/usr/lib/python2.7/dist-packages/csb/apps/hhfrag.py is in python-csb 1.2.3+dfsg-1.
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HHfrag: build a dynamic variable-length fragment library for protein structure
prediction with Rosetta AbInitio.
"""
import os
import multiprocessing
import csb.apps
import csb.apps.hhsearch as hhsearch
import csb.bio.io.hhpred
import csb.bio.fragments
import csb.bio.fragments.rosetta as rosetta
import csb.bio.structure
import csb.io.tsv
import csb.core
class ExitCodes(csb.apps.ExitCodes):
IO_ERROR = 2
INVALID_DATA = 3
HHSEARCH_FAILURE = 4
NO_OUTPUT = 5
class AppRunner(csb.apps.AppRunner):
@property
def target(self):
return HHfragApp
def command_line(self):
cmd = csb.apps.ArgHandler(self.program, __doc__)
cpu = multiprocessing.cpu_count()
cmd.add_scalar_option('hhsearch', 'H', str, 'path to the HHsearch executable', default='hhsearch')
cmd.add_scalar_option('database', 'd', str, 'database directory (containing PDBS25.hhm)', required=True)
cmd.add_scalar_option('min', 'm', int, 'minimum query segment length', default=6)
cmd.add_scalar_option('max', 'M', int, 'maximum query segment length', default=21)
cmd.add_scalar_option('step', 's', int, 'query segmentation step', default=3)
cmd.add_scalar_option('cpu', 'c', int, 'maximum degree of parallelism', default=cpu)
cmd.add_scalar_option('gap-filling', 'g', str, 'path to a fragment file (e.g. CSfrag or Rosetta NNmake), which will be used '
'to complement low-confidence regions (when specified, a hybrid fragment library be produced)')
cmd.add_scalar_option('filtered-filling', 'F', str, 'path to a filtered fragment file (e.g. filtered CSfrag-ments), which will '
'be mixed with the HHfrag-set and then filtered, resulting in a double-filtered library')
cmd.add_boolean_option('filtered-map', 'f', 'make an additional filtered fragment map of centroids and predict torsion angles', default=False)
cmd.add_boolean_option('c-alpha', None, 'include also C-alpha vectors in the output', default=False)
cmd.add_scalar_option('confidence-threshold', 't', float, 'confidence threshold for gap filling', default=0.7)
cmd.add_scalar_option('verbosity', 'v', int, 'verbosity level', default=2)
cmd.add_scalar_option('output', 'o', str, 'output directory', default='.')
cmd.add_positional_argument('QUERY', str, 'query profile HMM (e.g. created with csb.apps.buildhmm)')
return cmd
class HHfragApp(csb.apps.Application):
def main(self):
if not os.path.isdir(self.args.output):
HHfragApp.exit('Output directory does not exist', code=ExitCodes.INVALID_DATA, usage=True)
if self.args.c_alpha:
builder = rosetta.ExtendedOutputBuilder
else:
builder = rosetta.OutputBuilder
try:
hhf = HHfrag(self.args.QUERY, self.args.hhsearch, self.args.database, logger=self)
output = os.path.join(self.args.output, hhf.query.id)
hhf.slice_query(self.args.min, self.args.max, self.args.step, self.args.cpu)
frags = hhf.extract_fragments()
if len(frags) == 0:
HHfragApp.exit('No fragments found!', code=ExitCodes.NO_OUTPUT)
fragmap = hhf.build_fragment_map()
fragmap.dump(output + '.hhfrags.09', builder)
if self.args.filtered_map:
fragmap, events = hhf.build_filtered_map()
fragmap.dump(output + '.filtered.09', builder)
tsv = PredictionBuilder.create(events).product
tsv.dump(output + '.centroids.tsv')
if self.args.filtered_filling:
fragmap, events = hhf.build_hybrid_filtered_map(self.args.filtered_filling)
fragmap.dump(output + '.hybrid.filtered.09', builder)
tsv = PredictionBuilder.create(events).product
tsv.dump(output + '.hybrid.centroids.tsv')
if self.args.gap_filling:
fragmap = hhf.build_combined_map(self.args.gap_filling, self.args.confidence_threshold)
fragmap.dump(output + '.complemented.09', builder)
self.log('\nDONE.')
