"""
Protein
=======
"""
import logging
import requests
import shutil
import pandas as pd
import numpy as np
import os.path as op
import seaborn as sns
from slugify import Slugify
from collections import defaultdict
from six.moves.urllib.error import URLError
from Bio.Seq import Seq
from Bio import SeqIO
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.PDB.PDBExceptions import PDBException, PDBConstructionException
from msgpack.exceptions import ExtraData
from cobra.core import DictList
import ssbio.utils
import ssbio.databases.pdb
import ssbio.protein.sequence.utils.alignment
import ssbio.protein.sequence.utils.fasta
import ssbio.protein.structure.properties.quality
from ssbio.core.object import Object
from ssbio.protein.sequence.seqprop import SeqProp
from ssbio.databases.kegg import KEGGProp
from ssbio.databases.uniprot import UniProtProp
from ssbio.protein.structure.structprop import StructProp
from ssbio.databases.pdb import PDBProp
from ssbio.protein.structure.homology.itasser.itasserprop import ITASSERProp
from ssbio.protein.structure.homology.itasser.itasserprep import ITASSERPrep
custom_slugify = Slugify(safe_chars='-_.')
log = logging.getLogger(__name__)
[docs]class Protein(Object):
"""Store information about a protein, which represents the monomeric translated unit of a gene.
The main utilities of this class are to:
* Load, parse, and store the same (ie. from different database sources) or similar (ie. from different strains)
protein sequences as :ref:`SeqProp <sequence>` objects in the :attr:`~ssbio.core.protein.Protein.sequences`
attribute
* Load, parse, and store multiple experimental or predicted protein structures as :ref:`StructProp <structure>`
objects in the :attr:`~ssbio.core.protein.Protein.structures` attribute
* Set a single :attr:`~ssbio.core.protein.Protein.representative_sequence` and
:attr:`~ssbio.core.protein.Protein.representative_structure`
* Calculate, store, and access pairwise sequence alignments to the representative sequence or structure
* Provide summaries of alignments and mutations seen
* Map between residue numbers of sequences and structures
Args:
ident (str): Unique identifier for this protein
description (str): Optional description for this protein
root_dir (str): Path to where the folder named by this protein's ID will be created.
Default is current working directory.
pdb_file_type (str): ``pdb``, ``mmCif``, ``xml``, ``mmtf`` - file type for files downloaded from the PDB
Todo:
- Implement structural alignment objects with FATCAT
"""
__representative_sequence_attributes = ['gene', 'uniprot', 'kegg', 'pdbs',
'sequence_path', 'metadata_path']
__representative_structure_attributes = ['is_experimental', 'reference_seq_top_coverage', 'date', 'description',
'resolution','taxonomy_name']
def __init__(self, ident, description=None, root_dir=None, pdb_file_type='mmtf'):
Object.__init__(self, id=ident, description=description)
self.pdb_file_type = pdb_file_type
"""str: ``pdb``, ``pdb.gz``, ``mmcif``, ``cif``, ``cif.gz``, ``xml.gz``, ``mmtf``, ``mmtf.gz`` - choose a file
type for files downloaded from the PDB"""
# Create directories
self._root_dir = None
if root_dir:
self.root_dir = root_dir
# Sequences
self.sequences = DictList()
"""DictList: Stored protein sequences which are related to this protein"""
self.representative_sequence = None
"""SeqProp: Sequence set to represent this protein"""
# Structures
self.structures = DictList()
"""DictList: Stored protein structures which are related to this protein"""
self.representative_structure = None
"""StructProp: Structure set to represent this protein, usually in monomeric form"""
self.representative_chain = None
"""str: Chain ID in the representative structure which best represents a sequence"""
self.representative_chain_seq_coverage = 0
"""float: Percent identity of sequence coverage for the representative chain"""
# Alignments
self.sequence_alignments = DictList()
"""DictList: Pairwise or multiple sequence alignments stored as ``Bio.Align.MultipleSeqAlignment`` objects"""
self.structure_alignments = DictList()
"""DictList: Pairwise or multiple structure alignments - currently a placeholder"""
@property
def root_dir(self):
"""str: Path to where the folder named by this protein's ID will be created. Default is current working
directory."""
return self._root_dir
@root_dir.setter
def root_dir(self, path):
if not path:
raise ValueError('No path specified')
if not op.exists(path):
raise ValueError('{}: folder does not exist'.format(path))
if self._root_dir:
log.debug('Changing root directory of Protein "{}" from {} to {}'.format(self.id, self.root_dir, path))
if not op.exists(op.join(path, self.id)) and not op.exists(op.join(path, self.id) + '_protein'):
raise IOError('Protein "{}" does not exist in folder {}'.format(self.id, path))
self._root_dir = path
for d in [self.protein_dir, self.sequence_dir, self.structure_dir]:
ssbio.utils.make_dir(d)
# Propagate changes to SeqProps
if hasattr(self, 'sequences'):
for seq in self.sequences:
seq.sequence_dir = self.sequence_dir
seq.metadata_dir = self.sequence_dir
if hasattr(self, 'representative_sequence'):
if self.representative_sequence:
self.representative_sequence.sequence_dir = self.sequence_dir
self.representative_sequence.metadata_dir = self.sequence_dir
# Propagate changes to StructProps
if hasattr(self, 'structures'):
for struct in self.structures:
struct.structure_dir = self.structure_dir
if hasattr(self, 'representative_structure'):
if self.representative_structure:
self.representative_structure.structure_dir = self.structure_dir
@property
def protein_dir(self):
"""str: Protein folder"""
if self.root_dir:
# Add a _protein suffix to the folder if it has the same name as its root folder
folder_name = self.id
if folder_name == op.basename(self.root_dir):
folder_name = self.id + '_protein'
return op.join(self.root_dir, folder_name)
else:
log.warning('Root directory not set')
return None
@property
def sequence_dir(self):
"""str: Directory where sequence related files are stored"""
if self.root_dir:
return op.join(self.protein_dir, 'sequences')
else:
log.debug('Root directory not set')
return None
@property
def structure_dir(self):
"""str: Directory where structure related files are stored"""
if self.root_dir:
return op.join(self.protein_dir, 'structures')
else:
log.debug('Root directory not set')
return None
@property
def protein_statistics(self):
"""Get a dictionary of basic statistics describing this protein"""
# TODO: can i use get_dict here instead
d = {}
d['id'] = self.id
d['sequences'] = [x.id for x in self.sequences]
d['num_sequences'] = self.num_sequences
if self.representative_sequence:
d['representative_sequence'] = self.representative_sequence.id
d['repseq_gene_name'] = self.representative_sequence.gene_name
d['repseq_uniprot'] = self.representative_sequence.uniprot
d['repseq_description'] = self.representative_sequence.description
d['num_structures'] = self.num_structures
d['experimental_structures'] = [x.id for x in self.get_experimental_structures()]
d['num_experimental_structures'] = self.num_structures_experimental
d['homology_models'] = [x.id for x in self.get_homology_models()]
d['num_homology_models'] = self.num_structures_homology
if self.representative_structure:
d['representative_structure'] = self.representative_structure.id
d['representative_chain'] = self.representative_chain
d['representative_chain_seq_coverage'] = self.representative_chain_seq_coverage
d['repstruct_description'] = self.description
if self.representative_structure.is_experimental:
d['repstruct_resolution'] = self.representative_structure.resolution
d['num_sequence_alignments'] = len(self.sequence_alignments)
d['num_structure_alignments'] = len(self.structure_alignments)
return d
@property
def num_sequences(self):
"""int: Return the total number of sequences"""
return len(self.sequences)
@property
def num_structures(self):
"""int: Return the total number of structures"""
return len(self.structures)
@property
def num_structures_experimental(self):
"""int: Return the total number of experimental structures"""
return len(self.get_experimental_structures())
@property
def num_structures_homology(self):
"""int: Return the total number of homology models"""
return len(self.get_homology_models())
[docs] def get_experimental_structures(self):
"""DictList: Return a DictList of all experimental structures in self.structures"""
if self.representative_structure:
return DictList(x for x in self.structures if x.is_experimental and x.id != self.representative_structure.id)
else:
return DictList(x for x in self.structures if x.is_experimental)
[docs] def get_homology_models(self):
"""DictList: Return a DictList of all homology models in self.structures"""
if self.representative_structure:
return DictList(x for x in self.structures if not x.is_experimental and x.id != self.representative_structure.id)
else:
return DictList(x for x in self.structures if not x.is_experimental)
[docs] def filter_sequences(self, seq_type):
"""Return a DictList of only specified types in the sequences attribute.
Args:
seq_type (SeqProp): Object type
Returns:
DictList: A filtered DictList of specified object type only
"""
return DictList(x for x in self.sequences if isinstance(x, seq_type))
[docs] def load_kegg(self, kegg_id, kegg_organism_code=None, kegg_seq_file=None, kegg_metadata_file=None,
set_as_representative=False, download=False, outdir=None, force_rerun=False):
"""Load a KEGG ID, sequence, and metadata files into the sequences attribute.
Args:
kegg_id (str): KEGG ID
kegg_organism_code (str): KEGG organism code to prepend to the kegg_id if not part of it already.
Example: ``eco:b1244``, ``eco`` is the organism code
kegg_seq_file (str): Path to KEGG FASTA file
kegg_metadata_file (str): Path to KEGG metadata file (raw KEGG format)
set_as_representative (bool): If this KEGG ID should be set as the representative sequence
download (bool): If the KEGG sequence and metadata files should be downloaded if not provided
outdir (str): Where the sequence and metadata files should be downloaded to
force_rerun (bool): If ID should be reloaded and files redownloaded
Returns:
KEGGProp: object contained in the sequences attribute
"""
if download:
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
if kegg_organism_code:
kegg_id = kegg_organism_code + ':' + kegg_id
# If we have already loaded the KEGG ID
if self.sequences.has_id(kegg_id):
# Remove it if we want to force rerun things
if force_rerun:
existing = self.sequences.get_by_id(kegg_id)
self.sequences.remove(existing)
# Otherwise just get that KEGG object
else:
log.debug('{}: KEGG ID already present in list of sequences'.format(kegg_id))
kegg_prop = self.sequences.get_by_id(kegg_id)
# Check again (instead of else) in case we removed it if force rerun
if not self.sequences.has_id(kegg_id):
kegg_prop = KEGGProp(id=kegg_id, seq=None, fasta_path=kegg_seq_file, txt_path=kegg_metadata_file)
if download:
kegg_prop.download_seq_file(outdir, force_rerun)
kegg_prop.download_metadata_file(outdir, force_rerun)
# Check if KEGG sequence matches a potentially set representative sequence
# Do not add any info if a UniProt ID was already mapped though, we want to use that
if self.representative_sequence:
if not self.representative_sequence.uniprot:
if kegg_prop.equal_to(self.representative_sequence):
# Update the representative sequence field with KEGG metadata
self.representative_sequence.update(kegg_prop.get_dict(), only_keys=['sequence_path',
'metadata_path',
'kegg',
'description',
'taxonomy',
'id',
'pdbs',
'uniprot',
'seq_record',
'gene_name',
'refseq'])
else:
# TODO: add option to use manual or kegg sequence if things do not match
log.warning('{}: representative sequence does not match mapped KEGG sequence.'.format(self.id))
self.sequences.append(kegg_prop)
if set_as_representative:
self.representative_sequence = kegg_prop
return self.sequences.get_by_id(kegg_id)
[docs] def load_uniprot(self, uniprot_id, uniprot_seq_file=None, uniprot_xml_file=None, download=False, outdir=None,
set_as_representative=False, force_rerun=False):
"""Load a UniProt ID and associated sequence/metadata files into the sequences attribute.
Sequence and metadata files can be provided, or alternatively downloaded with the download flag set to True.
Metadata files will be downloaded as XML files.
