGB_BACME"], "7_[2]_123_[1]_74")
-        self.assertEqual(record.diagrams["NODG_RHIME"], "6_[2]_116_[1]_65")
-        self.assertEqual(record.diagrams["RIDH_KLEAE"], "14_[2]_116_[1]_61")
-        self.assertEqual(record.diagrams["YINL_LISMO"], "5_[2]_75_[2]_15_[1]_66")
-        self.assertEqual(record.diagrams["DHMA_FLAS1"], "14_[2]_121_[1]_77")
-        self.assertEqual(record.diagrams["HMTR_LEIMA"], "6_[2]_157_[1]_66")
-        self.assertEqual(record.diagrams["2BHD_STREX"], "6_[2]_116_[1]_75")
-        self.assertEqual(record.diagrams["ENTA_ECOLI"], "5_[2]_109_[1]_76")
-        self.assertEqual(record.diagrams["DHB2_HUMAN"], "82_[2]_120_[1]_127")
-        self.assertEqual(record.diagrams["BDH_HUMAN"], "55_[2]_123_[1]_107")
-        self.assertEqual(record.diagrams["BA72_EUBSP"], "6_[2]_121_[1]_64")
-        self.assertEqual(record.diagrams["FVT1_HUMAN"], "32_[2]_124_[1]_118")
-        self.assertEqual(record.diagrams["GUTD_ECOLI"], "2_[2]_122_[1]_77")
-        self.assertEqual(record.diagrams["DHB3_HUMAN"], "48_[2]_120_[1]_84")
-        self.assertEqual(record.diagrams["3BHD_COMTE"], "6_[2]_115_[1]_74")
-        self.assertEqual(record.diagrams["LIGD_PSEPA"], "6_[2]_121_[1]_120")
-        self.assertEqual(record.diagrams["DHES_HUMAN"], "2_[2]_50_[2]_44_[1]_144")
-        self.assertEqual(record.diagrams["RFBB_NEIGO"], "6_[2]_129_[1]_153")
-        self.assertEqual(record.diagrams["BPHB_PSEPS"], "5_[2]_118_[1]_94")
-        self.assertEqual(record.diagrams["YURA_MYXXA"], "65_[2]_22_[2]_14_[1]_70")
-        self.assertEqual(record.diagrams["PCR_PEA"], "25_[1]_32_[2]_284")
-        self.assertEqual(record.diagrams["DHCA_HUMAN"], "4_[2]_159_[1]_55")
-        self.assertEqual(record.diagrams["ADH_DROME"], "6_[2]_116_[1]_75")
-        self.assertEqual(record.diagrams["MAS1_AGRRA"], "245_[2]_74_[1]_14_[1]_56")
-        self.assertEqual(record.diagrams["FABI_ECOLI"], "6_[2]_123_[1]_75")
-        self.assertEqual(record.diagrams["CSGA_MYXXA"], "51_[2]_7_[1]_50")
-        handle.close()
-
-    def test_mast_parser_3(self):
-        """Test if Motif can parse MAST output files (third test)
-        """
-        from Bio.Motif.Parsers import MAST
-        handle = open("Motif/mast.protein.tcm.txt")
-        record = MAST.read(handle)
-        self.assertEqual(record.version, "3.0")
-        self.assertEqual(record.database, "farntrans5.s")
-        self.assertEqual(record.alphabet, IUPAC.protein)
-        self.assertEqual(len(record.motifs), 2)
-        self.assertEqual(record.motifs[0].alphabet, IUPAC.protein)
-        self.assertEqual(record.motifs[0].length, 30)
-        self.assertEqual(record.motifs[0].name, "1")
-        self.assertEqual(record.motifs[1].alphabet, IUPAC.protein)
-        self.assertEqual(record.motifs[1].length, 14)
-        self.assertEqual(record.motifs[1].name, "2")
-        self.assertEqual(len(record.sequences), 5)
-        self.assertEqual(record.sequences[0], "BET2_YEAST")
-        self.assertEqual(record.sequences[1], "RATRABGERB")
-        self.assertEqual(record.sequences[2], "CAL1_YEAST")
-        self.assertEqual(record.sequences[3], "PFTB_RAT")
-        self.assertEqual(record.sequences[4], "RAM1_YEAST")
-        self.assertEqual(record.diagrams["BET2_YEAST"], "6_[2]_3_[1]_1_[2]_4_[1]_4_[2]_3_[1]_1_[2]_3_[1]_21_[1]_1_[2]_4_[1]_24")
-        self.assertEqual(record.diagrams["RATRABGERB"], "65_[2]_3_[1]_1_[2]_3_[1]_1_[2]_3_[1]_18_[1]_1_[2]_4_[1]_26")
-        self.assertEqual(record.diagrams["CAL1_YEAST"], "125_[2]_50_[2]_1_[1]_4_[2]_22_[1]_22_[1]_5_[2]_1")
-        self.assertEqual(record.diagrams["PFTB_RAT"], "120_[2]_3_[1]_4_[2]_3_[1]_1_[2]_3_[1]_1_[2]_4_[1]_14_[2]_4_[1]_60")
-        self.assertEqual(record.diagrams["RAM1_YEAST"], "144_[1]_5_[2]_4_[1]_1_[2]_4_[1]_1_[2]_4_[1]_4_[2]_5_[1]_35_[2]_4")
-        handle.close()
-
-
-class MotifTestPWM(unittest.TestCase):
-    def setUp(self):
-        handle = open("Motif/SRF.pfm")
-        self.m = Motif.read(handle, "jaspar-pfm")
-        handle.close()
-        self.s = Seq("ACGTGTGCGTAGTGCGT", self.m.alphabet)
-
-    def test_simple(self):
-        """Test if Motif PWM scoring works."""
-        result = self.m.scanPWM(self.s)
-        self.assertEqual(6, len(result))
-        # The fast C-code in Bio/Motif/_pwm.c stores all results as 32-bit
-        # floats; the slower Python code in Bio/Motif/_Motif.py uses 64-bit
-        # doubles. The C-code and Python code results will therefore not be
-        # exactly equal. Test the first 5 decimal places only to avoid either
-        # the C-code or the Python code to inadvertently fail this test.
-        self.assertAlmostEqual(result[0], -29.18363571, places=5)
-        self.assertAlmostEqual(result[1], -38.3365097, places=5)
-        self.assertAlmostEqual(result[2], -29.17756271, places=5)
-        self.assertAlmostEqual(result[3], -38.04542542, places=5)
-        self.assertAlmostEqual(result[4], -20.3014183, places=5)
-        self.assertAlmostEqual(result[5], -25.18009186, places=5)
-
-    def test_with_alt_alphabet(self):
-        """Test motif search using alternative instance of alphabet."""
-        self.s = Seq(str(self.s), IUPAC.IUPACUnambiguousDNA())
-        self.test_simple()
-
-
-if __name__ == "__main__":
-    runner = unittest.TextTestRunner(verbosity=2)
-    unittest.main(testRunner=runner)
diff --git a/Tests/test_Muscle_tool.py b/Tests/test_Muscle_tool.py
index 80c2ce1..23548ce 100644
--- a/Tests/test_Muscle_tool.py
+++ b/Tests/test_Muscle_tool.py
@@ -89,8 +89,8 @@ class MuscleApplication(unittest.TestCase):
         cmdline = MuscleCommandline(muscle_exe,
                                     input=self.infile1,
                                     out=self.outfile1)
-        self.assertEqual(str(cmdline), _escape_filename(muscle_exe)
-                         + ' -in Fasta/f002 -out "Fasta/temp align out1.fa"')
+        self.assertEqual(str(cmdline), _escape_filename(muscle_exe) +
+                         ' -in Fasta/f002 -out "Fasta/temp align out1.fa"')
         self.assertEqual(str(eval(repr(cmdline))), str(cmdline))
         output, error = cmdline()
         self.assertEqual(output, "")
diff --git a/Tests/test_NCBI_BLAST_tools.py b/Tests/test_NCBI_BLAST_tools.py
index 369bf95..511908a 100644
--- a/Tests/test_NCBI_BLAST_tools.py
+++ b/Tests/test_NCBI_BLAST_tools.py
@@ -39,7 +39,8 @@ else:
 for folder in likely_dirs:
     if not os.path.isdir(folder):
         continue
-    for name in wanted:
+    # Loop over copy as will remove entries from wanted:
+    for name in wanted[:]:
         if sys.platform == "win32":
             exe_name = os.path.join(folder, name + ".exe")
         else:
@@ -61,6 +62,7 @@ for folder in likely_dirs:
             if name == "blast_formatter" and " -archive " not in output:
                 continue
             exe_names[name] = exe_name
+            wanted.remove(name)  # can stop search for this now
         # else:
         #    print("Rejecting %r" % exe_name)
         del exe_name, name
@@ -88,9 +90,9 @@ class Pairwise(unittest.TestCase):
                         query="Fasta/rose.pro",
                         subject="GenBank/NC_005816.faa",
                         evalue=1)
-        self.assertEqual(str(cline), _escape_filename(exe_names["blastp"])
-                         + " -query Fasta/rose.pro -evalue 1"
-                         + " -subject GenBank/NC_005816.faa")
+        self.assertEqual(str(cline), _escape_filename(exe_names["blastp"]) +
+                         " -query Fasta/rose.pro -evalue 1" +
+                         " -subject GenBank/NC_005816.faa")
         child = subprocess.Popen(str(cline),
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,
@@ -100,12 +102,18 @@ class Pairwise(unittest.TestCase):
         return_code = child.returncode
         self.assertEqual(return_code, 0, "Got error code %i back from:\n%s"
                          % (return_code, cline))
-        self.assertEqual(10, stdoutdata.count("Query= "))
-        if stdoutdata.count("***** No hits found *****") == 7:
-            # This happens with BLAST 2.2.26+ which is potentially a bug
-            pass
+        # Used to get 10 matches from 10 pairwise searches,
+        # as of NCBI BLAST+ 2.3.0 only get 1 Query= line:
+        if stdoutdata.count("Query= ") == 10:
+            if stdoutdata.count("***** No hits found *****") == 7:
+                # This happens with BLAST 2.2.26+ which is potentially a bug
+                pass
+            else:
+                self.assertEqual(9, stdoutdata.count("***** No hits found *****"))
         else:
-            self.assertEqual(9, stdoutdata.count("***** No hits found *****"))
+            # Assume this is NCBI BLAST+ 2.3.0 or later,
+            self.assertEqual(1, stdoutdata.count("Query= "))
+            self.assertEqual(0, stdoutdata.count("***** No hits found *****"))
 
         # TODO - Parse it? I think we'd need to update this obsole code :(
         # records = list(NCBIStandalone.Iterator(StringIO(stdoutdata),
@@ -118,9 +126,9 @@ class Pairwise(unittest.TestCase):
                         query="GenBank/NC_005816.ffn",
                         subject="GenBank/NC_005816.fna",
                         evalue="0.000001")
-        self.assertEqual(str(cline), _escape_filename(exe_names["blastn"])
-                         + " -query GenBank/NC_005816.ffn -evalue 0.000001"
-                         + " -subject GenBank/NC_005816.fna")
+        self.assertEqual(str(cline), _escape_filename(exe_names["blastn"]) +
+                         " -query GenBank/NC_005816.ffn -evalue 0.000001" +
+                         " -subject GenBank/NC_005816.fna")
         child = subprocess.Popen(str(cline),
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,
@@ -141,9 +149,9 @@ class Pairwise(unittest.TestCase):
                         query="GenBank/NC_005816.faa",
                         subject="GenBank/NC_005816.fna",
                         evalue="1e-6")
-        self.assertEqual(str(cline), _escape_filename(exe_names["tblastn"])
-                         + " -query GenBank/NC_005816.faa -evalue 1e-6"
-                         + " -subject GenBank/NC_005816.fna")
+        self.assertEqual(str(cline), _escape_filename(exe_names["tblastn"]) +
+                         " -query GenBank/NC_005816.faa -evalue 1e-6" +
+                         " -subject GenBank/NC_005816.fna")
         child = subprocess.Popen(str(cline),
                                  stdout=subprocess.PIPE,
                                  stderr=subprocess.PIPE,
@@ -269,6 +277,9 @@ class CheckCompleteArgList(unittest.TestCase):
                         "rpstblastn", "rpsblast", "tblastn", "tblastx"]:
             # New in BLAST+ 2.2.30 so will look like extra args on BLAST+ 2.2.29 etc
             extra = extra.difference(["-line_length", "-qcov_hsp_perc", "-sum_stats"])
+        if exe_name in ["deltablast", "psiblast"]:
+            # New in BLAST+ 2.3.0 so will look like extra args on older verions
+            extra = extra.difference(["-save_each_pssm", "-save_pssm_after_last_round"])
 
         if extra or missing:
             import warnings
diff --git a/Tests/test_NCBI_qblast.py b/Tests/test_NCBI_qblast.py
index 76a5fb1..35ff12b 100644
--- a/Tests/test_NCBI_qblast.py
+++ b/Tests/test_NCBI_qblast.py
@@ -1,4 +1,4 @@
-# Copyright 2008 by Peter Cock.  All rights reserved.
+# Copyright 2008-2016 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -18,14 +18,14 @@ import unittest
 from Bio._py3k import HTTPError
 from Bio._py3k import StringIO
 
-import requires_internet
-requires_internet.check()
 from Bio import MissingExternalDependencyError
 
 # We want to test these:
 from Bio.Blast import NCBIWWW
 from Bio.Blast import NCBIXML
 
+import requires_internet
+requires_internet.check()
 
 #####################################################################
 
@@ -51,7 +51,10 @@ class TestQblast(unittest.TestCase):
     def test_pcr_primers(self):
         # This next example finds PCR primer matches in Chimpanzees, e.g. BRCA1:
         self.run_qblast("blastn", "nr", "GTACCTTGATTTCGTATTC" + ("N" * 30) + "GACTCTACTACCTTTACCC",
-                        10, "pan [ORGN]", ["37953274", "51104367", "51104367", "51104367"])
+                        10, "pan [ORGN]", ["XM_009432096.2", "XM_009432102.2", "XM_009432101.2",
+                                           "XM_016930487.1", "XM_009432104.2", "XM_009432099.2",
+                                           "XR_001710553.1", "XM_016930485.1", "XM_009432089.2",
+                                           "XM_016930484.1"])
 
     def test_orchid_est(self):
         # Try an orchid EST (nucleotide) sequence against NR using BLASTX
@@ -95,9 +98,13 @@ class TestQblast(unittest.TestCase):
             # We used a FASTA record as the query
             expected = query[1:].split("\n", 1)[0]
             self.assertEqual(expected, record.query)
+        elif record.query_id.startswith("Query_") and len(query) == record.query_letters:
+            # We used a sequence as the entry and it was given a placeholder name
+            pass
         else:
             # We used an identifier as the query
-            self.assertTrue(query in record.query_id.split("|"))
+            self.assertTrue(query in record.query_id.split("|"),
+                            "Expected %r within query_id %r" % (query, record.query_id))
 
         # Check the recorded input parameters agree with those requested
         self.assertEqual(float(record.expect), e_value)
diff --git a/Tests/test_NNGene.py b/Tests/test_NNGene.py
index 60eac54..bc9b3b4 100644
--- a/Tests/test_NNGene.py
+++ b/Tests/test_NNGene.py
@@ -52,14 +52,12 @@ class PatternIOTest(unittest.TestCase):
         """
         # write to a file
         motifs = ["GAC", "AAA", "TTT", "GGG"]
-        output_handle = open(self.test_file, "w")
-        self.pattern_io.write(motifs, output_handle)
-        output_handle.close()
+        with open(self.test_file, "w") as output_handle:
+            self.pattern_io.write(motifs, output_handle)
 
         # read 'em back
-        input_handle = open(self.test_file, "r")
-        read_motifs = self.pattern_io.read(input_handle)
-        input_handle.close()
+        with open(self.test_file, "r") as input_handle:
+            read_motifs = self.pattern_io.read(input_handle)
         self.assertEqual(read_motifs, motifs,
                          "Failed to get back expected motifs %s, got %s"
                          % (motifs, read_motifs))
@@ -68,14 +66,12 @@ class PatternIOTest(unittest.TestCase):
         seq_motifs = []
         for motif in motifs:
             seq_motifs.append(Seq(motif, self.alphabet))
-        output_handle = open(self.test_file, "w")
-        self.pattern_io.write_seq(seq_motifs, output_handle)
-        output_handle.close()
+        with open(self.test_file, "w") as output_handle:
+            self.pattern_io.write_seq(seq_motifs, output_handle)
 
         # read the seqs back
-        input_handle = open(self.test_file, "r")
-        read_motifs = self.pattern_io.read(input_handle)
-        input_handle.close()
+        with open(self.test_file, "r") as input_handle:
+            read_motifs = self.pattern_io.read(input_handle)
         self.assertEqual(read_motifs, motifs,
                          "Failed to get back expected motifs %s from seqs, got %s"
                          % (motifs, read_motifs))
@@ -85,23 +81,20 @@ class PatternIOTest(unittest.TestCase):
         """
         schemas = ["GTR", "GAC"]
         # write out the schemas
-        output_handle = open(self.test_file, "w")
-        self.pattern_io.write(schemas, output_handle)
-        output_handle.close()
+        with open(self.test_file, "w") as output_handle:
+            self.pattern_io.write(schemas, output_handle)
 
         # read back the schemas
-        input_handle = open(self.test_file, "r")
-        read_schemas = self.pattern_io.read(input_handle)
-        input_handle.close()
+        with open(self.test_file, "r") as input_handle:
+            read_schemas = self.pattern_io.read(input_handle)
         self.assertEqual(schemas, read_schemas,
-                         "Read incorrect schemas %s, expected %s."
-                         % (read_schemas, schemas))
+                             "Read incorrect schemas %s, expected %s."
+                             % (read_schemas, schemas))
 
         # --- make sure inappropriate alphabets are reported
         schemas = ["GTR", "G*C"]  # '*' not in the unambigous alphabet
-        output_handle = open(self.test_file, "w")
-        self.pattern_io.write(schemas, output_handle)
-        output_handle.close()
+        with open(self.test_file, "w") as output_handle:
+            self.pattern_io.write(schemas, output_handle)
 
         input_handle = open(self.test_file, "r")
         try:
@@ -111,16 +104,14 @@ class PatternIOTest(unittest.TestCase):
             pass  # expected behavior
         except:
             raise AssertionError("Got unexpected error while reading.")
-
         input_handle.close()
 
     def test_signature(self):
         """Reading and writing signatures to a file.
         """
         signatures = [("GAC", "GAC"), ("AAA", "TTT")]
-        output_handle = open(self.test_file, "w")
-        self.pattern_io.write(signatures, output_handle)
-        output_handle.close()
+        with open(self.test_file, "w") as output_handle:
+            self.pattern_io.write(signatures, output_handle)
 
         input_handle = open(self.test_file, "r")
         read_sigs = self.pattern_io.read(input_handle)
@@ -481,8 +472,8 @@ class SchemaCoderTest(unittest.TestCase):
 
 
 class SchemaMatchingTest(unittest.TestCase):
-    """Matching schema to strings works correctly.
-    """
+    """Matching schema to strings works correctly."""
+
     def shortDescription(self):
         return "%s:%s" % (self.__class__.__name__, self.__doc__)
 
diff --git a/Tests/test_NaiveBayes.py b/Tests/test_NaiveBayes.py
new file mode 100644
index 0000000..3835100
--- /dev/null
+++ b/Tests/test_NaiveBayes.py
@@ -0,0 +1,116 @@
+# coding=utf-8
+import copy
+import unittest
+
+try:
+    import numpy
+except ImportError:
+    from Bio import MissingPythonDependencyError
+    raise MissingPythonDependencyError(
+        "Install NumPy if you want to use Bio.NaiveBayes.")
+
+from Bio import NaiveBayes
+
+
+class CarTest(unittest.TestCase):
+    def test_car_data(self):
+        """Simple example using car data."""
+        # Car data from example 'Naive Bayes Classifier example'
+        # by Eric Meisner November 22, 2003
+        # http://www.inf.u-szeged.hu/~ormandi/teaching/mi2/02-naiveBayes-example.pdf
+        xcar = [
+            ['Red', 'Sports', 'Domestic'],
+            ['Red', 'Sports', 'Domestic'],
+            ['Red', 'Sports', 'Domestic'],
+            ['Yellow', 'Sports', 'Domestic'],
+            ['Yellow', 'Sports', 'Imported'],
+            ['Yellow', 'SUV', 'Imported'],
+            ['Yellow', 'SUV', 'Imported'],
+            ['Yellow', 'SUV', 'Domestic'],
+            ['Red', 'SUV', 'Imported'],
+            ['Red', 'Sports', 'Imported'],
+            ]
+
+        ycar = [
+            'Yes',
+            'No',
+            'Yes',
+            'No',
+            'Yes',
+            'No',
+            'Yes',
+            'No',
+            'No',
+            'Yes',
+            ]
+
+        carmodel = NaiveBayes.train(xcar, ycar)
+        self.assertEqual("Yes", NaiveBayes.classify(carmodel, ['Red', 'Sports', 'Domestic']))
+        self.assertEqual("No", NaiveBayes.classify(carmodel, ['Red', 'SUV', 'Domestic']))
+
+
+class NaiveBayesTest(unittest.TestCase):
+    def setUp(self):
+        # Using example from https://en.wikipedia.org/wiki/Naive_Bayes_classifier
+        # height (feet), weight (lbs), foot size (inches)
+        self.xs = [
+            [6, 180, 12],
+            [5.92, 190, 11],
+            [5.58, 170, 12],
+            [5.92, 165, 10],
+            [5, 100, 6],
+            [5.5, 150, 8],
+            [5.42, 130, 7],
+            [5.75, 150, 9],
+        ]
+        self.ys = [
+            'male',
+            'male',
+            'male',
+            'male',
+            'female',
+            'female',
+            'female',
+            'female',
+        ]
+        self.model = NaiveBayes.train(self.xs, self.ys)
+        self.test = [6, 130, 8]
+
+    def test_train_function_no_training_set(self):
+        self.assertRaises(ValueError, NaiveBayes.train, [], self.ys)
+
+    def test_train_function_input_lengths(self):
+        ys = copy.copy(self.ys)
+        ys.pop()
+        self.assertRaises(ValueError, NaiveBayes.train, self.xs, ys)
+
+    def test_train_function_uneven_dimension_of_training_set(self):
+        xs = copy.copy(self.xs)
+        xs[0] = [1]
+        self.assertRaises(ValueError, NaiveBayes.train, xs, self.ys)
+
+    def test_train_function_with_priors(self):
+        model = NaiveBayes.train(self.xs, self.ys, priors={'male': 0.1, 'female': 0.9})
+        result = NaiveBayes.calculate(model, self.test, scale=True)
+        expected = -692.0
+        self.assertEqual(expected, round(result['male']))
+
+    def test_classify_function(self):
+        expected = "female"
+        result = NaiveBayes.classify(self.model, self.test)
+        self.assertEqual(expected, result)
+
+    def test_calculate_function_wrong_dimensionality(self):
+        xs = self.xs[0]
+        xs.append(100)
+        self.assertRaises(ValueError, NaiveBayes.calculate, self.model, xs)
+
+    def test_calculate_function_with_scale(self):
+        result = NaiveBayes.calculate(self.model, self.test, scale=True)
+        expected = -689.0
+        self.assertEqual(expected, round(result['male']))
+
+
+if __name__ == "__main__":
+    runner = unittest.TextTestRunner(verbosity=2)
+    unittest.main(testRunner=runner)
diff --git a/Tests/test_Nexus.py b/Tests/test_Nexus.py
index 5fcb94b..e8cc7ec 100755
--- a/Tests/test_Nexus.py
+++ b/Tests/test_Nexus.py
@@ -13,7 +13,11 @@ import sys
 from Bio._py3k import StringIO
 from Bio._py3k import range
 
+from Bio.SeqRecord import SeqRecord
 from Bio.Nexus import Nexus, Trees
+from Bio.Seq import Seq
+from Bio.Alphabet.IUPAC import ambiguous_dna
+from Bio import SeqIO
 
 
 class NexusTest1(unittest.TestCase):
@@ -35,6 +39,12 @@ class NexusTest1(unittest.TestCase):
         self.assertTrue(os.path.isfile(filename))
         os.remove(filename)
 
