def modelling(self, optd, rosetta_modeller=None):
if not (optd['make_frags'] or optd['make_models']
or optd['nmr_remodel']):
return
# Set the direcotry where the final models will end up
optd['models_dir'] = os.path.join(optd['work_dir'], 'models')
if not os.path.isdir(optd['models_dir']):
os.mkdir(optd['models_dir'])
if not rosetta_modeller:
rosetta_modeller = options_processor.process_rosetta_options(optd)
# Make Rosetta fragments
if optd['make_frags']:
rosetta_modeller.generate_fragments(optd)
optd['frags_3mers'] = rosetta_modeller.frags_3mers
optd['frags_9mers'] = rosetta_modeller.frags_9mers
optd['psipred_ss2'] = rosetta_modeller.psipred_ss2
con_util = self.handle_contacts(optd)
if optd['restraints_file']:
rosetta_modeller.restraints_file = optd['restraints_file']
if optd['make_models']:
logger.info('----- making Rosetta models--------')
logger.info('Making %s models...', optd['nmodels'])
try:
optd['processed_models'] = rosetta_modeller.ab_initio_model(
processed_models=optd['processed_models'])
except Exception as e:
msg = "Error running ROSETTA to create models: {0}".format(e)
exit_util.exit_error(msg, sys.exc_info()[2])
logger.info('Modelling complete - models stored in: %s',
optd['models_dir'])
# Sub-select the decoys using contact information
if con_util and optd['subselect_mode'] and not (optd['nmr_model_in'] or
optd['nmr_remodel']):
logger.info(
'Subselecting models from directory using provided contact information'
)
subselect_data = con_util.subselect_decoys(
optd['processed_models'],
'pdb',
mode=optd['subselect_mode'],
**optd)
optd['processed_models'] = zip(*subselect_data)[0]
optd['subselect_data'] = dict(subselect_data)
def modelling(self, optd, rosetta_modeller=None):
if not (optd['make_frags'] or optd['make_models'] or optd['nmr_remodel']):
return
# Set the direcotry where the final models will end up
optd['models_dir'] = os.path.join(optd['work_dir'], 'models')
if not os.path.isdir(optd['models_dir']):
os.mkdir(optd['models_dir'])
if not rosetta_modeller:
rosetta_modeller = options_processor.process_rosetta_options(optd)
# Make Rosetta fragments
if optd['make_frags']:
rosetta_modeller.generate_fragments(optd)
optd['frags_3mers'] = rosetta_modeller.frags_3mers
optd['frags_9mers'] = rosetta_modeller.frags_9mers
optd['psipred_ss2'] = rosetta_modeller.psipred_ss2
con_util = self.handle_contacts(optd)
if optd['restraints_file']:
rosetta_modeller.restraints_file = optd['restraints_file']
if optd['make_models']:
logger.info('----- making Rosetta models--------')
logger.info('Making %s models...', optd['nmodels'])
try:
optd['processed_models'] = rosetta_modeller.ab_initio_model(processed_models = optd['processed_models'])
except Exception as e:
msg = "Error running ROSETTA to create models: {0}".format(e)
exit_util.exit_error(msg, sys.exc_info()[2])
logger.info('Modelling complete - models stored in: %s', optd['models_dir'])
# Sub-select the decoys using contact information
if con_util and optd['subselect_mode'] and not (optd['nmr_model_in'] or optd['nmr_remodel']):
logger.info('Subselecting models from directory using provided contact information')
subselect_data = con_util.subselect_decoys(optd['processed_models'], 'pdb', mode=optd['subselect_mode'], **optd)
optd['processed_models'] = zip(*subselect_data)[0]
optd['subselect_data'] = dict(subselect_data)
def main(self, args=None):
"""Main AMPLE routine.
We require this as the multiprocessing module (only on **!!*%$$!! Windoze)
requires that the main module can be imported. We there need ample to be
a python script that can be imported, hence the main routine with its
calling protected by the if __name__=="__main__":...
args is an option argument that can contain the command-line arguments
for the program - required for testing.
