def test_atoms_by_type():
mol = next(oddt.toolkit.readfile('sdf', actives_sdf))
for mode, types, num_atoms in (('atomic_nums', (6, 7, 8), (33, 4, 4)),
('atom_types_sybyl',
('C.ar', 'C.2', 'C.3', 'N.3', 'N.am',
'O.3', 'O.2'), (12, 3, 18, 1, 3, 1, 3)),
('atom_types_ad4', ('A', 'C', 'N', 'OA'),
(12, 21, 4, 4))):
types_dict = descriptors.atoms_by_type(mol.atom_dict,
types=types,
mode=mode)
for t, n in zip(types, num_atoms):
assert_in(t, types_dict)
assert_equal(len(types_dict[t]), n)
def build(self, ligands, protein=None):
""" Descriptor building method
Parameters
----------
ligands: array-like
An array of generator of oddt.toolkit.Molecule objects for which the descriptor is computed
protein: oddt.toolkit.Molecule object (default=None)
Protein object to be used while generating descriptors.
If none, then the default protein (from constructor) is used.
Otherwise, protein becomes new global and default protein.
Returns
-------
descs: numpy array, shape=[n_samples, 351]
An array of binana descriptors, aligned with input ligands
"""
if protein:
self.set_protein(protein)
else:
protein = self.protein
protein_dict = protein.atom_dict
desc = None
for mol in ligands:
mol_dict = mol.atom_dict
vec = np.array([], dtype=float)
vec = tuple()
# Vina
# TODO: Asynchronous output from vina, push command to score and retrieve at the end?
# TODO: Check if ligand has vina scores
vec += tuple(self.vina.build(mol).flatten())
# Close Contacts (<4A)
vec += tuple(self.cc_4.build(mol).flatten())
# Electrostatics (<4A)
ele_rec_types, ele_lig_types = zip(*self.ele_types)
ele_mol_atoms = atoms_by_type(mol_dict, ele_lig_types,
'atom_types_ad4')
ele_rec_atoms = atoms_by_type(protein_dict, ele_rec_types,
'atom_types_ad4')
ele = tuple()
for r_t, m_t in self.ele_types:
mol_ele_dict, rec_ele_dict = close_contacts(
ele_mol_atoms[m_t], ele_rec_atoms[r_t], 4)
if len(mol_ele_dict) and len(rec_ele_dict):
ele += (mol_ele_dict['charge'] * rec_ele_dict['charge'] /
np.sqrt((mol_ele_dict['coords'] -
rec_ele_dict['coords'])**2).sum(axis=-1) *
138.94238460104697e4).sum(), # convert to J/mol
else:
ele += 0,
vec += tuple(np.nan_to_num(ele))
# Ligand Atom Types
atoms = atoms_by_type(mol_dict, self.ligand_atom_types,
'atom_types_ad4')
vec += tuple([len(atoms[t]) for t in self.ligand_atom_types])
# Close Contacts (<2.5A)
vec += tuple(self.cc_25.build(mol).flatten())
# H-Bonds (<4A)
hbond_mol, hbond_rec, strict = hbonds(mol, protein, 4)
# Retain only strict hbonds
hbond_mol = hbond_mol[strict]
hbond_rec = hbond_rec[strict]
backbone = hbond_rec['isbackbone']
alpha = hbond_rec['isalpha']
beta = hbond_rec['isbeta']
other = ~alpha & ~beta
donor_mol = hbond_mol['isdonor']
donor_rec = hbond_rec['isdonor']
hbond_vec = ((donor_mol & backbone
& alpha).sum(), (donor_mol & backbone & beta).sum(),
(donor_mol & backbone & other).sum(),
(donor_mol & ~backbone
& alpha).sum(), (donor_mol & ~backbone & beta).sum(),
(donor_mol & ~backbone
& other).sum(), (donor_rec & backbone & alpha).sum(),