+    def test_write_with_dups(self):
+        # see issue: biopython/Bio/Nexus/Nexus.py _unique_label() eval error #633
+        records = [SeqRecord(Seq("ATGCTGCTGAT", alphabet=ambiguous_dna), id="foo") for _ in range(4)]
+        out_file = StringIO()
+        self.assertEqual(4, SeqIO.write(records, out_file, "nexus"))
+
     def test_NexusTest1(self):
         """Test Nexus module"""
         # check data of main nexus file
@@ -67,68 +77,68 @@ class NexusTest1(unittest.TestCase):
                                         29: "1,2,3 can't decide for a name?!",
                                         47: "final"})
         self.assertEqual(n.charsets, {
-            "big":        [0, 2, 4, 6],
-            "bigchunk":   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46],
-            "byname":     [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
-            "c1":         [0, 1, 2, 3, 4, 5, 6, 7],
-            "c2":         [8, 9, 10, 11, 12, 13, 14, 15],
-            "c3":         [16, 17, 18, 19, 20, 21, 22, 23],
-            "firsthalf":  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
-            "mix":        [0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
-            "mux":        [0, 1, 4, 7, 8, 10, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 28, 31, 34, 37, 40, 43, 46],
-            "pos1":       [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
-            "pos2":       [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
-            "pos3":       [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
-            "secondhalf": [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
-                                     })
+                "big": [0, 2, 4, 6],
+                "bigchunk": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46],
+                "byname": [0, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
+                "c1": [0, 1, 2, 3, 4, 5, 6, 7],
+                "c2": [8, 9, 10, 11, 12, 13, 14, 15],
+                "c3": [16, 17, 18, 19, 20, 21, 22, 23],
+                "firsthalf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
+                "mix": [0, 1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
+                "mux": [0, 1, 4, 7, 8, 10, 13, 16, 17, 18, 19, 20, 21, 22, 23, 25, 28, 31, 34, 37, 40, 43, 46],
+                "pos1": [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
+                "pos2": [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
+                "pos3": [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
+                "secondhalf": [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
+                })
         self.assertEqual(n.taxsets, {
-            "normal":    ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't1',
-                          't5',
-                          't6',
-                          't8'],
-            "reference": ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't1',
-                          't2 the name',
-                          't5',
-                          't6'],
-            "tbyname1": ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't1',
-                          't2 the name',
-                          't5',
-                          't6'],
-            "tbyname2": ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't2 the name',
-                          't5',
-                          't6',
-                          't7'],
-            "tbyname3": ['t1',
-                          't2 the name'],
-                                    })
+                "normal": ["isn'that [a] strange name?",
+                           'one should be punished, for (that)!',
+                           't1',
+                           't5',
+                           't6',
+                           't8'],
+                "reference": ["isn'that [a] strange name?",
+                              'one should be punished, for (that)!',
+                              't1',
+                              't2 the name',
+                              't5',
+                              't6'],
+                "tbyname1": ["isn'that [a] strange name?",
+                             'one should be punished, for (that)!',
+                             't1',
+                             't2 the name',
+                             't5',
+                             't6'],
+                "tbyname2": ["isn'that [a] strange name?",
+                             'one should be punished, for (that)!',
+                             't2 the name',
+                             't5',
+                             't6',
+                             't7'],
+                "tbyname3": ['t1',
+                             't2 the name'],
+                })
         self.assertEqual(len(n.charpartitions), 2)
         self.assertTrue('codons' in n.charpartitions)
         self.assertTrue('part' in n.charpartitions)
         self.assertEqual(n.charpartitions['codons'],
-            {'a': [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
-             'b': [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
-             'c': [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
-            })
+                         {'a': [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45],
+                          'b': [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46],
+                          'c': [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38, 41, 44, 47],
+                          })
         self.assertEqual(n.charpartitions['part'],
-            {"one":   [0, 1, 2, 3, 4, 5, 6, 7],
-             "three": [16, 17, 18, 19, 20, 21, 22, 23],
-             "two":   [8, 9, 10, 11, 12, 13, 14, 15],
-            })
+                         {"one": [0, 1, 2, 3, 4, 5, 6, 7],
+                          "three": [16, 17, 18, 19, 20, 21, 22, 23],
+                          "two": [8, 9, 10, 11, 12, 13, 14, 15],
+                          })
         self.assertEqual(list(n.taxpartitions), ['taxpart'])
         self.assertEqual(n.taxpartitions['taxpart'],
-            {"badnames":  ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't2 the name'],
-             "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
-            })
+                         {"badnames": ["isn'that [a] strange name?",
+                                       'one should be punished, for (that)!',
+                                       't2 the name'],
+                          "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
+                          })
 
         # now we check excluding characters, deleting taxa,
         # and exporting adjusted sets
@@ -154,40 +164,40 @@ class NexusTest1(unittest.TestCase):
                                          "t8",
                                          "t9"])
         self.assertEqual(nf1.charlabels, {0: 'a', 1: 'c', 2: 'f'})
-        self.assertEqual(nf1.charsets, {'big':       [0, 1, 2, 3],
-                                        'bigchunk':  [1, 2, 3],
-                                        'byname':    [0, 2, 3],
-                                        'c1':        [0, 1, 2, 3],
+        self.assertEqual(nf1.charsets, {'big': [0, 1, 2, 3],
+                                        'bigchunk': [1, 2, 3],
+                                        'byname': [0, 2, 3],
+                                        'c1': [0, 1, 2, 3],
                                         'firsthalf': [0, 1, 2, 3],
-                                        'mix':       [0, 2],
-                                        'mux':       [0, 2],
-                                        'pos1':      [0, 3],
-                                        'pos2':      [2],
-                                        'pos3':      [1],
-                                       })
+                                        'mix': [0, 2],
+                                        'mux': [0, 2],
+                                        'pos1': [0, 3],
+                                        'pos2': [2],
+                                        'pos3': [1],
+                                        })
         self.assertEqual(nf1.taxsets, {
-            'normal':    ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't5',
-                          't6',
-                          't8'],
-            'reference': ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't2 the name',
-                          't5',
-                          't6'],
-            'tbyname1':  ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't2 the name',
-                          't5',
-                          't6'],
-            'tbyname2':  ["isn'that [a] strange name?",
-                          'one should be punished, for (that)!',
-                          't2 the name',
-                          't5',
-                          't6'],
-            'tbyname3': ['t2 the name'],
-                                       })
+                'normal': ["isn'that [a] strange name?",
+                           'one should be punished, for (that)!',
+                           't5',
+                           't6',
+                           't8'],
+                'reference': ["isn'that [a] strange name?",
+                              'one should be punished, for (that)!',
+                              't2 the name',
+                              't5',
+                              't6'],
+                'tbyname1': ["isn'that [a] strange name?",
+                             'one should be punished, for (that)!',
+                             't2 the name',
+                             't5',
+                             't6'],
+                'tbyname2': ["isn'that [a] strange name?",
+                             'one should be punished, for (that)!',
+                             't2 the name',
+                             't5',
+                             't6'],
+                'tbyname3': ['t2 the name'],
+                })
         self.assertEqual(len(nf1.charpartitions), 2)
         self.assertTrue('codons' in nf1.charpartitions)
         self.assertTrue('part' in nf1.charpartitions)
@@ -198,11 +208,11 @@ class NexusTest1(unittest.TestCase):
 
         self.assertEqual(list(nf1.taxpartitions), ['taxpart'])
         self.assertEqual(nf1.taxpartitions['taxpart'],
-            {"badnames":  ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't2 the name'],
-             "goodnames": ['t5', 't6', 't8', 't9'],
-            })
+                         {"badnames": ["isn'that [a] strange name?",
+                                       'one should be punished, for (that)!',
+                                       't2 the name'],
+                          "goodnames": ['t5', 't6', 't8', 't9'],
+                          })
 
         f2 = tempfile.NamedTemporaryFile("w+")
         n.write_nexus_data(f2,
@@ -237,68 +247,68 @@ class NexusTest1(unittest.TestCase):
                                           22: "1,2,3 can't decide for a name?!",
                                           37: "final"})
         self.assertEqual(nf2.charsets,
-            {"big":        [0, 2, 3, 5],
-             "bigchunk":   [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],
-             "byname":     [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22],
-             "c1":         [0, 1, 2, 3, 4, 5],
-             "c2":         [6, 7, 8, 9, 10, 11],
-             "c3":         [12, 13, 14, 15, 16, 17],
-             "firsthalf":  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
-             "mix":        [0, 1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
-             "mux":        [0, 1, 3, 6, 8, 10, 12, 13, 14, 15, 16, 17, 19, 21, 26, 28, 30, 33, 36],
-             "pos1":       [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
-             "pos2":       [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
-             "pos3":       [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
-             "secondhalf": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37],
-            })
+                         {"big": [0, 2, 3, 5],
+                          "bigchunk": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36],
+                          "byname": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22],
+                          "c1": [0, 1, 2, 3, 4, 5],
+                          "c2": [6, 7, 8, 9, 10, 11],
+                          "c3": [12, 13, 14, 15, 16, 17],
+                          "firsthalf": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
+                          "mix": [0, 1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
+                          "mux": [0, 1, 3, 6, 8, 10, 12, 13, 14, 15, 16, 17, 19, 21, 26, 28, 30, 33, 36],
+                          "pos1": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
+                          "pos2": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
+                          "pos3": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
+                          "secondhalf": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37],
+                          })
 
         self.assertEqual(nf2.taxsets,
-            {"normal":    ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't1',
-                           't5',
-                           't6',
-                           't8'],
-             "reference": ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't1',
-                           't2 the name',
-                           't5',
-                           't6'],
-             "tbyname1":  ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't1',
-                           't2 the name',
-                           't5',
-                           't6'],
-             "tbyname2":  ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't2 the name',
-                           't5',
-                           't6',
-                           't7'],
-             "tbyname3":  ['t1',
-                           't2 the name']})
+                         {"normal": ["isn'that [a] strange name?",
+                                     'one should be punished, for (that)!',
+                                     't1',
+                                     't5',
+                                     't6',
+                                     't8'],
+                          "reference": ["isn'that [a] strange name?",
+                                        'one should be punished, for (that)!',
+                                        't1',
+                                        't2 the name',
+                                        't5',
+                                        't6'],
+                          "tbyname1": ["isn'that [a] strange name?",
+                                       'one should be punished, for (that)!',
+                                       't1',
+                                       't2 the name',
+                                       't5',
+                                       't6'],
+                          "tbyname2": ["isn'that [a] strange name?",
+                                       'one should be punished, for (that)!',
+                                       't2 the name',
+                                       't5',
+                                       't6',
+                                       't7'],
+                          "tbyname3": ['t1',
+                                       't2 the name']})
         self.assertEqual(len(nf2.charpartitions), 2)
         self.assertTrue('codons' in nf2.charpartitions)
         self.assertTrue('part' in nf2.charpartitions)
         self.assertEqual(nf2.charpartitions['codons'],
-            {"a": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
-             "b": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
-             "c": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
-            })
+                         {"a": [0, 5, 7, 9, 14, 16, 18, 23, 25, 27, 32, 35],
+                          "b": [1, 3, 8, 10, 12, 17, 19, 21, 26, 28, 30, 33, 36],
+                          "c": [2, 4, 6, 11, 13, 15, 20, 22, 24, 29, 31, 34, 37],
+                          })
         self.assertEqual(nf2.charpartitions['part'],
-            {"one":   [0, 1, 2, 3, 4, 5],
-             "three": [12, 13, 14, 15, 16, 17],
-             "two":   [6, 7, 8, 9, 10, 11],
-            })
+                         {"one": [0, 1, 2, 3, 4, 5],
+                          "three": [12, 13, 14, 15, 16, 17],
+                          "two": [6, 7, 8, 9, 10, 11],
+                          })
         self.assertEqual(list(nf2.taxpartitions), ['taxpart'])
         self.assertEqual(nf2.taxpartitions['taxpart'],
-            {"badnames":  ["isn'that [a] strange name?",
-                           'one should be punished, for (that)!',
-                           't2 the name'],
-             "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
-            })
+                         {"badnames": ["isn'that [a] strange name?",
+                                       'one should be punished, for (that)!',
+                                       't2 the name'],
+                          "goodnames": ['t1', 't5', 't6', 't7', 't8', 't9'],
+                          })
         # check the stepmatrix
         self.assertEqual(n.weighted_stepmatrix(name='matrix_test'),
         """\
@@ -385,10 +395,9 @@ Root:  16
           "t1:0.130208)F:0.0318288)D:0.0273876);"
         tree = Trees.Tree(ts2)
 
-        large_ex_handle = open(os.path.join(self.testfile_dir,
-            "int_node_labels.nwk"))
-        tree = Trees.Tree(large_ex_handle.read())
-        large_ex_handle.close()
+    def test_large_newick(self):
+        with open(os.path.join(self.testfile_dir, "int_node_labels.nwk")) as large_ex_handle:
+            tree = Trees.Tree(large_ex_handle.read())
 
     def _get_flat_nodes(self, tree):
         cur_nodes = [tree.node(tree.root)]
diff --git a/Tests/test_PAML_baseml.py b/Tests/test_PAML_baseml.py
index 0ff7283..7519b98 100644
--- a/Tests/test_PAML_baseml.py
+++ b/Tests/test_PAML_baseml.py
@@ -29,8 +29,8 @@ class ModTest(unittest.TestCase):
     def tearDown(self):
         """Just in case BASEML creates some junk files, do a clean-up."""
         del_files = [self.out_file, "2base.t",
-            "in.basemlg", "baseml.ctl", "lnf", "rates", "rst", "rst1",
-            "rub"]
+                     "in.basemlg", "baseml.ctl", "lnf", "rates", "rst", "rst1",
+                     "rub"]
         for filename in del_files:
             if os.path.exists(filename):
                 os.remove(filename)
@@ -45,39 +45,39 @@ class ModTest(unittest.TestCase):
 
     def testAlignmentFileIsValid(self):
         self.assertRaises((AttributeError, TypeError, OSError),
-            baseml.Baseml, alignment=list())
+                          baseml.Baseml, alignment=list())
         self.bml.alignment = list()
         self.bml.tree = self.tree_file
         self.bml.out_file = self.out_file
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.bml.run)
+                          self.bml.run)
 
     def testAlignmentExists(self):
         self.assertRaises((EnvironmentError, IOError), baseml.Baseml,
-            alignment="nonexistent")
+                          alignment="nonexistent")
         self.bml.alignment = "nonexistent"
         self.bml.tree = self.tree_file
         self.bml.out_file = self.out_file
         self.assertRaises((EnvironmentError, IOError),
-            self.bml.run)
+                          self.bml.run)
 
     def testTreeFileValid(self):
         self.assertRaises((AttributeError, TypeError, OSError),
-            baseml.Baseml, tree=list())
+                          baseml.Baseml, tree=list())
         self.bml.alignment = self.align_file
         self.bml.tree = list()
         self.bml.out_file = self.out_file
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.bml.run)
+                          self.bml.run)
 
     def testTreeExists(self):
         self.assertRaises((EnvironmentError, IOError), baseml.Baseml,
-            tree="nonexistent")
+                          tree="nonexistent")
         self.bml.alignment = self.align_file
         self.bml.tree = "nonexistent"
         self.bml.out_file = self.out_file
         self.assertRaises((EnvironmentError, IOError),
-            self.bml.run)
+                          self.bml.run)
 
     def testWorkingDirValid(self):
         self.bml.tree = self.tree_file
@@ -85,14 +85,14 @@ class ModTest(unittest.TestCase):
         self.bml.out_file = self.out_file
         self.bml.working_dir = list()
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.bml.run)
+                          self.bml.run)
 
     def testOutputFileValid(self):
         self.bml.tree = self.tree_file
         self.bml.alignment = self.align_file
         self.bml.out_file = list()
         self.assertRaises((AttributeError, ValueError, OSError),
-            self.bml.run)
+                          self.bml.run)
 
     def testOptionExists(self):
         self.assertRaises((AttributeError, KeyError),
@@ -104,73 +104,73 @@ class ModTest(unittest.TestCase):
         self.bml.tree = self.tree_file
         self.bml.out_file = self.out_file
         self.assertRaises((AttributeError, ValueError),
-            self.bml.run)
+                          self.bml.run)
 
     def testTreeSpecified(self):
         self.bml.alignment = self.align_file
         self.bml.out_file = self.out_file
         self.assertRaises((AttributeError, ValueError),
-            self.bml.run)
+                          self.bml.run)
 
     def testOutputFileSpecified(self):
         self.bml.alignment = self.align_file
         self.bml.tree = self.tree_file
         self.assertRaises((AttributeError, ValueError),
-            self.bml.run)
+                          self.bml.run)
 
     def testPamlErrorsCaught(self):
         self.bml.alignment = self.align_file
         self.bml.tree = self.bad_tree_file
         self.bml.out_file = self.out_file
         self.assertRaises((EnvironmentError, PamlError),
-            self.bml.run)
+                          self.bml.run)
 
     def testCtlFileValidOnRun(self):
         self.bml.alignment = self.align_file
         self.bml.tree = self.tree_file
         self.bml.out_file = self.out_file
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.bml.run, ctl_file=list())
+                          self.bml.run, ctl_file=list())
 
     def testCtlFileExistsOnRun(self):
         self.bml.alignment = self.align_file
         self.bml.tree = self.tree_file
         self.bml.out_file = self.out_file
         self.assertRaises((EnvironmentError, IOError),
-            self.bml.run, ctl_file="nonexistent")
+                          self.bml.run, ctl_file="nonexistent")
 
     def testCtlFileValidOnRead(self):
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.bml.read_ctl_file, list())
+                          self.bml.read_ctl_file, list())
         self.assertRaises((AttributeError, KeyError),
-            self.bml.read_ctl_file, self.bad_ctl_file1)
+                          self.bml.read_ctl_file, self.bad_ctl_file1)
         self.assertRaises(AttributeError,
-            self.bml.read_ctl_file, self.bad_ctl_file2)
+                          self.bml.read_ctl_file, self.bad_ctl_file2)
         target_options = {"noisy": 0,
-                        "verbose": 0,
-                        "runmode": 0,
-                        "model": 6,
-                        "model_options": None,
-                        "Mgene": 1,
-                        "ndata": None,
-                        "clock": 0,
-                        "fix_kappa": 0,
-                        "kappa": 5,
-                        "fix_alpha": 0,
-                        "alpha": 0.5,
-                        "Malpha": 0,
-                        "ncatG": 5,
-                        "fix_rho": 1,
-                        "rho": 0,
-                        "nparK": 0,
-                        "nhomo": 0,
-                        "getSE": 0,
-                        "RateAncestor": 0,
-                        "Small_Diff": 7e-6,
-                        "cleandata": 1,
-                        "icode": None,
-                        "fix_blength": None,
-                        "method": 0}
+                          "verbose": 0,
+                          "runmode": 0,
+                          "model": 6,
+                          "model_options": None,
+                          "Mgene": 1,
+                          "ndata": None,
+                          "clock": 0,
+                          "fix_kappa": 0,
+                          "kappa": 5,
+                          "fix_alpha": 0,
+                          "alpha": 0.5,
+                          "Malpha": 0,
+                          "ncatG": 5,
+                          "fix_rho": 1,
+                          "rho": 0,
+                          "nparK": 0,
+                          "nhomo": 0,
+                          "getSE": 0,
+                          "RateAncestor": 0,
+                          "Small_Diff": 7e-6,
+                          "cleandata": 1,
+                          "icode": None,
+                          "fix_blength": None,
+                          "method": 0}
         self.bml.read_ctl_file(self.ctl_file)
         # Compare the dictionary keys:
         self.assertEqual(sorted(self.bml._options), sorted(target_options))
@@ -181,11 +181,11 @@ class ModTest(unittest.TestCase):
 
     def testCtlFileExistsOnRead(self):
         self.assertRaises(IOError,
-            self.bml.read_ctl_file, ctl_file="nonexistent")
+                          self.bml.read_ctl_file, ctl_file="nonexistent")
 
     def testResultsValid(self):
         self.assertRaises((AttributeError, TypeError, OSError),
-            baseml.read, list())
+                          baseml.read, list())
 
     def testResultsExist(self):
         self.assertRaises(IOError, baseml.read, "nonexistent")
@@ -208,7 +208,7 @@ class ModTest(unittest.TestCase):
             version = results_file.split('-')[1].split('.')[0]
             model = results_file[5]
             version_msg = "Improper parsing for model %s version %s" \
-                        % (model, version.replace('_', '.'))
+                          % (model, version.replace('_', '.'))
             results_path = os.path.join(res_dir, results_file)
             results = baseml.read(results_path)
             # There are 6 top-levels: parameters, tree, lnL, version,
@@ -242,7 +242,7 @@ class ModTest(unittest.TestCase):
             version = results_file.split('-')[1].split('.')[0]
             model = results_file[5]
             version_msg = "Improper parsing for model %s version %s" \
-                        % (model, version.replace('_', '.'))
+                          % (model, version.replace('_', '.'))
             results_path = os.path.join(res_dir, results_file)
             results = baseml.read(results_path)
             # There are 6 top-levels: parameters, tree, lnL, version,
@@ -262,7 +262,7 @@ class ModTest(unittest.TestCase):
             version = results_file.split('-')[1].split('.')[0]
             n = results_file[5]
             version_msg = "Improper parsing for nhomo %s version %s" \
-                        % (n, version.replace('_', '.'))
+                          % (n, version.replace('_', '.'))
             results_path = os.path.join(res_dir, results_file)
             results = baseml.read(results_path)
             # There are 6 top-levels: parameters, tree, lnL, version,
@@ -283,7 +283,7 @@ class ModTest(unittest.TestCase):
         for results_file in os.listdir(res_dir):
             version = results_file.split('-')[1].split('.')[0]
             version_msg = "Improper parsing for version %s" \
-                        % version.replace('_', '.')
+                          % version.replace('_', '.')
             results_path = os.path.join(res_dir, results_file)
             results = baseml.read(results_path)
             # There are 6 top-levels: parameters, tree, lnL, version,
diff --git a/Tests/test_PAML_codeml.py b/Tests/test_PAML_codeml.py
index 601b48d..cca7964 100644
--- a/Tests/test_PAML_codeml.py
+++ b/Tests/test_PAML_codeml.py
@@ -1,4 +1,4 @@
-# Copyright (C) 2011, 2015 by Brandon Invergo (b.invergo at gmail.com)
+# Copyright (C) 2011, 2015, 2016 by Brandon Invergo (b.invergo at gmail.com)
 # This code is part of the Biopython distribution and governed by its
 # license. Please see the LICENSE file that should have been included
 # as part of this package.
@@ -13,10 +13,10 @@ from Bio.Phylo.PAML._paml import PamlError
 # Some constants to assist with testing:
 # This is the number of parameters that should be parsed for each
 # NSsites site class model
-SITECLASS_PARAMS = {0: 6, 1: 4, 2: 4, 3: 4, 7: 5, 8: 8}
+SITECLASS_PARAMS = {0: 6, 1: 4, 2: 4, 3: 4, 7: 5, 8: 8, 22: 4}
 # This is the default number of site classes per NSsites site
 # class model
-SITECLASSES = {0: None, 1: 2, 2: 3, 3: 3, 7: 10, 8: 11}
+SITECLASSES = {0: None, 1: 2, 2: 3, 3: 3, 7: 10, 8: 11, 22: 3}
 
 
 class ModTest(unittest.TestCase):
@@ -40,7 +40,7 @@ class ModTest(unittest.TestCase):
     def tearDown(self):
         """Just in case CODEML creates some junk files, do a clean-up."""
         del_files = [self.out_file, "2NG.dN", "2NG.dS", "2NG.t", "codeml.ctl",
-                "lnf", "rst", "rst1", "rub"]
+                     "lnf", "rst", "rst1", "rub"]
         for filename in del_files:
             if os.path.exists(filename):
                 os.remove(filename)
@@ -55,16 +55,16 @@ class ModTest(unittest.TestCase):
 
     def testAlignmentFileIsValid(self):
         self.assertRaises((AttributeError, TypeError, OSError),
-            codeml.Codeml, alignment=list())
+                          codeml.Codeml, alignment=list())
         self.cml.alignment = list()
         self.cml.tree = self.tree_file
         self.cml.out_file = self.out_file
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.cml.run)
+                          self.cml.run)
 
     def testAlignmentExists(self):
         self.assertRaises((EnvironmentError, IOError), codeml.Codeml,
-            alignment="nonexistent")
+                          alignment="nonexistent")
         self.cml.alignment = "nonexistent"
         self.cml.tree = self.tree_file
         self.cml.out_file = self.out_file
@@ -72,16 +72,16 @@ class ModTest(unittest.TestCase):
 
     def testTreeFileValid(self):
         self.assertRaises((AttributeError, TypeError, OSError),
-            codeml.Codeml, tree=list())
+                          codeml.Codeml, tree=list())
         self.cml.alignment = self.align_file
         self.cml.tree = list()
         self.cml.out_file = self.out_file
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.cml.run)
+                          self.cml.run)
 
     def testTreeExists(self):
         self.assertRaises((EnvironmentError, IOError), codeml.Codeml,
-            tree="nonexistent")
+                          tree="nonexistent")
         self.cml.alignment = self.align_file
         self.cml.tree = "nonexistent"
         self.cml.out_file = self.out_file
@@ -93,14 +93,14 @@ class ModTest(unittest.TestCase):
         self.cml.out_file = self.out_file
         self.cml.working_dir = list()
         self.assertRaises((AttributeError, TypeError, OSError),
-            self.cml.run)
+                          self.cml.run)
 
     def testOutputFileValid(self):
         self.cml.tree = self.tree_file
         self.cml.alignment = self.align_file
         self.cml.out_file = list()
         self.assertRaises((AttributeError, ValueError, OSError),
-            self.cml.run)
+                          self.cml.run)
 
     def testOptionExists(self):
         self.assertRaises((AttributeError, KeyError),
@@ -112,26 +112,26 @@ class ModTest(unittest.TestCase):
         self.cml.tree = self.tree_file
         self.cml.out_file = self.out_file
         self.assertRaises((AttributeError, ValueError),
-            self.cml.run)
+                          self.cml.run)
 
     def testTreeSpecified(self):
         self.cml.alignment = self.align_file
         self.cml.out_file = self.out_file
         self.assertRaises((AttributeError, ValueError),
-            self.cml.run)
+                          self.cml.run)
 
     def testOutputFileSpecified(self):
         self.cml.alignment = self.align_file
         self.cml.tree = self.tree_file
         self.assertRaises((AttributeError, ValueError),
-            self.cml.run)
+                          self.cml.run)
 
     def testPamlErrorsCaught(self):
         self.cml.alignment = self.align_file
         self.cml.tree = self.bad_tree_file
         self.cml.out_file = self.out_file
         self.assertRaises((EnvironmentError, PamlError),
-            self.cml.run)
+                          self.cml.run)
 
     def testCtlFileValidOnRun(self):
         self.cml.alignment = self.align_file
@@ -524,6 +524,24 @@ class ModTest(unittest.TestCase):
         self.assertTrue(m0["tree"] is not None)
         self.assertNotEqual(len(m0["tree"]), 0)
 
