"""Internal implementation of binana software
(http://nbcr.ucsd.edu/data/sw/hosted/binana/)
"""
import numpy as np
from oddt.scoring.descriptors import (atoms_by_type,
close_contacts_descriptor,
oddt_vina_descriptor)
from oddt.interactions import (close_contacts, hbonds, hydrophobic_contacts,
pi_cation, pi_stacking, salt_bridges)
[docs]class binana_descriptor(object):
def __init__(self, protein=None):
""" Descriptor build from binana script (as used in NNScore 2.0
Parameters
----------
protein: oddt.toolkit.Molecule object (default=None)
Protein object to be used while generating descriptors.
"""
self.protein = protein
self.titles = []
self.vina = oddt_vina_descriptor(protein, vina_scores=['vina_gauss1',
'vina_gauss2',
'vina_repulsion',
'vina_hydrophobic',
'vina_hydrogen'])
self.titles += self.vina.titles
# Close contacts descriptor generators
cc_4_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'), ('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'),
('A', 'CU'), ('C', 'CD'))
cc_4_rec_types, cc_4_lig_types = zip(*cc_4_types)
self.titles += ['cc_%s.%s_4' % (t1, t2) for t1, t2 in cc_4_types]
self.cc_4 = close_contacts_descriptor(protein,
cutoff=4,
protein_types=cc_4_rec_types,
ligand_types=cc_4_lig_types,
mode='atom_types_ad4',
aligned_pairs=True)
self.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'))
self.titles += ['ele_%s.%s_4' % (t1, t2) for t1, t2 in self.ele_types]
self.ligand_atom_types = ['A', 'BR', 'C', 'CL', 'F', 'HD', 'I', 'N', 'NA', 'OA', 'P', 'S', 'SA']
self.titles += ['lig_%s' % t1 for t1 in self.ligand_atom_types]
cc_25_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'), ('CD', 'OA'),
('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')]
cc_25_rec_types, cc_25_lig_types = zip(*cc_25_types)
self.cc_25 = close_contacts_descriptor(protein,
cutoff=2.5,
protein_types=cc_25_rec_types,
ligand_types=cc_25_lig_types,
mode='atom_types_ad4',
aligned_pairs=True)
self.titles += ['cc_%s.%s_2.5' % (t1, t2) for t1, t2 in cc_25_types]
# H-Bonds (<4A)
self.titles += ['hb_4_mol_backbone_alpha',
'hb_4_mol_backbone_beta',
'hb_4_mol_backbone_other',
'hb_4_mol_sidechain_alpha',
'hb_4_mol_sidechain_beta',
'hb_4_mol_sidechain_other',
'hb_4_rec_backbone_alpha',
'hb_4_rec_backbone_beta',
'hb_4_rec_backbone_other',
'hb_4_rec_sidechain_alpha',
'hb_4_rec_sidechain_beta',
'hb_4_rec_sidechain_other']
# Hydrophobic Contact <4A
self.titles += ['hyd_4_backbone_alpha',
'hyd_4_backbone_beta',
'hyd_4_backbone_other',
'hyd_4_sidechain_alpha',
'hyd_4_sidechain_beta',
'hyd_4_sidechain_other',
'hyd_4_all']
# Pi-stacking (<7.5A)
self.titles += ['pi_stack_7.5_alpha',
'pi_stack_7.5_beta',
'pi_stack_7.5_other']
# T-shaped Pi-Pi interaction
self.titles += ['pi_t_7.5_alpha',
'pi_t_7.5_beta',
'pi_t_7.5_other']
# Pi-cation (<6A)
self.titles += ['pi_cat_mol_6_alpha',
'pi_cat_mol_6_beta',
'pi_cat_mol_6_other',
'pi_cat_rec_6_alpha',
'pi_cat_rec_6_beta',
'pi_cat_rec_6_other']
# Active site flexibility (<4A)
self.titles += ['as_flex_backbone_alpha',
'as_flex_backbone_beta',
'as_flex_backbone_other',
'as_flex_sidechain_alpha',
'as_flex_sidechain_beta',
'as_flex_sidechain_other',
'as_flex_all']
# Salt bridges (<5.5)
self.titles += ['salt_bridge_5.5_alpha',
'salt_bridge_5.5_beta',
'salt_bridge_5.5_other',
'salt_bridge_5.5_all']
# Rotatable bonds
self.titles += ['num_rotors']
assert len(self.titles) == len(self)
[docs] def set_protein(self, protein):
""" One function to change all relevant proteins
Parameters
----------
protein: oddt.toolkit.Molecule object
Protein object to be used while generating descriptors.
Protein becomes new global and default protein.
"""
self.protein = protein
self.vina.set_protein(protein)
self.cc_4.protein = protein
self.cc_25.protein = protein
[docs] 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 __len__(self):
""" Returns the dimensions of descriptors """
return 350
def __reduce__(self):
return binana_descriptor, (self.protein,)