"""
argso = argparse_util.process_command_line(args=args)
self.amopt = amopt = config_util.AMPLEConfigOptions()
amopt.populate(argso)
# Setup things like logging, file structure, etc...
amopt.d = self.setup(amopt.d)
rosetta_modeller = options_processor.process_rosetta_options(amopt.d)
# Display the parameters used
logger.debug(amopt.prettify_parameters())
amopt.write_config_file()
#######################################################
# SCRIPT PROPER STARTS HERE
time_start = time.time()
# Create function for monitoring jobs - static function decorator?
if self.ample_output:
def monitor():
return self.ample_output.display_results(amopt.d)
else:
monitor = None
if amopt.d['benchmark_mode'] and amopt.d['native_pdb']:
# Process the native before we do anything else
benchmark_util.analysePdb(amopt.d)
# Create constituent models from an NMR ensemble
if amopt.d['nmr_model_in']:
nmr_mdir = os.path.join(amopt.d['work_dir'], 'nmr_models')
amopt.d['modelling_workdir'] = nmr_mdir
logger.info(
'Splitting NMR ensemble into constituent models in directory: {0}'
.format(nmr_mdir))
amopt.d['models'] = pdb_edit.split_pdb(amopt.d['nmr_model_in'],
directory=nmr_mdir,
strip_hetatm=True,
same_size=True)
logger.info('NMR ensemble contained {0} models'.format(
len(amopt.d['models'])))
# Modelling business happens here
self.modelling(amopt.d, rosetta_modeller)
amopt.write_config_file()
# Ensembling business next
if amopt.d['make_ensembles']:
self.ensembling(amopt.d)
amopt.write_config_file()
# Some MR here
if amopt.d['do_mr']:
self.molecular_replacement(amopt.d)
amopt.write_config_file()
# Timing data
time_stop = time.time()
elapsed_time = time_stop - time_start
run_in_min = elapsed_time / 60
run_in_hours = run_in_min / 60
msg = os.linesep + \
'All processing completed (in {0:6.2F} hours)'.format(
run_in_hours) + os.linesep
msg += '----------------------------------------' + os.linesep
logging.info(msg)
# Benchmark mode
if amopt.d['benchmark_mode']:
self.benchmarking(amopt.d)
amopt.write_config_file()
amopt.write_config_file()
# Flag to show that we reached the end without error - useful for integration testing
amopt.d['AMPLE_finished'] = True
ample_util.save_amoptd(amopt.d)
logger.info("AMPLE finished at: %s",
time.strftime("%a, %d %b %Y %H:%M:%S", time.gmtime()))
ref_mgr = reference_manager.ReferenceManager(amopt.d)
ref_mgr.save_citations_to_file(amopt.d)
logger.info(ref_mgr.citations_as_text)
logger.info(reference_manager.footer)
# Finally update pyrvapi results
if self.ample_output:
self.ample_output.display_results(amopt.d)
self.ample_output.rvapi_shutdown(amopt.d)
self.cleanup(amopt.d)
return
def modelling(self, optd, rosetta_modeller=None):
if not (optd['import_models'] or optd['make_frags']
or optd['make_models'] or optd['nmr_remodel']):
return
# Set the direcotry where the final models will end up
optd['models_dir'] = os.path.join(optd['work_dir'], 'models')
if not os.path.isdir(optd['models_dir']):
os.mkdir(optd['models_dir'])
if not rosetta_modeller:
rosetta_modeller = options_processor.process_rosetta_options(optd)
# Make Rosetta fragments
if optd['make_frags']:
rosetta_modeller.generate_fragments(optd)
optd['frags_3mers'] = rosetta_modeller.frags_3mers
optd['frags_9mers'] = rosetta_modeller.frags_9mers
optd['psipred_ss2'] = rosetta_modeller.psipred_ss2
if optd["use_contacts"] and not optd['restraints_file']:
con_util = contact_util.ContactUtil(
optd['fasta'],
'fasta',
contact_file=optd['contact_file'],
contact_format=optd['contact_format'],