Args:
uniprot_id (str): UniProt ID/ACC
uniprot_seq_file (str): Path to FASTA file
uniprot_xml_file (str): Path to UniProt XML file
download (bool): If sequence and metadata files should be downloaded
outdir (str): Output directory for sequence and metadata files
set_as_representative (bool): If this sequence should be set as the representative one
force_rerun (bool): If files should be redownloaded and metadata reloaded
Returns:
UniProtProp: Sequence that was loaded into the ``sequences`` attribute
"""
if download:
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
# If we have already loaded the UniProt ID
if self.sequences.has_id(uniprot_id):
# Remove it if we want to force rerun things
if force_rerun:
existing = self.sequences.get_by_id(uniprot_id)
self.sequences.remove(existing)
# Otherwise just get that UniProt object
else:
log.debug('{}: UniProt ID already present in list of sequences'.format(uniprot_id))
uniprot_prop = self.sequences.get_by_id(uniprot_id)
if not self.sequences.has_id(uniprot_id):
uniprot_prop = UniProtProp(id=uniprot_id,
seq=None,
fasta_path=uniprot_seq_file,
xml_path=uniprot_xml_file)
if download:
uniprot_prop.download_metadata_file(outdir=outdir, force_rerun=force_rerun)
uniprot_prop.download_seq_file(outdir=outdir, force_rerun=force_rerun)
# Also check if UniProt sequence matches a potentially set representative sequence
if self.representative_sequence:
# Test equality
if uniprot_prop.equal_to(self.representative_sequence):
# Update the representative sequence field with UniProt metadata if it is the same
self.representative_sequence.update(uniprot_prop.get_dict(), only_keys=['sequence_path',
'metadata_path',
'kegg',
'description',
'taxonomy',
'id',
'pdbs',
'uniprot',
'seq_record',
'gene_name',
'refseq'])
else:
# TODO: add option to use manual or uniprot sequence if things do not match
log.warning('{}: representative sequence does not match mapped UniProt sequence'.format(self.id))
self.sequences.append(uniprot_prop)
if set_as_representative:
self.representative_sequence = uniprot_prop
return self.sequences.get_by_id(uniprot_id)
[docs] def load_manual_sequence_file(self, ident, seq_file, copy_file=False, outdir=None, set_as_representative=False):
"""Load a manual sequence, given as a FASTA file and optionally set it as the representative sequence.
Also store it in the sequences attribute.
Args:
ident (str): Sequence ID
seq_file (str): Path to sequence FASTA file
copy_file (bool): If the FASTA file should be copied to the protein's sequences folder or the ``outdir``, if
protein folder has not been set
outdir (str): Path to output directory
set_as_representative (bool): If this sequence should be set as the representative one
Returns:
SeqProp: Sequence that was loaded into the ``sequences`` attribute
"""
if copy_file:
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
shutil.copy(seq_file, outdir)
seq_file = op.join(outdir, seq_file)
manual_sequence = SeqProp(id=ident, sequence_path=seq_file, seq=None)
self.sequences.append(manual_sequence)
if set_as_representative:
self.representative_sequence = manual_sequence
return self.sequences.get_by_id(ident)
[docs] def load_manual_sequence(self, seq, ident=None, write_fasta_file=False, outdir=None,
set_as_representative=False, force_rewrite=False):
"""Load a manual sequence given as a string and optionally set it as the representative sequence.
Also store it in the sequences attribute.
Args:
seq (str, Seq, SeqRecord): Sequence string, Biopython Seq or SeqRecord object
ident (str): Optional identifier for the sequence, required if seq is a string. Also will override existing
IDs in Seq or SeqRecord objects if set.
write_fasta_file (bool): If this sequence should be written out to a FASTA file
outdir (str): Path to output directory
set_as_representative (bool): If this sequence should be set as the representative one
force_rewrite (bool): If the FASTA file should be overwritten if it already exists
Returns:
SeqProp: Sequence that was loaded into the ``sequences`` attribute
"""
if write_fasta_file:
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
outfile = op.join(outdir, '{}.faa'.format(ident))
else:
outfile = None
if isinstance(seq, str) or isinstance(seq, Seq):
if not ident:
raise ValueError('ID must be specified if sequence is a string or Seq object')
else:
if not ident:
# Use ID from SeqRecord ID if new ID not provided
ident = seq.id
else:
# Overwrite SeqRecord ID with new ID if provided
seq.id = ident
manual_sequence = SeqProp(id=ident, seq=seq)
if write_fasta_file:
manual_sequence.write_fasta_file(outfile=outfile, force_rerun=force_rewrite)
self.sequences.append(manual_sequence)
if set_as_representative:
self.representative_sequence = manual_sequence
return self.sequences.get_by_id(ident)
[docs] def set_representative_sequence(self, force_rerun=False):
"""Automatically consolidate loaded sequences (manual, UniProt, or KEGG) and set a single representative
sequence.
Manually set representative sequences override all existing mappings. UniProt mappings override KEGG mappings
except when KEGG mappings have PDBs associated with them and UniProt doesn't.
Args:
force_rerun (bool): Set to True to recheck stored sequences
Returns:
SeqProp: Which sequence was set as representative
"""
if len(self.sequences) == 0:
log.error('{}: no sequences mapped'.format(self.id))
return self.representative_sequence
kegg_mappings = self.filter_sequences(KEGGProp)
if len(kegg_mappings) > 0:
kegg_to_use = kegg_mappings[0]
if len(kegg_mappings) > 1:
log.warning('{}: multiple KEGG mappings found, using the first entry {}'.format(self.id, kegg_to_use.id))
uniprot_mappings = self.filter_sequences(UniProtProp)
# If a representative sequence has already been set, nothing needs to be done
if self.representative_sequence and not force_rerun:
log.debug('{}: representative sequence already set'.format(self.id))
# If there is a KEGG annotation and no UniProt annotations, set KEGG as representative
elif len(kegg_mappings) > 0 and len(uniprot_mappings) == 0:
self.representative_sequence = kegg_to_use
log.debug('{}: representative sequence set from KEGG ID {}'.format(self.id, kegg_to_use.id))
# If there are UniProt annotations and no KEGG annotations, set UniProt as representative
elif len(kegg_mappings) == 0 and len(uniprot_mappings) > 0:
# If there are multiple uniprots rank them by the sum of reviewed (bool) + num_pdbs
# This way, UniProts with PDBs get ranked to the top, or if no PDBs, reviewed entries
u_ranker = []
for u in uniprot_mappings:
u_ranker.append((u.id, u.ranking_score()))
sorted_by_second = sorted(u_ranker, key=lambda tup: tup[1], reverse=True)
best_u_id = sorted_by_second[0][0]
best_u = uniprot_mappings.get_by_id(best_u_id)
self.representative_sequence = best_u
log.debug('{}: representative sequence set from UniProt ID {}'.format(self.id, best_u_id))
# If there are both UniProt and KEGG annotations...
elif len(kegg_mappings) > 0 and len(uniprot_mappings) > 0:
# Use KEGG if the mapped UniProt is unique, and it has PDBs
if kegg_to_use.num_pdbs > 0 and not uniprot_mappings.has_id(kegg_to_use.uniprot):
self.representative_sequence = kegg_to_use
log.debug('{}: representative sequence set from KEGG ID {}'.format(self.id, kegg_to_use.id))
else:
# If there are multiple uniprots rank them by the sum of reviewed (bool) + num_pdbs
u_ranker = []
for u in uniprot_mappings:
u_ranker.append((u.id, u.ranking_score()))
sorted_by_second = sorted(u_ranker, key=lambda tup: tup[1], reverse=True)
best_u_id = sorted_by_second[0][0]
best_u = uniprot_mappings.get_by_id(best_u_id)
self.representative_sequence = best_u
log.debug('{}: representative sequence set from UniProt ID {}'.format(self.id, best_u_id))
return self.representative_sequence
[docs] def pairwise_align_sequences_to_representative(self, gapopen=10, gapextend=0.5, outdir=None,
engine='needle', parse=True, force_rerun=False):
"""Pairwise all sequences in the sequences attribute to the representative sequence. Stores the alignments
in the ``sequence_alignments`` DictList attribute.
Args:
gapopen (int): Only for ``engine='needle'`` - Gap open penalty is the score taken away when a gap is created
gapextend (float): Only for ``engine='needle'`` - Gap extension penalty is added to the standard gap penalty
for each base or residue in the gap
outdir (str): Only for ``engine='needle'`` - Path to output directory. Default is the protein sequence
directory.
engine (str): ``biopython`` or ``needle`` - which pairwise alignment program to use.
``needle`` is the standard EMBOSS tool to run pairwise alignments.
``biopython`` is Biopython's implementation of needle. Results can differ!
parse (bool): Store locations of mutations, insertions, and deletions in the alignment object (as an
annotation)
force_rerun (bool): Only for ``engine='needle'`` - Default False, set to True if you want to rerun the
alignment if outfile exists.
"""
if not self.representative_sequence:
raise ValueError('{}: no representative sequence set'.format(self.id))
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
for seq in self.sequences:
aln_id = '{}_{}'.format(self.id, seq.id)
outfile = '{}.needle'.format(aln_id)
if self.sequence_alignments.has_id(aln_id):
log.debug('{}: alignment already completed'.format(seq.id))
continue
if not seq.seq_str:
log.error('{}: no sequence stored, skipping alignment'.format(seq.id))
continue
# Don't need to compare sequence to itself
if seq.id == self.representative_sequence.id:
continue
aln = ssbio.protein.sequence.utils.alignment.pairwise_sequence_alignment(a_seq=self.representative_sequence.seq_str,
a_seq_id=self.id,
b_seq=seq.seq_str,
b_seq_id=seq.id,
gapopen=gapopen, gapextend=gapextend,
engine=engine,
outdir=outdir,
outfile=outfile,
force_rerun=force_rerun)
# Add an identifier to the MultipleSeqAlignment object for storage in a DictList
aln.id = aln_id
aln.annotations['a_seq'] = self.representative_sequence.id
aln.annotations['b_seq'] = seq.id
if parse:
aln_df = ssbio.protein.sequence.utils.alignment.get_alignment_df(a_aln_seq=str(list(aln)[0].seq),
b_aln_seq=str(list(aln)[1].seq))
aln.annotations['ssbio_type'] = 'seqalign'
aln.annotations['mutations'] = ssbio.protein.sequence.utils.alignment.get_mutations(aln_df)
aln.annotations['deletions'] = ssbio.protein.sequence.utils.alignment.get_deletions(aln_df)
aln.annotations['insertions'] = ssbio.protein.sequence.utils.alignment.get_insertions(aln_df)
self.sequence_alignments.append(aln)
[docs] def pairwise_align_sequences_to_representative_parallelize(self, sc, gapopen=10, gapextend=0.5, outdir=None,
engine='needle', parse=True, force_rerun=False):
"""Pairwise all sequences in the sequences attribute to the representative sequence. Stores the alignments
in the ``sequence_alignments`` DictList attribute.
Args:
sc (SparkContext): Configured spark context for parallelization
gapopen (int): Only for ``engine='needle'`` - Gap open penalty is the score taken away when a gap is created
gapextend (float): Only for ``engine='needle'`` - Gap extension penalty is added to the standard gap penalty
for each base or residue in the gap
outdir (str): Only for ``engine='needle'`` - Path to output directory. Default is the protein sequence
directory.
engine (str): ``biopython`` or ``needle`` - which pairwise alignment program to use.
``needle`` is the standard EMBOSS tool to run pairwise alignments.
``biopython`` is Biopython's implementation of needle. Results can differ!
parse (bool): Store locations of mutations, insertions, and deletions in the alignment object (as an
annotation)
force_rerun (bool): Only for ``engine='needle'`` - Default False, set to True if you want to rerun the
alignment if outfile exists.
"""
if not self.representative_sequence:
raise ValueError('{}: no representative sequence set'.format(self.id))
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
def pairwise_sc(self, seqprop):
aln_id = '{}_{}'.format(self.id, seqprop.id)
outfile = '{}.needle'.format(aln_id)
aln = ssbio.protein.sequence.utils.alignment.pairwise_sequence_alignment(a_seq=self.representative_sequence.seq_str,
a_seq_id=self.id,
b_seq=seqprop.seq_str,
b_seq_id=seqprop.id,
gapopen=gapopen,
gapextend=gapextend,
engine=engine,
outdir=outdir,
outfile=outfile,
force_rerun=force_rerun)
aln.id = aln_id
aln.annotations['a_seq'] = self.representative_sequence.id
aln.annotations['b_seq'] = seqprop.id
if parse:
aln_df = ssbio.protein.sequence.utils.alignment.get_alignment_df(a_aln_seq=str(list(aln)[0].seq),
b_aln_seq=str(list(aln)[1].seq))
aln.annotations['ssbio_type'] = 'seqalign'
aln.annotations['mutations'] = ssbio.protein.sequence.utils.alignment.get_mutations(aln_df)
aln.annotations['deletions'] = ssbio.protein.sequence.utils.alignment.get_deletions(aln_df)
aln.annotations['insertions'] = ssbio.protein.sequence.utils.alignment.get_insertions(aln_df)
return aln
sequences_rdd = sc.parallelize(filter(lambda x: x.id != self.representative_sequence.id, self.sequences))
result = sequences_rdd.map(lambda x: pairwise_sc(self, x)).collect()
for r in result:
self.sequence_alignments.append(r)
[docs] def write_all_sequences_file(self, outname, outdir=None):
"""Write all the stored sequences as a single FASTA file. By default, sets IDs to model gene IDs.
