def build_bfactor(self, ligands, protein, protein_pdb):
"""Builds b-factor descriptors for series of ligands.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
protein_pdb: the pdb id of the protein.
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types,
self.mode)
if self.aligned_pairs:
pairs = zip(self.ligand_types, self.protein_types)
else:
pairs = [(mol_type, prot_type)
for mol_type in self.ligand_types
for prot_type in self.protein_types]
dist = distance(self.protein.atom_dict['coords'],
mol.atom_dict['coords'])
within_cutoff = (dist <= self.cutoff.max()).any(axis=1)
local_protein_dict = self.protein.atom_dict[within_cutoff]
prot_dict = atoms_by_type(local_protein_dict, self.protein_types,
self.mode)
desc = []
for mol_type, prot_type in pairs:
d = distance(prot_dict[prot_type]['coords'],
mol_dict[mol_type]['coords'])[..., np.newaxis]
if len(self.cutoff) > 1:
count = ((d > self.cutoff[..., 0]) &
(d <= self.cutoff[..., 1])).sum(axis=(0, 1))
else:
count = (d <= self.cutoff).sum()
desc.append(count)
desc = np.array(desc, dtype=int).flatten()
out.append(desc)
parser = PDBParser()
structure = parser.get_structure('pdb', protein_pdb)
atoms = structure.get_atoms()
# Get the b_factors for each atom the structure
for a in atoms:
out = [np.append(out[0], np.array(a.get_bfactor()))]
return np.vstack(out)
def build(self, ligands, protein=None):
"""Builds descriptors for series of ligands
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
if self.protein is None:
out.append(self.func(mol))
else:
out.append(self.func(mol, protein=self.protein))
if self.sparse:
# out = list(map(partial(sparse_to_csr_matrix, size=self.shape), out))
return sparse_vstack(map(
partial(sparse_to_csr_matrix, size=self.shape), out),
format='csr')
else:
return np.vstack(out)
def similarity(self, method, query, cutoff=0.9, protein=None):
"""Similarity filter. Supported structural methods:
* ift: interaction fingerprints
* sift: simple interaction fingerprints
* usr: Ultrafast Shape recognition
* usr_cat: Ultrafast Shape recognition, Credo Atom Types
* electroshape: Electroshape, an USR method including partial charges
Parameters
----------
method: string
Similarity method used to compare molecules. Avaiale methods:
* `ifp` - interaction fingerprint (requires a receptor)
* `sifp` - simple interaction fingerprint (requires a receptor)
* `usr` - Ultrafast Shape Reckognition
* `usr_cat` - USR, with CREDO atom types
* `electroshape` - Electroshape, USR with moments representing
partial charge
query: oddt.toolkit.Molecule or list of oddt.toolkit.Molecule
Query molecules to compare the pipeline to.
cutoff: float
Similarity cutoff for filtering molecules. Any similarity lower
than it will be filtered out.
protein: oddt.toolkit.Molecule (default = None)
Protein for underling method. By default it's empty, but
sturctural fingerprints need one.
"""
if is_molecule(query):
query = [query]
# choose fp/usr and appropriate distance
if method.lower() == 'ifp':
gen = partial(InteractionFingerprint, protein=protein)
dist = dice
elif method.lower() == 'sifp':
gen = partial(SimpleInteractionFingerprint, protein=protein)
dist = dice
elif method.lower() == 'usr':
gen = usr
dist = usr_similarity
elif method.lower() == 'usr_cat':
gen = usr_cat
dist = usr_similarity
elif method.lower() == 'electroshape':
gen = electroshape
dist = usr_similarity
else:
raise ValueError('Similarity filter "%s" is not supported.' % method)
# generate FPs for query molecules once
query_fps = [gen(q) for q in query]
self._pipe.append(partial(_filter_similarity,
distance=dist,
generator=gen, # same generator for pipe mols
query_fps=query_fps,
cutoff=cutoff))
def similarity(self, method, query, cutoff=0.9, protein=None):
"""Similarity filter. Supported structural methods:
* ift: interaction fingerprints
* sift: simple interaction fingerprints
* usr: Ultrafast Shape recognition
* usr_cat: Ultrafast Shape recognition, Credo Atom Types
* electroshape: Electroshape, an USR method including partial charges
Parameters
----------
method: string
Similarity method used to compare molecules. Avaiale methods:
* `ifp` - interaction fingerprint (requires a receptor)
* `sifp` - simple interaction fingerprint (requires a receptor)
* `usr` - Ultrafast Shape Reckognition
* `usr_cat` - USR, with CREDO atom types
* `electroshape` - Electroshape, USR with moments representing
partial charge
query: oddt.toolkit.Molecule or list of oddt.toolkit.Molecule
Query molecules to compare the pipeline to.
