def calcANM(pdb,
selstr='calpha',
cutoff=15.,
gamma=1.,
n_modes=20,
zeros=False):
"""Returns an :class:`ANM` instance and atoms used for the calculations.
By default only alpha carbons are considered, but selection string helps
selecting a subset of it. *pdb* can be :class:`.Atomic` instance."""
if isinstance(pdb, str):
ag = parsePDB(pdb)
title = ag.getTitle()
elif isinstance(pdb, Atomic):
ag = pdb
if isinstance(pdb, AtomGroup):
title = ag.getTitle()
else:
title = ag.getAtomGroup().getTitle()
else:
raise TypeError('pdb must be an atomic class, not {0}'.format(
type(pdb)))
anm = ANM(title)
sel = ag.select(selstr)
anm.buildHessian(sel, cutoff, gamma)
anm.calcModes(n_modes, zeros)
return anm, sel
def parsePDBs(self, **kwargs):
"""Load PDB into memory as :class:`.AtomGroup` instances using :func:`.parsePDB` and
perform selection based on residue ranges given by CATH."""
pdbs = self.getPDBs(True)
selstrs = self.getSelStrs()
header = kwargs.get('header', False)
model = kwargs.get('model', None)
LOGGER.timeit('_cath_parsePDB')
LOGGER.info('Parsing {0} PDB files...'.format(len(pdbs)))
ret = parsePDB(*pdbs, **kwargs)
if model != 0:
if header:
prots, _ = ret
else:
prots = ret
LOGGER.info('Extracting domains...')
for i in range(len(prots)):
sel = prots[i].select(selstrs[i])
prots[i] = sel
LOGGER.report('CATH domains are parsed and extracted in %.2fs', '_cath_parsePDB')
return ret
def test2(pdb='2nwl-mem.pdb'):
from prody import parsePDB
structure = parsePDB(pdb, subset='ca')
exanm = exANM('2nwl')
exanm.buildHessian(structure)
exanm.calcModes()
return exanm
def test2(pdb='2nwl-mem.pdb'):
from prody import parsePDB
structure = parsePDB(pdb, subset='ca')
exanm = exANM('2nwl')
exanm.buildHessian(structure)
exanm.calcModes()
return exanm
def test(pdb='2ci2'):
from prody import parsePDB
from numpy import zeros
pdb = parsePDB(pdb, subset='ca')
bbenm = bbENM()
bbenm.buildHessian(pdb, cutoff=12.)
return bbenm
def test(pdb='2ci2'):
from prody import parsePDB
from numpy import zeros
pdb = parsePDB(pdb, subset='ca')
bbenm = bbENM()
bbenm.buildHessian(pdb, cutoff=12.)
return bbenm
def test(pdb='2ci2'):
from prody import parsePDB
from numpy import zeros
pdb = parsePDB(pdb, subset='ca')
bbenm = bbENM('2ci2')
bbenm.buildHessian(pdb, cutoff=7.)
bbenm.calcModes(n_modes = None)
return bbenm
def test():
from prody import parsePDB, GNM
from prody.dynamics.analysis import calcOverallNetEntropyTransfer
import matplotlib.pyplot as plt
pdb = parsePDB('1z83', subset='ca', chain='A')
gnm = GNM()
gnm.buildKirchhoff(pdb, cutoff=7.0)
gnm.calcModes(n_modes=None)
entTransfer = calcOverallNetEntropyTransfer(gnm,turbo=True)
# f = open('/data/Manuscript_data/Data/1Z83A/monomer_overallnet_A_cihan2.txt','w')
# for i in range(gnm.numAtoms()):
# for j in range(gnm.numAtoms()):
# if i != j:
# f.write('%d\t%d\t%f\n' % (i+1,j+1,entTransfer[i,j]))
# f.close()
return entTransfer
def test(pdb='2nwl-mem.pdb', blk='2nwl.blk'):
from prody import parsePDB
from numpy import zeros
pdb = parsePDB(pdb, subset='ca')
pdb.setData('block', zeros(len(pdb), int))
with open(blk) as inp:
for line in inp:
if line.startswith('BLOCK'):
_, b, n1, c1, r1, n2, c2, r2 = line.split()
sel = pdb.select('chain {} and resnum {} to {}'.format(
c1, r1, r2))
if sel:
sel.setData('block', int(b))
pdb.setBetas(pdb.getData('block'))
from prody import writePDB
writePDB('pdb2gb1_truncated.pdb', pdb)
rtb = RTB('2nwl')
rtb.buildHessian(pdb, pdb.getData('block'))
return rtb
def test(pdb='2nwl-mem.pdb', blk='2nwl.blk'):
from prody import parsePDB
from numpy import zeros
pdb = parsePDB(pdb, subset='ca')
pdb.setData('block', zeros(len(pdb), int))
with open(blk) as inp:
for line in inp:
if line.startswith('BLOCK'):
_, b, n1, c1, r1, n2, c2, r2 = line.split()
sel = pdb.select('chain {} and resnum {} to {}'
.format(c1, r1, r2))
if sel:
sel.setData('block', int(b))
pdb.setBetas(pdb.getData('block'))
from prody import writePDB
writePDB('pdb2gb1_truncated.pdb', pdb)
rtb = RTB('2nwl')
rtb.buildHessian(pdb, pdb.getData('block'))
return rtb
def calcGNM(pdb, selstr="calpha", cutoff=15.0, gamma=1.0, n_modes=20, zeros=False):
"""Return a :class:`GNM` instance and atoms used for the calculations.
By default only alpha carbons are considered, but selection string helps
selecting a subset of it. *pdb* can be :class:`.Atomic` instance."""
if isinstance(pdb, str):
ag = parsePDB(pdb)
title = ag.getTitle()
elif isinstance(pdb, Atomic):
ag = pdb
if isinstance(pdb, AtomGroup):
title = ag.getTitle()
else:
title = ag.getAtomGroup().getTitle()
else:
raise TypeError("pdb must be an atom container, not {0}".format(type(pdb)))
gnm = GNM(title)
sel = ag.select(selstr)
gnm.buildKirchhoff(sel, cutoff, gamma)
gnm.calcModes(n_modes)
return gnm, sel
def calcANM(pdb, selstr='calpha', cutoff=15., gamma=1., n_modes=20,
zeros=False):
"""Returns an :class:`ANM` instance and atoms used for the calculations.
By default only alpha carbons are considered, but selection string helps
selecting a subset of it. *pdb* can be :class:`.Atomic` instance."""
if isinstance(pdb, str):
ag = parsePDB(pdb)
title = ag.getTitle()
elif isinstance(pdb, Atomic):
ag = pdb
if isinstance(pdb, AtomGroup):
title = ag.getTitle()
else:
title = ag.getAtomGroup().getTitle()
else:
raise TypeError('pdb must be an atomic class, not {0}'
.format(type(pdb)))
anm = ANM(title)
sel = ag.select(selstr)
anm.buildHessian(sel, cutoff, gamma)
anm.calcModes(n_modes, zeros)
return anm, sel
def parsePDBs(self, **kwargs):
"""Load PDB into memory as :class:`.AtomGroup` instances using :func:`.parsePDB` and
perform selection based on residue ranges given by CATH."""
pdbs = self.getPDBs()
# selstrs = self.getSelstrs()
header = kwargs.get('header', False)
model = kwargs.get('model', None)
LOGGER.timeit('_uniprot_parsePDB')
LOGGER.info('Parsing {0} PDB files...'.format(len(pdbs)))
ret = parsePDB(*pdbs, **kwargs)
if model != 0:
headers = None
if header:
prots, headers = ret
else:
prots = ret
if not isinstance(prots, list):
prots = [prots]
if header:
headers = [headers]
ret = (prots, headers)
else:
ret = prots
LOGGER.info('Extracting domains...')
# for i in range(len(prots)):
# sel = prots[i].select(selstrs[i])
# prots[i] = sel
LOGGER.report('Uniprot domains are parsed and extracted in %.2fs',
'_uniprot_parsePDB')
return ret
def test(pdb="2nwl-mem.pdb", blk="2nwl.blk"):
from prody import parsePDB
from numpy import zeros, linalg
pdb = parsePDB(pdb, subset="ca")
pdb.setData("block", zeros(len(pdb), int))
with open(blk) as inp:
for line in inp:
if line.startswith("BLOCK"):
_, b, n1, c1, r1, n2, c2, r2 = line.split()
sel = pdb.select("chain {} and resnum {} to {}".format(c1, r1, r2))
if sel:
sel.setData("block", int(b))
pdb.setBetas(pdb.getData("block"))
coords = pdb.getCoords()
blocks = pdb.getBetas()
from prody import writePDB
writePDB("pdb2gb1_truncated.pdb", pdb)
rtb = RTB("2nwl")
rtb.buildHessian(coords, blocks, scale=64)
# rtb.calcModes()
return rtb
def parseScipionModes(run_path, title=None, pdb=None):
"""Returns :class:`.NMA` containing eigenvectors and eigenvalues
parsed from a ContinuousFlex FlexProtNMA Run directory.
