def simple_model(alignment,
instances,
output=None,
optimize=False,
verbose=False):
contents = identify_pir(alignment)
# We redirect STDOUT and STDERR to output files
if output is None: output = contents['seq']
warnings.warn('STDOUT & STDERR will be redirected to log files.')
sys.stdout = open(output + '.log', 'w')
sys.stderr = open(output + '.err', 'w')
if verbose: log.verbose() # Commands MODELLER to display all log output
env = environ() # Initializes the 'environment' for this modeling run
env.io.hetatm = True # Allow the presence of hetatoms
a = automodel(
env, # Loading the environment
alnfile=alignment, # Assigning the PIR alignment
knowns=contents['str'], # Listing the known structures
sequence=contents['seq'], # Identify the Query Sequence
assess_methods=(assess.DOPE, assess.GA341,
assess.DOPEHR)) # Energy evaluation methods
# Setting Starting and Ending Model number
a.starting_model, a.ending_model = 1, instances
if optimize:
# Very thorough VTFM optimization:
a.library_schedule = autosched.slow
a.max_var_iterations = 300
# Thorough MD optimization:
a.md_level = refine.slow
# Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6
a.repeat_optimization = 2
a.max_molpdf = 1e6
a.initial_malign3d = True
a.make() # Create the Model
sys.stdout = sys.__stdout__ # restore stdout back to normal
sys.stderr = sys.__stderr__ # restore stdout back to normal
warnings.warn('STDOUT & STDERR have been restored.')
# We delete the error file if no error has occurred
ERRsize = os.path.getsize(output + '.err')
if int(ERRsize) is 0:
os.remove(output + '.err')
def simple_model(alignment, instances,
output=None, optimize=False, verbose=False):
contents = identify_pir(alignment)
# We redirect STDOUT and STDERR to output files
if output is None: output = contents['seq']
warnings.warn('STDOUT & STDERR will be redirected to log files.')
sys.stdout = open(output + '.log', 'w')
sys.stderr = open(output + '.err', 'w')
if verbose: log.verbose() # Commands MODELLER to display all log output
env = environ() # Initializes the 'environment' for this modeling run
env.io.hetatm = True # Allow the presence of hetatoms
a = automodel(env, # Loading the environment
alnfile=alignment, # Assigning the PIR alignment
knowns=contents['str'], # Listing the known structures
sequence=contents['seq'], # Identify the Query Sequence
assess_methods=(assess.DOPE,
assess.GA341,
assess.DOPEHR)) # Energy evaluation methods
# Setting Starting and Ending Model number
a.starting_model, a.ending_model = 1, instances
if optimize:
# Very thorough VTFM optimization:
a.library_schedule = autosched.slow
a.max_var_iterations = 300
# Thorough MD optimization:
a.md_level = refine.slow
# Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6
a.repeat_optimization = 2
a.max_molpdf = 1e6
a.initial_malign3d = True
a.make() # Create the Model
sys.stdout = sys.__stdout__ # restore stdout back to normal
sys.stderr = sys.__stderr__ # restore stdout back to normal
warnings.warn('STDOUT & STDERR have been restored.')
# We delete the error file if no error has occurred
ERRsize = os.path.getsize(output + '.err')
if int(ERRsize) is 0:
os.remove(output + '.err')
def restrained_model(alignment,
instances,
helices=None,
betas=None,
distances=None,
output=None,
optimize=False,
verbose=False):
contents = identify_pir(alignment)
# We redirect STDOUT and STDERR to output files
if output is None: output = contents['seq']
warnings.warn('STDOUT & STDERR will be redirected to log files.')