cutoff: float
Similarity cutoff for filtering molecules. Any similarity lower
than it will be filtered out.
protein: oddt.toolkit.Molecule (default = None)
Protein for underling method. By default it's empty, but
sturctural fingerprints need one.
"""
if is_molecule(query):
query = [query]
# choose fp/usr and appropriate distance
if method.lower() == 'ifp':
gen = partial(InteractionFingerprint, protein=protein)
dist = dice
elif method.lower() == 'sifp':
gen = partial(SimpleInteractionFingerprint, protein=protein)
dist = dice
elif method.lower() == 'usr':
gen = usr
dist = usr_similarity
elif method.lower() == 'usr_cat':
gen = usr_cat
dist = usr_similarity
elif method.lower() == 'electroshape':
gen = electroshape
dist = usr_similarity
else:
raise ValueError('Similarity filter "%s" is not supported.' % method)
# generate FPs for query molecules once
query_fps = [gen(q) for q in query]
self._pipe.append(partial(_filter_similarity,
distance=dist,
generator=gen, # same generator for pipe mols
query_fps=query_fps,
cutoff=cutoff))
def build(self, mols):
if is_molecule(mols):
mols = [mols]
out = []
for mol in mols:
fp = self._get_fingerprint(mol)
out.append(fp)
return np.vstack(out)
def build_num_aromat_rings(self, ligands, protein, ligand_sdf):
"""Builds number of aromatic rings descriptors for series of ligands.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
ligand_sdf: the path to the sdf-file of the ligand.
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types,
self.mode)
if self.aligned_pairs:
pairs = zip(self.ligand_types, self.protein_types)
else:
pairs = [(mol_type, prot_type)
for mol_type in self.ligand_types
for prot_type in self.protein_types]
dist = distance(self.protein.atom_dict['coords'],
mol.atom_dict['coords'])
within_cutoff = (dist <= self.cutoff.max()).any(axis=1)
local_protein_dict = self.protein.atom_dict[within_cutoff]
prot_dict = atoms_by_type(local_protein_dict, self.protein_types,
self.mode)
desc = []
for mol_type, prot_type in pairs:
d = distance(prot_dict[prot_type]['coords'],
mol_dict[mol_type]['coords'])[..., np.newaxis]
if len(self.cutoff) > 1:
count = ((d > self.cutoff[..., 0]) &
(d <= self.cutoff[..., 1])).sum(axis=(0, 1))
else:
count = (d <= self.cutoff).sum()
desc.append(count)
desc = np.array(desc, dtype=int).flatten()
out.append(desc)
for mol in pybel.readfile("sdf", ligand_sdf): # could be sdf or mol2
result = [
"Aromatic" for r in mol.OBMol.GetSSSR() if r.IsAromatic()
]
out = [np.append(out[0], np.array(len(result)))]
output = np.vstack(out)
return output
def build(self, ligands, protein=None):
"""Builds descriptors for series of ligands
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types,
self.mode)
if self.aligned_pairs:
pairs = zip(self.ligand_types, self.protein_types)
else:
pairs = [(mol_type, prot_type)
for mol_type in self.ligand_types
for prot_type in self.protein_types]
dist = distance(self.protein.atom_dict['coords'],
mol.atom_dict['coords'])
within_cutoff = (dist <= self.cutoff.max()).any(axis=1)
local_protein_dict = self.protein.atom_dict[within_cutoff]
prot_dict = atoms_by_type(local_protein_dict, self.protein_types,
self.mode)
desc = []
for mol_type, prot_type in pairs:
d = distance(prot_dict[prot_type]['coords'],
mol_dict[mol_type]['coords'])[..., np.newaxis]
if len(self.cutoff) > 1:
count = ((d > self.cutoff[..., 0]) &
(d <= self.cutoff[..., 1])).sum(axis=(0, 1))
else:
count = (d <= self.cutoff).sum()
desc.append(count)
desc = np.array(desc, dtype=int).flatten()
out.append(desc)
return np.vstack(out)
def score(self, ligands, protein=None):
"""Automated scoring procedure.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecule objects
Ligands to score
protein: oddt.toolkit.Molecule object or None
Protein object to be used. If None, then the default
one is used, else the protein is new default.