:arg run_path: path to the Run directory
:type run_path: str
:arg title: title for :class:`.NMA` object
:type title: str
"""
if run_path.endswith("/"):
run_path = run_path[:-1]
run_name = os.path.split(run_path)[-1]
top_dirs = os.path.split(run_path)[0][:-4] # exclude "Runs"
star_data = parseSTAR(run_path + '/modes.xmd')
star_loop = star_data[0][0]
n_modes = star_loop.numRows()
row1 = star_loop[0]
mode1 = parseArray(top_dirs + row1['_nmaModefile']).reshape(-1)
dof = mode1.shape[0]
if pdb is not None:
atoms = parsePDB(pdb)
n_atoms = atoms.numAtoms()
else:
# assume standard NMA
n_atoms = dof // 3
vectors = np.zeros((dof, n_modes))
vectors[:, 0] = mode1
eigvals = np.zeros(n_modes)
try:
eigvals[0] = float(row1['_nmaEigenval'])
found_eigvals = True
except:
found_eigvals = False
for i, row in enumerate(star_loop[1:]):
vectors[:,
i + 1] = parseArray(top_dirs + row['_nmaModefile']).reshape(-1)
if found_eigvals:
eigvals[i + 1] = float(row['_nmaEigenval'])
if not found_eigvals:
log_fname = run_path + '/logs/run.stdout'
fi = open(log_fname, 'r')
lines = fi.readlines()
fi.close()
for line in lines:
if line.find('Eigenvector number') != -1:
j = int(line.strip().split()[-1]) - 1
if line.find('Corresponding eigenvalue') != -1:
eigvals[j] = float(line.strip().split()[-1])
if not found_eigvals:
found_eigvals = True
if title is None:
title = run_name
if not found_eigvals:
LOGGER.warn('No eigenvalues found')
eigvals = None
if dof == n_atoms * 3:
nma = NMA(title)
else:
nma = GNM(title)
nma.setEigens(vectors, eigvals)
return nma
def alignPDBEnsemble(ensemble, suffix='_aligned', outdir='.', gzip=False):
"""Align PDB files using transformations from *ensemble*, which may be
a :class:`.PDBEnsemble` or a :class:`.PDBConformation` instance. Label of
the conformation (see :meth:`~.PDBConformation.getLabel`) will be used to
determine the PDB structure and model number. First four characters of
the label is expected to be the PDB identifier and ending numbers to be the
model number. For example, the :class:`.Transformation` from conformation
with label *2k39_ca_selection_'resnum_<_71'_m116* will be applied to 116th
model of structure **2k39**. After applicable transformations are made,
structure will be written into *outputdir* as :file:`2k39_aligned.pdb`.
If ``gzip=True``, output files will be compressed. Return value is
the output filename or list of filenames, in the order files are processed.
Note that if multiple models from a file are aligned, that filename will
appear in the list multiple times."""
if not isinstance(ensemble, (PDBEnsemble, PDBConformation)):
raise TypeError('ensemble must be a PDBEnsemble or PDBConformation')
if isinstance(ensemble, PDBConformation):
ensemble = [ensemble]
if gzip:
gzip = '.gz'
else:
gzip = ''
output = []
pdbdict = {}
for conf in ensemble:
trans = conf.getTransformation()
if trans is None:
raise ValueError('transformations are not calculated, call '
'`superpose` or `iterpose`')
label = conf.getLabel()
pdb = label[:4]
filename = pdbdict.get(pdb, fetchPDB(pdb))
if filename is None:
LOGGER.warning(
'PDB file for conformation {0} is not found.'.format(label))
output.append(None)
continue
LOGGER.info('Parsing PDB file {0} for conformation {1}.'.format(
pdb, label))
acsi = None
model = label.rfind('m')
if model > 3:
model = label[model + 1:]
if model.isdigit():
acsi = int(model) - 1
LOGGER.info('Applying transformation to model {0}.'.format(model))
if isinstance(filename, str):
ag = parsePDB(filename)
else:
ag = filename
if acsi is not None:
if acsi >= ag.numCoordsets():
LOGGER.warn('Model number {0} for {1} is out of range.'.format(
model, pdb))
output.append(None)
continue
ag.setACSIndex(acsi)
trans.apply(ag)
outfn = os.path.join(outdir, pdb + suffix + '.pdb' + gzip)
if ag.numCoordsets() > 1:
pdbdict[pdb] = ag
else:
writePDB(outfn, ag)
output.append(os.path.normpath(outfn))
for pdb, ag in pdbdict.items(): # PY3K: OK
writePDB(os.path.join(outdir, pdb + suffix + '.pdb' + gzip), ag)
if len(output) == 1:
return output[0]
else:
return output
def parsePfamPDBs(query, data=[], **kwargs):
"""Returns a list of AtomGroups containing sections of chains that
correspond to a particular PFAM domain family. These are defined by
alignment start and end residue numbers.
:arg query: UniProt ID or PDB ID
If a PDB ID is provided the corresponding UniProt ID is used.
If this returns multiple matches then start or end must also be provided.
This query is also used for label refinement of the Pfam domain MSA.
:type query: str
:arg data: If given the data list from the Pfam mapping table will
be output through this argument.
:type data: list
:keyword start: Residue number for defining the start of the domain.
The PFAM domain that starts closest to this will be selected.
Default is **1**
:type start: int
:keyword end: Residue number for defining the end of the domain.
The PFAM domain that ends closest to this will be selected.
:type end: int
"""
start = kwargs.pop('start', 1)
end = kwargs.pop('end', None)
if len(query) > 4 and query.startswith('PF'):
pfam_acc = query
else:
pfam_matches = searchPfam(query)
keys = list(pfam_matches.keys())
if isinstance(start, Integral):
start_diff = []
for i, key in enumerate(pfam_matches):
start_diff.append(int(pfam_matches[key]['locations'][0]['start']) - start)
start_diff = np.array(start_diff)
pfam_acc = keys[np.where(abs(start_diff) == min(abs(start_diff)))[0][0]]
elif isinstance(end, Integral):
end_diff = []
for i, key in enumerate(pfam_matches):
end_diff.append(int(pfam_matches[key]['locations'][0]['end']) - end)
end_diff = np.array(end_diff)
pfam_acc = keys[np.where(abs(end_diff) == min(abs(end_diff)))[0][0]]
else:
raise ValueError('Please provide an integer for start or end '
'when using a UniProt ID or PDB ID.')