(donor_rec & backbone & beta).sum(),
(donor_rec & backbone & other).sum(),
(donor_rec & ~backbone & alpha).sum(),
(donor_rec & ~backbone
& beta).sum(), (donor_rec & ~backbone & other).sum())
vec += tuple(hbond_vec)
# Hydrophobic contacts (<4A)
hydrophobic = hydrophobic_contacts(mol, protein, 4)[1]
backbone = hydrophobic['isbackbone']
alpha = hydrophobic['isalpha']
beta = hydrophobic['isbeta']
other = ~alpha & ~beta
hyd_vec = ((backbone & alpha).sum(), (backbone & beta).sum(),
(backbone & other).sum(), (~backbone & alpha).sum(),
(~backbone & beta).sum(), (~backbone & other).sum(),
len(hydrophobic))
vec += tuple(hyd_vec)
# Pi-stacking (<7.5A)
pi_mol, pi_rec, pi_paralel, pi_tshaped = pi_stacking(
mol, protein, 7.5)
alpha = pi_rec['isalpha'] & pi_paralel
beta = pi_rec['isbeta'] & pi_paralel
other = ~alpha & ~beta & pi_paralel
pi_vec = (alpha.sum(), beta.sum(), other.sum())
vec += tuple(pi_vec)
# T-shaped Pi-Pi interaction
alpha = pi_rec['isalpha'] & pi_tshaped
beta = pi_rec['isbeta'] & pi_tshaped
other = ~alpha & ~beta & pi_tshaped
pi_t_vec = (alpha.sum(), beta.sum(), other.sum())
# Pi-cation (<6A)
pi_rec, cat_mol, strict = pi_cation(protein, mol, 6)
alpha = pi_rec['isalpha'] & strict
beta = pi_rec['isbeta'] & strict
other = ~alpha & ~beta & strict
pi_cat_vec = (alpha.sum(), beta.sum(), other.sum())
pi_mol, cat_rec, strict = pi_cation(mol, protein, 6)
alpha = cat_rec['isalpha'] & strict
beta = cat_rec['isbeta'] & strict
other = ~alpha & ~beta & strict
pi_cat_vec += (alpha.sum(), beta.sum(), other.sum())
vec += tuple(pi_cat_vec)
# T-shape (perpendicular Pi's) (<7.5A)
vec += tuple(pi_t_vec)
# Active site flexibility (<4A)
acitve_site = close_contacts(
mol_dict[mol_dict['atomicnum'] != 1],
protein_dict[protein_dict['atomicnum'] != 1],
cutoff=4)[1]
backbone = acitve_site['isbackbone']
alpha = acitve_site['isalpha']
beta = acitve_site['isbeta']
other = ~alpha & ~beta
as_flex = ((backbone & alpha).sum(), (backbone & beta).sum(),
(backbone & other).sum(), (~backbone & alpha).sum(),
(~backbone & beta).sum(), (~backbone & other).sum(),
len(acitve_site))
vec += tuple(as_flex)
# Salt bridges (<5.5)
salt_bridge_dict = salt_bridges(mol, protein, 5.5)[1]
vec += (salt_bridge_dict['isalpha'].sum(),
salt_bridge_dict['isbeta'].sum(),
(~salt_bridge_dict['isalpha']
& ~salt_bridge_dict['isbeta']).sum(),
len(salt_bridge_dict))
# Rotatable bonds
vec += mol.num_rotors,
if desc is None:
desc = np.zeros(len(vec), dtype=float)
desc = np.vstack((desc, np.array(vec, dtype=float)))
return desc[1:]
def build(self, ligands, protein = None):
""" Descriptor building method
Parameters
----------
ligands: array-like
An array of generator of oddt.toolkit.Molecule objects for which the descriptor is computed
protein: oddt.toolkit.Molecule object (default=None)
Protein object to be used while generating descriptors. If none, then the default protein (from constructor) is used. Otherwise, protein becomes new global and default protein.