+    def testParseM2arel(self):
+        res_dir = os.path.join(self.results_dir, "codeml", "m2a_rel")
+        for results_file in os.listdir(res_dir):
+            version = results_file.split('-')[1].split('.')[0]
+            version_msg = "Improper parsing for version %s" \
+                        % version.replace('_', '.')
+            results_path = os.path.join(res_dir, results_file)
+            results = codeml.read(results_path)
+            self.assertTrue("NSsites" in results)
+            self.assertTrue(22 in results["NSsites"])
+            model = results["NSsites"][22]
+            self.assertEqual(len(model), 5, version_msg)
+            params = model["parameters"]
+            self.assertEqual(len(params), SITECLASS_PARAMS[22],
+                             version_msg)
+
+
+
 if __name__ == "__main__":
     runner = unittest.TextTestRunner(verbosity=2)
     unittest.main(testRunner=runner)
diff --git a/Tests/test_PDB.py b/Tests/test_PDB.py
index 0d7bb3e..aa3f64c 100644
--- a/Tests/test_PDB.py
+++ b/Tests/test_PDB.py
@@ -48,47 +48,33 @@ class A_ExceptionTest(unittest.TestCase):
     works -- a warning is only logged the first time it is encountered.
     """
     def test_1_warnings(self):
-        """Check warnings: Parse a flawed PDB file in permissive mode.
-
-        NB: The try/finally block is adapted from the warnings.catch_warnings
-        context manager in the Python 2.6 standard library.
-
-        TODO: Now we require Python 2.6, switch to using warnings.catch_warnings
-        """
-        warnings.simplefilter('always', PDBConstructionWarning)
-        try:
-            # Equivalent to warnings.catch_warnings -- hackmagic
-            orig_showwarning = warnings.showwarning
-            all_warns = []
-
-            def showwarning(*args, **kwargs):
-                all_warns.append(args[0])
+        """Check warnings: Parse a flawed PDB file in permissive mode."""
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always', PDBConstructionWarning)
 
-            warnings.showwarning = showwarning
             # Trigger warnings
             p = PDBParser(PERMISSIVE=True)
             p.get_structure("example", "PDB/a_structure.pdb")
-            self.assertEqual(len(all_warns), 14)
-            for wrn, msg in zip(all_warns, [
-              # Expected warning messages:
-              "Used element 'N' for Atom (name=N) with given element ''",
-              "Used element 'C' for Atom (name=CA) with given element ''",
-              "Atom names ' CA ' and 'CA  ' differ only in spaces at line 17.",
-              "Used element 'CA' for Atom (name=CA  ) with given element ''",
-              'Atom N defined twice in residue <Residue ARG het=  resseq=2 icode= > at line 21.',
-              'disordered atom found with blank altloc before line 33.',
-              "Residue (' ', 4, ' ') redefined at line 43.",
-              "Blank altlocs in duplicate residue SER (' ', 4, ' ') at line 43.",
-              "Residue (' ', 10, ' ') redefined at line 75.",
-              "Residue (' ', 14, ' ') redefined at line 106.",
-              "Residue (' ', 16, ' ') redefined at line 135.",
-              "Residue (' ', 80, ' ') redefined at line 633.",
-              "Residue (' ', 81, ' ') redefined at line 646.",
-              'Atom O defined twice in residue <Residue HOH het=W resseq=67 icode= > at line 822.'
-              ]):
+
+            self.assertEqual(len(w), 14)
+            for wrn, msg in zip(w, [
+                    # Expected warning messages:
+                    "Used element 'N' for Atom (name=N) with given element ''",
+                    "Used element 'C' for Atom (name=CA) with given element ''",
+                    "Atom names ' CA ' and 'CA  ' differ only in spaces at line 17.",
+                    "Used element 'CA' for Atom (name=CA  ) with given element ''",
+                    'Atom N defined twice in residue <Residue ARG het=  resseq=2 icode= > at line 21.',
+                    'disordered atom found with blank altloc before line 33.',
+                    "Residue (' ', 4, ' ') redefined at line 43.",
+                    "Blank altlocs in duplicate residue SER (' ', 4, ' ') at line 43.",
+                    "Residue (' ', 10, ' ') redefined at line 75.",
+                    "Residue (' ', 14, ' ') redefined at line 106.",
+                    "Residue (' ', 16, ' ') redefined at line 135.",
+                    "Residue (' ', 80, ' ') redefined at line 633.",
+                    "Residue (' ', 81, ' ') redefined at line 646.",
+                    'Atom O defined twice in residue <Residue HOH het=W resseq=67 icode= > at line 822.'
+                    ]):
                 self.assertTrue(msg in str(wrn), str(wrn))
-        finally:
-            warnings.showwarning = orig_showwarning
 
     def test_2_strict(self):
         """Check error: Parse a flawed PDB file in strict mode."""
@@ -519,6 +505,24 @@ class ParseTest(unittest.TestCase):
                          "C C O C S N C C O C C C O N N C C O C C C C C C C O "
                          "N C C O C C C N C N N N C C O C S")
 
+    def test_pdbio_write_truncated(self):
+        """Test parsing of truncated lines"""
+        io = PDBIO()
+        struct = self.structure
+        # Write to temp file
+        io.set_structure(struct)
+        filenumber, filename = tempfile.mkstemp()
+        os.close(filenumber)
+        try:
+            io.save(filename)
+            # Check if there are lines besides 'ATOM', 'TER' and 'END'
+            with open(filename, 'rU') as handle:
+                record_set = set([l[0:6] for l in handle])
+            record_set -= set(('ATOM  ', 'HETATM', 'MODEL ', 'ENDMDL', 'TER\n', 'END\n'))
+            self.assertEqual(record_set, set())
+        finally:
+            os.remove(filename)
+
 
 class ParseReal(unittest.TestCase):
     """Testing with real PDB files."""
@@ -684,13 +688,25 @@ class ParseReal(unittest.TestCase):
     def test_model_numbering(self):
         """Preserve model serial numbers during I/O."""
         def confirm_numbering(struct):
-            self.assertEqual(len(struct), 20)
+            self.assertEqual(len(struct), 3)
             for idx, model in enumerate(struct):
-                self.assertTrue(model.serial_num, idx + 1)
-                self.assertTrue(model.serial_num, model.id + 1)
-        parser = PDBParser()
-        struct1 = parser.get_structure("1mot", "PDB/1MOT.pdb")
+                self.assertEqual(model.serial_num, idx + 1)
+                self.assertEqual(model.serial_num, model.id + 1)
+
+        def confirm_single_end(fname):
+            """Ensure there is only one END statement in multi-model files"""
+            with open(fname) as handle:
+                end_stment = []
+                for iline, line in enumerate(handle):
+                    if line.strip() == 'END':
+                        end_stment.append((line, iline))
+            self.assertEqual(len(end_stment), 1)  # Only one?
+            self.assertEqual(end_stment[0][1], iline)  # Last line of the file?
+
+        parser = PDBParser(QUIET=1)
+        struct1 = parser.get_structure("1lcd", "PDB/1LCD.pdb")
         confirm_numbering(struct1)
+
         # Round trip: serialize and parse again
         io = PDBIO()
         io.set_structure(struct1)
@@ -698,8 +714,9 @@ class ParseReal(unittest.TestCase):
         os.close(filenumber)
         try:
             io.save(filename)
-            struct2 = parser.get_structure("1mot", filename)
+            struct2 = parser.get_structure("1lcd", filename)
             confirm_numbering(struct2)
+            confirm_single_end(filename)
         finally:
             os.remove(filename)
 
@@ -956,7 +973,9 @@ class Atom_Element(unittest.TestCase):
 class IterationTests(unittest.TestCase):
 
     def setUp(self):
-        self.struc = PDBParser(PERMISSIVE=True).get_structure('X', "PDB/a_structure.pdb")
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", PDBConstructionWarning)
+            self.struc = PDBParser(PERMISSIVE=True).get_structure('X', "PDB/a_structure.pdb")
 
     def test_get_chains(self):
         """Yields chains from different models separately."""
@@ -993,8 +1012,10 @@ class IterationTests(unittest.TestCase):
 class TransformTests(unittest.TestCase):
 
     def setUp(self):
-        self.s = PDBParser(PERMISSIVE=True).get_structure(
-            'X', "PDB/a_structure.pdb")
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", PDBConstructionWarning)
+            self.s = PDBParser(PERMISSIVE=True).get_structure(
+                'X', "PDB/a_structure.pdb")
         self.m = self.s.get_list()[0]
         self.c = self.m.get_list()[0]
         self.r = self.c.get_list()[0]
@@ -1038,8 +1059,10 @@ class TransformTests(unittest.TestCase):
 class CopyTests(unittest.TestCase):
 
     def setUp(self):
-        self.s = PDBParser(PERMISSIVE=True).get_structure(
-            'X', "PDB/a_structure.pdb")
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", PDBConstructionWarning)
+            self.s = PDBParser(PERMISSIVE=True).get_structure(
+                'X', "PDB/a_structure.pdb")
         self.m = self.s.get_list()[0]
         self.c = self.m.get_list()[0]
         self.r = self.c.get_list()[0]
diff --git a/Tests/test_PDB_KDTree.py b/Tests/test_PDB_KDTree.py
index edcf70c..70d5dd8 100644
--- a/Tests/test_PDB_KDTree.py
+++ b/Tests/test_PDB_KDTree.py
@@ -35,7 +35,7 @@ class NeighborTest(unittest.TestCase):
 
         Based on the self test in Bio.PDB.NeighborSearch.
         """
-        class RandomAtom:
+        class RandomAtom(object):
             def __init__(self):
                 self.coord = 100 * random(3)
 
diff --git a/Tests/test_Pathway.py b/Tests/test_Pathway.py
index 7059602..b3c49ff 100644
--- a/Tests/test_Pathway.py
+++ b/Tests/test_Pathway.py
@@ -14,7 +14,7 @@ from Bio.Pathway.Rep.MultiGraph import MultiGraph
 
 class GraphTestCase(unittest.TestCase):
 
-    def testEquals(self):
+    def test_Equals(self):
         a = Graph(['a', 'b', 'c'])
         a.add_edge('a', 'b', 'label1')
         a.add_edge('b', 'c', 'label1')
@@ -34,7 +34,7 @@ class GraphTestCase(unittest.TestCase):
         self.assertNotEqual(c, Graph(), "equal to empty graph")
         self.assertEqual(Graph(), Graph(), "empty graph not equal to self")
 
-    def testNodes(self):
+    def test_Nodes(self):
         a = Graph()
         self.assertEqual(a.nodes(), [], "default graph not empty")
         a.add_node('a')
@@ -45,7 +45,7 @@ class GraphTestCase(unittest.TestCase):
         l = sorted(a.nodes())
         self.assertEqual(l, ['a', 'b'], "second node not added")
 
-    def testEdges(self):
+    def test_Edges(self):
         a = Graph(['a', 'b', 'c', 'd'])
         a.add_edge('a', 'b', 'label1')
         self.assertEqual(a.child_edges('a'), [('b', 'label1')])  # , "incorrect child edges")
@@ -58,7 +58,7 @@ class GraphTestCase(unittest.TestCase):
         self.assertEqual(a.children('d'), [], "incorrect children for singleton")
         self.assertEqual(a.parents('a'), ['b'], "incorrect parents")
 
-    def testRemoveNode(self):
+    def test_RemoveNode(self):
         a = Graph(['a', 'b', 'c', 'd', 'e'])
         a.add_edge('a', 'e', 'label1')
         a.add_edge('b', 'e', 'label1')
@@ -74,7 +74,7 @@ class GraphTestCase(unittest.TestCase):
 
 class MultiGraphTestCase(unittest.TestCase):
 
-    def testEquals(self):
+    def test_Equals(self):
         a = MultiGraph(['a', 'b', 'c'])
         a.add_edge('a', 'b', 'label1')
         a.add_edge('b', 'c', 'label1')
@@ -94,7 +94,7 @@ class MultiGraphTestCase(unittest.TestCase):
         self.assertNotEqual(c, MultiGraph(), "equal to empty graph")
         self.assertEqual(MultiGraph(), MultiGraph(), "empty graph not equal to self")
 
-    def testNodes(self):
+    def test_Nodes(self):
         a = MultiGraph()
         self.assertEqual(a.nodes(), [], "default graph not empty")
         a.add_node('a')
@@ -105,7 +105,7 @@ class MultiGraphTestCase(unittest.TestCase):
         l = sorted(a.nodes())
         self.assertEqual(l, ['a', 'b'], "second node not added")
 
-    def testEdges(self):
+    def test_Edges(self):
         a = MultiGraph(['a', 'b', 'c', 'd'])
         a.add_edge('a', 'b', 'label1')
         self.assertEqual(a.child_edges('a'), [('b', 'label1')])  # , "incorrect child edges")
@@ -122,7 +122,7 @@ class MultiGraphTestCase(unittest.TestCase):
         self.assertEqual(a.children('d'), [], "incorrect children for singleton")
         self.assertEqual(a.parents('a'), ['b'], "incorrect parents")
 
-    def testRemoveNode(self):
+    def test_RemoveNode(self):
         a = MultiGraph(['a', 'b', 'c', 'd', 'e'])
         a.add_edge('a', 'e', 'label1')
         self.assertEqual(repr(a), "<MultiGraph: ('a': ('e', 'label1'))('b': )('c': )('d': )('e': )>")
@@ -153,11 +153,11 @@ class ReactionTestCase(unittest.TestCase):
         self.r_3 = Reaction({"a": -1, "d": 2})
         self.r_4 = Reaction({"c": -1, "d": -1, "a": 1, "e": 2})
 
-    def testEq(self):
+    def test_eq(self):
         self.assertEqual(self.r_1, self.r_1i)  # , "not equal to similar")
         self.assertNotEqual(self.r_3, self.r_4)  # , "equal to different")
 
-    def testRev(self):
+    def test_rev(self):
         self.assertEqual(self.r_empty.reverse(), self.r_empty, "empty reversed not empty")
         self.assertEqual(self.r_prod.reverse(), self.r_dest,
                           "reversed reaction not equal to similar")
diff --git a/Tests/test_Phylo.py b/Tests/test_Phylo.py
index 6d6251d..3ce25df 100644
--- a/Tests/test_Phylo.py
+++ b/Tests/test_Phylo.py
@@ -93,6 +93,34 @@ class IOTests(unittest.TestCase):
         self.assertEqual(clade_a.name, 'foo')
         self.assertAlmostEqual(clade_a.branch_length, 0.1)
 
+
+        """Additional tests to check correct parsing"""
+        tree = Phylo.read(StringIO("(A:1, B:-2, (C:3, D:4):-2)"),'newick')
+        self.assertEqual(tree.distance('A'),1)
+        self.assertEqual(tree.distance('B'),-2)
+        self.assertEqual(tree.distance('C'),1)
+        self.assertEqual(tree.distance('D'),2)
+
+        tree = Phylo.read(StringIO("((A:1, B:-2):-5, (C:3, D:4):-2)"),'newick')
+        self.assertEqual(tree.distance('A'),-4)
+        self.assertEqual(tree.distance('B'),-7)
+        self.assertEqual(tree.distance('C'),1)
+        self.assertEqual(tree.distance('D'),2)
+
+        tree = Phylo.read(StringIO("((:1, B:-2):-5, (C:3, D:4):-2)"),'newick')
+        distances = {-4.0:1,-7.0:1,1:1,2:1}
+        for x in tree.get_terminals():
+            entry = int(tree.distance(x))
+            distances[entry] -= distances[entry]
+            self.assertEqual(distances[entry],0)
+
+        tree = Phylo.read(StringIO("((:\n1\n,\n B:-2):-5, (C:3, D:4):-2);"),'newick')
+        distances = {-4.0:1,-7.0:1,1:1,2:1}
+        for x in tree.get_terminals():
+            entry = int(tree.distance(x))
+            distances[entry] -= distances[entry]
+            self.assertEqual(distances[entry],0)
+
     def test_format_branch_length(self):
         """Custom format string for Newick branch length serialization."""
         tree = Phylo.read(StringIO('A:0.1;'), 'newick')
diff --git a/Tests/test_PhyloXML.py b/Tests/test_PhyloXML.py
index 0e1c5bd..31ff16b 100644
--- a/Tests/test_PhyloXML.py
+++ b/Tests/test_PhyloXML.py
@@ -202,7 +202,6 @@ class TreeTests(unittest.TestCase):
                 'a pylogeny of some monitor lizards')
         self.assertEqual(trees[9].rooted, True)
         # Network (unrooted)
-        tree6 = trees[6]
         self.assertEqual(trees[6].name,
                 'network, node B is connected to TWO nodes: AB and C')
         self.assertEqual(trees[6].rooted, False)
@@ -233,11 +232,8 @@ class TreeTests(unittest.TestCase):
 
     def test_BinaryCharacters(self):
         """Instantiation of BinaryCharacters objects."""
-        # Because we short circult interation, must close handle explicitly
-        # to avoid a ResourceWarning
-        handle = open(EX_DOLLO)
-        tree = next(PhyloXMLIO.parse(handle))
-        handle.close()
+        with open(EX_DOLLO) as handle:
+            tree = next(PhyloXMLIO.parse(handle))
         bchars = tree.clade[0, 0].binary_characters
         self.assertTrue(isinstance(bchars, PX.BinaryCharacters))
         self.assertEqual(bchars.type, 'parsimony inferred')
@@ -344,9 +340,9 @@ class TreeTests(unittest.TestCase):
                 (6, 109, 605, 647, 689, 733, 872, 993, 1075, 1117, 1168),
                 (90, 414, 643, 685, 729, 771, 910, 1031, 1113, 1155, 1204),
                 (7.0e-26, 7.2e-117, 2.4e-6, 1.1e-12, 2.4e-7, 4.7e-14, 2.5e-8,
-                    4.6e-6, 6.3e-7, 1.4e-7, 0.3),
+                 4.6e-6, 6.3e-7, 1.4e-7, 0.3),
                 ('CARD', 'NB-ARC', 'WD40', 'WD40', 'WD40', 'WD40', 'WD40',
-                    'WD40', 'WD40', 'WD40', 'WD40')):
+                 'WD40', 'WD40', 'WD40', 'WD40')):
             self.assertTrue(isinstance(domain, PX.ProteinDomain))
             self.assertEqual(domain.start + 1, start)
             self.assertEqual(domain.end, end)
diff --git a/Tests/test_Phylo_NeXML.py b/Tests/test_Phylo_NeXML.py
index 87c6110..2597bfb 100644
--- a/Tests/test_Phylo_NeXML.py
+++ b/Tests/test_Phylo_NeXML.py
@@ -11,7 +11,7 @@ import tempfile
 import unittest
 
 import Bio.Phylo as bp
-from Bio.Phylo import NeXML, NeXMLIO
+from Bio.Phylo import NeXMLIO
 
 # Example NeXML files
 nexml_files = (
@@ -74,10 +74,6 @@ def _test_write_factory(source):
     tests on files (re)generated by NeXMLIO's own writer.
     """
     filename = os.path.join('NeXML/', source)
-    if source in tree_counts:
-        count = tree_counts[source]
-    else:
-        count = 1
 
     def test_write(self):
         """Parse, rewrite and retest an example file."""
diff --git a/Tests/test_Phylo_depend.py b/Tests/test_Phylo_depend.py
index a2f03a0..d7d741b 100644
--- a/Tests/test_Phylo_depend.py
+++ b/Tests/test_Phylo_depend.py
@@ -64,6 +64,36 @@ class UtilTests(unittest.TestCase):
         Phylo.draw(apaf, do_show=False, branch_labels={apaf.root: 'Root'})
         Phylo.draw(apaf, do_show=False, branch_labels=lambda c: c.branch_length)
 
+    def test_draw_with_label_colors_dict(self):
+        """Run the tree layout algorithm with a label_colors argument passed in
+        as a dictionary. Don't display tree."""
+        pyplot.ioff()   # Turn off interactive display
+        dollo = Phylo.read(EX_DOLLO, 'phyloxml')
+        apaf = Phylo.read(EX_APAF, 'phyloxml')
+        label_colors_dollo = {
+            'f_50': 'red',
+            'f_34': 'blue',
+        }
+        label_colors_apaf = {
+            '22_MOUSE': 'red',
+            '18_NEMVE': 'blue',
+        }
+        Phylo.draw(dollo, label_colors=label_colors_dollo, do_show=False)
+        Phylo.draw(apaf, label_colors=label_colors_apaf, do_show=False)
+
+    def test_draw_with_label_colors_callable(self):
+        """Run the tree layout algorithm with a label_colors argument passed in
+        as a callable. Don't display tree."""
+        pyplot.ioff()   # Turn off interactive display
+        dollo = Phylo.read(EX_DOLLO, 'phyloxml')
+        apaf = Phylo.read(EX_APAF, 'phyloxml')
+
+        label_colors_dollo = lambda label: 'r' if label == 'f_50' else 'k'
+        label_colors_apaf = lambda label: 'r'
+
+        Phylo.draw(dollo, label_colors=label_colors_dollo, do_show=False)
+        Phylo.draw(apaf, label_colors=label_colors_apaf, do_show=False)
+
     def test_draw_ascii(self):
         """Tree to Graph conversion."""
         handle = StringIO()
diff --git a/Tests/test_PopGen_GenePop_nodepend.py b/Tests/test_PopGen_GenePop_nodepend.py
index 2f2a5e2..1c57530 100644
--- a/Tests/test_PopGen_GenePop_nodepend.py
+++ b/Tests/test_PopGen_GenePop_nodepend.py
@@ -64,13 +64,12 @@ class ParserTest(unittest.TestCase):
     def test_wrong_file_parser(self):
         """Testing the ability to deal with wrongly formatted files
         """
-        f = open(os.path.join("PopGen", "fdist1"))
-        try:
-            rec = GenePop.read(f)
-            raise Error("Should have raised exception")
-        except ValueError:
-            pass
-        f.close()
+        with open(os.path.join("PopGen", "fdist1")) as f:
+            try:
+                rec = GenePop.read(f)
+                raise Exception("Should have raised exception")
+            except ValueError:
+                pass
 
 
 class FileParserTest(unittest.TestCase):
@@ -98,9 +97,9 @@ class FileParserTest(unittest.TestCase):
             assert len(rec.loci_list) == self.num_loci[index]
             for skip in range(self.pops_indivs[index][0]):
                 if rec.skip_population() is False:
-                    raise Error("Not enough populations")
+                    raise Exception("Not enough populations")
             if rec.skip_population() is True:
-                    raise Error("Too much populations")
+                    raise Exception("Too much populations")
             for i in range(self.pops_indivs[index][0]):
                 continue
                 assert len(rec.populations[i]) == \
@@ -110,13 +109,12 @@ class FileParserTest(unittest.TestCase):
     def test_wrong_file_parser(self):
         """Testing the ability to deal with wrongly formatted files
         """
-        f = open(os.path.join("PopGen", "fdist1"))
-        try:
-            rec = GenePop.read(f)
-            raise Error("Should have raised exception")
-        except ValueError:
-            pass
-        f.close()
+        with open(os.path.join("PopGen", "fdist1")) as f:
+            try:
+                rec = GenePop.read(f)
+                raise Exception("Should have raised exception")
+            except ValueError:
+                pass
 
 
 class UtilsTest(unittest.TestCase):
diff --git a/Tests/test_PopGen_SimCoal_nodepend.py b/Tests/test_PopGen_SimCoal_nodepend.py
index cdf06ed..ad5e1e5 100644
--- a/Tests/test_PopGen_SimCoal_nodepend.py
+++ b/Tests/test_PopGen_SimCoal_nodepend.py
@@ -7,7 +7,6 @@ from Bio._py3k import _universal_read_mode
 
 import os
 import unittest
-from Bio.PopGen import SimCoal
 from Bio.PopGen.SimCoal.Template import generate_simcoal_from_template
 
 # Tests simcoal related code. Note: this case doesn't require simcoal
@@ -25,12 +24,10 @@ class TemplateTest(unittest.TestCase):
             'PopGen')
         # Confirm the files match (ignoring any switch of line endings
         # possible if the input file used a different OS convention)
-        handle = open(os.path.join('PopGen', 'simple.par'), _universal_read_mode)
-        old = handle.readlines()
-        handle.close()
-        handle = open(os.path.join('PopGen', 'simple_100_30.par'))
-        new = handle.readlines()
-        handle.close()
+        with open(os.path.join('PopGen', 'simple.par'), _universal_read_mode) as handle:
+            old = handle.readlines()
+        with open(os.path.join('PopGen', 'simple_100_30.par')) as handle:
+            new = handle.readlines()
         assert old == new, "Error - Old:\n%s\n\nNew:\n%s\n" % (old, new)
         # assert(os.stat('PopGen' + os.sep + 'simple.par').st_size ==
         #       os.stat('PopGen' + os.sep + 'simple_100_30.par').st_size)
diff --git a/Tests/test_Prank_tool.py b/Tests/test_Prank_tool.py
index f271fb7..952e9f3 100644
--- a/Tests/test_Prank_tool.py
+++ b/Tests/test_Prank_tool.py
@@ -161,11 +161,11 @@ class PrankConversion(unittest.TestCase):
         cmdline = PrankCommandline(prank_exe, d=self.input,
                                    convert=True, f=prank_number,
                                    o='"%s"' % self.output)
-        self.assertEqual(str(cmdline), _escape_filename(prank_exe)
-                         + ' -d=%s' % self.input
-                         + ' -o="%s"' % self.output
-                         + ' -f=%i' % prank_number
-                         + ' -convert')
+        self.assertEqual(str(cmdline), _escape_filename(prank_exe) +
+                         ' -d=%s' % self.input +
+                         ' -o="%s"' % self.output +
+                         ' -f=%i' % prank_number +
+                         ' -convert')
         self.assertEqual(str(eval(repr(cmdline))), str(cmdline))
         message, error = cmdline()
         self.assertTrue("PRANK" in message, message)
diff --git a/Tests/test_QCPSuperimposer.py b/Tests/test_QCPSuperimposer.py
index 1724c3d..9e2d43a 100644
--- a/Tests/test_QCPSuperimposer.py
+++ b/Tests/test_QCPSuperimposer.py
@@ -2,10 +2,11 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-# TODO - Don't use "from XXX import *"
 from __future__ import print_function
+import warnings
 
 try:
+    # TODO - Don't use "from XXX import *"
     from numpy import *
     from numpy import dot  # missing in old PyPy's micronumpy
 except ImportError:
@@ -13,7 +14,10 @@ except ImportError:
     raise MissingPythonDependencyError(
         "Install NumPy if you want to use Bio.QCPSuperimposer.")
 
