dcoid_rf2.py

from mpi4py import MPI
from mpi4py.futures import MPICommExecutor


from oddt.scoring.functions import RFScore
import oddt
from Bio.PDB import PDBParser, PDBIO, Select
import tempfile
import os
import sys

import pandas as pd

scorer = RFScore.rfscore.load(version=2)

def is_het(residue):
    res = residue.id[0]
    return res != " " and res != "W"

class ResidueSelect(Select):
    def __init__(self, het):
        self.het = het

    def accept_residue(self, residue):
        """ Recognition of heteroatoms - Remove water molecules """
        return (self.het and is_het(residue) or not self.het and not is_het(residue))

def get_complex(fn):
    try:
        io = PDBIO()
        parser = PDBParser()

        structure = parser.get_structure('complex',fn)
        io.set_structure(structure)

        with tempfile.NamedTemporaryFile(mode='w',delete=False) as f:
            name_receptor = f.name

        with tempfile.NamedTemporaryFile(mode='w',delete=False) as f:
            name_ligand = f.name

        io.save(name_receptor,ResidueSelect(het=False))
        io.save(name_ligand,ResidueSelect(het=True))

        receptor = next(oddt.toolkit.readfile('pdb',name_receptor))
        ligand = next(oddt.toolkit.readfile('pdb',name_ligand))

        scorer.set_protein(receptor)
        scorer.predict_ligand(ligand)

        os.unlink(name_ligand)
        os.unlink(name_receptor)
        return float(ligand.data['rfscore_v2'])
    except Exception as e:
        print(e)
        pass

if __name__ == '__main__':
    import glob

    filenames = glob.glob(sys.argv[2])
    comm = MPI.COMM_WORLD
    with MPICommExecutor(comm, root=0) as executor:
        if executor is not None:
            result = executor.map(get_complex, filenames)

            names = []
            scores = []
            for n,r in zip(filenames,result):
                try:
                    scores.append(r)
                    names.append(os.path.basename(n))
                except:
                    pass
            df = pd.DataFrame({'name': names, 'rf2': scores})
            df.to_parquet(sys.argv[1])