def save_models(self):
# output folder
output_folder = os.path.join(self.work_dir, 'output_pdbs')
logger.log_file(module_name=_name,
msg="Saving pdb files to " + str(output_folder))
try:
os.mkdir(output_folder)
except OSError:
logger.warning(
_name,
"Possibly overwriting previous pdb files. Use --work-dir <DIR> to avoid that."
)
# Saving the trajectory to PDBs:
if 'R' in self.pdb_output:
logger.log_file(module_name=_name, msg='Saving replicas...')
self.trajectory.to_pdb(mode='replicas', to_dir=output_folder)
# Saving top1000 models to PDB:
if 'F' in self.pdb_output:
logger.log_file(module_name=_name, msg='Saving filtered models...')
self.filtered_trajectory.to_pdb(mode='replicas',
to_dir=output_folder,
name='top1000')
# Saving clusters in CA representation
if 'C' in self.pdb_output:
logger.log_file(module_name=_name, msg='Saving clusters...')
for i, cluster in enumerate(self.clusters):
cluster.to_pdb(mode='replicas',
to_dir=output_folder,
name='cluster_{0}'.format(i))
# Saving final models:
if 'M' in self.pdb_output:
if self.aa_rebuild:
logger.log_file(
module_name=_name,
msg='Saving final models (in AA representation)')
pdb_medoids = self.medoids.to_pdb()
from CABS.ca2all import ca2all
from CABS.pdblib import Pdb
for i, fname in enumerate(pdb_medoids):
ca2all(fname,
output=os.path.join(output_folder,
'model_{0}.pdb'.format(i)),
iterations=self.modeller_iterations,
out_mdl=os.path.join(
self.work_dir, 'output_data',
'modeller_output_{0}.txt'.format(i)),
work_dir=self.work_dir)
pth_tmp = os.path.join(self.work_dir, 'output_pdbs',
'model_{0}.pdb'.format(i))
mod = Pdb(pth_tmp)
ssh = mod.mk_ss_header()
mod.atoms.save_to_pdb(pth_tmp, header=ssh)
else:
logger.log_file(
module_name=_name,
msg='Saving final models (in CA representation)')
self.medoids.to_pdb(mode='models',
to_dir=output_folder,
name='model')
def run(self):
monitor = logger.CabsObserver(interval=0.2,
progress_file=join(
self.cfg['cwd'], 'PROGRESS'))
cabs_proc = Popen(self.cfg['exe'],
cwd=self.cfg['cwd'],
stderr=PIPE,
stdin=PIPE)
(stdout, stderr) = cabs_proc.communicate()
if stderr:
logger.warning(module_name=_name, msg=stderr)
monitor.exit()
def parse_reference(self, ref):
try:
try:
dummy, trg_chids = ref.split(":")
self.reference = (pdblib.Pdb(ref,
selection='name CA',
no_exit=True,
verify=True).atoms, trg_chids)
super(FlexTask, self).parse_reference(ref)
except AttributeError: # if ref is None it has no split mth
self.reference = (self.initial_complex,
self.initial_complex.protein_chains)
except (pdblib.Pdb.InvalidPdbInput, ValueError):
logger.warning(_name, 'Invalid reference {}'.format(ref))
def save_config_file(self):
if self.save_config:
with open(os.path.join(self.work_dir, 'config.ini'),
'w') as configfile:
configfile.write(CONFIG_HEADER)
for k in sorted(self.config):
value = self.config[k]
name = re.sub("_", "-", str(k))
option = opt_parser.option_formatter(name, value)
try:
configfile.write(option)
except Exception as e:
logger.warning(
_name,
"Failed to save %s option to config file. Reason: %s."
