def _create_models(self, env, base_models, model_id, query_id):
a = automodel(env,
alnfile=self._aln_file(model_id, query_id),
knowns=base_models,
sequence=str(query_id),
assess_methods=self._assess_methods)
a.starting_model = 1
a.ending_model = self.model_count
a.md_level = self._refinement
# TODO assessing with {assess_methods}
_log.debug(
"Generating {model_count} model/s for {orf} with {base_models} ".
format(orf=query_id,
base_models=base_models,
model_count=self.model_count))
if self.parallel_jobs > 1:
j = job()
for _ in range(0, self.parallel_jobs):
j.append(local_slave())
a.use_parallel_job(j)
model = a.make()
if "OverflowError" in str(a.outputs[0]["failure"].__class__):
raise ModellerOverflowError(a.outputs[0]["failure"], base_models)
_log.debug(
"{model_count} models for {orf} with {base_models} where generated "
.format(orf=query_id,
base_models=base_models,
model_count=self.model_count))
def _create_models(self, env, base_models, model_id, query_id):
class MyModel(automodel):
def special_patches(self, aln):
# Rename both chains and renumber the residues in each
start = int(aln[1].range[0].split(":")[0])
self.rename_segments(segment_ids=['A'],
renumber_residues=start)
# self.chains[0].name = 'A'
a = MyModel(env,
alnfile=self._aln_file(model_id, query_id),
knowns=base_models,
sequence=str(query_id),
assess_methods=self._assess_methods)
a.starting_model = 1
a.ending_model = self.model_count
a.md_level = self._refinement
# TODO assessing with {assess_methods}
_log.debug(
"Generating {model_count} model/s for {orf} with {base_models} ".
format(orf=query_id,
base_models=base_models,
model_count=self.model_count))
if self.parallel_jobs > 1:
j = job()
for _ in range(0, self.parallel_jobs):
j.append(local_slave())
a.use_parallel_job(j)
model = a.make()
if "OverflowError" in str(a.outputs[0]["failure"].__class__):
raise ModellerOverflowError(a.outputs[0]["failure"], base_models)
_log.debug(
"{model_count} models for {orf} with {base_models} where generated "
.format(orf=query_id,
base_models=base_models,
model_count=self.model_count))
def peptide_rebuild_modeller(name, selection='all', hetatm=0, sequence=None,
nmodels=1, hydro=0, quiet=1, *, _self=cmd):
'''
DESCRIPTION
Remodel the given selection using modeller. This is useful for example to
build incomplete sidechains. More complicated modelling tasks are not
the intention of this simple interface.
Side effects: Alters "type" property for MSE residues in selection
(workaround for bug #3512313).
USAGE
peptide_rebuild_modeller name [, selection [, hetatm [, sequence ]]]
ARGUMENTS
name = string: new object name
selection = string: atom selection
hetatm = 0/1: read and model HETATMs (ligands) {default: 0}
sequence = string: if provided, use this sequence instead of the
template sequence {default: None}
nmodels = int: number of models (states) to generate {default: 1}
'''
import modeller
from modeller.automodel import automodel, allhmodel
import tempfile, shutil, os
_assert_package_import()
from .editing import update_identifiers
nmodels, hetatm, quiet = int(nmodels), int(hetatm), int(quiet)
if int(hydro):
automodel = allhmodel
tempdir = tempfile.mkdtemp()
pdbfile = os.path.join(tempdir, 'template.pdb')
alnfile = os.path.join(tempdir, 'aln.pir')
cwd = os.getcwd()
os.chdir(tempdir)
if not quiet:
print(' Notice: PWD=%s' % (tempdir))
try:
modeller.log.none()
env = modeller.environ()
env.io.hetatm = hetatm
# prevent PyMOL to put TER records before MSE residues (bug #3512313)
_self.alter('(%s) and polymer' % (selection), 'type="ATOM"')