except ArgumentIOError as ae:
HHfragApp.exit(str(ae), code=ExitCodes.IO_ERROR)
except ArgumentError as ae:
HHfragApp.exit(str(ae), code=ExitCodes.INVALID_DATA)
except csb.io.InvalidCommandError as ose:
msg = '{0!s}: {0.program}'.format(ose)
HHfragApp.exit(msg, ExitCodes.IO_ERROR)
except csb.bio.io.hhpred.HHProfileFormatError as hfe:
msg = 'Corrupt HMM: {0!s}'.format(hfe)
HHfragApp.exit(msg, code=ExitCodes.INVALID_DATA)
except csb.io.ProcessError as pe:
message = 'Bad exit code from HHsearch: #{0.code}.\nSTDERR: {0.stderr}\nSTDOUT: {0.stdout}'.format(pe.context)
HHfragApp.exit(message, ExitCodes.HHSEARCH_FAILURE)
def log(self, message, ending='\n', level=1):
if level <= self.args.verbosity:
super(HHfragApp, self).log(message, ending)
class ArgumentError(ValueError):
pass
class ArgumentIOError(ArgumentError):
pass
class InvalidOperationError(ValueError):
pass
class HHfrag(object):
"""
The HHfrag dynamic fragment detection protocol.
@param query: query HMM path and file name
@type query: str
@param binary: the HHsearch binary
@type binary: str
@param database: path to the PDBS25 directory
@type database: str
@param logger: logging client (needs to have a C{log} method)
@type logger: L{Application}
"""
PDBS = 'pdbs25.hhm'
def __init__(self, query, binary, database, logger=None):
try:
self._query = csb.bio.io.HHProfileParser(query).parse()
except IOError as io:
raise ArgumentIOError(str(io))
self._hsqs = None
self._matches = None
self._app = logger
self._database = None
self._pdbs25 = None
self._aligner = None
self.database = database
self.aligner = hhsearch.HHsearch(binary, self.pdbs25, cpu=2)
if self.query.layers.length < 1:
raise ArgumentError("Zero-length sequence profile")
@property
def query(self):
return self._query
@property
def pdbs25(self):
return self._pdbs25
@property
def database(self):
return self._database
@database.setter
def database(self, value):
database = value
pdbs25 = os.path.join(value, HHfrag.PDBS)
if not os.path.isfile(pdbs25):
raise ArgumentError('PDBS25 not found here: ' + pdbs25)
self._database = database
self._pdbs25 = pdbs25
@property
def aligner(self):
return self._aligner
@aligner.setter
def aligner(self, value):
if hasattr(value, 'run') and hasattr(value, 'runmany'):
self._aligner = value
else:
raise TypeError(value)
def log(self, *a, **ka):
if self._app:
self._app.log(*a, **ka)
def slice_query(self, min=6, max=21, step=3, cpu=None):
"""
Run the query slicer and collect the optimal query segments.
@param min: min segment length
@type min: int
@param max: max segment length
@type max: int
@param step: slicing step
@type step: int
@param cpu: degree of parallelism
@type cpu: int
@rtype: tuple of L{SliceContext}
"""
if not 0 < min <= max:
raise ArgumentError('min and max must be positive numbers, with max >= min')
if not 0 < step:
raise ArgumentError('step must be positive number')
self.log('\n# Processing profile HMM "{0}"...'.format(self.query.id))
self.log('', level=2)
qp = self.query
hsqs = []
if not cpu:
cpu = max - min + 1
for start in range(1, qp.layers.length - min + 1 + 1, step):
self.log('{0:3}. '.format(start), ending='', level=1)
probes = []
for end in range(start + min - 1, start + max):
if end > qp.layers.length:
break
context = SliceContext(qp.segment(start, end), start, end)
probes.append(context)
probes = self.aligner.runmany(probes, workers=cpu)
probes.sort()
if len(probes) > 0:
rep = probes[-1]
hsqs.append(rep)
self.log('{0.start:3} {0.end:3} ({0.length:2} aa) {0.recurrence:3} hits'.format(rep), level=1)
else:
self.log(' no hits', level=1)
self._hsqs = hsqs
return tuple(hsqs)
def extract_fragments(self):
"""
Extract all matching fragment instances, given the list of optimal
query slices, generated during the first stage.