% (name, e.message))
def parse_reference(self, ref):
try:
source, rec, pep = ref.split(':')
self.reference = (pdblib.Pdb(ref,
selection='name CA',
no_exit=True,
verify=True).atoms, rec, pep)
super(DockTask, self).parse_reference(ref)
self.reference = (pdblib.Pdb(ref,
selection='name CA',
no_exit=True,
verify=True).atoms, rec, pep)
if len(self.initial_complex.peptide_chains) != len(
self.reference[2]):
raise ValueError
except (ValueError, pdblib.Pdb.InvalidPdbInput):
logger.warning(_name, 'Invalid reference {}'.format(ref))
self.reference = None
def if_append(option_name, value):
""" Handles appended arguments that come as both lists of lists and lists of arguments"""
try:
if options[option_name]["action"] == "append":
try:
nargs = options[option_name]['nargs'] # TODO: options[option_name].get('nargs')
if type(value) == list:
line = ""
for single_value in value:
line += "\n" + option_name + " : " + " ".join([str(i) for i in single_value])
return line
else:
logger.warning("OptParse", "Issues while saving multiple argument option: %s" % option_name)
raise KeyError
except KeyError:
if type(value) == list:
line = ""
for single_value in value:
line += "\n" + option_name + " : " + str(single_value)
return line
else:
logger.warning("OptParse", "Issues while saving appended argument option: %s" % option_name)
raise KeyError
except Exception:
logger.warning("OptParse", "Issues while saving %s option" % option_name)
raise KeyError
else:
raise KeyError
except KeyError:
raise
def __init__(
self,
source,
selection='',
remove_alternative_locations=True,
fix_non_standard_aa=True,
remove_water=True,
remove_hetero=True,
verify=False,
no_exit=False, # does not exit on error, raises InvalidPdbInput instead
):
logger.debug(_name, 'Creating Pdb object from {}'.format(source))
self.atoms = Atoms()
words = source.split(':')
try:
name, rec, pep = words
chains = rec + pep
except ValueError:
try:
name, chains = words
except ValueError:
name = words[0]
chains = ''
try:
self.body = self.read(name)
self.name = os.path.basename(name).split('.')[0]
except IOError:
try:
self.body = self.read(self.fetch(name))
self.name = name
except ConnectionError as e:
if no_exit:
raise Pdb.InvalidPdbInput(e.message)
else:
logger.exit_program(
module_name=_name,
msg='Cannot connect to the PDB database',
exc=e
)
except HTTPError as e:
if no_exit:
raise Pdb.InvalidPdbInput(e.message)
else:
logger.exit_program(
module_name=_name,
msg='Invalid PDB code: {}'.format(name),
exc=e
)
except IOError as e:
if no_exit:
raise Pdb.InvalidPdbInput(e.message)
else:
logger.exit_program(
module_name=_name,
msg='File {} not found'.format(name),
exc=e
)
try:
logger.debug(_name, 'Processing {}'.format(name))
current_model = 0
for line in self.body.split('\n'):
match = re.match(r'(ATOM|HETATM)', line)
if match:
self.atoms.append(Atom(line, current_model))
else:
match = re.match(r'MODEL\s+(\d+)', line)
if match:
current_model = int(match.group(1))
if chains:
logger.debug(_name, 'Selected chains {}'.format(chains))
if selection:
selection = '({}) and chain {}'.format(selection, ','.join(chains))
else:
selection = 'chain {}'.format(','.join(chains))
if remove_alternative_locations:
logger.debug(_name, 'Removing alternative locations from {}'.format(name))
self.atoms.remove_alternative_locations()
if remove_water:
logger.debug(_name, 'Removing water molecules from {}'.format(name))
self.atoms = self.atoms.drop('resname HOH')
if fix_non_standard_aa:
logger.debug(_name, 'Scanning {} for non-standard amino acids'.format(name))
aa_names = [AA_NAMES[k] for k in AA_NAMES]
for model in self.atoms.models():
for residue in model.residues():
resname = residue[0].resname
if resname not in aa_names:
if resname not in AA_SUB_NAMES:
logger.warning(
_name, 'Unknown residue {} at {} in {}'.format(
resname, residue[0].resid_id(), name
)
)
else:
sub_name = AA_SUB_NAMES[resname]
for atom in residue:
atom.resname = sub_name
atom.hetatm = False
logger.warning(
_name, 'Replacing {} -> {} for {} in {}'.format(
resname, sub_name, residue[0].resid_id(), name
)
)
if remove_hetero:
logger.debug(_name, 'Removing heteroatoms from {}'.format(name))
self.atoms = self.atoms.drop('hetero')
if selection:
logger.debug(_name, 'Selecting [{}] from {}'.format(selection, name))
self.atoms = self.atoms.select(selection)
if ' ' in set([i.chid for i in self.atoms]):
raise ValueError('Atoms with empty chain ID in selected part of PDB file detected.')