from ftplib import FTP
from .uniprot import queryUniprot
data_stream = BytesIO()
ftp_host = 'ftp.ebi.ac.uk'
ftp = FTP(ftp_host)
ftp.login()
ftp.cwd('pub/databases/Pfam/current_release')
ftp.retrbinary('RETR pdbmap.gz', data_stream.write)
ftp.quit()
zip_data = data_stream.getvalue()
data_stream.close()
rawdata = gunzip(zip_data)
if PY3K:
rawdata = rawdata.decode()
fields = ['PDB_ID', 'chain', 'nothing', 'PFAM_Name', 'PFAM_ACC',
'UniprotAcc', 'UniprotResnumRange']
data_dicts = []
for line in rawdata.split('\n'):
if line.find(pfam_acc) != -1:
data_dicts.append({})
for j, entry in enumerate(line.strip().split('\t')):
data_dicts[-1][fields[j]] = entry.strip(';')
pdb_ids = [data_dict['PDB_ID'] for data_dict in data_dicts]
chains = [data_dict['chain'] for data_dict in data_dicts]
header = kwargs.pop('header', False)
model = kwargs.get('model', None)
results = parsePDB(*pdb_ids, chain=chains, header=True, **kwargs)
ags, headers = results
ags, headers = list(ags), list(headers)
if model == 0:
LOGGER.info('only header is requested and returned')
return results
if header:
results = (ags, headers)
else:
# ags = results
# ags = list(ags)
results = ags
LOGGER.progress('Extracting Pfam domains...', len(ags))
comma_splitter = re.compile(r'\s*,\s*').split
no_info = []
for i, ag in enumerate(ags):
LOGGER.update(i)
data_dict = data_dicts[i]
pfamRange = data_dict['UniprotResnumRange'].split('-')
uniprotAcc = data_dict['UniprotAcc']
try:
uniData = queryUniprot(uniprotAcc)
except:
LOGGER.warn('No Uniprot record found for {0}'.format(data_dict['PBD_ID']))
continue
resrange = None
found = False
for key, value in uniData.items():
if not key.startswith('dbReference'):
continue
try:
pdbid = value['PDB']
except:
continue
if pdbid != data_dict['PDB_ID']:
continue
pdbchains = value['chains']
# example chain strings: "A=27-139, B=140-150" or "A/B=27-150"
pdbchains = comma_splitter(pdbchains)
for chain in pdbchains:
chids, resrange = chain.split('=')
chids = [chid.strip() for chid in chids.split('/')]
if data_dict['chain'] in chids:
resrange = resrange.split('-')
found = True
break
if found:
break
if found:
header = headers[i]
chain_accessions = [dbref.accession
for dbref in header[data_dict['chain']].dbrefs]
try:
if len(chain_accessions) > 0:
right_part = np.where(np.array(chain_accessions) ==
data_dict['UniprotAcc'])[0][0]
else:
raise ValueError('There is no accession for a chain in the Header')
except:
LOGGER.warn('Could not map domains in {0}'
.format(data_dict['PDB_ID']
+ data_dict['chain']))
no_info.append(i)
continue
right_dbref = header[data_dict['chain']].dbrefs[right_part]
chainStart = ag.select('chain {0}'.format(data_dict['chain'])
).getResnums()[0]
missing = chainStart - right_dbref.first[0]
partStart = ag.getResindices()[np.where(ag.getResnums() ==
right_dbref.first[0] + missing)][0]
pfStart, pfEnd = int(pfamRange[0]), int(pfamRange[1])
uniStart, uniEnd = int(resrange[0]), int(resrange[1])
resiStart = pfStart - uniStart + partStart - missing
resiEnd = pfEnd - uniStart + partStart - missing
ags[i] = ag.select('resindex {0} to {1}'.format(
resiStart, resiEnd))
else:
no_info.append(i)
LOGGER.finish()
for i in reversed(no_info):
ags.pop(i)
if header:
headers.pop(i)
if isinstance(data, list):
data.extend(data_dicts)
else:
LOGGER.warn('data should be a list in order to get output')
return results
def searchPfam(query, **kwargs):
"""Returns Pfam search results in a dictionary. Matching Pfam accession
as keys will map to evalue, alignment start and end residue positions.
:arg query: UniProt ID, PDB identifier, a protein sequence, or a sequence
file. Sequence queries must not contain without gaps and must be at
least 16 characters long
:type query: str
:arg timeout: timeout for blocking connection attempt in seconds, default
is 60
:type timeout: int
*query* can also be a PDB identifier, e.g. ``'1mkp'`` or ``'1mkpA'`` with
chain identifier. UniProt ID of the specified chain, or the first
protein chain will be used for searching the Pfam database."""
if isfile(query):
from prody.sequence import MSAFile
try:
seq = next(MSAFile(query))
except:
with openFile(query) as inp:
seq = ''.join(inp.read().split())
else:
seq = seq[0][1]
if not seq.isalpha():
raise ValueError('could not parse a sequence without gaps from ' +
query)
else:
seq = ''.join(query.split())
import xml.etree.cElementTree as ET
LOGGER.timeit('_pfam')
timeout = int(kwargs.get('timeout', 60))
if len(seq) >= MINSEQLEN:
if not seq.isalpha():
raise ValueError(repr(seq) + ' is not a valid sequence')
fseq = '>Seq\n' + seq
parameters = { 'hmmdb' : 'pfam', 'seq': fseq }
enc_params = urllib.urlencode(parameters).encode('utf-8')
request = urllib2.Request('https://www.ebi.ac.uk/Tools/hmmer/search/hmmscan', enc_params)
results_url = urllib2.urlopen(request).geturl()
#res_params = { 'output' : 'xml' }
res_params = { 'format' : 'tsv' }
enc_res_params = urllib.urlencode(res_params)
#modified_res_url = results_url + '?' + enc_res_params
modified_res_url = results_url.replace('results','download') + '?' + enc_res_params
result_request = urllib2.Request(modified_res_url)
# url = ( urllib2.urlopen(request).geturl() + '?output=xml')
LOGGER.debug('Submitted Pfam search for sequence "{0}...".'
.format(seq[:MINSEQLEN]))
#xml = urllib2.urlopen(result_request).read()
tsv = urllib2.urlopen(result_request).read()
# openURL(url, timeout=timeout).read()
# try:
# root = ET.XML(xml)
# except Exception as err:
# raise ValueError('failed to parse results XML, check URL: ' + modified_res_url)
matches = {}
#for child in root[0]:
#if child.tag == 'hits':
# accession = child.get('acc')
# pfam_id = accession.split('.')[0]
# matches[pfam_id]={}
# matches[pfam_id]['accession']=accession
# matches[pfam_id]['class']='Domain'
# matches[pfam_id]['id']=child.get('name')
# matches[pfam_id]['locations']={}
# matches[pfam_id]['locations']['ali_end']=child[0].get('alisqto')
# matches[pfam_id]['locations']['ali_start']=child[0].get('alisqfrom')
# matches[pfam_id]['locations']['bitscore']=child[0].get('bitscore')
# matches[pfam_id]['locations']['end']=child[0].get('alisqto')
# matches[pfam_id]['locations']['evalue']=child.get('evalue')
# matches[pfam_id]['locations']['evidence']='hmmer v3.0'
# matches[pfam_id]['locations']['hmm_end']=child[0].get('alihmmto')
# matches[pfam_id]['locations']['hmm_start']=child[0].get('alihmmfrom')
# matches[pfam_id]['locations']['significant']=child[0].get('significant')
# matches[pfam_id]['locations']['start']=child[0].get('alisqfrom')
# matches[pfam_id]['type']='Pfam-A'
# return matches
lines = tsv.split('\n')
keys = lines[0].split('\t')
root = {}
for i, line in enumerate(lines[1:-1]):
root[i] = {}
for j, key in enumerate(keys):
root[i][key] = line.split('\t')[j]
for child in root.values():
accession = child['Family Accession']
pfam_id = accession.split('.')[0]
matches[pfam_id]={}
matches[pfam_id]['accession'] = accession
matches[pfam_id]['class'] = 'Domain'
matches[pfam_id]['id'] = child['Family id']
matches[pfam_id]['locations'] = {}
matches[pfam_id]['locations']['ali_end'] = child['Ali. End']
matches[pfam_id]['locations']['ali_start'] = child['Ali. Start']
matches[pfam_id]['locations']['bitscore'] = child['Bit Score']
matches[pfam_id]['locations']['end'] = child['Env. End']
matches[pfam_id]['locations']['cond_evalue'] = child['Cond. E-value']
matches[pfam_id]['locations']['ind_evalue'] = child['Ind. E-value']
matches[pfam_id]['locations']['evidence'] = 'hmmer v3.0'
matches[pfam_id]['locations']['hmm_end'] = child['Model End']
matches[pfam_id]['locations']['hmm_start'] = child['Model Start']
#matches[pfam_id]['locations']['significant'] = child['significant']
matches[pfam_id]['locations']['start'] = child['Env. Start']
matches[pfam_id]['type'] = 'Pfam-A'
return matches
else:
if len(seq) <= 5:
idcode = None
from prody import parsePDBHeader
try:
polymers = parsePDBHeader(seq[:4], 'polymers')
except Exception as err:
LOGGER.warn('failed to parse header for {0} ({1})'
.format(seq[:4], str(err)))
else:
chid = seq[4:].upper()
for poly in polymers:
if chid and poly.chid != chid:
continue
for dbref in poly.dbrefs:
if dbref.database != 'UniProt':
continue
idcode = dbref.idcode
accession = dbref.accession
LOGGER.info('UniProt ID code {0} for {1} chain '
'{2} will be used.'