@rtype: tuple of L{Assignment}s
"""
if self._hsqs is None:
self.slice_query()
self.log('\n# Extracting fragments...')
fragments = []
for si in self._hsqs:
self.log('\nSEGMENT: {0.start:3} {0.end:3} ({0.recurrence})'.format(si), level=2)
for hit in si.hits:
cn = 0
for chunk in hit.alignment.segments:
chunk.qstart = chunk.qstart + si.start - 1
chunk.qend = chunk.qend + si.start - 1
cn += 1
self.log(' {0.id:5} L{0.qstart:3} {0.qend:3} {0.length:2}aa P:{0.probability:5.3f}'.format(chunk), ending='', level=2)
sourcefile = os.path.join(self.database, hit.id + '.pdb')
if not os.path.isfile(sourcefile):
self.log(' missing', level=2)
continue
source = csb.bio.io.StructureParser(sourcefile).parse().first_chain
assert hit.id[-1] == source.id
source.compute_torsion()
try:
fragment = csb.bio.fragments.Assignment(source, chunk.start, chunk.end,
chunk.qstart, chunk.qend,
probability=chunk.probability)
fragments.append(fragment)
if cn > 1:
self.log(' (chunk #{0})'.format(cn), level=2)
else:
self.log('', level=2)
except csb.bio.structure.Broken3DStructureError:
self.log(' corrupt', level=2)
continue
self._matches = fragments
return tuple(fragments)
def _plot_lengths(self):
self.log('\n {0} ungapped assignments'.format(len(self._matches)))
self.log('', level=2)
histogram = {}
for f in self._matches:
histogram[f.length] = histogram.get(f.length, 0) + 1
for length in sorted(histogram):
percent = histogram[length] * 100.0 / len(self._matches)
bar = '{0:3} |{1} {2:5.1f}%'.format(length, 'o' * int(percent), percent)
self.log(bar, level=2)
def build_fragment_map(self):
"""
Build a full Rosetta fragset.
@rtype: L{RosettaFragmentMap}
"""
if self._matches is None:
self.extract_fragments()
self.log('\n# Building dynamic fragment map...')
self._plot_lengths()
target = csb.bio.fragments.Target.from_profile(self.query)
target.assignall(self._matches)
factory = csb.bio.fragments.RosettaFragsetFactory()
return factory.make_fragset(target)
def _filter_event_handler(self, ri):
if ri.gap is True or ri.confident is False:
self.log('{0.rank:3}. {0.confidence:5.3f} {0.count:3}'.format(ri), level=2)
else:
phi = PredictionBuilder.format_angle(ri.torsion.phi)
psi = PredictionBuilder.format_angle(ri.torsion.psi)
omega = PredictionBuilder.format_angle(ri.torsion.omega)
pred = "{0.source_id:5} {0.start:3} {0.end:3} {1} {2} {3}".format(ri.rep, phi, psi, omega)
self.log('{0.rank:3}. {0.confidence:5.3f} {0.count:3} {1}'.format(ri, pred), level=2)
def build_filtered_map(self):
"""
Build a filtered fragset of centroids.
@return: filtered fragset and a list of residue-wise predictions
(centroid and torsion angles)
@rtype: L{RosettaFragmentMap}, list of L{ResidueEventInfo}
"""
if self._matches is None:
self.extract_fragments()
self.log('\n# Building filtered map...')
self.log('\n Confidence Recurrence Representative Phi Psi Omega', level=2)
events = []
def logger(ri):
events.append(ri)
self._filter_event_handler(ri)
target = csb.bio.fragments.Target.from_profile(self.query)
target.assignall(self._matches)
factory = csb.bio.fragments.RosettaFragsetFactory()
fragset = factory.make_filtered(target, extend=True, callback=logger)
return fragset, events
def _merge_event_handler(self, ri):
marked = ""
if ri.gap is True or ri.confident is False:
marked = "*"
self.log('{0.rank:3}. {0.confidence:5.3f} {0.count:3} {1:>3}'.format(ri, marked), level=2)
def build_combined_map(self, fragfile, threshold=0.7, top=25):
"""
Build a hybrid map, where low-confidence regions are complemented
with the specified filling.