-from Bio.PDB.QCPSuperimposer import QCPSuperimposer
+from Bio import BiopythonExperimentalWarning
+with warnings.catch_warnings():
+    warnings.simplefilter('ignore', BiopythonExperimentalWarning)
+    from Bio.PDB.QCPSuperimposer import QCPSuperimposer
 
 # start with two coordinate sets (Nx3 arrays - Float0)
 
diff --git a/Tests/test_RCSBFormats.py b/Tests/test_RCSBFormats.py
new file mode 100644
index 0000000..5e0ad2e
--- /dev/null
+++ b/Tests/test_RCSBFormats.py
@@ -0,0 +1,69 @@
+# Copyright 2016 Joao P. Rodrigues (j.p.g.l.m.rodrigues at gmail.com).
+# All rights reserved.
+#
+# This code is part of the Biopython distribution and governed by its
+# license. Please see the LICENSE file that should have been included
+# as part of this package.
+
+"""Unit tests to compare objects parsed by different structure parsers."""
+
+import os
+import unittest
+import warnings
+
+try:
+    import numpy
+    from numpy import dot  # Missing on old PyPy's micronumpy
+    del dot
+    from numpy.linalg import svd, det  # Missing in PyPy 2.0 numpypy
+except ImportError:
+    from Bio import MissingPythonDependencyError
+    raise MissingPythonDependencyError(
+        "Install NumPy if you want to use Bio.PDB.")
+
+from Bio.PDB.MMCIFParser import MMCIFParser
+from Bio.PDB import PDBParser
+from Bio.PDB.PDBExceptions import PDBConstructionException, PDBConstructionWarning
+
+class CompareStructures(unittest.TestCase):
+    """Tests for comparing the same structure parsed by PDB and MMCIF parsers"""
+
+    def setUp(self):
+
+        # Silence!
+        warnings.simplefilter('ignore', PDBConstructionWarning)
+
+        pdbparser = PDBParser(QUIET=1)
+        cifparser = MMCIFParser(QUIET=1)
+
+        modpath = os.path.abspath(os.path.dirname(__file__))
+
+        pdb_file = os.path.join(modpath, "PDB", "1LCD.pdb")
+        cif_file = os.path.join(modpath, "PDB", "1LCD.cif")
+
+        self.pdbo = pdbparser.get_structure('pdb', pdb_file)
+        self.cifo = cifparser.get_structure('pdb', cif_file)
+
+    def test_compare_models(self):
+        """Compared parsed models"""
+
+        cif_models = [(m.id, len(m.child_list)) for m in self.cifo.get_models()]
+        pdb_models = [(m.id, len(m.child_list)) for m in self.pdbo.get_models()]
+
+        self.assertEqual( len(cif_models), len(pdb_models) )
+        self.assertEqual( [i[0] for i in cif_models], [i[0] for i in pdb_models] )
+        self.assertEqual( [i[1] for i in cif_models], [i[1] for i in pdb_models] )
+
+    def test_compare_chains(self):
+        """Compare parsed chains"""
+
+        cif_chains = [(c.id, len(c.child_list)) for c in self.cifo.get_chains()]
+        pdb_chains = [(c.id, len(c.child_list)) for c in self.pdbo.get_chains()]
+
+        self.assertEqual( len(cif_chains), len(pdb_chains) )
+        self.assertEqual( [i[0] for i in cif_chains], [i[0] for i in pdb_chains] )
+        self.assertEqual( [i[1] for i in cif_chains], [i[1] for i in pdb_chains] )
+
+if __name__ == '__main__':
+    runner = unittest.TextTestRunner(verbosity=2)
+    unittest.main(testRunner=runner)
diff --git a/Tests/test_SCOP_Raf.py b/Tests/test_SCOP_Raf.py
index 7f8a6d4..e9d8bbd 100644
--- a/Tests/test_SCOP_Raf.py
+++ b/Tests/test_SCOP_Raf.py
@@ -9,6 +9,7 @@
 import unittest
 
 from Bio.SCOP import Raf
+from Bio._py3k import StringIO
 
 
 class RafTests(unittest.TestCase):
@@ -60,6 +61,14 @@ class RafTests(unittest.TestCase):
         r = r[r.index("124"): r.index("135") + 1]
         self.assertEqual(len(r.res), 12)
 
+    def test_SeqMap_getAtoms_err(self):
+        r = Raf.SeqMap(self.rafLine)
+        # There is no overlap with this PDB file...
+        with open("PDB/1A8O.pdb") as pdb_handle:
+            out_handle = StringIO()
+            self.assertRaises(RuntimeError, r.getAtoms,
+                              *(pdb_handle, out_handle))
+
     def testSeqMapIndex(self):
         filename = ("./SCOP/raftest.txt")
 
diff --git a/Tests/test_SCOP_Scop.py b/Tests/test_SCOP_Scop.py
index f3f8730..269ae68 100644
--- a/Tests/test_SCOP_Scop.py
+++ b/Tests/test_SCOP_Scop.py
@@ -13,7 +13,7 @@ import unittest
 from Bio._py3k import StringIO
 from Bio._py3k import zip
 
-from Bio.SCOP import *
+from Bio.SCOP import Scop, cmp_sccs, parse_domain
 
 
 class ScopTests(unittest.TestCase):
diff --git a/Tests/test_SVDSuperimposer.py b/Tests/test_SVDSuperimposer.py
index 5b5cc2f..9dac0a4 100644
--- a/Tests/test_SVDSuperimposer.py
+++ b/Tests/test_SVDSuperimposer.py
@@ -2,11 +2,10 @@
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
-# TODO - Don't use "from XXX import *"
 from __future__ import print_function
 
 try:
-    from numpy import *
+    from numpy import array
     from numpy import dot  # missing in old PyPy's micronumpy
     from numpy.linalg import svd, det  # Missing in PyPy 2.0 numpypy
 except ImportError:
@@ -19,14 +18,14 @@ from Bio.SVDSuperimposer import SVDSuperimposer
 # start with two coordinate sets (Nx3 arrays - Float0)
 
 x = array([[51.65, -1.90, 50.07],
-         [50.40, -1.23, 50.65],
-         [50.68, -0.04, 51.54],
-         [50.22, -0.02, 52.85]], 'f')
+          [50.40, -1.23, 50.65],
+          [50.68, -0.04, 51.54],
+          [50.22, -0.02, 52.85]], 'f')
 
 y = array([[51.30, -2.99, 46.54],
-         [51.09, -1.88, 47.58],
-         [52.36, -1.20, 48.03],
-         [52.71, -1.18, 49.38]], 'f')
+          [51.09, -1.88, 47.58],
+          [52.36, -1.20, 48.03],
+          [52.71, -1.18, 49.38]], 'f')
 
 sup = SVDSuperimposer()
 
diff --git a/Tests/test_SearchIO_model.py b/Tests/test_SearchIO_model.py
index 8e846b1..937d320 100644
--- a/Tests/test_SearchIO_model.py
+++ b/Tests/test_SearchIO_model.py
@@ -484,7 +484,7 @@ class QueryResultCases(unittest.TestCase):
         filtered = self.qresult.hit_filter(filter_func)
         self.assertEqual([hit11, hit31], list(filtered.hits))
         # make sure all remaining hits return True for the filter function
-        self.assertTrue(all([filter_func(hit) for hit in filtered]))
+        self.assertTrue(all(filter_func(hit) for hit in filtered))
         self.assertEqual(1102, filtered.seq_len)
         self.assertEqual('refseq_rna', filtered.target)
 
@@ -555,11 +555,11 @@ class QueryResultCases(unittest.TestCase):
         self.assertTrue('hit2' not in filtered)
         self.assertTrue('hit3' in filtered)
         # test hsps in hit11
-        self.assertTrue(all([hsp in filtered['hit1'] for hsp in
-                [hsp111, hsp112, hsp114]]))
+        self.assertTrue(all(hsp in filtered['hit1'] for hsp in
+                            [hsp111, hsp112, hsp114]))
         # test hsps in hit31
-        self.assertTrue(all([hsp in filtered['hit3'] for hsp in
-                [hsp311, hsp312]]))
+        self.assertTrue(all(hsp in filtered['hit3'] for hsp in
+                            [hsp311, hsp312]))
 
     def test_hsp_filter_no_func(self):
         """Test QueryResult.hsp_filter, no arguments"""
@@ -944,7 +944,7 @@ class HitCases(unittest.TestCase):
         filtered = self.hit.filter(filter_func)
         self.assertEqual([hsp111, hsp113], filtered.hsps)
         # make sure all remaining hits return True for the filter function
-        self.assertTrue(all([filter_func(hit) for hit in filtered]))
+        self.assertTrue(all(filter_func(hit) for hit in filtered))
         self.assertEqual(5e-10, filtered.evalue)
         self.assertEqual('test', filtered.name)
 
diff --git a/Tests/test_SeqFeature.py b/Tests/test_SeqFeature.py
new file mode 100644
index 0000000..7f04128
--- /dev/null
+++ b/Tests/test_SeqFeature.py
@@ -0,0 +1,28 @@
+# Copyright 2015 by Kai Blin.  All rights reserved.
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+
+"""Tests Bio.SeqFeature.
+"""
+import unittest
+from os import path
+from Bio import SeqIO
+
+
+class TestReference(unittest.TestCase):
+    """Tests for the SeqFeature.Reference class"""
+
+    def test_eq_identical(self):
+        """Test two identical references eq() to True"""
+        testfile = path.join('GenBank', 'origin_line.gb')
+        rec1 = SeqIO.read(testfile, 'genbank')
+        rec2 = SeqIO.read(testfile, 'genbank')
+
+        self.assertEqual(rec1.annotations['references'][0], rec1.annotations['references'][0])
+        cmp1, cmp2 = rec1.annotations['references'][0], rec2.annotations['references'][0]
+        self.assertEqual(cmp1, cmp2)
+        self.assertNotEqual(rec1.annotations['references'][0], rec1.annotations['references'][1])
+        self.assertNotEqual(rec1.annotations['references'][0], rec2.annotations['references'][1])
+        self.assertEqual(rec1.annotations['references'][1], rec1.annotations['references'][1])
+        self.assertEqual(rec1.annotations['references'][1], rec2.annotations['references'][1])
diff --git a/Tests/test_SeqIO.py b/Tests/test_SeqIO.py
index 6d1c52f..e3b1152 100644
--- a/Tests/test_SeqIO.py
+++ b/Tests/test_SeqIO.py
@@ -1,4 +1,4 @@
-# Copyright 2007-2015 by Peter Cock.  All rights reserved.
+# Copyright 2007-2016 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -159,6 +159,8 @@ test_files = [
     ("genbank", False, 'GenBank/NP_416719.gbwithparts', 1),
     # GenPept file with nasty bond locations,
     ("genbank", False, 'GenBank/1MRR_A.gp', 1),
+    # These are a pair, and should be roughly equivalent:
+    ("genbank", False, 'GenBank/DS830848.gb', 1), ("embl", False, 'EMBL/DS830848.embl', 1),
     # Following files are currently only used here or in test_SeqIO_index.py:
     ("embl", False, 'EMBL/epo_prt_selection.embl', 9),  # proteins
     ("embl", False, 'EMBL/patents.embl', 4),  # more proteins, but no seq
@@ -321,14 +323,14 @@ def alignment_summary(alignment, index=" "):
     alignment_len = alignment.get_alignment_length()
     rec_count = len(alignment)
     for i in range(min(5, alignment_len)):
-        answer.append(index + col_summary(alignment[:, i])
-                            + " alignment column %i" % i)
+        answer.append(index + col_summary(alignment[:, i]) +
+                      " alignment column %i" % i)
     if alignment_len > 5:
         i = alignment_len - 1
-        answer.append(index + col_summary("|" * rec_count)
-                            + " ...")
-        answer.append(index + col_summary(alignment[:, i])
-                            + " alignment column %i" % i)
+        answer.append(index + col_summary("|" * rec_count) +
+                      " ...")
+        answer.append(index + col_summary(alignment[:, i]) +
+                      " alignment column %i" % i)
     return "\n".join(answer)
 
 
@@ -438,8 +440,10 @@ def check_simple_write_read(records, indent=" "):
                 handle = BytesIO()
             else:
                 handle = StringIO()
-            SeqIO.write(records[0], handle, format)
-            assert handle.getvalue() == records[0].format(format)
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", BiopythonWarning)
+                SeqIO.write(records[0], handle, format)
+                assert handle.getvalue() == records[0].format(format)
 
 
 # Check parsers can cope with an empty file
@@ -528,135 +532,135 @@ for (t_format, t_alignment, t_filename, t_count) in test_files:
         assert len(records5) == t_count
         h.close()
 
-    for i in range(t_count):
-        record = records[i]
+        for i in range(t_count):
+            record = records[i]
 
-        # Check returned expected object type
-        assert isinstance(record, SeqRecord)
-        if t_format in possible_unknown_seq_formats:
-            assert isinstance(record.seq, Seq) or \
-                isinstance(record.seq, UnknownSeq)
-        else:
-            assert isinstance(record.seq, Seq)
-        assert isinstance(record.id, basestring)
-        assert isinstance(record.name, basestring)
-        assert isinstance(record.description, basestring)
-        assert record.id != ""
-
-        if "accessions" in record.annotations:
-            accs = record.annotations["accessions"]
-            # Check for blanks, or entries with leading/trailing spaces
-            for acc in accs:
-                assert acc and acc == acc.strip(), \
-                    "Bad accession in annotations: %s" % repr(acc)
-            assert len(set(accs)) == len(accs), \
-                "Repeated accession in annotations: %s" % repr(accs)
-        for ref in record.dbxrefs:
-            assert ref and ref == ref.strip(), \
-                "Bad cross reference in dbxrefs: %s" % repr(ref)
-        assert len(record.dbxrefs) == len(record.dbxrefs), \
-            "Repeated cross reference in dbxrefs: %s" % repr(record.dbxrefs)
-
-        # Check the lists obtained by the different methods agree
-        assert compare_record(record, records2[i])
-        assert compare_record(record, records3[i])
-        assert compare_record(record, records4[i])
-        assert compare_record(record, records5[i])
-
-        if i < 3:
-            print(record_summary(record))
-    # Only printed the only first three records: 0,1,2
-    if t_count > 4:
-        print(" ...")
-    if t_count > 3:
-        print(record_summary(records[-1]))
-
-    # Check Bio.SeqIO.read(...)
-    if t_count == 1:
-        record = SeqIO.read(t_filename, format=t_format)
-        assert isinstance(record, SeqRecord)
-    else:
-        try:
-            record = SeqIO.read(t_filename, t_format)
-            assert False, "Bio.SeqIO.read(...) should have failed"
-        except ValueError:
-            # Expected to fail
-            pass
-
-    # Check alphabets
-    for record in records:
-        base_alpha = Alphabet._get_base_alphabet(record.seq.alphabet)
-        if isinstance(base_alpha, Alphabet.SingleLetterAlphabet):
-            if t_format in no_alpha_formats:
-                # Too harsh?
-                assert base_alpha == Alphabet.single_letter_alphabet
+            # Check returned expected object type
+            assert isinstance(record, SeqRecord)
+            if t_format in possible_unknown_seq_formats:
+                assert isinstance(record.seq, Seq) or \
+                    isinstance(record.seq, UnknownSeq)
+            else:
+                assert isinstance(record.seq, Seq)
+            assert isinstance(record.id, basestring)
+            assert isinstance(record.name, basestring)
+            assert isinstance(record.description, basestring)
+            assert record.id != ""
+
+            if "accessions" in record.annotations:
+                accs = record.annotations["accessions"]
+                # Check for blanks, or entries with leading/trailing spaces
+                for acc in accs:
+                    assert acc and acc == acc.strip(), \
+                        "Bad accession in annotations: %s" % repr(acc)
+                assert len(set(accs)) == len(accs), \
+                    "Repeated accession in annotations: %s" % repr(accs)
+            for ref in record.dbxrefs:
+                assert ref and ref == ref.strip(), \
+                    "Bad cross reference in dbxrefs: %s" % repr(ref)
+            assert len(record.dbxrefs) == len(record.dbxrefs), \
+                "Repeated cross reference in dbxrefs: %s" % repr(record.dbxrefs)
+
+            # Check the lists obtained by the different methods agree
+            assert compare_record(record, records2[i])
+            assert compare_record(record, records3[i])
+            assert compare_record(record, records4[i])
+            assert compare_record(record, records5[i])
+
+            if i < 3:
+                print(record_summary(record))
+        # Only printed the only first three records: 0,1,2
+        if t_count > 4:
+            print(" ...")
+        if t_count > 3:
+            print(record_summary(records[-1]))
+
+        # Check Bio.SeqIO.read(...)
+        if t_count == 1:
+            record = SeqIO.read(t_filename, format=t_format)
+            assert isinstance(record, SeqRecord)
         else:
-            base_alpha = None
-    if base_alpha is None:
-        good = []
-        bad = []
-        given_alpha = None
-    elif isinstance(base_alpha, Alphabet.ProteinAlphabet):
-        good = protein_alphas
-        bad = dna_alphas + rna_alphas + nucleotide_alphas
-    elif isinstance(base_alpha, Alphabet.RNAAlphabet):
-        good = nucleotide_alphas + rna_alphas
-        bad = protein_alphas + dna_alphas
-    elif isinstance(base_alpha, Alphabet.DNAAlphabet):
-        good = nucleotide_alphas + dna_alphas
-        bad = protein_alphas + rna_alphas
-    elif isinstance(base_alpha, Alphabet.NucleotideAlphabet):
-        good = nucleotide_alphas
-        bad = protein_alphas
-    else:
-        assert t_format in no_alpha_formats, "Got %s from %s file" \
-            % (repr(base_alpha), t_format)
-        good = protein_alphas + dna_alphas + rna_alphas + nucleotide_alphas
-        bad = []
-    for given_alpha in good:
-        # These should all work...
-        given_base = Alphabet._get_base_alphabet(given_alpha)
-        for record in SeqIO.parse(t_filename, t_format, given_alpha):
+            try:
+                record = SeqIO.read(t_filename, t_format)
+                assert False, "Bio.SeqIO.read(...) should have failed"
+            except ValueError:
+                # Expected to fail
+                pass
+
+        # Check alphabets
+        for record in records:
             base_alpha = Alphabet._get_base_alphabet(record.seq.alphabet)
-            assert isinstance(base_alpha, given_base.__class__)
-            assert base_alpha == given_base
-        if t_count == 1:
+            if isinstance(base_alpha, Alphabet.SingleLetterAlphabet):
+                if t_format in no_alpha_formats:
+                    # Too harsh?
+                    assert base_alpha == Alphabet.single_letter_alphabet
+            else:
+                base_alpha = None
+        if base_alpha is None:
+            good = []
+            bad = []
+            given_alpha = None
+        elif isinstance(base_alpha, Alphabet.ProteinAlphabet):
+            good = protein_alphas
+            bad = dna_alphas + rna_alphas + nucleotide_alphas
+        elif isinstance(base_alpha, Alphabet.RNAAlphabet):
+            good = nucleotide_alphas + rna_alphas
+            bad = protein_alphas + dna_alphas
+        elif isinstance(base_alpha, Alphabet.DNAAlphabet):
+            good = nucleotide_alphas + dna_alphas
+            bad = protein_alphas + rna_alphas
+        elif isinstance(base_alpha, Alphabet.NucleotideAlphabet):
+            good = nucleotide_alphas
+            bad = protein_alphas
+        else:
+            assert t_format in no_alpha_formats, "Got %s from %s file" \
+                % (repr(base_alpha), t_format)
+            good = protein_alphas + dna_alphas + rna_alphas + nucleotide_alphas
+            bad = []
+        for given_alpha in good:
+            # These should all work...
+            given_base = Alphabet._get_base_alphabet(given_alpha)
+            for record in SeqIO.parse(t_filename, t_format, given_alpha):
+                base_alpha = Alphabet._get_base_alphabet(record.seq.alphabet)
+                assert isinstance(base_alpha, given_base.__class__)
+                assert base_alpha == given_base
+            if t_count == 1:
+                h = open(t_filename, mode)
+                record = SeqIO.read(h, t_format, given_alpha)
+                h.close()
+                assert isinstance(base_alpha, given_base.__class__)
+                assert base_alpha == given_base
+        for given_alpha in bad:
+            # These should all fail...
             h = open(t_filename, mode)
-            record = SeqIO.read(h, t_format, given_alpha)
-            h.close()
-            assert isinstance(base_alpha, given_base.__class__)
-            assert base_alpha == given_base
-    for given_alpha in bad:
-        # These should all fail...
-        h = open(t_filename, mode)
-        try:
-            print(next(SeqIO.parse(h, t_format, given_alpha)))
+            try:
+                print(next(SeqIO.parse(h, t_format, given_alpha)))
+                h.close()
+                assert False, "Forcing wrong alphabet, %s, should fail (%s)" \
+                    % (repr(given_alpha), t_filename)
+            except ValueError:
+                # Good - should fail
+                pass
             h.close()
-            assert False, "Forcing wrong alphabet, %s, should fail (%s)" \
-                % (repr(given_alpha), t_filename)
-        except ValueError:
-            # Good - should fail
-            pass
-        h.close()
-    del good, bad, given_alpha, base_alpha
+        del good, bad, given_alpha, base_alpha
 
-    if t_alignment:
-        print("Testing reading %s format file %s as an alignment"
-              % (t_format, t_filename))
+        if t_alignment:
+            print("Testing reading %s format file %s as an alignment"
+                  % (t_format, t_filename))
 
-        alignment = MultipleSeqAlignment(SeqIO.parse(
-            handle=t_filename, format=t_format))
-        assert len(alignment) == t_count
+            alignment = MultipleSeqAlignment(SeqIO.parse(
+                    handle=t_filename, format=t_format))
+            assert len(alignment) == t_count
 
-        alignment_len = alignment.get_alignment_length()
+            alignment_len = alignment.get_alignment_length()
 
-        # Check the record order agrees, and double check the
-        # sequence lengths all agree too.
-        for i in range(t_count):
-            assert compare_record(records[i], alignment[i])
-            assert len(records[i].seq) == alignment_len
+            # Check the record order agrees, and double check the
+            # sequence lengths all agree too.
+            for i in range(t_count):
+                assert compare_record(records[i], alignment[i])
+                assert len(records[i].seq) == alignment_len
 
-        print(alignment_summary(alignment))
+            print(alignment_summary(alignment))
 
     # Some alignment file formats have magic characters which mean
     # use the letter in this position in the first sequence.
diff --git a/Tests/test_SeqIO_FastaIO.py b/Tests/test_SeqIO_FastaIO.py
index 20389b9..e7f1868 100644
--- a/Tests/test_SeqIO_FastaIO.py
+++ b/Tests/test_SeqIO_FastaIO.py
@@ -44,29 +44,27 @@ def title_to_ids(title):
 
 def read_single_with_titles(filename, alphabet):
     global title_to_ids
-    handle = open(filename)
-    iterator = FastaIterator(handle, alphabet, title_to_ids)
-    record = next(iterator)
-    try:
-        second = next(iterator)
-    except StopIteration:
-        second = None
-    handle.close()
+    with open(filename) as handle:
+        iterator = FastaIterator(handle, alphabet, title_to_ids)
+        record = next(iterator)
+        try:
+            second = next(iterator)
+        except StopIteration:
+            second = None
     assert record is not None and second is None
     return record
 
 
 def read_title_and_seq(filename):
     """Crude parser that gets the first record from a FASTA file."""
-    handle = open(filename)
-    title = handle.readline().rstrip()
-    assert title.startswith(">")
-    seq = ""
-    for line in handle:
-        if line.startswith(">"):
-            break
-        seq += line.strip()
-    handle.close()
+    with open(filename) as handle:
+        title = handle.readline().rstrip()
+        assert title.startswith(">")
+        seq = ""
+        for line in handle:
+            if line.startswith(">"):
+                break
+            seq += line.strip()
     return title[1:], seq
 
 
diff --git a/Tests/test_SeqIO_PdbIO.py b/Tests/test_SeqIO_PdbIO.py
index 913a51f..7b67348 100644
--- a/Tests/test_SeqIO_PdbIO.py
+++ b/Tests/test_SeqIO_PdbIO.py
@@ -51,7 +51,7 @@ class TestPdbSeqres(unittest.TestCase):
 
     def test_seqres_missing(self):
         """Parse a PDB with no SEQRES entries."""
-        chains = list(SeqIO.parse('PDB/1MOT.pdb', 'pdb-seqres'))
+        chains = list(SeqIO.parse('PDB/a_structure.pdb', 'pdb-seqres'))
         self.assertEqual(len(chains), 0)
 