Args:
outname (str): Name of the output FASTA file without the extension
outdir (str): Path to output directory for the file, default is the sequences directory
"""
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
outfile = op.join(outdir, outname + '.faa')
SeqIO.write(self.sequences, outfile, "fasta")
log.info('{}: wrote all protein sequences to file'.format(outfile))
return outfile
[docs] def get_sequence_properties(self, representative_only=True):
"""Run Biopython ProteinAnalysis and EMBOSS pepstats to summarize basic statistics of the protein sequences.
Results are stored in the protein's respective SeqProp objects at ``.annotations``
Args:
representative_only (bool): If analysis should only be run on the representative sequence
"""
if representative_only:
# Check if a representative sequence was set
if not self.representative_sequence:
log.warning('{}: no representative sequence set, cannot get sequence properties'.format(self.id))
return
# Also need to check if a sequence has been stored
if not self.representative_sequence.seq:
log.warning('{}: representative sequence {} set, but no sequence stored. '
'Cannot get sequence properties.'.format(self.id, self.representative_sequence.id))
return
self.representative_sequence.get_biopython_pepstats()
self.representative_sequence.get_emboss_pepstats()
if not representative_only:
for s in self.sequences:
# Need to check if a sequence has been stored
if not s.seq:
log.warning('{}: no sequence stored. '
'Cannot get sequence properties.'.format(s.id))
continue
else:
s.get_biopython_pepstats()
s.get_emboss_pepstats()
[docs] def prep_itasser_modeling(self, itasser_installation, itlib_folder, runtype, create_in_dir=None,
execute_from_dir=None, print_exec=False, **kwargs):
"""Prepare to run I-TASSER homology modeling for the representative sequence.
Args:
itasser_installation (str): Path to I-TASSER folder, i.e. ``~/software/I-TASSER4.4``
itlib_folder (str): Path to ITLIB folder, i.e. ``~/software/ITLIB``
runtype: How you will be running I-TASSER - local, slurm, or torque
create_in_dir (str): Local directory where folders will be created
execute_from_dir (str): Optional path to execution directory - use this if you are copying the homology
models to another location such as a supercomputer for running
all_genes (bool): If all genes should be prepped, or only those without any mapped structures
print_exec (bool): If the execution statement should be printed to run modelling
Todo:
* Document kwargs - extra options for I-TASSER, SLURM or Torque execution
* Allow modeling of any sequence in sequences attribute, select by ID or provide SeqProp?
"""
if not create_in_dir:
if not self.structure_dir:
raise ValueError('Output directory must be specified')
self.homology_models_dir = op.join(self.structure_dir, 'homology_models')
else:
self.homology_models_dir = create_in_dir
ssbio.utils.make_dir(self.homology_models_dir)
if not execute_from_dir:
execute_from_dir = self.homology_models_dir
repseq = self.representative_sequence
itasser_kwargs = {'light': True,
'java_home': None,
'binding_site_pred': False,
'ec_pred': False,
'go_pred': False,
'job_scheduler_header': None,
'additional_options': None}
if kwargs:
itasser_kwargs.update(kwargs)
ITASSERPrep(ident=self.id, seq_str=repseq.seq_str, root_dir=self.homology_models_dir,
itasser_path=itasser_installation, itlib_path=itlib_folder,
runtype=runtype, print_exec=print_exec, execute_dir=execute_from_dir,
java_home=itasser_kwargs['java_home'],
light=itasser_kwargs['light'],
binding_site_pred=itasser_kwargs['binding_site_pred'],
ec_pred=itasser_kwargs['ec_pred'],
go_pred=itasser_kwargs['go_pred'],
job_scheduler_header=itasser_kwargs['job_scheduler_header'],
additional_options=itasser_kwargs['additional_options'])
log.debug('Prepared I-TASSER modeling folder {}'.format(self.homology_models_dir))
[docs] def blast_representative_sequence_to_pdb(self, seq_ident_cutoff=0, evalue=0.0001, display_link=False,
outdir=None, force_rerun=False):
"""BLAST the representative protein sequence to the PDB. Saves a raw BLAST result file (XML file).
Args:
seq_ident_cutoff (float, optional): Cutoff results based on percent coverage (in decimal form)
evalue (float, optional): Cutoff for the E-value - filters for significant hits. 0.001 is liberal,
0.0001 is stringent (default).
display_link (bool, optional): Set to True if links to the HTML results should be displayed
outdir (str): Path to output directory of downloaded XML files, must be set if protein directory
was not initialized
force_rerun (bool, optional): If existing BLAST results should not be used, set to True. Default is False.
Returns:
list: List of new ``PDBProp`` objects added to the ``structures`` attribute
"""
# Check if a representative sequence was set
if not self.representative_sequence:
log.warning('{}: no representative sequence set, cannot BLAST'.format(self.id))
return None
# Also need to check if a sequence has been stored
if not self.representative_sequence.seq:
log.warning('{}: no representative sequence loaded, cannot BLAST'.format(self.id))
return None
# BLAST the sequence to the PDB
blast_results = self.representative_sequence.blast_pdb(seq_ident_cutoff=seq_ident_cutoff,
evalue=evalue,
display_link=display_link,
outdir=outdir,
force_rerun=force_rerun)
new_pdbs = []
# Add BLAST results to the list of structures
if blast_results:
# Filter for new BLASTed PDBs
pdbs = [x['hit_pdb'].lower() for x in blast_results]
new_pdbs = [y for y in pdbs if not self.structures.has_id(y)]
if new_pdbs:
log.debug('{}: adding {} PDBs from BLAST results'.format(self.id, len(new_pdbs)))
else:
already_have = [y for y in pdbs if self.structures.has_id(y)]
log.debug('{}: PDBs already contained in structures list'.format(';'.join(already_have)))
blast_results = [z for z in blast_results if z['hit_pdb'].lower() in new_pdbs]
for blast_result in blast_results:
pdb = blast_result['hit_pdb'].lower()
chains = blast_result['hit_pdb_chains']
for chain in chains:
# load_pdb will append this protein to the list of structures
new_pdb = self.load_pdb(pdb_id=pdb, mapped_chains=chain)
new_pdb.add_chain_ids(chain)
new_chain = new_pdb.chains.get_by_id(chain)
# Store BLAST results within the chain
new_chain.blast_results = blast_result
return new_pdbs
@property
def df_pdb_blast(self):
"""DataFrame: Get a dataframe of PDB BLAST results"""
blast_results_pre_df = []
for p in self.get_experimental_structures():
for c in p.chains:
if hasattr(c, 'blast_results'):
# Summary dataframe
infodict = p.get_dict_with_chain(chain=c.id)['blast_results']
infodict['pdb_id'] = p.id
infodict['pdb_chain_id'] = c.id
blast_results_pre_df.append(infodict)
cols = ['pdb_id', 'pdb_chain_id', 'hit_score', 'hit_evalue', 'hit_percent_similar',
'hit_percent_ident', 'hit_percent_gaps', 'hit_num_ident', 'hit_num_similar', 'hit_num_gaps']
df = pd.DataFrame.from_records(blast_results_pre_df, columns=cols).set_index('pdb_id')
return ssbio.utils.clean_df(df)
[docs] def map_uniprot_to_pdb(self, seq_ident_cutoff=0.0, outdir=None, force_rerun=False):
"""Map the representative sequence's UniProt ID to PDB IDs using the PDBe "Best Structures" API.
Will save a JSON file of the results to the protein sequences folder.
The "Best structures" API is available at https://www.ebi.ac.uk/pdbe/api/doc/sifts.html
The list of PDB structures mapping to a UniProt accession sorted by coverage of the protein and,
if the same, resolution.
Args:
seq_ident_cutoff (float): Sequence identity cutoff in decimal form
outdir (str): Output directory to cache JSON results of search
force_rerun (bool): Force re-downloading of JSON results if they already exist
Returns:
list: A rank-ordered list of PDBProp objects that map to the UniProt ID
"""
if not self.representative_sequence:
log.error('{}: no representative sequence set, cannot use best structures API'.format(self.id))
return None
# Check if a UniProt ID is attached to the representative sequence
uniprot_id = self.representative_sequence.uniprot
if not uniprot_id:
log.error('{}: no representative UniProt ID set, cannot use best structures API'.format(self.id))
return None
if '-' in uniprot_id:
log.debug('{}: "-" detected in UniProt ID, isoform specific sequences are ignored with best structures API'.format(self.id))
uniprot_id = uniprot_id.split('-')[0]
if not outdir:
outdir = self.sequence_dir
if not outdir:
raise ValueError('Output directory must be specified')
best_structures = ssbio.databases.pdb.best_structures(uniprot_id,
outname='{}_best_structures'.format(custom_slugify(uniprot_id)),
outdir=outdir,
seq_ident_cutoff=seq_ident_cutoff,
force_rerun=force_rerun)
new_pdbs = []
if best_structures:
rank = 1
for best_structure in best_structures:
currpdb = str(best_structure['pdb_id'].lower())
new_pdbs.append(currpdb)
currchain = str(best_structure['chain_id'])
# load_pdb will append this protein to the list
new_pdb = self.load_pdb(pdb_id=currpdb, mapped_chains=currchain)
# Also add this chain to the chains attribute so we can save the
# info we get from best_structures
new_pdb.add_chain_ids(currchain)
pdb_specific_keys = ['experimental_method', 'resolution']
chain_specific_keys = ['coverage', 'start', 'end', 'unp_start', 'unp_end']
new_pdb.update(best_structure, only_keys=pdb_specific_keys)
new_chain = new_pdb.chains.get_by_id(currchain)
new_chain.update(best_structure, only_keys=chain_specific_keys)
new_chain.update({'rank': rank})
rank += 1
log.debug('{}, {}: {} PDB/chain pairs mapped'.format(self.id, uniprot_id, len(best_structures)))
else:
log.debug('{}, {}: no PDB/chain pairs mapped'.format(self.id, uniprot_id))
return new_pdbs
@property
def df_pdb_ranking(self):
"""DataFrame: Get a dataframe of UniProt -> best structure in PDB results"""
best_structures_pre_df = []
chain_specific_keys = ['coverage', 'start', 'end', 'unp_start', 'unp_end', 'rank']
for p in self.get_experimental_structures():
for c in p.chains:
if hasattr(c, 'rank'):
# Summary dataframe
infodict = p.get_dict_with_chain(chain=c.id, df_format=True, chain_keys=chain_specific_keys)
infodict['pdb_id'] = p.id
infodict['pdb_chain_id'] = c.id
infodict['uniprot'] = self.representative_sequence.uniprot
best_structures_pre_df.append(infodict)
cols = ['uniprot', 'pdb_id', 'pdb_chain_id', 'experimental_method', 'resolution', 'coverage',
'taxonomy_name', 'start', 'end', 'unp_start', 'unp_end', 'rank']
df = pd.DataFrame.from_records(best_structures_pre_df, columns=cols).set_index(['pdb_id', 'pdb_chain_id'])
return ssbio.utils.clean_df(df)
[docs] def load_pdb(self, pdb_id, mapped_chains=None, pdb_file=None, file_type=None, is_experimental=True,
set_as_representative=False, representative_chain=None, force_rerun=False):
"""Load a structure ID and optional structure file into the structures attribute.