if not len(self.atoms):
raise Exception('{} contains no atoms'.format(source))
if chains and verify:
actual_chains = ''.join(self.atoms.list_chains().keys())
logger.debug(
module_name=_name,
msg='Matching declared [{}] with actual [{}] chain IDs in {}.'.format(chains, actual_chains, name)
)
if set(chains) != set(actual_chains):
msg = 'Mismatch in chain IDs in {}: {} differs from {}'.format(name, chains, actual_chains)
logger.warning(_name, msg)
raise Exception(msg)
except Exception as e:
if no_exit:
raise Pdb.InvalidPdbInput(e.message)
else:
logger.exit_program(
module_name=_name,
msg=e.message,
exc=e
)
def dssp(self, output=''):
"""Runs dssp on the read pdb file and returns a dictionary with secondary structure"""
out = err = None
try:
proc = Popen([self.DSSP_COMMAND, '/dev/stdin'], stdin=PIPE, stdout=PIPE, stderr=PIPE)
out, err = proc.communicate(input=self.body)
logger.debug(
module_name=_name,
msg='Running DSSP'
)
except OSError:
logger.warning(
module_name=_name,
msg='DSSP not found.'
)
tempfile = mkstemp(suffix='.pdb', prefix='.tmp.dssp.', dir=PDB_CACHE)[1]
with open(tempfile, 'wb') as f:
f.write(self.body)
try:
logger.debug(
module_name=_name,
msg='Submitting structure to the DSSP server'
)
out, err = self.xssp(tempfile)
except (HTTPError, ConnectionError):
logger.warning(
module_name=_name,
msg='Cannot connect to the DSSP server. DSSP was not ran at all.'
)
return None
finally:
try:
os.remove(tempfile)
except OSError:
pass
if err:
logger.critical(
module_name=_name,
msg='DSSP ERROR: %s' % err.replace('\n', ' ')
)
return None
else:
logger.debug(_name, 'DSSP successful')
if logger.log_files() and output:
output = os.path.join(output, 'output_data', 'DSSP_output_%s.txt' % self.name)
d = os.path.dirname(output)
if not isdir(d):
os.makedirs(d)
logger.to_file(
filename=output,
content=out,
msg='Saving DSSP output to %s' % output
)
sec = OrderedDict()
p = '^([0-9 ]{5}) ([0-9 ]{4}.)([A-Z ]) ([A-Z]) ([HBEGITS ])(.*)$'
for line in out.split('\n'):
m = re.match(p, line)
if m:
key = str(m.group(2).strip() + ':' + m.group(3))
if m.group(5) in 'HGI':
val = 'H'
elif m.group(5) in 'BE':
val = 'E'
elif m.group(5) in 'T':
val = 'T'
else:
val = 'C'
sec[key] = val
return sec
def __init__(self,
source,
flexibility=None,
exclude=None,
weights=None,
work_dir='.'):
Atoms.__init__(self)
pdb = Pdb(source=source, selection='name CA')
self.atoms = pdb.atoms.models()[0]
logger.info(module_name=_name,
msg="Loading %s as input protein" % source)
# setup flexibility
if flexibility:
try:
bfac = float(flexibility)
self.atoms.set_bfac(bfac)
except ValueError:
if flexibility.lower() == 'bf':
pass
elif flexibility.lower() == 'bfi':
for a in self.atoms:
if a.bfac > 1.:
a.bfac = 0.