.format(idcode, seq[:4], poly.chid))
break
if idcode is not None:
break
if idcode is None:
LOGGER.warn('A UniProt ID code for PDB {0} could not be '
'parsed.'.format(repr(seq)))
url = prefix + 'protein/' + seq + '?output=xml'
else:
url = prefix + 'protein/' + idcode + '?output=xml'
else:
url = prefix + 'protein/' + seq + '?output=xml'
LOGGER.debug('Retrieving Pfam search results: ' + url)
xml = None
while LOGGER.timing('_pfam') < timeout:
try:
xml = openURL(url, timeout=timeout).read()
except Exception:
pass
else:
if xml not in ['PEND','RUN']:
break
if not xml:
raise IOError('Pfam search timed out or failed to parse results '
'XML, check URL: ' + url)
else:
LOGGER.report('Pfam search completed in %.2fs.', '_pfam')
if xml.find(b'There was a system error on your last request.') > 0:
LOGGER.warn('No Pfam matches found for: ' + seq)
return None
elif xml.find(b'No valid UniProt accession or ID') > 0:
try:
url = prefix + 'protein/' + accession + '?output=xml'
xml = openURL(url, timeout=timeout).read()
except:
try:
ag = parsePDB(seq, subset='ca')
ag_seq = ag.getSequence()
return searchPfam(ag_seq)
except:
raise ValueError('No valid UniProt accession or ID for: ' + seq)
try:
root = ET.XML(xml)
except Exception as err:
raise ValueError('failed to parse results XML, check URL: ' + url)
if len(seq) >= MINSEQLEN:
try:
xml_matches = root[0][0][0][0]
except IndexError:
raise ValueError('failed to parse results XML, check URL: ' + url)
else:
key = '{' + prefix + '}'
results = dictElement(root[0], key)
try:
xml_matches = results['matches']
except KeyError:
raise ValueError('failed to parse results XML, check URL: ' + url)
matches = dict()
for child in xml_matches:
try:
accession = child.attrib['accession'][:7]
except KeyError:
raise ValueError('failed to parse results XML, check URL: ' + url)
if not re.search('^P(F|B)[0-9]{5}$', accession):
raise ValueError('{0} does not match pfam accession'
' format'.format(accession))
match = matches.setdefault(accession, dict(child.items()))
locations = match.setdefault('locations', [])
for loc in child:
locations.append(dict(loc.items()))
if len(seq) < MINSEQLEN:
query = 'Query ' + repr(query)
else:
query = 'Query sequence'
if matches:
LOGGER.info(query + ' matched {0} Pfam families.'.format(len(matches)))
else:
LOGGER.info(query + ' did not match any Pfam families.')
return matches
def renumber_InputAlign(alnfile,pdbid,refid,selection="protein"\
,outfile="renumbered.pdb",pdbfile="",newAA=None,first=1):
'''
Renumber input pdb using an exsiting multiple alignment.
- alnfile: alignment in .fasta format. Beware of weird characters in the
sequence ids, eg "|"
- pdbid: sequence id in the alginment file that corresponds to the input
structure. Must be the same number of residues
- refid: sequence id corresponding to the reference sequence by which to
renumber the pdbid sequence. pdbid musnt' align to any gaps in refid.
- selection: atom selection(s) in the the structure file to renumber.
Will iterate over comma separated selections to renumber each.
- pdbfile: original structure file
- outfile: output structure file
- newAA: comma separated list of unrepresented amino acids
XXXYCA:
XXX = three letter abbrevation as in pdbfile
Y = one letter code in the alignment
CA = atom to use as CA if different from "CA", eg
C1 in PVL of 1JEN
'''
selections = selection.split(",")
tmp=tempfile.gettempdir()
modified_selections = []
if os.path.exists(alnfile):
aln = AlignIO.read(alnfile, "fasta",alphabet=IUPAC.protein)
else:
print("ERROR, no such alignment: %s"%alnfile)
exit(1)
aln_ids = [x.id for x in aln]
if pdbid in aln_ids and refid in aln_ids:
pdbSeqRec = seqbyname(aln, pdbid)
if not pdbSeqRec:
print("ERROR, bad pdbid name")
exit(1)
refSeqRec = seqbyname(aln, refid)
if not refSeqRec:
print("ERROR, bad refid name")
exit(1)
if pdbfile != '':
if os.path.exists(pdbfile):
structure = parsePDB(pdbfile)
updateAA(structure,newAA)
else:
print("ERROR, no such pdb file: %s"%pdbfile)
exit(1)
renumber_aln(aln, refid, pdbid,first)
for polymer in selections:
currentSel = structure.select("not hetero and protein and name CA and %s"%polymer)
if currentSel:
renumber_struct(structure, pdbSeqRec, polymer)
modified_selections.append(polymer)
else:
print('ERROR: Selection \"%s\" has zero CA atoms'%polymer)
else:
if pdbid not in [x.id for x in aln]:
print("ERROR, no such sequence to renumber: %s"%pdbid)
if refid not in [x.id for x in aln]:
print("ERROR, no such sequence to renumber by: %s"%refid)
exit(1)
if writePDB(outfile, structure):
print("Wrote renumbered %s selections from %s to %s"\
%(str(modified_selections),pdbfile,outfile))
def renumber_noInputAlign(pdbfile,refseqfile,selection="protein",\
outfile="renumbered.pdb",newAA=None,first=1):
'''
Renumber pdb file (pdbfile) according to reference sequence in refseqfile.
Pdb sequence is extracted and aligned with reference sequence using needle
from EMBOSS.
- refseqfile: .fasta file containing the reference sequence by which to
renumber
- selection: atom selection(s) in the the structure file to renumber.
Will iterate over comma separated selections to renumber each.
- pdbfile: original structure file
- outfile: output structure file
- newAA: comma separated list of unrepresented amino acids
XXXYCA:
XXX = three letter abbrevation as in pdbfile
Y = one letter code in the alignment
CA = atom to use as CA if different from "CA", eg
C1 in PVL of 1JEN
'''
# selections = selection.split(",")
selections = selection
tmp=tempfile.gettempdir()
tmp_refseqfile="%s/refseq.fasta"%tmp
pdbID = re.search("\w+\.\w+", pdbfile).group(0)
tmp_pdbseqfile="%s/%s.fasta"%(tmp,pdbID)
tmp_needle="%s/needle.out"%tmp
if os.path.exists(refseqfile):
refseqRec = SeqIO.read(refseqfile,"fasta",alphabet=IUPAC.protein )
refseqRec.id = "refseq"
SeqIO.write(refseqRec,tmp_refseqfile,"fasta")
else:
print ("ERROR, no such file: %s"%refseqfile)
exit(1)
if os.path.exists(pdbfile):
structure=parsePDB("%s"%pdbfile)
updateAA(structure,newAA)
else:
print ("ERROR, no such file: %s"%pdbfile)
exit(1)
modified_selections = []
for polymer in selections:
currentSel = structure.select("protein and name CA and %s"%polymer)
if currentSel:
pdbseq_str=''.join([oneletter[i] for i in currentSel.getResnames()])
pdbseqRec=SeqRecord(Seq(pdbseq_str,IUPAC.protein),id=pdbID)
SeqIO.write(pdbseqRec,tmp_pdbseqfile,"fasta")
needle_cli = NeedleCommandline(asequence=tmp_pdbseqfile,bsequence=tmp_refseqfile,\
gapopen=10,gapextend=0.5,outfile=tmp_needle)
needle_cli()
aln = AlignIO.read(tmp_needle, "emboss",alphabet=IUPAC.protein )
# os.remove(tmp_needle)
# os.remove(tmp_pdbseqfile)
gpdb.renumber_aln(aln,"refseq",pdbID,first)
pdbRenSeq = gpdb.seqbyname(aln, pdbID)
gpdb.renumber_struct(structure, pdbRenSeq,polymer)
pdbRenSeq.annotations["resnum"]=str(pdbRenSeq.letter_annotations["resnum"])
modified_selections.append(polymer)
# seems to be the only way to store pret residue annotations
# AlignIO.write(aln,"pdb.outseq","seqxml")
else:
print ('ERROR: Selection \"%s\" has zero CA atoms'%polymer)
if writePDB(outfile, structure):
print ("Wrote renumbered %s selections from %s to %s"%\
(str(modified_selections),pdbfile,outfile))
os.remove(tmp_refseqfile)
def alignPDBEnsemble(ensemble, suffix='_aligned', outdir='.', gzip=False):
"""Align PDB files using transformations from *ensemble*, which may be
a :class:`.PDBEnsemble` or a :class:`.PDBConformation` instance. Label of
the conformation (see :meth:`~.PDBConformation.getLabel`) will be used to
determine the PDB structure and model number. First four characters of
the label is expected to be the PDB identifier and ending numbers to be the
model number. For example, the :class:`.Transformation` from conformation
with label *2k39_ca_selection_'resnum_<_71'_m116* will be applied to 116th
model of structure **2k39**. After applicable transformations are made,
structure will be written into *outputdir* as :file:`2k39_aligned.pdb`.
If *gzip* is **True**, output files will be compressed. Return value is
the output filename or list of filenames, in the order files are processed.
Note that if multiple models from a file are aligned, that filename will
appear in the list multiple times."""
if not isinstance(ensemble, (PDBEnsemble, PDBConformation)):
raise TypeError('ensemble must be a PDBEnsemble or PDBConformation')
if isinstance(ensemble, PDBConformation):
ensemble = [ensemble]
if gzip:
gzip = '.gz'
else:
gzip = ''
output = []
pdbdict = {}
for conf in ensemble:
trans = conf.getTransformation()
if trans is None:
raise ValueError('transformations are not calculated, call '
'`superpose` or `iterpose`')
label = conf.getLabel()
pdb = label[:4]
filename = pdbdict.get(pdb, fetchPDB(pdb))
if filename is None:
LOGGER.warning('PDB file for conformation {0} is not found.'