Returns
-------
descs: numpy array, shape=[n_samples, 351]
An array of binana descriptors, aligned with input ligands
"""
if protein:
self.set_protein(protein)
else:
protein = self.protein
protein_dict = protein.atom_dict
desc = None
for mol in ligands:
mol_dict = mol.atom_dict
vec = np.array([], dtype=float)
vec = tuple()
# 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, single=True)[0].data
vina_scores = ['vina_affinity', 'vina_gauss1', 'vina_gauss2', 'vina_repulsion', 'vina_hydrophobic', 'vina_hydrogen']
vec += tuple([scored_mol[key] for key in vina_scores])
# Close Contacts (<4A)
vec += tuple(self.cc_4.build(mol, single=True).flatten())
# Electrostatics (<4A)
ele_types = (('A', 'A'), ('A', 'C'), ('A', 'CL'), ('A', 'F'), ('A', 'FE'), ('A', 'HD'), ('A', 'MG'), ('A', 'MN'), ('A', 'N'), ('A', 'NA'), ('A', 'OA'), ('A', 'SA'), ('A', 'ZN'), ('BR', 'C'), ('BR', 'HD'), ('BR', 'OA'), ('C', 'C'), ('C', 'CL'), ('C', 'F'), ('C', 'HD'), ('C', 'MG'), ('C', 'MN'), ('C', 'N'), ('C', 'NA'), ('C', 'OA'), ('C', 'SA'), ('C', 'ZN'), ('CL', 'FE'), ('CL', 'HD'), ('CL', 'MG'), ('CL', 'N'), ('CL', 'OA'), ('CL', 'ZN'), ('F', 'HD'), ('F', 'N'), ('F', 'OA'), ('F', 'SA'), ('F', 'ZN'), ('FE', 'HD'), ('FE', 'N'), ('FE', 'OA'), ('HD', 'HD'), ('HD', 'I'), ('HD', 'MG'), ('HD', 'MN'), ('HD', 'N'), ('HD', 'NA'), ('HD', 'OA'), ('HD', 'P'), ('HD', 'S'), ('HD', 'SA'), ('HD', 'ZN'), ('MG', 'NA'), ('MG', 'OA'), ('MN', 'N'), ('MN', 'OA'), ('N', 'N'), ('N', 'NA'), ('N', 'OA'), ('N', 'SA'), ('N', 'ZN'), ('NA', 'OA'), ('NA', 'SA'), ('NA', 'ZN'), ('OA', 'OA'), ('OA', 'SA'), ('OA', 'ZN'), ('S', 'ZN'), ('SA', 'ZN'), ('A', 'BR'), ('A', 'I'), ('A', 'P'), ('A', 'S'), ('BR', 'N'), ('BR', 'SA'), ('C', 'FE'), ('C', 'I'), ('C', 'P'), ('C', 'S'), ('CL', 'MN'), ('CL', 'NA'), ('CL', 'P'), ('CL', 'S'), ('CL', 'SA'), ('CU', 'HD'), ('CU', 'N'), ('FE', 'NA'), ('FE', 'SA'), ('I', 'N'), ('I', 'OA'), ('MG', 'N'), ('MG', 'P'), ('MG', 'S'), ('MG', 'SA'), ('MN', 'NA'), ('MN', 'P'), ('MN', 'S'), ('MN', 'SA'), ('N', 'P'), ('N', 'S'), ('NA', 'P'), ('NA', 'S'), ('OA', 'P'), ('OA', 'S'), ('P', 'S'), ('P', 'SA'), ('P', 'ZN'), ('S', 'SA'), ('SA', 'SA'))
ele_rec_types, ele_lig_types = zip(*ele_types)
ele_mol_atoms = atoms_by_type(mol_dict, ele_lig_types, 'atom_types_ad4')
ele_rec_atoms = atoms_by_type(protein_dict, ele_rec_types, 'atom_types_ad4')
ele = tuple()
for r_t, m_t in ele_types:
mol_ele_dict, rec_ele_dict = interactions.close_contacts(ele_mol_atoms[m_t], ele_rec_atoms[r_t], 4)
if len(mol_ele_dict) and len(rec_ele_dict):
ele += (mol_ele_dict['charge'] * rec_ele_dict['charge']/ np.sqrt((mol_ele_dict['coords'] - rec_ele_dict['coords'])**2).sum(axis=-1) * 138.94238460104697e4).sum(), # convert to J/mol
else:
ele += 0,
vec += tuple(ele)
# Ligand Atom Types
ligand_atom_types = ['A', 'BR', 'C', 'CL', 'F', 'HD', 'I', 'N', 'NA', 'OA', 'P', 'S', 'SA']
atoms = atoms_by_type(mol_dict, ligand_atom_types, 'atom_types_ad4')
atoms_counts = [len(atoms[t]) for t in ligand_atom_types]
vec += tuple(atoms_counts)
# Close Contacts (<2.5A)
vec += tuple(self.cc_25.build(mol, single=True).flatten())
