Datasets:

jglaser
/

protein_ligand_contacts

	from mpi4py import MPI
	from mpi4py.futures import MPICommExecutor

	import warnings
	from Bio.PDB import PDBParser, PPBuilder, CaPPBuilder
	from Bio.PDB.NeighborSearch import NeighborSearch
	from Bio.PDB.Selection import unfold_entities

	import numpy as np
	import dask.array as da

	from rdkit import Chem

	import os
	import re
	import sys

	# all punctuation
	punctuation_regex = r"""($\|$\|\.\|=\|#\|-\|\+\|\\\|\/\|:\|~\|@\|\?\|>>?\|\*\|\$\|\%[0-9]{2}\|[0-9])"""

	# tokenization regex (Schwaller)
	molecule_regex = r"""(\[[^\]]+]\|Br?\|Cl?\|N\|O\|S\|P\|F\|I\|b\|c\|n\|o\|s\|p\|$\|$\|\.\|=\|#\|-\|\+\|\\\|\/\|:\|~\|@\|\?\|>>?\|\*\|\$\|\%[0-9]{2}\|[0-9])"""

	cutoff = int(sys.argv[1])
	max_seq = 2046 # = 2048 - 2 (accounting for [CLS] and [SEP])
	max_smiles = 510 # = 512 - 2
	chunk_size = '1G'

	def parse_complex(fn):
	try:
	name = os.path.basename(fn)

	# parse protein sequence and coordinates
	parser = PDBParser()
	with warnings.catch_warnings():
	warnings.simplefilter("ignore")
	structure = parser.get_structure('protein',fn+'/'+name+'_protein.pdb')

	# ppb = PPBuilder()
	ppb = CaPPBuilder()
	seq = []
	for pp in ppb.build_peptides(structure):
	seq.append(str(pp.get_sequence()))
	seq = ''.join(seq)

	# parse ligand, convert to SMILES and map atoms
	suppl = Chem.SDMolSupplier(fn+'/'+name+'_ligand.sdf')
	mol = next(suppl)
	smi = Chem.MolToSmiles(mol)

	# position of atoms in SMILES (not counting punctuation)
	atom_order = [int(s) for s in list(filter(None,re.sub(r'[\[\]]','',mol.GetProp("_smilesAtomOutputOrder")).split(',')))]

	# tokenize the SMILES
	tokens = list(filter(None, re.split(molecule_regex, smi)))

	# remove punctuation
	masked_tokens = [re.sub(punctuation_regex,'',s) for s in tokens]

	k = 0
	token_pos = []
	token_id = []
	for i,token in enumerate(masked_tokens):
	if token != '':
	token_pos.append(tuple(mol.GetConformer().GetAtomPosition(atom_order[k])))
	token_id.append(i)
	k += 1

	# query protein for ligand contacts
	atoms = unfold_entities(structure, 'A')
	neighbor_search = NeighborSearch(atoms)

	close_residues = [neighbor_search.search(center=t, level='R', radius=cutoff) for t in token_pos]
	first_residue = next(structure.get_residues()).get_id()[1]
	residue_id = [[c.get_id()[1]-first_residue for c in query if not c.get_id()[0].startswith(('H','W'))]
	for query in close_residues] # zero-based, exclude hetero atoms

	# contact map
	contact_map = np.zeros((max_seq, max_smiles),dtype=np.float32)

	for query,t in zip(residue_id,token_id):
	for r in query:
	contact_map[r,t] = 1

	return name, seq, smi, contact_map
	except Exception as e:
	print(e)
	return None


	if __name__ == '__main__':
	import glob

	filenames = glob.glob('data/pdbbind/v2020-other-PL/*')
	filenames.extend(glob.glob('data/pdbbind/refined-set/*'))
	filenames = sorted(filenames)
	comm = MPI.COMM_WORLD
	with MPICommExecutor(comm, root=0) as executor:
	if executor is not None:
	result = executor.map(parse_complex, filenames)
	result = list(result)
	names = [r[0] for r in result if r is not None]
	seqs = [r[1] for r in result if r is not None]
	all_smiles = [r[2] for r in result if r is not None]
	all_contacts = [r[3] for r in result if r is not None]

	import pandas as pd
	df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles})
	all_contacts = da.from_array(all_contacts, chunks=chunk_size)
	da.to_npy_stack('data/pdbbind_contacts_{}/'.format(cutoff), all_contacts)
	df.to_parquet('data/pdbbind_complex_{}.parquet'.format(cutoff))