.format(label))
output.append(None)
continue
LOGGER.info('Parsing PDB file {0} for conformation {1}.'
.format(pdb, label))
acsi = None
model = label.rfind('m')
if model > 3:
model = label[model+1:]
if model.isdigit():
acsi = int(model) - 1
LOGGER.info('Applying transformation to model {0}.'
.format(model))
if isinstance(filename, str):
ag = parsePDB(filename)
else:
ag = filename
if acsi is not None:
if acsi >= ag.numCoordsets():
LOGGER.warn('Model number {0} for {1} is out of range.'
.format(model, pdb))
output.append(None)
continue
ag.setACSIndex(acsi)
trans.apply(ag)
outfn = os.path.join(outdir, pdb + suffix + '.pdb' + gzip)
if ag.numCoordsets() > 1:
pdbdict[pdb] = ag
else:
writePDB(outfn, ag)
output.append(os.path.normpath(outfn))
for pdb, ag in pdbdict.items(): # PY3K: OK
writePDB(os.path.join(outdir, pdb + suffix + '.pdb' + gzip), ag)
if len(output) == 1:
return output[0]
else:
return output
def searchPfam(query, **kwargs):
"""Returns Pfam search results in a dictionary. Matching Pfam accession
as keys will map to evalue, alignment start and end residue positions.
:arg query: UniProt ID, PDB identifier, a protein sequence, or a sequence
file. Sequence queries must not contain without gaps and must be at
least 16 characters long
:type query: str
:arg timeout: timeout for blocking connection attempt in seconds, default
is 60
:type timeout: int
*query* can also be a PDB identifier, e.g. ``'1mkp'`` or ``'1mkpA'`` with
chain identifier. UniProt ID of the specified chain, or the first
protein chain will be used for searching the Pfam database."""
if isfile(query):
from prody.sequence import MSAFile
try:
seq = next(MSAFile(query))
except:
with openFile(query) as inp:
seq = ''.join(inp.read().split())
else:
seq = seq[0][1]
if not seq.isalpha():
raise ValueError('could not parse a sequence without gaps from ' +
query)
else:
seq = ''.join(query.split())
import xml.etree.cElementTree as ET
LOGGER.timeit('_pfam')
timeout = int(kwargs.get('timeout', 60))
if len(seq) >= MINSEQLEN:
if not seq.isalpha():
raise ValueError(repr(seq) + ' is not a valid sequence')
fseq = '>Seq\n' + seq
parameters = {'hmmdb': 'pfam', 'seq': fseq}
enc_params = urllib.urlencode(parameters).encode('utf-8')
request = urllib2.Request(
'https://www.ebi.ac.uk/Tools/hmmer/search/hmmscan', enc_params)
results_url = urllib2.urlopen(request).geturl()
#res_params = { 'output' : 'xml' }
res_params = {'format': 'tsv'}
enc_res_params = urllib.urlencode(res_params)
#modified_res_url = results_url + '?' + enc_res_params
modified_res_url = results_url.replace(
'results', 'download') + '?' + enc_res_params
result_request = urllib2.Request(modified_res_url)
# url = ( urllib2.urlopen(request).geturl() + '?output=xml')
LOGGER.debug('Submitted Pfam search for sequence "{0}...".'.format(
seq[:MINSEQLEN]))
try:
#xml = urllib2.urlopen(result_request).read()
tsv = urllib2.urlopen(result_request).read()
# openURL(url, timeout=timeout).read()
except:
raise ValueError('No matching Pfam domains were found.')
# try:
# root = ET.XML(xml)
# except Exception as err:
# raise ValueError('failed to parse results XML, check URL: ' + modified_res_url)
matches = {}
#for child in root[0]:
#if child.tag == 'hits':
# accession = child.get('acc')
# pfam_id = accession.split('.')[0]
# matches[pfam_id]={}
# matches[pfam_id]['accession']=accession
# matches[pfam_id]['class']='Domain'
# matches[pfam_id]['id']=child.get('name')
# matches[pfam_id]['locations']={}
# matches[pfam_id]['locations']['ali_end']=child[0].get('alisqto')
# matches[pfam_id]['locations']['ali_start']=child[0].get('alisqfrom')
# matches[pfam_id]['locations']['bitscore']=child[0].get('bitscore')
# matches[pfam_id]['locations']['end']=child[0].get('alisqto')
# matches[pfam_id]['locations']['evalue']=child.get('evalue')
# matches[pfam_id]['locations']['evidence']='hmmer v3.0'
# matches[pfam_id]['locations']['hmm_end']=child[0].get('alihmmto')
# matches[pfam_id]['locations']['hmm_start']=child[0].get('alihmmfrom')
# matches[pfam_id]['locations']['significant']=child[0].get('significant')
# matches[pfam_id]['locations']['start']=child[0].get('alisqfrom')
# matches[pfam_id]['type']='Pfam-A'
# return matches
if PY3K:
tsv = tsv.decode()
lines = tsv.split('\n')
keys = lines[0].split('\t')
root = {}
for i, line in enumerate(lines[1:-1]):
root[i] = {}
for j, key in enumerate(keys):
root[i][key] = line.split('\t')[j]
for child in root.values():
accession = child['Family Accession']
pfam_id = accession.split('.')[0]
matches[pfam_id] = {}
matches[pfam_id]['accession'] = accession
matches[pfam_id]['class'] = 'Domain'
matches[pfam_id]['id'] = child['Family id']
matches[pfam_id]['locations'] = {}
matches[pfam_id]['locations']['ali_end'] = child['Ali. End']
matches[pfam_id]['locations']['ali_start'] = child['Ali. Start']
matches[pfam_id]['locations']['bitscore'] = child['Bit Score']
matches[pfam_id]['locations']['end'] = child['Env. End']
matches[pfam_id]['locations']['cond_evalue'] = child[
'Cond. E-value']
matches[pfam_id]['locations']['ind_evalue'] = child['Ind. E-value']
matches[pfam_id]['locations']['evidence'] = 'hmmer v3.0'
matches[pfam_id]['locations']['hmm_end'] = child['Model End']
matches[pfam_id]['locations']['hmm_start'] = child['Model Start']
#matches[pfam_id]['locations']['significant'] = child['significant']
matches[pfam_id]['locations']['start'] = child['Env. Start']
matches[pfam_id]['type'] = 'Pfam-A'
return matches
else:
if len(seq) <= 5:
idcode = None
from prody import parsePDBHeader
try:
polymers = parsePDBHeader(seq[:4], 'polymers')
except Exception as err:
LOGGER.warn('failed to parse header for {0} ({1})'.format(
seq[:4], str(err)))
else:
chid = seq[4:].upper()
for poly in polymers:
if chid and poly.chid != chid:
continue
for dbref in poly.dbrefs:
if dbref.database != 'UniProt':
continue
idcode = dbref.idcode
accession = dbref.accession
LOGGER.info('UniProt ID code {0} for {1} chain '
'{2} will be used.'.format(
idcode, seq[:4], poly.chid))
break
if idcode is not None:
break
if idcode is None:
LOGGER.warn('A UniProt ID code for PDB {0} could not be '
'parsed.'.format(repr(seq)))
url = prefix + 'protein/' + seq + '?output=xml'
else:
url = prefix + 'protein/' + idcode + '?output=xml'
else:
url = prefix + 'protein/' + seq + '?output=xml'
LOGGER.debug('Retrieving Pfam search results: ' + url)
xml = None
while LOGGER.timing('_pfam') < timeout:
try:
xml = openURL(url, timeout=timeout).read()
except Exception:
pass
else:
if xml not in ['PEND', 'RUN']:
break
if not xml:
raise IOError('Pfam search timed out or failed to parse results '
'XML, check URL: ' + url)
else:
LOGGER.report('Pfam search completed in %.2fs.', '_pfam')
if xml.find(b'There was a system error on your last request.') > 0:
LOGGER.warn('No Pfam matches found for: ' + seq)
return None
elif xml.find(b'No valid UniProt accession or ID') > 0:
try:
url = prefix + 'protein/' + accession + '?output=xml'
xml = openURL(url, timeout=timeout).read()
except:
try:
ag = parsePDB(seq, subset='ca')
ag_seq = ag.getSequence()
return searchPfam(ag_seq)
except:
raise ValueError('No valid UniProt accession or ID for: ' +
seq)
try:
root = ET.XML(xml)
except Exception as err:
raise ValueError('failed to parse results XML, check URL: ' + url)
if len(seq) >= MINSEQLEN:
try:
xml_matches = root[0][0][0][0]
except IndexError:
raise ValueError('failed to parse results XML, check URL: ' + url)
else:
key = '{' + prefix + '}'
results = dictElement(root[0], key)
try:
xml_matches = results['matches']
except KeyError:
raise ValueError('failed to parse results XML, check URL: ' + url)
matches = dict()
for child in xml_matches:
try:
accession = child.attrib['accession'][:7]
except KeyError:
raise ValueError('failed to parse results XML, check URL: ' + url)
if not re.search('^P(F|B)[0-9]{5}$', accession):
raise ValueError('{0} does not match pfam accession'
' format'.format(accession))
match = matches.setdefault(accession, dict(child.items()))
locations = match.setdefault('locations', [])
for loc in child:
locations.append(dict(loc.items()))
if len(seq) < MINSEQLEN:
query = 'Query ' + repr(query)
else:
query = 'Query sequence'
if matches:
LOGGER.info(query + ' matched {0} Pfam families.'.format(len(matches)))
else:
LOGGER.info(query + ' did not match any Pfam families.')