 
@@ -102,10 +102,14 @@ class TestPdbAtom(unittest.TestCase):
 
     def test_atom_noheader(self):
         """Parse a PDB with no HEADER line."""
-        warnings.simplefilter('ignore', UserWarning)
-        chains = list(SeqIO.parse('PDB/1MOT.pdb', 'pdb-atom'))
+
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', PDBConstructionWarning)
+            warnings.simplefilter('ignore', UserWarning)
+            chains = list(SeqIO.parse('PDB/1LCD.pdb', 'pdb-atom'))
+
         self.assertEqual(len(chains), 1)
-        self.assertEqual(str(chains[0].seq), 'APARVGLGITTVLTMTTQSSGSRASLPK')
+        self.assertEqual(str(chains[0].seq), 'MKPVTLYDVAEYAGVSYQTVSRVVNQASHVSAKTREKVEAAMAELNYIPNR')
 
 
 if __name__ == "__main__":
diff --git a/Tests/test_SeqIO_QualityIO.py b/Tests/test_SeqIO_QualityIO.py
index 011862f..5273278 100644
--- a/Tests/test_SeqIO_QualityIO.py
+++ b/Tests/test_SeqIO_QualityIO.py
@@ -289,7 +289,6 @@ class TestReferenceFastqConversions(unittest.TestCase):
                 if out_variant != "sanger":
                     # Ignore data loss warnings from max qualities
                     warnings.simplefilter("ignore", BiopythonWarning)
-                    warnings.simplefilter("ignore", UserWarning)
                 # Check matches using convert...
                 handle = StringIO()
                 SeqIO.convert(in_filename, "fastq-" + in_variant,
@@ -500,8 +499,11 @@ class TestWriteRead(unittest.TestCase):
         """Write and read back sanger_93.fastq"""
         self.check(os.path.join("Quality", "sanger_93.fastq"), "fastq",
                    ["fastq", "fastq-sanger", "fasta", "qual", "phd"])
-        self.check(os.path.join("Quality", "sanger_93.fastq"), "fastq",
-                   ["fastq-solexa", "fastq-illumina"])
+        with warnings.catch_warnings():
+            # TODO - Have a Biopython defined "DataLossWarning?"
+            warnings.simplefilter('ignore', BiopythonWarning)
+            self.check(os.path.join("Quality", "sanger_93.fastq"), "fastq",
+                       ["fastq-solexa", "fastq-illumina"])
 
     def test_sanger_faked(self):
         """Write and read back sanger_faked.fastq"""
diff --git a/Tests/test_SeqIO_convert.py b/Tests/test_SeqIO_convert.py
index 58e1362..db3f4bf 100644
--- a/Tests/test_SeqIO_convert.py
+++ b/Tests/test_SeqIO_convert.py
@@ -8,6 +8,7 @@ from __future__ import print_function
 
 import unittest
 import warnings
+from Bio import BiopythonWarning
 from Bio.Seq import UnknownSeq
 from Bio import SeqIO
 from Bio.SeqIO import QualityIO
@@ -34,7 +35,7 @@ def check_convert(in_filename, in_format, out_format, alphabet=None):
     qual_truncate = truncation_expected(out_format)
     with warnings.catch_warnings():
         if qual_truncate:
-            warnings.simplefilter('ignore', UserWarning)
+            warnings.simplefilter('ignore', BiopythonWarning)
         SeqIO.write(records, handle, out_format)
     handle.seek(0)
     # Now load it back and check it agrees,
@@ -44,7 +45,7 @@ def check_convert(in_filename, in_format, out_format, alphabet=None):
     handle2 = StringIO()
     with warnings.catch_warnings():
         if qual_truncate:
-            warnings.simplefilter('ignore', UserWarning)
+            warnings.simplefilter('ignore', BiopythonWarning)
         SeqIO.convert(in_filename, in_format, handle2, out_format, alphabet)
     # We could re-parse this, but it is simpler and stricter:
     assert handle.getvalue() == handle2.getvalue()
@@ -59,7 +60,7 @@ def check_convert_fails(in_filename, in_format, out_format, alphabet=None):
         handle = StringIO()
         with warnings.catch_warnings():
             if qual_truncate:
-                warnings.simplefilter('ignore', UserWarning)
+                warnings.simplefilter('ignore', BiopythonWarning)
             SeqIO.write(records, handle, out_format)
         handle.seek(0)
         assert False, "Parse or write should have failed!"
@@ -70,7 +71,7 @@ def check_convert_fails(in_filename, in_format, out_format, alphabet=None):
         handle2 = StringIO()
         with warnings.catch_warnings():
             if qual_truncate:
-                warnings.simplefilter('ignore', UserWarning)
+                warnings.simplefilter('ignore', BiopythonWarning)
             SeqIO.convert(in_filename, in_format, handle2, out_format, alphabet)
         assert False, "Convert should have failed!"
     except ValueError as err2:
diff --git a/Tests/test_SeqIO_features.py b/Tests/test_SeqIO_features.py
index 8b3820a..f0381f3 100644
--- a/Tests/test_SeqIO_features.py
+++ b/Tests/test_SeqIO_features.py
@@ -21,8 +21,8 @@ from Bio.Data.CodonTable import TranslationError
 from Bio.Seq import Seq, UnknownSeq, MutableSeq, reverse_complement
 from Bio.SeqRecord import SeqRecord
 from Bio.SeqFeature import SeqFeature, FeatureLocation, CompoundLocation
-from Bio.SeqFeature import ExactPosition, BeforePosition, AfterPosition, \
-                           OneOfPosition, WithinPosition
+from Bio.SeqFeature import ExactPosition, BeforePosition, AfterPosition
+from Bio.SeqFeature import OneOfPosition, WithinPosition, UnknownPosition
 from Bio.SeqIO.InsdcIO import _insdc_location_string
 from Bio.SeqIO.InsdcIO import _insdc_feature_location_string
 
@@ -37,7 +37,7 @@ def _get_location_string(feature, record_length):
 
 
 # Top level function as this makes it easier to use for debugging:
-def write_read(filename, in_format="gb", out_formats=["gb", "embl", "imgt"]):
+def write_read(filename, in_format="gb", out_formats=("gb", "embl", "imgt")):
     for out_format in out_formats:
         gb_records = list(SeqIO.parse(filename, in_format))
         # Write it out...
@@ -421,7 +421,7 @@ class SeqFeatureExtractionWritingReading(unittest.TestCase):
         f = make_join_feature([f1, f2])
         self.check(s, f, "YWSMKVDN", "join(6..10,13..15)")
 
-    def test_simple_dna_join(self):
+    def test_simple_dna_join_strand_minus(self):
         """Feature on DNA (join, strand -1)"""
         s = Seq("AAAAACCCCCTTTTTGGGGG", generic_dna)
         f1 = SeqFeature(FeatureLocation(5, 10), strand=-1)
@@ -430,7 +430,7 @@ class SeqFeatureExtractionWritingReading(unittest.TestCase):
         self.check(s, f, reverse_complement("CCCCC" + "TTT"),
                    "complement(join(6..10,13..15))")
 
-    def test_simple_dna_join(self):
+    def test_simple_dna_join_before(self):
         """Feature on DNA (join, strand -1, before position)"""
         s = Seq("AAAAACCCCCTTTTTGGGGG", generic_dna)
         f1 = SeqFeature(FeatureLocation(BeforePosition(5), 10), strand=-1)
@@ -550,7 +550,7 @@ class FeatureWriting(unittest.TestCase):
         record2 = SeqIO.read(handle, format)
         compare_record(self.record, record2)
 
-    def write_read_checks(self, formats=["gb", "embl", "imgt"]):
+    def write_read_checks(self, formats=("gb", "embl", "imgt")):
         for f in formats:
             self.write_read_check(f)
 
@@ -626,6 +626,34 @@ class FeatureWriting(unittest.TestCase):
         self.record.features.append(f)
         self.write_read_checks()
 
+    def test_unknown(self):
+        """GenBank/EMBL write/read with unknown end points."""
+        f = SeqFeature(FeatureLocation(10, 15), strand=+1, type="region")
+        self.assertEqual(_get_location_string(f, 100),
+                         "11..15")
+        self.record.features.append(f)
+        f = SeqFeature(FeatureLocation(10, UnknownPosition()), strand=+1, type="region")
+        self.assertEqual(_get_location_string(f, 100),
+                         "11..>11")
+        self.record.features.append(f)
+        f = SeqFeature(FeatureLocation(UnknownPosition(), 15), strand=+1, type="region")
+        self.assertEqual(_get_location_string(f, 100),
+                         "<15..15")
+        self.record.features.append(f)
+        f = SeqFeature(FeatureLocation(10, 15), strand=-1, type="region")
+        self.assertEqual(_get_location_string(f, 100),
+                         "complement(11..15)")
+        f = SeqFeature(FeatureLocation(10, UnknownPosition()), strand=-1, type="region")
+        self.assertEqual(_get_location_string(f, 100),
+                         "complement(11..>11)")
+        self.record.features.append(f)
+        f = SeqFeature(FeatureLocation(UnknownPosition(), 15), strand=-1, type="region")
+        self.assertEqual(_get_location_string(f, 100),
+                         "complement(<15..15)")
+        self.record.features.append(f)
+        # This doesn't round trip
+        # self.write_read_checks()
+
     def test_join(self):
         """GenBank/EMBL write/read simple join locations."""
         f1 = SeqFeature(FeatureLocation(10, 20), strand=+1)
diff --git a/Tests/test_SeqIO_index.py b/Tests/test_SeqIO_index.py
index fd4b1e6..add9717 100644
--- a/Tests/test_SeqIO_index.py
+++ b/Tests/test_SeqIO_index.py
@@ -1,4 +1,4 @@
-# Copyright 2009-2012 by Peter Cock.  All rights reserved.
+# Copyright 2009-2015 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -314,62 +314,69 @@ class IndexDictTests(unittest.TestCase):
         else:
             id_list = [rec.id for rec in SeqIO.parse(filename, format, alphabet)]
 
-        rec_dict = SeqIO.index(filename, format, alphabet)
-        self.check_dict_methods(rec_dict, id_list, id_list)
-        rec_dict.close()
-        del rec_dict
+        with warnings.catch_warnings():
+            if "_alt_index_" in filename:
+                # BiopythonParserWarning: Could not parse the SFF index:
+                # Unknown magic number b'.diy' in SFF index header:
+                # b'.diy1.00'
+                warnings.simplefilter('ignore', BiopythonParserWarning)
 
-        if not sqlite3:
-            return
+            rec_dict = SeqIO.index(filename, format, alphabet)
+            self.check_dict_methods(rec_dict, id_list, id_list)
+            rec_dict.close()
+            del rec_dict
 
-        # In memory,
-        # note here give filenames as list of strings
-        rec_dict = SeqIO.index_db(":memory:", [filename], format,
-                                  alphabet)
-        self.check_dict_methods(rec_dict, id_list, id_list)
-        rec_dict.close()
-        del rec_dict
+            if not sqlite3:
+                return
 
-        # check error conditions
-        self.assertRaises(ValueError, SeqIO.index_db,
-                          ":memory:", format="dummy")
-        self.assertRaises(ValueError, SeqIO.index_db,
-                          ":memory:", filenames=["dummy"])
+            # In memory,
+            # note here give filenames as list of strings
+            rec_dict = SeqIO.index_db(":memory:", [filename], format,
+                                      alphabet)
+            self.check_dict_methods(rec_dict, id_list, id_list)
+            rec_dict.close()
+            del rec_dict
 
-        # Saving to file...
-        index_tmp = self.index_tmp
-        if os.path.isfile(index_tmp):
-            os.remove(index_tmp)
-
-        # To disk,
-        # note here we give the filename as a single string
-        # to confirm that works too (convience feature).
-        rec_dict = SeqIO.index_db(index_tmp, filename, format,
-                                  alphabet)
-        self.check_dict_methods(rec_dict, id_list, id_list)
-        rec_dict.close()
-        rec_dict._con.close()  # hack for PyPy
-        del rec_dict
-
-        # Now reload it...
-        rec_dict = SeqIO.index_db(index_tmp, [filename], format,
-                                  alphabet)
-        self.check_dict_methods(rec_dict, id_list, id_list)
-        rec_dict.close()
-        rec_dict._con.close()  # hack for PyPy
-        del rec_dict
-
-        # Now reload without passing filenames and format
-        # and switch directory to check  paths still work
-        index_tmp = os.path.abspath(index_tmp)
-        os.chdir(os.path.dirname(filename))
-        rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet)
-        self.check_dict_methods(rec_dict, id_list, id_list)
-        rec_dict.close()
-        rec_dict._con.close()  # hack for PyPy
-        del rec_dict
+            # check error conditions
+            self.assertRaises(ValueError, SeqIO.index_db,
+                              ":memory:", format="dummy")
+            self.assertRaises(ValueError, SeqIO.index_db,
+                              ":memory:", filenames=["dummy"])
+
+            # Saving to file...
+            index_tmp = self.index_tmp
+            if os.path.isfile(index_tmp):
+                os.remove(index_tmp)
+
+            # To disk,
+            # note here we give the filename as a single string
+            # to confirm that works too (convience feature).
+            rec_dict = SeqIO.index_db(index_tmp, filename, format,
+                                      alphabet)
+            self.check_dict_methods(rec_dict, id_list, id_list)
+            rec_dict.close()
+            rec_dict._con.close()  # hack for PyPy
+            del rec_dict
+
+            # Now reload it...
+            rec_dict = SeqIO.index_db(index_tmp, [filename], format,
+                                      alphabet)
+            self.check_dict_methods(rec_dict, id_list, id_list)
+            rec_dict.close()
+            rec_dict._con.close()  # hack for PyPy
+            del rec_dict
+
+            # Now reload without passing filenames and format
+            # and switch directory to check  paths still work
+            index_tmp = os.path.abspath(index_tmp)
+            os.chdir(os.path.dirname(filename))
+            rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet)
+            self.check_dict_methods(rec_dict, id_list, id_list)
+            rec_dict.close()
+            rec_dict._con.close()  # hack for PyPy
+            del rec_dict
 
-        os.remove(index_tmp)
+            os.remove(index_tmp)
 
     def key_check(self, filename, format, alphabet, comp):
         """Check indexing with a key function."""
@@ -381,56 +388,64 @@ class IndexDictTests(unittest.TestCase):
             id_list = [rec.id for rec in SeqIO.parse(filename, format, alphabet)]
 
         key_list = [add_prefix(id) for id in id_list]
-        rec_dict = SeqIO.index(filename, format, alphabet, add_prefix)
-        self.check_dict_methods(rec_dict, key_list, id_list)
-        rec_dict.close()
-        del rec_dict
 
-        if not sqlite3:
-            return
-
-        # In memory,
-        rec_dict = SeqIO.index_db(":memory:", [filename], format, alphabet,
-                                  add_prefix)
-        self.check_dict_methods(rec_dict, key_list, id_list)
-        # check error conditions
-        self.assertRaises(ValueError, SeqIO.index_db,
-                          ":memory:", format="dummy",
-                          key_function=add_prefix)
-        self.assertRaises(ValueError, SeqIO.index_db,
-                          ":memory:", filenames=["dummy"],
-                          key_function=add_prefix)
-        rec_dict.close()
-        del rec_dict
+        with warnings.catch_warnings():
+            if "_alt_index_" in filename:
+                # BiopythonParserWarning: Could not parse the SFF index:
+                # Unknown magic number b'.diy' in SFF index header:
+                # b'.diy1.00'
+                warnings.simplefilter('ignore', BiopythonParserWarning)
 
-        # Saving to file...
-        index_tmp = filename + ".key.idx"
-        if os.path.isfile(index_tmp):
-            os.remove(index_tmp)
-        rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
-                                  add_prefix)
-        self.check_dict_methods(rec_dict, key_list, id_list)
-        rec_dict.close()
-        rec_dict._con.close()  # hack for PyPy
-        del rec_dict
+            rec_dict = SeqIO.index(filename, format, alphabet, add_prefix)
+            self.check_dict_methods(rec_dict, key_list, id_list)
+            rec_dict.close()
+            del rec_dict
 
-        # Now reload it...
-        rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
-                                  add_prefix)
-        self.check_dict_methods(rec_dict, key_list, id_list)
-        rec_dict.close()
-        rec_dict._con.close()  # hack for PyPy
-        del rec_dict
+            if not sqlite3:
+                return
 
-        # Now reload without passing filenames and format
-        rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet,
-                                  key_function=add_prefix)
-        self.check_dict_methods(rec_dict, key_list, id_list)
-        rec_dict.close()
-        rec_dict._con.close()  # hack for PyPy
-        del rec_dict
-        os.remove(index_tmp)
-        # Done
+            # In memory,
+            rec_dict = SeqIO.index_db(":memory:", [filename], format, alphabet,
+                                      add_prefix)
+            self.check_dict_methods(rec_dict, key_list, id_list)
+            # check error conditions
+            self.assertRaises(ValueError, SeqIO.index_db,
+                              ":memory:", format="dummy",
+                              key_function=add_prefix)
+            self.assertRaises(ValueError, SeqIO.index_db,
+                              ":memory:", filenames=["dummy"],
+                              key_function=add_prefix)
+            rec_dict.close()
+            del rec_dict
+
+            # Saving to file...
+            index_tmp = filename + ".key.idx"
+            if os.path.isfile(index_tmp):
+                os.remove(index_tmp)
+            rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
+                                      add_prefix)
+            self.check_dict_methods(rec_dict, key_list, id_list)
+            rec_dict.close()
+            rec_dict._con.close()  # hack for PyPy
+            del rec_dict
+
+            # Now reload it...
+            rec_dict = SeqIO.index_db(index_tmp, [filename], format, alphabet,
+                                      add_prefix)
+            self.check_dict_methods(rec_dict, key_list, id_list)
+            rec_dict.close()
+            rec_dict._con.close()  # hack for PyPy
+            del rec_dict
+
+            # Now reload without passing filenames and format
+            rec_dict = SeqIO.index_db(index_tmp, alphabet=alphabet,
+                                      key_function=add_prefix)
+            self.check_dict_methods(rec_dict, key_list, id_list)
+            rec_dict.close()
+            rec_dict._con.close()  # hack for PyPy
+            del rec_dict
+            os.remove(index_tmp)
+            # Done
 
     def check_dict_methods(self, rec_dict, keys, ids):
         self.assertEqual(set(keys), set(rec_dict))
@@ -510,6 +525,8 @@ class IndexDictTests(unittest.TestCase):
             self.assertEqual(key, rec_dict[key].id.lower())
             self.assertEqual(key, rec_dict.get(key).id.lower())
             raw = rec_dict.get_raw(key)
+            self.assertTrue(isinstance(raw, bytes),
+                            "Didn't get bytes from %s get_raw" % format)
             self.assertTrue(raw.strip())
             self.assertTrue(raw in raw_file)
             rec1 = rec_dict[key]
@@ -627,20 +644,20 @@ tests = [
     ("Roche/paired.sff", "sff", None),
     ("Roche/paired.sff", "sff-trim", None),
     ]
-for filename, format, alphabet in tests:
+for filename1, format, alphabet in tests:
     assert format in _FormatToRandomAccess
-    tasks = [(filename, None)]
-    if do_bgzf and os.path.isfile(filename + ".bgz"):
-        tasks.append((filename + ".bgz", "bgzf"))
-    for filename, comp in tasks:
+    tasks = [(filename1, None)]
+    if do_bgzf and os.path.isfile(filename1 + ".bgz"):
+        tasks.append((filename1 + ".bgz", "bgzf"))
+    for filename2, comp in tasks:
 
         def funct(fn, fmt, alpha, c):
             f = lambda x: x.simple_check(fn, fmt, alpha, c)
             f.__doc__ = "Index %s file %s defaults" % (fmt, fn)
             return f
         setattr(IndexDictTests, "test_%s_%s_simple"
-                    % (format, filename.replace("/", "_").replace(".", "_")),
-                funct(filename, format, alphabet, comp))
+                    % (format, filename2.replace("/", "_").replace(".", "_")),
+                funct(filename2, format, alphabet, comp))
         del funct
 
         def funct(fn, fmt, alpha, c):
@@ -648,8 +665,8 @@ for filename, format, alphabet in tests:
             f.__doc__ = "Index %s file %s with key function" % (fmt, fn)
             return f
         setattr(IndexDictTests, "test_%s_%s_keyf"
-                    % (format, filename.replace("/", "_").replace(".", "_")),
-                funct(filename, format, alphabet, comp))
+                    % (format, filename2.replace("/", "_").replace(".", "_")),
+                funct(filename2, format, alphabet, comp))
         del funct
 
         def funct(fn, fmt, alpha, c):
@@ -657,8 +674,8 @@ for filename, format, alphabet in tests:
             f.__doc__ = "Index %s file %s get_raw" % (fmt, fn)
             return f
         setattr(IndexDictTests, "test_%s_%s_get_raw"
-                    % (format, filename.replace("/", "_").replace(".", "_")),
-                funct(filename, format, alphabet, comp))
+                    % (format, filename2.replace("/", "_").replace(".", "_")),
+                funct(filename2, format, alphabet, comp))
         del funct
 
 if __name__ == "__main__":
diff --git a/Tests/test_SeqIO_online.py b/Tests/test_SeqIO_online.py
index d5f0507..0300581 100644
--- a/Tests/test_SeqIO_online.py
+++ b/Tests/test_SeqIO_online.py
@@ -60,8 +60,8 @@ class EntrezTests(unittest.TestCase):
             handle.close()
             self.assertTrue((entry in record.name) or
                          (entry in record.id) or
-                         ("gi" in record.annotations
-                          and record.annotations["gi"] == entry),
+                         ("gi" in record.annotations and
+                          record.annotations["gi"] == entry),
                          "%s got %s, %s" % (entry, record.name, record.id))
             self.assertEqual(len(record), length)
             self.assertEqual(seguid(record.seq), checksum)
diff --git a/Tests/test_SeqIO_write.py b/Tests/test_SeqIO_write.py
index b2f745b..7a65f76 100644
--- a/Tests/test_SeqIO_write.py
+++ b/Tests/test_SeqIO_write.py
@@ -62,11 +62,11 @@ test_records = [
 # Meddle with the annotation too:
 assert test_records[4][1] == "3 DNA seq alignment with CR/LF in name/descr"
 # Add a list of strings,
-test_records[4][0][2].annotations["note"] = ["Note%salso" % os.linesep
-                                    + "\r\nhas\n evil line\rbreaks!", "Wow"]
+test_records[4][0][2].annotations["note"] = ["Note%salso" % os.linesep +
+                                             "\r\nhas\n evil line\rbreaks!", "Wow"]
 # Add a simple string
-test_records[4][0][2].annotations["comment"] = "More%sof" % os.linesep \
-                                          + "\r\nthese\n evil line\rbreaks!"
+test_records[4][0][2].annotations["comment"] = ("More%sof" % os.linesep +
+                                               "\r\nthese\n evil line\rbreaks!")
 # Add a float too:
 test_records[4][0][2].annotations["weight"] = 2.5
 
diff --git a/Tests/test_SeqRecord.py b/Tests/test_SeqRecord.py
index 71cb7a4..23393c2 100644
--- a/Tests/test_SeqRecord.py
+++ b/Tests/test_SeqRecord.py
@@ -338,6 +338,36 @@ Seq('ABCDEFGHIJKLMNOPQRSTUVWZYX', ProteinAlphabet())"""
         s = SeqRecord(MutableSeq("ACTG"))
         self.assertEqual("CAGT", str(s.reverse_complement().seq))
 
+    def test_gt_exception(self):
+        def lt():
+            SeqRecord(Seq("A")) < SeqRecord(Seq("A"))
+        self.assertRaises(NotImplementedError, lt)
+
+    def test_le_exception(self):
+        def le():
+            SeqRecord(Seq("A")) <= SeqRecord(Seq("A"))
+            self.assertRaises(NotImplementedError, le)
+
+    def test_eq_exception(self):
+        def equality():
+            SeqRecord(Seq("A")) == SeqRecord(Seq("A"))
+        self.assertRaises(NotImplementedError, equality)
+
+    def test_ne_exception(self):
+        def notequality():
+            SeqRecord(Seq("A")) != SeqRecord(Seq("A"))
+        self.assertRaises(NotImplementedError, notequality)
+
+    def test_gt_exception(self):
+        def gt():
+            SeqRecord(Seq("A")) > SeqRecord(Seq("A"))
+        self.assertRaises(NotImplementedError, gt)
+
+    def test_ge_exception(self):
+        def ge():
+            SeqRecord(Seq("A")) >= SeqRecord(Seq("A"))
+        self.assertRaises(NotImplementedError, ge)
+
 
 if __name__ == "__main__":
     runner = unittest.TextTestRunner(verbosity=2)
diff --git a/Tests/test_Seq_objs.py b/Tests/test_Seq_objs.py
index e4ca35d..c42613b 100644
--- a/Tests/test_Seq_objs.py
+++ b/Tests/test_Seq_objs.py
@@ -500,6 +500,7 @@ class StringMethodTests(unittest.TestCase):
             self.assertEqual("***RR", str(nuc.translate(table=11)))
             self.assertEqual("***RR", str(nuc.translate(table='11')))
             self.assertEqual("***RR", str(nuc.translate(table='Bacterial')))
+            self.assertEqual("**GRR", str(nuc.translate(table=25)))
             self.assertEqual("", str(nuc.translate(to_stop=True)))
             self.assertEqual("O*ORR", str(nuc.translate(table=special_table)))
             self.assertEqual("*QWRR", str(nuc.translate(table=Chilodonella_uncinata_table)))
diff --git a/Tests/test_SffIO.py b/Tests/test_SffIO.py
index 7e6912e..6edde57 100644
--- a/Tests/test_SffIO.py
+++ b/Tests/test_SffIO.py
@@ -1,12 +1,17 @@
 # Copyright 2012 by Jeff Hussmann.  All rights reserved.
-# Revisions copyright 2013 by Peter Cock.  All rights reserved.
+# Revisions copyright 2013-2016 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
 
+import sys
 import re
 import unittest
+from io import BytesIO
 
+from Bio.SeqIO.SffIO import _sff_find_roche_index, _sff_read_roche_index
+from Bio.SeqIO.SffIO import _sff_do_slow_index
+from Bio.SeqIO.SffIO import SffIterator, SffWriter, ReadRocheXmlManifest
 from Bio import SeqIO
 
 # sffinfo E3MFGYR02_random_10_reads.sff | sed -n '/>\|Run Prefix\|Region\|XY/p'
@@ -85,6 +90,224 @@ class TestUAN(unittest.TestCase):
             self.assertEqual(record.annotations["coords"], self.test_annotations[record.name]["coords"])
 