Returns
-------
ligands : array of oddt.toolkit.Molecule objects
Array of ligands (scores are stored in mol.data method)
"""
if protein:
self.set_protein(protein)
if not self.protein_file:
raise IOError("No receptor.")
if is_molecule(ligands):
ligands = [ligands]
ligand_dir = mkdtemp(dir=self.tmp_dir, prefix='ligands_')
output_array = []
for n, ligand in enumerate(ligands):
check_molecule(ligand, force_coords=True)
ligand_file = write_vina_pdbqt(ligand, ligand_dir, name_id=n)
try:
scores = parse_vina_scoring_output(
subprocess.check_output([
self.executable, '--score_only', '--receptor',
self.protein_file, '--ligand', ligand_file
] + self.params,
stderr=subprocess.STDOUT))
except subprocess.CalledProcessError as e:
sys.stderr.write(e.output.decode('ascii'))
if self.skip_bad_mols:
continue
else:
raise Exception('Autodock Vina failed. Command: "%s"' %
' '.join(e.cmd))
ligand.data.update(scores)
output_array.append(ligand)
rmtree(ligand_dir)
return output_array
def score(self, ligands, protein=None):
"""Automated scoring procedure.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecule objects
Ligands to score
protein: oddt.toolkit.Molecule object or None
Protein object to be used. If None, then the default
one is used, else the protein is new default.
Returns
-------
ligands : array of oddt.toolkit.Molecule objects
Array of ligands (scores are stored in mol.data method)
"""
if protein:
self.set_protein(protein)
if not self.protein_file:
raise IOError("No receptor.")
if is_molecule(ligands):
ligands = [ligands]
ligand_dir = mkdtemp(dir=self.tmp_dir, prefix='ligands_')
output_array = []
for n, ligand in enumerate(ligands):
check_molecule(ligand, force_coords=True)
ligand_file = write_vina_pdbqt(ligand, ligand_dir, name_id=n)
try:
scores = parse_vina_scoring_output(
subprocess.check_output([self.executable, '--score_only',
'--receptor', self.protein_file,
'--ligand', ligand_file] + self.params,
stderr=subprocess.STDOUT))
except subprocess.CalledProcessError as e:
sys.stderr.write(e.output.decode('ascii'))
if self.skip_bad_mols:
continue
else:
raise Exception('Autodock Vina failed. Command: "%s"' %
' '.join(e.cmd))
ligand.data.update(scores)
output_array.append(ligand)
rmtree(ligand_dir)
return output_array
def build(self, ligands, protein=None):
if protein:
self.set_protein(protein)
else:
protein = self.protein
if is_molecule(ligands):
ligands = [ligands]
desc = None
for mol in ligands:
# Vina
# TODO: Asynchronous output from vina, push command to score and retrieve at the end?
# TODO: Check if ligand has vina scores
scored_mol = self.vina.score(mol)[0].data
vec = np.array(([scored_mol[key] for key in self.vina_scores]),
dtype=np.float32).flatten()
if desc is None:
desc = vec
else:
desc = np.vstack((desc, vec))
return np.atleast_2d(desc)
def build(self, ligands, protein=None):
if protein:
self.set_protein(protein)
else:
protein = self.protein
if is_molecule(ligands):
ligands = [ligands]
desc = None
for mol in ligands:
mol_keys = mol.data.keys()
if any(x not in mol_keys for x in self.vina_scores):
self.vina.set_ligand(mol)
inter = self.vina.score_inter()
intra = self.vina.score_intra()
num_rotors = self.vina.num_rotors
# could use self.vina.score(), but better to reuse variables
affinity = ((inter * self.vina.weights[:5]).sum() /
(1 + self.vina.weights[5] * num_rotors))
assert len(self.all_vina_scores) == len(inter) + len(intra) + 2
score = dict(
zip(
self.all_vina_scores,
np.hstack(
(affinity, inter, intra, num_rotors)).flatten()))
mol.data.update(score)
else:
score = mol.data.to_dict()
try:
vec = np.array([score[s] for s in self.vina_scores],
dtype=np.float32).flatten()
except Exception as e:
print(score, affinity, inter, intra, num_rotors)
print(mol.title)
raise e
if desc is None:
desc = vec
else:
desc = np.vstack((desc, vec))
return np.atleast_2d(desc)
def dock(self, ligands, protein=None):
"""Automated docking procedure.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecule objects
Ligands to dock
protein: oddt.toolkit.Molecule object or None
Protein object to be used. If None, then the default one
is used, else the protein is new default.
Returns
-------
ligands : array of oddt.toolkit.Molecule objects
Array of ligands (scores are stored in mol.data method)
"""
if protein:
self.set_protein(protein)
if not self.protein_file:
raise IOError("No receptor.")
if is_molecule(ligands):
ligands = [ligands]
ligand_dir = mkdtemp(dir=self.tmp_dir, prefix='ligands_')
output_array = []
for n, ligand in enumerate(ligands):
check_molecule(ligand, force_coords=True)
ligand_file = write_vina_pdbqt(ligand, ligand_dir, name_id=n)
ligand_outfile = ligand_file[:-6] + '_out.pdbqt'
try:
scores = parse_vina_docking_output(
subprocess.check_output([
self.executable, '--receptor', self.protein_file,
'--ligand', ligand_file, '--out', ligand_outfile
] + self.params + ['--cpu', str(self.n_cpu)],
stderr=subprocess.STDOUT))
except subprocess.CalledProcessError as e:
sys.stderr.write(e.output.decode('ascii'))
if self.skip_bad_mols:
continue # TODO: print some warning message
else:
raise Exception('Autodock Vina failed. Command: "%s"' %
' '.join(e.cmd))
# docked conformations may have wrong connectivity - use source ligand
if is_openbabel_molecule(ligand):
if oddt.toolkits.ob.__version__ >= '2.4.0':
# find the order of PDBQT atoms assigned by OpenBabel
with open(ligand_file) as f:
write_order = [
int(line[7:12].strip()) for line in f
if line[:4] == 'ATOM'
]
new_order = sorted(range(len(write_order)),
key=write_order.__getitem__)
new_order = [i + 1
for i in new_order] # OBMol has 1 based idx
assert len(new_order) == len(ligand.atoms)
else:
# Openbabel 2.3.2 does not support perserving atom order.
# We read back the PDBQT ligand to get "correct" bonding.
ligand = next(oddt.toolkit.readfile('pdbqt', ligand_file))
if 'REMARK' in ligand.data:
del ligand.data['REMARK']
docked_ligands = oddt.toolkit.readfile('pdbqt', ligand_outfile)
for docked_ligand, score in zip(docked_ligands, scores):
# Renumber atoms to match the input ligand
if (is_openbabel_molecule(docked_ligand)
and oddt.toolkits.ob.__version__ >= '2.4.0'):
docked_ligand.OBMol.RenumberAtoms(new_order)
# HACK: copy docked coordinates onto source ligand
# We assume that the order of atoms match between ligands
clone = ligand.clone
clone.clone_coords(docked_ligand)
clone.data.update(score)
# Calculate RMSD to the input pose
try:
clone.data['vina_rmsd_input'] = rmsd(ligand, clone)
clone.data['vina_rmsd_input_min'] = rmsd(
ligand, clone, method='min_symmetry')
except Exception:
pass
output_array.append(clone)
rmtree(ligand_dir)
return output_array
def build_stand_alone(self, ligands, lig_sdf, protein, prot_pdb,
nmbr_modes, schroedinger_path):
"""Combines qikprop properties, #aromatic rings, #rotatable bons, #eigenvectors
and #eigenvalues as descriptors. Used in ET-Score.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
protein_pdb: the pdb id of the protein.
ligand_sdf: the path to the sdf-file of the ligand.