elif a.bfac < 0.:
a.bfac = 1.
else:
a.bfac = 1. - a.bfac
elif flexibility.lower() == 'bfg':
for a in self.atoms:
if a.bfac < 0.:
a.bfac = 1.
else:
a.bfac = exp(-0.5 * a.bfac**2)
else:
try:
d, de = self.read_flexibility(flexibility)
self.atoms.update_bfac(d, de)
except IOError:
logger.warning(
_name, 'Could not read flexibility file: %s' %
flexibility)
logger.warning(
_name,
'Using default flexibility(1.0) for all residues.')
self.atoms.set_bfac(1.0)
else:
self.atoms.set_bfac(1.0)
# setup excluding
self.exclude = {}
if exclude:
for s in exclude:
words = s.split('@')
if len(words) == 1:
key = 'ALL'
else:
key = utils.pep2pep1(words[-1])
if key in self.exclude:
self.exclude[key] += '+' + words[0]
else:
self.exclude[key] = words[0]
for k, v in self.exclude.items():
self.exclude[k] = []
for word in v.split('+'):
if ':' in word:
if '-' in word:
beg, end = word.split('-')
self.exclude[k].extend(
self.atoms.atom_range(beg, end))
else:
self.exclude[k].append(word)
else:
chains = re.sub(r'[^%s]*' % word, '', ascii_uppercase)
self.exclude[k].extend(
a.resid_id()
for a in self.atoms.select('chain %s' % chains))
ss = pdb.dssp(output=work_dir)
self.old_ids = self.atoms.update_sec(ss).fix_broken_chains()
self.new_ids = {v: k for k, v in self.old_ids.items()}
for key, val in self.exclude.items():
self.exclude[key] = [self.new_ids[r] for r in val]
# setup rmsd_weights
self.weights = None
if weights and weights.lower() == 'flex':
self.weights = [a.bfac for a in self.atoms]
if weights and weights.lower() == 'ss':
self.weights = [(a.occ + 1.) % 2 for a in self.atoms]
else:
try:
default = 1.0
self.weights = []
weights_dict = {}
with open(weights, 'rb') as _file:
for line in _file:
k, v = line.split()[:2]
weights_dict[k] = v
if 'default' in weights_dict:
default = float(weights_dict['default'])
for a in self.atoms:
w = weights_dict.get(a.resid_id())
w = float(w) if w else default
self.weights.append(w)
except (IOError, ValueError):
logger.warning(_name,
'Could not read weights file: %s' % weights)
logger.warning(_name,
'Using default weights(1.0) for all atoms.')
self.center = self.cent_of_mass()
self.dimension = self.max_dimension()
self.patches = {}
def __init__(self, **kwargs):
# self.__dict__.update(kwargs)
self.aa_rebuild = kwargs.get('aa_rebuild')
self.add_peptide = kwargs.get('add_peptide')
self.align = kwargs.get('align')
self.align_options = dict(kwargs.get('align_options', []))
self.align_peptide_options = dict(
kwargs.get('align_peptide_options', []))
self.ca_rest_add = kwargs.get('ca_rest_add')
self.ca_rest_file = kwargs.get('ca_rest_file')
self.ca_rest_weight = kwargs.get('ca_rest_weight')
self.clustering_iterations = kwargs.get('clustering_iterations')
self.clustering_medoids = kwargs.get('clustering_medoids')
self.contact_map_colors = kwargs.get('contact_map_colors')
self.contact_maps = kwargs.get('contact_maps')
self.contact_threshold = kwargs.get('contact_threshold')
self.dssp_command = kwargs.get('dssp_command')
self.exclude = kwargs.get('exclude')
self.excluding_distance = kwargs.get('excluding_distance')
self.filtering_count = kwargs.get('filtering_count')
self.filtering_mode = kwargs.get('filtering_mode')
self.fortran_command = kwargs.get('fortran_command')