 
+class TestErrors(unittest.TestCase):
+    with open("Roche/E3MFGYR02_random_10_reads.sff", "rb") as handle:
+        good = handle.read()
+
+    def test_empty(self):
+        fh = BytesIO()
+        try:
+            records = list(SeqIO.parse(fh, "sff"))
+        except ValueError as err:
+            self.assertEqual(str(err), "Empty file.")
+        else:
+            self.assertTrue(False, "Empty file did not raise exception")
+
+    def check_bad_header(self, header, msg):
+        try:
+            records = list(SeqIO.parse(BytesIO(header), "sff"))
+        except ValueError as err:
+            if isinstance(msg, (tuple, list)):
+                self.assertTrue(str(err) in msg, "Unexpected error: %s" % err)
+            else:
+                self.assertEqual(str(err), msg)
+        else:
+            self.assertTrue(False, "Test SFF header only did not raise exception")
+
+    def test_30bytes(self):
+        self.check_bad_header(b"x" * 30,
+                              "File too small to hold a valid SFF header.")
+
+    def test_31bytes(self):
+        self.check_bad_header(b"x" * 31,
+                              ("SFF file did not start '.sff', but 'xxxx'",
+                               "SFF file did not start '.sff', but b'xxxx'"))
+
+    def test_31bytes_index_header(self):
+        self.check_bad_header(b".srt" + b"x" * 27,
+                              "Handle seems to be at SFF index block, not start")
+
+    def test_31bytes_bad_ver(self):
+        self.check_bad_header(b".sff1.00" + b"x" * 23,
+                              "Unsupported SFF version in header, 49.46.48.48")
+
+    def test_31bytes_bad_flowgram(self):
+        self.check_bad_header(b".sff\x00\x00\x00\x01" + b"x" * 23,
+                              "Flowgram format code 120 not supported")
+
+    def test_bad_index_offset(self):
+        bad = self.good[:12] + b"\x00\x00\x00\x00" + self.good[16:]
+        self.check_bad_header(bad,
+                              "Index offset 0 but index length 764")
+
+    def test_bad_index_length(self):
+        bad = self.good[:16] + b"\x00\x00\x00\x00" + self.good[20:]
+        self.check_bad_header(bad,
+                              "Index offset 16824 but index length 0")
+
+    def test_bad_index_eof(self):
+        # Semi-random edit to the index offset value,
+        bad = self.good[:13] + b"\x01" + self.good[14:]
+        self.check_bad_header(bad,
+                              "Gap of 65536 bytes after final record end 16824, "
+                              "before 82360 where index starts?")
+
+    def test_no_index(self):
+        # Does a lot of work to create a no-index SFF file
+        # (in the process checking this bit of SffWriter works)
+        records = list(SeqIO.parse(BytesIO(self.good), "sff"))
+        with BytesIO() as handle:
+            writer = SffWriter(handle, index=False)
+            count = writer.write_file(records)
+            self.assertEqual(count, len(records))
+            handle.seek(0)
+            new = list(SeqIO.parse(handle, "sff"))
+            self.assertEqual(len(records), len(new))
+            for a, b in zip(records, new):
+                self.assertEqual(a.id, b.id)
+            handle.seek(0)
+            try:
+                values = _sff_find_roche_index(handle)
+            except ValueError as err:
+                self.assertEqual(str(err), "No index present in this SFF file")
+            else:
+                self.assertTrue(False, "Test _sff_find_roche_index did not raise exception")
+
+    def test_unknown_index(self):
+        # TODO - Add SFF file with no index,
+        # self.assertEqual(str(err), "No index present in this SFF file")
+        with open("Roche/E3MFGYR02_alt_index_in_middle.sff", "rb") as handle:
+            try:
+                values = _sff_find_roche_index(handle)
+            except ValueError as err:
+                self.assertTrue(str(err) in ("Unknown magic number '.diy' in SFF index header:\n'.diy1.00'",
+                                             "Unknown magic number b'.diy' in SFF index header:\nb'.diy1.00'"))
+            else:
+                self.assertTrue(False, "Test _sff_find_roche_index did not raise exception")
+
+    def check_sff_read_roche_index(self, data, msg):
+        handle = BytesIO(data)
+        try:
+            index = list(_sff_read_roche_index(handle))
+        except ValueError as err:
+            self.assertEqual(str(err), msg)
+        else:
+            self.assertTrue(False, "_sff_read_roche_index did not raise exception")
+
+    def test_premature_end_of_index(self):
+        self.check_sff_read_roche_index(self.good[:-50],
+                                        "Premature end of file!")
+
+    def test_index_name_no_null(self):
+        self.assertEqual(self.good[17502:17503], b"\x00")
+        self.check_sff_read_roche_index(self.good[:17502] + b"x" + self.good[17503:],
+                                        "Expected a null terminator to the read name.")
+
+    def test_index_mft_version(self):
+        self.assertEqual(self.good[16824:16832], b".mft1.00")
+        self.check_sff_read_roche_index(self.good[:16828] + b"\x01\x02\x03\x04" + self.good[16832:],
+                                        "Unsupported version in .mft index header, 1.2.3.4")
+
+    def test_index_mft_data_size(self):
+        self.assertEqual(self.good[16824:16832], b".mft1.00")
+        self.check_sff_read_roche_index(self.good[:16836] + b"\x00\x00\x00\x00" + self.good[16840:],
+                                        "Problem understanding .mft index header, 764 != 8 + 8 + 548 + 0")
+
+    def test_index_lengths(self):
+        # Reduce the number of reads from 10 to 9 so index loading fails...
+        self.assertEqual(self.good[20:24], b"\x00\x00\x00\x0A")
+        self.check_sff_read_roche_index(self.good[:20] + b"\x00\x00\x00\x09" + self.good[24:],
+                                        "Problem with index length? 17568 vs 17588")
+
+    def test_no_manifest_xml(self):
+        with open("Roche/E3MFGYR02_no_manifest.sff", "rb") as handle:
+            try:
+                xml = ReadRocheXmlManifest(handle)
+            except ValueError as err:
+                self.assertEqual(str(err), "No XML manifest found")
+            else:
+                self.assertTrue(False, "ReadRocheXmlManifest did not raise exception")
+
+
+class TestIndex(unittest.TestCase):
+
+    def test_manifest(self):
+        filename = "Roche/E3MFGYR02_random_10_reads.sff"
+        with open(filename, "rb") as handle:
+            metadata = ReadRocheXmlManifest(handle)
+
+    def test_both_ways(self):
+        filename = "Roche/E3MFGYR02_random_10_reads.sff"
+        with open(filename, "rb") as handle:
+            index1 = sorted(_sff_read_roche_index(handle))
+        with open(filename, "rb") as handle:
+            index2 = sorted(_sff_do_slow_index(handle))
+        self.assertEqual(index1, index2)
+        with open(filename, "rb") as handle:
+            self.assertEqual(len(index1), len(list(SffIterator(handle))))
+        with open(filename, "rb") as handle:
+            self.assertEqual(len(index1), len(list(SffIterator(BytesIO(handle.read())))))
+
+        if sys.platform != "win32" and sys.version_info[0] < 3:
+            # Can be lazy and treat as binary...
+            with open(filename, "r") as handle:
+                self.assertEqual(len(index1), len(list(SffIterator(handle))))
+            with open(filename) as handle:
+                index2 = sorted(_sff_read_roche_index(handle))
+            self.assertEqual(index1, index2)
+            with open(filename, "r") as handle:
+                index2 = sorted(_sff_do_slow_index(handle))
+            self.assertEqual(index1, index2)
+            with open(filename, "r") as handle:
+                self.assertEqual(len(index1), len(list(SffIterator(handle))))
+            with open(filename, "r") as handle:
+                self.assertEqual(len(index1), len(list(SffIterator(BytesIO(handle.read())))))
+
+
+class TestAlternativeIndexes(unittest.TestCase):
+    filename = "Roche/E3MFGYR02_random_10_reads.sff"
+    with open(filename, "rb") as handle:
+        sff = list(SffIterator(handle))
+
+    def check_same(self, new_sff):
+        self.assertEqual(len(self.sff), len(new_sff))
+        for old, new in zip(self.sff, new_sff):
+            self.assertEqual(old.id, new.id)
+            self.assertEqual(str(old.seq), str(new.seq))
+
+    def test_alt_index_at_end(self):
+        with open("Roche/E3MFGYR02_alt_index_at_end.sff", "rb") as handle:
+            sff2 = list(SffIterator(handle))
+        self.check_same(sff2)
+
+    def test_alt_index_at_start(self):
+        with open("Roche/E3MFGYR02_alt_index_at_start.sff", "rb") as handle:
+            sff2 = list(SffIterator(handle))
+        self.check_same(sff2)
+
+    def test_alt_index_in_middle(self):
+        with open("Roche/E3MFGYR02_alt_index_in_middle.sff", "rb") as handle:
+            sff2 = list(SffIterator(handle))
+        self.check_same(sff2)
+
+    def test_index_at_start(self):
+        with open("Roche/E3MFGYR02_index_at_start.sff", "rb") as handle:
+            sff2 = list(SffIterator(handle))
+        self.check_same(sff2)
+
+    def test_index_in_middle(self):
+        with open("Roche/E3MFGYR02_index_in_middle.sff", "rb") as handle:
+            sff2 = list(SffIterator(handle))
+        self.check_same(sff2)
+
+    def test_trim(self):
+        with open(self.filename, "rb") as handle:
+            sff_trim = list(SffIterator(handle, trim=True))
+        self.assertEqual(len(self.sff), len(sff_trim))
+        for old, new in zip(self.sff, sff_trim):
+            self.assertEqual(old.id, new.id)
+
+
 class TestConcatenated(unittest.TestCase):
     def test_parses_gzipped_stream(self):
         import gzip
diff --git a/Tests/test_SwissProt.py b/Tests/test_SwissProt.py
index e9febdf..15f1435 100644
--- a/Tests/test_SwissProt.py
+++ b/Tests/test_SwissProt.py
@@ -23,9 +23,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -34,9 +33,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "N33 PROTEIN.")
         self.assertEqual(repr(seq_record.seq), "Seq('MGARGAPSRRRQAGRRLRYLPTGSFPFLLLLLLLCIQLGGGQKKKENLLAEKVE...DFE', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "N33_HUMAN")
@@ -68,9 +66,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -82,9 +79,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -103,9 +99,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -114,9 +109,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "CYSTEINE STRING PROTEIN (CSP).")
         self.assertEqual(repr(seq_record.seq), "Seq('MADQRQRSLSTSGESLYHVLGLDKNATSDDIKKSYRKLALKYHPDKNPDNPEAA...GFN', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "CSP_MOUSE")
@@ -151,9 +145,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -165,9 +158,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -186,9 +178,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -197,9 +188,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "14-3-3 PROTEIN EPSILON (MITOCHONDRIAL IMPORT STIMULATION FACTOR L SUBUNIT) (PROTEIN KINASE C INHIBITOR PROTEIN-1) (KCIP-1) (14-3-3E).")
         self.assertEqual(repr(seq_record.seq), "Seq('MDDREDLVYQAKLAEQAERYDEMVESMKKVAGMDVELTVEERNLLSVAYKNVIG...ENQ', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "143E_HUMAN")
@@ -274,9 +264,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -288,9 +277,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -309,9 +297,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -320,9 +307,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "NAPHTHALENE 1,2-DIOXYGENASE SYSTEM FERREDOXIN COMPONENT.")
         self.assertEqual(repr(seq_record.seq), "Seq('TVKWIEAVALSDILEGDVLGVTVEGKELALYEVEGEIYATDNLCTHGSARMSDG...DLS', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "NDOA_PSEPU")
@@ -371,9 +357,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -385,9 +370,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -406,9 +390,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -417,9 +400,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "NADH-UBIQUINONE OXIDOREDUCTASE CHAIN 3 (EC 1.6.5.3).")
         self.assertEqual(repr(seq_record.seq), "Seq('MNLLLTLLTNTTLALLLVFIAFWLPQLNVYAEKTSPYECGFDPMGSARLPFSMK...WAE', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "NU3M_BALPH")
@@ -449,9 +431,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -463,9 +444,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -484,9 +464,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -495,9 +474,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "TETRACENOMYCIN POLYKETIDE SYNTHESIS 8-O-METHYL TRANSFERASE TCMO (EC 2.1.1.-).")
         self.assertEqual(repr(seq_record.seq), "Seq('MTPHTHVRGPGDILQLTMAFYGSRALISAVELDLFTLLAGKPLPLGELCERAGI...KPR', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "TCMO_STRGA")
@@ -523,9 +501,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -537,9 +514,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -558,9 +534,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -569,9 +544,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "CLAUDIN-1 (SENESCENCE-ASSOCIATED EPITHELIAL MEMBRANE PROTEIN).")
         self.assertEqual(repr(seq_record.seq), "Seq('MANAGLQLLGFILAFLGWIGAIVSTALPQWRIYSYAGDNIVTAQAMYEGLWMSC...DYV', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "CLD1_HUMAN")
@@ -606,9 +580,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -620,9 +593,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -641,9 +613,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -652,9 +623,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "HLA CLASS I HISTOCOMPATIBILITY ANTIGEN, A-2 ALPHA CHAIN PRECURSOR.")
         self.assertEqual(repr(seq_record.seq), "Seq('MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFFTSVSRPGRGEPRFIAVGYVDD...CKV', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "1A02_HUMAN")
@@ -852,9 +822,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -866,9 +835,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -887,9 +855,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -898,9 +865,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "CHALCONE SYNTHASE 3 (EC 2.3.1.74) (NARINGENIN-CHALCONE SYNTHASE 3).")
         self.assertEqual(repr(seq_record.seq), "Seq('MAPAMEEIRQAQRAEGPAAVLAIGTSTPPNALYQADYPDYYFRITKSEHLTELK...GAE', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "CHS3_BROFI")
@@ -926,9 +892,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -940,9 +905,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -961,9 +925,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -972,9 +935,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "5-HYDROXYTRYPTAMINE 4 RECEPTOR (5-HT-4) (SEROTONIN RECEPTOR) (5-HT4).")
         self.assertEqual(repr(seq_record.seq), "Seq('MDKLDANVSSEEGFGSVEKVVLLTFLSTVILMAILGNLLVMVAVCWDRQLRKIK...SDT', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "5H4_HUMAN")
@@ -1042,9 +1004,7 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        records = list(SeqIO.parse(datafile, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1056,9 +1016,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1076,10 +1035,7 @@ class TestSwissProt(unittest.TestCase):
         # test the record parser
 
         datafile = os.path.join('SwissProt', filename)
-
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        seq_record = SeqIO.read(datafile, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -1088,9 +1044,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "LUTROPIN-CHORIOGONADOTROPIC HORMONE RECEPTOR PRECURSOR (LH/CG-R) (LSH-R) (LUTEINIZING HORMONE RECEPTOR).")
         self.assertEqual(repr(seq_record.seq), "Seq('MGRRVPALRQLLVLAVLLLKPSQLQSRELSGSRCPEPCDCAPDGALRCPGPRAG...LTH', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "LSHR_RAT")
@@ -1204,9 +1159,7 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        records = list(SeqIO.parse(datafile, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1218,9 +1171,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1238,10 +1190,7 @@ class TestSwissProt(unittest.TestCase):
         # test the record parser
 
         datafile = os.path.join('SwissProt', filename)
-
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        seq_record = SeqIO.read(datafile, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -1250,9 +1199,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "UBIQUITIN.")
         self.assertEqual(repr(seq_record.seq), "Seq('MQIFVKTLTGKTITLEVESSDTIDNVKTKIQDKEGIPPDQQRLIFAGKQLEDGR...GGN', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "Q9Y736")
@@ -1280,9 +1228,7 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        records = list(SeqIO.parse(datafile, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1294,9 +1240,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1314,10 +1259,7 @@ class TestSwissProt(unittest.TestCase):
         # test the record parser
 
         datafile = os.path.join('SwissProt', filename)
-
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        seq_record = SeqIO.read(datafile, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -1326,9 +1268,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "MITOCHONDRIAL 28S RIBOSOMAL PROTEIN S36 (MRP-S36).")
         self.assertEqual(repr(seq_record.seq), "Seq('MGSKMASASRVVQVVKPHTPLIRFPDRRDNPKPNVSEALRSAGLPSHSSVISQH...GPE', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "P82909")
@@ -1356,9 +1297,7 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        records = list(SeqIO.parse(datafile, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1370,9 +1309,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1390,10 +1328,7 @@ class TestSwissProt(unittest.TestCase):
         # test the record parser
 
         datafile = os.path.join('SwissProt', filename)
-
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        seq_record = SeqIO.read(datafile, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -1402,9 +1337,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "PHOTOSYSTEM II REACTION CENTER L PROTEIN (PSII 5 KDA PROTEIN).")
         self.assertEqual(repr(seq_record.seq), "Seq('TQSNPNEQNVELNRTSLYWGLLLIFVLAVLFSNYFFN', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "PSBL_ORYSA")
@@ -1523,9 +1457,7 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        records = list(SeqIO.parse(datafile, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1537,9 +1469,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
@@ -1558,9 +1489,8 @@ class TestSwissProt(unittest.TestCase):
 
         datafile = os.path.join('SwissProt', filename)
 
-        test_handle = open(datafile)
-        seq_record = SeqIO.read(test_handle, "swiss")
-        test_handle.close()
+        with open(datafile) as test_handle:
+            seq_record = SeqIO.read(test_handle, "swiss")
 
         self.assertTrue(isinstance(seq_record, SeqRecord))
 
@@ -1569,9 +1499,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(seq_record.description, "")
         self.assertEqual(repr(seq_record.seq), "Seq('MSFQAPRRLLELAGQSLLRDQALAISVLDELPRELFPRLFVEAFTSRRCEVLKV...TPC', ProteinAlphabet())")
 
-        test_handle = open(datafile)
-        record = SwissProt.read(test_handle)
-        test_handle.close()
+        with open(datafile) as test_handle:
+            record = SwissProt.read(test_handle)
 
         # test a couple of things on the record -- this is not exhaustive
         self.assertEqual(record.entry_name, "IPI00383150.2")
@@ -1592,9 +1521,8 @@ class TestSwissProt(unittest.TestCase):
         # test cases have only one record.
 
         # With the SequenceParser
-        test_handle = open(datafile)
-        records = list(SeqIO.parse(test_handle, "swiss"))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SeqIO.parse(test_handle, "swiss"))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SeqRecord))
@@ -1606,9 +1534,8 @@ class TestSwissProt(unittest.TestCase):
         self.assertEqual(records[0].id, seq_record.id)
 
         # With the RecordParser
-        test_handle = open(datafile)
-        records = list(SwissProt.parse(test_handle))
-        test_handle.close()
+        with open(datafile) as test_handle:
+            records = list(SwissProt.parse(test_handle))
 
         self.assertEqual(len(records), 1)
         self.assertTrue(isinstance(records[0], SwissProt.Record))
diff --git a/Tests/test_TogoWS.py b/Tests/test_TogoWS.py
index 1b7453d..52316ae 100644
--- a/Tests/test_TogoWS.py
+++ b/Tests/test_TogoWS.py
@@ -254,7 +254,7 @@ class TogoEntry(unittest.TestCase):
         handle.close()
         self.assertEqual(data, "X52960")
 
-    def test_nucleotide_genbank_accession(self):
+    def test_nucleotide_genbank_version(self):
         """Bio.TogoWS.entry("nucleotide", "X52960", field="version")"""
         handle = TogoWS.entry("nucleotide", "X52960", field="version")
         data = handle.read().strip()  # ignore trailing \n
@@ -457,7 +457,7 @@ class TogoSearch(unittest.TestCase):
         """
         self.check("uniprot", "terminal+lung+cancer", limit=150)
 
-    def check(self, database, search_term, expected_matches=[], limit=None):
+    def check(self, database, search_term, expected_matches=(), limit=None):
         if expected_matches and limit:
             raise ValueError("Bad test - TogoWS makes no promises about order")
         search_count = TogoWS.search_count(database, search_term)
diff --git a/Tests/test_Tutorial.py b/Tests/test_Tutorial.py
index f0acf4b..97963b5 100644
--- a/Tests/test_Tutorial.py
+++ b/Tests/test_Tutorial.py
@@ -1,4 +1,4 @@
-# Copyright 2011-2013 by Peter Cock.  All rights reserved.
+# Copyright 2011-2016 by Peter Cock.  All rights reserved.
 # This code is part of the Biopython distribution and governed by its
 # license.  Please see the LICENSE file that should have been included
 # as part of this package.
@@ -27,18 +27,30 @@ if sys.version_info[0] >= 3:
                                  'Two plus two is 4\n', "example2") == \
                                  '>>> print("Two plus two is", 2+2)\nTwo plus two is 4\n'
 
+# Cache this to restore the cwd at the end of the tests
+original_path = os.path.abspath(".")
 
-tutorial = os.path.join(os.path.dirname(sys.argv[0]), "../Doc/Tutorial.tex")
+if os.path.basename(sys.argv[0]) == "test_Tutorial.py":
+    # sys.argv[0] will be (relative) path to test_Turorial.py - use this to allow, e.g.
+    # [base]$ python Tests/test_Tutorial.py
+    # [Tests/]$ python test_Tutorial.py
+    tutorial_base = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "../Doc/"))
+    tutorial = os.path.join(tutorial_base, "Tutorial.tex")
+else:
+    # Probably called via run_tests.py so current directory should (now) be Tests/
+    # but may have been changed by run_tests.py so can't infer from sys.argv[0] with e.g.
+    # [base]$ python Tests/run_tests.py test_Tutorial
+    tutorial_base = os.path.abspath("../Doc/")
+    tutorial = os.path.join(tutorial_base, "Tutorial.tex")
 if not os.path.isfile(tutorial):
     from Bio import MissingExternalDependencyError
     raise MissingExternalDependencyError("Could not find ../Doc/Tutorial.tex file")
+
+# Build a list of all the Tutorial LaTeX files:
 files = [tutorial]
-for latex in os.listdir("../Doc/Tutorial/"):
+for latex in os.listdir(os.path.join(tutorial_base, "Tutorial/")):
     if latex.startswith("chapter_") and latex.endswith(".tex"):
-        files.append(os.path.join(os.path.dirname(sys.argv[0]), "../Doc/Tutorial", latex))
-
-tutorial_base = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "../Doc/"))
-original_path = os.path.abspath(".")
+        files.append(os.path.join(tutorial_base, "Tutorial", latex))
 
 
 def _extract(handle):
@@ -67,38 +79,37 @@ def extract_doctests(latex_filename):
     This is a generator, yielding one tuple per doctest.
     """
     base_name = os.path.splitext(os.path.basename(latex_filename))[0]
-    handle = open(latex_filename, _universal_read_mode)
-    line_number = 0
-    in_test = False
-    lines = []
-    name = None
-    while True:
-        line = handle.readline()
-        line_number += 1
-        if not line:
-            # End of file
-            break
-        elif line.startswith("%cont-doctest"):
-            x = _extract(handle)
-            lines.extend(x)
-            line_number += len(x) + 2
-        elif line.startswith("%doctest"):
-            if lines:
-                if not lines[0].startswith(">>> "):
-                    raise ValueError("Should start '>>> ' not %r" % lines[0])
-                yield name, "".join(lines), folder, deps
-                lines = []
-            deps = [x.strip() for x in line.split()[1:]]
-            if deps:
-                folder = deps[0]
-                deps = deps[1:]
-            else:
-                folder = ""
-            name = "test_%s_line_%05i" % (base_name, line_number)
-            x = _extract(handle)
-            lines.extend(x)
-            line_number += len(x) + 2
-    handle.close()
+    with open(latex_filename, _universal_read_mode) as handle:
+        line_number = 0
+        in_test = False
+        lines = []
+        name = None
+        while True:
+            line = handle.readline()
+            line_number += 1
+            if not line:
+                # End of file
+                break
+            elif line.startswith("%cont-doctest"):
+                x = _extract(handle)
+                lines.extend(x)
+                line_number += len(x) + 2
+            elif line.startswith("%doctest"):
+                if lines:
+                    if not lines[0].startswith(">>> "):
+                        raise ValueError("Should start '>>> ' not %r" % lines[0])
+                    yield name, "".join(lines), folder, deps
+                    lines = []
+                deps = [x.strip() for x in line.split()[1:]]
+                if deps:
+                    folder = deps[0]
+                    deps = deps[1:]
+                else:
+                    folder = ""
+                name = "test_%s_line_%05i" % (base_name, line_number)
+                x = _extract(handle)
+                lines.extend(x)
+                line_number += len(x) + 2
     if lines:
         if not lines[0].startswith(">>> "):
             raise ValueError("Should start '>>> ' not %r" % lines[0])
@@ -188,7 +199,6 @@ if __name__ == "__main__":
     print("Running Tutorial doctests...")
     import doctest
     tests = doctest.testmod()
-    if tests[0]:
-        # Note on Python 2.5+ can use tests.failed rather than tests[0]
+    if tests.failed:
         raise RuntimeError("%i/%i tests failed" % tests)
     print("Tests done")
diff --git a/Tests/test_XXmotif_tool.py b/Tests/test_XXmotif_tool.py
index 50ae2e7..521498d 100644
--- a/Tests/test_XXmotif_tool.py
+++ b/Tests/test_XXmotif_tool.py
@@ -12,7 +12,7 @@ import unittest
 from Bio import MissingExternalDependencyError
 from Bio import SeqIO
 from Bio.Application import ApplicationError
-from Bio.Motif.Applications import XXmotifCommandline
+from Bio.motifs.applications import XXmotifCommandline
 
 
 # Try to avoid problems when the OS is in another language
diff --git a/Tests/test_codonalign.py b/Tests/test_codonalign.py
index 51cf54b..ebdf06a 100644
--- a/Tests/test_codonalign.py
+++ b/Tests/test_codonalign.py
@@ -5,13 +5,11 @@
 
 """Unit tests for the Bio.codonalign modules.
 """
-import sys
 import warnings
 import tempfile
-import platform
 import unittest
 
-from Bio import BiopythonExperimentalWarning
+from Bio import BiopythonWarning, BiopythonExperimentalWarning
 from Bio import SeqIO
 from Bio import AlignIO
 from Bio.Seq import Seq
@@ -163,7 +161,9 @@ class Test_dn_ds(unittest.TestCase):
         prot = AlignIO.read(TEST_ALIGN_FILE6[0][1], 'clustal', alphabet=IUPAC.protein)
         with open(TEST_ALIGN_FILE6[0][2]) as handle:
             id_corr = dict((i.split()[0], i.split()[1]) for i in handle)
-        aln = codonalign.build(prot, nucl, corr_dict=id_corr, alphabet=codonalign.default_codon_alphabet)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', BiopythonWarning)
+            aln = codonalign.build(prot, nucl, corr_dict=id_corr, alphabet=codonalign.default_codon_alphabet)
         self.aln = aln
 
     def test_dn_ds(self):
diff --git a/Tests/test_motifs.py b/Tests/test_motifs.py
index da78b62..f8e49ed 100644
--- a/Tests/test_motifs.py
+++ b/Tests/test_motifs.py
@@ -6,10 +6,12 @@
 
 import os
 import unittest
+import math
 
+from Bio.Alphabet import generic_dna
+from Bio.Alphabet import Gapped
 from Bio.Alphabet import IUPAC
 from Bio import motifs
-from Bio.motifs.matrix import _isnan
 from Bio.Seq import Seq
 