Args:
pdb_id (str): PDB ID
mapped_chains (str, list): Chain ID or list of IDs which you are interested in
pdb_file (str): Path to PDB file
file_type (str): Type of PDB file
is_experimental (bool): If this structure file is experimental
set_as_representative (bool): If this structure should be set as the representative structure
representative_chain (str): If ``set_as_representative`` is ``True``, provide the representative chain ID
force_rerun (bool): If the PDB should be reloaded if it is already in the list of structures
Returns:
PDBProp: The object that is now contained in the structures attribute
"""
if self.structures.has_id(pdb_id):
# Remove the structure if set to force rerun
if force_rerun:
existing = self.structures.get_by_id(pdb_id)
self.structures.remove(existing)
# Otherwise just retrieve it
else:
log.debug('{}: PDB ID already present in list of structures'.format(pdb_id))
pdb = self.structures.get_by_id(pdb_id)
if pdb_file:
pdb.load_structure_path(pdb_file, file_type)
if mapped_chains:
pdb.add_mapped_chain_ids(mapped_chains)
# Create a new StructProp entry
if not self.structures.has_id(pdb_id):
if is_experimental:
pdb = PDBProp(ident=pdb_id, mapped_chains=mapped_chains, structure_path=pdb_file, file_type=file_type)
else:
pdb = StructProp(ident=pdb_id, mapped_chains=mapped_chains, structure_path=pdb_file, file_type=file_type)
self.structures.append(pdb)
if set_as_representative:
# Parse structure so chains are stored before setting representative
pdb.parse_structure()
self._representative_structure_setter(structprop=pdb, keep_chain=representative_chain, force_rerun=force_rerun)
return self.structures.get_by_id(pdb_id)
[docs] def load_itasser_folder(self, ident, itasser_folder, organize=False, outdir=None, organize_name=None,
set_as_representative=False, representative_chain='X', force_rerun=False):
"""Load the results folder from an I-TASSER run (local, not from the website) and copy relevant files over to
the protein structures directory.
Args:
ident (str): I-TASSER ID
itasser_folder (str): Path to results folder
organize (bool): If select files from modeling should be copied to the Protein directory
outdir (str): Path to directory where files will be copied and organized to
organize_name (str): Basename of files to rename results to. If not provided, will use id attribute.
set_as_representative: If this structure should be set as the representative structure
representative_chain (str): If ``set_as_representative`` is ``True``, provide the representative chain ID
force_rerun (bool): If the PDB should be reloaded if it is already in the list of structures
Returns:
ITASSERProp: The object that is now contained in the structures attribute
"""
if organize:
if not outdir:
outdir = self.structure_dir
if not outdir:
raise ValueError('Directory to copy results to must be specified')
if self.structures.has_id(ident):
if force_rerun:
existing = self.structures.get_by_id(ident)
self.structures.remove(existing)
else:
log.debug('{}: already present in list of structures'.format(ident))
itasser = self.structures.get_by_id(ident)
if not self.structures.has_id(ident):
itasser = ITASSERProp(ident, itasser_folder)
self.structures.append(itasser)
if set_as_representative:
self._representative_structure_setter(structprop=itasser, keep_chain=representative_chain, force_rerun=force_rerun)
if organize:
if itasser.structure_file:
# The name of the actual pdb file will be $GENEID_model1.pdb
if not organize_name:
new_itasser_name = self.id + '_model1'
else:
new_itasser_name = organize_name
# Additional results will be stored in a subdirectory
dest_itasser_extra_dir = op.join(outdir, '{}_itasser'.format(new_itasser_name))
ssbio.utils.make_dir(dest_itasser_extra_dir)
# Copy the model1.pdb and also create summary dataframes
itasser.copy_results(copy_to_dir=outdir, rename_model_to=new_itasser_name, force_rerun=force_rerun)
return self.structures.get_by_id(ident)
@property
def df_homology_models(self):
"""DataFrame: Get a dataframe of I-TASSER homology model results"""
# TODO: add definitions of column names
# model_date Date the model was created
# difficulty Difficulty level of the modeling run (easy, medium, hard)
# top_template_pdb Top template used to model the protein (PDB ID)
# top_template_chain Chain of the top template used to model the protein
# c_score Confidence score of the homology model from [-5,2]
# tm_score Structural similarity score to best template
# tm_score_err Standard error of TM score
# rmsd RMSD to best template
# rmsd_err Standard error of RMSD
# top_bsite_site_num Cluster which contained the consensus binding site
# top_bsite_c_score Confidence score of the consensus binding site prediction from [0,1]
# top_bsite_cluster_size Number of predictions within this cluster
# top_bsite_binding_residues Residue numbers of top ranked binding site prediction
# top_bsite_binding_location_coords Cartesian coordinates of top ranked binding site prediction
# top_bsite_pdb_ligand Top predicted ligand to bind
# top_bsite_ligand_cluster_counts Number of predictions for all ligands
# top_bsite_algorithm Algorithm used for top ranked binding site prediction
# top_bsite_c_score_method Confidence score of just the top algorithm's binding site prediction from [0,1]. >0.35 is somewhat reliable.
# top_bsite_pdb_template_id PDB ID of the template used to make the prediction
# top_bsite_pdb_template_chain Chain of the PDB which has the ligand
# top_ec_ec_number Top predicted EC number
# top_ec_c_score Confidence score of the consensus EC number prediction from [0,1]
# top_ec_pdb_template_id Top PDB ID used as template for EC number prediction
# top_ec_pdb_template_chain Chain of PDB ID used as template for EC number prediction
# top_ec_rmsd RMSD to best template
# top_ec_seq_coverage Sequence coverage percentage to best template
# top_ec_seq_ident Sequence identity percentage to best template
# top_ec_tm_score Structural similarity score to best template
# top_go_bp_go_id Predicted GO ID for biological process
# top_go_bp_go_term Predicted GO term for biological process
# top_go_bp_c_score Confidence score of the GO BP prediction from [0,1]
# top_go_cc_go_id Predicted GO ID for cellular compartment
# top_go_cc_go_term Predicted GO term for cellular compartment
# top_go_cc_c_score Confidence score of the GO CC prediction from [0,1]
# top_go_mf_go_id Predicted GO ID for molecular function
# top_go_mf_go_term Predicted GO term for molecular function
# top_go_mf_c_score Confidence score of the GO MF prediction from [0,1]
itasser_pre_df = []
df_cols = ['id', 'structure_file', 'model_date', 'difficulty',
'top_template_pdb', 'top_template_chain', 'c_score',
'tm_score', 'tm_score_err', 'rmsd', 'rmsd_err',
'top_bsite_site_num', 'top_bsite_c_score', 'top_bsite_cluster_size', 'top_bsite_algorithm',
'top_bsite_pdb_template_id', 'top_bsite_pdb_template_chain', 'top_bsite_pdb_ligand',
'top_bsite_binding_location_coords', 'top_bsite_c_score_method', 'top_bsite_binding_residues',
'top_bsite_ligand_cluster_counts',
'top_ec_pdb_template_id', 'top_ec_pdb_template_chain', 'top_ec_tm_score', 'top_ec_rmsd',
'top_ec_seq_ident', 'top_ec_seq_coverage', 'top_ec_c_score', 'top_ec_ec_number',
'top_ec_binding_residues',
'top_go_mf_go_id', 'top_go_mf_go_term', 'top_go_mf_c_score', 'top_go_bp_go_id', 'top_go_bp_go_term',
'top_go_bp_c_score', 'top_go_cc_go_id', 'top_go_cc_go_term', 'top_go_cc_c_score']
for p in self.get_homology_models():
# Summary dataframe
new_homology_dict = p.get_dict(df_format=True, only_attributes=df_cols)
itasser_pre_df.append(new_homology_dict)
df = pd.DataFrame.from_records(itasser_pre_df, columns=df_cols).set_index('id')
return ssbio.utils.clean_df(df)
return downloaded_pdb_ids
[docs] def download_all_pdbs(self, outdir=None, pdb_file_type=None, load_metadata=False, force_rerun=False):
"""Downloads all structures from the PDB. load_metadata flag sets if metadata should be parsed and stored in
StructProp, otherwise filepaths are just linked"""
# TODO: will replace pdb_downloader_and_metadata function
if not outdir:
outdir = self.structure_dir
if not outdir:
raise ValueError('Output directory must be specified')
if not pdb_file_type:
pdb_file_type = self.pdb_file_type
# Check if we have any PDBs
if self.num_structures_experimental == 0:
log.debug('{}: no structures available - nothing will be downloaded'.format(self.id))
return
downloaded_pdb_ids = []
# Download the PDBs
for s in self.get_experimental_structures():
log.debug('{}: downloading structure file from the PDB...'.format(s.id))
s.download_structure_file(outdir=outdir, file_type=pdb_file_type,
load_header_metadata=load_metadata, force_rerun=force_rerun)
downloaded_pdb_ids.append(s.id)
return downloaded_pdb_ids
[docs] def parse_all_stored_structures(self, outdir=None, pdb_file_type=None, force_rerun=False):
"""Runs parse_structure for any stored structure with a file available"""
# TODO: will replace pdb_downloader_and_metadata function
if not outdir:
outdir = self.structure_dir
if not outdir:
raise ValueError('Output directory must be specified')
if not pdb_file_type:
pdb_file_type = self.pdb_file_type
# Check if we have any PDBs
if self.num_structures_experimental == 0:
log.debug('{}: no structures available - nothing will be downloaded'.format(self.id))
return
downloaded_pdb_ids = []
# Download the PDBs
for s in self.get_experimental_structures():
log.debug('{}: downloading structure file from the PDB...'.format(s.id))
s.download_structure_file(outdir=outdir, file_type=pdb_file_type, force_rerun=force_rerun)
downloaded_pdb_ids.append(s.id)
return downloaded_pdb_ids
@property
def df_pdb_metadata(self):
"""DataFrame: Get a dataframe of PDB metadata (PDBs have to be downloaded first)"""
if self.num_structures == 0:
log.error('No experimental PDB structures have been mapped to protein')
return pd.DataFrame()
pdb_pre_df = []
not_showing_info = []
for p in self.get_experimental_structures():
if not p.structure_file:
not_showing_info.append(p.id)
log.debug('{}: PDB file has not been downloaded, run "pdb_downloader_and_metadata" \
to download all structures'.format(p.id))
continue
infodict = p.get_dict(df_format=True)
infodict['pdb_id'] = p.id
pdb_pre_df.append(infodict)
log.warning('Not showing info for {} mapped PDB entries, as they have not been downloaded yet. Run ')
cols = ['pdb_id', 'pdb_title', 'description', 'experimental_method', 'mapped_chains',
'resolution', 'chemicals', 'date', 'taxonomy_name',
'structure_file']
df = pd.DataFrame.from_records(pdb_pre_df, columns=cols).set_index('pdb_id')
return ssbio.utils.clean_df(df)
[docs] def align_seqprop_to_structprop(self, seqprop, structprop, chains=None, outdir=None,
engine='needle', structure_already_parsed=False, parse=True, force_rerun=False,
**kwargs):
"""Run and store alignments of a SeqProp to chains in the ``mapped_chains`` attribute of a StructProp.
Alignments are stored in the sequence_alignments attribute, with the IDs formatted as
``<SeqProp_ID>_<StructProp_ID>-<Chain_ID>``. Although it is more intuitive to align to individual ChainProps,
StructProps should be loaded as little as possible to reduce run times so the alignment is done to the entire
structure.
Args:
seqprop (SeqProp): SeqProp object with a loaded sequence
structprop (StructProp): StructProp object with a loaded structure
chains (str, list): Chain ID or IDs to map to. If not specified, ``mapped_chains`` attribute is inspected
for chains. If no chains there, all chains will be aligned to.
outdir (str): Directory to output sequence alignment files (only if running with needle)
engine (str): ``biopython`` or ``needle`` - which pairwise alignment program to use.
``needle`` is the standard EMBOSS tool to run pairwise alignments.