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types,
self.mode)
if self.aligned_pairs:
pairs = zip(self.ligand_types, self.protein_types)
else:
pairs = [(mol_type, prot_type)
for mol_type in self.ligand_types
for prot_type in self.protein_types]
dist = distance(self.protein.atom_dict['coords'],
mol.atom_dict['coords'])
within_cutoff = (dist <= self.cutoff.max()).any(axis=1)
local_protein_dict = self.protein.atom_dict[within_cutoff]
prot_dict = atoms_by_type(local_protein_dict, self.protein_types,
self.mode)
desc = []
for mol_type, prot_type in pairs:
d = distance(prot_dict[prot_type]['coords'],
mol_dict[mol_type]['coords'])[..., np.newaxis]
if len(self.cutoff) > 1:
count = ((d > self.cutoff[..., 0]) &
(d <= self.cutoff[..., 1])).sum(axis=(0, 1))
else:
count = (d <= self.cutoff).sum()
desc.append(count)
desc = np.array(desc, dtype=int).flatten()
out.append(desc)
if schroedinger_path[-1] != "/":
schroedinger_path += "/"
subprocess.call(schroedinger_path + "qikprop -NOJOBID %s" % lig_sdf,
shell=True)
if ("/" in lig_sdf):
pattern = re.compile(r'/([^/]+)\.sdf')
lig_name = re.search(pattern, lig_sdf).group(1)
print(lig_name)
else:
lig_name = lig_sdf[:-4] # remove .sdf
qikprops = {}
with open("%s.CSV" % lig_name) as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
qikprops["FOSA"] = row["FOSA"]
qikprops["FISA"] = row["FISA"]
qikprops["WPSA"] = row["WPSA"]
qikprops["QPlogPo/w"] = row["QPlogPo/w"]
qikprops["QPlogHERG"] = row["QPlogHERG"]
qikprops["QPlogKhsa"] = row["QPlogKhsa"]
qikprops["QPPMDCK"] = row["QPPMDCK"]
qikprops["QPlogKp"] = row["QPlogKp"]
# Add QikProp properties as descriptors
fail = 0
for prop in qikprops:
qikprops[prop]
if qikprops[prop] == '': # QikProp has failed
if fail == 0:
with open("./qikFail_eigv.txt", "a+") as results:
results.write("%s \n" % lig_name)
fail = 1
out = [np.append(out[0], np.array(0))]
else:
out = [np.append(out[0], np.array(float(qikprops[prop])))]
# Add Number of rotatable bonds
for mol in pybel.readfile("sdf", lig_sdf): # can be sdf or mol2
out = [np.append(out[0], np.array(mol.OBMol.NumRotors()))]
# Add number of aromatic rings
for mol in pybel.readfile("sdf", lig_sdf): # could be sdf or mol2
result = [
"Aromatic" for r in mol.OBMol.GetSSSR() if r.IsAromatic()
]
out = [np.append(out[0], np.array(len(result)))]
print("Protein:")
print(prot_pdb)
pdb = parsePDB(prot_pdb)
calphas = pdb.select('calpha')
anm = ANM('pdb ANM analysis')
anm.buildHessian(calphas, cutoff=12.0)
anm.getHessian().round(3)
anm.calcModes(n_modes=nmbr_modes)
# Add NMA Length
out = [np.append(out[0], np.array(len(anm.getEigvals())))]
out = [np.append(out[0], np.array(len(anm.getEigvecs())))]
return np.vstack(out)
def build_eigval_qik(self, ligands, protein, protein_pdb, ligand_sdf):
"""Combines nma_eigenvalues and qikprop-properties descriptors for a series of ligands.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
protein_pdb: the pdb id of the protein.
ligand_sdf: the path to the sdf-file of the ligand.
