Files
Delete/utils/pdb_parser.py
HaotianZhang 7652568ecc update
2024-11-25 09:59:13 -08:00

181 lines
7.2 KiB
Python

from rdkit import Chem
import numpy as np
class PDBProtein(object):
AA_NAME_SYM = {
'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E', 'PHE': 'F', 'GLY': 'G', 'HIS': 'H',
'ILE': 'I', 'LYS': 'K', 'LEU': 'L', 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'GLN': 'Q',
'ARG': 'R', 'SER': 'S', 'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y',
}
AA_NAME_NUMBER = {
k: i for i, (k, _) in enumerate(AA_NAME_SYM.items())
}
BACKBONE_NAMES = ["CA", "C", "N", "O"]
def __init__(self, data, mode='auto'):
super().__init__()
if (data[-4:].lower() == '.pdb' and mode == 'auto') or mode == 'path':
with open(data, 'r') as f:
self.block = f.read()
else:
self.block = data
self.ptable = Chem.GetPeriodicTable()
# Molecule properties
self.title = None
# Atom properties
self.atoms = []
self.element = []
self.atomic_weight = []
self.pos = []
self.atom_name = []
self.is_backbone = []
self.atom_to_aa_type = []
# Residue properties
self.residues = []
self.amino_acid = []
self.center_of_mass = []
self.pos_CA = []
self.pos_C = []
self.pos_N = []
self.pos_O = []
self._parse()
def _enum_formatted_atom_lines(self):
for line in self.block.splitlines():
if line[0:6].strip() == 'ATOM':
element_symb = line[76:78].strip().capitalize()
if len(element_symb) == 0:
element_symb = line[13:14]
yield {
'line': line,
'type': 'ATOM',
'atom_id': int(line[6:11]),
'atom_name': line[12:16].strip(),
'res_name': line[17:20].strip(),
'chain': line[21:22].strip(),
'res_id': int(line[22:26]),
'res_insert_id': line[26:27].strip(),
'x': float(line[30:38]),
'y': float(line[38:46]),
'z': float(line[46:54]),
'occupancy': float(line[54:60]),
'segment': line[72:76].strip(),
'element_symb': element_symb,
'charge': line[78:80].strip(),
}
elif line[0:6].strip() == 'HEADER':
yield {
'type': 'HEADER',
'value': line[10:].strip()
}
elif line[0:6].strip() == 'ENDMDL':
break # Some PDBs have more than 1 model.
def _parse(self):
# Process atoms
residues_tmp = {}
for atom in self._enum_formatted_atom_lines():
if atom['type'] == 'HEADER':
self.title = atom['value'].lower()
continue
self.atoms.append(atom)
atomic_number = self.ptable.GetAtomicNumber(atom['element_symb'])
next_ptr = len(self.element)
self.element.append(atomic_number)
self.atomic_weight.append(self.ptable.GetAtomicWeight(atomic_number))
self.pos.append(np.array([atom['x'], atom['y'], atom['z']], dtype=np.float32))
self.atom_name.append(atom['atom_name'])
self.is_backbone.append(atom['atom_name'] in self.BACKBONE_NAMES)
self.atom_to_aa_type.append(self.AA_NAME_NUMBER[atom['res_name']])
chain_res_id = '%s_%s_%d_%s' % (atom['chain'], atom['segment'], atom['res_id'], atom['res_insert_id'])
if chain_res_id not in residues_tmp:
residues_tmp[chain_res_id] = {
'name': atom['res_name'],
'atoms': [next_ptr],
'chain': atom['chain'],
'segment': atom['segment'],
}
else:
assert residues_tmp[chain_res_id]['name'] == atom['res_name']
assert residues_tmp[chain_res_id]['chain'] == atom['chain']
residues_tmp[chain_res_id]['atoms'].append(next_ptr)
# Process residues
self.residues = [r for _, r in residues_tmp.items()]
for residue in self.residues:
sum_pos = np.zeros([3], dtype=np.float32)
sum_mass = 0.0
for atom_idx in residue['atoms']:
sum_pos += self.pos[atom_idx] * self.atomic_weight[atom_idx]
sum_mass += self.atomic_weight[atom_idx]
if self.atom_name[atom_idx] in self.BACKBONE_NAMES:
residue['pos_%s' % self.atom_name[atom_idx]] = self.pos[atom_idx]
residue['center_of_mass'] = sum_pos / sum_mass
# Process backbone atoms of residues
for residue in self.residues:
self.amino_acid.append(self.AA_NAME_NUMBER[residue['name']])
self.center_of_mass.append(residue['center_of_mass'])
for name in self.BACKBONE_NAMES:
pos_key = 'pos_%s' % name # pos_CA, pos_C, pos_N, pos_O
if pos_key in residue:
getattr(self, pos_key).append(residue[pos_key])
else:
getattr(self, pos_key).append(residue['center_of_mass'])
def to_dict_atom(self):
return {
'element': np.array(self.element, dtype=np.long),
'molecule_name': self.title,
'pos': np.array(self.pos, dtype=np.float32),
'is_backbone': np.array(self.is_backbone, dtype=np.bool),
'atom_name': self.atom_name,
'atom_to_aa_type': np.array(self.atom_to_aa_type, dtype=np.long)
}
def to_dict_residue(self):
return {
'amino_acid': np.array(self.amino_acid, dtype=np.long),
'center_of_mass': np.array(self.center_of_mass, dtype=np.float32),
'pos_CA': np.array(self.pos_CA, dtype=np.float32),
'pos_C': np.array(self.pos_C, dtype=np.float32),
'pos_N': np.array(self.pos_N, dtype=np.float32),
'pos_O': np.array(self.pos_O, dtype=np.float32),
}
def query_residues_radius(self, center, radius, criterion='center_of_mass'):
center = np.array(center).reshape(3)
selected = []
for residue in self.residues:
distance = np.linalg.norm(residue[criterion] - center, ord=2)
#print(residue[criterion], distance)
if distance < radius:
selected.append(residue)
return selected
def query_residues_ligand(self, ligand, radius, criterion='center_of_mass'):
selected = []
sel_idx = set()
# The time-complexity is O(mn).
for center in ligand['pos']:
for i, residue in enumerate(self.residues):
distance = np.linalg.norm(residue[criterion] - center, ord=2)
if distance < radius and i not in sel_idx:
selected.append(residue)
sel_idx.add(i)
return selected
def residues_to_pdb_block(self, residues, name='POCKET'):
block = "HEADER %s\n" % name
block += "COMPND %s\n" % name
for residue in residues:
for atom_idx in residue['atoms']:
block += self.atoms[atom_idx]['line'] + "\n"
block += "END\n"
return block