bbcontacts_file=optd['bbcontacts_file'],
bbcontacts_format=optd["bbcontacts_format"],
cutoff_factor=optd['restraints_factor'],
distance_to_neighbor=optd['distance_to_neighbour'])
optd["contacts_dir"] = os.path.join(optd["work_dir"], "contacts")
if not os.path.isdir(optd["contacts_dir"]):
os.mkdir(optd["contacts_dir"])
if con_util.require_contact_prediction:
if con_util.found_ccmpred_contact_prediction_deps:
con_util.predict_contacts_from_sequence(
wdir=optd["contacts_dir"])
optd["contact_file"] = con_util.contact_file
optd["contact_format"] = con_util.contact_format
if con_util.do_contact_analysis:
plot_file = os.path.join(optd['contacts_dir'],
optd['name'] + ".cm.png")
if optd['native_pdb'] and optd['native_pdb_std']:
structure_file = optd['native_pdb_std']
elif optd["native_pdb"]:
structure_file = optd['native_std']
else:
structure_file = None
optd['contact_map'], optd['contact_ppv'] = con_util.summarize(
plot_file, structure_file, 'pdb', optd['native_cutoff'])
restraints_file = os.path.join(optd['contacts_dir'],
optd['name'] + ".cst")
optd['restraints_file'] = con_util.write_restraints(
restraints_file, optd['restraints_format'],
optd['energy_function'])
else:
con_util = None
else:
con_util = None
if optd['make_models'] and optd['restraints_file']:
rosetta_modeller.restraints_file = optd['restraints_file']
if optd['make_models']:
logger.info('----- making Rosetta models--------')
if optd['nmr_remodel']:
try:
optd['models'] = rosetta_modeller.nmr_remodel(
models=optd['models'],
ntimes=optd['nmr_process'],
alignment_file=optd['alignment_file'],
remodel_fasta=optd['nmr_remodel_fasta'],
monitor=monitor)
except Exception as e:
msg = "Error remodelling NMR ensemble: {0}".format(e)
exit_util.exit_error(msg, sys.exc_info()[2])
else:
logger.info('making %s models...', optd['nmodels'])
try:
optd['models'] = rosetta_modeller.ab_initio_model(
monitor=monitor)
except Exception as e:
msg = "Error running ROSETTA to create models: {0}".format(
e)
exit_util.exit_error(msg, sys.exc_info()[2])
if not pdb_edit.check_pdb_directory(optd['models_dir'],
sequence=optd['sequence']):
msg = "Problem with rosetta pdb files - please check the log for more information"
exit_util.exit_error(msg)
logger.info('Modelling complete - models stored in: %s\n',
optd['models_dir'])
elif optd['import_models']:
logger.info('Importing models from directory: %s\n',
optd['models_dir'])
if optd['homologs']:
optd['models'] = ample_util.extract_and_validate_models(
optd, sequence=None, single=True, allsame=False)
else:
optd['models'] = ample_util.extract_and_validate_models(optd)
# Need to check if Quark and handle things accordingly
if optd['quark_models']:
# We always add sidechains to QUARK models if SCWRL is installed
if ample_util.is_exe(optd['scwrl_exe']):
optd['use_scwrl'] = True
else:
# No SCWRL so don't do owt with the side chains
logger.info(
'Using QUARK models but SCWRL is not installed '
'so only using %s sidechains', UNMODIFIED)
optd['side_chain_treatments'] = [UNMODIFIED]
# Sub-select the decoys using contact information
if con_util and optd['subselect_mode'] and not (optd['nmr_model_in'] or
optd['nmr_remodel']):
logger.info('Subselecting models from directory using '
'provided contact information')
subselect_data = con_util.subselect_decoys(
optd['models'], 'pdb', mode=optd['subselect_mode'], **optd)
optd['models'] = zip(*subselect_data)[0]
optd['subselect_data'] = dict(subselect_data)
ample_util.save_amoptd(optd)
def main(self, args=None):
"""Main AMPLE routine.