@param threshold: confidence threshold
@type threshold: float
@param fragfile: filling fragset (Rosetta fragment file)
@type fragfile: str
@return: filtered fragset and a list of residue-wise predictions
(centroid and torsion angles)
@rtype: L{RosettaFragmentMap}, list of L{ResidueEventInfo}
"""
if self._matches is None:
self.extract_fragments()
self.log('\n# Building complemented map...')
try:
filling = rosetta.RosettaFragmentMap.read(fragfile, top=top)
except IOError as io:
raise ArgumentIOError(str(io))
self.log('\n {0} supplementary fragments loaded'.format(filling.size))
self.log(' Confidence Recurrence Fill?', level=2)
target = csb.bio.fragments.Target.from_profile(self.query)
target.assignall(self._matches)
factory = csb.bio.fragments.RosettaFragsetFactory()
return factory.make_combined(target, filling, threshold=threshold,
callback=self._merge_event_handler)
def build_hybrid_filtered_map(self, fragfile):
"""
Mix the fragset with the specified (filtered)filling and then filter
the mixture. If the filling is a filtered CSfrag library, this will
produce a double-filtered map.
@param fragfile: filtered filling (filtered CSfrag fragment file)
@type fragfile: str
@rtype: L{RosettaFragmentMap}
"""
if self._matches is None:
self.extract_fragments()
self.log('\n# Building hybrid filtered map...')
filling = []
events = []
def logger(ri):
events.append(ri)
self._filter_event_handler(ri)
try:
db = csb.bio.io.wwpdb.FileSystemStructureProvider(self.database)
for f in rosetta.RosettaFragmentMap.read(fragfile):
filling.append(csb.bio.fragments.Assignment.from_fragment(f, db))
except IOError as io:
raise ArgumentIOError(str(io))
except csb.bio.io.wwpdb.StructureNotFoundError as sne:
msg = "{0} is not a PDBS25-derived fragset (template {1} not found)"
raise ArgumentIOError(msg.format(fragfile, str(sne)))
self.log('\n {0} supplementary fragments loaded'.format(len(filling)))
self.log('\n Confidence Recurrence Representative Phi Psi Omega', level=2)
if len(filling) > self.query.layers.length:
msg = "{0} does not look like a filtered fragset (too many centroids)"
raise ArgumentError(msg.format(fragfile))
target = csb.bio.fragments.Target.from_profile(self.query)
target.assignall(self._matches)
target.assignall(filling)
factory = csb.bio.fragments.RosettaFragsetFactory()
fragset = factory.make_filtered(target, extend=False, callback=logger)
return fragset, events
class SliceContext(hhsearch.Context):
def __init__(self, segment, start, end):
self.start = start
self.end = end
if not isinstance(segment, csb.core.string):
segment = segment.to_hmm(convert_scores=True)
super(SliceContext, self).__init__(segment)
@property
def length(self):
return self.end - self.start + 1
@property
def hits(self):
return self.result
@property
def recurrence(self):
return len(self.result)
def __lt__(self, other):
if self.recurrence == other.recurrence:
return self.length < other.length
else:
return self.recurrence < other.recurrence
class PredictionBuilder(object):
HEADER = "rank:int residue:str confidence:float centroid:str phi:float psi:float omega:float"
@staticmethod
def format_angle(angle):
"""
@param angle: torsion angle value
@type angle: float
"""
if angle is None:
return '{0:>6}'.format("-")
else:
return '{0:6.1f}'.format(angle)
@staticmethod
def create(ri):
"""
@param ri: all predictions
@type ri: list of L{ResidueEventInfo}
"""
builder = PredictionBuilder()
builder.addall(ri)
return builder
def __init__(self):
self._tsv = csb.io.tsv.Table(PredictionBuilder.HEADER)
@property
def product(self):
"""
@rtype: L{Table}
"""
return self._tsv
def add(self, ri):
"""
@param ri: single residue prediction
@type ri: L{ResidueEventInfo}
"""
row = [ri.rank, repr(ri.type), ri.confidence]
if ri.rep:
row.append(ri.rep.id)
row.append(ri.torsion.phi)
row.append(ri.torsion.psi)
row.append(ri.torsion.omega)
else:
row.extend([None] * 4)
self.product.insert(row)
def addall(self, ri):
"""
@param ri: all predictions
@type ri: list of L{ResidueEventInfo}
"""
ri = list(ri)
ri.sort(key=lambda i: i.rank)
for i in ri:
self.add(i)
def main():
AppRunner().run()
if __name__ == '__main__':
main()
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