# H-Bonds (<4A)
hbond_mol, hbond_rec, strict = interactions.hbond(mol, protein, 4)
# Retain only strict hbonds
hbond_mol = hbond_mol[strict]
hbond_rec = hbond_rec[strict]
backbone = hbond_rec['isbackbone']
alpha = hbond_rec['isalpha']
beta = hbond_rec['isbeta']
other = ~alpha & ~beta
donor_mol = hbond_mol['isdonor']
donor_rec = hbond_rec['isdonor']
hbond_vec = ((donor_mol & backbone & alpha).sum(), (donor_mol & backbone & beta).sum(), (donor_mol & backbone & other).sum(),
(donor_mol & ~backbone & alpha).sum(), (donor_mol & ~backbone & beta).sum(), (donor_mol & ~backbone & other).sum(),
(donor_rec & backbone & alpha).sum(), (donor_rec & backbone & beta).sum(), (donor_rec & backbone & other).sum(),
(donor_rec & ~backbone & alpha).sum(), (donor_rec & ~backbone & beta).sum(), (donor_rec & ~backbone & other).sum())
vec += tuple(hbond_vec)
# Hydrophobic contacts (<4A)
hydrophobic = interactions.hydrophobic_contacts(mol, protein, 4)[1]
backbone = hydrophobic['isbackbone']
alpha = hydrophobic['isalpha']
beta = hydrophobic['isbeta']
other = ~alpha & ~beta
hyd_vec = ((backbone & alpha).sum(), (backbone & beta).sum(), (backbone & other).sum(),
(~backbone & alpha).sum(), (~backbone & beta).sum(), (~backbone & other).sum(), len(hydrophobic))
vec += tuple(hyd_vec)
# Pi-stacking (<7.5A)
pi_mol, pi_rec, pi_paralel, pi_tshaped = interactions.pi_stacking(mol, protein, 7.5)
alpha = pi_rec['isalpha'] & pi_paralel
beta = pi_rec['isbeta'] & pi_paralel
other = ~alpha & ~beta & pi_paralel
pi_vec = (alpha.sum(), beta.sum(), other.sum())
vec += tuple(pi_vec)
# count T-shaped Pi-Pi interaction
alpha = pi_rec['isalpha'] & pi_tshaped
beta = pi_rec['isbeta'] & pi_tshaped
other = ~alpha & ~beta & pi_tshaped
pi_t_vec = (alpha.sum(), beta.sum(), other.sum())
# Pi-cation (<6A)
pi_rec, cat_mol, strict = interactions.pi_cation(protein, mol, 6)
alpha = pi_rec['isalpha'] & strict
beta = pi_rec['isbeta'] & strict
other = ~alpha & ~beta & strict
pi_cat_vec = (alpha.sum(), beta.sum(), other.sum())
pi_mol, cat_rec, strict = interactions.pi_cation(mol, protein, 6)
alpha = cat_rec['isalpha'] & strict
beta = cat_rec['isbeta'] & strict
other = ~alpha & ~beta & strict
pi_cat_vec += (alpha.sum(), beta.sum(), other.sum())
vec += tuple(pi_cat_vec)
# T-shape (perpendicular Pi's) (<7.5A)
vec += tuple(pi_t_vec)
# Active site flexibility (<4A)
acitve_site = interactions.close_contacts(mol_dict, protein_dict, 4)[1]
backbone = acitve_site['isbackbone']
alpha = acitve_site['isalpha']
beta = acitve_site['isbeta']
other = ~alpha & ~beta
as_flex = ((backbone & alpha).sum(), (backbone & beta).sum(), (backbone & other).sum(),
(~backbone & alpha).sum(), (~backbone & beta).sum(), (~backbone & other).sum(), len(acitve_site))
vec += tuple(as_flex)
# Salt bridges (<5.5)
salt_bridges = interactions.salt_bridges(mol, protein, 5.5)[1]
vec += (salt_bridges['isalpha'].sum(), salt_bridges['isbeta'].sum(),
(~salt_bridges['isalpha'] & ~salt_bridges['isbeta']).sum(), len(salt_bridges))
# Rotatable bonds
vec += mol.num_rotors,
if desc is None:
desc = np.zeros(len(vec), dtype=float)
desc = np.vstack((desc, np.array(vec, dtype=float)))
return desc[1:]