self.image_file_format = kwargs.get('image_file_format')
self.input_protein = kwargs.get('input_protein')
self.insertion_attempts = kwargs.get('insertion_attempts')
self.insertion_clash = kwargs.get('insertion_clash')
self.load_cabs_files = kwargs.get('load_cabs_files')
self.mc_annealing = kwargs.get('mc_annealing')
self.mc_cycles = kwargs.get('mc_cycles')
self.mc_steps = kwargs.get('mc_steps')
self.modeller_iterations = kwargs.get('modeller_iterations')
self.pdb_output = kwargs.get('pdb_output')
self.peptide = kwargs.get('peptide')
self.protein_flexibility = kwargs.get('protein_flexibility')
self.protein_restraints = kwargs.get('protein_restraints')
self.protein_restraints_reduce = kwargs.get(
'protein_restraints_reduce')
self.random_seed = kwargs.get('random_seed')
self.reference_pdb = kwargs.get('reference_pdb')
self.remote = kwargs.get('remote')
self.replicas = kwargs.get('replicas')
self.replicas_dtemp = kwargs.get('replicas_dtemp')
self.save_cabs_files = kwargs.get('save_cabs_files')
self.save_config = kwargs.get('save_config')
self.sc_rest_add = kwargs.get('sc_rest_add')
self.sc_rest_file = kwargs.get('sc_rest_file')
self.sc_rest_weight = kwargs.get('sc_rest_weight')
self.separation = kwargs.get('separation')
self.temperature = kwargs.get('temperature')
self.verbose = kwargs.get('verbose')
self.work_dir = kwargs.get('work_dir')
self.weighted_fit = kwargs.get('weighted_fit')
# Job attributes collected.
self.config = kwargs
self.initial_complex = None
self.restraints = None
self.cabsrun = None
self.trajectory = None
self.filtered_trajectory = None
self.filtered_ndx = None
self.medoids = None
self.clusters_dict = None
self.clusters = None
self.rmslst = {}
self.results = None
self.reference = None
# Work_dir processing: making sure work_dir is abspath
self.work_dir = os.path.abspath(self.work_dir)
try:
logger.setup(log_level=self.verbose,
remote=self.remote,
work_dir=self.work_dir)
os.makedirs(self.work_dir)
except OSError:
if os.path.isdir(self.work_dir):
logger.warning(
_name,
'{} already exists. Output data will be overwritten.'.
format(self.work_dir))
else:
logger.exit_program(
_name,
'{} already exists and is not a directory. Choose different name.'
.format(self.work_dir))
if self.dssp_command:
pdblib.Pdb.DSSP_COMMAND = self.dssp_command
if self.fortran_command:
cabs.CabsRun.FORTRAN_COMMAND = self.fortran_command
self.file_TRAF = self.file_SEQ = None
if self.load_cabs_files:
try:
self.load_cabs_results()
self.file_TRAF = os.path.join(self.work_dir, "TRAF")
self.file_SEQ = os.path.join(self.work_dir, "SEQ")
except (ValueError, TypeError, IOError) as e:
logger.exit_program(
module_name=_name,
msg=
"Could not load CABS files from %s. An error occurred: %s"
% (self.load_cabs_files, e),
exc=e)
# self.peptide + self.add_peptide -> self.ligand
self.peptides = []
if self.peptide:
self.peptides.extend([[p, 'random', 'random']
for p in self.peptide])
if self.add_peptide:
self.peptides.extend([p for p in self.add_peptide if p])
# Pdb output processing
if 'A' in self.pdb_output:
self.pdb_output = 'RFCM'
elif 'N' in self.pdb_output:
self.pdb_output = ''
if self.contact_map_colors:
self.colors = self.contact_map_colors
else:
self.colors = DEFAULT_COLORS