sys.stdout = open(output + '.log', 'w')
sys.stderr = open(output + '.err', 'w')
if verbose: log.verbose() # Commands MODELLER to display all log output
env = environ() # Initializes the 'environment' for this modeling run
env.io.hetatm = True # Allow the presence of hetatoms
class MyModel(automodel):
def special_restraints(self, aln):
rsr = self.restraints
at = self.atoms
# Setting forced alpha helices
try:
for helix in helices:
rsr.add(
secondary_structure.alpha(
self.residue_range(
str(helix[0]) + ':',
str(helix[1]) + ':')))
except TypeError:
pass
# Setting forced beta strands
try:
for strand in betas:
rsr.add(
secondary_structure.strand(
self.residue_range(
str(strand[0]) + ':',
str(strand[1]) + ':')))
except TypeError:
pass
# Setting distance restraints
try:
for restraint in distances:
rsr.add(
forms.gaussian(group=physical.xy_distance,
feature=features.distance(
at['CA:' + str(restraint[0])],
at['CA:' + str(restraint[1])]),
mean=float(restraint[2]),
stdev=0.1))
except TypeError:
pass
a = automodel(
env, # Loading the environment
alnfile=alignment, # Assigning the PIR alignment
knowns=contents['str'], # Listing the known structures
sequence=contents['seq'], # Identify the Query Sequence
assess_methods=(assess.DOPE, assess.GA341,
assess.DOPEHR)) # Energy evaluation methods
# Setting Starting and Ending Model number
a.starting_model, a.ending_model = 1, instances
if optimize:
# Very thorough VTFM optimization:
a.library_schedule = autosched.slow
a.max_var_iterations = 300
# Thorough MD optimization:
a.md_level = refine.slow
# Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6
a.repeat_optimization = 2
a.max_molpdf = 1e6
a.initial_malign3d = True
a.make() # Create the Model
sys.stdout = sys.__stdout__ # restore stdout back to normal
sys.stderr = sys.__stderr__ # restore stdout back to normal
warnings.warn('STDOUT & STDERR have been restored.')
# We delete the error file if no error has occurred
ERRsize = os.path.getsize(output + '.err')
if int(ERRsize) is 0:
os.remove(output + '.err')
def restrained_model(alignment, instances, helices=None, betas=None, distances=None,
output=None, optimize=False, verbose=False):
contents = identify_pir(alignment)
# We redirect STDOUT and STDERR to output files
if output is None: output = contents['seq']
warnings.warn('STDOUT & STDERR will be redirected to log files.')
sys.stdout = open(output + '.log', 'w')
sys.stderr = open(output + '.err', 'w')
if verbose: log.verbose() # Commands MODELLER to display all log output
env = environ() # Initializes the 'environment' for this modeling run
env.io.hetatm = True # Allow the presence of hetatoms
class MyModel(automodel):
def special_restraints(self, aln):
rsr = self.restraints
at = self.atoms
# Setting forced alpha helices
try:
for helix in helices:
rsr.add(secondary_structure.alpha(self.residue_range(str(helix[0])+':',
str(helix[1])+':')))
except TypeError: pass
# Setting forced beta strands
try:
for strand in betas:
rsr.add(secondary_structure.strand(self.residue_range(str(strand[0])+':',
str(strand[1])+':')))
except TypeError: pass
# Setting distance restraints
try:
for restraint in distances:
rsr.add(forms.gaussian(group=physical.xy_distance,
feature=features.distance(at['CA:' + str(restraint[0])],
at['CA:' + str(restraint[1])]),
mean=float(restraint[2]),
stdev=0.1)
)
except TypeError: pass
a = automodel(env, # Loading the environment
alnfile=alignment, # Assigning the PIR alignment
knowns=contents['str'], # Listing the known structures
sequence=contents['seq'], # Identify the Query Sequence
assess_methods=(assess.DOPE,
assess.GA341,
assess.DOPEHR)) # Energy evaluation methods
# Setting Starting and Ending Model number
a.starting_model, a.ending_model = 1, instances
if optimize:
# Very thorough VTFM optimization:
a.library_schedule = autosched.slow
a.max_var_iterations = 300
# Thorough MD optimization:
a.md_level = refine.slow
# Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6
a.repeat_optimization = 2
a.max_molpdf = 1e6
a.initial_malign3d = True
a.make() # Create the Model
sys.stdout = sys.__stdout__ # restore stdout back to normal
sys.stderr = sys.__stderr__ # restore stdout back to normal
warnings.warn('STDOUT & STDERR have been restored.')
# We delete the error file if no error has occurred
ERRsize = os.path.getsize(output + '.err')
if int(ERRsize) is 0:
os.remove(output + '.err')