We require this as the multiprocessing module (only on **!!*%$$!! Windoze)
requires that the main module can be imported. We there need ample to be
a python script that can be imported, hence the main routine with its
calling protected by the if __name__=="__main__":...
args is an option argument that can contain the command-line arguments
for the program - required for testing.
"""
argso = argparse_util.process_command_line(args=args)
# Work directory and loggers need to be setup before we do anything else
self.setup_workdir(argso)
global logger
logger = logging_util.setup_logging(argso)
# Logging and work directories in place so can start work
self.amopt = amopt = config_util.AMPLEConfigOptions()
amopt.populate(argso)
amopt.d = self.setup(amopt.d)
rosetta_modeller = options_processor.process_rosetta_options(amopt.d)
logger.debug(
amopt.prettify_parameters()) # Display the parameters used
amopt.write_config_file()
time_start = time.time()
if self.ample_output:
def monitor():
return self.ample_output.display_results(amopt.d)
else:
monitor = None
# Highlight deprecated command line arguments
if amopt.d['submit_cluster']:
message = "-%s has been deprecated and will be removed in version %s!" % (
'submit_cluster', 1.6)
warnings.warn(message, DeprecationWarning)
if amopt.d["submit_pe_lsf"]:
message = "-%s has been deprecated and will be removed in version %s! Use -submit_pe instead" % (
'submit_pe_lsf', 1.6)
warnings.warn(message, DeprecationWarning)
if amopt.d["submit_pe_sge"]:
message = "-%s has been deprecated and will be removed in version %s! Use -submit_pe instead" % (
'submit_pe_sge', 1.6)
warnings.warn(message, DeprecationWarning)
# Process any files we may have been given
model_results = process_models.extract_and_validate_models(amopt.d)
if model_results:
process_models.handle_model_import(amopt.d, model_results)
if amopt.d['benchmark_mode'] and amopt.d['native_pdb']:
# Process the native before we do anything else
benchmark_util.analysePdb(amopt.d)
# Create constituent models from an NMR ensemble
if amopt.d['nmr_model_in']:
nmr_mdir = os.path.join(amopt.d['work_dir'], 'nmr_models')
amopt.d['modelling_workdir'] = nmr_mdir
logger.info(
'Splitting NMR ensemble into constituent models in directory: {0}'
.format(nmr_mdir))
amopt.d['processed_models'] = pdb_edit.split_pdb(
amopt.d['nmr_model_in'],
directory=nmr_mdir,
strip_hetatm=True,
same_size=True)
logger.info('NMR ensemble contained {0} models'.format(
len(amopt.d['processed_models'])))
# Modelling business happens here
if self.modelling_required(amopt.d):
self.modelling(amopt.d, rosetta_modeller)
ample_util.save_amoptd(amopt.d)
amopt.write_config_file()
# Ensembling business next
if amopt.d['make_ensembles']:
self.ensembling(amopt.d)
amopt.write_config_file()
# Some MR here
if amopt.d['do_mr']:
self.molecular_replacement(amopt.d)
amopt.write_config_file()
# Timing data
time_stop = time.time()
elapsed_time = time_stop - time_start
run_in_min = elapsed_time / 60
run_in_hours = run_in_min / 60
msg = os.linesep + 'All processing completed (in {0:6.2F} hours)'.format(
run_in_hours) + os.linesep
msg += '----------------------------------------' + os.linesep
logging.info(msg)
# Benchmark mode
if amopt.d['benchmark_mode']:
self.benchmarking(amopt.d)
amopt.write_config_file()
amopt.write_config_file()
# Flag to show that we reached the end without error - useful for integration testing
amopt.d['AMPLE_finished'] = True
ample_util.save_amoptd(amopt.d)
logger.info("AMPLE finished at: %s",
time.strftime("%a, %d %b %Y %H:%M:%S", time.gmtime()))
ref_mgr = reference_manager.ReferenceManager(amopt.d)
ref_mgr.save_citations_to_file(amopt.d)
logger.info(ref_mgr.citations_as_text)
logger.info(reference_manager.footer)
# Finally update pyrvapi results
if self.ample_output:
self.ample_output.display_results(amopt.d)
self.ample_output.rvapi_shutdown(amopt.d)
self.cleanup(amopt.d)
return
def main(self, args=None):
"""Main AMPLE routine.