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types,
self.mode)
if self.aligned_pairs:
pairs = zip(self.ligand_types, self.protein_types)
else:
pairs = [(mol_type, prot_type)
for mol_type in self.ligand_types
for prot_type in self.protein_types]
dist = distance(self.protein.atom_dict['coords'],
mol.atom_dict['coords'])
within_cutoff = (dist <= self.cutoff.max()).any(axis=1)
local_protein_dict = self.protein.atom_dict[within_cutoff]
prot_dict = atoms_by_type(local_protein_dict, self.protein_types,
self.mode)
desc = []
for mol_type, prot_type in pairs:
d = distance(prot_dict[prot_type]['coords'],
mol_dict[mol_type]['coords'])[..., np.newaxis]
if len(self.cutoff) > 1:
count = ((d > self.cutoff[..., 0]) &
(d <= self.cutoff[..., 1])).sum(axis=(0, 1))
else:
count = (d <= self.cutoff).sum()
desc.append(count)
desc = np.array(desc, dtype=int).flatten()
out.append(desc)
lig_id = ligand_sdf[-15:-4]
subprocess.call("/opt/schrodinger2017-4/qikprop -NOJOBID %s" %
ligand_sdf,
shell=True)
qikprops = {}
with open("%s.CSV" % lig_id) as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
qikprops["FOSA"] = row["FOSA"]
qikprops["FISA"] = row["FISA"]
qikprops["WPSA"] = row["WPSA"]
qikprops["QPlogPo/w"] = row["QPlogPo/w"]
qikprops["QPlogHERG"] = row["QPlogHERG"]
qikprops["QPlogKhsa"] = row["QPlogKhsa"]
qikprops["QPPMDCK"] = row["QPPMDCK"]
qikprops["QPlogKp"] = row["QPlogKp"]
lig_name = lig_id[:-7]
subprocess.call("rm " + lig_name + "*", shell=True)
# Add QikProp properties as descriptors
fail = 0
for prop in qikprops:
qikprops[prop]
if qikprops[prop] == '': # QikProp has failed
if fail == 0:
with open("./qikFail_eigv.txt", "a+") as results:
results.write("%s \n" % lig_name)
fail = 1
out = [np.append(out[0], np.array(0))]
else:
out = [np.append(out[0], np.array(float(qikprops[prop])))]
# Add NMA Eigenvalues
pdb = parsePDB(protein_pdb)
calphas = pdb.select('calpha')
anm = ANM('pdb ANM analysis')
anm.buildHessian(calphas, cutoff=12.0)
anm.getHessian().round(3)
anm.calcModes()
for mode in anm:
desc = np.array(mode.getEigval(), dtype=int).flatten()
out = [np.append(out[0], np.array(mode.getEigval()))]
return np.vstack(out)
def build_nmaLength(self, ligands, protein, protein_pdb, nmbr_modes):
"""Builds descriptors with number of eigenvalues
and eigenvectors for series of ligands and proteins.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecules or oddt.toolkit.Molecule
A list or iterable of ligands to build the descriptor or a
single molecule.
protein: oddt.toolkit.Molecule or None (default=None)
Default protein to use as reference
protein_pdb: the pdb id of the protein.
nmbr_modes: the number of normal modes that will be calculated.