 
@@ -1686,7 +1688,20 @@ class MotifTestPWM(unittest.TestCase):
         self.assertAlmostEqual(result[3], -38.04542542, places=5)
         self.assertAlmostEqual(result[4], -20.3014183, places=5)
         self.assertAlmostEqual(result[5], -25.18009186, places=5)
-        self.assertTrue(_isnan(result[6]), "Expected nan, not %r" % result[6])
+        self.assertTrue(math.isnan(result[6]), "Expected nan, not %r" % result[6])
+
+    def test_mixed_alphabets(self):
+        """Test creating motif with mixed alphabets."""
+        # TODO - Can we support this?
+        seqs = [Seq("TACAA", IUPAC.unambiguous_dna),
+                Seq("TACGC", IUPAC.ambiguous_dna),
+                Seq("TACAC", IUPAC.extended_dna),
+                Seq("TACCC", Gapped(IUPAC.unambiguous_dna)),
+                Seq("AACCC", IUPAC.unambiguous_dna),
+                Seq("AATGC", IUPAC.unambiguous_dna),
+                Seq("AATGC", generic_dna)]
+        # ValueError: Alphabets are inconsistent
+        self.assertRaises(ValueError, motifs.create, seqs)
 
 
 if __name__ == "__main__":
diff --git a/Tests/test_motifs_online.py b/Tests/test_motifs_online.py
index 9541fb8..0ed259a 100644
--- a/Tests/test_motifs_online.py
+++ b/Tests/test_motifs_online.py
@@ -8,24 +8,46 @@
 import os
 import unittest
 
-import requires_internet
-requires_internet.check()
-
 # We want to test these:
 from Bio import motifs
+from Bio.Alphabet.IUPAC import extended_dna, unambiguous_rna
+from Bio.Alphabet.IUPAC import extended_protein
 
 # In order to check any sequences returned
 from Bio.Seq import Seq
 
+import requires_internet
+requires_internet.check()
 
-class TestMotifWeblogo(unittest.TestCase):
-    def setUp(self):
-        self.m = motifs.create([
-            Seq("TACAA"), Seq("TACGC"), Seq("TACAC"), Seq("TACCC"),
-            Seq("AACCC"), Seq("AATGC"), Seq("AATGC")])
 
-    def test_weblogo(self):
-        self.m.weblogo(os.devnull)
+class TestotifWeblogo(unittest.TestCase):
+    """Tests Bio.motifs online code."""
+
+    def check(self, seqs_as_strs, alpha):
+        # Using Seq objects and passing exactly the same alphabet:
+        m = motifs.create([Seq(s, alpha) for s in seqs_as_strs], alpha)
+        m.weblogo(os.devnull)
+        # Using Seq objects but not passing alphabet:
+        m = motifs.create([Seq(s, alpha) for s in seqs_as_strs])
+        m.weblogo(os.devnull)
+        # Using strings and passing alphabet:
+        m = motifs.create(seqs_as_strs, alpha)
+        m.weblogo(os.devnull)
+
+    def test_dna(self):
+        """Test Bio.Motif.weblogo with a DNA sequence."""
+        self.check(["TACAA", "TACGC", "TACAC", "TACCC",
+                    "AACCC", "AATGC", "AATGC"], extended_dna)
+
+    def test_rna(self):
+        """Test Bio.Motif.weblogo with an RNA sequence."""
+        self.check(["UACAA", "UACGC", "UACAC", "UACCC",
+                    "AACCC", "AAUGC", "AAUGC"], unambiguous_rna)
+
+    def test_protein(self):
+        """Test Bio.Motif.weblogo with a protein sequence."""
+        self.check(["ACDEG", "AYCRN", "HYLID", "AYHEL",
+                    "ACDEH", "AYYRN", "HYIID"], extended_protein)
 
 
 if __name__ == "__main__":
diff --git a/Tests/test_phenotype.py b/Tests/test_phenotype.py
new file mode 100644
index 0000000..50ba87c
--- /dev/null
+++ b/Tests/test_phenotype.py
@@ -0,0 +1,304 @@
+# Copyright 2014-2016 Marco Galardini.  All rights reserved.
+# Adapted from test_Mymodule.py by Jeff Chang
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+
+try:
+    import numpy
+except ImportError:
+    from Bio import MissingExternalDependencyError
+    raise MissingExternalDependencyError(
+        "Install NumPy if you want to use Bio.phenotype.")
+
+import json
+import unittest
+
+from Bio._py3k import StringIO
+
+from Bio import BiopythonExperimentalWarning
+
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter('ignore', BiopythonExperimentalWarning)
+    from Bio import phenotype
+
+# Example plate files
+SMALL_JSON_PLATE = 'phenotype/SmallPlate.json'
+SMALL_JSON_PLATE_2 = 'phenotype/SmallPlate_2.json'
+JSON_PLATE = 'phenotype/Plate.json'
+JSON_PLATE_2 = 'phenotype/Plate_2.json'
+JSON_PLATE_3 = 'phenotype/Plate_3.json'
+JSON_BAD = 'phenotype/BadPlate.json'
+
+SMALL_CSV_PLATES = 'phenotype/SmallPlates.csv'
+CSV_PLATES = 'phenotype/Plates.csv'
+
+
+class TestPhenoMicro(unittest.TestCase):
+
+    def test_phenotype_IO_errors(self):
+        '''Test bad arguments to phenotype IO methods'''
+        self.assertRaises(ValueError, phenotype.read, CSV_PLATES, 'pm-csv')
+        self.assertRaises(ValueError, phenotype.read, CSV_PLATES, 'pm-json')
+        self.assertRaises(
+            ValueError,
+            phenotype.read,
+            CSV_PLATES,
+            'pm-noformat')
+        self.assertRaises(ValueError, phenotype.read, CSV_PLATES, 'PM-CSV')
+        self.assertRaises(TypeError, phenotype.read, CSV_PLATES, 1)
+        self.assertRaises(KeyError, phenotype.read, JSON_BAD, 'pm-json')
+
+    def test_phenotype_IO(self):
+        '''Test basic functionalities of phenotype IO methods'''
+        p1 = phenotype.read(SMALL_JSON_PLATE, 'pm-json')
+        p2 = next(phenotype.parse(SMALL_CSV_PLATES, 'pm-csv'))
+
+        handle = StringIO()
+
+        c = phenotype.write([p1, p2], handle, 'pm-json')
+        self.assertEqual(c, 2)
+
+        handle.flush()
+        handle.seek(0)
+        # Now ready to read back from the handle...
+        try:
+            records = list(phenotype.parse(handle, 'pm-json'))
+        except ValueError as e:
+            # This is BAD.  We can't read our own output.
+            # I want to see the output when called from the test harness,
+            # run_tests.py (which can be funny about new lines on Windows)
+            handle.seek(0)
+            raise ValueError("%s\n\n%s\n\n%s"
+                             % (str(e), repr(handle.read()), repr(records)))
+
+        self.assertEqual(p1, records[0])
+
+        handle.close()
+        handle = StringIO()
+        self.assertRaises(TypeError, phenotype.write, p1, handle, 1)
+        self.assertRaises(ValueError, phenotype.write, p1, handle, 'PM-JSON')
+        self.assertRaises(ValueError, phenotype.write, p1, handle, 'pm-csv')
+        handle.close()
+
+    def test_PlateRecord_errors(self):
+        '''Test bad arguments with PlateRecord objects'''
+        self.assertRaises(ValueError,
+                          phenotype.phen_micro.PlateRecord, 'test', [1, 2, 3])
+        self.assertRaises(TypeError,
+                          phenotype.phen_micro.PlateRecord, 'test', 1)
+
+    def test_PlateRecord(self):
+        '''Test basic functionalities of PlateRecord objects'''
+        with open(SMALL_JSON_PLATE) as handle:
+            j = json.load(handle)
+
+        p = phenotype.phen_micro.PlateRecord(j['csv_data']['Plate Type'])
+
+        times = j['measurements']['Hour']
+        for k in j['measurements']:
+            if k == 'Hour':
+                continue
+            p[k] = phenotype.phen_micro.WellRecord(k,
+                                                   signals=dict([(times[i], j['measurements'][k][i])
+                                                                 for i in range(len(times))]))
+
+        del j['measurements']
+        p.qualifiers = j
+
+        self.assertEqual(p.id, 'PM01')
+        self.assertEqual(len(p), 24)
+        self.assertEqual(p.qualifiers, j)
+        self.assertRaises(ValueError, p._is_well, 'a')
+        self.assertEqual(p['A01'].id, 'A01')
+        self.assertRaises(KeyError, p.__getitem__, 'test')
+        self.assertEqual(len(p[1]), 12)
+        self.assertEqual(len(p[1:2:2]), 12)
+        self.assertEqual(p[1, 2], p['B03'])
+        self.assertEqual(len(p[:, 1]), 2)
+        self.assertEqual(len(p[:, 1:4:2]), 4)
+        self.assertRaises(TypeError, p.__getitem__, 1, 2, 3)
+        self.assertRaises(IndexError, p.__getitem__, 13)
+        self.assertRaises(ValueError, p.__setitem__, 'A02', p['A01'])
+        self.assertRaises(ValueError, p.__setitem__, 'A02', 'a')
+        p['A02'] = p['A02']
+        for w in p:
+            pass
+        self.assertEqual('A01' in p, True)
+        self.assertEqual('test' in p, False)
+        self.assertRaises(ValueError, next, p.get_row('test'))
+        self.assertEqual(next(p.get_row('A')), p['A01'])
+        self.assertRaises(ValueError, next, p.get_column('test'))
+        self.assertEqual(next(p.get_column('12')), p['A12'])
+        self.assertEqual(next(p.get_column('1')), p['A01'])
+        self.assertRaises(ValueError, p.subtract_control, 'A121')
+        self.assertRaises(ValueError, p.subtract_control, wells=['A121'])
+        p2 = p.subtract_control()
+        self.assertEqual(p2.id, p.id)
+        self.assertEqual(p2['A02'], p['A02'] - p['A01'])
+        self.assertEqual(repr(p), "PlateRecord('WellRecord['A01'], WellRecord" +
+                         "['A02'], WellRecord['A03'], ..., WellRecord['B12']')")
+        self.assertEqual(str(p), "Plate ID: PM01\nWell: 24\nRows: 2\nColumns: " +
+                         "12\nPlateRecord('WellRecord['A01'], WellRecord['A02'], WellRecord" +
+                         "['A03'], ..., WellRecord['B12']')")
+
+        with open(SMALL_JSON_PLATE_2) as handle:
+            j = json.load(handle)
+
+        p1 = phenotype.phen_micro.PlateRecord(j['csv_data']['Plate Type'])
+
+        times = j['measurements']['Hour']
+        for k in j['measurements']:
+            if k == 'Hour':
+                continue
+            p1[k] = phenotype.phen_micro.WellRecord(k,
+                                                    signals=dict([(times[i], j['measurements'][k][i])
+                                                                  for i in range(len(times))]))
+
+        del j['measurements']
+        p1.qualifiers = j
+
+        self.assertRaises(TypeError, p.__add__, 'a')
+        self.assertRaises(TypeError, p.__sub__, 'a')
+
+        p3 = p + p1
+        self.assertEqual(p3['A02'], p['A02'] + p1['A02'])
+
+        p3 = p - p1
+        self.assertEqual(p3['A02'], p['A02'] - p1['A02'])
+
+        del p['A02']
+        self.assertRaises(ValueError, p.__add__, p1)
+        self.assertRaises(ValueError, p.__sub__, p1)
+
+    def test_bad_fit_args(self):
+        """Test error handling of the fit method."""
+        with open(JSON_PLATE) as handle:
+            p = json.load(handle)
+
+        times = p['measurements']['Hour']
+        w = phenotype.phen_micro.WellRecord('A10',
+                                            signals=dict([(times[i], p['measurements']['A10'][i])
+                                                          for i in range(len(times))]))
+
+        self.assertRaises(ValueError, w.fit, "wibble")
+        self.assertRaises(ValueError, w.fit, ["wibble"])
+        self.assertRaises(ValueError, w.fit, ("logistic", "wibble"))
+        self.assertRaises(ValueError, w.fit, ("wibble", "logistic"))
+        self.assertRaises(ValueError, w.fit, "logistic")  # should be a list/tuple!
+
+    def test_WellRecord(self):
+        '''Test basic functionalities of WellRecord objects'''
+        with open(JSON_PLATE) as handle:
+            p = json.load(handle)
+
+        times = p['measurements']['Hour']
+        w = phenotype.phen_micro.WellRecord('A10',
+                                            signals=dict([(times[i], p['measurements']['A10'][i])
+                                                          for i in range(len(times))]))
+
+        w1 = phenotype.phen_micro.WellRecord('H12',
+                                             signals=dict([(times[i], p['measurements']['H12'][i])
+                                                           for i in range(len(times))]))
+
+        # self.assertIsInstance(w.plate,
+        #                       phenotype.phen_micro.PlateRecord)
+        self.assertTrue(isinstance(w.plate, phenotype.phen_micro.PlateRecord))
+        self.assertEqual(w.id, 'A10')
+        self.assertEqual(len(w), len(times))
+        self.assertEqual(len(w), 384)
+        self.assertEqual(max(w), (95.75, 217.0))
+        self.assertEqual(min(w), (0.0, 37.0))
+        self.assertEqual(max(w, key=lambda x: x[1]),
+                         (16.75, 313.0))
+        self.assertEqual(min(w, key=lambda x: x[1]),
+                         (0.25, 29.0))
+        self.assertEqual(len(w[:]), 96)
+        self.assertEqual(w[1], 29.)
+        self.assertEqual(w[12], 272.)
+        self.assertEqual(w[1:5], [29., 35., 39., 43.])
+        self.assertRaises(ValueError, w.__getitem__, 'a')
+        self.assertAlmostEqual(w[1:2:.25][0], 29.)
+        self.assertAlmostEqual(w[1.3567], 33.7196)
+        self.assertEqual(w.get_raw()[0], (0.0, 37.0))
+        self.assertEqual(w.get_raw()[-1], (95.75, 217.0))
+        self.assertEqual(w.get_times()[0], 0.0)
+        self.assertEqual(w.get_times()[-1], 95.75)
+        self.assertEqual(w.get_signals()[0], 37.0)
+        self.assertEqual(w.get_signals()[-1], 217.0)
+        self.assertEqual(repr(w),
+                         "WellRecord('(0.0, 37.0), (0.25, 29.0), (0.5, 32.0)," +
+                         " (0.75, 30.0), (1.0, 29.0), ..., (95.75, 217.0)')")
+        self.assertEqual(str(w),
+                         "Well ID: A10\nTime points: 384\nMinum signal 0.25 at " +
+                         "time 29.00\nMaximum signal 16.75 at time " +
+                         "313.00\nWellRecord('(0.0, 37.0), (0.25, 29.0), " +
+                         "(0.5, 32.0), (0.75, 30.0), " +
+                         "(1.0, 29.0), ..., (95.75, 217.0)')")
+
+        w.fit(None)
+        self.assertEqual(w.area, None)
+        self.assertEqual(w.model, None)
+        self.assertEqual(w.lag, None)
+        self.assertEqual(w.plateau, None)
+        self.assertEqual(w.slope, None)
+        self.assertEqual(w.v, None)
+        self.assertEqual(w.y0, None)
+        self.assertEqual(w.max, 313.0)
+        self.assertEqual(w.min, 29.0)
+        self.assertEqual(w.average_height, 217.82552083333334)
+
+        self.assertRaises(TypeError, w.__add__, 'a')
+
+        w2 = w + w1
+        self.assertEqual(w2.id, 'A10')
+        self.assertEqual(len(w2), len(times))
+        self.assertEqual(len(w2), 384)
+        self.assertEqual(max(w2), (95.75, 327.0))
+        self.assertEqual(min(w2), (0.0, 63.0))
+        self.assertEqual(max(w2, key=lambda x: x[1]),
+                         (18.25, 357.0))
+        self.assertEqual(min(w2, key=lambda x: x[1]),
+                         (0.25, 55.0))
+        self.assertEqual(w2[1], 71.)
+        self.assertEqual(w2[12], 316.)
+        self.assertEqual(w2[1:5], [71.0, 88.0, 94.0, 94.0])
+        self.assertAlmostEqual(w2[1:2:.25][0], 71.0)
+        self.assertAlmostEqual(w2[1.3567], 77.7196)
+        self.assertEqual(w2.get_raw()[0], (0.0, 63.0))
+        self.assertEqual(w2.get_raw()[-1], (95.75, 327.0))
+        self.assertEqual(w2.get_times()[0], 0.0)
+        self.assertEqual(w2.get_times()[-1], 95.75)
+        self.assertEqual(w2.get_signals()[0], 63.0)
+        self.assertEqual(w2.get_signals()[-1], 327.0)
+
+        self.assertRaises(TypeError, w.__sub__, 'a')
+
+        w2 = w - w1
+        self.assertEqual(w2.id, 'A10')
+        self.assertEqual(len(w2), len(times))
+        self.assertEqual(len(w2), 384)
+        self.assertEqual(max(w2), (95.75, 107.0))
+        self.assertEqual(min(w2), (0.0, 11.0))
+        self.assertEqual(max(w2, key=lambda x: x[1]),
+                         (15.75, 274.0))
+        self.assertEqual(min(w2, key=lambda x: x[1]),
+                         (3.25, -20.0))
+        self.assertEqual(w2[1], -13.)
+        self.assertEqual(w2[12], 228.)
+        self.assertEqual(w2[1:5], [-13.0, -18.0, -16.0, -8.0])
+        self.assertAlmostEqual(w2[1:2:.25][0], -13.0)
+        self.assertAlmostEqual(w2[1.3567], -10.2804)
+        self.assertEqual(w2.get_raw()[0], (0.0, 11.0))
+        self.assertEqual(w2.get_raw()[-1], (95.75, 107.0))
+        self.assertEqual(w2.get_times()[0], 0.0)
+        self.assertEqual(w2.get_times()[-1], 95.75)
+        self.assertEqual(w2.get_signals()[0], 11.0)
+        self.assertEqual(w2.get_signals()[-1], 107.0)
+
+        w[1] = 1
+
+if __name__ == "__main__":
+    runner = unittest.TextTestRunner(verbosity=2)
+    unittest.main(testRunner=runner)
diff --git a/Tests/test_phenotype_fit.py b/Tests/test_phenotype_fit.py
new file mode 100644
index 0000000..a010976
--- /dev/null
+++ b/Tests/test_phenotype_fit.py
@@ -0,0 +1,63 @@
+# Copyright 2014-2016 Marco Galardini.  All rights reserved.
+# Adapted from test_Mymodule.py by Jeff Chang
+# This code is part of the Biopython distribution and governed by its
+# license.  Please see the LICENSE file that should have been included
+# as part of this package.
+
+try:
+    import numpy
+except ImportError:
+    from Bio import MissingExternalDependencyError
+    raise MissingExternalDependencyError(
+        "Install NumPy if you want to use Bio.phenotype.")
+try:
+    import scipy
+    from scipy.optimize import OptimizeWarning
+except ImportError:
+    from Bio import MissingExternalDependencyError
+    raise MissingExternalDependencyError(
+        "Install SciPy if you want to use Bio.phenotype fit functionality.")
+
+import json
+import unittest
+
+from Bio import BiopythonExperimentalWarning
+
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter('ignore', BiopythonExperimentalWarning)
+    from Bio import phenotype
+
+# Example plate files
+JSON_PLATE = 'phenotype/Plate.json'
+
+
+class TestPhenoMicro(unittest.TestCase):
+
+    def test_WellRecord(self):
+        '''Test basic functionalities of WellRecord objects'''
+        with open(JSON_PLATE) as handle:
+            p = json.load(handle)
+
+        times = p['measurements']['Hour']
+        w = phenotype.phen_micro.WellRecord('A10',
+                                            signals=dict([(times[i], p['measurements']['A10'][i])
+                                                          for i in range(len(times))]))
+
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', OptimizeWarning)
+            w.fit()
+        self.assertAlmostEqual(w.area, 20879.5)
+        self.assertEqual(w.model, 'gompertz')
+        self.assertAlmostEqual(w.lag, 6.0425868725090357, places=5)
+        self.assertAlmostEqual(w.plateau, 188.51404344898586, places=5)
+        self.assertAlmostEqual(w.slope, 48.190618284831132, places=5)
+        self.assertAlmostEqual(w.v, 0.10000000000000001, places=5)
+        self.assertAlmostEqual(w.y0, 45.879770069807989, places=5)
+        self.assertEqual(w.max, 313.0)
+        self.assertEqual(w.min, 29.0)
+        self.assertEqual(w.average_height, 217.82552083333334)
+
+if __name__ == "__main__":
+    runner = unittest.TextTestRunner(verbosity=2)
+    unittest.main(testRunner=runner)
diff --git a/Tests/test_phyml_tool.py b/Tests/test_phyml_tool.py
index 940a64b..8894697 100644
--- a/Tests/test_phyml_tool.py
+++ b/Tests/test_phyml_tool.py
@@ -17,14 +17,19 @@ from Bio import MissingExternalDependencyError
 os.environ['LANG'] = 'C'
 
 phyml_exe = None
-if sys.platform == "win32":
-    raise MissingExternalDependencyError(
-        "Testing PhyML on Windows not supported yet")
-else:
-    from Bio._py3k import getoutput
-    output = getoutput("phyml --version")
+exe_name = "PhyML-3.1_win32.exe" if sys.platform == "win32" else "phyml"
+from Bio._py3k import getoutput
+try:
+    output = getoutput(exe_name + " --version")
     if "not found" not in output and "20" in output:
-        phyml_exe = "phyml"
+        phyml_exe = exe_name
+except OSError:
+    # TODO: Use FileNotFoundError once we drop Python 2
+    # Python 2.6 or 2.7 on Windows XP:
+    # WindowsError: [Error 2] The system cannot find the file specified
+    # Python 3.3 or 3.4 on Windows XP:
+    # FileNotFoundError: [WinError 2] The system cannot find the file specified
+    pass
 
 if not phyml_exe:
     raise MissingExternalDependencyError(
diff --git a/Tests/test_prodoc.py b/Tests/test_prodoc.py
index 7d3dbcb..5985b51 100644
--- a/Tests/test_prodoc.py
+++ b/Tests/test_prodoc.py
@@ -14,9 +14,8 @@ class TestProdocRead(unittest.TestCase):
     def test_read_pdoc00100(self):
         """Reading Prodoc record PDOC00100"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00100.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00100")
         self.assertEqual(len(record.prosite_refs), 4)
@@ -173,9 +172,8 @@ PubMed=2165531""")
     def test_read_pdoc00113(self):
         """Reading Prodoc record PDOC00113"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00113.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00113")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -244,9 +242,8 @@ PubMed=1654502""")
     def test_read_pdoc00144(self):
         """Reading Prodoc record PDOC00144"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00144.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00144")
         self.assertEqual(len(record.prosite_refs), 2)
@@ -304,9 +301,8 @@ PubMed=3136164""")
     def test_read_pdoc00149(self):
         "Reading Prodoc record PDOC00149"
         filename = os.path.join('Prosite', 'Doc', 'pdoc00149.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00149")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -384,9 +380,8 @@ PubMed=8236444""")
     def test_read_pdoc00340(self):
         "Reading Prodoc record PDOC00340"
         filename = os.path.join('Prosite', 'Doc', 'pdoc00340.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00340")
         self.assertEqual(len(record.prosite_refs), 3)
@@ -487,9 +482,8 @@ PubMed=2203335""")
     def test_read_pdoc00424(self):
         """Reading Prodoc record PDOC00424"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00424.txt',)
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00424")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -568,9 +562,8 @@ PubMed=7961661""")
     def test_read_pdoc00472(self):
         """Reading Prodoc record PDOC00472"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00472.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00472")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -662,9 +655,8 @@ PubMed=1584806""")
     def test_read_pdoc00640(self):
         """Reading Prodoc record PDOC00640"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00640.txt',)
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00640")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -757,9 +749,8 @@ PubMed=8805535""")
     def test_read_pdoc00787(self):
         """Reading Prodoc record PDOC00787"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00787.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00787")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -820,9 +811,8 @@ PubMed=7624375""")
     def test_read_pdoc0933(self):
         """Reading Prodoc record PDOC00933"""
         filename = os.path.join('Prosite', 'Doc', 'pdoc00933.txt')
-        handle = open(filename)
-        record = Prodoc.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prodoc.read(handle)
 
         self.assertEqual(record.accession, "PDOC00933")
         self.assertEqual(len(record.prosite_refs), 1)
@@ -892,14 +882,14 @@ class TestProdocParse(unittest.TestCase):
         """Parsing an excerpt of prosite.doc"""
         filename = os.path.join('Prosite', 'Doc',
                                 'prosite.excerpt.doc')
-        handle = open(filename)
-        records = Prodoc.parse(handle)
-
-        # Testing the first parsed record
-        record = next(records)
-        self.assertEqual(record.accession, "PDOC00000")
-        self.assertEqual(len(record.prosite_refs), 0)
-        self.assertEqual(record.text, """\
+        with open(filename) as handle:
+            records = Prodoc.parse(handle)
+
+            # Testing the first parsed record
+            record = next(records)
+            self.assertEqual(record.accession, "PDOC00000")
+            self.assertEqual(len(record.prosite_refs), 0)
+            self.assertEqual(record.text, """\
 **********************************
 *** PROSITE documentation file ***
 **********************************
@@ -957,13 +947,13 @@ Acknowledgements:
 
 """)
 
-        # Testing the second parsed record
-        record = next(records)
-        self.assertEqual(record.accession, "PDOC00001")
-        self.assertEqual(len(record.prosite_refs), 1)
-        self.assertEqual(record.prosite_refs[0],
-                         ("PS00001", "ASN_GLYCOSYLATION"))
-        self.assertEqual(record.text, """\
+            # Testing the second parsed record
+            record = next(records)
+            self.assertEqual(record.accession, "PDOC00001")
+            self.assertEqual(len(record.prosite_refs), 1)
+            self.assertEqual(record.prosite_refs[0],
+                             ("PS00001", "ASN_GLYCOSYLATION"))
+            self.assertEqual(record.text, """\
 ************************
 * N-glycosylation site *
 ************************
@@ -987,52 +977,52 @@ of such a non-standard site is found in the plasma protein C [5].
 -Last update: May 1991 / Text revised.
 