We require this as the multiprocessing module (only on **!!*%$$!! Windoze)
requires that the main module can be imported. We there need ample to be
a python script that can be imported, hence the main routine with its
calling protected by the if __name__=="__main__":...
args is an option argument that can contain the command-line arguments
for the program - required for testing.
"""
argso = argparse_util.process_command_line(args=args)
# SWork directory and loggers need to be setup before we do anything else
self.setup_workdir(argso)
global logger
logger = logging_util.setup_logging(argso)
# Logging and work directories in place so can start work
self.amopt = amopt = config_util.AMPLEConfigOptions()
amopt.populate(argso)
amopt.d = self.setup(amopt.d)
rosetta_modeller = options_processor.process_rosetta_options(amopt.d)
# Display the parameters used
logger.debug(amopt.prettify_parameters())
amopt.write_config_file()
#######################################################
# SCRIPT PROPER STARTS HERE
time_start = time.time()
# Create function for monitoring jobs - static function decorator?
if self.ample_output:
def monitor():
return self.ample_output.display_results(amopt.d)
else:
monitor = None
# Process any files we may have been given
model_results = process_models.extract_and_validate_models(amopt.d)
if model_results:
process_models.handle_model_import(amopt.d, model_results)
if amopt.d['benchmark_mode'] and amopt.d['native_pdb']:
# Process the native before we do anything else
benchmark_util.analysePdb(amopt.d)
# Create constituent models from an NMR ensemble
if amopt.d['nmr_model_in']:
nmr_mdir = os.path.join(amopt.d['work_dir'], 'nmr_models')
amopt.d['modelling_workdir'] = nmr_mdir
logger.info('Splitting NMR ensemble into constituent models in directory: {0}'.format(nmr_mdir))
amopt.d['processed_models'] = pdb_edit.split_pdb(
amopt.d['nmr_model_in'], directory=nmr_mdir, strip_hetatm=True, same_size=True)
logger.info('NMR ensemble contained {0} models'.format(len(amopt.d['processed_models'])))
# Modelling business happens here
if self.modelling_required(amopt.d):
self.modelling(amopt.d, rosetta_modeller)
ample_util.save_amoptd(amopt.d)
amopt.write_config_file()
# Ensembling business next
if amopt.d['make_ensembles']:
self.ensembling(amopt.d)
amopt.write_config_file()
# Some MR here
if amopt.d['do_mr']:
self.molecular_replacement(amopt.d)
amopt.write_config_file()
# Timing data
time_stop = time.time()
elapsed_time = time_stop - time_start
run_in_min = elapsed_time / 60
run_in_hours = run_in_min / 60
msg = os.linesep + \
'All processing completed (in {0:6.2F} hours)'.format(
run_in_hours) + os.linesep
msg += '----------------------------------------' + os.linesep
logging.info(msg)
# Benchmark mode
if amopt.d['benchmark_mode']:
self.benchmarking(amopt.d)
amopt.write_config_file()
amopt.write_config_file()
# Flag to show that we reached the end without error - useful for integration testing
amopt.d['AMPLE_finished'] = True
ample_util.save_amoptd(amopt.d)
logger.info("AMPLE finished at: %s", time.strftime("%a, %d %b %Y %H:%M:%S", time.gmtime()))
ref_mgr = reference_manager.ReferenceManager(amopt.d)
ref_mgr.save_citations_to_file(amopt.d)
logger.info(ref_mgr.citations_as_text)
logger.info(reference_manager.footer)
# Finally update pyrvapi results
if self.ample_output:
self.ample_output.display_results(amopt.d)
self.ample_output.rvapi_shutdown(amopt.d)
self.cleanup(amopt.d)
return