_self.save(pdbfile, selection)
mdl = modeller.model(env, file=pdbfile)
aln = modeller.alignment(env)
aln.append_model(mdl, align_codes='foo', atom_files=pdbfile)
# get sequence from non-present atoms
if not sequence and _self.count_atoms('(%s) & !present' % (selection)):
sequence = get_seq(selection)
if sequence:
aln.append_sequence(sequence)
aln[-1].code = 'bar'
aln.malign()
aln.write(alnfile)
a = automodel(env, alnfile=alnfile, sequence=aln[-1].code,
knowns=[s.code for s in aln if s.prottyp.startswith('structure')])
a.max_ca_ca_distance = 30.0
if nmodels > 1:
a.ending_model = nmodels
from multiprocessing import cpu_count
ncpu = min(cpu_count(), nmodels)
if ncpu > 1:
from modeller import parallel
job = parallel.job(parallel.local_slave()
for _ in range(ncpu))
a.use_parallel_job(job)
a.make()
for output in a.outputs:
_self.load(output['name'], name, quiet=quiet)
finally:
os.chdir(cwd)
shutil.rmtree(tempdir)
_self.align(name, selection, cycles=0)
if not sequence:
update_identifiers(name, selection, _self=_self)
if not quiet:
print(' peptide_rebuild_modeller: done')
def peptide_rebuild_modeller(name, selection='all', hetatm=0, sequence=None,
nmodels=1, hydro=0, quiet=1):
'''
DESCRIPTION
Remodel the given selection using modeller. This is useful for example to
build incomplete sidechains. More complicated modelling tasks are not
the intention of this simple interface.
Side effects: Alters "type" property for MSE residues in selection
(workaround for bug #3512313).
USAGE
peptide_rebuild_modeller name [, selection [, hetatm [, sequence ]]]
ARGUMENTS
name = string: new object name
selection = string: atom selection
hetatm = 0/1: read and model HETATMs (ligands) {default: 0}
sequence = string: if provided, use this sequence instead of the
template sequence {default: None}
nmodels = int: number of models (states) to generate {default: 1}
'''
try:
import modeller
from modeller.automodel import automodel, allhmodel
except ImportError:
print(' Error: failed to import "modeller"')
raise CmdException
import tempfile, shutil, os
from .editing import update_identifiers
nmodels, hetatm, quiet = int(nmodels), int(hetatm), int(quiet)
if int(hydro):
automodel = allhmodel
tempdir = tempfile.mkdtemp()
pdbfile = os.path.join(tempdir, 'template.pdb')
alnfile = os.path.join(tempdir, 'aln.pir')
cwd = os.getcwd()
os.chdir(tempdir)
if not quiet:
print(' Notice: PWD=%s' % (tempdir))
try:
modeller.log.none()
env = modeller.environ()
env.io.hetatm = hetatm
# prevent PyMOL to put TER records before MSE residues (bug #3512313)
cmd.alter('(%s) and polymer' % (selection), 'type="ATOM"')
cmd.save(pdbfile, selection)
mdl = modeller.model(env, file=pdbfile)
aln = modeller.alignment(env)
aln.append_model(mdl, align_codes='foo', atom_files=pdbfile)
if sequence:
aln.append_sequence(sequence)
aln[-1].code = 'bar'
aln.malign()
aln.write(alnfile)
a = automodel(env, alnfile=alnfile, sequence=aln[-1].code,
knowns=[s.code for s in aln if s.prottyp.startswith('structure')])
a.max_ca_ca_distance = 30.0
if nmodels > 1:
a.ending_model = nmodels
from multiprocessing import cpu_count
ncpu = min(cpu_count(), nmodels)
if ncpu > 1:
from modeller import parallel
job = parallel.job(parallel.local_slave()
for _ in range(ncpu))
a.use_parallel_job(job)
a.make()
for output in a.outputs:
cmd.load(output['name'], name, quiet=quiet)
finally:
os.chdir(cwd)
shutil.rmtree(tempdir)
cmd.align(name, selection, cycles=0)
if not sequence:
update_identifiers(name, selection)
if not quiet:
print(' peptide_rebuild_modeller: done')