return matches
def parsePfamPDBs(query, data=[], **kwargs):
"""Returns a list of AtomGroups containing sections of chains that
correspond to a particular PFAM domain family. These are defined by
alignment start and end residue numbers.
:arg query: UniProt ID or PDB ID
If a PDB ID is provided the corresponding UniProt ID is used.
If this returns multiple matches then start or end must also be provided.
This query is also used for label refinement of the Pfam domain MSA.
:type query: str
:arg data: If given the data list from the Pfam mapping table will
be output through this argument.
:type data: list
:keyword start: Residue number for defining the start of the domain.
The PFAM domain that starts closest to this will be selected.
Default is **1**
:type start: int
:keyword end: Residue number for defining the end of the domain.
The PFAM domain that ends closest to this will be selected.
:type end: int
"""
start = kwargs.pop('start', 1)
end = kwargs.pop('end', None)
if len(query) > 4 and query.startswith('PF'):
pfam_acc = query
else:
pfam_matches = searchPfam(query)
keys = list(pfam_matches.keys())
if isinstance(start, Integral):
start_diff = []
for i, key in enumerate(pfam_matches):
start_diff.append(
int(pfam_matches[key]['locations'][0]['start']) - start)
start_diff = np.array(start_diff)
pfam_acc = keys[np.where(
abs(start_diff) == min(abs(start_diff)))[0][0]]
elif isinstance(end, Integral):
end_diff = []
for i, key in enumerate(pfam_matches):
end_diff.append(
int(pfam_matches[key]['locations'][0]['end']) - end)
end_diff = np.array(end_diff)
pfam_acc = keys[np.where(
abs(end_diff) == min(abs(end_diff)))[0][0]]
else:
raise ValueError('Please provide an integer for start or end '
'when using a UniProt ID or PDB ID.')
from ftplib import FTP
from .uniprot import queryUniprot
data_stream = BytesIO()
ftp_host = 'ftp.ebi.ac.uk'
ftp = FTP(ftp_host)
ftp.login()
ftp.cwd('pub/databases/Pfam/current_release')
ftp.retrbinary('RETR pdbmap.gz', data_stream.write)
ftp.quit()
zip_data = data_stream.getvalue()
data_stream.close()
rawdata = gunzip(zip_data)
if PY3K:
rawdata = rawdata.decode()
fields = [
'PDB_ID', 'chain', 'nothing', 'PFAM_Name', 'PFAM_ACC', 'UniprotAcc',
'UniprotResnumRange'
]
data_dicts = []
for line in rawdata.split('\n'):
if line.find(pfam_acc) != -1:
data_dicts.append({})
for j, entry in enumerate(line.strip().split('\t')):
data_dicts[-1][fields[j]] = entry.strip(';')
pdb_ids = [data_dict['PDB_ID'] for data_dict in data_dicts]
chains = [data_dict['chain'] for data_dict in data_dicts]
header = kwargs.pop('header', False)
model = kwargs.get('model', None)
results = parsePDB(*pdb_ids, chain=chains, header=True, **kwargs)
ags, headers = results
ags, headers = list(ags), list(headers)
if model == 0:
LOGGER.info('only header is requested and returned')
return results
if header:
results = (ags, headers)
else:
# ags = results
# ags = list(ags)
results = ags
LOGGER.progress('Extracting Pfam domains...', len(ags))
comma_splitter = re.compile(r'\s*,\s*').split
no_info = []
for i, ag in enumerate(ags):
LOGGER.update(i)
data_dict = data_dicts[i]
pfamRange = data_dict['UniprotResnumRange'].split('-')
uniprotAcc = data_dict['UniprotAcc']
try:
uniData = queryUniprot(uniprotAcc)
except:
LOGGER.warn('No Uniprot record found for {0}'.format(
data_dict['PBD_ID']))
continue
resrange = None
found = False
for key, value in uniData.items():
if not key.startswith('dbReference'):
continue
try:
pdbid = value['PDB']
except:
continue
if pdbid != data_dict['PDB_ID']:
continue
pdbchains = value['chains']
# example chain strings: "A=27-139, B=140-150" or "A/B=27-150"
pdbchains = comma_splitter(pdbchains)
for chain in pdbchains:
chids, resrange = chain.split('=')
chids = [chid.strip() for chid in chids.split('/')]
if data_dict['chain'] in chids:
resrange = resrange.split('-')
found = True
break
if found:
break
if found:
header = headers[i]
chain_accessions = [
dbref.accession for dbref in header[data_dict['chain']].dbrefs
]
try:
if len(chain_accessions) > 0:
right_part = np.where(
np.array(chain_accessions) ==
data_dict['UniprotAcc'])[0][0]
else:
raise ValueError(
'There is no accession for a chain in the Header')
except:
LOGGER.warn(
'Could not map domains in {0}'.format(data_dict['PDB_ID'] +
data_dict['chain']))
no_info.append(i)
continue
right_dbref = header[data_dict['chain']].dbrefs[right_part]
chainStart = ag.select('chain {0}'.format(
data_dict['chain'])).getResnums()[0]
missing = chainStart - right_dbref.first[0]
partStart = ag.getResindices()[np.where(
ag.getResnums() == right_dbref.first[0] + missing)][0]
pfStart, pfEnd = int(pfamRange[0]), int(pfamRange[1])
uniStart, uniEnd = int(resrange[0]), int(resrange[1])
resiStart = pfStart - uniStart + partStart - missing
resiEnd = pfEnd - uniStart + partStart - missing
ags[i] = ag.select('resindex {0} to {1}'.format(
resiStart, resiEnd))
else:
no_info.append(i)
LOGGER.finish()
for i in reversed(no_info):
ags.pop(i)
if header:
headers.pop(i)
if isinstance(data, list):
data.extend(data_dicts)
else:
LOGGER.warn('data should be a list in order to get output')
return results
def scanPockets(self):
'Generates ESSA z-scores for pockets and parses pocket features. It requires both Fpocket 3.0 and Pandas being installed in your system.'
from re import findall
fpocket = which('fpocket')
if fpocket is None:
LOGGER.warning(
'Fpocket (version >= 3.0) was not found, please install it.')
return None
try:
from pandas import Index, DataFrame
except ImportError as ie:
LOGGER.warning(ie.__str__() + ' was found, please install it.')
return None
rcr = {(i, j): k if self._rib else self._ri[k]
for i, j, k in zip(self._ca.getChids(), self._ca.getResnums(),
self._ca.getResindices())}
writePDB('{}_pro'.format(self._title), self._heavy)
direc = '{}_pro_out'.format(self._title)
if not isdir(direc):
system('fpocket -f {}_pro.pdb'.format(self._title))
chdir(direc + '/pockets')
l = [x for x in listdir('.') if x.endswith('.pdb')]
l.sort(key=lambda x: int(x.partition('_')[0][6:]))
ps = []
for x in l:
with open(x, 'r') as f:
tmp0 = f.read()
tmp1 = [(x[1].strip(), float(x[2])) for x in findall(
r'(\w+\s\w+\s*-\s*)(.+):\s*([\d.-]+)(\n)', tmp0)]
fea, sco = list(zip(*tmp1))
ps.append(sco)
pdbs = parsePDB(l)
chdir('../..')
