def run_filter(self, f):
if f.name() == 'FragmentScoreFilter':
# FragmentScoreFilter can be finicky and sometimes requires you
# to run sparks-x outside of C++. I have no idea why, just roll
# with it.
try:
# First try to run FSF because it needs to generate the
# other sequence profile files
score = f.report_sm(self.pose)
except:
import subprocess
# Now get rid of the empty .fasta.pssm file it creates
# and try again in Python
rempath = os.path.join(self.workspace.seqprof_dir,
'{}.fasta.phipsi'.format(self.task_id))
print('REMOVING {}'.format(rempath))
os.remove(rempath)
print('CWD: {}'.format(os.getcwd()))
print('TASK: {}'.format(self.task_id))
print('FILES IN CWD:')
print(os.listdir(os.getcwd()))
cmd = [
'/wynton/home/kortemme/krivacic/software/fragments/sparks-x/bin/buildinp_query.sh',
os.path.join(self.workspace.seqprof_dir,
'{}.fasta'.format(self.task_id)),
]
process = subprocess.run(
cmd,
env=dict(
SPARKSXDIR=
'/wynton/home/kortemme/krivacic/software/fragments/sparks-x',
**os.environ))
score = f.report_sm(self.pose)
elif f.name() == 'BuriedUnsatHbondFilter':
try:
score = f.report_sm(self.pose)
except:
print('Buried Unsats FAILED; make sure DAlphaBall is '\
'installed and working. You may need to install '\
'gfortran \n(conda install -c anaconda '\
'gfortran_<OS>-64 gmp)')
print('Once installed, add the library path to your '\
'environment.\n'\
'Ex. export '\
'LD_LIBRARY_PATH=anaconda/lib:LD_LIBRARY_PATH')
else:
score = f.report_sm(self.pose)
fname = 'EXTRA_METRIC_' + f.get_user_defined_name()
setPoseExtraScore(self.pose, fname, score)
def __setitem__(self, key, value):
if key in self._reserved:
raise ValueError(
"Can not set score key with reserved energy name: %r" % key
)
# Bit of a two-step to deal with potential duplicate keys in the
# score maps. First check if a key, of either type, exists. If so
# *try* to set the extra score, triggering type conversion checking
# etc...
#
# If set is successful then clear the score cache and set again,
# eliminating any potential duplicate keys.
had_score = key in self
setPoseExtraScore(self.pose, key, value)
if had_score:
self.__delitem__(key)
setPoseExtraScore(self.pose, key, value)
print(fd.movemap)
print(fd.task_factory)
fd.apply()
# Create new pose from input file for comparison
input_pose = pose_from_file(pdbpath)
#input_pose = pose_from_file(workspace.input_pdb_path)
# Calculate several different types of RMSD
ca_rmsd = CA_rmsd(fd.pose, input_pose)
all_atom_rmsd = all_atom_rmsd(fd.pose, input_pose)
filters = workspace.get_filters(fd.pose,
task_id=job_info['task_id'],
score_fragments=False,
test_run=test_run)
filters.run_filters()
# Add RMSDs as extra metrics, which will be printed at the end of
# the PDB file.
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_CA_RMSD', ca_rmsd)
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_AllAtom_RMSD', all_atom_rmsd)
total_time = time.time() - start_time
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_Run time', total_time)
# Save final pose as a pdb file.
input_name = os.path.basename(pdbpath).split(".")[0]
out = workspace.output_prefix(
job_info) + input_name + workspace.output_suffix(job_info) + '.pdb.gz'
pose.dump_pdb(out)
chi=designable.extend(repackable))
if test_run:
fd.rounds = 1
print(fd.movemap)
print(fd.task_factory)
fd.apply()
# Create new pose from input file for comparison
input_pose = pose_from_file(pdbpath)
#input_pose = pose_from_file(workspace.input_pdb_path)
# Calculate several different types of RMSD
ca_rmsd = CA_rmsd(fd.pose, input_pose)
all_atom_rmsd = all_atom_rmsd(fd.pose, input_pose)
filters = workspace.get_filters(fd.pose,
task_id=job_info['task_id'], score_fragments=False,
test_run=test_run)
filters.run_filters()