``biopython`` is Biopython's implementation of needle. Results can differ!
structure_already_parsed (bool): If the structure has already been parsed and the chain sequences are
stored. Temporary option until Hadoop sequence file is implemented to reduce number of times a
structure is parsed.
parse (bool): Store locations of mutations, insertions, and deletions in the alignment object (as an annotation)
force_rerun (bool): If alignments should be rerun
**kwargs: Other alignment options
Todo:
* Document **kwargs for alignment options
"""
if not outdir:
outdir = self.sequence_dir
if not structure_already_parsed:
# Parse the structure so chain sequences are stored
structprop.parse_structure()
# XTODO: remove and use the "parsed" attribute in a structprop instead
if chains:
chains_to_align_to = ssbio.utils.force_list(chains)
elif structprop.mapped_chains:
chains_to_align_to = structprop.mapped_chains
else:
log.warning('{}-{}: no chains specified in structure to align to, all chains will be aligned to'.format(seqprop.id,
structprop.id))
chains_to_align_to = structprop.chains.list_attr('id')
for chain_id in chains_to_align_to:
full_structure_id = '{}-{}'.format(structprop.id, chain_id)
aln_id = '{}_{}'.format(seqprop.id, full_structure_id)
outfile = '{}.needle'.format(aln_id)
if self.sequence_alignments.has_id(aln_id) and not force_rerun:
log.debug('{}: alignment already completed, skipping'.format(aln_id))
continue
log.debug('{}: running pairwise alignment to structure {}, chain {}'.format(seqprop.id,
structprop.id,
chain_id))
# Check if the chain ID actually exists
if structprop.chains.has_id(chain_id):
chain_prop = structprop.chains.get_by_id(chain_id)
chain_seq_record = chain_prop.seq_record
else:
log.error('{}: chain not present in structure file!'.format(full_structure_id))
continue
# Check if the chain sequence was parsed
if not chain_seq_record:
log.error('{}: chain sequence not available, was structure parsed?'.format(full_structure_id))
continue
# Run the pairwise alignment
try:
aln = ssbio.protein.sequence.utils.alignment.pairwise_sequence_alignment(a_seq=seqprop,
a_seq_id=seqprop.id,
b_seq=chain_seq_record,
b_seq_id=full_structure_id,
engine=engine,
outdir=outdir,
outfile=outfile,
force_rerun=force_rerun)
except ValueError:
log.error('{}: alignment failed to run, unable to check structure\'s chain'.format(full_structure_id))
continue
# Add an identifier to the MultipleSeqAlignment object for storage in a DictList
aln.id = aln_id
# Add annotations to keep track of what was aligned
aln.annotations['a_seq'] = seqprop.id
aln.annotations['b_seq'] = full_structure_id
aln.annotations['structure_id'] = structprop.id
aln.annotations['chain_id'] = chain_id
aln.annotations['ssbio_type'] = 'structalign'
# Optionally parse for locations of mutations, deletions, and insertions
# Store mapping to chain index as letter annotations in the sequence
# Store locations in the alignment's annotations
if parse:
aln_df = ssbio.protein.sequence.utils.alignment.get_alignment_df(a_aln_seq=str(list(aln)[0].seq),
b_aln_seq=str(list(aln)[1].seq))
chain_indices = aln_df[pd.notnull(aln_df.id_a_pos)].id_b_pos.tolist()
seqprop.letter_annotations['{}_chain_index'.format(aln_id)] = chain_indices
aln.annotations['mutations'] = ssbio.protein.sequence.utils.alignment.get_mutations(aln_df)
aln.annotations['deletions'] = ssbio.protein.sequence.utils.alignment.get_deletions(aln_df)
aln.annotations['insertions'] = ssbio.protein.sequence.utils.alignment.get_insertions(aln_df)
if force_rerun and self.sequence_alignments.has_id(aln.id):
self.sequence_alignments.remove(aln.id)
self.sequence_alignments.append(aln)
def _get_seqprop_to_structprop_alignment(self, seqprop, structprop, chain_id):
"""Return the alignment stored in self.sequence_alignments given a seqprop, structuprop, and chain_id"""
full_structure_id = '{}-{}'.format(structprop.id, chain_id)
aln_id = '{}_{}'.format(seqprop.id, full_structure_id)
if self.sequence_alignments.has_id(aln_id):
alignment = self.sequence_alignments.get_by_id(aln_id)
return alignment
else:
raise ValueError('{}: structure alignment not found, please run the alignment first'.format(aln_id))
[docs] def get_seqprop_to_structprop_alignment_stats(self, seqprop, structprop, chain_id):
"""Get the sequence alignment information for a sequence to a structure's chain."""
alignment = self._get_seqprop_to_structprop_alignment(seqprop=seqprop, structprop=structprop, chain_id=chain_id)
return ssbio.protein.structure.properties.quality.seq_to_struct_alignment_stats(reference_seq_aln=alignment[0],
structure_seq_aln=alignment[1])
[docs] def check_structure_chain_quality(self, seqprop, structprop, chain_id,
seq_ident_cutoff=0.5, allow_missing_on_termini=0.2,
allow_mutants=True, allow_deletions=False,
allow_insertions=False, allow_unresolved=True):
"""Report if a structure's chain meets the defined cutoffs for sequence quality."""
alignment = self._get_seqprop_to_structprop_alignment(seqprop=seqprop, structprop=structprop, chain_id=chain_id)
# Compare sequence to structure's sequence using the alignment
chain_passes_quality_check = ssbio.protein.structure.properties.quality.sequence_checker(reference_seq_aln=alignment[0],
structure_seq_aln=alignment[1],
seq_ident_cutoff=seq_ident_cutoff,
allow_missing_on_termini=allow_missing_on_termini,
allow_mutants=allow_mutants,
allow_deletions=allow_deletions,
allow_insertions=allow_insertions,
allow_unresolved=allow_unresolved)
return chain_passes_quality_check
[docs] def find_representative_chain(self, seqprop, structprop, chains_to_check=None,
seq_ident_cutoff=0.5, allow_missing_on_termini=0.2,
allow_mutants=True, allow_deletions=False,
allow_insertions=False, allow_unresolved=True):
"""Set and return the representative chain based on sequence quality checks to a reference sequence.
Args:
seqprop (SeqProp): SeqProp object to compare to chain sequences
structprop (StructProp): StructProp object with chains to compare to in the ``mapped_chains`` attribute. If
there are none present, ``chains_to_check`` can be specified, otherwise all chains are checked.
chains_to_check (str, list): Chain ID or IDs to check for sequence coverage quality
seq_ident_cutoff (float): Percent sequence identity cutoff, in decimal form
allow_missing_on_termini (float): Percentage of the total length of the reference sequence which will be
ignored when checking for modifications. Example: if 0.1, and reference sequence is 100 AA, then only
residues 5 to 95 will be checked for modifications.
allow_mutants (bool): If mutations should be allowed or checked for
allow_deletions (bool): If deletions should be allowed or checked for
allow_insertions (bool): If insertions should be allowed or checked for
allow_unresolved (bool): If unresolved residues should be allowed or checked for
Returns:
str: the best chain ID, if any
"""
if chains_to_check:
chains_to_check = ssbio.utils.force_list(chains_to_check)
elif structprop.mapped_chains:
chains_to_check = structprop.mapped_chains
else:
log.warning('{}-{}: no chains specified in structure to align to, all chains will be checked'.format(seqprop.id,
structprop.id))
chains_to_check = structprop.chains.list_attr('id')
for chain_id in chains_to_check:
try:
# Compare sequence to structure's sequence using the alignment
found_good_chain = self.check_structure_chain_quality(seqprop=seqprop, structprop=structprop, chain_id=chain_id,
seq_ident_cutoff=seq_ident_cutoff,
allow_missing_on_termini=allow_missing_on_termini,
allow_mutants=allow_mutants,
allow_deletions=allow_deletions,
allow_insertions=allow_insertions,
allow_unresolved=allow_unresolved)
except ValueError:
log.error('{}-{}: unable to check chain "{}"'.format(seqprop.id, structprop.id, chain_id))
found_good_chain = False
# If found_good_chain = True, return chain ID
# If not, move on to the next potential chain
if found_good_chain:
stats = self.get_seqprop_to_structprop_alignment_stats(seqprop=seqprop, structprop=structprop, chain_id=chain_id)
self.representative_chain = chain_id
self.representative_chain_seq_coverage = stats['percent_identity']
return chain_id
else:
log.debug('{}: no chains meet quality checks'.format(structprop.id))
return None
def _map_seqprop_resnums_to_structprop_chain_index(self, resnums, seqprop=None, structprop=None, chain_id=None,
use_representatives=False):
"""Map a residue number in any SeqProp to the mapping index in the StructProp + chain ID. This does not provide
a mapping to residue number, only a mapping to the index which then can be mapped to the structure resnum!
Args:
resnums (int, list): Residue numbers in the sequence
seqprop (SeqProp): SeqProp object
structprop (StructProp): StructProp object
chain_id (str): Chain ID to map to index
use_representatives (bool): If representative sequence/structure/chain should be used in mapping
Returns:
dict: Mapping of resnums to indices
"""
resnums = ssbio.utils.force_list(resnums)
if use_representatives:
seqprop = self.representative_sequence
structprop = self.representative_structure
chain_id = self.representative_chain
full_structure_id = '{}-{}'.format(structprop.id, chain_id).replace('REP-', '')
aln_id = '{}_{}'.format(seqprop.id, full_structure_id)
else:
if not seqprop or not structprop or not chain_id:
raise ValueError('Please specify sequence, structure, and chain ID')
full_structure_id = '{}-{}'.format(structprop.id, chain_id)
aln_id = '{}_{}'.format(seqprop.id, full_structure_id)
access_key = '{}_chain_index'.format(aln_id)
if access_key not in seqprop.letter_annotations:
raise KeyError('{}: structure mapping {} not available in sequence letter annotations. Was alignment parsed? '
'Run ``align_seqprop_to_structprop`` with ``parse=True``.'.format(access_key, aln_id))
chain_index_mapping = seqprop.letter_annotations[access_key]
resnum_to_chain_index = {}
for x in resnums:
ix = chain_index_mapping[x - 1] - 1
if np.isnan(ix):
log.warning('{}-{}, {}: no equivalent residue found in structure sequence'.format(structprop.id,
chain_id,
x))
else:
resnum_to_chain_index[int(x)] = int(ix)
return resnum_to_chain_index
[docs] def map_seqprop_resnums_to_structprop_resnums(self, resnums, seqprop=None, structprop=None, chain_id=None,
use_representatives=False):
"""Map a residue number in any SeqProp to the structure's residue number for a specified chain.
Args:
resnums (int, list): Residue numbers in the sequence
seqprop (SeqProp): SeqProp object
structprop (StructProp): StructProp object
chain_id (str): Chain ID to map to
use_representatives (bool): If the representative sequence and structure should be used. If True, seqprop,
structprop, and chain_id do not need to be defined.
Returns:
dict: Mapping of sequence residue numbers to structure residue numbers
"""
resnums = ssbio.utils.force_list(resnums)
if use_representatives:
seqprop = self.representative_sequence
structprop = self.representative_structure
chain_id = self.representative_chain
else:
if not seqprop or not structprop or not chain_id:
raise ValueError('Please specify sequence, structure, and chain ID')
mapping_to_repchain_index = self._map_seqprop_resnums_to_structprop_chain_index(resnums=resnums,
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
chain = structprop.chains.get_by_id(chain_id)
chain_structure_resnum_mapping = chain.seq_record.letter_annotations['structure_resnums']
final_mapping = {}
for k, v in mapping_to_repchain_index.items():
k = int(k)
rn = chain_structure_resnum_mapping[v]
if rn == float('Inf'):
log.warning('{}-{}, {}: structure file does not contain coordinates for this residue'.format(structprop.id,
chain_id,
k))
else:
rn = int(rn)
final_mapping[k] = rn
# Additionally report if residues are the same - they could be different in the structure though
format_data = {'seqprop_id' : seqprop.id,
'seqprop_resid' : seqprop[k - 1],
'seqprop_resnum' : k,
'structprop_id' : structprop.id,
'structprop_chid' : chain_id,
'structprop_resid' : chain.seq_record[rn - 1],
'structprop_resnum': rn}
if seqprop[k-1] != chain.seq_record[rn-1]:
log.warning('Sequence {seqprop_id} residue {seqprop_resid}{seqprop_resnum} does not match to '
'structure {structprop_id}-{structprop_chid} residue '
'{structprop_resid}{structprop_resnum}. NOTE: this may be due to '
'structural differences'.format(**format_data))
else:
log.debug('Sequence {seqprop_id} residue {seqprop_resid}{seqprop_resnum} is mapped to '
'structure {structprop_id}-{structprop_chid} residue '
'{structprop_resid}{structprop_resnum}'.format(**format_data))
return final_mapping
[docs] def map_structprop_resnums_to_seqprop_resnums(self, resnums, structprop=None, chain_id=None, seqprop=None,
use_representatives=False):
"""Map a residue number in any StructProp + chain ID to any SeqProp's residue number.
Args:
resnums (int, list): Residue numbers in the structure
structprop (StructProp): StructProp object
chain_id (str): Chain ID to map from
seqprop (SeqProp): SeqProp object
use_representatives (bool): If the representative sequence and structure should be used. If True, seqprop,
structprop, and chain_id do not need to be defined.