 """)
-        self.assertEqual(record.references[0].number, "1")
-        self.assertEqual(record.references[0].authors,
-                         "Marshall R.D.")
-        self.assertEqual(record.references[0].citation, """\
+            self.assertEqual(record.references[0].number, "1")
+            self.assertEqual(record.references[0].authors,
+                             "Marshall R.D.")
+            self.assertEqual(record.references[0].citation, """\
 "Glycoproteins."
 Annu. Rev. Biochem. 41:673-702(1972).
 PubMed=4563441; DOI=10.1146/annurev.bi.41.070172.003325""")
-        self.assertEqual(record.references[1].number, "2")
-        self.assertEqual(record.references[1].authors,
-                         "Pless D.D., Lennarz W.J.")
-        self.assertEqual(record.references[1].citation, """\
+            self.assertEqual(record.references[1].number, "2")
+            self.assertEqual(record.references[1].authors,
+                             "Pless D.D., Lennarz W.J.")
+            self.assertEqual(record.references[1].citation, """\
 "Enzymatic conversion of proteins to glycoproteins."
 Proc. Natl. Acad. Sci. U.S.A. 74:134-138(1977).
 PubMed=264667""")
-        self.assertEqual(record.references[2].number, "3")
-        self.assertEqual(record.references[2].authors, "Bause E.")
-        self.assertEqual(record.references[2].citation, """\
+            self.assertEqual(record.references[2].number, "3")
+            self.assertEqual(record.references[2].authors, "Bause E.")
+            self.assertEqual(record.references[2].citation, """\
 "Structural requirements of N-glycosylation of proteins. Studies with
 proline peptides as conformational probes."
 Biochem. J. 209:331-336(1983).
 PubMed=6847620""")
-        self.assertEqual(record.references[3].number, "4")
-        self.assertEqual(record.references[3].authors,
-                         "Gavel Y., von Heijne G.")
-        self.assertEqual(record.references[3].citation, """\
+            self.assertEqual(record.references[3].number, "4")
+            self.assertEqual(record.references[3].authors,
+                             "Gavel Y., von Heijne G.")
+            self.assertEqual(record.references[3].citation, """\
 "Sequence differences between glycosylated and non-glycosylated
 Asn-X-Thr/Ser acceptor sites: implications for protein engineering."
 Protein Eng. 3:433-442(1990).
 PubMed=2349213""")
-        self.assertEqual(record.references[4].number, "5")
-        self.assertEqual(record.references[4].authors,
-                         "Miletich J.P., Broze G.J. Jr.")
-        self.assertEqual(record.references[4].citation, """\
+            self.assertEqual(record.references[4].number, "5")
+            self.assertEqual(record.references[4].authors,
+                             "Miletich J.P., Broze G.J. Jr.")
+            self.assertEqual(record.references[4].citation, """\
 "Beta protein C is not glycosylated at asparagine 329. The rate of
 translation may influence the frequency of usage at
 asparagine-X-cysteine sites."
 J. Biol. Chem. 265:11397-11404(1990).
 PubMed=1694179""")
 
-        # Testing the third parsed record
-        record = next(records)
-        self.assertEqual(record.accession, "PDOC00004")
-        self.assertEqual(len(record.prosite_refs), 1)
-        self.assertEqual(record.prosite_refs[0],
-                         ("PS00004", "CAMP_PHOSPHO_SITE"))
-        self.assertEqual(record.text, """\
+            # Testing the third parsed record
+            record = next(records)
+            self.assertEqual(record.accession, "PDOC00004")
+            self.assertEqual(len(record.prosite_refs), 1)
+            self.assertEqual(record.prosite_refs[0],
+                             ("PS00004", "CAMP_PHOSPHO_SITE"))
+            self.assertEqual(record.text, """\
 ****************************************************************
 * cAMP- and cGMP-dependent protein kinase phosphorylation site *
 ****************************************************************
@@ -1049,36 +1039,36 @@ to note that there are quite a number of exceptions to this rule.
 
 """)
 
-        self.assertEqual(record.references[0].number, "1")
-        self.assertEqual(record.references[0].authors,
-                         "Fremisco J.R., Glass D.B., Krebs E.G.")
-        self.assertEqual(record.references[0].citation, """\
+            self.assertEqual(record.references[0].number, "1")
+            self.assertEqual(record.references[0].authors,
+                             "Fremisco J.R., Glass D.B., Krebs E.G.")
+            self.assertEqual(record.references[0].citation, """\
 J. Biol. Chem. 255:4240-4245(1980).""")
-        self.assertEqual(record.references[1].number, "2")
-        self.assertEqual(record.references[1].authors,
-                         "Glass D.B., Smith S.B.")
-        self.assertEqual(record.references[1].citation, """\
+            self.assertEqual(record.references[1].number, "2")
+            self.assertEqual(record.references[1].authors,
+                             "Glass D.B., Smith S.B.")
+            self.assertEqual(record.references[1].citation, """\
 "Phosphorylation by cyclic GMP-dependent protein kinase of a synthetic
 peptide corresponding to the autophosphorylation site in the enzyme."
 J. Biol. Chem. 258:14797-14803(1983).
 PubMed=6317673""")
-        self.assertEqual(record.references[2].number, "3")
-        self.assertEqual(record.references[2].authors,
-                         "Glass D.B., el-Maghrabi M.R., Pilkis S.J.")
-        self.assertEqual(record.references[2].citation, """\
+            self.assertEqual(record.references[2].number, "3")
+            self.assertEqual(record.references[2].authors,
+                             "Glass D.B., el-Maghrabi M.R., Pilkis S.J.")
+            self.assertEqual(record.references[2].citation, """\
 "Synthetic peptides corresponding to the site phosphorylated in
 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase as substrates of
 cyclic nucleotide-dependent protein kinases."
 J. Biol. Chem. 261:2987-2993(1986).
 PubMed=3005275""")
 
-        # Testing the fourth parsed record
-        record = next(records)
-        self.assertEqual(record.accession, "PDOC60030")
-        self.assertEqual(len(record.prosite_refs), 1)
-        self.assertEqual(record.prosite_refs[0],
-                         ("PS60030", "BACTERIOCIN_IIA"))
-        self.assertEqual(record.text, """\
+            # Testing the fourth parsed record
+            record = next(records)
+            self.assertEqual(record.accession, "PDOC60030")
+            self.assertEqual(len(record.prosite_refs), 1)
+            self.assertEqual(record.prosite_refs[0],
+                             ("PS60030", "BACTERIOCIN_IIA"))
+            self.assertEqual(record.text, """\
 ******************************************
 * Bacteriocin class IIa family signature *
 ******************************************
@@ -1133,26 +1123,26 @@ The pattern  we  developed  for  the  class  IIa bacteriocin family covers the
 
 """)
 
-        self.assertEqual(record.references[0].number, "1")
-        self.assertEqual(record.references[0].authors,
-                         "Ennahar S., Sonomoto K., Ishizaki A.")
-        self.assertEqual(record.references[0].citation, """\
+            self.assertEqual(record.references[0].number, "1")
+            self.assertEqual(record.references[0].authors,
+                             "Ennahar S., Sonomoto K., Ishizaki A.")
+            self.assertEqual(record.references[0].citation, """\
 "Class IIa bacteriocins from lactic acid bacteria: antibacterial
 activity and food preservation."
 J. Biosci. Bioeng. 87:705-716(1999).
 PubMed=16232543""")
-        self.assertEqual(record.references[1].number, "2")
-        self.assertEqual(record.references[1].authors,
-                         "Johnsen L., Fimland G., Nissen-Meyer J.")
-        self.assertEqual(record.references[1].citation, """\
+            self.assertEqual(record.references[1].number, "2")
+            self.assertEqual(record.references[1].authors,
+                             "Johnsen L., Fimland G., Nissen-Meyer J.")
+            self.assertEqual(record.references[1].citation, """\
 "The C-terminal domain of pediocin-like antimicrobial peptides (class
 IIa bacteriocins) is involved in specific recognition of the
 C-terminal part of cognate immunity proteins and in determining the
 antimicrobial spectrum."
 J. Biol. Chem. 280:9243-9250(2005).
 PubMed=15611086; DOI=10.1074/jbc.M412712200""")
-        self.assertEqual(record.references[2].number, "3")
-        self.assertEqual(record.references[2].authors, "Fimland G., Johnsen L., Dalhus B., Nissen-Meyer J.")
+            self.assertEqual(record.references[2].number, "3")
+            self.assertEqual(record.references[2].authors, "Fimland G., Johnsen L., Dalhus B., Nissen-Meyer J.")
         self.assertEqual(record.references[2].citation, """\
 "Pediocin-like antimicrobial peptides (class IIa bacteriocins) and
 their immunity proteins: biosynthesis, structure, and mode of
@@ -1160,8 +1150,6 @@ action."
 J. Pept. Sci. 11:688-696(2005).
 PubMed=16059970; DOI=10.1002/psc.699""")
 
-        handle.close()
-
 
 if __name__ == "__main__":
     runner = unittest.TextTestRunner(verbosity=2)
diff --git a/Tests/test_prosite1.py b/Tests/test_prosite1.py
index c5665ab..78ea784 100644
--- a/Tests/test_prosite1.py
+++ b/Tests/test_prosite1.py
@@ -14,11 +14,10 @@ from Bio.ExPASy import Prosite
 class TestPrositeRead(unittest.TestCase):
 
     def test_read1(self):
-        "Parsing Prosite record ps00107.txt"
+        """Parsing Prosite record ps00107.txt"""
         filename = os.path.join('Prosite', 'ps00107.txt')
-        handle = open(filename)
-        record = Prosite.read(handle)
-        handle.close()
+        with open(filename) as handle:
+           record = Prosite.read(handle)
         self.assertEqual(record.name, "PROTEIN_KINASE_ATP")
         self.assertEqual(record.type, "PATTERN")
         self.assertEqual(record.accession, "PS00107")
@@ -38,6 +37,7 @@ class TestPrositeRead(unittest.TestCase):
         self.read1_positive1(record)
         self.read1_positive2(record)
         self.read1_positive3(record)
+        self.read1_positive4(record)
         self.read1_false_neg(record)
         self.read1_false_pos(record)
         self.read1_potential(record)
@@ -1551,7 +1551,7 @@ class TestPrositeRead(unittest.TestCase):
         self.assertEqual(record.dr_positive[1498], ('Q62270', 'SRMS_MOUSE'))
         self.assertEqual(record.dr_positive[1499], ('Q96SB4', 'SRPK1_HUMAN'))
 
-    def read1_positive3(self, record):
+    def read1_positive4(self, record):
         self.assertEqual(len(record.dr_positive), 1689)
         self.assertEqual(record.dr_positive[1500], ('O70551', 'SRPK1_MOUSE'))
         self.assertEqual(record.dr_positive[1501], ('Q5RD27', 'SRPK1_PONPY'))
@@ -2523,11 +2523,10 @@ class TestPrositeRead(unittest.TestCase):
         self.assertEqual(record.pdb_structs[330], '4ERK')
 
     def test_read2(self):
-        "Parsing Prosite record ps00159.txt"
+        """Parsing Prosite record ps00159.txt"""
         filename = os.path.join('Prosite', 'ps00159.txt')
-        handle = open(filename)
-        record = Prosite.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prosite.read(handle)
         self.assertEqual(record.name, "ALDOLASE_KDPG_KHG_1")
         self.assertEqual(record.type, "PATTERN")
         self.assertEqual(record.accession, "PS00159")
@@ -2572,11 +2571,10 @@ class TestPrositeRead(unittest.TestCase):
         self.assertEqual(record.pdb_structs[5], "1VHC")
 
     def test_read3(self):
-        "Parsing Prosite record ps00165.txt"
+        """Parsing Prosite record ps00165.txt"""
         filename = os.path.join('Prosite', 'ps00165.txt')
-        handle = open(filename)
-        record = Prosite.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prosite.read(handle)
         self.assertEqual(record.name, "DEHYDRATASE_SER_THR")
         self.assertEqual(record.type, "PATTERN")
         self.assertEqual(record.accession, "PS00165")
diff --git a/Tests/test_prosite2.py b/Tests/test_prosite2.py
index e4b2cd7..53be98d 100644
--- a/Tests/test_prosite2.py
+++ b/Tests/test_prosite2.py
@@ -14,11 +14,10 @@ from Bio.ExPASy import Prosite
 class TestPrositeRead(unittest.TestCase):
 
     def test_read4(self):
-        "Parsing Prosite record ps00432.txt"
+        """Parsing Prosite record ps00432.txt"""
         filename = os.path.join('Prosite', 'ps00432.txt')
-        handle = open(filename)
-        record = Prosite.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prosite.read(handle)
         self.assertEqual(record.name, "ACTINS_2")
         self.assertEqual(record.type, "PATTERN")
         self.assertEqual(record.accession, "PS00432")
@@ -453,11 +452,10 @@ class TestPrositeRead(unittest.TestCase):
         self.assertEqual(record.pdb_structs[59], '2BTF')
 
     def test_read5(self):
-        "Parsing Prosite record ps00488.txt"
+        """Parsing Prosite record ps00488.txt"""
         filename = os.path.join('Prosite', 'ps00488.txt')
-        handle = open(filename)
-        record = Prosite.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prosite.read(handle)
         self.assertEqual(record.name, "PAL_HISTIDASE")
         self.assertEqual(record.type, "PATTERN")
         self.assertEqual(record.accession, "PS00488")
@@ -621,11 +619,10 @@ class TestPrositeRead(unittest.TestCase):
         self.assertEqual(record.pdb_structs[5], "1Y2M")
 
     def test_read6(self):
-        "Parsing Prosite record ps00546.txt"
+        """Parsing Prosite record ps00546.txt"""
         filename = os.path.join('Prosite', 'ps00546.txt')
-        handle = open(filename)
-        record = Prosite.read(handle)
-        handle.close()
+        with open(filename) as handle:
+            record = Prosite.read(handle)
         self.assertEqual(record.name, "CYSTEINE_SWITCH")
         self.assertEqual(record.type, "PATTERN")
         self.assertEqual(record.accession, "PS00546")
diff --git a/Tests/test_raxml_tool.py b/Tests/test_raxml_tool.py
index fc0e82b..44c15fd 100644
--- a/Tests/test_raxml_tool.py
+++ b/Tests/test_raxml_tool.py
@@ -14,14 +14,15 @@ from Bio.Phylo.Applications import RaxmlCommandline
 from Bio import MissingExternalDependencyError
 
 raxml_exe = None
-if sys.platform == "win32":
-    raise MissingExternalDependencyError(
-          "Testing RAxML on Windows not supported yet")
-else:
+try:
     from Bio._py3k import getoutput
     output = getoutput("raxmlHPC -v")
     if "not found" not in output and "This is RAxML" in output:
         raxml_exe = "raxmlHPC"
+except OSError:
+    # TODO: Use FileNotFoundError once we drop Python 2
+    pass
+
 if not raxml_exe:
     raise MissingExternalDependencyError(
         "Install RAxML (binary raxmlHPC) if you want to test the Bio.Phylo.Applications wrapper.")
diff --git a/Tests/test_samtools_tool.py b/Tests/test_samtools_tool.py
index f7059d4..c16a17e 100644
--- a/Tests/test_samtools_tool.py
+++ b/Tests/test_samtools_tool.py
@@ -11,6 +11,8 @@ from Bio import MissingExternalDependencyError
 import sys
 import os
 import unittest
+
+from Bio.Application import ApplicationError
 from Bio.Sequencing.Applications import SamtoolsViewCommandline
 from Bio.Sequencing.Applications import SamtoolsCalmdCommandline
 from Bio.Sequencing.Applications import SamtoolsCatCommandline
@@ -180,7 +182,14 @@ class SamtoolsTestCase(unittest.TestCase):
         cmdline = SamtoolsSortCommandline(samtools_exe)
         cmdline.set_parameter("input_bam", self.bamfile1)
         cmdline.set_parameter("out_prefix", "SamBam/out")
-        stdout, stderr = cmdline()
+        try:
+            stdout, stderr = cmdline()
+        except ApplicationError as err:
+            if "[bam_sort] Use -T PREFIX / -o FILE to specify temporary and final output files" in str(err):
+                # TODO: The samtools sort API changed...
+                return
+            else:
+                raise
         self.assertFalse(stderr,
                          "Samtools sort failed:\n%s\nStderr:%s"
                          % (cmdline, stderr))
@@ -209,7 +218,9 @@ class SamtoolsTestCase(unittest.TestCase):
         cmdline.set_parameter("out_bam", self.outbamfile)
         cmdline.set_parameter("f", True)  # Overwrite out.bam if it exists
         stdout, stderr = cmdline()
-        self.assertFalse(stderr,
+        # Worked up to v1.2, then there was a regression failing with message
+        # but as of v1.3 expect a warning: [W::bam_merge_core2] No @HD tag found.
+        self.assertTrue(not stderr or stderr.strip() == "[W::bam_merge_core2] No @HD tag found.",
                          "Samtools merge failed:\n%s\nStderr:%s"
                          % (cmdline, stderr))
         self.assertTrue(os.path.exists(self.outbamfile))
diff --git a/Tests/test_seq.py b/Tests/test_seq.py
index 5f1fc19..f3a3360 100644
--- a/Tests/test_seq.py
+++ b/Tests/test_seq.py
@@ -20,7 +20,7 @@ else:
 
 from Bio import Alphabet
 from Bio import Seq
-from Bio.Alphabet import IUPAC
+from Bio.Alphabet import IUPAC, Gapped
 from Bio.Data.IUPACData import ambiguous_dna_complement, ambiguous_rna_complement
 from Bio.Data.IUPACData import ambiguous_dna_values, ambiguous_rna_values
 from Bio.Data.CodonTable import TranslationError
@@ -950,6 +950,105 @@ class TestTranslating(unittest.TestCase):
                 expected = Seq.translate(nucleotide_seq)
                 self.assertEqual(repr(expected), repr(nucleotide_seq.translate()))
 
+    def test_alphabets_of_translated_seqs(self):
+
+        def triple_pad(s):
+            """Add N to ensure length is a multiple of three (whole codons)."""
+            while len(s) % 3:
+                s += "N"
+            return s
+
+        self.assertEqual("IUPACProtein()", repr(self.test_seqs[0].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[1].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[2].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[3].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[10].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[11].translate().alphabet))
+        self.assertEqual("IUPACProtein()", repr(self.test_seqs[12].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()",
+                         repr(triple_pad(self.test_seqs[13]).translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()", repr(self.test_seqs[14].translate().alphabet))
+        self.assertEqual("IUPACProtein()", repr(self.test_seqs[15].translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()",
+                         repr(triple_pad(self.test_seqs[16]).translate().alphabet))
+        self.assertEqual("ExtendedIUPACProtein()",
+                         repr(triple_pad(self.test_seqs[17]).translate().alphabet))
+
+    def test_translation_of_gapped_seq_with_gap_char_given(self):
+        seq = Seq.Seq("ATG---AAACTG")
+        self.assertEqual("M-KL", seq.translate(gap="-"))
+        self.assertRaises(TranslationError, seq.translate, gap="~")
+
+    def test_translation_of_gapped_seq_with_stop_codon_and_gap_char_given(self):
+        seq = Seq.Seq("GTG---GCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG")
+        self.assertEqual("V-AIVMGR*KGAR*", seq.translate(gap="-"))
+        self.assertRaises(TranslationError, seq.translate)
+
+    def test_translation_of_gapped_seq_with_gap_char_given_and_inferred_from_alphabet(self):
+        seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna))
+        self.assertEqual("M-KL", seq.translate(gap="-"))
+        self.assertRaises(ValueError, seq.translate, gap="~")
+
+        seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna))
+        self.assertRaises(ValueError, seq.translate, gap="~")
+        self.assertRaises(TranslationError, seq.translate, gap="-")
+
+    def test_translation_of_gapped_seq_with_gap_char_given_and_inferred_from_alphabet2(self):
+        """Test using stop codon in sequence"""
+        seq = Seq.Seq("ATG---AAACTGTAG", Gapped(IUPAC.unambiguous_dna))
+        self.assertEqual("M-KL*", seq.translate(gap="-"))
+        self.assertRaises(ValueError, seq.translate, gap="~")
+
+        seq = Seq.Seq("ATG---AAACTGTAG", Gapped(IUPAC.unambiguous_dna))
+        self.assertEqual("M-KL@", seq.translate(gap="-", stop_symbol="@"))
+        self.assertRaises(ValueError, seq.translate, gap="~")
+
+        seq = Seq.Seq("ATG~~~AAACTGTAG", Gapped(IUPAC.unambiguous_dna))
+        self.assertRaises(ValueError, seq.translate, gap="~")
+        self.assertRaises(TranslationError, seq.translate, gap="-")
+
+    def test_translation_of_gapped_seq_no_gap_char_given(self):
+        seq = Seq.Seq("ATG---AAACTG")
+        self.assertRaises(TranslationError, seq.translate)
+
+    def test_translation_of_gapped_seq_no_gap_char_given_and_inferred_from_alphabet(self):
+        seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna))
+        self.assertEqual("M-KL", seq.translate())
+
+        seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna))
+        self.assertRaises(TranslationError, seq.translate)
+
+        seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna, "~"))
+        self.assertEqual("M~KL", seq.translate())
+
+    def test_alphabet_of_translated_gapped_seq(self):
+        seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna))
+        self.assertEqual("Gapped(ExtendedIUPACProtein(), '-')", repr(seq.translate().alphabet))
+
+        seq = Seq.Seq("ATG---AAACTG", Gapped(IUPAC.unambiguous_dna, "-"))
+        self.assertEqual("Gapped(ExtendedIUPACProtein(), '-')", repr(seq.translate().alphabet))
+
+        seq = Seq.Seq("ATG~~~AAACTG", Gapped(IUPAC.unambiguous_dna, "~"))
+        self.assertEqual("Gapped(ExtendedIUPACProtein(), '~')", repr(seq.translate().alphabet))
+
+        seq = Seq.Seq("ATG---AAACTG")
+        self.assertEqual("Gapped(ExtendedIUPACProtein(), '-')", repr(seq.translate(gap="-").alphabet))
+
+        seq = Seq.Seq("ATG~~~AAACTG")
+        self.assertEqual("Gapped(ExtendedIUPACProtein(), '~')", repr(seq.translate(gap="~").alphabet))
+
+        seq = Seq.Seq("ATG~~~AAACTGTAG")
+        self.assertEqual("HasStopCodon(Gapped(ExtendedIUPACProtein(), '~'), '*')",
+                         repr(seq.translate(gap="~").alphabet))
+
+        seq = Seq.Seq("ATG---AAACTGTGA")
+        self.assertEqual("HasStopCodon(Gapped(ExtendedIUPACProtein(), '-'), '*')",
+                         repr(seq.translate(gap="-").alphabet))
+
+        seq = Seq.Seq("ATG---AAACTGTGA")
+        self.assertEqual("HasStopCodon(Gapped(ExtendedIUPACProtein(), '-'), '@')",
+                         repr(seq.translate(gap="-", stop_symbol="@").alphabet))
+
     def test_translation_wrong_type(self):
         """Test translation table cannot be CodonTable"""
         seq = Seq.Seq("ATCGTA")
@@ -960,6 +1059,17 @@ class TestTranslating(unittest.TestCase):
         seq = "GTGGCCATTGTAATGGGCCGC"
         self.assertEqual("VAIVMGR", Seq.translate(seq))
 
+    def test_translation_of_gapped_string_with_gap_char_given(self):
+        seq = "GTG---GCCATTGTAATGGGCCGC"
+        expected = "V-AIVMGR"
+        self.assertEqual(expected, Seq.translate(seq, gap="-"))
+        self.assertRaises(TypeError, Seq.translate, seq, gap=[])
+        self.assertRaises(ValueError, Seq.translate, seq, gap="-*")
+
+    def test_translation_of_gapped_string_no_gap_char_given(self):
+        seq = "GTG---GCCATTGTAATGGGCCGC"
+        self.assertRaises(TranslationError, Seq.translate, seq)
+
     def test_translation_to_stop(self):
         for nucleotide_seq in self.test_seqs:
             nucleotide_seq = nucleotide_seq[:3 * (len(nucleotide_seq) // 3)]
diff --git a/setup.py b/setup.py
index 54d4f87..8b94436 100644
--- a/setup.py
+++ b/setup.py
@@ -136,6 +136,8 @@ elif sys.version_info[0] == 3 and sys.version_info[:2] < (3, 3):
     print("Biopython requires Python 3.3 or later (or Python 2.6 or 2.7). "
           "Python %d.%d detected" % sys.version_info[:2])
     sys.exit(1)
+elif sys.version_info[:2] == (3, 3):
+    print("WARNING: Biopython support for Python 3.3 is now deprecated.")
 
 
 def check_dependencies_once():
@@ -339,9 +341,6 @@ PACKAGES = [
     'Bio.KEGG.Map',
     'Bio.KEGG.KGML',
     'Bio.Medline',
-    'Bio.Motif',
-    'Bio.Motif.Parsers',
-    'Bio.Motif.Applications',
     'Bio.motifs',
     'Bio.motifs.applications',
     'Bio.motifs.jaspar',
@@ -391,6 +390,7 @@ NUMPY_PACKAGES = [
     'Bio.Affy',
     'Bio.Cluster',
     'Bio.KDTree',
+    'Bio.phenotype',
 ]
 
 if os.name == 'java':
@@ -431,11 +431,6 @@ if is_Numpy_installed():
                   include_dirs=[numpy_include_dir],
                   ))
     EXTENSIONS.append(
-        Extension('Bio.Motif._pwm',
-                  ["Bio/Motif/_pwm.c"],
-                  include_dirs=[numpy_include_dir],
-                  ))
-    EXTENSIONS.append(
         Extension('Bio.motifs._pwm',
                   ["Bio/motifs/_pwm.c"],
                   include_dirs=[numpy_include_dir],

-- 
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