# Add RMSDs as extra metrics, which will be printed at the end of
# the PDB file.
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_CA_RMSD', ca_rmsd)
setPoseExtraScore(fd.pose, 'EXTRA_METRIC_AllAtom_RMSD', all_atom_rmsd)
# Save final pose as a pdb file.
input_name = os.path.basename(pdbpath).split(".")[0]
out = workspace.output_prefix(job_info) + input_name + workspace.output_suffix(job_info) + '.pdb.gz'
pose.dump_pdb(out)
def main():
args = docopt.docopt(__doc__)
print(args)
cluster.require_qsub()
start_time = time.time()
workspace, job_info = big_jobs.initiate()
pdbpath = workspace.input_path(job_info)
if not os.path.exists(workspace.output_prefix(job_info)):
os.mkdir(workspace.output_prefix(job_info))
outpath = workspace.output_path(job_info)
test_run = job_info.get('test_run', False)
# append location of Rosetta binaries to path
sys.path.append('/wynton/home/kortemme/krivacic/source/bin')
# find necessary files
tmers = glob.glob(
os.path.join(workspace.fragments_dir,
workspace.fragments_tag(pdbpath) + '?', '*3mers.gz'))
nmers = glob.glob(
os.path.join(workspace.fragments_dir,
workspace.fragments_tag(pdbpath) + '?', '*9mers.gz'))
ss2 = glob.glob(
os.path.join(workspace.fragments_dir,
workspace.fragments_tag(pdbpath) + '?', '*psipred_ss2'))
# Run the ab initio relaxation script.
relax_abinitio = [
'AbinitioRelax.linuxgccrelease',
'-abinitio:relax',
'-use_filters',
'true',
'-abinitio::increase_cycles',
'10',
'-abinitio::rg_reweight',
'0.5',
'-abinitio::rsd_wt_helix',
'0.5',
'-abinitio::rsd_wt_loop',
'0.5',
'-relax::fast',
'-in:file:fasta',
workspace.fasta_path,
'-in:file:frag3',
tmers,
'-in:file:frag9',
nmers,
'-in:file:psipred_ss2',
ss2,
'-nstruct',
args.nstruct,
'-out:pdb_gz',
'-out:prefix',
workspace.output_prefix(job_info),
'-out:suffix',
workspace.output_suffix(job_info),
# '--outdir', workspace.fragments_dir,
# '--memfree', args['--mem-free']
]
if args['--dry-run']:
print(' '.join(relax_abinitio))
else:
subprocess.call(relax_abinitio)
init()
pose = pose_from_file(outpath)
input_pose = pose_from_file(pdbpath)
ca_rmsd = CA_rmsd(pose, input_pose)
all_atom_rmsd = all_atom_rmsd(pose, input_pose)
setPoseExtraScore(pose, 'EXTRA_METRIC_CA_RMSD [[-]]', ca_rmsd)
setPoseExtraScore(pose, 'EXTRA_METRIC_AllAtom_RMSD [[-]]', all_atom_rmsd)
filters = workspace.get_filters(pose,
task_id=job_info['task_id'],
score_fragments=not test_run,
test_run=test_run)
filters.run_filters()
total_time = time.time() - start_time
setPoseExtraScore(pose, 'EXTRA_METRIC_Run time', total_time)
outname = os.path.basename(outpath)
outfolder = os.path.join(workspace.output_prefix(job_info), 'filtered')
pose.dump_pdb(os.path.join(outfolder, outpath))
dalphaball_path, pdbpath))
relax.add_init_arg('-total_threads 1')
if test_run:
relax.rounds = 1
relax.pose = pose
# Warning: This is an all-atom movemap. Constrain to input coords if
# you don't want things to move around a lot, or set a different
# movemap.
relax.setup_default_movemap()
# Constrain relax to start coords.
relax.mover.constrain_relax_to_start_coords(True)
relax.apply()
input_pose = pose_from_file(workspace.input_pdb_path)
ca_rmsd = CA_rmsd(relax.pose, input_pose)
score_fragments = os.path.exists(workspace.loops_path)
filters = workspace.get_filters(relax.pose,
task_id=job_info['task_id'],
score_fragments=score_fragments,
test_run=test_run)
filters.run_filters()
input_name = os.path.basename(pdbpath).split(".")[0]
out = workspace.output_prefix(
job_info) + input_name + workspace.output_suffix(job_info) + '.pdb.gz'
setPoseExtraScore(relax.pose, 'EXTRA_METRIC_CA_RMSD', ca_rmsd)
pose.dump_pdb(out)
i += 1
sfxn = create_score_function('ref2015')
# Create new pose from input file for comparison
input_pose = pose_from_file(pdbpath)
for i in posedict:
pose = posedict[i]
sfxn(input_pose)
sfxn(pose)
ca_rmsd = CA_rmsd(pose, input_pose)
aa_rmsd = all_atom_rmsd(pose, input_pose)
# Add RMSDs to pose for parsing
setPoseExtraScore(pose, 'EXTRA_METRIC_CA_RMSD', ca_rmsd)
setPoseExtraScore(pose, 'EXTRA_METRIC_AllAtom_RMSD', aa_rmsd)
#input_pose = pose_from_file(workspace.input_pdb_path)
# Calculate several different types of RMSD
filters = workspace.get_filters(pose,
task_id=str(job_info['task_id']) + '_' + str(i),
score_fragments=False,
test_run=test_run,
fragment_full_chain=1)
filters.run_filters()
# Save final pose as a pdb file.
input_name = os.path.basename(pdbpath).split('.')[0]
out = workspace.output_prefix(job_info) + \
input_name + '_{}'.format(i) + '.pdb.gz'