Returns:
dict: Mapping of structure residue numbers to sequence residue numbers
"""
pass
# TODO: implementation
# resnums = ssbio.utils.force_list(resnums)
#
# if use_representatives:
# seqprop = self.representative_sequence
# structprop = self.representative_structure
# chain_id = self.representative_chain
# else:
# if not seqprop or not structprop or not chain_id:
# raise ValueError('Please specify sequence, structure, and chain ID')
#
# mapping_to_repchain_index = self.map_seqprop_resnums_to_structprop_chain_index(resnums=resnums,
# seqprop=seqprop,
# structprop=structprop,
# chain_id=chain_id,
# use_representatives=use_representatives)
#
# chain = structprop.chains.get_by_id(chain_id)
# chain_structure_resnum_mapping = chain.seq_record.letter_annotations['structure_resnums']
#
# final_mapping = {}
# for k, v in mapping_to_repchain_index.items():
# k = int(k)
# rn = chain_structure_resnum_mapping[v]
#
# if rn == float('Inf'):
# log.warning('{}-{}, {}: structure file does not contain coordinates for this residue'.format(structprop.id,
# chain_id,
# k))
# else:
# rn = int(rn)
# final_mapping[k] = rn
#
# # Additionally report if residues are the same - they could be different in the structure though
# format_data = {'seqprop_id' : seqprop.id,
# 'seqprop_resid' : seqprop[k - 1],
# 'seqprop_resnum' : k,
# 'structprop_id' : structprop.id,
# 'structprop_chid' : chain_id,
# 'structprop_resid' : chain.seq_record[rn - 1],
# 'structprop_resnum': rn}
#
# if seqprop[k-1] != chain.seq_record[rn-1]:
# log.warning('Sequence {seqprop_id} residue {seqprop_resid}{seqprop_resnum} does not match to '
# 'structure {structprop_id}-{structprop_chid} residue '
# '{structprop_resid}{structprop_resnum}. NOTE: this may be due to '
# 'structural differences'.format(**format_data))
# else:
# log.debug('Sequence {seqprop_id} residue {seqprop_resid}{seqprop_resnum} is mapped to '
# 'structure {structprop_id}-{structprop_chid} residue '
# '{structprop_resid}{structprop_resnum}'.format(**format_data))
#
# return final_mapping
def _representative_structure_setter(self, structprop, keep_chain, clean=True, keep_chemicals=None,
out_suffix='_clean', outdir=None, force_rerun=False):
"""Set the representative structure by 1) cleaning it and 2) copying over attributes of the original structure.
The structure is copied because the chains stored may change, and cleaning it makes a new PDB file.
Args:
structprop (StructProp): StructProp object to set as representative
keep_chain (str): Chain ID to keep
clean (bool): If the PDB file should be cleaned (see ssbio.structure.utils.cleanpdb)
keep_chemicals (str, list): Keep specified chemical names
out_suffix (str): Suffix to append to clean PDB file
outdir (str): Path to output directory
Returns:
StructProp: representative structure
"""
# Set output directory for cleaned PDB file
if not outdir:
outdir = self.structure_dir
if not outdir:
raise ValueError('Output directory must be specified')
# Create new ID for this representative structure, it cannot be the same as the original one
new_id = 'REP-{}'.format(structprop.id)
# Remove the previously set representative structure if set to force rerun
if self.structures.has_id(new_id):
if force_rerun:
existing = self.structures.get_by_id(new_id)
self.structures.remove(existing)
# If the structure is to be cleaned, and which chain to keep
if clean:
final_pdb = structprop.clean_structure(outdir=outdir, out_suffix=out_suffix,
keep_chemicals=keep_chemicals, keep_chains=keep_chain,
force_rerun=force_rerun)
log.debug('{}: cleaned structure and saved new file at {}'.format(structprop.id, final_pdb))
else:
final_pdb = structprop.structure_path
self.representative_structure = StructProp(ident=new_id, chains=keep_chain, mapped_chains=keep_chain,
structure_path=final_pdb, file_type='pdb')
self.representative_chain = keep_chain
self.representative_structure.update(structprop.get_dict_with_chain(chain=keep_chain),
only_keys=self.__representative_structure_attributes,
overwrite=True)
# Save the original structure ID as an extra attribute
self.representative_structure.original_structure_id = structprop.id
# Also need to parse the clean structure and save its sequence..
self.representative_structure.parse_structure()
# And finally add it to the list of structures
self.structures.append(self.representative_structure)
[docs] def set_representative_structure(self, seq_outdir=None, struct_outdir=None, pdb_file_type=None,
engine='needle', always_use_homology=False, rez_cutoff=0.0,
seq_ident_cutoff=0.5, allow_missing_on_termini=0.2,
allow_mutants=True, allow_deletions=False,
allow_insertions=False, allow_unresolved=True,
clean=True, keep_chemicals=None, skip_large_structures=False,
force_rerun=False):
"""Set a representative structure from a structure in the structures attribute.
Each gene can have a combination of the following, which will be analyzed to set a representative structure.
* Homology model(s)
* Ranked PDBs
* BLASTed PDBs
If the ``always_use_homology`` flag is true, homology models are always set as representative when they exist.
If there are multiple homology models, we rank by the percent sequence coverage.
Args:
seq_outdir (str): Path to output directory of sequence alignment files, must be set if Protein directory
was not created initially
struct_outdir (str): Path to output directory of structure files, must be set if Protein directory
was not created initially
pdb_file_type (str): ``pdb``, ``mmCif``, ``xml``, ``mmtf`` - file type for files downloaded from the PDB
engine (str): ``biopython`` or ``needle`` - which pairwise alignment program to use.
``needle`` is the standard EMBOSS tool to run pairwise alignments.
``biopython`` is Biopython's implementation of needle. Results can differ!
always_use_homology (bool): If homology models should always be set as the representative structure
rez_cutoff (float): Resolution cutoff, in Angstroms (only if experimental structure)
seq_ident_cutoff (float): Percent sequence identity cutoff, in decimal form
allow_missing_on_termini (float): Percentage of the total length of the reference sequence which will be ignored
when checking for modifications. Example: if 0.1, and reference sequence is 100 AA, then only residues
5 to 95 will be checked for modifications.
allow_mutants (bool): If mutations should be allowed or checked for
allow_deletions (bool): If deletions should be allowed or checked for
allow_insertions (bool): If insertions should be allowed or checked for
allow_unresolved (bool): If unresolved residues should be allowed or checked for
clean (bool): If structure should be cleaned
keep_chemicals (str, list): Keep specified chemical names if structure is to be cleaned
skip_large_structures (bool): Default False -- currently, large structures can't be saved as a PDB file even
if you just want to save a single chain, so Biopython will throw an error when trying to do so. As an
alternative, if a large structure is selected as representative, the pipeline will currently point to it
and not clean it. If you don't want this to happen, set this to true.
force_rerun (bool): If sequence to structure alignment should be rerun
Returns:
StructProp: Representative structure from the list of structures. This is a not a map to the original
structure, it is copied and optionally cleaned from the original one.
Todo:
- Remedy large structure representative setting
"""
log.debug('{}: setting representative structure'.format(self.id))
if len(self.structures) == 0:
log.debug('{}: no structures available'.format(self.id))
return None
if not self.representative_sequence:
log.error('{}: no representative sequence to compare structures to'.format(self.id))
return None
if self.representative_structure and not force_rerun:
log.debug('{}: representative structure already set'.format(self.id))
return self.representative_structure
if self.representative_structure and force_rerun:
log.debug('{}: representative structure previously set, unsetting'.format(self.id))
self.representative_structure = None
if not pdb_file_type:
pdb_file_type = self.pdb_file_type
if not seq_outdir:
seq_outdir = self.sequence_dir
if not seq_outdir:
raise ValueError('Sequence output directory must be specified')
if not struct_outdir:
struct_outdir = self.structure_dir
if not struct_outdir:
raise ValueError('Structure output directory must be specified')
has_homology = False
has_pdb = False
use_homology = False
use_pdb = False
if self.num_structures_homology > 0:
has_homology = True
if self.num_structures_experimental > 0:
has_pdb = True
# If we mark to always use homology, use it if it exists
if always_use_homology:
if has_homology:
use_homology = True
elif has_pdb:
use_pdb = True
# If we don't always want to use homology, use PDB if it exists
else:
if has_homology and has_pdb:
use_pdb = True
use_homology = True
elif has_homology and not has_pdb:
use_homology = True
elif has_pdb and not has_homology:
use_pdb = True
if use_pdb:
# Put PDBs through QC/QA
all_pdbs = self.get_experimental_structures()
log.debug('{}: checking quality of {} experimental structures'.format(self.id, len(all_pdbs)))
for pdb in all_pdbs:
# Download the structure and parse it
# This will add all chains to the mapped_chains attribute if there are none
try:
pdb.download_structure_file(outdir=struct_outdir, file_type=pdb_file_type, force_rerun=force_rerun, load_header_metadata=True)
except (requests.exceptions.HTTPError, URLError):
log.error('{}: structure file could not be downloaded in {} format'.format(pdb, pdb_file_type))
continue
# TODO: add try/except to download cif file as fallback like below?
if rez_cutoff and pdb.resolution:
if pdb.resolution > rez_cutoff:
log.debug('{}: structure does not meet experimental resolution cutoff'.format(pdb, pdb_file_type))
continue
# TODO: clean up these try/except things
try:
self.align_seqprop_to_structprop(seqprop=self.representative_sequence,
structprop=pdb,
outdir=seq_outdir,
engine=engine,
parse=True,
force_rerun=force_rerun)
except (PDBConstructionException, ExtraData, KeyError) as e:
log.error('{}: unable to parse structure file as {}. Falling back to mmCIF format.'.format(pdb, pdb_file_type))
print(e)
# Fall back to using mmCIF file if structure cannot be parsed
try:
pdb.download_structure_file(outdir=struct_outdir, file_type='mmCif',
force_rerun=force_rerun, load_header_metadata=True)
except (requests.exceptions.HTTPError, URLError):
log.error('{}: structure file could not be downloaded'.format(pdb))
continue
self.align_seqprop_to_structprop(seqprop=self.representative_sequence,
structprop=pdb,
outdir=seq_outdir,
engine=engine,
parse=True,
force_rerun=force_rerun)
best_chain = self.find_representative_chain(seqprop=self.representative_sequence,
structprop=pdb,
seq_ident_cutoff=seq_ident_cutoff,
allow_missing_on_termini=allow_missing_on_termini,
allow_mutants=allow_mutants, allow_deletions=allow_deletions,
allow_insertions=allow_insertions, allow_unresolved=allow_unresolved)
if best_chain:
try:
self._representative_structure_setter(structprop=pdb,
clean=clean,
out_suffix='-{}_clean'.format(best_chain),
keep_chain=best_chain,
keep_chemicals=keep_chemicals,
outdir=struct_outdir,
force_rerun=force_rerun)
except TypeError:
if skip_large_structures == True:
log.warning("{}: unable to save large PDB {}-{} in PDB file format, trying next "
"structure.".format(self.id, pdb.id, best_chain))
continue
else:
log.warning("{}: unable to save large PDB {}-{} in PDB file format, setting original "
"structure as representative. Set skip_large_structures=True if you don't "
"want this to happen".format(self.id, pdb.id, best_chain))
self.representative_structure = pdb
except Exception as e:
# Try force rerunning first if there exists a corrupt clean PDB file
try:
log.debug('{}: unknown error with {}, trying force_rerun first'.format(self.id, pdb.id))
self._representative_structure_setter(structprop=pdb,
clean=clean,
out_suffix='-{}_clean'.format(best_chain),
keep_chain=best_chain,
keep_chemicals=keep_chemicals,
outdir=struct_outdir,
force_rerun=True)
except Exception as e:
# TODO: inspect causes of these errors - most common is Biopython PDBParser error
logging.exception("{}: unknown error with PDB ID {}".format(self.id, pdb.id))
print(e)
continue
log.debug('{}-{}: set as representative structure'.format(pdb.id, best_chain))
pdb.reset_chain_seq_records()
return self.representative_structure
else:
pdb.reset_chain_seq_records()
else:
log.debug('{}: no experimental structures meet cutoffs'.format(self.id))
# If we are to use homology, save its information in the representative structure field
if use_homology:
log.debug('{}: checking quality of homology models'.format(self.id))
all_models = self.get_homology_models()
# TODO: homology models are not ordered in any other way other than how they are loaded,
# rethink this for multiple homology models
for homology in all_models:
if not homology.structure_file:
log.debug('{}: no homology structure file'.format(self.id))
continue
self.align_seqprop_to_structprop(seqprop=self.representative_sequence,
structprop=homology,
outdir=seq_outdir,
engine=engine,
parse=True,
force_rerun=force_rerun)
best_chain = self.find_representative_chain(seqprop=self.representative_sequence,
structprop=homology,
seq_ident_cutoff=seq_ident_cutoff,
allow_missing_on_termini=allow_missing_on_termini,
allow_mutants=allow_mutants,
allow_deletions=allow_deletions,
allow_insertions=allow_insertions,
allow_unresolved=allow_unresolved)
if best_chain:
# If chain ID is empty (some homology models are like that), use ID "X"
if not best_chain.strip():
best_chain = 'X'
try:
self._representative_structure_setter(structprop=homology,
# new_id='{}-{}'.format(homology.id, best_chain), # 170906 Deprecated use of new_id
clean=True,
out_suffix='-{}_clean'.format(best_chain),
keep_chain=best_chain,
outdir=struct_outdir,
force_rerun=force_rerun)
except:
# TODO: inspect causes of these errors - most common is Biopython PDBParser error
logging.exception("Unknown error with homology model {}".format(homology.id))
continue
log.debug('{}-{}: set as representative structure'.format(homology.id, best_chain))
homology.reset_chain_seq_records()
return self.representative_structure
else:
homology.reset_chain_seq_records()
log.warning('{}: no structures meet quality checks'.format(self.id))
return None
[docs] def get_dssp_annotations(self, representative_only=True, force_rerun=False):
"""Run DSSP on structures and store calculations.