# Flag to check if dynamic weights should be used
self.gauss = self.weighted_fit == 'gauss'
def ca2all(filename,
output=None,
iterations=1,
work_dir='.',
out_mdl=os.path.join(os.getcwd(), 'output_data',
'modeller_output_0.txt')):
"""
Rebuilds ca to all-atom
"""
old_stdout = sys.stdout
if logger.log_files():
sys.stdout = open(out_mdl, 'w')
else:
sys.stdout = open('/dev/null', 'w')
pdb = mkstemp(prefix='.', suffix='.pdb', dir=work_dir, text=True)[1]
prefix = basename(pdb).rsplit('.', 1)[0]
aa_names = {
'A': 'ALA',
'C': 'CYS',
'D': 'ASP',
'E': 'GLU',
'F': 'PHE',
'G': 'GLY',
'H': 'HIS',
'I': 'ILE',
'K': 'LYS',
'L': 'LEU',
'M': 'MET',
'N': 'ASN',
'P': 'PRO',
'Q': 'GLN',
'R': 'ARG',
'S': 'SER',
'T': 'THR',
'V': 'VAL',
'W': 'TRP',
'Y': 'TYR'
}
aa_names = {v: k for k, v in aa_names.items()}
atoms = []
pattern = re.compile('ATOM.{9}CA .([A-Z]{3}) ([A-Z ])(.{5}).{27}(.{12}).*')
try:
with closing(filename) as f, open(pdb, 'w') as tmp:
for line in f:
if line.startswith('ENDMDL'):
break
else:
match = re.match(pattern, line)
if match:
atoms.append(match.groups())
tmp.write(line)
if not len(atoms):
raise Exception('File %s contains no CA atoms' % filename)
chains = [atoms[0][1]]
seq = ''
for a in atoms:
s, c = a[:2]
if c not in chains:
chains += c
seq += '/'
seq += aa_names[s]
pir = prefix + '.pir'
with open(pir, 'w') as f:
f.write(_PIR_TEMPLATE % (prefix, seq, pdb))
env = environ()
env.io.atom_files_directory = ['.']
class MyModel(automodel):
def special_patches(self, aln):
self.rename_segments(segment_ids=chains)
mdl = MyModel(env,
alnfile=pir,
knowns='model_ca',
sequence=prefix,
assess_methods=assess.DOPE)
mdl.md_level = refine.slow
mdl.auto_align(matrix_file=prefix + '.mat')
mdl.starting_model = 1
mdl.ending_model = int(iterations)
mdl.final_malign3d = True
mdl.make()
models = [m for m in mdl.outputs if m['failure'] is None]
cmp_key = 'DOPE score'
models.sort(lambda x, y: cmp(x[cmp_key], y[cmp_key]))
final = models[0]['name'].rsplit('.', 1)[0] + '_fit.pdb'
sys.stdout.close()
sys.stdout = old_stdout
if output:
outfile = open(output, 'w')
else:
outfile = sys.stdout
with open(final) as f:
a = iter(atoms)
current = ch = r = t = nl = None
for line in f:
if line.startswith('ATOM'):
res = line[21:27]
if not current or current != res:
current = res
ch, r, t = a.next()[1:]
nl = line[:21] + ch + r + line[27:54] + t
if len(line) > 66:
nl += line[66:]
outfile.write(nl)
elif line.startswith('TER '):
outfile.write(line[:22] + nl[22:27] + '\n')
else:
outfile.write(line)
finally:
junk = glob.glob(prefix + '*')
try:
map(os.remove, junk)
except OSError as e:
logger.warning(_name, e.message)
from tempfile import mkstemp
from os.path import basename
from contextlib import closing
from CABS import logger
_name = 'MODELLER'
_PIR_TEMPLATE = '\n'.join([
'>P1;%s', 'sequence:::::::::', '%s', '*', '', '>P1;model_ca',
'structure:%s:FIRST:@:END:@::::', '*'
])
try:
from modeller import *
from modeller.automodel import *
except ImportError:
logger.warning(_name, 'MODELLER NOT FOUND')
def ca2all(filename,
output=None,
iterations=1,
work_dir='.',
out_mdl=os.path.join(os.getcwd(), 'output_data',
'modeller_output_0.txt')):
"""
Rebuilds ca to all-atom
"""
old_stdout = sys.stdout
if logger.log_files():
sys.stdout = open(out_mdl, 'w')