# ----- # ----- #
ps = array(ps)
pcn = {
int(pdb.getTitle().partition('_')[0][6:]):
set(zip(pdb.getChids().tolist(),
pdb.getResnums().tolist()))
for pdb in pdbs
}
pi = {p: [rcr[x] for x in crn] for p, crn in pcn.items()}
pzs_max = {k: max(self._zscore[v]) for k, v in pi.items()}
pzs_med = {k: median(self._zscore[v]) for k, v in pi.items()}
# ----- # ----- #
indices = Index(range(1, ps.shape[0] + 1), name='Pocket #')
columns = Index(fea, name='Feature')
self._df = DataFrame(index=indices, columns=columns, data=ps)
# ----- # ----- #
columns_zs = Index(['ESSA_max', 'ESSA_med', 'LHD'], name='Z-score')
zps = c_[list(pzs_max.values())]
zps = hstack((zps, c_[list(pzs_med.values())]))
zps = hstack(
(zps, zscore(self._df[['Local hydrophobic density Score']])))
self._df_zs = DataFrame(index=indices, columns=columns_zs, data=zps)
def writePerturbResponsePDB(prs_matrix, pdbIn=None, **kwargs):
""" Write the average response to perturbation of
a particular residue (a row of a perturbation response matrix)
or the average effect of perturbation of a particular residue
(a column of a normalized perturbation response matrix)
into the b-factor field of a PDB file for visualisation in a
molecular graphics program.
If no chain is given this will be done for that residue in all chains.
If no residue number is given then the effectiveness and sensitivity
profiles will be written out instead. These two profiles are also returned
as arrays for further analysis if they aren't already provided.
:arg prs_matrix: a perturbation response matrix
or a :class:`.AtomGroup` object with a PRS matrix associated as data
:type prs_matrix: array or :class:`.AtomGroup`
:arg pdbIn: file name for the input PDB file where you would like the PRS
data mapped
:type pdbIn: str
:arg pdbOut: a list of file names (enclosed in square
brackets) for the output PDB file, default is to append
the chain and residue info (name and number) onto the pdbIn stem.
The input for pdbOut can also be used as a stem if you enter a
single string enclosed in quotes.
If no residue number is supplied, chain is ignored and the default
is to append '_effectiveness' and '_sensitivity' onto the stem.
:type pdbOut: list
:arg chain: chain identifier for the residue of interest, default is all chains
If you want to analyse residues in a subset of chains, concatentate them
together e.g. 'AC'
:type chain: str
:arg resnum: residue number for the residue of interest
:type resnum: int
:arg direction: the direction you want to use to read data out
of the PRS matrix for plotting: the options are 'effect' or 'response'.
Default is 'effect'.
A row gives the effect on each residue of peturbing the specified
residue.
A column gives the response of the specified residue to perturbing
each residue.
If no residue number is provided then this option will be ignored
:type direction: str
:arg returnData: whether to return effectiveness and sensitivity for analysis
default is False
:type returnProfiles: bool
:arg effectiveness: effectiveness profile
:type array
:arg sensitivity: sensitivity profile
:type array
"""
if not isinstance(prs_matrix, np.ndarray):
try:
prs_matrix = prs_matrix.getData('prs_matrix')
except:
raise TypeError(
'Please provide a valid PRS matrix in numpy ndarray format.')
try:
fi = open(pdbIn, 'r')
lines = fi.readlines()
fi.close()
except:
raise PRSMatrixParseError(
'Please provide a valid file name for the input PDB.')
chain = kwargs.get('chain', None)
structure = parsePDB(pdbIn, subset='ca')
structure.setData('prs_matrix', prs_matrix)
hv = structure.getHierView()
chains = []
for i in range(len(list(hv))):
chainAg = list(hv)[i]
chains.append(chainAg.getChids()[0])
chains = np.array(chains)
if chain is None:
chain = ''.join(chains)
resnum = kwargs.get('resnum', None)
pdbOut = kwargs.get('pdbOut', None)
if pdbOut is None:
out_stem = pdbIn.split('.')[0]
elif type(pdbOut) is str:
out_stem = pdbOut.split('.')[0]
pdbOut = None
if resnum is None:
effectiveness = kwargs.get('effectiveness', None)
sensitivity = kwargs.get('sensitivity', None)
if effectiveness is None or sensitivity is None:
effectiveness, sensitivity = calcPerturbResponseProfiles(
prs_matrix)
structure.setData('effectiveness', effectiveness)
structure.setData('sensitivity', sensitivity)
file_effs_name = '{0}_effectiveness.pdb'.format(out_stem)
file_sens_name = '{0}_sensitivity.pdb'.format(out_stem)
fileEffs = open(file_effs_name, 'w')
fileSens = open(file_sens_name, 'w')
for line in lines:
if line.find('ATOM') != 0 and line.find(
'HETATM') != 0 and line.find('ANISOU') != 0:
fileEffs.write(line)
fileSens.write(line)
elif line.find('ATOM') == 0:
fileEffs.write(line[:60] + '{:6.2f}'.format(float(structure.select( \
'chain {0} and resnum {1}'.format(line[21],line[22:26])) \
.getData('effectiveness')) * 100/np.max( \
structure.getData('effectiveness'))) + line[66:])
fileSens.write(line[:60] + '{:6.2f}'.format(float(structure.select( \
'chain {0} and resnum {1}'.format(line[21],line[22:26])) \
.getData('sensitivity')) * 100/np.max( \
structure.getData('sensitivity'))) + line[66:])
elif line.find('HETATM') == 0:
fileEffs.write(line[:60] + ' 0.00' + line[66:])
fileSens.write(line[:60] + ' 0.00' + line[66:])
fileEffs.close()
fileSens.close()
LOGGER.info('The effectiveness and sensitivity profiles were written' \
' to {0} and {1}.'.format(file_effs_name,file_sens_name))
returnData = kwargs.get('returnData', False)
if returnData:
return structure, effectiveness, sensitivity
else:
return
direction = kwargs.get('direction', 'effect')
for n in range(len(chain)):
if not chain[n] in chains:
raise PRSMatrixParseError('Chain {0} was not found in {1}'.format(
chain[n], pdbIn))
if pdbOut is None:
pdbOut = []
for c in chain:
pdbOut.append('{0}_{1}_{2}{3}_{4}.pdb' \
.format(out_stem, c, \
str(structure.select('chain {0} and resnum {1}' \
.format(c, resnum)).getResnames()), \
resnum, direction))
for c in chain:
fo = open(pdbOut[n], 'w')
for line in lines:
if line.find('ATOM') != 0 and line.find(
'HETATM') != 0 and line.find('ANISOU') != 0:
fo.write(line)
elif line.find('ATOM') == 0:
if direction is 'effect':
fo.write(line[:60] + '{:6.2f}'.format(float(structure.getData('prs_matrix') \
[structure.select('chain {0} and resnum {1}' \
.format(c, resnum)).getResindices(), \
structure.select('chain {0} and resnum {1}' \
.format(line[21], line[22:26])).getResindices()])*100) \
+ line[66:])
else:
fo.write(line[:60] + '{:6.2f}'.format(float(structure.getData('prs_matrix') \
[structure.select('chain {0} and resnum {1}' \
.format(line[21], line[22:26])).getResindices(), \
structure.select('chain {0} and resnum {1}' \
.format(c, resnum)).getResindices()])*100) \
+ line[66:])
elif line.find('HETATM') == 0:
fo.write(line[:60] + ' 0.00' + line[66:])
LOGGER.info('Perturbation responses for specific residues were written' \
' to {0}.'.format(', '.join(pdbOut)))
def writePerturbResponsePDB(prs_matrix, pdbIn, **kwargs):
""" Write the average response to perturbation of
a particular residue (a row of a perturbation response matrix)
or the average effect of perturbation of a particular residue
(a column of a normalized perturbation response matrix)
into the b-factor field of a PDB file for visualisation in a
molecular graphics program.
If no chain is given this will be done for that residue in all chains.
If no residue number is given then the effectiveness and sensitivity
profiles will be written out instead. These two profiles are also returned
as arrays for further analysis if they aren't already provided.
:arg prs_matrix: a perturbation response matrix
:type prs_matrix: ndarray
:arg pdbIn: file name for the input PDB file where you would like the PRS
data mapped
:type pdbIn: str
:arg pdbOut: a list of file names (enclosed in square
brackets) for the output PDB file, default is to append
the chain and residue info (name and number) onto the pdbIn stem.
The input for pdbOut can also be used as a stem if you enter a
single string enclosed in quotes.
If no residue number is supplied, chain is ignored and the default
is to append '_effectiveness' and '_sensitivity' onto the stem.
:type pdbOut: list
:arg chain: chain identifier for the residue of interest, default is all chains
If you want to analyse residues in a subset of chains, concatentate them
together e.g. 'AC'
:type chain: str
:arg resnum: residue number for the residue of interest
:type resnum: int
:arg direction: the direction you want to use to read data out
of the PRS matrix for plotting: the options are 'row' or 'column'.