Annotations are stored in the protein structure's chain sequence at:
``<chain_prop>.seq_record.letter_annotations['*-dssp']``
Args:
representative_only (bool): If analysis should only be run on the representative structure
force_rerun (bool): If calculations should be rerun even if an output file exists
Todo:
* Some errors arise from storing annotations for nonstandard amino acids, need to run DSSP separately for those
"""
if representative_only:
if self.representative_structure:
try:
self.representative_structure.get_dssp_annotations(outdir=self.structure_dir, force_rerun=force_rerun)
except PDBException as e:
log.error('{}: Biopython error, issue matching sequences with {}'.format(self.id, self.representative_structure))
print(e)
except TypeError as e:
log.error('{}: Biopython error, DSSP SeqRecord length mismatch with {}'.format(self.id, self.representative_structure))
print(e)
except Exception as e:
log.error('{}: DSSP failed to run on {}'.format(self.id, self.representative_structure))
print(e)
else:
log.warning('{}: no representative structure set, cannot run DSSP'.format(self.id))
else:
for s in self.structures:
try:
s.get_dssp_annotations(outdir=self.structure_dir)
except PDBException as e:
log.error('{}: Biopython error, issue matching sequences with {}'.format(self.id, s.id))
print(e)
except TypeError as e:
log.error('{}: Biopython error, DSSP SeqRecord length mismatch with {}'.format(self.id, s.id))
print(e)
except Exception as e:
log.error('{}: DSSP failed to run on {}'.format(self.id, s.id))
print(e)
[docs] def get_msms_annotations(self, representative_only=True, force_rerun=False):
"""Run MSMS on structures and store calculations.
Annotations are stored in the protein structure's chain sequence at:
``<chain_prop>.seq_record.letter_annotations['*-msms']``
Args:
representative_only (bool): If analysis should only be run on the representative structure
force_rerun (bool): If calculations should be rerun even if an output file exists
"""
if representative_only:
if self.representative_structure:
try:
self.representative_structure.get_msms_annotations(outdir=self.structure_dir, force_rerun=force_rerun)
except TypeError:
log.error('{}: MSMS SeqRecord length mismatch with {}'.format(self.id, self.representative_structure))
except:
log.error('{}: unknown MSMS error with {}'.format(self.id, self.representative_structure))
else:
log.warning('{}: no representative structure set, cannot run MSMS'.format(self.id))
else:
for s in self.structures:
try:
s.get_msms_annotations(outdir=self.structure_dir)
except TypeError:
log.error('{}: MSMS SeqRecord length mismatch with {}'.format(self.id, s.id))
except Exception as e:
log.error('{}: unknown MSMS error with {}'.format(self.id, s.id))
print(e)
[docs] def get_freesasa_annotations(self, include_hetatms=False, representative_only=True, force_rerun=False):
"""Run freesasa on structures and store calculations.
Annotations are stored in the protein structure's chain sequence at:
``<chain_prop>.seq_record.letter_annotations['*-freesasa']``
Args:
include_hetatms (bool): If HETATMs should be included in calculations. Defaults to ``False``.
representative_only (bool): If analysis should only be run on the representative structure
force_rerun (bool): If calculations should be rerun even if an output file exists
"""
if representative_only:
if self.representative_structure:
try:
self.representative_structure.get_freesasa_annotations(outdir=self.structure_dir,
include_hetatms=include_hetatms,
force_rerun=force_rerun)
except TypeError:
log.error('{}: freesasa SeqRecord length mismatch with {}'.format(self.id, self.representative_structure))
except:
log.error('{}: unknown freesasa error with {}'.format(self.id, self.representative_structure))
else:
log.warning('{}: no representative structure set, cannot run freesasa'.format(self.id))
else:
for s in self.structures:
try:
s.get_freesasa_annotations(outdir=self.structure_dir, include_hetatms=include_hetatms)
except TypeError:
log.error('{}: freesasa SeqRecord length mismatch with {}'.format(self.id, s.id))
except Exception as e:
log.error('{}: unknown freesasa error with {}'.format(self.id, s.id))
print(e)
[docs] def find_disulfide_bridges(self, representative_only=True):
"""Run Biopython's disulfide bridge finder and store found bridges.
Annotations are stored in the protein structure's chain sequence at:
``<chain_prop>.seq_record.annotations['SSBOND-biopython']``
Args:
representative_only (bool): If analysis should only be run on the representative structure
"""
if representative_only:
if self.representative_structure:
try:
self.representative_structure.find_disulfide_bridges()
except KeyError:
log.error('{}: unable to run disulfide bridge finder on {}'.format(self.id, self.representative_structure))
else:
log.warning('{}: no representative structure set, cannot run disulfide bridge finder'.format(self.id))
else:
for s in self.structures:
try:
s.find_disulfide_bridges()
except KeyError:
log.error('{}: unable to run disulfide bridge finder on {}'.format(self.id, s.id))
[docs] def get_residue_annotations(self, seq_resnum, seqprop=None, structprop=None, chain_id=None,
use_representatives=False):
"""Get all residue-level annotations stored in the SeqProp ``letter_annotations`` field for a given residue number.
Uses the representative sequence, structure, and chain ID stored by default. If other properties from other
structures are desired, input the proper IDs. An alignment for the given sequence to the structure must
be present in the sequence_alignments list.
Args:
seq_resnum (int): Residue number in the sequence
seqprop (SeqProp): SeqProp object
structprop (StructProp): StructProp object
chain_id (str): ID of the structure's chain to get annotation from
use_representatives (bool): If the representative sequence/structure/chain IDs should be used
Returns:
dict: All available letter_annotations for this residue number
"""
if use_representatives:
if seqprop and structprop and chain_id:
raise ValueError('Overriding sequence, structure, and chain IDs with representatives. '
'Set use_representatives to False if custom IDs are to be used.')
else:
if not seqprop or not structprop or not chain_id:
raise ValueError('Input sequence, structure, and chain to map between, or set use_representatives '
'to True.')
if use_representatives:
seqprop = self.representative_sequence
structprop = self.representative_structure
chain_id = self.representative_chain
log.debug('Using sequence: {}, structure: {}, chain: {}'.format(seqprop.id, structprop.id, chain_id))
# Create a new SeqFeature
f = SeqFeature(FeatureLocation(seq_resnum-1, seq_resnum))
# Get sequence properties
seq_features = f.extract(seqprop)
# Store in dictionary to return, clean it up
all_info = ssbio.utils.clean_single_dict(indict=seq_features.letter_annotations,
prepend_to_keys='seq_',
remove_keys_containing='_chain_index')
all_info['seq_resnum'] = seq_resnum
all_info['seq_residue'] = str(seq_features.seq)
if structprop:
chain = structprop.chains.get_by_id(chain_id)
# Get structure properties
mapping_to_structure_resnum = self.map_seqprop_resnums_to_structprop_resnums(resnums=seq_resnum,
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
# Try finding the residue in the structure
if f.location.end.position in mapping_to_structure_resnum:
struct_resnum = mapping_to_structure_resnum[f.location.end.position]
struct_f = SeqFeature(FeatureLocation(struct_resnum-1, struct_resnum))
struct_seq_features = struct_f.extract(chain.seq_record)
struct_info = ssbio.utils.clean_single_dict(indict=struct_seq_features.letter_annotations,
prepend_to_keys='struct_',
remove_keys_containing='structure_resnums')
struct_info['struct_resnum'] = struct_resnum
struct_info['struct_residue'] = str(struct_seq_features.seq)
all_info.update(struct_info)
# Warn if residue differs from sequence
if seq_features.seq != struct_seq_features.seq:
log.warning('Sequence residue ({}{}) does not match structure residue ({}{}). '
'This may simply be due to differences in the structure'.format(seq_features.seq,
seq_resnum,
struct_seq_features.seq,
struct_resnum))
return all_info
[docs] def sequence_mutation_summary(self, alignment_ids=None, alignment_type=None):
"""Summarize all mutations found in the sequence_alignments attribute.
Returns 2 dictionaries, single_counter and fingerprint_counter.
single_counter:
Dictionary of ``{point mutation: list of genes/strains}``
Example::
{
('A', 24, 'V'): ['Strain1', 'Strain2', 'Strain4'],
('R', 33, 'T'): ['Strain2']
}
Here, we report which genes/strains have the single point mutation.
fingerprint_counter:
Dictionary of ``{mutation group: list of genes/strains}``
Example::
{
(('A', 24, 'V'), ('R', 33, 'T')): ['Strain2'],
(('A', 24, 'V')): ['Strain1', 'Strain4']
}
Here, we report which genes/strains have the specific combinations (or "fingerprints") of point mutations
Args:
alignment_ids (str, list): Specified alignment ID or IDs to use
alignment_type (str): Specified alignment type contained in the ``annotation`` field of an alignment object,
``seqalign`` or ``structalign`` are the current types.
Returns:
dict, dict: single_counter, fingerprint_counter
"""
if alignment_ids:
ssbio.utils.force_list(alignment_ids)
if len(self.sequence_alignments) == 0:
log.error('{}: no sequence alignments'.format(self.id))
return {}, {}
fingerprint_counter = defaultdict(list)
single_counter = defaultdict(list)
for alignment in self.sequence_alignments:
# Ignore alignments if a list of identifiers is provided
if alignment_ids:
if alignment.id not in alignment_ids:
continue
# Ignore alignments if type is specified
if alignment_type:
if alignment.annotations['ssbio_type'] != alignment_type:
continue
other_sequence = alignment.annotations['b_seq']
mutations = alignment.annotations['mutations']
if mutations:
# Turn this list of mutations into a tuple so it can be a dictionary key
mutations = tuple(tuple(x) for x in mutations)
fingerprint_counter[mutations].append(other_sequence)
for m in mutations:
single_counter[m].append(other_sequence)
return dict(single_counter), dict(fingerprint_counter)
[docs] def add_features_to_nglview(self, view, seqprop=None, structprop=None, chain_id=None, use_representatives=False):
"""Add select features from the selected SeqProp object to an NGLWidget view object.
