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:]