Default is 'row'.
A row gives the effect on each residue of peturbing the specified
residue.
A column gives the response of the specified residue to perturbing
each residue.
If no residue number is provided then this option will be ignored
:type direction: str
:arg returnData: whether to return effectiveness and sensitivity for analysis
default is False
:type returnProfiles: bool
"""
if not type(prs_matrix) is np.ndarray:
raise TypeError(
'Please provide a valid PRS matrix in numpy ndarray format.')
try:
fi = open(pdbIn, 'r')
lines = fi.readlines()
fi.close()
except:
raise PRSMatrixParseError(
'Please provide a valid file name for the input PDB.')
chain = kwargs.get('chain', None)
structure = parsePDB(pdbIn).calpha
hv = structure.getHierView()
chains = []
for i in range(len(list(hv))):
chainAg = list(hv)[i]
chains.append(chainAg.getChids()[0])
chains = np.array(chains)
if chain is None:
chain = ''.join(chains)
resnum = kwargs.get('resnum', None)
pdbOut = kwargs.get('pdbOut', None)
if pdbOut is None:
out_stem = pdbIn.split('.')[0]
elif type(pdbOut) is str:
out_stem = pdbOut.split('.')[0]
pdbOut = None
if resnum is None:
effectiveness = kwargs.get('effectiveness')
sensitivity = kwargs.get('sensitivity')
if effectiveness is None or sensitivity is None:
effectiveness, sensitivity = calcPerturbResponseProfiles(
prs_matrix)
file_effs_name = '{0}_effectiveness.pdb'.format(out_stem)
file_sens_name = '{0}_sensitivity.pdb'.format(out_stem)
fileEffs = open(file_effs_name, 'w')
fileSens = open(file_sens_name, 'w')
for line in lines:
if line.find('ATOM') != 0 and line.find(
'HETATM') != 0 and line.find('ANISOU') != 0:
fileEffs.write(line)
fileSens.write(line)
elif line.find('ATOM') == 0:
sel_line_res = structure.select('resid {0}'.format(
line[22:26]))
j = np.where(structure.getResnums() == int(line[22:26]))[0] \
[np.where(sel_line_res.getChids() == line[21])[0][0]]
fileEffs.write(line[:60] + ' '*(6-len('{:3.2f}'.format(( \
effectiveness[j]*100/np.max(effectiveness))))) \
+ '{:3.2f}'.format((effectiveness[j]) \
*100/np.max(effectiveness)) + line[66:])
fileSens.write(line[:60] + ' '*(6-len('{:3.2f}'.format((\
sensitivity[j]*100/np.max(sensitivity))))) \
+ '{:3.2f}'.format((sensitivity[j]) \
*100/np.max(sensitivity)) + line[66:])
elif line.find('HETATM') == 0:
fileEffs.write(line[:60] + ' ' * 2 + '0.00' + line[66:])
fileSens.write(line[:60] + ' ' * 2 + '0.00' + line[66:])
fileEffs.close()
fileSens.close()
LOGGER.info('The effectiveness and sensitivity profiles were written' \
' to {0} and {1}.'.format(file_effs_name,file_sens_name))
returnData = kwargs.get('returnData', False)
if returnData:
return effectiveness, sensitivity
else:
return
timesNF = 0
direction = kwargs.get('direction', 'row')
for n in range(len(chain)):
if not chain[n] in chains:
raise PRSMatrixParseError('Chain {0} was not found in {1}'.format(
chain[n], pdbIn))
chainNum = int(np.where(chains == chain[n])[0])
chainAg = list(hv)[chainNum]
if not resnum in chainAg.getResnums():
LOGGER.info('A residue with number {0} was not found',
' in chain {1}. Continuing to next chain.' \
.format(resnum, chain[n]))
timesNF += 1
continue
if pdbOut is None:
pdbOut = []
for n in range(len(chain)):
chainNum = int(np.where(chains == chain[n])[0])
i = np.where(structure.getResnums() == resnum)[0][chainNum -
timesNF]
pdbOut.append('{0}_{1}_{2}{3}_{4}.pdb'.format(out_stem, chain[n], \
structure.getResnames()[i], resnum, direction))
for n in range(len(chain)):
chainNum = int(np.where(chains == chain)[0])
i = np.where(structure.getResnums() == resnum)[0][chainNum - timesNF]
fo = open(pdbOut[n], 'w')
for line in lines:
if line.find('ATOM') != 0 and line.find(
'HETATM') != 0 and line.find('ANISOU') != 0:
fo.write(line)
elif line.find('ATOM') == 0:
sel_line_res = structure.select('resid {0}'.format(
line[22:26]))
j = np.where(structure.getResnums() == int(line[22:26]))[0] \
[np.where(sel_line_res.getChids() == line[21])[0][0]]
if direction is 'row':
fo.write(line[:60] + ' '*(6-len('{:3.2f}'.format(( \
prs_matrix[i][j])*100/np.max(prs_matrix)))) \
+ '{:3.2f}'.format((prs_matrix[i][j]) \
*100/np.max(prs_matrix)) + line[66:])
else:
fo.write(line[:60] + ' '*(6-len('{:3.2f}'.format(( \
prs_matrix[j][i])*100/np.max(prs_matrix)))) \
+ '{:3.2f}'.format((prs_matrix[j][i]) \
*100/np.max(prs_matrix)) + line[66:])
elif line.find('HETATM') == 0:
fo.write(line[:60] + ' ' * 2 + '0.00' + line[66:])
LOGGER.info('Perturbation responses for specific residues were written' \
' to {0}.'.format(', '.join(pdbOut)))
return
def fetchPfamPDBs(**kwargs):
"""Returns a list of AtomGroups containing sections of chains that
correspond to a particular PFAM domain family. These are defined by
alignment start and end residue numbers.
:arg pfam_acc: The accession number for a pfam domain family, if known.
Alternatively you can select a family based on a query (see below).
:type pfam_acc: str
:arg query: UniProt ID or PDB ID
If a PDB ID is provided the corresponding UniProt ID is used.
If no query is provided but a pfam_acc is then the first entry
will be used as a query.
This query is also used for label refinement of the pfam domain MSA.
:type query: str
You must provide one of these two arguments.
Use of query requires start or end to also be provided.
:arg start: Residue number for defining the start of the domain.
The PFAM domain that starts closest to this will be selected.
:type start: int
:arg end: Residue number for defining the end of the domain.
The PFAM domain that ends closest to this will be selected.
:type end: int
:arg return_data: Whether to return the data dictionary from
the Pfam mapping table, default is False
:type return_data: bool
"""
pfam_acc = kwargs.pop('pfam_acc', None)
query = kwargs.pop('query', None)
start = kwargs.pop('start', None)
end = kwargs.pop('end', None)
return_data = kwargs.pop('return_data', False)
if pfam_acc is None:
if query is None:
raise ValueError('Please provide a value for pfam_acc or query.')
else:
pfam_matches = searchPfam(query)
if start is not None and type(start) is int:
start_diff = []
for i, key in enumerate(pfam_matches):
start_diff.append(
int(pfam_matches[key]['locations'][0]['start']) -
start)
start_diff = np.array(start_diff)
pfam_acc = pfam_matches.keys()[np.where(
abs(start_diff) == min(abs(start_diff)))[0][0]]
elif end is not None and type(end) is int:
end_diff = []
for i, key in enumerate(pfam_matches):
end_diff.append(
int(pfam_matches[key]['locations'][0]['end']) - end)
end_diff = np.array(end_diff)
pfam_acc = pfam_matches.keys()[np.where(
abs(end_diff) == min(abs(end_diff)))[0][0]]
else:
raise ValueError(
'Please provide an integer for start or end when using query.'
)
from ftplib import FTP
data = []
ftp_host = 'ftp.ebi.ac.uk'
ftp = FTP(ftp_host)
ftp.login('')
ftp.cwd('pub/databases/Pfam/mappings')
ftp.retrlines('RETR pdb_pfam_mapping.txt', data.append)
fields = []
for field in data[0].strip().split('\t'):
fields.append(field)
data_dict = []
for line in data[1:]:
if line.find(pfam_acc) != -1:
data_dict.append({})
for j, entry in enumerate(line.strip().split('\t')):
data_dict[-1][fields[j]] = entry
pdb_ids = []
pdbs = []
headers = []
for i in range(len(data_dict)):
pdb_id = data_dict[i]['PDB_ID']
if not pdb_id in pdb_ids:
pdb_ids.append(pdb_id)
result = parsePDB(*pdb_ids, **kwargs)
if return_data:
return data_dict, result
else:
return result