"""
if protein:
self.protein = protein
if is_molecule(ligands):
ligands = [ligands]
out = []
for mol in ligands:
mol_dict = atoms_by_type(mol.atom_dict, self.ligand_types,
self.mode)
if self.aligned_pairs:
pairs = zip(self.ligand_types, self.protein_types)
else:
pairs = [(mol_type, prot_type)
for mol_type in self.ligand_types
for prot_type in self.protein_types]
dist = distance(self.protein.atom_dict['coords'],
mol.atom_dict['coords'])
within_cutoff = (dist <= self.cutoff.max()).any(axis=1)
local_protein_dict = self.protein.atom_dict[within_cutoff]
prot_dict = atoms_by_type(local_protein_dict, self.protein_types,
self.mode)
desc = []
for mol_type, prot_type in pairs:
d = distance(prot_dict[prot_type]['coords'],
mol_dict[mol_type]['coords'])[..., np.newaxis]
if len(self.cutoff) > 1:
count = ((d > self.cutoff[..., 0]) &
(d <= self.cutoff[..., 1])).sum(axis=(0, 1))
else:
count = (d <= self.cutoff).sum()
desc.append(count)
desc = np.array(desc, dtype=int).flatten()
out.append(desc)
# New normal modes descriptors
# print(protein_pdb)
pdb = parsePDB(protein_pdb)
calphas = pdb.select('calpha')
anm = ANM('pdb ANM analysis')
anm.buildHessian(calphas, cutoff=12.0)
anm.getHessian().round(3)
anm.calcModes(n_modes=nmbr_modes)
out = [np.append(out[0], np.array(len(anm.getEigvals())))]
out = [np.append(out[0], np.array(len(anm.getEigvecs())))]
output = np.vstack(out)
return output
def dock(self, ligands, protein=None):
"""Automated docking procedure.
Parameters
----------
ligands: iterable of oddt.toolkit.Molecule objects
Ligands to dock
protein: oddt.toolkit.Molecule object or None
Protein object to be used. If None, then the default one
is used, else the protein is new default.
Returns
-------
ligands : array of oddt.toolkit.Molecule objects
Array of ligands (scores are stored in mol.data method)
"""
if protein:
self.set_protein(protein)
if not self.protein_file:
raise IOError("No receptor.")
if is_molecule(ligands):
ligands = [ligands]
ligand_dir = mkdtemp(dir=self.tmp_dir, prefix='ligands_')
output_array = []
for n, ligand in enumerate(ligands):
check_molecule(ligand, force_coords=True)
ligand_file = write_vina_pdbqt(ligand, ligand_dir, name_id=n)
ligand_outfile = ligand_file[:-6] + '_out.pdbqt'
try:
scores = parse_vina_docking_output(
subprocess.check_output([self.executable, '--receptor',
self.protein_file,
'--ligand', ligand_file,
'--out', ligand_outfile] +
self.params +
['--cpu', str(self.n_cpu)],
stderr=subprocess.STDOUT))
except subprocess.CalledProcessError as e:
sys.stderr.write(e.output.decode('ascii'))
if self.skip_bad_mols:
continue # TODO: print some warning message
else:
raise Exception('Autodock Vina failed. Command: "%s"' %
' '.join(e.cmd))
# docked conformations may have wrong connectivity - use source ligand
if is_openbabel_molecule(ligand):
if oddt.toolkits.ob.__version__ >= '2.4.0':
# find the order of PDBQT atoms assigned by OpenBabel
with open(ligand_file) as f:
write_order = [int(line[7:12].strip())
for line in f
if line[:4] == 'ATOM']
new_order = sorted(range(len(write_order)),
key=write_order.__getitem__)
new_order = [i + 1 for i in new_order] # OBMol has 1 based idx
assert len(new_order) == len(ligand.atoms)
else:
# Openbabel 2.3.2 does not support perserving atom order.
# We read back the PDBQT ligand to get "correct" bonding.
ligand = next(oddt.toolkit.readfile('pdbqt', ligand_file))
if 'REMARK' in ligand.data:
del ligand.data['REMARK']
docked_ligands = oddt.toolkit.readfile('pdbqt', ligand_outfile)
for docked_ligand, score in zip(docked_ligands, scores):
# Renumber atoms to match the input ligand
if (is_openbabel_molecule(docked_ligand) and
oddt.toolkits.ob.__version__ >= '2.4.0'):
docked_ligand.OBMol.RenumberAtoms(new_order)
# HACK: copy docked coordinates onto source ligand
# We assume that the order of atoms match between ligands
clone = ligand.clone
clone.clone_coords(docked_ligand)
clone.data.update(score)
# Calculate RMSD to the input pose
clone.data['vina_rmsd_input'] = rmsd(ligand, clone)
clone.data['vina_rmsd_input_min'] = rmsd(ligand, clone,
method='min_symmetry')
output_array.append(clone)
rmtree(ligand_dir)
return output_array