Currently parsing for:
* Single residue features (ie. metal binding sites)
* Disulfide bonds
Args:
view (NGLWidget): NGLWidget view object
seqprop (SeqProp): SeqProp object
structprop (StructProp): StructProp object
chain_id (str): ID of the structure's chain to get annotation from
use_representatives (bool): If the representative sequence/structure/chain IDs should be used
"""
if use_representatives:
if seqprop and structprop and chain_id:
raise ValueError('Overriding sequence, structure, and chain IDs with representatives. '
'Set use_representatives to False if custom IDs are to be used.')
else:
if not seqprop or not structprop or not chain_id:
raise ValueError('Input sequence, structure, and chain to map between, or set use_representatives '
'to True.')
if use_representatives:
seqprop = self.representative_sequence
structprop = self.representative_structure
chain_id = self.representative_chain
# Parse and store chain seq if not already stored
if not structprop.chains.has_id(chain_id):
structprop.parse_structure()
if not structprop.chains.has_id(chain_id):
raise ValueError('Chain {} not present in structure {}'.format(chain_id, structprop.id))
if not seqprop.features:
log.warning('{}: no stored features'.format(seqprop.id))
# Loop through any stored features
for f in seqprop.features:
# Display disulfide bonds
if f.type.lower() == 'disulfide bond':
# TODO: double check if .start or .start + 1
disulfide = self.map_seqprop_resnums_to_structprop_resnums(resnums=[f.location.start + 1, f.location.end],
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
to_view = [str(x)+'.CA' for x in list(disulfide.values())]
view.add_distance(atom_pair=[to_view], color='black')
log.info('Disulfide bridge at residues {} & {}'.format(f.location.start + 1, f.location.end))
# Display DNA-binding regions
if f.type.lower() == 'dna-binding region' or f.type.lower() == 'nucleotide phosphate-binding region':
impres = self.map_seqprop_resnums_to_structprop_resnums(resnums=[f.location.start + 1,
f.location.end],
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
# TODO: need to check if f.location.start was mapped and if not, try incrementing. or input the list
# of resnums, not just the start and end
if f.location.start + 1 in impres and f.location.end in impres:
mapped_start = impres[f.location.start + 1]
mapped_end = impres[f.location.end]
view.add_ball_and_stick(selection=':{} and ( {}-{} )'.format(chain_id,
mapped_start,
mapped_end), color='black')
log.info('{} at sequence region {}-{}, structure residues {}-{}'.format(f.type,
f.location.start,
f.location.end,
mapped_start,
mapped_end))
# Display other single residues
if f.location.end - 1 == f.location.start:
if f.type.lower() == 'sequence variant' or f.type.lower() == 'mutagenesis site':
continue
impres = self.map_seqprop_resnums_to_structprop_resnums(resnums=f.location.end,
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
if f.location.end in impres:
impres_mapped = impres[f.location.end]
view.add_ball_and_stick(selection=str(impres_mapped), color='black')
view.add_label(selection=':{} and {}'.format(chain_id, impres_mapped), label_type='res', color='black')
log.info('{} at sequence residue {}, structure residue {}'.format(f.type, f.location.end, impres_mapped))
[docs] def add_mutations_to_nglview(self, view, alignment_type='seqalign', alignment_ids=None,
seqprop=None, structprop=None, chain_id=None, use_representatives=False,
grouped=False, color='red', unique_colors=True,
opacity_range=(0.8,1), scale_range=(1,5)):
"""Add representations to an NGLWidget view object for residues that are mutated in the
``sequence_alignments`` attribute.
Args:
view (NGLWidget): NGLWidget view object
alignment_type (str): Specified alignment type contained in the ``annotation`` field of an alignment object,
``seqalign`` or ``structalign`` are the current types.
alignment_ids (str, list): Specified alignment ID or IDs to use
seqprop (SeqProp): SeqProp object
structprop (StructProp): StructProp object
chain_id (str): ID of the structure's chain to get annotation from
use_representatives (bool): If the representative sequence/structure/chain IDs should be used
grouped (bool): If groups of mutations should be colored and sized together
color (str): Color of the mutations (overridden if unique_colors=True)
unique_colors (bool): If each mutation/mutation group should be colored uniquely
opacity_range (tuple): Min/max opacity values (mutations that show up more will be opaque)
scale_range (tuple): Min/max size values (mutations that show up more will be bigger)
"""
if use_representatives:
if seqprop and structprop and chain_id:
raise ValueError('Overriding sequence, structure, and chain IDs with representatives. '
'Set use_representatives to False if custom IDs are to be used.')
else:
if not seqprop or not structprop or not chain_id:
raise ValueError('Input sequence, structure, and chain to map between, or set use_representatives '
'to True.')
if use_representatives:
seqprop = self.representative_sequence
structprop = self.representative_structure
chain_id = self.representative_chain
log.debug('Using sequence: {}, structure: {}, chain: {}'.format(seqprop.id, structprop.id, chain_id))
# Get the summary of mutations
single, fingerprint = self.sequence_mutation_summary(alignment_type=alignment_type, alignment_ids=alignment_ids)
# Map residues from sequence to structure
if not grouped:
single_lens = {k: len(v) for k, v in single.items()}
single_map_to_structure = {}
for k, v in single_lens.items():
resnum = int(k[1])
resnum_to_structure = self.map_seqprop_resnums_to_structprop_resnums(resnums=resnum,
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
if resnum not in resnum_to_structure:
log.warning('{}: residue is not available in structure {}'.format(resnum, structprop.id))
continue
new_key = resnum_to_structure[resnum]
single_map_to_structure[new_key] = v
structprop.add_scaled_residues_highlight_to_nglview(view=view,
structure_resnums=single_map_to_structure,
chain=chain_id,
color=color,
unique_colors=unique_colors,
opacity_range=opacity_range,
scale_range=scale_range)
else:
log.warning('Viewing mutation groups is currently in beta -- groups may overwrite each other')
fingerprint_lens = {k: len(v) for k, v in fingerprint.items()}
fingerprint_map_to_structure = {}
for k, v in fingerprint_lens.items():
k_list = [int(x[1]) for x in k]
resnums_to_structure = self.map_seqprop_resnums_to_structprop_resnums(resnums=k_list,
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
new_key = tuple(y for y in resnums_to_structure.values())
fingerprint_map_to_structure[new_key] = v
structprop.add_scaled_residues_highlight_to_nglview(view=view,
structure_resnums=fingerprint_map_to_structure,
chain=chain_id,
color=color,
unique_colors=unique_colors,
opacity_range=opacity_range,
scale_range=scale_range)
[docs] def add_fingerprint_to_nglview(self, view, fingerprint,
seqprop=None, structprop=None, chain_id=None, use_representatives=False,
color='red', opacity_range=(0.8, 1), scale_range=(1, 5)):
"""Add representations to an NGLWidget view object for residues that are mutated in the
``sequence_alignments`` attribute.
Args:
view (NGLWidget): NGLWidget view object
fingerprint (dict): Single mutation group from the ``sequence_mutation_summary`` function
seqprop (SeqProp): SeqProp object
structprop (StructProp): StructProp object
chain_id (str): ID of the structure's chain to get annotation from
use_representatives (bool): If the representative sequence/structure/chain IDs should be used
color (str): Color of the mutations (overridden if unique_colors=True)
opacity_range (tuple): Min/max opacity values (mutations that show up more will be opaque)
scale_range (tuple): Min/max size values (mutations that show up more will be bigger)
"""
if use_representatives:
if seqprop and structprop and chain_id:
raise ValueError('Overriding sequence, structure, and chain IDs with representatives. '
'Set use_representatives to False if custom IDs are to be used.')
else:
if not seqprop or not structprop or not chain_id:
raise ValueError('Input sequence, structure, and chain to map between, or set use_representatives '
'to True.')
if use_representatives:
seqprop = self.representative_sequence
structprop = self.representative_structure
chain_id = self.representative_chain
log.debug('Using sequence: {}, structure: {}, chain: {}'.format(seqprop.id, structprop.id, chain_id))
fingerprint_lens = {k: len(v) for k, v in fingerprint.items()}
fingerprint_map_to_structure = {}
for k, v in fingerprint_lens.items():
k_list = [int(x[1]) for x in k]
resnums_to_structure = self.map_seqprop_resnums_to_structprop_resnums(resnums=k_list,
seqprop=seqprop,
structprop=structprop,
chain_id=chain_id,
use_representatives=use_representatives)
new_key = tuple(y for y in resnums_to_structure.values())
fingerprint_map_to_structure[new_key] = v
structprop.add_scaled_residues_highlight_to_nglview(view=view,
structure_resnums=fingerprint_map_to_structure,
chain=chain_id,
color=color,
opacity_range=opacity_range,
scale_range=scale_range)
# def view_all_mutations(self, alignment_type, grouped=False, color='red', unique_colors=True, structure_opacity=0.5,
# opacity_range=(0.8,1), scale_range=(1,5), gui=False):
# """Map all sequence alignment mutations to the structure.
#
# Args:
# alignment_type (str): Specified alignment type contained in the ``annotation`` field of an alignment object,
# ``seqalign`` or ``structalign`` are the current types.
# grouped (bool): If groups of mutations should be colored and sized together
# color (str): Color of the mutations (overridden if unique_colors=True)
# unique_colors (bool): If each mutation/mutation group should be colored uniquely
# structure_opacity (float): Opacity of the protein structure cartoon representation
# opacity_range (tuple): Min/max opacity values (mutations that show up more will be opaque)
# scale_range (tuple): Min/max size values (mutations that show up more will be bigger)
# gui (bool): If the NGLview GUI should show up
#
# Returns:
# NGLviewer object
#
# """
# single, fingerprint = self.sequence_mutation_summary(alignment_type=alignment_type)
#
# single_lens = {k: len(v) for k, v in single.items()}
# single_map_to_structure = {}
# for k, v in single_lens.items():
# resnum = int(k[1])
# resnum_to_structure = self.map_seqprop_resnums_to_structprop_resnums(resnums=resnum, use_representatives=True)
# if resnum not in resnum_to_structure:
# log.warning('{}: residue is not available in structure {}'.format(resnum, self.representative_structure.id))
# continue
# new_key = resnum_to_structure[resnum]
# single_map_to_structure[new_key] = v
#
# if not grouped:
# view = self.representative_structure.view_structure_and_highlight_residues_scaled(single_map_to_structure,
# color=color, unique_colors=unique_colors,
# structure_opacity=structure_opacity,
# opacity_range=opacity_range,
# scale_range=scale_range,
# gui=gui)
# return view
#
# else:
# fingerprint_lens = {k: len(v) for k, v in fingerprint.items()}
# fingerprint_map_to_structure = {}
# for k, v in fingerprint_lens.items():
# k_list = [int(x[1]) for x in k]
# resnums_to_structure = self.map_seqprop_resnums_to_structprop_resnums(resnums=k_list, use_representatives=True)
# new_key = tuple(y for y in resnums_to_structure.values())
# fingerprint_map_to_structure[new_key] = v
#
# view = self.representative_structure.view_structure_and_highlight_residues_scaled(fingerprint_map_to_structure,
# color=color, unique_colors=unique_colors,
# opacity_range=opacity_range,
# scale_range=scale_range,
# gui=gui)
# return view
# def summarize_protein(self):
# """Gather all possible attributes in the sequences and structures and summarize everything.
#
# Returns:
# dict:
#
# """
# d = OrderedDict()
# repseq = self.representative_sequence
# if not self.representative_structure:
# repstruct = StructProp(self.id)
# repchain = repstruct.representative_chain
# else:
# repstruct = self.representative_structure
# repchain = self.representative_structure.representative_chain
# single, fingerprint = self.representative_sequence.sequence_mutation_summary()
# numstrains = len(self.sequences) - 1
#
# d['Gene ID'] = self.id
# d['Number of sequences'] = len(self.sequences)
# d['Number of structures (total)'] = self.num_structures
# d['Number of structures (experimental)'] = self.num_structures_experimental
# d['Number of structures (homology models)'] = self.num_structures_homology
#
# # d['------REPRESENTATIVE SEQUENCE PROPERTIES------')
# # d['Sequence ID'] = repseq.id
# d['Sequence length'] = repseq.sequence_len
# d['Predicted number of transmembrane helices'] = repseq.seq_record.annotations['num_tm_helix-tmhmm']
#
# # d['------REPRESENTATIVE STRUCTURE PROPERTIES------')
# d['Structure ID'] = repstruct.id
# # d['Structure representative chain'] = format(repchain.id)))
# d['Structure is experimental'] = repstruct.is_experimental
# # d['Structure origin'] = repstruct.taxonomy_name))
# # d['Structure description'] = repstruct.description))
# if repstruct.reference_seq_top_coverage:
# d['Structure coverage of sequence'] = str(repstruct.reference_seq_top_coverage) + '%'
#
# # d['------ALIGNMENTS SUMMARY------')
# # d['Number of sequence alignments'] = len(repseq.sequence_alignments)))
# # d['Number of structure alignments'] = len(repseq.structure_alignments)))
#
# singles = []
# for k, v in single.items():
# k = [str(x) for x in k]
# if len(v) / numstrains >= 0.01:
# singles.append(''.join(k)) # len(v) is the number of strains
# d['Mutations that show up in more than 10% of strains'] = ';'.join(singles)
#
# allfingerprints = []
# for k, v in fingerprint.items():
# if len(v) / numstrains >= 0.01:
# fingerprints = []
# for m in k:
# y = [str(x) for x in m]
# fingerprints.append(''.join(y))
# allfingerprints.append('-'.join(fingerprints))
# d['Mutation groups that show up in more than 10% of strains'] = ';'.join(allfingerprints)
#
# return d
def __json_decode__(self, **attrs):
for k, v in attrs.items():
if k == 'sequences' or k == 'structures' or k == 'sequence_alignments' or k == 'structure_alignments':
setattr(self, k, DictList(v))
else:
setattr(self, k, v)